HYDROLOGICAL PROCESSESHydrol Process 27 1426ndash1439 (2013)Published online 23 April 2012 in Wiley Online Library(wileyonlinelibrarycom) DOI 101002hyp9284

Assessing the potential of reservoir outflow management toreduce sedimentation using continuous turbidity monitoring

and reservoir modellingdagger

Casey Lee and Guy FosterUS Geological Survey Lawrence KS 66049 USA

CPlaE-mdaggerTh

Co

Abstract

In-stream sensors are increasingly deployed as part of ambient water quality-monitoring networks Temporally dense data fromthese networks can be used to better understand the transport of constituents through streams lakes or reservoirs Data fromexisting continuously recording in-stream flow and water quality monitoring stations were coupled with the two-dimensionalhydrodynamic CE-QUAL-W2 model to assess the potential of altered reservoir outflow management to reduce sediment trappingin John Redmond Reservoir located in east-central Kansas Monitoring stations upstream and downstream from the reservoirwere used to estimate 56 million metric tons of sediment transported to John Redmond Reservoir from 2007 through 2010 88of which was trapped within the reservoir The two-dimensional model was used to estimate the residence time of 55 equal-volume releases from the reservoir sediment trapping for these releases varied from 48 to 97 Smaller trapping efficiencieswere observed when the reservoir was maintained near the normal operating capacity (relative to higher flood pool levels) andwhen average residence times were relatively short An idealized alternative outflow management scenario was constructedwhich minimized reservoir elevations and the length of time water was in the reservoir while continuing to meet downstreamflood control end points identified in the reservoir water control manual The alternative scenario is projected to reduce sedimenttrapping in the reservoir by approximately 3 preventing approximately 45 000 metric tons of sediment from being depositedwithin the reservoir annually This article presents an approach to quantify the potential of reservoir management using existingin-stream data actual management decisions need to consider the effects on other reservoir benefits such as downstream floodcontrol and aquatic life Copyright copy 2012 John Wiley amp Sons Ltd

KEY WORDS suspended sediment turbidity sediment trapping efficiency reservoir modelling

Received 8 August 2011 Accepted 17 February 2012

INTRODUCTION

In addition to flood control communities are reliant onreservoir storage for drinking water agricultural use andindustrial use In Kansas reservoir storage is the source ofdrinking water for more than two thirds of the statepopulation studies project that a severe drought will resultin water supply shortages in multiple basins (Kansas WaterOffice 2008) Water supply shortages will become morelikely as human populations grow and as sedimentaccumulation continues to decrease available reservoirstorage Solutions to maintaining reservoir storage arelimited because (i) sediment is naturally transported instreams and rivers (ii) improved erosion controls maynot affect sedimentation for decades because of fieldfloodplain and in-stream sediment storage of previouslyeroded sediments (Trimble 1999 Evans et al 2000) and(iii) dredging of large reservoirs such as in Kansas has thusfar been cost-prohibitive and disposal of sediments isdifficult (Kansas Water Office 2008)Internationally the effects of sediment accumulation in

reservoirs have long been realized Because of the

orrespondence to Casey Lee US Geological Survey 4821 Quail Crestce Lawrence KS 66049 USAail cjleeusgsgovis article is aUSGovernmentwork and is in the public domain in theUSA

pyright copy 2012 John Wiley amp Sons Ltd

immediacy of the problem and the expense and difficultyof dredging decommissioning or building new reservoirsthe management of reservoir outflows has been altered todecrease or arrest sediment accumulation (Fan and Morris1992a 1992bMorris and Fan 1998White 2001 Palmieriet al 2003 Morris et al 2008) These reservoirmanagement strategies use the velocity of incoming flood-waters to transport incoming and previously depositedsediments through reservoirs but require varied levels ofreservoir drawdown to maximize effectiveness Thefeasibility of reservoir management to reduce sedimentdeposition varies depending on reservoir watershed andeconomic considerations (White 2001 Palmieri et al2003 Morris et al 2008) Compared with reservoirsworldwide the percentage of storage loss in large reservoirsin the United States has been limited because thesereservoirs typically have large storage capacities relativeto incoming inflow volumes (G Morris written communi-cation 2009)Numerical models are improving the ability to simulate

the movement of turbidity currents (Gelda and Effler 2007Chung et al 2009) and cohesive sediment throughreservoirs (Simotildees and Yang 2008 Yang and Simotildees2008) However the episodic nature of sediment transportto reservoirs and the spatial complexity of sediment withinreservoirs often make it difficult to test model simulations

1427CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

Water quality sensors are increasingly being deployedcontinuously in streams and rivers as part of ambient waterquality programs (eg see httpwaterwatchusgsgovwqwatch) These data can accurately represent the flux ofsuspended sediment at fine temporal scales (Rasmussenet al 2009) and when collected upstream and downstreamfrom a lake or reservoir can be used quantify sedimenttrapping efficiency more accurately than through periodicsample collection (Lee et al 2008) Further when coupledwith an understanding of reservoir hydrodynamics thesedata can be used to characterize how short-term processessuch as how variation in reservoir outflow managementaffects sediment flux through reservoirs This study coupleda CE-QUAL W2 hydrodynamic reservoir model withexisting continuous turbidity data at US Geological Survey(USGS) streamgage sites upstream and downstream fromJohn Redmond Reservoir to assess the potential of alteredreservoir management to reduce reservoir sedimentation

1Any use of trade firm or product names is for descriptive purposes onlyand does not imply endorsement by the US Government

Study area

John Redmond Reservoir was constructed on theNeosho River from 1959 through 1964 for purposes offlood control water supply and recreation [USArmy Corpsof Engineers (USACE) 1996] Since the dam wascompleted in 1964 sediment deposition has reduced waterstorage at the normal operational level (termed theconservation pool) by 42 which is among the largestpercentage loss of reservoirs owned by the USACE in theState of Kansas (KansasWater Office 2010) Approximatesedimentation rates in John Redmond Reservoir from 1964to 2006 (~910 000m3year) are nearly double the expectedsedimentation rate expected at the time of reservoir design(~500 000m3year Kansas Water Office 2010) Seventy-one percent of water rights downstream from JohnRedmond Reservoir are allocated for cooling of the WolfCreek Nuclear Power Plant (USACE 2002) Most of thiswater is lost to evaporation after cooling (Barfield 2010)In addition 14 of the water rights are allocated tomunicipalities 10 to irrigation and recreational uses and5 for other industrial uses (USACE 2002) JohnRedmond Reservoir typically is not thermally stratifiedbecause it is shallow (19m average depth) and is easilymixed by wave action (USACE 2002)John Redmond Reservoir is downstream from 7810 km2

of predominantly grass and cropland in east-central Kansas(Figure 1) The Neosho River (excluding the CottonwoodRiver) drains approximately 2870 km2 of land upstreamfrom JohnRedmondReservoir and has a slope ranging from158 mkm in the headwaters to 79mkm near theconfluence with the Cottonwood River (Carswell and Hart1985) The largest tributary to the Neosho River is theCottonwood River which runs a length of approximately329 river kilometres drains 4920 km2 and has river slopesranging from 185mkm in the headwaters to 79mkm nearthe confluence with the Neosho River (Jordan and Hart1985) Silty-clay loam (material with 27ndash40 clay andless than 20 sand) predominates along riparian areas andin the downstream part of the basin Silty clay (material with

Copyright copy 2012 John Wiley amp Sons Ltd

40 or more clay and 40 or more silt) is the dominant soiltype in the upstream part of the basin (US Department ofAgriculture 1994)John Redmond Reservoir was completed in 1964 and

had a capacity of approximately 101 million m3 in theconservation pool and with approximately 650 millionm3 acre-feet of capacity including the flood control poolThe deepest point of the reservoir is approximately 3127m above mean sea level (NGVD29 Kansas BiologicalSurvey 2010) and the top of the flood pool is 3255 mabove mean sea level (Figure 2) The primary outletstructure is a 1707-m-wide ogee weir and the crest of thespillway is located at 3149m above mean sea level Theoutlet structure has a maximum discharge capacity of16 400m3s at maximum pool level two additional low-flow outlet pipes exist at an elevation of 3095 m abovemean sea level with a maximum discharge capacity of37m3s These pipes are typically used for improvingdownstream water quality during low-flow conditions(USACE 1996) but were not incorporated into thereservoir model because specific information regardingtheir use was not made available and because their sizerelative to the larger gates precludes them from having asubstantial effect on sediment flux from the reservoir Themaximum bankfull capacity of the channel downstreamfrom the dam is 340m3s releases are typically keptbelow this value (USACE 1996)The USGS streamgages located on the Neosho River

near Americus (Americus) and on the Cottonwood Rivernear Plymouth (Plymouth) were the farthest downstreamgages before stream entry to John Redmond Reservoirfrom February 2007 to May 2009 (Table I) These gagescover 6118 km2 (78) of the 7809 km2 that drains to thereservoir (USACE 2002) A streamgage was installed onthe Neosho River at Neosho Rapids (Neosho Rapids) inAugust 2009 which better quantified the amount andtiming of sediment transport to the reservoir (draining7130 km2 of the basin upstream from John RedmondReservoir) The downstream gage is located on the NeoshoRiver at Burlington (Burlington) approximately 5 milesdownstream from John Redmond Reservoir (with 70 km2

of unregulated drainage area) Two large USACEreservoirs regulate approximately 15 of the watersheddraining to John Redmond Reservoir Council GroveReservoir which has a drainage area of 637 km2 and islocated on the upper Neosho River and Marion Reservoirwhich has a drainage area of 518 km2 and is located on theupper Cottonwood River (Figure 1)

MATERIALS AND METHODS

USGS streamgages near Americus Plymouth NeoshoRapids and Burlington (Table I Figure 1) were equippedwith YSI1 6600 continuous water quality monitors whichmeasured specific conductance (SC) water temperature

Hydrol Process 27 1426ndash1439 (2013)

1427CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

Water quality sensors are increasingly being deployedcontinuously in streams and rivers as part of ambient waterquality programs (eg see httpwaterwatchusgsgovwqwatch) These data can accurately represent the flux ofsuspended sediment at fine temporal scales (Rasmussenet al 2009) and when collected upstream and downstreamfrom a lake or reservoir can be used quantify sedimenttrapping efficiency more accurately than through periodicsample collection (Lee et al 2008) Further when coupledwith an understanding of reservoir hydrodynamics thesedata can be used to characterize how short-term processessuch as how variation in reservoir outflow managementaffects sediment flux through reservoirs This study coupleda CE-QUAL W2 hydrodynamic reservoir model withexisting continuous turbidity data at US Geological Survey(USGS) streamgage sites upstream and downstream fromJohn Redmond Reservoir to assess the potential of alteredreservoir management to reduce reservoir sedimentation

1Any use of trade firm or product names is for descriptive purposes onlyand does not imply endorsement by the US Government

Study area

John Redmond Reservoir was constructed on theNeosho River from 1959 through 1964 for purposes offlood control water supply and recreation [USArmy Corpsof Engineers (USACE) 1996] Since the dam wascompleted in 1964 sediment deposition has reduced waterstorage at the normal operational level (termed theconservation pool) by 42 which is among the largestpercentage loss of reservoirs owned by the USACE in theState of Kansas (KansasWater Office 2010) Approximatesedimentation rates in John Redmond Reservoir from 1964to 2006 (~910 000m3year) are nearly double the expectedsedimentation rate expected at the time of reservoir design(~500 000m3year Kansas Water Office 2010) Seventy-one percent of water rights downstream from JohnRedmond Reservoir are allocated for cooling of the WolfCreek Nuclear Power Plant (USACE 2002) Most of thiswater is lost to evaporation after cooling (Barfield 2010)In addition 14 of the water rights are allocated tomunicipalities 10 to irrigation and recreational uses and5 for other industrial uses (USACE 2002) JohnRedmond Reservoir typically is not thermally stratifiedbecause it is shallow (19m average depth) and is easilymixed by wave action (USACE 2002)John Redmond Reservoir is downstream from 7810 km2

of predominantly grass and cropland in east-central Kansas(Figure 1) The Neosho River (excluding the CottonwoodRiver) drains approximately 2870 km2 of land upstreamfrom JohnRedmondReservoir and has a slope ranging from158 mkm in the headwaters to 79mkm near theconfluence with the Cottonwood River (Carswell and Hart1985) The largest tributary to the Neosho River is theCottonwood River which runs a length of approximately329 river kilometres drains 4920 km2 and has river slopesranging from 185mkm in the headwaters to 79mkm nearthe confluence with the Neosho River (Jordan and Hart1985) Silty-clay loam (material with 27ndash40 clay andless than 20 sand) predominates along riparian areas andin the downstream part of the basin Silty clay (material with

Copyright copy 2012 John Wiley amp Sons Ltd

40 or more clay and 40 or more silt) is the dominant soiltype in the upstream part of the basin (US Department ofAgriculture 1994)John Redmond Reservoir was completed in 1964 and

had a capacity of approximately 101 million m3 in theconservation pool and with approximately 650 millionm3 acre-feet of capacity including the flood control poolThe deepest point of the reservoir is approximately 3127m above mean sea level (NGVD29 Kansas BiologicalSurvey 2010) and the top of the flood pool is 3255 mabove mean sea level (Figure 2) The primary outletstructure is a 1707-m-wide ogee weir and the crest of thespillway is located at 3149m above mean sea level Theoutlet structure has a maximum discharge capacity of16 400m3s at maximum pool level two additional low-flow outlet pipes exist at an elevation of 3095 m abovemean sea level with a maximum discharge capacity of37m3s These pipes are typically used for improvingdownstream water quality during low-flow conditions(USACE 1996) but were not incorporated into thereservoir model because specific information regardingtheir use was not made available and because their sizerelative to the larger gates precludes them from having asubstantial effect on sediment flux from the reservoir Themaximum bankfull capacity of the channel downstreamfrom the dam is 340m3s releases are typically keptbelow this value (USACE 1996)The USGS streamgages located on the Neosho River

near Americus (Americus) and on the Cottonwood Rivernear Plymouth (Plymouth) were the farthest downstreamgages before stream entry to John Redmond Reservoirfrom February 2007 to May 2009 (Table I) These gagescover 6118 km2 (78) of the 7809 km2 that drains to thereservoir (USACE 2002) A streamgage was installed onthe Neosho River at Neosho Rapids (Neosho Rapids) inAugust 2009 which better quantified the amount andtiming of sediment transport to the reservoir (draining7130 km2 of the basin upstream from John RedmondReservoir) The downstream gage is located on the NeoshoRiver at Burlington (Burlington) approximately 5 milesdownstream from John Redmond Reservoir (with 70 km2

of unregulated drainage area) Two large USACEreservoirs regulate approximately 15 of the watersheddraining to John Redmond Reservoir Council GroveReservoir which has a drainage area of 637 km2 and islocated on the upper Neosho River and Marion Reservoirwhich has a drainage area of 518 km2 and is located on theupper Cottonwood River (Figure 1)

MATERIALS AND METHODS

USGS streamgages near Americus Plymouth NeoshoRapids and Burlington (Table I Figure 1) were equippedwith YSI1 6600 continuous water quality monitors whichmeasured specific conductance (SC) water temperature

Hydrol Process 27 1426ndash1439 (2013)

Top of conservation

pool3167 m

Top of flood- control pool

3255 m

Water level on31811 at3164 m

Deepest pointof reservoir

3127 m

Primaryoutlet

structure3149 m

Simplified representation of the dam and pool levelsat John Redmond Reservoir eastcentral Kansas

(adapted from the US Army Corps of Engineers 2010)[m meters above mean sea level NGVD 29]

John

Red

mon

d D

am

Figure 2 Simplified representation of the dam and pool levels at JohnRedmond Reservoir east-central Kansas

0 10 20 MILES

0 10 20 KILOMETERS

Land use from US Geological Survey NationalLandcover Database (Homer et al 2004)

Cottonwood

River

River

Neosho

Council GroveReservoir

Marion Reservoir

CottonwoodRiver nearPlymouth

Neosho Riverat Burlington

JohnRedmond Reservoir

Base map from US Geological Survey digital data 12000000 1994Albers Conic Equal-Area ProjectionStandard parallels 29deg30 and 45deg30 central meridian 96deg

Horizontal coordinate information is referenced to theNorth American Datum of 1983 (NAD 83)

Neosho Rivernear Americus

Neosho Riverat Neosho Rapids

Emporia

Boundary of drainage basin upstream from streamgage station at Burlington

Boundary of subbasin

US Geological Survey streamgage and turbidity monitoring station (turbiditysensor operated from February 2007-May 2009

US Geological Survey streamgage and turbidity monitoring station (turbiditysensor operated from February 2007-September 2010

US Geological Survey streamgage and turbidity monitoring station operatedfrom August 2009-September 2010

EXPLANATION

Land use (within basin)

Open waterUrban developmentroadsForestGrasslandshrublandPasturehayCultivated croplandWetlands

Index map

KANSAS

Location of study area

3097deg97deg30 96deg39deg

38deg30

38deg

COFFEY

WOODSONGREENWOODBUTLER

HARVEY

MARIONMC-PHERSON

MORRIS

GEARY

SALINE

DICKINSON

LYON

WABAUNSEE SHAWNEE

OSAGE

CHASE

Figure 1 Sampling sites and land use upstream and downstream from John Redmond Reservoir east-central Kansas

1428 C LEE AND G FOSTER

Copyright copy 2012 John Wiley amp Sons Ltd

and turbidity (model 6136) and Hach Solitax suspended-solids optical backscatterturbidity sensors Sensorscollected values in stream and were housed in polyvinylchloride pipes with holes drilled to allow stream water toflow through the installation Sensors near Americus andPlymouth were installed along the bank nearest thestreamgage and sensors at Neosho Rapids and Burlingtonwere suspended from a bridge by chain near the centre ofthe stream Measurements were logged every 15 minhistorical and real-time continuous data are available onthe USGSWeb page httpnrtwqusgsgovks Water qualitysample results are available online at httpwaterdatausgsgovksnwisqwTurbidity sensor maintenance and data reporting

followed the USGS procedures described by Wagneret al (2006) with the exception of increased length betweencalibration checks (because dissolved oxygen and pHdata were not collected at monitoring sites) Sensors werecleaned and calibrated approximately every 2 monthsadditional cleaning visits were made when real-time data

Hydrol Process 27 1426ndash1439 (2013)

Table

ILocationandcontributin

gdrainage

area

ofsamplingsitesupstream

anddownstream

from

John

RedmondReservoireast-central

Kansas

USGS

identifi

catio

nnu

mber

Site

name

Total

drainage

area

(km

2)

Unregulated

drainage

area

(km

2)

Nearestupstream

reservoir

andcorrespondingregulated

drainage

area

(km

2)

Latitu

deLongitude

Periodof

stream

flow

andcontinuous

turbidity

operation

07179730

NeoshoRiver

near

Americus

Kansas

1611

974

CouncilGrove

Reservoir(637)

38 28prime01

0096

15prime01

00February2007

ndashMay

2009

07182250

Cottonw

oodRiver

near

PlymouthKansas

4507

3989

MarionReservoir(518)

38 23prime51

0096

21prime21

00February2007

ndashMay

2009

07182390

NeoshoRiver

atNeosho

RapidsKansas

7130

5975

CouncilGrove

andMarion

Reservoirs(1155)

38 22prime03

0096

00prime07

00August2009ndashS

eptember2010

07182510

NeoshoRiver

atBurlin

gton

Kansas

7879

70John

RedmondReservoir(7808)

38 11prime40

0095

44prime40

00February2007

ndashSeptember2010

1429CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

Copyright copy 2012 John Wiley amp Sons Ltd

indicated sensor fouling Quality-assurance checks weremade before and after sensor cleaning and calibration withan independently calibrated sensor Because in-streamturbidity conditions occasionally exceeded the uppermeasurement limit of YSI 6136 turbidity sensors HachSolitax SC turbidityoptical backscatter sensors (Solitax)were operated at Americus Plymouth and Neosho Rapidsadjacent to YSI sensors The Solitax sensor uses an internalalgorithm to convert a ratiometric turbidityoptical back-scatter signal to an estimate of suspended-solids concentra-tion Solitax sensors have an approximate range from 0 to50 000 mgl of suspended solids (Hach Company 2005)and were installed to estimate suspended-sediment concen-tration (SSC) when YSI turbidity values were missing orgreater than the range of the sensor (1000ndash1500 formazinnephelometric units)Streamflow data were computed using the standard

USGS methods (Turnipseed and Sauer 2010) Riverstage was continuously measured in 15-min incrementsusing automated methods and was cross-checked with awire-weight gage during periodic site visits Streamflowmeasurements were collected approximately every 6weeks and during extreme flow conditions to establishand continually update a stagedischarge relation at eachsite which was then used to compute a continuous15-min record of streamflowSuspended-sediment samples were collected using equal-

width or equal-discharge increment methods using manualdepth-integrated sampling techniques described by Nolanet al (2005) All samples were analysed for SSC and 16inflow samples collected during high streamflow conditionswere analysed for a selected grain-size distribution (percentof sediment less than 2 4 8 16 31 and 63 mm in diameter)at the USGS Sediment Laboratory in Iowa City Iowa usingthe pipet method described by Guy (1969) Turbidity valueswere measured across the width of the stream during thecollection of suspended-sediment samples Median valuesof cross-sectional measurements were compared with in-stream sensors to assess the ability of each in-stream sensorto represent turbidity conditions across the width of thestream (for more details see Lee et al 2008) In-streamsensors accounted for 92 to 97 of the variability acrossstream cross sections and had a near 11 relation in slope(090ndash111 among sites) Cross-sectional variability inturbidity was minimal at all sites where measurementsmuch outside of the 11 fit typically were during periods ofrapidly changing turbidity conditions Because consistentbias was not observed in the relation at any monitoringlocation values from continuous water quality monitors aredeemed representative of stream water quality across thewidth of the stream cross section Turbidity recordsgenerally were rated good (error of 5ndash10) andoccasionally fair (10ndash15) on the basis of the guidelinesdeveloped by Wagner et al (2006)

Computation of continuous SSC

Ordinary least squares regressions were developed tocompute a continuous record of SSC and suspended-sediment

Hydrol Process 27 1426ndash1439 (2013)

1430 C LEE AND G FOSTER

load using periodically collected SSC and continuousturbidity continuous Solitax and continuous streamflowdata upstream and downstream from John RedmondReservoir (Lee et al 2008) Continuous turbidity sensorswere occasionally not operational or were malfunctioningduring sample collection in these instances cross-sectional turbidity measurements were used in place ofin-stream turbidity measurements in regression relationsAll values were log-transformed to better approximatenormality evenly distribute regression residuals and toavoid the prediction of negative values Regressionrelations between in-stream turbidity Solitax and SSCwere applied to the continuously recorded values andmultiplied by continuous streamflow data and a conversionfactor (as described in Rasmussen et al 2009) to obtaincontinuous (15-min) estimates of suspended-sediment loadAfter applying the regression model to log-transformedturbidity data log-transformed SSC values were retrans-formed back to linear space Because this retransformationcan cause bias when adding instantaneous values of loadestimates with time a log-transformation bias correctionfactor (Duanrsquos smearing estimator Duan 1983) wasmultiplied to correct for potential bias (Cohn and Gilroy1991 Helsel and Hirsch 1992) Regression methods used inthis study were developed using protocols described inRasmussen et al (2009)Occasionally turbidity data are recorded at a sensor-

specific maximum reporting limit typically between1000 and 1500 formazin nephelometric units Duringthese periods which were only observed at the Americusand Plymouth sampling sites continuous Solitax-derivedestimates of SSC were used when they exceededturbidity-derived estimates Solitax-derived estimates ofSSC also were used if and when turbidity data weremissing because of environmental fouling or sensormalfunction Solitax-derived estimates of SSC duringperiods of sensor truncation are 3 of the total load at theAmericus and Plymouth sites (which were operationalthrough June of 2009)Occasionally both continuous turbidity and Solitax

measurements were missing or deleted from the continuousrecord because of equipment malfunction environmentalfouling or bothWhen these dataweremissing during stablelow-flow conditions SSC values were estimated byinterpolating betweenmeasured data points When turbidityand Solitax were missing during changing flow andturbidity conditions suspended-sediment loads wereestimated using continuous streamflow data as the explana-tory variable (Figure 3B) These periods accounted forapproximately 11 of the total sediment load transported toJohnRedmondReservoirModel standard percentage errors(Rasmussen et al 2009) for turbidity-based estimates ofSSC ranged from30 to 40 atAmericus to approximately15 at BurlingtonAnnual suspended-sediment loads and 95 CIs were

quantified by the USGS LOADEST program (Runkelet al 2004) in 2007 using both turbidity and streamflowas surrogates at Americus Plymouth and Burlington toestimate and compare the uncertainty of annual load

Copyright copy 2012 John Wiley amp Sons Ltd

estimates Turbidity-computed loads were generally lessthan streamflow computed loads and were more certainranging from approximately 20 (Americus) to 10(Plymouth and Burlington) of annual load estimates(Figure 4) Conversely streamflow-computed annual loadswere consistently larger than turbidity-based models andwere much less certain 95 uncertainty bands wereapproximately 60 of the annual load at Americus 40of the annual load at Plymouth and 50of the annual load atBurlington Although 89 of incoming sediment loadswere estimated using turbidity and it can be estimated with95 certainty that annual loads from these sites are within10ndash20 the use of multiple data sources and thecontributions of sediment from ungaged areas make itimpossible to exactly quantify the uncertainty of loadestimates to JohnRedmondReservoir Because the turbiditysensor was operational during practically entire period ofrecord at Burlington there is a 95 chance that reportedannual loads from John Redmond Reservoir are within 10of reported values

Reservoir modelling

CE-QUAL-W2V36 is a two-dimensional hydrodynamicwater qualitymodel used in this study to simulate the averagedaily residence time of water leaving John RedmondReservoir (Cole and Wells 2008) Estimates of residencetime are necessary to match flow transported from reservoiroutflows to corresponding inflows to estimate sedimenttrapping efficiency at relatively short (days to months) timescales Daily estimates of the average residence time ofoutflows were obtained by simulating the length of time aconservative tracer would remain within the reservoir (Coleand Wells 2008) Reservoir bathymetry was represented by26 vertical and 21 horizontal cells on the basis of an existingUSACE model developed in 2007 (D Gade writtencommunication 2010) and an updated conservation poolbathymetry survey conducted in 2007 (Kansas BiologicalSurvey 2010) and by interpolating range lines surveyed bythe USACE in 1957 for flood pool elevations (C Gnauwritten communication 2010) The bathymetry of the floodpool upstream from available spatial data sets was initiallycharacterized using the existing USACE model bathymetryand then adjusted on the basis of the observed differencesbetween the USACE model and the available spatial dataThe USACE (2010) daily computed inflow and outflow

data were input into the model along with daily USGStemperature SC and continuously computed SSC values(computed as a flow-weighted average from 15-min data)collected at upstream gage sites The USACE daily inflowdata were input into the model because the USGS gagesites were upstream from the reservoir and thus wouldless accurately represent the timing and quantity ofreservoir inflows Before computing daily flow-weightedaverages water quality data from Americus and Plymouthwere lagged by the average approximate travel times ofstreamflow from these sites to the Neosho Rapids site (18and 20 h respectively) Values were not lagged from theNeosho Rapids site because it is near where backwater

Hydrol Process 27 1426ndash1439 (2013)

Figure 3 Regression analysis between (A) YSI model 6136 turbidity and Hach Solitax sensors with SSC and (B) streamflow with suspended-sediment load

1431CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

conditions have been observed from John RedmondReservoir during extreme flood events In CE-QUAL-W2 it is necessary to compute incoming total dissolvedsolids (TDS) to simulate water density As described byHem (1985) TDS is linearly related to SC at a slopebetween 055 and 075 For this study a value of 067 was

Copyright copy 2012 John Wiley amp Sons Ltd

used to compute daily TDS values from YSI SC values(as was performed by Sullivan et al 2007)The CE-QUAL W2 model was calibrated into the

USACE reservoir elevation data from February 2007through September 2010 (USACE 2010) and the USGScollected temperature and SC and continuously computed

Hydrol Process 27 1426ndash1439 (2013)

0

200000

400000

600000

800000

1000000

1200000

Neosho River nearAmericus Kansas

Cottonwood Rivernear Plymouth Kansas

Neosho River nearBurlington Kansas

Sedi

men

t loa

d in

met

ric

tons

Sediment load computed usinghourly turbidity streamflow andperiodic SSC data

Sediment load computed usinghourly streamflow and periodicSSC data

(Upstream) (Upstream) (Downstream)

95 confidence intervals

Figure 4 Comparison of continuous turbidity and streamflow-computedestimates of annual suspended-sediment loads

1432 C LEE AND G FOSTER

SSC values at the Burlington site (downstream from JohnRedmond Reservoir) The entire period of record wasused for calibration because the model was developedexclusively to represent the residence time of waterthrough the reservoir for the further purpose of estimatingsediment trapping efficiency at temporal scales of days toweeks Data input into the model included reservoirelevation (USACE 2010) precipitation air and dewpoint temperature wind speed and direction and cloudcover (National Weather Service 2010a) incomingtemperature TDS SSC and incoming and outgoingstreamflow were input into the model Modifications todefault model conditions were as follows (i) thepartitioning of incoming SSCs into four classes withdifferent settling rates (0001 08 15 and 8mday)which were adjusted to approximate outflow sedimentconcentration and load and (ii) the adjustment of wind-sheltering coefficients to 080 representing that windobserved at the nearby Emporia weather station (NationalWeather Service 2010a) was observed at 80 strength atthe surface of John Redmond Reservoir Althoughadjustments to the model were not verified to representreal-world conditions they did result in a relativelyconsistent simulation of reservoir elevation relative toobserved values through the study period (Figure 5A)Simulated reservoir elevations compared well with the

observed values of the USACE [root mean squared error(RMSE) of 010m Figure 5A] Simulated outflow watertemperatures also closely matched observed watertemperature at Burlington (RMSE of 146 C betweensimulated and observed values Figure 5B) Simulatedtemperature profiles indicated that the reservoir was rarelystratified during the study period which was consistent withavailable in-reservoir data (USACE profiles collected in2007 D Gade written communication 2010 Figure 6)Reservoir temperatures were well mixed from top to bottomduringmost of the spring summer and fall of 2007 but weresomewhat stratified (approximate decrease of 1 Cm)through a 7-m water column in July of 2007Simulated SC from the reservoir was frequently greater

than observed SC (Figure 5C) especially during periodswith low flows and longer residence times This wasprimarily because major ions that increase SC were not

Copyright copy 2012 John Wiley amp Sons Ltd

transported conservatively through the reservoir AverageSC values in inflows were 149 mScm greater than thosein reservoir outflows These results indicate the potentialof the biomediated precipitation of calcium carbonate(typically the dominant cationsanions in temperatereservoirs Wetzel 2001) within the reservoir as otherstudies have determined decalcification within reservoirswith similar SC values (Wetzel 2001) Further discussionof this phenomenon is beyond the scope of this studyother than to indicate that SC was not an adequate tracerof water movement through John Redmond Reservoirespecially during longer residence timesAlthough it was necessary to calibrate the model to

outgoing suspended-sediment flux to represent reservoirhydrodynamics through the period of study (Figure 5D)model results were not used to evaluate the effect ofreservoirmanagement on sediment trapping This is becauseno in-reservoir sediment data were collected and thus it wasimpossible to represent spatial patterns of sedimentdeposition or the degree to which previously depositedsedimentswere resuspended bywaves or incomingflows Inaddition because the entire data record was used to bestrepresent model hydrodynamics we were not able tovalidate the results of sediment modelling

Evaluation of sediment trapping efficiency using modellingoutput and continuous data

To evaluate the sediment-trapping efficiency of JohnRedmond Reservoir relative to the observed differences inreservoir management daily values of streamflow andsediment load (computed from 15-min data) were dividedinto 55 individual releases which accounted for 97 ofoutgoing water Releases were delineated by firstcharacterizing individual instances in which outflow gateswere adjusted to release more or less water and then byfurther dividing these periods into approximately equal-volume releases Equal-volume releases consisted ofapproximately 123 million m3 of water (approximatelydouble the volume of the conservation pool in 2010which was 59 million m3) which were larger or smallerdepending on the total volume of water transported fromwhen the gates were opened and closed The calibratedCE-QUAL-W2 model simulated the residence times foroutflows at a daily time step which were then used tomatch releases to corresponding inflows for the purposeof computing sediment trapping efficiency from incomingand outgoing sediment loads Residence timendashassignedinflow events were adjusted further to match more closelythe volume of corresponding outflow events Incomingflow volumes typically were within 10 of equivalentoutgoing volumesmdashthe remaining differences werebecause of the daily time step of streamflow and sedimentloads To account for these differences when computingsediment trapping efficiency differences in incoming andoutgoing water volumes were multiplied by the flow-weighted sediment concentration (FWSC) of the incom-ing event The FWSC is defined as the total sediment loadof the inflow event divided by the total water volume for a

Hydrol Process 27 1426ndash1439 (2013)

314

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Ele

vati

on a

bove

mea

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a le

vel

in m

Observed bathymetry Root -mean squared error = 010 m

Simulated bathymetry Average error = 007 mTop of flood pool

Top of conservation poolAverage error = 007 m

A

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-10

-5

0

5

10

15

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40

11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Wat

er te

mpr

eatu

re i

n de

gree

s C

elci

us Observed water temperature

Modeled water temperature

Root-mean squared error = 146 degrees CelciusAverage error = 118 degrees Celcius

0

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910St

ream

flow

in

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er s

econ

d

Spe

cifi

c co

nduc

tanc

e in

mic

rosi

emen

s Observed specific conductanceSimulated specific conductanceStreamflow

Root-mean squared error = 117 microScmAverage error = 69 microScm

C

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Stre

amfl

ow f

rom

Joh

n R

edm

ond

Res

ervo

ir i

n m

3 per

sec

ond

Cum

ulat

ive

sedi

men

t flu

x fr

om J

ohn

Red

mon

d R

eser

voir

in m

etri

c to

ns

Turbidity-computed sediment load

Simulated sediment load

StreamflowRoot-mean squared error of observed and modeled SSC values = 65 mgL

Average error = 38 mgL

Figure 5 Comparison of simulated and observed (A) reservoir elevation (B) outflow water temperature (C) outflow SC and (D) outflow cumulativesuspended-sediment load from February 2007 through September 2010

1433CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

specified period as indicated in Equation 1

FWSCin frac14 SLin=WVin 106 (1)

where FWSCin is the FWSC of the incoming event(mgl) SLin is the incoming sediment load of the event(metric tons) and WVin is the volume of water of theincoming event (m3) The FWSC is then multiplied by thedifference in inflowoutflow volumes and the resulting

Copyright copy 2012 John Wiley amp Sons Ltd

sediment load is added (or subtracted) from the originalincoming sediment load as indicated in Equation 2

SLinadj frac14 SLin thorn FWSCin WVout WVineth THORN=106 (2)

where SLinadj is the adjusted incoming sediment load(metric tons) and WVout is the volume of water released(m3) Sediment trapping efficiency for each eventreleasepair is then defined as the percentage of the adjusted

Hydrol Process 27 1426ndash1439 (2013)

0

02

04

06

08

1

12

0 02 04 06 08 1 12

Obs

erve

d av

erag

e di

ffer

ence

in w

ater

tem

pera

ture

in

degr

ees

Cm

Modeled average difference in watertemperature in degrees Cm

July 2007

March April May JuneAugust September October

and November 2007

Figure 6 Modelled versus observed differences in water temperature fromthe surface to the bottom near John Reservoir dam 2007

1434 C LEE AND G FOSTER

incoming sediment load trapped in the reservoir asindicated in Equation 3

TE frac14 100 SLinadj SLout

=SLinadj (3)

Equal-volume inflow events were transported between3100 and 260 000 metric tons of suspended sedimentinto John Redmond Reservoir The largest sedimentloads were transported during relatively short-termrainfallrunoff events whereas smaller loads weretransported during low-flow conditions Least squaresregression was used to explore relations between thesediment trapping efficiency of eventrelease pairs andthe measures of residence time reservoir elevation andsediment concentration and load entering John RedmondReservoir Different measures of residence time and lakeelevation were computed for each eventrelease byweighting these variables by the length of time thewater (flow weighted) or sediment (sediment weighted)for a particular eventrelease pair was present within thereservoir (eg the reservoir elevation for a particular daywas weighted by 05 if one half the water or sedimentfor an individual eventrelease pair was withinthe reservoir or by 10 if all of the water or sedimentwas within the reservoir)Only releases with incoming sediment loads greater

than 37 000 metric tons were used in this analysis as thesediment trapping efficiency of smaller events wasjudged to be primarily affected by background levelsof sediment within the reservoir (such as from algae orwind-related resuspension of bottom sediment) orpotentially from sediment suspended from the streamchannel between the reservoir outflow and Burlingtonmonitoring site These releases are less important topredict because they represent a relatively small part ofthe total sediment load transported to the reservoirStepwise multiple linear regression was then used tocharacterize variables which best estimated sedimenttrapping efficiency among eventrelease pairs withoutexhibiting multicollinearity

Copyright copy 2012 John Wiley amp Sons Ltd

RESULTS AND DISCUSSION

Hydrologic conditions

Precipitation upstream from John Redmond Reservoirduring the study period (2007ndash2010) was generally greaterthan historical averages The National Weather Servicestation at the Neosho Rapids (directly upstream from JohnRedmond Reservoir) recorded 815 in of precipitation in2007 1298 in in 2008 and 1196 cm in 2009 comparedwith the annual average of 914 in from 1950 to 2006(National Weather Service 2010b) Annual flows weresummed from theAmericus and Plymouth sites whichwere839 km3 in 2007 1591 km3 in 2008 and 1332 km3 in2009 the median combined annual flow from these siteswas 1061 km3 from 1964 to 2006 Rivers exceeded the2-year (50 annual) USGS flood-frequency estimates 14times at the Plymouth and Neosho Rapids sites (Perry et al2004) indicating that relatively extreme storms (andcorresponding high sediment loads) were frequentlyobserved during the study period Increased rainfall andflow during the study period indicate that sediment flux to(and likely from) the reservoir is likely larger than duringa typical 4-year study period

Sediment transport to and from John Redmond Reservoir

The maximum computed SSC upstream from thereservoir was 7690mgl whereas the maximum SSC was1080mgl downstream from the reservoir From February2007 through September 2010 approximately 5 600 000metric tons of sediment entered John Redmond Reservoir660 000 of which were transported past the Burlington site(trapping efficiency of 88) Nearly all of the suspendedsediment at upstream sampling sites consisted of silt andclay (the median sample was 96 less than 63mm indiameter) Approximately one half of the sedimentstransported to John Redmond Reservoir during high-flowconditions were clays (lt2mm) the remaining sedimentswere distributed somewhat equally between 2 and 63mmSimilar to inflow samples 98 of the reservoir outflowsuspended sediment sampled had diameters of less than63 mm (full grain-size analysis was not conducted)Because suspended-sediment and reservoir-bottom sam-ples (Juracek 2010) indicate a lack of sand and larger-sized material and because streambed substrates at gagesites were observed to consist primarily of cobble androck bedload transport of sediment is not considered asubstantial component of the sediment load transported toJohn Redmond ReservoirThe annual volume of flow and sediment load transported

into John Redmond Reservoir generally corresponded withannual patterns in precipitation (Figure 7) The trappingefficiency of the reservoir initially decreased during yearswith greater precipitation and streamflow in 2008 and 2009but continued to decrease despite smaller flows andincoming sediment loads in 2010 potentially because ofdifferences in the manner in which the reservoir wasoperated An examination of inflows outflows and reservoirlevels in John Redmond Reservoir indicated that although

Hydrol Process 27 1426ndash1439 (2013)

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cipi

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dse

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load

in

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IncomingstreamflowIncomingsedimentOutgoingsedimentPrecipitation

2007 2008 2009 2010

929

886

858

853

Sediment trapping efficiency

Figure 7 Annual precipitation streamflow and sediment transport to andfrom John Redmond Reservoir February 2007 to September 2010

1435CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

inflows were smaller in 2010 than those in 2008 and 2009water was released more rapidly after sediment-ladeninflows in 2010 in part because of maintenance activitiesthat necessitated lower reservoir levels (T Lyons USACEoral communication 2010)

Sediment trapping efficiency relative to variation inreservoir management

Among the 48 eventrelease pairs with greater than37 000 metric tons of incoming sediment sedimenttrapping efficiency varied from 48 to 97 Relations

A

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316 318 320 322 324 326

Flow-weighted reservoir elevation in meters

Linear fit

Top of conservation pool Top of flood pool

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Tur

bidi

ty-c

ompu

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sedi

men

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g ef

fici

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perc

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Tur

bidi

ty-c

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sedi

men

t tra

ppin

gef

fici

ency

in

pres

ent

Regression-predicted sep

C

Figure 8 Regression relations between turbidity-computed sediment trappinand (C) regression-predicted estimates of sedim

Copyright copy 2012 John Wiley amp Sons Ltd

established between sediment trapping efficiency flow-weighted reservoir elevation (Figure 8A) and flow-weighted residence time (Figure 8B) indicated that alarger proportion of incoming sediment is transportedthrough John Redmond Reservoir when the reservoir ismaintained at lower levels and when residence times areminimized (Table II) However these relations were alsomore variable during these conditions In additionpredicted sediment trapping efficiencies were nonlinearwith respect to the primary explanatory variables(reservoir elevation FWSCs and flow-weighted residencetime) single linear regressions typically overpredictedeventrelease pairs with small trapping efficiencies andunderpredicted eventrelease pairs with larger sedimenttrapping efficiencies To minimize bias in predictionsof sediment trapping efficiency separate linear regres-sions were identified for eventrelease pairs above andbelow a reservoir elevation threshold of 3185 m Theresulting predictions were similar to single regressionequations in terms of adjusted R2 and error but residualswere distributed more evenly throughout the range ofvaluesAmong eventrelease pairs in which reservoir elevation

was maintained lower than 3185m the variability inthe sediment trapping efficiency was best explained byflow-weighted reservoir elevation (EL) and by the FWSCof the inflow event (Figure 8C) Among eventrelease pairsin which reservoir elevation was maintained higher than3185 m the variability in sediment trapping efficiency

40

50

60

70

80

90

100

5 15 25 35 45

Flow-weighted residence time of event in days

B

80 90 100

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

gef

fici

ency

in

perc

ent

diment trapping efficiency in ercent

g efficiency and (A) flow-weighted reservoir elevation (B) residence timeent trapping efficiency for eventrelease pairs

Hydrol Process 27 1426ndash1439 (2013)

Table II Summary of sediment loading and reservoir characteristics during delineated inflows and releases from John RedmondReservoir

Range of flow-weighted averagereservoir elevations (ft abovemean sea level)

No eventrelease pairs

Turbidity-computedload in (metric tons)

Turbidity-computedload out

(metric tons)

Turbidity-computedtrapping efficiency

()

Regression-predictedsediment trappingefficiency ()

All releases 3161ndash3234 48 5 240 000 595 000 886 8873161ndash3173 8 708 000 179 000 747 7463173ndash3179 10 1 101 000 159 000 856 8623179ndash3197 8 892 000 88 000 901 8923197ndash3210 11 1 201 000 84 000 930 9253210ndash3234 11 1 337 000 85 000 936 945

Table III Maximum discharges and approximate travel times ofreleases to flood control points downstream from John Redmond

Reservoirdaggera

Flood controlend point

Approximate travel timefrom outflow gates (h)daggerb

Maximumdischarge (m3s)

Neosho River atBurlington Kansas

2 396

Neosho River atIola Kansas

24 510

Neosho River atChanute Kansas

36 510

Neosho River atParsons Kansas

60 481

Neosho River atCommerce Oklahoma

84 623

a In addition to flood control end points the water control manual specifiesthat outgoing discharges do not rise or fall by more than 566m3s every 3h the manual also includes the consideration of conditions at otherreservoirs in the basin (USACE 1996)b Data from USACE (1996)

326s

1436 C LEE AND G FOSTER

was best explained by flow-weighted hydraulic residencetime (RES) and by the FWSC of the inflow event(Figure 8C) All explanatory variables were significantlyrelated to sediment trapping efficiency (P valuelt 005) andthe regression equations chosen resulted in the largestadjusted R2 the smallest RMSE values and the smallestprediction error sum of squares values of other potentialcombinations of independent variables The varianceinflation factors among independent variables were lessthan 12 indicating that multicollinearity among incomingFWSC values and flow-weighted reservoir elevations didnot inflate or adversely affect regression estimates (Helseland Hirsch 1992) In addition to decreased sedimenttrapping efficiency during low reservoir levels and residencetimes more sediment was trapped when incoming sedimentconcentrations were larger (as indicated by larger FWSCvalues) potentially because of increased flocculation (andthus larger effective grain size and fall velocity Droppo andOngley 1994)Although sediment trapping predictions were variable

for individual eventrelease pairs they were muchmore accurate with respect to turbidity-computed resultsfor multiple event releases when grouped by flow-weighted reservoir elevation (Table II) Results suggestthat (i) reductions in trapping efficiency consistently areobserved when reservoir elevations are maintained nearconservation pool levels and (ii) although regression-derived estimates of sediment trapping may be inaccuratefor individual eventrelease pairs this method can producerelatively accurate long-term estimates of sedimenttrapping efficiency with respect to factors that can beaffected by altered reservoir management

315

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Ele

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Observed reservoir level

Altered outflow scenario

Flood-control pool elevation

Conservationpool

elevation

Example period (fig 10)

Figure 9 Observed and altered elevation at John Redmond ReservoirFebruary 2007 through September 2010

Potential of altered reservoir management to reducesediment trapping

The USACE water control manual for John RedmondReservoir identifies specific flood control end points thatmust not be exceeded to ensure that reservoir outflows donot contribute to flood-related damages to crops andstructures (Table III) Any changes to reservoirmanagementmust also meet these end points to fulfil the mission forwhich John Redmond Reservoir was constructed Tosimulate the potential effect of changes in reservoir outflowmanagement on sediment accumulation in John RedmondReservoir an idealized and altered outflow management

Copyright copy 2012 John Wiley amp Sons Ltd

scenario was constructed which continued to meet theseend points while also preserving storage for drinking wateragricultural use and industrial use (Figure 9) This scenariois lsquoidealizedrsquo in that it benefits from the knowledge ofincoming and downstream flows whereas in practicereservoir operators rely on weather forecasts and modellingto ensure consistent water supplies and to preventdownstream flooding Because of the inability to access

Hydrol Process 27 1426ndash1439 (2013)

1437CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

historical rainfall forecasts or models that predict flows toand from John Redmond Reservoir results from thisscenario are presented as the maximum potential reductionin sediment trapping that could be achieved within currentoperational plansRelative to observed reservoir management the altered

scenario purposefully minimized reservoir elevation andresidence time through larger more rapid releases ofwater after periods of high inflows (Figure 9) To partiallycompensate for uncertainties in weather forecasts andpredicted streamflows the altered management scenariowas somewhat more conservative compared with watercontrol plan restrictions in that (i) outflows were notincreased bymore than 850m3sday and (ii) outflows werenever reduced by more than 566m3sday (other than whenthe reservoir was actuallymanaged in this fashion) After theconstruction of the altered scenario reservoir releases wereredelineated and matched to inflows as done with observeddata Flow-weighted reservoir elevations residence timesand FWSCs for incoming events were recomputed toestimate the effect of the altered management scenario onsediment trapping efficiency (using regressions developedwith observed values)Although data and models were not available to test the

uncertainty of forecasted flow conditions an exampleperiod (Figures 9 and 10) in July 2007 is highlighted toillustrate how the consideration of sediment trapping withinexisting reservoir operational plans could preserve reservoirstorage During late June and early July 2007 heavy rainsupstream and downstream from John Redmond Reservoirraised reservoir levels and caused flooding downstreamfrom the reservoir (see the Neosho River at Parsons as anexample Figure 10) These flows raised reservoir levelswell into the flood pool where it was held until 16 July2007 when John Redmond Reservoir gates began releasingmore water (as indicated by the observed Burlingtonstreamflow record Figure 10) Historical weather forecastsfrom the National Weather Service for Iola Kansas(between Burlington and Parsons National Weather

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Res

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Observed reservoir elevation

Alternate reservoir elevation

Observed flow at Burlington KS

Alternate flow at Burlington KS

Observed flow at Parsons KS

Alternate flow at Parsons KS

Figure 10 Comparison of observed reservoir elevations and downstreamflow conditions relative to the altered reservoir management scenario June

to August 2007

Copyright copy 2012 John Wiley amp Sons Ltd

service written communication 2011) projected con-sistent (20ndash50) chances of thunderstorms throughoutJuly Compared with observed reservoir managementthe altered outflow scenario began discharging wateron 5 July 2007 while keeping downstream flows belowmaximum levels indicated in John Redmond Reservoircontrol manual (Table III) Two eventrelease pairs weredelineated during this period modelled residence timesbased on observed reservoir levels were 72 and 26 daysMore rapid release of water from John RedmondReservoir through the alternative scenario reduced theseto 66 and 16 days respectively Sediment trappingfor these periods is projected to have decreased from971 to 893 and from 957 to 822 Althoughthis example illustrates that consideration of sedimenttrapping within reservoir operational plans can reducesediment trapping uncertainty of weather forecasts andrainfall runoff models will often limit the ability topreserve reservoir storage within existing operationlimitsFrom 2007 to 2010 the altered management scenario

decreased the average reservoir elevation from 3178 to3173 m and decreased the average daily residence timesfrom 296 to 269 days Forty-six releases with more than37 000 tons of incoming sediment were delineated under thealtered scenario (compared with 48 observed releases)corresponding to 51 million metric tons of incomingsediment (compared with 53 million metric tons computedwith observed data Table IV) The average reservoirelevations for eventrelease pairs decreased from 3194 to3186 m and for residence times from 199 to 131 daysUnder the altered management scenario regression-predicted sediment trapping efficiency was reduced by39 passing approximately 180 000 additional metric tonsof sediment through the reservoir Estimates indicate thatwithin existing operational constraints altered reservoirmanagement has a maximum potential of decreasingsediment trapping by approximately 45 000 metric tonsper year or approximately 3 of the annual load of 141million metric tons transported during the study periodAn annual reduction of 45 000 tons of sediment from

John Redmond Reservoir would preserve approximately74 000m3 of storage in the reservoir per year This rate isequivalent to 15 of the designed reservoir sedimentationrate and equivalent to 81 of the observed sedimentationrate in the conservation pool given the average bulk densityestimates of Juracek (2010) As more reservoir storage islost to deposited sediments the residence time of sediment-laden inflows will continue to decrease and reservoirmanagement is likely to become a more effective alternativeto reducing reservoir sedimentation If existing reservoiroperational plans were adapted to accommodate watersupply as well as flood control uses of the reservoir alteredreservoir management might further reduce sedimentaccumulationAlthough continuous flow and sediment data have proven

useful in determining how short-term variability inhydrology and reservoir management affect sedimentationimproved understanding of in-reservoir processes is needed

Hydrol Process 27 1426ndash1439 (2013)

Table IV Sediment transport to and from John Redmond Reservoir during delineated inflows and releases under the alteredmanagement scenario

Range of flow-weighted averagereservoir elevations (meters abovemean sea level)

No releaseevents

Turbidity-computedload in (metric tons)

Regression-predictedload out (metric tons)

Regression-predicted sedimenttrapping efficiency ()

All releases 46 5 126 000 778 000 8483161ndash3173 17 1 813 000 426 000 7653173ndash3179 7 855 000 151 000 8243179ndash3197 7 690 000 76 000 8903197ndash3210 10 992 000 94 000 9053210ndash3234 5 775 000 31 000 960

1438 C LEE AND G FOSTER

to better characterize how sediment moves through isdeposited within and is resuspended from reservoirs duringvarious hydrologic and outflow management scenarios Forexample although the maintenance of low-reservoir levelsmay reduce the total amount of sedimentation in thereservoir it could change the predominant location ofsediment deposition from the flood to the conservation poolpossibly decreasing storage where it is most needed Anexpanded collection of turbidity data within reservoirscan improve understanding of in-reservoir processes (Effleret al 2006) and could better calibrate two- or three-dimensional reservoir models In addition while increasedsediment transport downstream from the reservoir underaltered management plans would still be less than thehistorical pre-impoundment conditions investigationswould need to ascertain potential effects on infrastructureand aquatic life Any potential changes to reservoirmanagement also would need to fully evaluate the degreeto which altered management plans could increase the riskof downstream flooding However study results indicatethat despite limited in-reservoir data the coupling ofcontinuous streamflow and turbidity data with reservoirmodelling can effectively project the degree to whichchanges in reservoir management can affect sedimentationin the reservoir

CONCLUSIONS

Rapid sediment accumulation in large reservoirs willincreasingly threaten communities reliant on reservoirstorage for municipal agricultural and industrial usesDecisions will need to be made about which reservoiruses will be prioritized such as maintenance of watersupplies for nearby and downstream communities floodcontrol for downstream infrastructure and property ownersor recreational considerations Study results indicate thatcontinuous in-stream flow and turbidity monitoring can beused to quantify the potential of outflow management toreduce reservoir sedimentation Along with the collectionof hydrodynamic and sediment data within reservoirsthese data can help calibrate and validate models thatbetter quantify spatial patterns of sediment depositionand resuspension under varying hydrologic and manage-ment scenarios Study results indicate that depending onthe specific reservoir characteristics reservoir outflow

Copyright copy 2012 John Wiley amp Sons Ltd

management can help preserve water supplies especiallyas sedimentation continues to usurp storage capacityAlthough the approach presented can evaluate the potentialof altered reservoir management to preserve storagemanagement decisions need to consider potential effectschanging reservoir operations on downstream flood controland aquatic ecosystems

REFERENCES

Barfield DB 2010 Eastern Kansas Water Supply presentation given atthe 2010 Kansas Field Conference June 2 2010 Available on the Webat httpwwwksdagovincludesdocument_centerdwrPresentationsBarfield_KGSFieldTour2010pdf

Carswell WJ Hart RJ 1985 Transit losses and travel times for reservoirreleases during drought conditions along the Neosho River fromCouncil Grove Lake to Iola east-central Kansas US GeologicalSurvey Water-Resources Investigations Report 85ndash4003 40

Chung SW Hipsey MR Imberger J 2009 Modeling the propagation ofturbid density inflows into a stratified lake Daecheong ReservoirKorea Environmental Modelling and Software 24 1467ndash1482

Cohn TA Gilroy EJ 1991 Estimating loads from periodic records USGeological Survey Branch of Systems Analysis Technical Memo9101 81

Cole TM Wells SA 2008 CE-QUAL-W2 A two-dimensional laterallyaveraged hydrodynamic and water-quality model 36 userrsquos manualUS Army Corps of Engineers Waterways Experiment Station 125

Droppo IG Ongley ED 1994 Floccuation of suspended-sediment inrivers of Southeastern Canada Water Research 28(8) 1799ndash1809

Duan N 1983 Smearing estimatemdasha nonparametric retransformationmethod Journal of the American Statistical Asssociation 78 605ndash610

Effler SW Prestigiacomo AR Peng F Bulygina KB 2006 Resolution ofturbidity patterns from runoff events in a water supply reservoir and theadvantages of in situ beam attenuation measurements Lake andReservoir Management 22(1) 79ndash93

Evans JK Gottgens JF Gill WM Mackey SD 2000 Sediment yieldscontrolled by intrabasinal storage and sediment conveyance over theinterval 1842-1994 Chagrin River Northeast Ohio USA Journal ofSoil and Water Conservation 55 264ndash70

Fan J Morris GL 1992a Reservoir sedimentation I Delta and densitycurrent deposits Journal of Hydraulic Engineering 118 (3) 354ndash69

Fan J Morris GL 1992b Reservoir sedimentation II Reservoir desiltationand long-term storage capacity Journal of Hydraulic Engineering118 (3) 370ndash84

Gelda RK Effler SW 2007 Modeling turbidity in a water supply reservoiradvancements and issues Journal of Environmental Engineering 133(2)139ndash148

Guy HP 1969 Laboratory theory and methods for sediment analysis USGeological Survey Techniques of Water-Resources Investigations book5 chap C1 58

Hach Company 2005 SOLITAX sc turbidity and suspended solids usermanual Available on the Web at httpwwwhachcomfmmimghachCODEDOC0235403232_3ED_8864|1

Helsel DR Hirsch RM 1992 Statistical methods in water resourcesElsevier New York 522

Hydrol Process 27 1426ndash1439 (2013)

1430 C LEE AND G FOSTER

load using periodically collected SSC and continuousturbidity continuous Solitax and continuous streamflowdata upstream and downstream from John RedmondReservoir (Lee et al 2008) Continuous turbidity sensorswere occasionally not operational or were malfunctioningduring sample collection in these instances cross-sectional turbidity measurements were used in place ofin-stream turbidity measurements in regression relationsAll values were log-transformed to better approximatenormality evenly distribute regression residuals and toavoid the prediction of negative values Regressionrelations between in-stream turbidity Solitax and SSCwere applied to the continuously recorded values andmultiplied by continuous streamflow data and a conversionfactor (as described in Rasmussen et al 2009) to obtaincontinuous (15-min) estimates of suspended-sediment loadAfter applying the regression model to log-transformedturbidity data log-transformed SSC values were retrans-formed back to linear space Because this retransformationcan cause bias when adding instantaneous values of loadestimates with time a log-transformation bias correctionfactor (Duanrsquos smearing estimator Duan 1983) wasmultiplied to correct for potential bias (Cohn and Gilroy1991 Helsel and Hirsch 1992) Regression methods used inthis study were developed using protocols described inRasmussen et al (2009)Occasionally turbidity data are recorded at a sensor-

specific maximum reporting limit typically between1000 and 1500 formazin nephelometric units Duringthese periods which were only observed at the Americusand Plymouth sampling sites continuous Solitax-derivedestimates of SSC were used when they exceededturbidity-derived estimates Solitax-derived estimates ofSSC also were used if and when turbidity data weremissing because of environmental fouling or sensormalfunction Solitax-derived estimates of SSC duringperiods of sensor truncation are 3 of the total load at theAmericus and Plymouth sites (which were operationalthrough June of 2009)Occasionally both continuous turbidity and Solitax

measurements were missing or deleted from the continuousrecord because of equipment malfunction environmentalfouling or bothWhen these dataweremissing during stablelow-flow conditions SSC values were estimated byinterpolating betweenmeasured data points When turbidityand Solitax were missing during changing flow andturbidity conditions suspended-sediment loads wereestimated using continuous streamflow data as the explana-tory variable (Figure 3B) These periods accounted forapproximately 11 of the total sediment load transported toJohnRedmondReservoirModel standard percentage errors(Rasmussen et al 2009) for turbidity-based estimates ofSSC ranged from30 to 40 atAmericus to approximately15 at BurlingtonAnnual suspended-sediment loads and 95 CIs were

quantified by the USGS LOADEST program (Runkelet al 2004) in 2007 using both turbidity and streamflowas surrogates at Americus Plymouth and Burlington toestimate and compare the uncertainty of annual load

Copyright copy 2012 John Wiley amp Sons Ltd

estimates Turbidity-computed loads were generally lessthan streamflow computed loads and were more certainranging from approximately 20 (Americus) to 10(Plymouth and Burlington) of annual load estimates(Figure 4) Conversely streamflow-computed annual loadswere consistently larger than turbidity-based models andwere much less certain 95 uncertainty bands wereapproximately 60 of the annual load at Americus 40of the annual load at Plymouth and 50of the annual load atBurlington Although 89 of incoming sediment loadswere estimated using turbidity and it can be estimated with95 certainty that annual loads from these sites are within10ndash20 the use of multiple data sources and thecontributions of sediment from ungaged areas make itimpossible to exactly quantify the uncertainty of loadestimates to JohnRedmondReservoir Because the turbiditysensor was operational during practically entire period ofrecord at Burlington there is a 95 chance that reportedannual loads from John Redmond Reservoir are within 10of reported values

Reservoir modelling

CE-QUAL-W2V36 is a two-dimensional hydrodynamicwater qualitymodel used in this study to simulate the averagedaily residence time of water leaving John RedmondReservoir (Cole and Wells 2008) Estimates of residencetime are necessary to match flow transported from reservoiroutflows to corresponding inflows to estimate sedimenttrapping efficiency at relatively short (days to months) timescales Daily estimates of the average residence time ofoutflows were obtained by simulating the length of time aconservative tracer would remain within the reservoir (Coleand Wells 2008) Reservoir bathymetry was represented by26 vertical and 21 horizontal cells on the basis of an existingUSACE model developed in 2007 (D Gade writtencommunication 2010) and an updated conservation poolbathymetry survey conducted in 2007 (Kansas BiologicalSurvey 2010) and by interpolating range lines surveyed bythe USACE in 1957 for flood pool elevations (C Gnauwritten communication 2010) The bathymetry of the floodpool upstream from available spatial data sets was initiallycharacterized using the existing USACE model bathymetryand then adjusted on the basis of the observed differencesbetween the USACE model and the available spatial dataThe USACE (2010) daily computed inflow and outflow

data were input into the model along with daily USGStemperature SC and continuously computed SSC values(computed as a flow-weighted average from 15-min data)collected at upstream gage sites The USACE daily inflowdata were input into the model because the USGS gagesites were upstream from the reservoir and thus wouldless accurately represent the timing and quantity ofreservoir inflows Before computing daily flow-weightedaverages water quality data from Americus and Plymouthwere lagged by the average approximate travel times ofstreamflow from these sites to the Neosho Rapids site (18and 20 h respectively) Values were not lagged from theNeosho Rapids site because it is near where backwater

Hydrol Process 27 1426ndash1439 (2013)

Figure 3 Regression analysis between (A) YSI model 6136 turbidity and Hach Solitax sensors with SSC and (B) streamflow with suspended-sediment load

1431CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

conditions have been observed from John RedmondReservoir during extreme flood events In CE-QUAL-W2 it is necessary to compute incoming total dissolvedsolids (TDS) to simulate water density As described byHem (1985) TDS is linearly related to SC at a slopebetween 055 and 075 For this study a value of 067 was

Copyright copy 2012 John Wiley amp Sons Ltd

used to compute daily TDS values from YSI SC values(as was performed by Sullivan et al 2007)The CE-QUAL W2 model was calibrated into the

USACE reservoir elevation data from February 2007through September 2010 (USACE 2010) and the USGScollected temperature and SC and continuously computed

Hydrol Process 27 1426ndash1439 (2013)

0

200000

400000

600000

800000

1000000

1200000

Neosho River nearAmericus Kansas

Cottonwood Rivernear Plymouth Kansas

Neosho River nearBurlington Kansas

Sedi

men

t loa

d in

met

ric

tons

Sediment load computed usinghourly turbidity streamflow andperiodic SSC data

Sediment load computed usinghourly streamflow and periodicSSC data

(Upstream) (Upstream) (Downstream)

95 confidence intervals

Figure 4 Comparison of continuous turbidity and streamflow-computedestimates of annual suspended-sediment loads

1432 C LEE AND G FOSTER

SSC values at the Burlington site (downstream from JohnRedmond Reservoir) The entire period of record wasused for calibration because the model was developedexclusively to represent the residence time of waterthrough the reservoir for the further purpose of estimatingsediment trapping efficiency at temporal scales of days toweeks Data input into the model included reservoirelevation (USACE 2010) precipitation air and dewpoint temperature wind speed and direction and cloudcover (National Weather Service 2010a) incomingtemperature TDS SSC and incoming and outgoingstreamflow were input into the model Modifications todefault model conditions were as follows (i) thepartitioning of incoming SSCs into four classes withdifferent settling rates (0001 08 15 and 8mday)which were adjusted to approximate outflow sedimentconcentration and load and (ii) the adjustment of wind-sheltering coefficients to 080 representing that windobserved at the nearby Emporia weather station (NationalWeather Service 2010a) was observed at 80 strength atthe surface of John Redmond Reservoir Althoughadjustments to the model were not verified to representreal-world conditions they did result in a relativelyconsistent simulation of reservoir elevation relative toobserved values through the study period (Figure 5A)Simulated reservoir elevations compared well with the

observed values of the USACE [root mean squared error(RMSE) of 010m Figure 5A] Simulated outflow watertemperatures also closely matched observed watertemperature at Burlington (RMSE of 146 C betweensimulated and observed values Figure 5B) Simulatedtemperature profiles indicated that the reservoir was rarelystratified during the study period which was consistent withavailable in-reservoir data (USACE profiles collected in2007 D Gade written communication 2010 Figure 6)Reservoir temperatures were well mixed from top to bottomduringmost of the spring summer and fall of 2007 but weresomewhat stratified (approximate decrease of 1 Cm)through a 7-m water column in July of 2007Simulated SC from the reservoir was frequently greater

than observed SC (Figure 5C) especially during periodswith low flows and longer residence times This wasprimarily because major ions that increase SC were not

Copyright copy 2012 John Wiley amp Sons Ltd

transported conservatively through the reservoir AverageSC values in inflows were 149 mScm greater than thosein reservoir outflows These results indicate the potentialof the biomediated precipitation of calcium carbonate(typically the dominant cationsanions in temperatereservoirs Wetzel 2001) within the reservoir as otherstudies have determined decalcification within reservoirswith similar SC values (Wetzel 2001) Further discussionof this phenomenon is beyond the scope of this studyother than to indicate that SC was not an adequate tracerof water movement through John Redmond Reservoirespecially during longer residence timesAlthough it was necessary to calibrate the model to

outgoing suspended-sediment flux to represent reservoirhydrodynamics through the period of study (Figure 5D)model results were not used to evaluate the effect ofreservoirmanagement on sediment trapping This is becauseno in-reservoir sediment data were collected and thus it wasimpossible to represent spatial patterns of sedimentdeposition or the degree to which previously depositedsedimentswere resuspended bywaves or incomingflows Inaddition because the entire data record was used to bestrepresent model hydrodynamics we were not able tovalidate the results of sediment modelling

Evaluation of sediment trapping efficiency using modellingoutput and continuous data

To evaluate the sediment-trapping efficiency of JohnRedmond Reservoir relative to the observed differences inreservoir management daily values of streamflow andsediment load (computed from 15-min data) were dividedinto 55 individual releases which accounted for 97 ofoutgoing water Releases were delineated by firstcharacterizing individual instances in which outflow gateswere adjusted to release more or less water and then byfurther dividing these periods into approximately equal-volume releases Equal-volume releases consisted ofapproximately 123 million m3 of water (approximatelydouble the volume of the conservation pool in 2010which was 59 million m3) which were larger or smallerdepending on the total volume of water transported fromwhen the gates were opened and closed The calibratedCE-QUAL-W2 model simulated the residence times foroutflows at a daily time step which were then used tomatch releases to corresponding inflows for the purposeof computing sediment trapping efficiency from incomingand outgoing sediment loads Residence timendashassignedinflow events were adjusted further to match more closelythe volume of corresponding outflow events Incomingflow volumes typically were within 10 of equivalentoutgoing volumesmdashthe remaining differences werebecause of the daily time step of streamflow and sedimentloads To account for these differences when computingsediment trapping efficiency differences in incoming andoutgoing water volumes were multiplied by the flow-weighted sediment concentration (FWSC) of the incom-ing event The FWSC is defined as the total sediment loadof the inflow event divided by the total water volume for a

Hydrol Process 27 1426ndash1439 (2013)

314

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Ele

vati

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bove

mea

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vel

in m

Observed bathymetry Root -mean squared error = 010 m

Simulated bathymetry Average error = 007 mTop of flood pool

Top of conservation poolAverage error = 007 m

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-10

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Wat

er te

mpr

eatu

re i

n de

gree

s C

elci

us Observed water temperature

Modeled water temperature

Root-mean squared error = 146 degrees CelciusAverage error = 118 degrees Celcius

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ream

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econ

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Spe

cifi

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nduc

tanc

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mic

rosi

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s Observed specific conductanceSimulated specific conductanceStreamflow

Root-mean squared error = 117 microScmAverage error = 69 microScm

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Stre

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ervo

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3 per

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sedi

men

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x fr

om J

ohn

Red

mon

d R

eser

voir

in m

etri

c to

ns

Turbidity-computed sediment load

Simulated sediment load

StreamflowRoot-mean squared error of observed and modeled SSC values = 65 mgL

Average error = 38 mgL

Figure 5 Comparison of simulated and observed (A) reservoir elevation (B) outflow water temperature (C) outflow SC and (D) outflow cumulativesuspended-sediment load from February 2007 through September 2010

1433CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

specified period as indicated in Equation 1

FWSCin frac14 SLin=WVin 106 (1)

where FWSCin is the FWSC of the incoming event(mgl) SLin is the incoming sediment load of the event(metric tons) and WVin is the volume of water of theincoming event (m3) The FWSC is then multiplied by thedifference in inflowoutflow volumes and the resulting

Copyright copy 2012 John Wiley amp Sons Ltd

sediment load is added (or subtracted) from the originalincoming sediment load as indicated in Equation 2

SLinadj frac14 SLin thorn FWSCin WVout WVineth THORN=106 (2)

where SLinadj is the adjusted incoming sediment load(metric tons) and WVout is the volume of water released(m3) Sediment trapping efficiency for each eventreleasepair is then defined as the percentage of the adjusted

Hydrol Process 27 1426ndash1439 (2013)

0

02

04

06

08

1

12

0 02 04 06 08 1 12

Obs

erve

d av

erag

e di

ffer

ence

in w

ater

tem

pera

ture

in

degr

ees

Cm

Modeled average difference in watertemperature in degrees Cm

July 2007

March April May JuneAugust September October

and November 2007

Figure 6 Modelled versus observed differences in water temperature fromthe surface to the bottom near John Reservoir dam 2007

1434 C LEE AND G FOSTER

incoming sediment load trapped in the reservoir asindicated in Equation 3

TE frac14 100 SLinadj SLout

=SLinadj (3)

Equal-volume inflow events were transported between3100 and 260 000 metric tons of suspended sedimentinto John Redmond Reservoir The largest sedimentloads were transported during relatively short-termrainfallrunoff events whereas smaller loads weretransported during low-flow conditions Least squaresregression was used to explore relations between thesediment trapping efficiency of eventrelease pairs andthe measures of residence time reservoir elevation andsediment concentration and load entering John RedmondReservoir Different measures of residence time and lakeelevation were computed for each eventrelease byweighting these variables by the length of time thewater (flow weighted) or sediment (sediment weighted)for a particular eventrelease pair was present within thereservoir (eg the reservoir elevation for a particular daywas weighted by 05 if one half the water or sedimentfor an individual eventrelease pair was withinthe reservoir or by 10 if all of the water or sedimentwas within the reservoir)Only releases with incoming sediment loads greater

than 37 000 metric tons were used in this analysis as thesediment trapping efficiency of smaller events wasjudged to be primarily affected by background levelsof sediment within the reservoir (such as from algae orwind-related resuspension of bottom sediment) orpotentially from sediment suspended from the streamchannel between the reservoir outflow and Burlingtonmonitoring site These releases are less important topredict because they represent a relatively small part ofthe total sediment load transported to the reservoirStepwise multiple linear regression was then used tocharacterize variables which best estimated sedimenttrapping efficiency among eventrelease pairs withoutexhibiting multicollinearity

Copyright copy 2012 John Wiley amp Sons Ltd

RESULTS AND DISCUSSION

Hydrologic conditions

Precipitation upstream from John Redmond Reservoirduring the study period (2007ndash2010) was generally greaterthan historical averages The National Weather Servicestation at the Neosho Rapids (directly upstream from JohnRedmond Reservoir) recorded 815 in of precipitation in2007 1298 in in 2008 and 1196 cm in 2009 comparedwith the annual average of 914 in from 1950 to 2006(National Weather Service 2010b) Annual flows weresummed from theAmericus and Plymouth sites whichwere839 km3 in 2007 1591 km3 in 2008 and 1332 km3 in2009 the median combined annual flow from these siteswas 1061 km3 from 1964 to 2006 Rivers exceeded the2-year (50 annual) USGS flood-frequency estimates 14times at the Plymouth and Neosho Rapids sites (Perry et al2004) indicating that relatively extreme storms (andcorresponding high sediment loads) were frequentlyobserved during the study period Increased rainfall andflow during the study period indicate that sediment flux to(and likely from) the reservoir is likely larger than duringa typical 4-year study period

Sediment transport to and from John Redmond Reservoir

The maximum computed SSC upstream from thereservoir was 7690mgl whereas the maximum SSC was1080mgl downstream from the reservoir From February2007 through September 2010 approximately 5 600 000metric tons of sediment entered John Redmond Reservoir660 000 of which were transported past the Burlington site(trapping efficiency of 88) Nearly all of the suspendedsediment at upstream sampling sites consisted of silt andclay (the median sample was 96 less than 63mm indiameter) Approximately one half of the sedimentstransported to John Redmond Reservoir during high-flowconditions were clays (lt2mm) the remaining sedimentswere distributed somewhat equally between 2 and 63mmSimilar to inflow samples 98 of the reservoir outflowsuspended sediment sampled had diameters of less than63 mm (full grain-size analysis was not conducted)Because suspended-sediment and reservoir-bottom sam-ples (Juracek 2010) indicate a lack of sand and larger-sized material and because streambed substrates at gagesites were observed to consist primarily of cobble androck bedload transport of sediment is not considered asubstantial component of the sediment load transported toJohn Redmond ReservoirThe annual volume of flow and sediment load transported

into John Redmond Reservoir generally corresponded withannual patterns in precipitation (Figure 7) The trappingefficiency of the reservoir initially decreased during yearswith greater precipitation and streamflow in 2008 and 2009but continued to decrease despite smaller flows andincoming sediment loads in 2010 potentially because ofdifferences in the manner in which the reservoir wasoperated An examination of inflows outflows and reservoirlevels in John Redmond Reservoir indicated that although

Hydrol Process 27 1426ndash1439 (2013)

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IncomingstreamflowIncomingsedimentOutgoingsedimentPrecipitation

2007 2008 2009 2010

929

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Sediment trapping efficiency

Figure 7 Annual precipitation streamflow and sediment transport to andfrom John Redmond Reservoir February 2007 to September 2010

1435CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

inflows were smaller in 2010 than those in 2008 and 2009water was released more rapidly after sediment-ladeninflows in 2010 in part because of maintenance activitiesthat necessitated lower reservoir levels (T Lyons USACEoral communication 2010)

Sediment trapping efficiency relative to variation inreservoir management

Among the 48 eventrelease pairs with greater than37 000 metric tons of incoming sediment sedimenttrapping efficiency varied from 48 to 97 Relations

A

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Flow-weighted reservoir elevation in meters

Linear fit

Top of conservation pool Top of flood pool

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Tur

bidi

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Regression-predicted sep

C

Figure 8 Regression relations between turbidity-computed sediment trappinand (C) regression-predicted estimates of sedim

Copyright copy 2012 John Wiley amp Sons Ltd

established between sediment trapping efficiency flow-weighted reservoir elevation (Figure 8A) and flow-weighted residence time (Figure 8B) indicated that alarger proportion of incoming sediment is transportedthrough John Redmond Reservoir when the reservoir ismaintained at lower levels and when residence times areminimized (Table II) However these relations were alsomore variable during these conditions In additionpredicted sediment trapping efficiencies were nonlinearwith respect to the primary explanatory variables(reservoir elevation FWSCs and flow-weighted residencetime) single linear regressions typically overpredictedeventrelease pairs with small trapping efficiencies andunderpredicted eventrelease pairs with larger sedimenttrapping efficiencies To minimize bias in predictionsof sediment trapping efficiency separate linear regres-sions were identified for eventrelease pairs above andbelow a reservoir elevation threshold of 3185 m Theresulting predictions were similar to single regressionequations in terms of adjusted R2 and error but residualswere distributed more evenly throughout the range ofvaluesAmong eventrelease pairs in which reservoir elevation

was maintained lower than 3185m the variability inthe sediment trapping efficiency was best explained byflow-weighted reservoir elevation (EL) and by the FWSCof the inflow event (Figure 8C) Among eventrelease pairsin which reservoir elevation was maintained higher than3185 m the variability in sediment trapping efficiency

40

50

60

70

80

90

100

5 15 25 35 45

Flow-weighted residence time of event in days

B

80 90 100

Tur

bidi

ty-c

ompu

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sedi

men

t tra

ppin

gef

fici

ency

in

perc

ent

diment trapping efficiency in ercent

g efficiency and (A) flow-weighted reservoir elevation (B) residence timeent trapping efficiency for eventrelease pairs

Hydrol Process 27 1426ndash1439 (2013)

Table II Summary of sediment loading and reservoir characteristics during delineated inflows and releases from John RedmondReservoir

Range of flow-weighted averagereservoir elevations (ft abovemean sea level)

No eventrelease pairs

Turbidity-computedload in (metric tons)

Turbidity-computedload out

(metric tons)

Turbidity-computedtrapping efficiency

()

Regression-predictedsediment trappingefficiency ()

All releases 3161ndash3234 48 5 240 000 595 000 886 8873161ndash3173 8 708 000 179 000 747 7463173ndash3179 10 1 101 000 159 000 856 8623179ndash3197 8 892 000 88 000 901 8923197ndash3210 11 1 201 000 84 000 930 9253210ndash3234 11 1 337 000 85 000 936 945

Table III Maximum discharges and approximate travel times ofreleases to flood control points downstream from John Redmond

Reservoirdaggera

Flood controlend point

Approximate travel timefrom outflow gates (h)daggerb

Maximumdischarge (m3s)

Neosho River atBurlington Kansas

2 396

Neosho River atIola Kansas

24 510

Neosho River atChanute Kansas

36 510

Neosho River atParsons Kansas

60 481

Neosho River atCommerce Oklahoma

84 623

a In addition to flood control end points the water control manual specifiesthat outgoing discharges do not rise or fall by more than 566m3s every 3h the manual also includes the consideration of conditions at otherreservoirs in the basin (USACE 1996)b Data from USACE (1996)

326s

1436 C LEE AND G FOSTER

was best explained by flow-weighted hydraulic residencetime (RES) and by the FWSC of the inflow event(Figure 8C) All explanatory variables were significantlyrelated to sediment trapping efficiency (P valuelt 005) andthe regression equations chosen resulted in the largestadjusted R2 the smallest RMSE values and the smallestprediction error sum of squares values of other potentialcombinations of independent variables The varianceinflation factors among independent variables were lessthan 12 indicating that multicollinearity among incomingFWSC values and flow-weighted reservoir elevations didnot inflate or adversely affect regression estimates (Helseland Hirsch 1992) In addition to decreased sedimenttrapping efficiency during low reservoir levels and residencetimes more sediment was trapped when incoming sedimentconcentrations were larger (as indicated by larger FWSCvalues) potentially because of increased flocculation (andthus larger effective grain size and fall velocity Droppo andOngley 1994)Although sediment trapping predictions were variable

for individual eventrelease pairs they were muchmore accurate with respect to turbidity-computed resultsfor multiple event releases when grouped by flow-weighted reservoir elevation (Table II) Results suggestthat (i) reductions in trapping efficiency consistently areobserved when reservoir elevations are maintained nearconservation pool levels and (ii) although regression-derived estimates of sediment trapping may be inaccuratefor individual eventrelease pairs this method can producerelatively accurate long-term estimates of sedimenttrapping efficiency with respect to factors that can beaffected by altered reservoir management

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elevation

Example period (fig 10)

Figure 9 Observed and altered elevation at John Redmond ReservoirFebruary 2007 through September 2010

Potential of altered reservoir management to reducesediment trapping

The USACE water control manual for John RedmondReservoir identifies specific flood control end points thatmust not be exceeded to ensure that reservoir outflows donot contribute to flood-related damages to crops andstructures (Table III) Any changes to reservoirmanagementmust also meet these end points to fulfil the mission forwhich John Redmond Reservoir was constructed Tosimulate the potential effect of changes in reservoir outflowmanagement on sediment accumulation in John RedmondReservoir an idealized and altered outflow management

Copyright copy 2012 John Wiley amp Sons Ltd

scenario was constructed which continued to meet theseend points while also preserving storage for drinking wateragricultural use and industrial use (Figure 9) This scenariois lsquoidealizedrsquo in that it benefits from the knowledge ofincoming and downstream flows whereas in practicereservoir operators rely on weather forecasts and modellingto ensure consistent water supplies and to preventdownstream flooding Because of the inability to access

Hydrol Process 27 1426ndash1439 (2013)

1437CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

historical rainfall forecasts or models that predict flows toand from John Redmond Reservoir results from thisscenario are presented as the maximum potential reductionin sediment trapping that could be achieved within currentoperational plansRelative to observed reservoir management the altered

scenario purposefully minimized reservoir elevation andresidence time through larger more rapid releases ofwater after periods of high inflows (Figure 9) To partiallycompensate for uncertainties in weather forecasts andpredicted streamflows the altered management scenariowas somewhat more conservative compared with watercontrol plan restrictions in that (i) outflows were notincreased bymore than 850m3sday and (ii) outflows werenever reduced by more than 566m3sday (other than whenthe reservoir was actuallymanaged in this fashion) After theconstruction of the altered scenario reservoir releases wereredelineated and matched to inflows as done with observeddata Flow-weighted reservoir elevations residence timesand FWSCs for incoming events were recomputed toestimate the effect of the altered management scenario onsediment trapping efficiency (using regressions developedwith observed values)Although data and models were not available to test the

uncertainty of forecasted flow conditions an exampleperiod (Figures 9 and 10) in July 2007 is highlighted toillustrate how the consideration of sediment trapping withinexisting reservoir operational plans could preserve reservoirstorage During late June and early July 2007 heavy rainsupstream and downstream from John Redmond Reservoirraised reservoir levels and caused flooding downstreamfrom the reservoir (see the Neosho River at Parsons as anexample Figure 10) These flows raised reservoir levelswell into the flood pool where it was held until 16 July2007 when John Redmond Reservoir gates began releasingmore water (as indicated by the observed Burlingtonstreamflow record Figure 10) Historical weather forecastsfrom the National Weather Service for Iola Kansas(between Burlington and Parsons National Weather

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Observed flow at Burlington KS

Alternate flow at Burlington KS

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Alternate flow at Parsons KS

Figure 10 Comparison of observed reservoir elevations and downstreamflow conditions relative to the altered reservoir management scenario June

to August 2007

Copyright copy 2012 John Wiley amp Sons Ltd

service written communication 2011) projected con-sistent (20ndash50) chances of thunderstorms throughoutJuly Compared with observed reservoir managementthe altered outflow scenario began discharging wateron 5 July 2007 while keeping downstream flows belowmaximum levels indicated in John Redmond Reservoircontrol manual (Table III) Two eventrelease pairs weredelineated during this period modelled residence timesbased on observed reservoir levels were 72 and 26 daysMore rapid release of water from John RedmondReservoir through the alternative scenario reduced theseto 66 and 16 days respectively Sediment trappingfor these periods is projected to have decreased from971 to 893 and from 957 to 822 Althoughthis example illustrates that consideration of sedimenttrapping within reservoir operational plans can reducesediment trapping uncertainty of weather forecasts andrainfall runoff models will often limit the ability topreserve reservoir storage within existing operationlimitsFrom 2007 to 2010 the altered management scenario

decreased the average reservoir elevation from 3178 to3173 m and decreased the average daily residence timesfrom 296 to 269 days Forty-six releases with more than37 000 tons of incoming sediment were delineated under thealtered scenario (compared with 48 observed releases)corresponding to 51 million metric tons of incomingsediment (compared with 53 million metric tons computedwith observed data Table IV) The average reservoirelevations for eventrelease pairs decreased from 3194 to3186 m and for residence times from 199 to 131 daysUnder the altered management scenario regression-predicted sediment trapping efficiency was reduced by39 passing approximately 180 000 additional metric tonsof sediment through the reservoir Estimates indicate thatwithin existing operational constraints altered reservoirmanagement has a maximum potential of decreasingsediment trapping by approximately 45 000 metric tonsper year or approximately 3 of the annual load of 141million metric tons transported during the study periodAn annual reduction of 45 000 tons of sediment from

John Redmond Reservoir would preserve approximately74 000m3 of storage in the reservoir per year This rate isequivalent to 15 of the designed reservoir sedimentationrate and equivalent to 81 of the observed sedimentationrate in the conservation pool given the average bulk densityestimates of Juracek (2010) As more reservoir storage islost to deposited sediments the residence time of sediment-laden inflows will continue to decrease and reservoirmanagement is likely to become a more effective alternativeto reducing reservoir sedimentation If existing reservoiroperational plans were adapted to accommodate watersupply as well as flood control uses of the reservoir alteredreservoir management might further reduce sedimentaccumulationAlthough continuous flow and sediment data have proven

useful in determining how short-term variability inhydrology and reservoir management affect sedimentationimproved understanding of in-reservoir processes is needed

Hydrol Process 27 1426ndash1439 (2013)

Table IV Sediment transport to and from John Redmond Reservoir during delineated inflows and releases under the alteredmanagement scenario

Range of flow-weighted averagereservoir elevations (meters abovemean sea level)

No releaseevents

Turbidity-computedload in (metric tons)

Regression-predictedload out (metric tons)

Regression-predicted sedimenttrapping efficiency ()

All releases 46 5 126 000 778 000 8483161ndash3173 17 1 813 000 426 000 7653173ndash3179 7 855 000 151 000 8243179ndash3197 7 690 000 76 000 8903197ndash3210 10 992 000 94 000 9053210ndash3234 5 775 000 31 000 960

1438 C LEE AND G FOSTER

to better characterize how sediment moves through isdeposited within and is resuspended from reservoirs duringvarious hydrologic and outflow management scenarios Forexample although the maintenance of low-reservoir levelsmay reduce the total amount of sedimentation in thereservoir it could change the predominant location ofsediment deposition from the flood to the conservation poolpossibly decreasing storage where it is most needed Anexpanded collection of turbidity data within reservoirscan improve understanding of in-reservoir processes (Effleret al 2006) and could better calibrate two- or three-dimensional reservoir models In addition while increasedsediment transport downstream from the reservoir underaltered management plans would still be less than thehistorical pre-impoundment conditions investigationswould need to ascertain potential effects on infrastructureand aquatic life Any potential changes to reservoirmanagement also would need to fully evaluate the degreeto which altered management plans could increase the riskof downstream flooding However study results indicatethat despite limited in-reservoir data the coupling ofcontinuous streamflow and turbidity data with reservoirmodelling can effectively project the degree to whichchanges in reservoir management can affect sedimentationin the reservoir

CONCLUSIONS

Rapid sediment accumulation in large reservoirs willincreasingly threaten communities reliant on reservoirstorage for municipal agricultural and industrial usesDecisions will need to be made about which reservoiruses will be prioritized such as maintenance of watersupplies for nearby and downstream communities floodcontrol for downstream infrastructure and property ownersor recreational considerations Study results indicate thatcontinuous in-stream flow and turbidity monitoring can beused to quantify the potential of outflow management toreduce reservoir sedimentation Along with the collectionof hydrodynamic and sediment data within reservoirsthese data can help calibrate and validate models thatbetter quantify spatial patterns of sediment depositionand resuspension under varying hydrologic and manage-ment scenarios Study results indicate that depending onthe specific reservoir characteristics reservoir outflow

Copyright copy 2012 John Wiley amp Sons Ltd

management can help preserve water supplies especiallyas sedimentation continues to usurp storage capacityAlthough the approach presented can evaluate the potentialof altered reservoir management to preserve storagemanagement decisions need to consider potential effectschanging reservoir operations on downstream flood controland aquatic ecosystems

REFERENCES

Barfield DB 2010 Eastern Kansas Water Supply presentation given atthe 2010 Kansas Field Conference June 2 2010 Available on the Webat httpwwwksdagovincludesdocument_centerdwrPresentationsBarfield_KGSFieldTour2010pdf

Carswell WJ Hart RJ 1985 Transit losses and travel times for reservoirreleases during drought conditions along the Neosho River fromCouncil Grove Lake to Iola east-central Kansas US GeologicalSurvey Water-Resources Investigations Report 85ndash4003 40

Chung SW Hipsey MR Imberger J 2009 Modeling the propagation ofturbid density inflows into a stratified lake Daecheong ReservoirKorea Environmental Modelling and Software 24 1467ndash1482

Cohn TA Gilroy EJ 1991 Estimating loads from periodic records USGeological Survey Branch of Systems Analysis Technical Memo9101 81

Cole TM Wells SA 2008 CE-QUAL-W2 A two-dimensional laterallyaveraged hydrodynamic and water-quality model 36 userrsquos manualUS Army Corps of Engineers Waterways Experiment Station 125

Droppo IG Ongley ED 1994 Floccuation of suspended-sediment inrivers of Southeastern Canada Water Research 28(8) 1799ndash1809

Duan N 1983 Smearing estimatemdasha nonparametric retransformationmethod Journal of the American Statistical Asssociation 78 605ndash610

Effler SW Prestigiacomo AR Peng F Bulygina KB 2006 Resolution ofturbidity patterns from runoff events in a water supply reservoir and theadvantages of in situ beam attenuation measurements Lake andReservoir Management 22(1) 79ndash93

Evans JK Gottgens JF Gill WM Mackey SD 2000 Sediment yieldscontrolled by intrabasinal storage and sediment conveyance over theinterval 1842-1994 Chagrin River Northeast Ohio USA Journal ofSoil and Water Conservation 55 264ndash70

Fan J Morris GL 1992a Reservoir sedimentation I Delta and densitycurrent deposits Journal of Hydraulic Engineering 118 (3) 354ndash69

Fan J Morris GL 1992b Reservoir sedimentation II Reservoir desiltationand long-term storage capacity Journal of Hydraulic Engineering118 (3) 370ndash84

Gelda RK Effler SW 2007 Modeling turbidity in a water supply reservoiradvancements and issues Journal of Environmental Engineering 133(2)139ndash148

Guy HP 1969 Laboratory theory and methods for sediment analysis USGeological Survey Techniques of Water-Resources Investigations book5 chap C1 58

Hach Company 2005 SOLITAX sc turbidity and suspended solids usermanual Available on the Web at httpwwwhachcomfmmimghachCODEDOC0235403232_3ED_8864|1

Helsel DR Hirsch RM 1992 Statistical methods in water resourcesElsevier New York 522

Hydrol Process 27 1426ndash1439 (2013)

Figure 3 Regression analysis between (A) YSI model 6136 turbidity and Hach Solitax sensors with SSC and (B) streamflow with suspended-sediment load

1431CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

conditions have been observed from John RedmondReservoir during extreme flood events In CE-QUAL-W2 it is necessary to compute incoming total dissolvedsolids (TDS) to simulate water density As described byHem (1985) TDS is linearly related to SC at a slopebetween 055 and 075 For this study a value of 067 was

Copyright copy 2012 John Wiley amp Sons Ltd

used to compute daily TDS values from YSI SC values(as was performed by Sullivan et al 2007)The CE-QUAL W2 model was calibrated into the

USACE reservoir elevation data from February 2007through September 2010 (USACE 2010) and the USGScollected temperature and SC and continuously computed

Hydrol Process 27 1426ndash1439 (2013)

0

200000

400000

600000

800000

1000000

1200000

Neosho River nearAmericus Kansas

Cottonwood Rivernear Plymouth Kansas

Neosho River nearBurlington Kansas

Sedi

men

t loa

d in

met

ric

tons

Sediment load computed usinghourly turbidity streamflow andperiodic SSC data

Sediment load computed usinghourly streamflow and periodicSSC data

(Upstream) (Upstream) (Downstream)

95 confidence intervals

Figure 4 Comparison of continuous turbidity and streamflow-computedestimates of annual suspended-sediment loads

1432 C LEE AND G FOSTER

SSC values at the Burlington site (downstream from JohnRedmond Reservoir) The entire period of record wasused for calibration because the model was developedexclusively to represent the residence time of waterthrough the reservoir for the further purpose of estimatingsediment trapping efficiency at temporal scales of days toweeks Data input into the model included reservoirelevation (USACE 2010) precipitation air and dewpoint temperature wind speed and direction and cloudcover (National Weather Service 2010a) incomingtemperature TDS SSC and incoming and outgoingstreamflow were input into the model Modifications todefault model conditions were as follows (i) thepartitioning of incoming SSCs into four classes withdifferent settling rates (0001 08 15 and 8mday)which were adjusted to approximate outflow sedimentconcentration and load and (ii) the adjustment of wind-sheltering coefficients to 080 representing that windobserved at the nearby Emporia weather station (NationalWeather Service 2010a) was observed at 80 strength atthe surface of John Redmond Reservoir Althoughadjustments to the model were not verified to representreal-world conditions they did result in a relativelyconsistent simulation of reservoir elevation relative toobserved values through the study period (Figure 5A)Simulated reservoir elevations compared well with the

observed values of the USACE [root mean squared error(RMSE) of 010m Figure 5A] Simulated outflow watertemperatures also closely matched observed watertemperature at Burlington (RMSE of 146 C betweensimulated and observed values Figure 5B) Simulatedtemperature profiles indicated that the reservoir was rarelystratified during the study period which was consistent withavailable in-reservoir data (USACE profiles collected in2007 D Gade written communication 2010 Figure 6)Reservoir temperatures were well mixed from top to bottomduringmost of the spring summer and fall of 2007 but weresomewhat stratified (approximate decrease of 1 Cm)through a 7-m water column in July of 2007Simulated SC from the reservoir was frequently greater

than observed SC (Figure 5C) especially during periodswith low flows and longer residence times This wasprimarily because major ions that increase SC were not

Copyright copy 2012 John Wiley amp Sons Ltd

transported conservatively through the reservoir AverageSC values in inflows were 149 mScm greater than thosein reservoir outflows These results indicate the potentialof the biomediated precipitation of calcium carbonate(typically the dominant cationsanions in temperatereservoirs Wetzel 2001) within the reservoir as otherstudies have determined decalcification within reservoirswith similar SC values (Wetzel 2001) Further discussionof this phenomenon is beyond the scope of this studyother than to indicate that SC was not an adequate tracerof water movement through John Redmond Reservoirespecially during longer residence timesAlthough it was necessary to calibrate the model to

outgoing suspended-sediment flux to represent reservoirhydrodynamics through the period of study (Figure 5D)model results were not used to evaluate the effect ofreservoirmanagement on sediment trapping This is becauseno in-reservoir sediment data were collected and thus it wasimpossible to represent spatial patterns of sedimentdeposition or the degree to which previously depositedsedimentswere resuspended bywaves or incomingflows Inaddition because the entire data record was used to bestrepresent model hydrodynamics we were not able tovalidate the results of sediment modelling

Evaluation of sediment trapping efficiency using modellingoutput and continuous data

To evaluate the sediment-trapping efficiency of JohnRedmond Reservoir relative to the observed differences inreservoir management daily values of streamflow andsediment load (computed from 15-min data) were dividedinto 55 individual releases which accounted for 97 ofoutgoing water Releases were delineated by firstcharacterizing individual instances in which outflow gateswere adjusted to release more or less water and then byfurther dividing these periods into approximately equal-volume releases Equal-volume releases consisted ofapproximately 123 million m3 of water (approximatelydouble the volume of the conservation pool in 2010which was 59 million m3) which were larger or smallerdepending on the total volume of water transported fromwhen the gates were opened and closed The calibratedCE-QUAL-W2 model simulated the residence times foroutflows at a daily time step which were then used tomatch releases to corresponding inflows for the purposeof computing sediment trapping efficiency from incomingand outgoing sediment loads Residence timendashassignedinflow events were adjusted further to match more closelythe volume of corresponding outflow events Incomingflow volumes typically were within 10 of equivalentoutgoing volumesmdashthe remaining differences werebecause of the daily time step of streamflow and sedimentloads To account for these differences when computingsediment trapping efficiency differences in incoming andoutgoing water volumes were multiplied by the flow-weighted sediment concentration (FWSC) of the incom-ing event The FWSC is defined as the total sediment loadof the inflow event divided by the total water volume for a

Hydrol Process 27 1426ndash1439 (2013)

314

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Ele

vati

on a

bove

mea

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vel

in m

Observed bathymetry Root -mean squared error = 010 m

Simulated bathymetry Average error = 007 mTop of flood pool

Top of conservation poolAverage error = 007 m

A

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-10

-5

0

5

10

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Wat

er te

mpr

eatu

re i

n de

gree

s C

elci

us Observed water temperature

Modeled water temperature

Root-mean squared error = 146 degrees CelciusAverage error = 118 degrees Celcius

0

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ream

flow

in

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er s

econ

d

Spe

cifi

c co

nduc

tanc

e in

mic

rosi

emen

s Observed specific conductanceSimulated specific conductanceStreamflow

Root-mean squared error = 117 microScmAverage error = 69 microScm

C

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Stre

amfl

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rom

Joh

n R

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ond

Res

ervo

ir i

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3 per

sec

ond

Cum

ulat

ive

sedi

men

t flu

x fr

om J

ohn

Red

mon

d R

eser

voir

in m

etri

c to

ns

Turbidity-computed sediment load

Simulated sediment load

StreamflowRoot-mean squared error of observed and modeled SSC values = 65 mgL

Average error = 38 mgL

Figure 5 Comparison of simulated and observed (A) reservoir elevation (B) outflow water temperature (C) outflow SC and (D) outflow cumulativesuspended-sediment load from February 2007 through September 2010

1433CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

specified period as indicated in Equation 1

FWSCin frac14 SLin=WVin 106 (1)

where FWSCin is the FWSC of the incoming event(mgl) SLin is the incoming sediment load of the event(metric tons) and WVin is the volume of water of theincoming event (m3) The FWSC is then multiplied by thedifference in inflowoutflow volumes and the resulting

Copyright copy 2012 John Wiley amp Sons Ltd

sediment load is added (or subtracted) from the originalincoming sediment load as indicated in Equation 2

SLinadj frac14 SLin thorn FWSCin WVout WVineth THORN=106 (2)

where SLinadj is the adjusted incoming sediment load(metric tons) and WVout is the volume of water released(m3) Sediment trapping efficiency for each eventreleasepair is then defined as the percentage of the adjusted

Hydrol Process 27 1426ndash1439 (2013)

0

02

04

06

08

1

12

0 02 04 06 08 1 12

Obs

erve

d av

erag

e di

ffer

ence

in w

ater

tem

pera

ture

in

degr

ees

Cm

Modeled average difference in watertemperature in degrees Cm

July 2007

March April May JuneAugust September October

and November 2007

Figure 6 Modelled versus observed differences in water temperature fromthe surface to the bottom near John Reservoir dam 2007

1434 C LEE AND G FOSTER

incoming sediment load trapped in the reservoir asindicated in Equation 3

TE frac14 100 SLinadj SLout

=SLinadj (3)

Equal-volume inflow events were transported between3100 and 260 000 metric tons of suspended sedimentinto John Redmond Reservoir The largest sedimentloads were transported during relatively short-termrainfallrunoff events whereas smaller loads weretransported during low-flow conditions Least squaresregression was used to explore relations between thesediment trapping efficiency of eventrelease pairs andthe measures of residence time reservoir elevation andsediment concentration and load entering John RedmondReservoir Different measures of residence time and lakeelevation were computed for each eventrelease byweighting these variables by the length of time thewater (flow weighted) or sediment (sediment weighted)for a particular eventrelease pair was present within thereservoir (eg the reservoir elevation for a particular daywas weighted by 05 if one half the water or sedimentfor an individual eventrelease pair was withinthe reservoir or by 10 if all of the water or sedimentwas within the reservoir)Only releases with incoming sediment loads greater

than 37 000 metric tons were used in this analysis as thesediment trapping efficiency of smaller events wasjudged to be primarily affected by background levelsof sediment within the reservoir (such as from algae orwind-related resuspension of bottom sediment) orpotentially from sediment suspended from the streamchannel between the reservoir outflow and Burlingtonmonitoring site These releases are less important topredict because they represent a relatively small part ofthe total sediment load transported to the reservoirStepwise multiple linear regression was then used tocharacterize variables which best estimated sedimenttrapping efficiency among eventrelease pairs withoutexhibiting multicollinearity

Copyright copy 2012 John Wiley amp Sons Ltd

RESULTS AND DISCUSSION

Hydrologic conditions

Precipitation upstream from John Redmond Reservoirduring the study period (2007ndash2010) was generally greaterthan historical averages The National Weather Servicestation at the Neosho Rapids (directly upstream from JohnRedmond Reservoir) recorded 815 in of precipitation in2007 1298 in in 2008 and 1196 cm in 2009 comparedwith the annual average of 914 in from 1950 to 2006(National Weather Service 2010b) Annual flows weresummed from theAmericus and Plymouth sites whichwere839 km3 in 2007 1591 km3 in 2008 and 1332 km3 in2009 the median combined annual flow from these siteswas 1061 km3 from 1964 to 2006 Rivers exceeded the2-year (50 annual) USGS flood-frequency estimates 14times at the Plymouth and Neosho Rapids sites (Perry et al2004) indicating that relatively extreme storms (andcorresponding high sediment loads) were frequentlyobserved during the study period Increased rainfall andflow during the study period indicate that sediment flux to(and likely from) the reservoir is likely larger than duringa typical 4-year study period

Sediment transport to and from John Redmond Reservoir

The maximum computed SSC upstream from thereservoir was 7690mgl whereas the maximum SSC was1080mgl downstream from the reservoir From February2007 through September 2010 approximately 5 600 000metric tons of sediment entered John Redmond Reservoir660 000 of which were transported past the Burlington site(trapping efficiency of 88) Nearly all of the suspendedsediment at upstream sampling sites consisted of silt andclay (the median sample was 96 less than 63mm indiameter) Approximately one half of the sedimentstransported to John Redmond Reservoir during high-flowconditions were clays (lt2mm) the remaining sedimentswere distributed somewhat equally between 2 and 63mmSimilar to inflow samples 98 of the reservoir outflowsuspended sediment sampled had diameters of less than63 mm (full grain-size analysis was not conducted)Because suspended-sediment and reservoir-bottom sam-ples (Juracek 2010) indicate a lack of sand and larger-sized material and because streambed substrates at gagesites were observed to consist primarily of cobble androck bedload transport of sediment is not considered asubstantial component of the sediment load transported toJohn Redmond ReservoirThe annual volume of flow and sediment load transported

into John Redmond Reservoir generally corresponded withannual patterns in precipitation (Figure 7) The trappingefficiency of the reservoir initially decreased during yearswith greater precipitation and streamflow in 2008 and 2009but continued to decrease despite smaller flows andincoming sediment loads in 2010 potentially because ofdifferences in the manner in which the reservoir wasoperated An examination of inflows outflows and reservoirlevels in John Redmond Reservoir indicated that although

Hydrol Process 27 1426ndash1439 (2013)

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load

in

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IncomingstreamflowIncomingsedimentOutgoingsedimentPrecipitation

2007 2008 2009 2010

929

886

858

853

Sediment trapping efficiency

Figure 7 Annual precipitation streamflow and sediment transport to andfrom John Redmond Reservoir February 2007 to September 2010

1435CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

inflows were smaller in 2010 than those in 2008 and 2009water was released more rapidly after sediment-ladeninflows in 2010 in part because of maintenance activitiesthat necessitated lower reservoir levels (T Lyons USACEoral communication 2010)

Sediment trapping efficiency relative to variation inreservoir management

Among the 48 eventrelease pairs with greater than37 000 metric tons of incoming sediment sedimenttrapping efficiency varied from 48 to 97 Relations

A

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316 318 320 322 324 326

Flow-weighted reservoir elevation in meters

Linear fit

Top of conservation pool Top of flood pool

60

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60 70

Tur

bidi

ty-c

ompu

ted

sedi

men

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g ef

fici

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Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

gef

fici

ency

in

pres

ent

Regression-predicted sep

C

Figure 8 Regression relations between turbidity-computed sediment trappinand (C) regression-predicted estimates of sedim

Copyright copy 2012 John Wiley amp Sons Ltd

established between sediment trapping efficiency flow-weighted reservoir elevation (Figure 8A) and flow-weighted residence time (Figure 8B) indicated that alarger proportion of incoming sediment is transportedthrough John Redmond Reservoir when the reservoir ismaintained at lower levels and when residence times areminimized (Table II) However these relations were alsomore variable during these conditions In additionpredicted sediment trapping efficiencies were nonlinearwith respect to the primary explanatory variables(reservoir elevation FWSCs and flow-weighted residencetime) single linear regressions typically overpredictedeventrelease pairs with small trapping efficiencies andunderpredicted eventrelease pairs with larger sedimenttrapping efficiencies To minimize bias in predictionsof sediment trapping efficiency separate linear regres-sions were identified for eventrelease pairs above andbelow a reservoir elevation threshold of 3185 m Theresulting predictions were similar to single regressionequations in terms of adjusted R2 and error but residualswere distributed more evenly throughout the range ofvaluesAmong eventrelease pairs in which reservoir elevation

was maintained lower than 3185m the variability inthe sediment trapping efficiency was best explained byflow-weighted reservoir elevation (EL) and by the FWSCof the inflow event (Figure 8C) Among eventrelease pairsin which reservoir elevation was maintained higher than3185 m the variability in sediment trapping efficiency

40

50

60

70

80

90

100

5 15 25 35 45

Flow-weighted residence time of event in days

B

80 90 100

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

gef

fici

ency

in

perc

ent

diment trapping efficiency in ercent

g efficiency and (A) flow-weighted reservoir elevation (B) residence timeent trapping efficiency for eventrelease pairs

Hydrol Process 27 1426ndash1439 (2013)

Table II Summary of sediment loading and reservoir characteristics during delineated inflows and releases from John RedmondReservoir

Range of flow-weighted averagereservoir elevations (ft abovemean sea level)

No eventrelease pairs

Turbidity-computedload in (metric tons)

Turbidity-computedload out

(metric tons)

Turbidity-computedtrapping efficiency

()

Regression-predictedsediment trappingefficiency ()

All releases 3161ndash3234 48 5 240 000 595 000 886 8873161ndash3173 8 708 000 179 000 747 7463173ndash3179 10 1 101 000 159 000 856 8623179ndash3197 8 892 000 88 000 901 8923197ndash3210 11 1 201 000 84 000 930 9253210ndash3234 11 1 337 000 85 000 936 945

Table III Maximum discharges and approximate travel times ofreleases to flood control points downstream from John Redmond

Reservoirdaggera

Flood controlend point

Approximate travel timefrom outflow gates (h)daggerb

Maximumdischarge (m3s)

Neosho River atBurlington Kansas

2 396

Neosho River atIola Kansas

24 510

Neosho River atChanute Kansas

36 510

Neosho River atParsons Kansas

60 481

Neosho River atCommerce Oklahoma

84 623

a In addition to flood control end points the water control manual specifiesthat outgoing discharges do not rise or fall by more than 566m3s every 3h the manual also includes the consideration of conditions at otherreservoirs in the basin (USACE 1996)b Data from USACE (1996)

326s

1436 C LEE AND G FOSTER

was best explained by flow-weighted hydraulic residencetime (RES) and by the FWSC of the inflow event(Figure 8C) All explanatory variables were significantlyrelated to sediment trapping efficiency (P valuelt 005) andthe regression equations chosen resulted in the largestadjusted R2 the smallest RMSE values and the smallestprediction error sum of squares values of other potentialcombinations of independent variables The varianceinflation factors among independent variables were lessthan 12 indicating that multicollinearity among incomingFWSC values and flow-weighted reservoir elevations didnot inflate or adversely affect regression estimates (Helseland Hirsch 1992) In addition to decreased sedimenttrapping efficiency during low reservoir levels and residencetimes more sediment was trapped when incoming sedimentconcentrations were larger (as indicated by larger FWSCvalues) potentially because of increased flocculation (andthus larger effective grain size and fall velocity Droppo andOngley 1994)Although sediment trapping predictions were variable

for individual eventrelease pairs they were muchmore accurate with respect to turbidity-computed resultsfor multiple event releases when grouped by flow-weighted reservoir elevation (Table II) Results suggestthat (i) reductions in trapping efficiency consistently areobserved when reservoir elevations are maintained nearconservation pool levels and (ii) although regression-derived estimates of sediment trapping may be inaccuratefor individual eventrelease pairs this method can producerelatively accurate long-term estimates of sedimenttrapping efficiency with respect to factors that can beaffected by altered reservoir management

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

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Observed reservoir level

Altered outflow scenario

Flood-control pool elevation

Conservationpool

elevation

Example period (fig 10)

Figure 9 Observed and altered elevation at John Redmond ReservoirFebruary 2007 through September 2010

Potential of altered reservoir management to reducesediment trapping

The USACE water control manual for John RedmondReservoir identifies specific flood control end points thatmust not be exceeded to ensure that reservoir outflows donot contribute to flood-related damages to crops andstructures (Table III) Any changes to reservoirmanagementmust also meet these end points to fulfil the mission forwhich John Redmond Reservoir was constructed Tosimulate the potential effect of changes in reservoir outflowmanagement on sediment accumulation in John RedmondReservoir an idealized and altered outflow management

Copyright copy 2012 John Wiley amp Sons Ltd

scenario was constructed which continued to meet theseend points while also preserving storage for drinking wateragricultural use and industrial use (Figure 9) This scenariois lsquoidealizedrsquo in that it benefits from the knowledge ofincoming and downstream flows whereas in practicereservoir operators rely on weather forecasts and modellingto ensure consistent water supplies and to preventdownstream flooding Because of the inability to access

Hydrol Process 27 1426ndash1439 (2013)

1437CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

historical rainfall forecasts or models that predict flows toand from John Redmond Reservoir results from thisscenario are presented as the maximum potential reductionin sediment trapping that could be achieved within currentoperational plansRelative to observed reservoir management the altered

scenario purposefully minimized reservoir elevation andresidence time through larger more rapid releases ofwater after periods of high inflows (Figure 9) To partiallycompensate for uncertainties in weather forecasts andpredicted streamflows the altered management scenariowas somewhat more conservative compared with watercontrol plan restrictions in that (i) outflows were notincreased bymore than 850m3sday and (ii) outflows werenever reduced by more than 566m3sday (other than whenthe reservoir was actuallymanaged in this fashion) After theconstruction of the altered scenario reservoir releases wereredelineated and matched to inflows as done with observeddata Flow-weighted reservoir elevations residence timesand FWSCs for incoming events were recomputed toestimate the effect of the altered management scenario onsediment trapping efficiency (using regressions developedwith observed values)Although data and models were not available to test the

uncertainty of forecasted flow conditions an exampleperiod (Figures 9 and 10) in July 2007 is highlighted toillustrate how the consideration of sediment trapping withinexisting reservoir operational plans could preserve reservoirstorage During late June and early July 2007 heavy rainsupstream and downstream from John Redmond Reservoirraised reservoir levels and caused flooding downstreamfrom the reservoir (see the Neosho River at Parsons as anexample Figure 10) These flows raised reservoir levelswell into the flood pool where it was held until 16 July2007 when John Redmond Reservoir gates began releasingmore water (as indicated by the observed Burlingtonstreamflow record Figure 10) Historical weather forecastsfrom the National Weather Service for Iola Kansas(between Burlington and Parsons National Weather

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Alternate flow at Parsons KS

Figure 10 Comparison of observed reservoir elevations and downstreamflow conditions relative to the altered reservoir management scenario June

to August 2007

Copyright copy 2012 John Wiley amp Sons Ltd

service written communication 2011) projected con-sistent (20ndash50) chances of thunderstorms throughoutJuly Compared with observed reservoir managementthe altered outflow scenario began discharging wateron 5 July 2007 while keeping downstream flows belowmaximum levels indicated in John Redmond Reservoircontrol manual (Table III) Two eventrelease pairs weredelineated during this period modelled residence timesbased on observed reservoir levels were 72 and 26 daysMore rapid release of water from John RedmondReservoir through the alternative scenario reduced theseto 66 and 16 days respectively Sediment trappingfor these periods is projected to have decreased from971 to 893 and from 957 to 822 Althoughthis example illustrates that consideration of sedimenttrapping within reservoir operational plans can reducesediment trapping uncertainty of weather forecasts andrainfall runoff models will often limit the ability topreserve reservoir storage within existing operationlimitsFrom 2007 to 2010 the altered management scenario

decreased the average reservoir elevation from 3178 to3173 m and decreased the average daily residence timesfrom 296 to 269 days Forty-six releases with more than37 000 tons of incoming sediment were delineated under thealtered scenario (compared with 48 observed releases)corresponding to 51 million metric tons of incomingsediment (compared with 53 million metric tons computedwith observed data Table IV) The average reservoirelevations for eventrelease pairs decreased from 3194 to3186 m and for residence times from 199 to 131 daysUnder the altered management scenario regression-predicted sediment trapping efficiency was reduced by39 passing approximately 180 000 additional metric tonsof sediment through the reservoir Estimates indicate thatwithin existing operational constraints altered reservoirmanagement has a maximum potential of decreasingsediment trapping by approximately 45 000 metric tonsper year or approximately 3 of the annual load of 141million metric tons transported during the study periodAn annual reduction of 45 000 tons of sediment from

John Redmond Reservoir would preserve approximately74 000m3 of storage in the reservoir per year This rate isequivalent to 15 of the designed reservoir sedimentationrate and equivalent to 81 of the observed sedimentationrate in the conservation pool given the average bulk densityestimates of Juracek (2010) As more reservoir storage islost to deposited sediments the residence time of sediment-laden inflows will continue to decrease and reservoirmanagement is likely to become a more effective alternativeto reducing reservoir sedimentation If existing reservoiroperational plans were adapted to accommodate watersupply as well as flood control uses of the reservoir alteredreservoir management might further reduce sedimentaccumulationAlthough continuous flow and sediment data have proven

useful in determining how short-term variability inhydrology and reservoir management affect sedimentationimproved understanding of in-reservoir processes is needed

Hydrol Process 27 1426ndash1439 (2013)

Table IV Sediment transport to and from John Redmond Reservoir during delineated inflows and releases under the alteredmanagement scenario

Range of flow-weighted averagereservoir elevations (meters abovemean sea level)

No releaseevents

Turbidity-computedload in (metric tons)

Regression-predictedload out (metric tons)

Regression-predicted sedimenttrapping efficiency ()

All releases 46 5 126 000 778 000 8483161ndash3173 17 1 813 000 426 000 7653173ndash3179 7 855 000 151 000 8243179ndash3197 7 690 000 76 000 8903197ndash3210 10 992 000 94 000 9053210ndash3234 5 775 000 31 000 960

1438 C LEE AND G FOSTER

to better characterize how sediment moves through isdeposited within and is resuspended from reservoirs duringvarious hydrologic and outflow management scenarios Forexample although the maintenance of low-reservoir levelsmay reduce the total amount of sedimentation in thereservoir it could change the predominant location ofsediment deposition from the flood to the conservation poolpossibly decreasing storage where it is most needed Anexpanded collection of turbidity data within reservoirscan improve understanding of in-reservoir processes (Effleret al 2006) and could better calibrate two- or three-dimensional reservoir models In addition while increasedsediment transport downstream from the reservoir underaltered management plans would still be less than thehistorical pre-impoundment conditions investigationswould need to ascertain potential effects on infrastructureand aquatic life Any potential changes to reservoirmanagement also would need to fully evaluate the degreeto which altered management plans could increase the riskof downstream flooding However study results indicatethat despite limited in-reservoir data the coupling ofcontinuous streamflow and turbidity data with reservoirmodelling can effectively project the degree to whichchanges in reservoir management can affect sedimentationin the reservoir

CONCLUSIONS

Rapid sediment accumulation in large reservoirs willincreasingly threaten communities reliant on reservoirstorage for municipal agricultural and industrial usesDecisions will need to be made about which reservoiruses will be prioritized such as maintenance of watersupplies for nearby and downstream communities floodcontrol for downstream infrastructure and property ownersor recreational considerations Study results indicate thatcontinuous in-stream flow and turbidity monitoring can beused to quantify the potential of outflow management toreduce reservoir sedimentation Along with the collectionof hydrodynamic and sediment data within reservoirsthese data can help calibrate and validate models thatbetter quantify spatial patterns of sediment depositionand resuspension under varying hydrologic and manage-ment scenarios Study results indicate that depending onthe specific reservoir characteristics reservoir outflow

Copyright copy 2012 John Wiley amp Sons Ltd

management can help preserve water supplies especiallyas sedimentation continues to usurp storage capacityAlthough the approach presented can evaluate the potentialof altered reservoir management to preserve storagemanagement decisions need to consider potential effectschanging reservoir operations on downstream flood controland aquatic ecosystems

REFERENCES

Barfield DB 2010 Eastern Kansas Water Supply presentation given atthe 2010 Kansas Field Conference June 2 2010 Available on the Webat httpwwwksdagovincludesdocument_centerdwrPresentationsBarfield_KGSFieldTour2010pdf

Carswell WJ Hart RJ 1985 Transit losses and travel times for reservoirreleases during drought conditions along the Neosho River fromCouncil Grove Lake to Iola east-central Kansas US GeologicalSurvey Water-Resources Investigations Report 85ndash4003 40

Chung SW Hipsey MR Imberger J 2009 Modeling the propagation ofturbid density inflows into a stratified lake Daecheong ReservoirKorea Environmental Modelling and Software 24 1467ndash1482

Cohn TA Gilroy EJ 1991 Estimating loads from periodic records USGeological Survey Branch of Systems Analysis Technical Memo9101 81

Cole TM Wells SA 2008 CE-QUAL-W2 A two-dimensional laterallyaveraged hydrodynamic and water-quality model 36 userrsquos manualUS Army Corps of Engineers Waterways Experiment Station 125

Droppo IG Ongley ED 1994 Floccuation of suspended-sediment inrivers of Southeastern Canada Water Research 28(8) 1799ndash1809

Duan N 1983 Smearing estimatemdasha nonparametric retransformationmethod Journal of the American Statistical Asssociation 78 605ndash610

Effler SW Prestigiacomo AR Peng F Bulygina KB 2006 Resolution ofturbidity patterns from runoff events in a water supply reservoir and theadvantages of in situ beam attenuation measurements Lake andReservoir Management 22(1) 79ndash93

Evans JK Gottgens JF Gill WM Mackey SD 2000 Sediment yieldscontrolled by intrabasinal storage and sediment conveyance over theinterval 1842-1994 Chagrin River Northeast Ohio USA Journal ofSoil and Water Conservation 55 264ndash70

Fan J Morris GL 1992a Reservoir sedimentation I Delta and densitycurrent deposits Journal of Hydraulic Engineering 118 (3) 354ndash69

Fan J Morris GL 1992b Reservoir sedimentation II Reservoir desiltationand long-term storage capacity Journal of Hydraulic Engineering118 (3) 370ndash84

Gelda RK Effler SW 2007 Modeling turbidity in a water supply reservoiradvancements and issues Journal of Environmental Engineering 133(2)139ndash148

Guy HP 1969 Laboratory theory and methods for sediment analysis USGeological Survey Techniques of Water-Resources Investigations book5 chap C1 58

Hach Company 2005 SOLITAX sc turbidity and suspended solids usermanual Available on the Web at httpwwwhachcomfmmimghachCODEDOC0235403232_3ED_8864|1

Helsel DR Hirsch RM 1992 Statistical methods in water resourcesElsevier New York 522

Hydrol Process 27 1426ndash1439 (2013)

0

200000

400000

600000

800000

1000000

1200000

Neosho River nearAmericus Kansas

Cottonwood Rivernear Plymouth Kansas

Neosho River nearBurlington Kansas

Sedi

men

t loa

d in

met

ric

tons

Sediment load computed usinghourly turbidity streamflow andperiodic SSC data

Sediment load computed usinghourly streamflow and periodicSSC data

(Upstream) (Upstream) (Downstream)

95 confidence intervals

Figure 4 Comparison of continuous turbidity and streamflow-computedestimates of annual suspended-sediment loads

1432 C LEE AND G FOSTER

SSC values at the Burlington site (downstream from JohnRedmond Reservoir) The entire period of record wasused for calibration because the model was developedexclusively to represent the residence time of waterthrough the reservoir for the further purpose of estimatingsediment trapping efficiency at temporal scales of days toweeks Data input into the model included reservoirelevation (USACE 2010) precipitation air and dewpoint temperature wind speed and direction and cloudcover (National Weather Service 2010a) incomingtemperature TDS SSC and incoming and outgoingstreamflow were input into the model Modifications todefault model conditions were as follows (i) thepartitioning of incoming SSCs into four classes withdifferent settling rates (0001 08 15 and 8mday)which were adjusted to approximate outflow sedimentconcentration and load and (ii) the adjustment of wind-sheltering coefficients to 080 representing that windobserved at the nearby Emporia weather station (NationalWeather Service 2010a) was observed at 80 strength atthe surface of John Redmond Reservoir Althoughadjustments to the model were not verified to representreal-world conditions they did result in a relativelyconsistent simulation of reservoir elevation relative toobserved values through the study period (Figure 5A)Simulated reservoir elevations compared well with the

observed values of the USACE [root mean squared error(RMSE) of 010m Figure 5A] Simulated outflow watertemperatures also closely matched observed watertemperature at Burlington (RMSE of 146 C betweensimulated and observed values Figure 5B) Simulatedtemperature profiles indicated that the reservoir was rarelystratified during the study period which was consistent withavailable in-reservoir data (USACE profiles collected in2007 D Gade written communication 2010 Figure 6)Reservoir temperatures were well mixed from top to bottomduringmost of the spring summer and fall of 2007 but weresomewhat stratified (approximate decrease of 1 Cm)through a 7-m water column in July of 2007Simulated SC from the reservoir was frequently greater

than observed SC (Figure 5C) especially during periodswith low flows and longer residence times This wasprimarily because major ions that increase SC were not

Copyright copy 2012 John Wiley amp Sons Ltd

transported conservatively through the reservoir AverageSC values in inflows were 149 mScm greater than thosein reservoir outflows These results indicate the potentialof the biomediated precipitation of calcium carbonate(typically the dominant cationsanions in temperatereservoirs Wetzel 2001) within the reservoir as otherstudies have determined decalcification within reservoirswith similar SC values (Wetzel 2001) Further discussionof this phenomenon is beyond the scope of this studyother than to indicate that SC was not an adequate tracerof water movement through John Redmond Reservoirespecially during longer residence timesAlthough it was necessary to calibrate the model to

outgoing suspended-sediment flux to represent reservoirhydrodynamics through the period of study (Figure 5D)model results were not used to evaluate the effect ofreservoirmanagement on sediment trapping This is becauseno in-reservoir sediment data were collected and thus it wasimpossible to represent spatial patterns of sedimentdeposition or the degree to which previously depositedsedimentswere resuspended bywaves or incomingflows Inaddition because the entire data record was used to bestrepresent model hydrodynamics we were not able tovalidate the results of sediment modelling

Evaluation of sediment trapping efficiency using modellingoutput and continuous data

To evaluate the sediment-trapping efficiency of JohnRedmond Reservoir relative to the observed differences inreservoir management daily values of streamflow andsediment load (computed from 15-min data) were dividedinto 55 individual releases which accounted for 97 ofoutgoing water Releases were delineated by firstcharacterizing individual instances in which outflow gateswere adjusted to release more or less water and then byfurther dividing these periods into approximately equal-volume releases Equal-volume releases consisted ofapproximately 123 million m3 of water (approximatelydouble the volume of the conservation pool in 2010which was 59 million m3) which were larger or smallerdepending on the total volume of water transported fromwhen the gates were opened and closed The calibratedCE-QUAL-W2 model simulated the residence times foroutflows at a daily time step which were then used tomatch releases to corresponding inflows for the purposeof computing sediment trapping efficiency from incomingand outgoing sediment loads Residence timendashassignedinflow events were adjusted further to match more closelythe volume of corresponding outflow events Incomingflow volumes typically were within 10 of equivalentoutgoing volumesmdashthe remaining differences werebecause of the daily time step of streamflow and sedimentloads To account for these differences when computingsediment trapping efficiency differences in incoming andoutgoing water volumes were multiplied by the flow-weighted sediment concentration (FWSC) of the incom-ing event The FWSC is defined as the total sediment loadof the inflow event divided by the total water volume for a

Hydrol Process 27 1426ndash1439 (2013)

314

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Ele

vati

on a

bove

mea

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vel

in m

Observed bathymetry Root -mean squared error = 010 m

Simulated bathymetry Average error = 007 mTop of flood pool

Top of conservation poolAverage error = 007 m

A

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-10

-5

0

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Wat

er te

mpr

eatu

re i

n de

gree

s C

elci

us Observed water temperature

Modeled water temperature

Root-mean squared error = 146 degrees CelciusAverage error = 118 degrees Celcius

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ream

flow

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econ

d

Spe

cifi

c co

nduc

tanc

e in

mic

rosi

emen

s Observed specific conductanceSimulated specific conductanceStreamflow

Root-mean squared error = 117 microScmAverage error = 69 microScm

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Stre

amfl

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rom

Joh

n R

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3 per

sec

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ive

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men

t flu

x fr

om J

ohn

Red

mon

d R

eser

voir

in m

etri

c to

ns

Turbidity-computed sediment load

Simulated sediment load

StreamflowRoot-mean squared error of observed and modeled SSC values = 65 mgL

Average error = 38 mgL

Figure 5 Comparison of simulated and observed (A) reservoir elevation (B) outflow water temperature (C) outflow SC and (D) outflow cumulativesuspended-sediment load from February 2007 through September 2010

1433CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

specified period as indicated in Equation 1

FWSCin frac14 SLin=WVin 106 (1)

where FWSCin is the FWSC of the incoming event(mgl) SLin is the incoming sediment load of the event(metric tons) and WVin is the volume of water of theincoming event (m3) The FWSC is then multiplied by thedifference in inflowoutflow volumes and the resulting

Copyright copy 2012 John Wiley amp Sons Ltd

sediment load is added (or subtracted) from the originalincoming sediment load as indicated in Equation 2

SLinadj frac14 SLin thorn FWSCin WVout WVineth THORN=106 (2)

where SLinadj is the adjusted incoming sediment load(metric tons) and WVout is the volume of water released(m3) Sediment trapping efficiency for each eventreleasepair is then defined as the percentage of the adjusted

Hydrol Process 27 1426ndash1439 (2013)

0

02

04

06

08

1

12

0 02 04 06 08 1 12

Obs

erve

d av

erag

e di

ffer

ence

in w

ater

tem

pera

ture

in

degr

ees

Cm

Modeled average difference in watertemperature in degrees Cm

July 2007

March April May JuneAugust September October

and November 2007

Figure 6 Modelled versus observed differences in water temperature fromthe surface to the bottom near John Reservoir dam 2007

1434 C LEE AND G FOSTER

incoming sediment load trapped in the reservoir asindicated in Equation 3

TE frac14 100 SLinadj SLout

=SLinadj (3)

Equal-volume inflow events were transported between3100 and 260 000 metric tons of suspended sedimentinto John Redmond Reservoir The largest sedimentloads were transported during relatively short-termrainfallrunoff events whereas smaller loads weretransported during low-flow conditions Least squaresregression was used to explore relations between thesediment trapping efficiency of eventrelease pairs andthe measures of residence time reservoir elevation andsediment concentration and load entering John RedmondReservoir Different measures of residence time and lakeelevation were computed for each eventrelease byweighting these variables by the length of time thewater (flow weighted) or sediment (sediment weighted)for a particular eventrelease pair was present within thereservoir (eg the reservoir elevation for a particular daywas weighted by 05 if one half the water or sedimentfor an individual eventrelease pair was withinthe reservoir or by 10 if all of the water or sedimentwas within the reservoir)Only releases with incoming sediment loads greater

than 37 000 metric tons were used in this analysis as thesediment trapping efficiency of smaller events wasjudged to be primarily affected by background levelsof sediment within the reservoir (such as from algae orwind-related resuspension of bottom sediment) orpotentially from sediment suspended from the streamchannel between the reservoir outflow and Burlingtonmonitoring site These releases are less important topredict because they represent a relatively small part ofthe total sediment load transported to the reservoirStepwise multiple linear regression was then used tocharacterize variables which best estimated sedimenttrapping efficiency among eventrelease pairs withoutexhibiting multicollinearity

Copyright copy 2012 John Wiley amp Sons Ltd

RESULTS AND DISCUSSION

Hydrologic conditions

Precipitation upstream from John Redmond Reservoirduring the study period (2007ndash2010) was generally greaterthan historical averages The National Weather Servicestation at the Neosho Rapids (directly upstream from JohnRedmond Reservoir) recorded 815 in of precipitation in2007 1298 in in 2008 and 1196 cm in 2009 comparedwith the annual average of 914 in from 1950 to 2006(National Weather Service 2010b) Annual flows weresummed from theAmericus and Plymouth sites whichwere839 km3 in 2007 1591 km3 in 2008 and 1332 km3 in2009 the median combined annual flow from these siteswas 1061 km3 from 1964 to 2006 Rivers exceeded the2-year (50 annual) USGS flood-frequency estimates 14times at the Plymouth and Neosho Rapids sites (Perry et al2004) indicating that relatively extreme storms (andcorresponding high sediment loads) were frequentlyobserved during the study period Increased rainfall andflow during the study period indicate that sediment flux to(and likely from) the reservoir is likely larger than duringa typical 4-year study period

Sediment transport to and from John Redmond Reservoir

The maximum computed SSC upstream from thereservoir was 7690mgl whereas the maximum SSC was1080mgl downstream from the reservoir From February2007 through September 2010 approximately 5 600 000metric tons of sediment entered John Redmond Reservoir660 000 of which were transported past the Burlington site(trapping efficiency of 88) Nearly all of the suspendedsediment at upstream sampling sites consisted of silt andclay (the median sample was 96 less than 63mm indiameter) Approximately one half of the sedimentstransported to John Redmond Reservoir during high-flowconditions were clays (lt2mm) the remaining sedimentswere distributed somewhat equally between 2 and 63mmSimilar to inflow samples 98 of the reservoir outflowsuspended sediment sampled had diameters of less than63 mm (full grain-size analysis was not conducted)Because suspended-sediment and reservoir-bottom sam-ples (Juracek 2010) indicate a lack of sand and larger-sized material and because streambed substrates at gagesites were observed to consist primarily of cobble androck bedload transport of sediment is not considered asubstantial component of the sediment load transported toJohn Redmond ReservoirThe annual volume of flow and sediment load transported

into John Redmond Reservoir generally corresponded withannual patterns in precipitation (Figure 7) The trappingefficiency of the reservoir initially decreased during yearswith greater precipitation and streamflow in 2008 and 2009but continued to decrease despite smaller flows andincoming sediment loads in 2010 potentially because ofdifferences in the manner in which the reservoir wasoperated An examination of inflows outflows and reservoirlevels in John Redmond Reservoir indicated that although

Hydrol Process 27 1426ndash1439 (2013)

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IncomingstreamflowIncomingsedimentOutgoingsedimentPrecipitation

2007 2008 2009 2010

929

886

858

853

Sediment trapping efficiency

Figure 7 Annual precipitation streamflow and sediment transport to andfrom John Redmond Reservoir February 2007 to September 2010

1435CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

inflows were smaller in 2010 than those in 2008 and 2009water was released more rapidly after sediment-ladeninflows in 2010 in part because of maintenance activitiesthat necessitated lower reservoir levels (T Lyons USACEoral communication 2010)

Sediment trapping efficiency relative to variation inreservoir management

Among the 48 eventrelease pairs with greater than37 000 metric tons of incoming sediment sedimenttrapping efficiency varied from 48 to 97 Relations

A

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316 318 320 322 324 326

Flow-weighted reservoir elevation in meters

Linear fit

Top of conservation pool Top of flood pool

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Tur

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ty-c

ompu

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sedi

men

t tra

ppin

gef

fici

ency

in

pres

ent

Regression-predicted sep

C

Figure 8 Regression relations between turbidity-computed sediment trappinand (C) regression-predicted estimates of sedim

Copyright copy 2012 John Wiley amp Sons Ltd

established between sediment trapping efficiency flow-weighted reservoir elevation (Figure 8A) and flow-weighted residence time (Figure 8B) indicated that alarger proportion of incoming sediment is transportedthrough John Redmond Reservoir when the reservoir ismaintained at lower levels and when residence times areminimized (Table II) However these relations were alsomore variable during these conditions In additionpredicted sediment trapping efficiencies were nonlinearwith respect to the primary explanatory variables(reservoir elevation FWSCs and flow-weighted residencetime) single linear regressions typically overpredictedeventrelease pairs with small trapping efficiencies andunderpredicted eventrelease pairs with larger sedimenttrapping efficiencies To minimize bias in predictionsof sediment trapping efficiency separate linear regres-sions were identified for eventrelease pairs above andbelow a reservoir elevation threshold of 3185 m Theresulting predictions were similar to single regressionequations in terms of adjusted R2 and error but residualswere distributed more evenly throughout the range ofvaluesAmong eventrelease pairs in which reservoir elevation

was maintained lower than 3185m the variability inthe sediment trapping efficiency was best explained byflow-weighted reservoir elevation (EL) and by the FWSCof the inflow event (Figure 8C) Among eventrelease pairsin which reservoir elevation was maintained higher than3185 m the variability in sediment trapping efficiency

40

50

60

70

80

90

100

5 15 25 35 45

Flow-weighted residence time of event in days

B

80 90 100

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

gef

fici

ency

in

perc

ent

diment trapping efficiency in ercent

g efficiency and (A) flow-weighted reservoir elevation (B) residence timeent trapping efficiency for eventrelease pairs

Hydrol Process 27 1426ndash1439 (2013)

Table II Summary of sediment loading and reservoir characteristics during delineated inflows and releases from John RedmondReservoir

Range of flow-weighted averagereservoir elevations (ft abovemean sea level)

No eventrelease pairs

Turbidity-computedload in (metric tons)

Turbidity-computedload out

(metric tons)

Turbidity-computedtrapping efficiency

()

Regression-predictedsediment trappingefficiency ()

All releases 3161ndash3234 48 5 240 000 595 000 886 8873161ndash3173 8 708 000 179 000 747 7463173ndash3179 10 1 101 000 159 000 856 8623179ndash3197 8 892 000 88 000 901 8923197ndash3210 11 1 201 000 84 000 930 9253210ndash3234 11 1 337 000 85 000 936 945

Table III Maximum discharges and approximate travel times ofreleases to flood control points downstream from John Redmond

Reservoirdaggera

Flood controlend point

Approximate travel timefrom outflow gates (h)daggerb

Maximumdischarge (m3s)

Neosho River atBurlington Kansas

2 396

Neosho River atIola Kansas

24 510

Neosho River atChanute Kansas

36 510

Neosho River atParsons Kansas

60 481

Neosho River atCommerce Oklahoma

84 623

a In addition to flood control end points the water control manual specifiesthat outgoing discharges do not rise or fall by more than 566m3s every 3h the manual also includes the consideration of conditions at otherreservoirs in the basin (USACE 1996)b Data from USACE (1996)

326s

1436 C LEE AND G FOSTER

was best explained by flow-weighted hydraulic residencetime (RES) and by the FWSC of the inflow event(Figure 8C) All explanatory variables were significantlyrelated to sediment trapping efficiency (P valuelt 005) andthe regression equations chosen resulted in the largestadjusted R2 the smallest RMSE values and the smallestprediction error sum of squares values of other potentialcombinations of independent variables The varianceinflation factors among independent variables were lessthan 12 indicating that multicollinearity among incomingFWSC values and flow-weighted reservoir elevations didnot inflate or adversely affect regression estimates (Helseland Hirsch 1992) In addition to decreased sedimenttrapping efficiency during low reservoir levels and residencetimes more sediment was trapped when incoming sedimentconcentrations were larger (as indicated by larger FWSCvalues) potentially because of increased flocculation (andthus larger effective grain size and fall velocity Droppo andOngley 1994)Although sediment trapping predictions were variable

for individual eventrelease pairs they were muchmore accurate with respect to turbidity-computed resultsfor multiple event releases when grouped by flow-weighted reservoir elevation (Table II) Results suggestthat (i) reductions in trapping efficiency consistently areobserved when reservoir elevations are maintained nearconservation pool levels and (ii) although regression-derived estimates of sediment trapping may be inaccuratefor individual eventrelease pairs this method can producerelatively accurate long-term estimates of sedimenttrapping efficiency with respect to factors that can beaffected by altered reservoir management

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

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Observed reservoir level

Altered outflow scenario

Flood-control pool elevation

Conservationpool

elevation

Example period (fig 10)

Figure 9 Observed and altered elevation at John Redmond ReservoirFebruary 2007 through September 2010

Potential of altered reservoir management to reducesediment trapping

The USACE water control manual for John RedmondReservoir identifies specific flood control end points thatmust not be exceeded to ensure that reservoir outflows donot contribute to flood-related damages to crops andstructures (Table III) Any changes to reservoirmanagementmust also meet these end points to fulfil the mission forwhich John Redmond Reservoir was constructed Tosimulate the potential effect of changes in reservoir outflowmanagement on sediment accumulation in John RedmondReservoir an idealized and altered outflow management

Copyright copy 2012 John Wiley amp Sons Ltd

scenario was constructed which continued to meet theseend points while also preserving storage for drinking wateragricultural use and industrial use (Figure 9) This scenariois lsquoidealizedrsquo in that it benefits from the knowledge ofincoming and downstream flows whereas in practicereservoir operators rely on weather forecasts and modellingto ensure consistent water supplies and to preventdownstream flooding Because of the inability to access

Hydrol Process 27 1426ndash1439 (2013)

1437CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

historical rainfall forecasts or models that predict flows toand from John Redmond Reservoir results from thisscenario are presented as the maximum potential reductionin sediment trapping that could be achieved within currentoperational plansRelative to observed reservoir management the altered

scenario purposefully minimized reservoir elevation andresidence time through larger more rapid releases ofwater after periods of high inflows (Figure 9) To partiallycompensate for uncertainties in weather forecasts andpredicted streamflows the altered management scenariowas somewhat more conservative compared with watercontrol plan restrictions in that (i) outflows were notincreased bymore than 850m3sday and (ii) outflows werenever reduced by more than 566m3sday (other than whenthe reservoir was actuallymanaged in this fashion) After theconstruction of the altered scenario reservoir releases wereredelineated and matched to inflows as done with observeddata Flow-weighted reservoir elevations residence timesand FWSCs for incoming events were recomputed toestimate the effect of the altered management scenario onsediment trapping efficiency (using regressions developedwith observed values)Although data and models were not available to test the

uncertainty of forecasted flow conditions an exampleperiod (Figures 9 and 10) in July 2007 is highlighted toillustrate how the consideration of sediment trapping withinexisting reservoir operational plans could preserve reservoirstorage During late June and early July 2007 heavy rainsupstream and downstream from John Redmond Reservoirraised reservoir levels and caused flooding downstreamfrom the reservoir (see the Neosho River at Parsons as anexample Figure 10) These flows raised reservoir levelswell into the flood pool where it was held until 16 July2007 when John Redmond Reservoir gates began releasingmore water (as indicated by the observed Burlingtonstreamflow record Figure 10) Historical weather forecastsfrom the National Weather Service for Iola Kansas(between Burlington and Parsons National Weather

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Observed flow at Burlington KS

Alternate flow at Burlington KS

Observed flow at Parsons KS

Alternate flow at Parsons KS

Figure 10 Comparison of observed reservoir elevations and downstreamflow conditions relative to the altered reservoir management scenario June

to August 2007

Copyright copy 2012 John Wiley amp Sons Ltd

service written communication 2011) projected con-sistent (20ndash50) chances of thunderstorms throughoutJuly Compared with observed reservoir managementthe altered outflow scenario began discharging wateron 5 July 2007 while keeping downstream flows belowmaximum levels indicated in John Redmond Reservoircontrol manual (Table III) Two eventrelease pairs weredelineated during this period modelled residence timesbased on observed reservoir levels were 72 and 26 daysMore rapid release of water from John RedmondReservoir through the alternative scenario reduced theseto 66 and 16 days respectively Sediment trappingfor these periods is projected to have decreased from971 to 893 and from 957 to 822 Althoughthis example illustrates that consideration of sedimenttrapping within reservoir operational plans can reducesediment trapping uncertainty of weather forecasts andrainfall runoff models will often limit the ability topreserve reservoir storage within existing operationlimitsFrom 2007 to 2010 the altered management scenario

decreased the average reservoir elevation from 3178 to3173 m and decreased the average daily residence timesfrom 296 to 269 days Forty-six releases with more than37 000 tons of incoming sediment were delineated under thealtered scenario (compared with 48 observed releases)corresponding to 51 million metric tons of incomingsediment (compared with 53 million metric tons computedwith observed data Table IV) The average reservoirelevations for eventrelease pairs decreased from 3194 to3186 m and for residence times from 199 to 131 daysUnder the altered management scenario regression-predicted sediment trapping efficiency was reduced by39 passing approximately 180 000 additional metric tonsof sediment through the reservoir Estimates indicate thatwithin existing operational constraints altered reservoirmanagement has a maximum potential of decreasingsediment trapping by approximately 45 000 metric tonsper year or approximately 3 of the annual load of 141million metric tons transported during the study periodAn annual reduction of 45 000 tons of sediment from

John Redmond Reservoir would preserve approximately74 000m3 of storage in the reservoir per year This rate isequivalent to 15 of the designed reservoir sedimentationrate and equivalent to 81 of the observed sedimentationrate in the conservation pool given the average bulk densityestimates of Juracek (2010) As more reservoir storage islost to deposited sediments the residence time of sediment-laden inflows will continue to decrease and reservoirmanagement is likely to become a more effective alternativeto reducing reservoir sedimentation If existing reservoiroperational plans were adapted to accommodate watersupply as well as flood control uses of the reservoir alteredreservoir management might further reduce sedimentaccumulationAlthough continuous flow and sediment data have proven

useful in determining how short-term variability inhydrology and reservoir management affect sedimentationimproved understanding of in-reservoir processes is needed

Hydrol Process 27 1426ndash1439 (2013)

Table IV Sediment transport to and from John Redmond Reservoir during delineated inflows and releases under the alteredmanagement scenario

Range of flow-weighted averagereservoir elevations (meters abovemean sea level)

No releaseevents

Turbidity-computedload in (metric tons)

Regression-predictedload out (metric tons)

Regression-predicted sedimenttrapping efficiency ()

All releases 46 5 126 000 778 000 8483161ndash3173 17 1 813 000 426 000 7653173ndash3179 7 855 000 151 000 8243179ndash3197 7 690 000 76 000 8903197ndash3210 10 992 000 94 000 9053210ndash3234 5 775 000 31 000 960

1438 C LEE AND G FOSTER

to better characterize how sediment moves through isdeposited within and is resuspended from reservoirs duringvarious hydrologic and outflow management scenarios Forexample although the maintenance of low-reservoir levelsmay reduce the total amount of sedimentation in thereservoir it could change the predominant location ofsediment deposition from the flood to the conservation poolpossibly decreasing storage where it is most needed Anexpanded collection of turbidity data within reservoirscan improve understanding of in-reservoir processes (Effleret al 2006) and could better calibrate two- or three-dimensional reservoir models In addition while increasedsediment transport downstream from the reservoir underaltered management plans would still be less than thehistorical pre-impoundment conditions investigationswould need to ascertain potential effects on infrastructureand aquatic life Any potential changes to reservoirmanagement also would need to fully evaluate the degreeto which altered management plans could increase the riskof downstream flooding However study results indicatethat despite limited in-reservoir data the coupling ofcontinuous streamflow and turbidity data with reservoirmodelling can effectively project the degree to whichchanges in reservoir management can affect sedimentationin the reservoir

CONCLUSIONS

Rapid sediment accumulation in large reservoirs willincreasingly threaten communities reliant on reservoirstorage for municipal agricultural and industrial usesDecisions will need to be made about which reservoiruses will be prioritized such as maintenance of watersupplies for nearby and downstream communities floodcontrol for downstream infrastructure and property ownersor recreational considerations Study results indicate thatcontinuous in-stream flow and turbidity monitoring can beused to quantify the potential of outflow management toreduce reservoir sedimentation Along with the collectionof hydrodynamic and sediment data within reservoirsthese data can help calibrate and validate models thatbetter quantify spatial patterns of sediment depositionand resuspension under varying hydrologic and manage-ment scenarios Study results indicate that depending onthe specific reservoir characteristics reservoir outflow

Copyright copy 2012 John Wiley amp Sons Ltd

management can help preserve water supplies especiallyas sedimentation continues to usurp storage capacityAlthough the approach presented can evaluate the potentialof altered reservoir management to preserve storagemanagement decisions need to consider potential effectschanging reservoir operations on downstream flood controland aquatic ecosystems

REFERENCES

Barfield DB 2010 Eastern Kansas Water Supply presentation given atthe 2010 Kansas Field Conference June 2 2010 Available on the Webat httpwwwksdagovincludesdocument_centerdwrPresentationsBarfield_KGSFieldTour2010pdf

Carswell WJ Hart RJ 1985 Transit losses and travel times for reservoirreleases during drought conditions along the Neosho River fromCouncil Grove Lake to Iola east-central Kansas US GeologicalSurvey Water-Resources Investigations Report 85ndash4003 40

Chung SW Hipsey MR Imberger J 2009 Modeling the propagation ofturbid density inflows into a stratified lake Daecheong ReservoirKorea Environmental Modelling and Software 24 1467ndash1482

Cohn TA Gilroy EJ 1991 Estimating loads from periodic records USGeological Survey Branch of Systems Analysis Technical Memo9101 81

Cole TM Wells SA 2008 CE-QUAL-W2 A two-dimensional laterallyaveraged hydrodynamic and water-quality model 36 userrsquos manualUS Army Corps of Engineers Waterways Experiment Station 125

Droppo IG Ongley ED 1994 Floccuation of suspended-sediment inrivers of Southeastern Canada Water Research 28(8) 1799ndash1809

Duan N 1983 Smearing estimatemdasha nonparametric retransformationmethod Journal of the American Statistical Asssociation 78 605ndash610

Effler SW Prestigiacomo AR Peng F Bulygina KB 2006 Resolution ofturbidity patterns from runoff events in a water supply reservoir and theadvantages of in situ beam attenuation measurements Lake andReservoir Management 22(1) 79ndash93

Evans JK Gottgens JF Gill WM Mackey SD 2000 Sediment yieldscontrolled by intrabasinal storage and sediment conveyance over theinterval 1842-1994 Chagrin River Northeast Ohio USA Journal ofSoil and Water Conservation 55 264ndash70

Fan J Morris GL 1992a Reservoir sedimentation I Delta and densitycurrent deposits Journal of Hydraulic Engineering 118 (3) 354ndash69

Fan J Morris GL 1992b Reservoir sedimentation II Reservoir desiltationand long-term storage capacity Journal of Hydraulic Engineering118 (3) 370ndash84

Gelda RK Effler SW 2007 Modeling turbidity in a water supply reservoiradvancements and issues Journal of Environmental Engineering 133(2)139ndash148

Guy HP 1969 Laboratory theory and methods for sediment analysis USGeological Survey Techniques of Water-Resources Investigations book5 chap C1 58

Hach Company 2005 SOLITAX sc turbidity and suspended solids usermanual Available on the Web at httpwwwhachcomfmmimghachCODEDOC0235403232_3ED_8864|1

Helsel DR Hirsch RM 1992 Statistical methods in water resourcesElsevier New York 522

Hydrol Process 27 1426ndash1439 (2013)

314

316

318

320

322

324

326

11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Ele

vati

on a

bove

mea

n se

a le

vel

in m

Observed bathymetry Root -mean squared error = 010 m

Simulated bathymetry Average error = 007 mTop of flood pool

Top of conservation poolAverage error = 007 m

A

B

-10

-5

0

5

10

15

20

25

30

35

40

11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Wat

er te

mpr

eatu

re i

n de

gree

s C

elci

us Observed water temperature

Modeled water temperature

Root-mean squared error = 146 degrees CelciusAverage error = 118 degrees Celcius

0

50

100

150

200

250

300

350

400

450

0

100

200

300

400

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600

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800

11807 61707 111407 41208 9908 2609 7609 12309 5210 92910St

ream

flow

in

m3 p

er s

econ

d

Spe

cifi

c co

nduc

tanc

e in

mic

rosi

emen

s Observed specific conductanceSimulated specific conductanceStreamflow

Root-mean squared error = 117 microScmAverage error = 69 microScm

C

D

0

50

100

150

200

250

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400

450

0

100000

200000

300000

400000

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600000

700000

11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Stre

amfl

ow f

rom

Joh

n R

edm

ond

Res

ervo

ir i

n m

3 per

sec

ond

Cum

ulat

ive

sedi

men

t flu

x fr

om J

ohn

Red

mon

d R

eser

voir

in m

etri

c to

ns

Turbidity-computed sediment load

Simulated sediment load

StreamflowRoot-mean squared error of observed and modeled SSC values = 65 mgL

Average error = 38 mgL

Figure 5 Comparison of simulated and observed (A) reservoir elevation (B) outflow water temperature (C) outflow SC and (D) outflow cumulativesuspended-sediment load from February 2007 through September 2010

1433CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

specified period as indicated in Equation 1

FWSCin frac14 SLin=WVin 106 (1)

where FWSCin is the FWSC of the incoming event(mgl) SLin is the incoming sediment load of the event(metric tons) and WVin is the volume of water of theincoming event (m3) The FWSC is then multiplied by thedifference in inflowoutflow volumes and the resulting

Copyright copy 2012 John Wiley amp Sons Ltd

sediment load is added (or subtracted) from the originalincoming sediment load as indicated in Equation 2

SLinadj frac14 SLin thorn FWSCin WVout WVineth THORN=106 (2)

where SLinadj is the adjusted incoming sediment load(metric tons) and WVout is the volume of water released(m3) Sediment trapping efficiency for each eventreleasepair is then defined as the percentage of the adjusted

Hydrol Process 27 1426ndash1439 (2013)

0

02

04

06

08

1

12

0 02 04 06 08 1 12

Obs

erve

d av

erag

e di

ffer

ence

in w

ater

tem

pera

ture

in

degr

ees

Cm

Modeled average difference in watertemperature in degrees Cm

July 2007

March April May JuneAugust September October

and November 2007

Figure 6 Modelled versus observed differences in water temperature fromthe surface to the bottom near John Reservoir dam 2007

1434 C LEE AND G FOSTER

incoming sediment load trapped in the reservoir asindicated in Equation 3

TE frac14 100 SLinadj SLout

=SLinadj (3)

Equal-volume inflow events were transported between3100 and 260 000 metric tons of suspended sedimentinto John Redmond Reservoir The largest sedimentloads were transported during relatively short-termrainfallrunoff events whereas smaller loads weretransported during low-flow conditions Least squaresregression was used to explore relations between thesediment trapping efficiency of eventrelease pairs andthe measures of residence time reservoir elevation andsediment concentration and load entering John RedmondReservoir Different measures of residence time and lakeelevation were computed for each eventrelease byweighting these variables by the length of time thewater (flow weighted) or sediment (sediment weighted)for a particular eventrelease pair was present within thereservoir (eg the reservoir elevation for a particular daywas weighted by 05 if one half the water or sedimentfor an individual eventrelease pair was withinthe reservoir or by 10 if all of the water or sedimentwas within the reservoir)Only releases with incoming sediment loads greater

than 37 000 metric tons were used in this analysis as thesediment trapping efficiency of smaller events wasjudged to be primarily affected by background levelsof sediment within the reservoir (such as from algae orwind-related resuspension of bottom sediment) orpotentially from sediment suspended from the streamchannel between the reservoir outflow and Burlingtonmonitoring site These releases are less important topredict because they represent a relatively small part ofthe total sediment load transported to the reservoirStepwise multiple linear regression was then used tocharacterize variables which best estimated sedimenttrapping efficiency among eventrelease pairs withoutexhibiting multicollinearity

Copyright copy 2012 John Wiley amp Sons Ltd

RESULTS AND DISCUSSION

Hydrologic conditions

Precipitation upstream from John Redmond Reservoirduring the study period (2007ndash2010) was generally greaterthan historical averages The National Weather Servicestation at the Neosho Rapids (directly upstream from JohnRedmond Reservoir) recorded 815 in of precipitation in2007 1298 in in 2008 and 1196 cm in 2009 comparedwith the annual average of 914 in from 1950 to 2006(National Weather Service 2010b) Annual flows weresummed from theAmericus and Plymouth sites whichwere839 km3 in 2007 1591 km3 in 2008 and 1332 km3 in2009 the median combined annual flow from these siteswas 1061 km3 from 1964 to 2006 Rivers exceeded the2-year (50 annual) USGS flood-frequency estimates 14times at the Plymouth and Neosho Rapids sites (Perry et al2004) indicating that relatively extreme storms (andcorresponding high sediment loads) were frequentlyobserved during the study period Increased rainfall andflow during the study period indicate that sediment flux to(and likely from) the reservoir is likely larger than duringa typical 4-year study period

Sediment transport to and from John Redmond Reservoir

The maximum computed SSC upstream from thereservoir was 7690mgl whereas the maximum SSC was1080mgl downstream from the reservoir From February2007 through September 2010 approximately 5 600 000metric tons of sediment entered John Redmond Reservoir660 000 of which were transported past the Burlington site(trapping efficiency of 88) Nearly all of the suspendedsediment at upstream sampling sites consisted of silt andclay (the median sample was 96 less than 63mm indiameter) Approximately one half of the sedimentstransported to John Redmond Reservoir during high-flowconditions were clays (lt2mm) the remaining sedimentswere distributed somewhat equally between 2 and 63mmSimilar to inflow samples 98 of the reservoir outflowsuspended sediment sampled had diameters of less than63 mm (full grain-size analysis was not conducted)Because suspended-sediment and reservoir-bottom sam-ples (Juracek 2010) indicate a lack of sand and larger-sized material and because streambed substrates at gagesites were observed to consist primarily of cobble androck bedload transport of sediment is not considered asubstantial component of the sediment load transported toJohn Redmond ReservoirThe annual volume of flow and sediment load transported

into John Redmond Reservoir generally corresponded withannual patterns in precipitation (Figure 7) The trappingefficiency of the reservoir initially decreased during yearswith greater precipitation and streamflow in 2008 and 2009but continued to decrease despite smaller flows andincoming sediment loads in 2010 potentially because ofdifferences in the manner in which the reservoir wasoperated An examination of inflows outflows and reservoirlevels in John Redmond Reservoir indicated that although

Hydrol Process 27 1426ndash1439 (2013)

0

20

40

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140

0

200000

400000

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800000

1000000

1200000

1400000

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2000000

Pre

cipi

tati

on i

n ce

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eter

s

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amfl

ow i

n th

ousa

nds

of m

3 an

dse

dim

ent

load

in

met

ric

tons

IncomingstreamflowIncomingsedimentOutgoingsedimentPrecipitation

2007 2008 2009 2010

929

886

858

853

Sediment trapping efficiency

Figure 7 Annual precipitation streamflow and sediment transport to andfrom John Redmond Reservoir February 2007 to September 2010

1435CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

inflows were smaller in 2010 than those in 2008 and 2009water was released more rapidly after sediment-ladeninflows in 2010 in part because of maintenance activitiesthat necessitated lower reservoir levels (T Lyons USACEoral communication 2010)

Sediment trapping efficiency relative to variation inreservoir management

Among the 48 eventrelease pairs with greater than37 000 metric tons of incoming sediment sedimenttrapping efficiency varied from 48 to 97 Relations

A

40

50

60

70

80

90

100

316 318 320 322 324 326

Flow-weighted reservoir elevation in meters

Linear fit

Top of conservation pool Top of flood pool

60

70

80

90

100

60 70

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

g ef

fici

ency

in

perc

ent

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

gef

fici

ency

in

pres

ent

Regression-predicted sep

C

Figure 8 Regression relations between turbidity-computed sediment trappinand (C) regression-predicted estimates of sedim

Copyright copy 2012 John Wiley amp Sons Ltd

established between sediment trapping efficiency flow-weighted reservoir elevation (Figure 8A) and flow-weighted residence time (Figure 8B) indicated that alarger proportion of incoming sediment is transportedthrough John Redmond Reservoir when the reservoir ismaintained at lower levels and when residence times areminimized (Table II) However these relations were alsomore variable during these conditions In additionpredicted sediment trapping efficiencies were nonlinearwith respect to the primary explanatory variables(reservoir elevation FWSCs and flow-weighted residencetime) single linear regressions typically overpredictedeventrelease pairs with small trapping efficiencies andunderpredicted eventrelease pairs with larger sedimenttrapping efficiencies To minimize bias in predictionsof sediment trapping efficiency separate linear regres-sions were identified for eventrelease pairs above andbelow a reservoir elevation threshold of 3185 m Theresulting predictions were similar to single regressionequations in terms of adjusted R2 and error but residualswere distributed more evenly throughout the range ofvaluesAmong eventrelease pairs in which reservoir elevation

was maintained lower than 3185m the variability inthe sediment trapping efficiency was best explained byflow-weighted reservoir elevation (EL) and by the FWSCof the inflow event (Figure 8C) Among eventrelease pairsin which reservoir elevation was maintained higher than3185 m the variability in sediment trapping efficiency

40

50

60

70

80

90

100

5 15 25 35 45

Flow-weighted residence time of event in days

B

80 90 100

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

gef

fici

ency

in

perc

ent

diment trapping efficiency in ercent

g efficiency and (A) flow-weighted reservoir elevation (B) residence timeent trapping efficiency for eventrelease pairs

Hydrol Process 27 1426ndash1439 (2013)

Table II Summary of sediment loading and reservoir characteristics during delineated inflows and releases from John RedmondReservoir

Range of flow-weighted averagereservoir elevations (ft abovemean sea level)

No eventrelease pairs

Turbidity-computedload in (metric tons)

Turbidity-computedload out

(metric tons)

Turbidity-computedtrapping efficiency

()

Regression-predictedsediment trappingefficiency ()

All releases 3161ndash3234 48 5 240 000 595 000 886 8873161ndash3173 8 708 000 179 000 747 7463173ndash3179 10 1 101 000 159 000 856 8623179ndash3197 8 892 000 88 000 901 8923197ndash3210 11 1 201 000 84 000 930 9253210ndash3234 11 1 337 000 85 000 936 945

Table III Maximum discharges and approximate travel times ofreleases to flood control points downstream from John Redmond

Reservoirdaggera

Flood controlend point

Approximate travel timefrom outflow gates (h)daggerb

Maximumdischarge (m3s)

Neosho River atBurlington Kansas

2 396

Neosho River atIola Kansas

24 510

Neosho River atChanute Kansas

36 510

Neosho River atParsons Kansas

60 481

Neosho River atCommerce Oklahoma

84 623

a In addition to flood control end points the water control manual specifiesthat outgoing discharges do not rise or fall by more than 566m3s every 3h the manual also includes the consideration of conditions at otherreservoirs in the basin (USACE 1996)b Data from USACE (1996)

326s

1436 C LEE AND G FOSTER

was best explained by flow-weighted hydraulic residencetime (RES) and by the FWSC of the inflow event(Figure 8C) All explanatory variables were significantlyrelated to sediment trapping efficiency (P valuelt 005) andthe regression equations chosen resulted in the largestadjusted R2 the smallest RMSE values and the smallestprediction error sum of squares values of other potentialcombinations of independent variables The varianceinflation factors among independent variables were lessthan 12 indicating that multicollinearity among incomingFWSC values and flow-weighted reservoir elevations didnot inflate or adversely affect regression estimates (Helseland Hirsch 1992) In addition to decreased sedimenttrapping efficiency during low reservoir levels and residencetimes more sediment was trapped when incoming sedimentconcentrations were larger (as indicated by larger FWSCvalues) potentially because of increased flocculation (andthus larger effective grain size and fall velocity Droppo andOngley 1994)Although sediment trapping predictions were variable

for individual eventrelease pairs they were muchmore accurate with respect to turbidity-computed resultsfor multiple event releases when grouped by flow-weighted reservoir elevation (Table II) Results suggestthat (i) reductions in trapping efficiency consistently areobserved when reservoir elevations are maintained nearconservation pool levels and (ii) although regression-derived estimates of sediment trapping may be inaccuratefor individual eventrelease pairs this method can producerelatively accurate long-term estimates of sedimenttrapping efficiency with respect to factors that can beaffected by altered reservoir management

315

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11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Ele

vati

on a

bove

mea

n se

a le

vel

in m

eter

Observed reservoir level

Altered outflow scenario

Flood-control pool elevation

Conservationpool

elevation

Example period (fig 10)

Figure 9 Observed and altered elevation at John Redmond ReservoirFebruary 2007 through September 2010

Potential of altered reservoir management to reducesediment trapping

The USACE water control manual for John RedmondReservoir identifies specific flood control end points thatmust not be exceeded to ensure that reservoir outflows donot contribute to flood-related damages to crops andstructures (Table III) Any changes to reservoirmanagementmust also meet these end points to fulfil the mission forwhich John Redmond Reservoir was constructed Tosimulate the potential effect of changes in reservoir outflowmanagement on sediment accumulation in John RedmondReservoir an idealized and altered outflow management

Copyright copy 2012 John Wiley amp Sons Ltd

scenario was constructed which continued to meet theseend points while also preserving storage for drinking wateragricultural use and industrial use (Figure 9) This scenariois lsquoidealizedrsquo in that it benefits from the knowledge ofincoming and downstream flows whereas in practicereservoir operators rely on weather forecasts and modellingto ensure consistent water supplies and to preventdownstream flooding Because of the inability to access

Hydrol Process 27 1426ndash1439 (2013)

1437CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

historical rainfall forecasts or models that predict flows toand from John Redmond Reservoir results from thisscenario are presented as the maximum potential reductionin sediment trapping that could be achieved within currentoperational plansRelative to observed reservoir management the altered

scenario purposefully minimized reservoir elevation andresidence time through larger more rapid releases ofwater after periods of high inflows (Figure 9) To partiallycompensate for uncertainties in weather forecasts andpredicted streamflows the altered management scenariowas somewhat more conservative compared with watercontrol plan restrictions in that (i) outflows were notincreased bymore than 850m3sday and (ii) outflows werenever reduced by more than 566m3sday (other than whenthe reservoir was actuallymanaged in this fashion) After theconstruction of the altered scenario reservoir releases wereredelineated and matched to inflows as done with observeddata Flow-weighted reservoir elevations residence timesand FWSCs for incoming events were recomputed toestimate the effect of the altered management scenario onsediment trapping efficiency (using regressions developedwith observed values)Although data and models were not available to test the

uncertainty of forecasted flow conditions an exampleperiod (Figures 9 and 10) in July 2007 is highlighted toillustrate how the consideration of sediment trapping withinexisting reservoir operational plans could preserve reservoirstorage During late June and early July 2007 heavy rainsupstream and downstream from John Redmond Reservoirraised reservoir levels and caused flooding downstreamfrom the reservoir (see the Neosho River at Parsons as anexample Figure 10) These flows raised reservoir levelswell into the flood pool where it was held until 16 July2007 when John Redmond Reservoir gates began releasingmore water (as indicated by the observed Burlingtonstreamflow record Figure 10) Historical weather forecastsfrom the National Weather Service for Iola Kansas(between Burlington and Parsons National Weather

0

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627 77 717 727 86 816

Stre

amfl

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sec

ond

Res

ervo

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leva

tion

abo

ve m

ean

sea

leve

l in

met

ers

Observed reservoir elevation

Alternate reservoir elevation

Observed flow at Burlington KS

Alternate flow at Burlington KS

Observed flow at Parsons KS

Alternate flow at Parsons KS

Figure 10 Comparison of observed reservoir elevations and downstreamflow conditions relative to the altered reservoir management scenario June

to August 2007

Copyright copy 2012 John Wiley amp Sons Ltd

service written communication 2011) projected con-sistent (20ndash50) chances of thunderstorms throughoutJuly Compared with observed reservoir managementthe altered outflow scenario began discharging wateron 5 July 2007 while keeping downstream flows belowmaximum levels indicated in John Redmond Reservoircontrol manual (Table III) Two eventrelease pairs weredelineated during this period modelled residence timesbased on observed reservoir levels were 72 and 26 daysMore rapid release of water from John RedmondReservoir through the alternative scenario reduced theseto 66 and 16 days respectively Sediment trappingfor these periods is projected to have decreased from971 to 893 and from 957 to 822 Althoughthis example illustrates that consideration of sedimenttrapping within reservoir operational plans can reducesediment trapping uncertainty of weather forecasts andrainfall runoff models will often limit the ability topreserve reservoir storage within existing operationlimitsFrom 2007 to 2010 the altered management scenario

decreased the average reservoir elevation from 3178 to3173 m and decreased the average daily residence timesfrom 296 to 269 days Forty-six releases with more than37 000 tons of incoming sediment were delineated under thealtered scenario (compared with 48 observed releases)corresponding to 51 million metric tons of incomingsediment (compared with 53 million metric tons computedwith observed data Table IV) The average reservoirelevations for eventrelease pairs decreased from 3194 to3186 m and for residence times from 199 to 131 daysUnder the altered management scenario regression-predicted sediment trapping efficiency was reduced by39 passing approximately 180 000 additional metric tonsof sediment through the reservoir Estimates indicate thatwithin existing operational constraints altered reservoirmanagement has a maximum potential of decreasingsediment trapping by approximately 45 000 metric tonsper year or approximately 3 of the annual load of 141million metric tons transported during the study periodAn annual reduction of 45 000 tons of sediment from

John Redmond Reservoir would preserve approximately74 000m3 of storage in the reservoir per year This rate isequivalent to 15 of the designed reservoir sedimentationrate and equivalent to 81 of the observed sedimentationrate in the conservation pool given the average bulk densityestimates of Juracek (2010) As more reservoir storage islost to deposited sediments the residence time of sediment-laden inflows will continue to decrease and reservoirmanagement is likely to become a more effective alternativeto reducing reservoir sedimentation If existing reservoiroperational plans were adapted to accommodate watersupply as well as flood control uses of the reservoir alteredreservoir management might further reduce sedimentaccumulationAlthough continuous flow and sediment data have proven

useful in determining how short-term variability inhydrology and reservoir management affect sedimentationimproved understanding of in-reservoir processes is needed

Hydrol Process 27 1426ndash1439 (2013)

Table IV Sediment transport to and from John Redmond Reservoir during delineated inflows and releases under the alteredmanagement scenario

Range of flow-weighted averagereservoir elevations (meters abovemean sea level)

No releaseevents

Turbidity-computedload in (metric tons)

Regression-predictedload out (metric tons)

Regression-predicted sedimenttrapping efficiency ()

All releases 46 5 126 000 778 000 8483161ndash3173 17 1 813 000 426 000 7653173ndash3179 7 855 000 151 000 8243179ndash3197 7 690 000 76 000 8903197ndash3210 10 992 000 94 000 9053210ndash3234 5 775 000 31 000 960

1438 C LEE AND G FOSTER

to better characterize how sediment moves through isdeposited within and is resuspended from reservoirs duringvarious hydrologic and outflow management scenarios Forexample although the maintenance of low-reservoir levelsmay reduce the total amount of sedimentation in thereservoir it could change the predominant location ofsediment deposition from the flood to the conservation poolpossibly decreasing storage where it is most needed Anexpanded collection of turbidity data within reservoirscan improve understanding of in-reservoir processes (Effleret al 2006) and could better calibrate two- or three-dimensional reservoir models In addition while increasedsediment transport downstream from the reservoir underaltered management plans would still be less than thehistorical pre-impoundment conditions investigationswould need to ascertain potential effects on infrastructureand aquatic life Any potential changes to reservoirmanagement also would need to fully evaluate the degreeto which altered management plans could increase the riskof downstream flooding However study results indicatethat despite limited in-reservoir data the coupling ofcontinuous streamflow and turbidity data with reservoirmodelling can effectively project the degree to whichchanges in reservoir management can affect sedimentationin the reservoir

CONCLUSIONS

Rapid sediment accumulation in large reservoirs willincreasingly threaten communities reliant on reservoirstorage for municipal agricultural and industrial usesDecisions will need to be made about which reservoiruses will be prioritized such as maintenance of watersupplies for nearby and downstream communities floodcontrol for downstream infrastructure and property ownersor recreational considerations Study results indicate thatcontinuous in-stream flow and turbidity monitoring can beused to quantify the potential of outflow management toreduce reservoir sedimentation Along with the collectionof hydrodynamic and sediment data within reservoirsthese data can help calibrate and validate models thatbetter quantify spatial patterns of sediment depositionand resuspension under varying hydrologic and manage-ment scenarios Study results indicate that depending onthe specific reservoir characteristics reservoir outflow

Copyright copy 2012 John Wiley amp Sons Ltd

management can help preserve water supplies especiallyas sedimentation continues to usurp storage capacityAlthough the approach presented can evaluate the potentialof altered reservoir management to preserve storagemanagement decisions need to consider potential effectschanging reservoir operations on downstream flood controland aquatic ecosystems

REFERENCES

Barfield DB 2010 Eastern Kansas Water Supply presentation given atthe 2010 Kansas Field Conference June 2 2010 Available on the Webat httpwwwksdagovincludesdocument_centerdwrPresentationsBarfield_KGSFieldTour2010pdf

Carswell WJ Hart RJ 1985 Transit losses and travel times for reservoirreleases during drought conditions along the Neosho River fromCouncil Grove Lake to Iola east-central Kansas US GeologicalSurvey Water-Resources Investigations Report 85ndash4003 40

Chung SW Hipsey MR Imberger J 2009 Modeling the propagation ofturbid density inflows into a stratified lake Daecheong ReservoirKorea Environmental Modelling and Software 24 1467ndash1482

Cohn TA Gilroy EJ 1991 Estimating loads from periodic records USGeological Survey Branch of Systems Analysis Technical Memo9101 81

Cole TM Wells SA 2008 CE-QUAL-W2 A two-dimensional laterallyaveraged hydrodynamic and water-quality model 36 userrsquos manualUS Army Corps of Engineers Waterways Experiment Station 125

Droppo IG Ongley ED 1994 Floccuation of suspended-sediment inrivers of Southeastern Canada Water Research 28(8) 1799ndash1809

Duan N 1983 Smearing estimatemdasha nonparametric retransformationmethod Journal of the American Statistical Asssociation 78 605ndash610

Effler SW Prestigiacomo AR Peng F Bulygina KB 2006 Resolution ofturbidity patterns from runoff events in a water supply reservoir and theadvantages of in situ beam attenuation measurements Lake andReservoir Management 22(1) 79ndash93

Evans JK Gottgens JF Gill WM Mackey SD 2000 Sediment yieldscontrolled by intrabasinal storage and sediment conveyance over theinterval 1842-1994 Chagrin River Northeast Ohio USA Journal ofSoil and Water Conservation 55 264ndash70

Fan J Morris GL 1992a Reservoir sedimentation I Delta and densitycurrent deposits Journal of Hydraulic Engineering 118 (3) 354ndash69

Fan J Morris GL 1992b Reservoir sedimentation II Reservoir desiltationand long-term storage capacity Journal of Hydraulic Engineering118 (3) 370ndash84

Gelda RK Effler SW 2007 Modeling turbidity in a water supply reservoiradvancements and issues Journal of Environmental Engineering 133(2)139ndash148

Guy HP 1969 Laboratory theory and methods for sediment analysis USGeological Survey Techniques of Water-Resources Investigations book5 chap C1 58

Hach Company 2005 SOLITAX sc turbidity and suspended solids usermanual Available on the Web at httpwwwhachcomfmmimghachCODEDOC0235403232_3ED_8864|1

Helsel DR Hirsch RM 1992 Statistical methods in water resourcesElsevier New York 522

Hydrol Process 27 1426ndash1439 (2013)

0

02

04

06

08

1

12

0 02 04 06 08 1 12

Obs

erve

d av

erag

e di

ffer

ence

in w

ater

tem

pera

ture

in

degr

ees

Cm

Modeled average difference in watertemperature in degrees Cm

July 2007

March April May JuneAugust September October

and November 2007

Figure 6 Modelled versus observed differences in water temperature fromthe surface to the bottom near John Reservoir dam 2007

1434 C LEE AND G FOSTER

incoming sediment load trapped in the reservoir asindicated in Equation 3

TE frac14 100 SLinadj SLout

=SLinadj (3)

Equal-volume inflow events were transported between3100 and 260 000 metric tons of suspended sedimentinto John Redmond Reservoir The largest sedimentloads were transported during relatively short-termrainfallrunoff events whereas smaller loads weretransported during low-flow conditions Least squaresregression was used to explore relations between thesediment trapping efficiency of eventrelease pairs andthe measures of residence time reservoir elevation andsediment concentration and load entering John RedmondReservoir Different measures of residence time and lakeelevation were computed for each eventrelease byweighting these variables by the length of time thewater (flow weighted) or sediment (sediment weighted)for a particular eventrelease pair was present within thereservoir (eg the reservoir elevation for a particular daywas weighted by 05 if one half the water or sedimentfor an individual eventrelease pair was withinthe reservoir or by 10 if all of the water or sedimentwas within the reservoir)Only releases with incoming sediment loads greater

than 37 000 metric tons were used in this analysis as thesediment trapping efficiency of smaller events wasjudged to be primarily affected by background levelsof sediment within the reservoir (such as from algae orwind-related resuspension of bottom sediment) orpotentially from sediment suspended from the streamchannel between the reservoir outflow and Burlingtonmonitoring site These releases are less important topredict because they represent a relatively small part ofthe total sediment load transported to the reservoirStepwise multiple linear regression was then used tocharacterize variables which best estimated sedimenttrapping efficiency among eventrelease pairs withoutexhibiting multicollinearity

Copyright copy 2012 John Wiley amp Sons Ltd

RESULTS AND DISCUSSION

Hydrologic conditions

Precipitation upstream from John Redmond Reservoirduring the study period (2007ndash2010) was generally greaterthan historical averages The National Weather Servicestation at the Neosho Rapids (directly upstream from JohnRedmond Reservoir) recorded 815 in of precipitation in2007 1298 in in 2008 and 1196 cm in 2009 comparedwith the annual average of 914 in from 1950 to 2006(National Weather Service 2010b) Annual flows weresummed from theAmericus and Plymouth sites whichwere839 km3 in 2007 1591 km3 in 2008 and 1332 km3 in2009 the median combined annual flow from these siteswas 1061 km3 from 1964 to 2006 Rivers exceeded the2-year (50 annual) USGS flood-frequency estimates 14times at the Plymouth and Neosho Rapids sites (Perry et al2004) indicating that relatively extreme storms (andcorresponding high sediment loads) were frequentlyobserved during the study period Increased rainfall andflow during the study period indicate that sediment flux to(and likely from) the reservoir is likely larger than duringa typical 4-year study period

Sediment transport to and from John Redmond Reservoir

The maximum computed SSC upstream from thereservoir was 7690mgl whereas the maximum SSC was1080mgl downstream from the reservoir From February2007 through September 2010 approximately 5 600 000metric tons of sediment entered John Redmond Reservoir660 000 of which were transported past the Burlington site(trapping efficiency of 88) Nearly all of the suspendedsediment at upstream sampling sites consisted of silt andclay (the median sample was 96 less than 63mm indiameter) Approximately one half of the sedimentstransported to John Redmond Reservoir during high-flowconditions were clays (lt2mm) the remaining sedimentswere distributed somewhat equally between 2 and 63mmSimilar to inflow samples 98 of the reservoir outflowsuspended sediment sampled had diameters of less than63 mm (full grain-size analysis was not conducted)Because suspended-sediment and reservoir-bottom sam-ples (Juracek 2010) indicate a lack of sand and larger-sized material and because streambed substrates at gagesites were observed to consist primarily of cobble androck bedload transport of sediment is not considered asubstantial component of the sediment load transported toJohn Redmond ReservoirThe annual volume of flow and sediment load transported

into John Redmond Reservoir generally corresponded withannual patterns in precipitation (Figure 7) The trappingefficiency of the reservoir initially decreased during yearswith greater precipitation and streamflow in 2008 and 2009but continued to decrease despite smaller flows andincoming sediment loads in 2010 potentially because ofdifferences in the manner in which the reservoir wasoperated An examination of inflows outflows and reservoirlevels in John Redmond Reservoir indicated that although

Hydrol Process 27 1426ndash1439 (2013)

0

20

40

60

80

100

120

140

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

2000000

Pre

cipi

tati

on i

n ce

ntim

eter

s

Stre

amfl

ow i

n th

ousa

nds

of m

3 an

dse

dim

ent

load

in

met

ric

tons

IncomingstreamflowIncomingsedimentOutgoingsedimentPrecipitation

2007 2008 2009 2010

929

886

858

853

Sediment trapping efficiency

Figure 7 Annual precipitation streamflow and sediment transport to andfrom John Redmond Reservoir February 2007 to September 2010

1435CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

inflows were smaller in 2010 than those in 2008 and 2009water was released more rapidly after sediment-ladeninflows in 2010 in part because of maintenance activitiesthat necessitated lower reservoir levels (T Lyons USACEoral communication 2010)

Sediment trapping efficiency relative to variation inreservoir management

Among the 48 eventrelease pairs with greater than37 000 metric tons of incoming sediment sedimenttrapping efficiency varied from 48 to 97 Relations

A

40

50

60

70

80

90

100

316 318 320 322 324 326

Flow-weighted reservoir elevation in meters

Linear fit

Top of conservation pool Top of flood pool

60

70

80

90

100

60 70

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

g ef

fici

ency

in

perc

ent

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

gef

fici

ency

in

pres

ent

Regression-predicted sep

C

Figure 8 Regression relations between turbidity-computed sediment trappinand (C) regression-predicted estimates of sedim

Copyright copy 2012 John Wiley amp Sons Ltd

established between sediment trapping efficiency flow-weighted reservoir elevation (Figure 8A) and flow-weighted residence time (Figure 8B) indicated that alarger proportion of incoming sediment is transportedthrough John Redmond Reservoir when the reservoir ismaintained at lower levels and when residence times areminimized (Table II) However these relations were alsomore variable during these conditions In additionpredicted sediment trapping efficiencies were nonlinearwith respect to the primary explanatory variables(reservoir elevation FWSCs and flow-weighted residencetime) single linear regressions typically overpredictedeventrelease pairs with small trapping efficiencies andunderpredicted eventrelease pairs with larger sedimenttrapping efficiencies To minimize bias in predictionsof sediment trapping efficiency separate linear regres-sions were identified for eventrelease pairs above andbelow a reservoir elevation threshold of 3185 m Theresulting predictions were similar to single regressionequations in terms of adjusted R2 and error but residualswere distributed more evenly throughout the range ofvaluesAmong eventrelease pairs in which reservoir elevation

was maintained lower than 3185m the variability inthe sediment trapping efficiency was best explained byflow-weighted reservoir elevation (EL) and by the FWSCof the inflow event (Figure 8C) Among eventrelease pairsin which reservoir elevation was maintained higher than3185 m the variability in sediment trapping efficiency

40

50

60

70

80

90

100

5 15 25 35 45

Flow-weighted residence time of event in days

B

80 90 100

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

gef

fici

ency

in

perc

ent

diment trapping efficiency in ercent

g efficiency and (A) flow-weighted reservoir elevation (B) residence timeent trapping efficiency for eventrelease pairs

Hydrol Process 27 1426ndash1439 (2013)

Table II Summary of sediment loading and reservoir characteristics during delineated inflows and releases from John RedmondReservoir

Range of flow-weighted averagereservoir elevations (ft abovemean sea level)

No eventrelease pairs

Turbidity-computedload in (metric tons)

Turbidity-computedload out

(metric tons)

Turbidity-computedtrapping efficiency

()

Regression-predictedsediment trappingefficiency ()

All releases 3161ndash3234 48 5 240 000 595 000 886 8873161ndash3173 8 708 000 179 000 747 7463173ndash3179 10 1 101 000 159 000 856 8623179ndash3197 8 892 000 88 000 901 8923197ndash3210 11 1 201 000 84 000 930 9253210ndash3234 11 1 337 000 85 000 936 945

Table III Maximum discharges and approximate travel times ofreleases to flood control points downstream from John Redmond

Reservoirdaggera

Flood controlend point

Approximate travel timefrom outflow gates (h)daggerb

Maximumdischarge (m3s)

Neosho River atBurlington Kansas

2 396

Neosho River atIola Kansas

24 510

Neosho River atChanute Kansas

36 510

Neosho River atParsons Kansas

60 481

Neosho River atCommerce Oklahoma

84 623

a In addition to flood control end points the water control manual specifiesthat outgoing discharges do not rise or fall by more than 566m3s every 3h the manual also includes the consideration of conditions at otherreservoirs in the basin (USACE 1996)b Data from USACE (1996)

326s

1436 C LEE AND G FOSTER

was best explained by flow-weighted hydraulic residencetime (RES) and by the FWSC of the inflow event(Figure 8C) All explanatory variables were significantlyrelated to sediment trapping efficiency (P valuelt 005) andthe regression equations chosen resulted in the largestadjusted R2 the smallest RMSE values and the smallestprediction error sum of squares values of other potentialcombinations of independent variables The varianceinflation factors among independent variables were lessthan 12 indicating that multicollinearity among incomingFWSC values and flow-weighted reservoir elevations didnot inflate or adversely affect regression estimates (Helseland Hirsch 1992) In addition to decreased sedimenttrapping efficiency during low reservoir levels and residencetimes more sediment was trapped when incoming sedimentconcentrations were larger (as indicated by larger FWSCvalues) potentially because of increased flocculation (andthus larger effective grain size and fall velocity Droppo andOngley 1994)Although sediment trapping predictions were variable

for individual eventrelease pairs they were muchmore accurate with respect to turbidity-computed resultsfor multiple event releases when grouped by flow-weighted reservoir elevation (Table II) Results suggestthat (i) reductions in trapping efficiency consistently areobserved when reservoir elevations are maintained nearconservation pool levels and (ii) although regression-derived estimates of sediment trapping may be inaccuratefor individual eventrelease pairs this method can producerelatively accurate long-term estimates of sedimenttrapping efficiency with respect to factors that can beaffected by altered reservoir management

315

316

317

318

319

320

321

322

323

324

325

11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Ele

vati

on a

bove

mea

n se

a le

vel

in m

eter

Observed reservoir level

Altered outflow scenario

Flood-control pool elevation

Conservationpool

elevation

Example period (fig 10)

Figure 9 Observed and altered elevation at John Redmond ReservoirFebruary 2007 through September 2010

Potential of altered reservoir management to reducesediment trapping

The USACE water control manual for John RedmondReservoir identifies specific flood control end points thatmust not be exceeded to ensure that reservoir outflows donot contribute to flood-related damages to crops andstructures (Table III) Any changes to reservoirmanagementmust also meet these end points to fulfil the mission forwhich John Redmond Reservoir was constructed Tosimulate the potential effect of changes in reservoir outflowmanagement on sediment accumulation in John RedmondReservoir an idealized and altered outflow management

Copyright copy 2012 John Wiley amp Sons Ltd

scenario was constructed which continued to meet theseend points while also preserving storage for drinking wateragricultural use and industrial use (Figure 9) This scenariois lsquoidealizedrsquo in that it benefits from the knowledge ofincoming and downstream flows whereas in practicereservoir operators rely on weather forecasts and modellingto ensure consistent water supplies and to preventdownstream flooding Because of the inability to access

Hydrol Process 27 1426ndash1439 (2013)

1437CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

historical rainfall forecasts or models that predict flows toand from John Redmond Reservoir results from thisscenario are presented as the maximum potential reductionin sediment trapping that could be achieved within currentoperational plansRelative to observed reservoir management the altered

scenario purposefully minimized reservoir elevation andresidence time through larger more rapid releases ofwater after periods of high inflows (Figure 9) To partiallycompensate for uncertainties in weather forecasts andpredicted streamflows the altered management scenariowas somewhat more conservative compared with watercontrol plan restrictions in that (i) outflows were notincreased bymore than 850m3sday and (ii) outflows werenever reduced by more than 566m3sday (other than whenthe reservoir was actuallymanaged in this fashion) After theconstruction of the altered scenario reservoir releases wereredelineated and matched to inflows as done with observeddata Flow-weighted reservoir elevations residence timesand FWSCs for incoming events were recomputed toestimate the effect of the altered management scenario onsediment trapping efficiency (using regressions developedwith observed values)Although data and models were not available to test the

uncertainty of forecasted flow conditions an exampleperiod (Figures 9 and 10) in July 2007 is highlighted toillustrate how the consideration of sediment trapping withinexisting reservoir operational plans could preserve reservoirstorage During late June and early July 2007 heavy rainsupstream and downstream from John Redmond Reservoirraised reservoir levels and caused flooding downstreamfrom the reservoir (see the Neosho River at Parsons as anexample Figure 10) These flows raised reservoir levelswell into the flood pool where it was held until 16 July2007 when John Redmond Reservoir gates began releasingmore water (as indicated by the observed Burlingtonstreamflow record Figure 10) Historical weather forecastsfrom the National Weather Service for Iola Kansas(between Burlington and Parsons National Weather

0

500

1000

1500

2000

2500

3000

3500

4000

4500

315

316

317

318

319

320

321

322

323

324

325

627 77 717 727 86 816

Stre

amfl

ow i

n cu

bic

mee

ters

per

sec

ond

Res

ervo

ir e

leva

tion

abo

ve m

ean

sea

leve

l in

met

ers

Observed reservoir elevation

Alternate reservoir elevation

Observed flow at Burlington KS

Alternate flow at Burlington KS

Observed flow at Parsons KS

Alternate flow at Parsons KS

Figure 10 Comparison of observed reservoir elevations and downstreamflow conditions relative to the altered reservoir management scenario June

to August 2007

Copyright copy 2012 John Wiley amp Sons Ltd

service written communication 2011) projected con-sistent (20ndash50) chances of thunderstorms throughoutJuly Compared with observed reservoir managementthe altered outflow scenario began discharging wateron 5 July 2007 while keeping downstream flows belowmaximum levels indicated in John Redmond Reservoircontrol manual (Table III) Two eventrelease pairs weredelineated during this period modelled residence timesbased on observed reservoir levels were 72 and 26 daysMore rapid release of water from John RedmondReservoir through the alternative scenario reduced theseto 66 and 16 days respectively Sediment trappingfor these periods is projected to have decreased from971 to 893 and from 957 to 822 Althoughthis example illustrates that consideration of sedimenttrapping within reservoir operational plans can reducesediment trapping uncertainty of weather forecasts andrainfall runoff models will often limit the ability topreserve reservoir storage within existing operationlimitsFrom 2007 to 2010 the altered management scenario

decreased the average reservoir elevation from 3178 to3173 m and decreased the average daily residence timesfrom 296 to 269 days Forty-six releases with more than37 000 tons of incoming sediment were delineated under thealtered scenario (compared with 48 observed releases)corresponding to 51 million metric tons of incomingsediment (compared with 53 million metric tons computedwith observed data Table IV) The average reservoirelevations for eventrelease pairs decreased from 3194 to3186 m and for residence times from 199 to 131 daysUnder the altered management scenario regression-predicted sediment trapping efficiency was reduced by39 passing approximately 180 000 additional metric tonsof sediment through the reservoir Estimates indicate thatwithin existing operational constraints altered reservoirmanagement has a maximum potential of decreasingsediment trapping by approximately 45 000 metric tonsper year or approximately 3 of the annual load of 141million metric tons transported during the study periodAn annual reduction of 45 000 tons of sediment from

John Redmond Reservoir would preserve approximately74 000m3 of storage in the reservoir per year This rate isequivalent to 15 of the designed reservoir sedimentationrate and equivalent to 81 of the observed sedimentationrate in the conservation pool given the average bulk densityestimates of Juracek (2010) As more reservoir storage islost to deposited sediments the residence time of sediment-laden inflows will continue to decrease and reservoirmanagement is likely to become a more effective alternativeto reducing reservoir sedimentation If existing reservoiroperational plans were adapted to accommodate watersupply as well as flood control uses of the reservoir alteredreservoir management might further reduce sedimentaccumulationAlthough continuous flow and sediment data have proven

useful in determining how short-term variability inhydrology and reservoir management affect sedimentationimproved understanding of in-reservoir processes is needed

Hydrol Process 27 1426ndash1439 (2013)

Table IV Sediment transport to and from John Redmond Reservoir during delineated inflows and releases under the alteredmanagement scenario

Range of flow-weighted averagereservoir elevations (meters abovemean sea level)

No releaseevents

Turbidity-computedload in (metric tons)

Regression-predictedload out (metric tons)

Regression-predicted sedimenttrapping efficiency ()

All releases 46 5 126 000 778 000 8483161ndash3173 17 1 813 000 426 000 7653173ndash3179 7 855 000 151 000 8243179ndash3197 7 690 000 76 000 8903197ndash3210 10 992 000 94 000 9053210ndash3234 5 775 000 31 000 960

1438 C LEE AND G FOSTER

to better characterize how sediment moves through isdeposited within and is resuspended from reservoirs duringvarious hydrologic and outflow management scenarios Forexample although the maintenance of low-reservoir levelsmay reduce the total amount of sedimentation in thereservoir it could change the predominant location ofsediment deposition from the flood to the conservation poolpossibly decreasing storage where it is most needed Anexpanded collection of turbidity data within reservoirscan improve understanding of in-reservoir processes (Effleret al 2006) and could better calibrate two- or three-dimensional reservoir models In addition while increasedsediment transport downstream from the reservoir underaltered management plans would still be less than thehistorical pre-impoundment conditions investigationswould need to ascertain potential effects on infrastructureand aquatic life Any potential changes to reservoirmanagement also would need to fully evaluate the degreeto which altered management plans could increase the riskof downstream flooding However study results indicatethat despite limited in-reservoir data the coupling ofcontinuous streamflow and turbidity data with reservoirmodelling can effectively project the degree to whichchanges in reservoir management can affect sedimentationin the reservoir

CONCLUSIONS

Rapid sediment accumulation in large reservoirs willincreasingly threaten communities reliant on reservoirstorage for municipal agricultural and industrial usesDecisions will need to be made about which reservoiruses will be prioritized such as maintenance of watersupplies for nearby and downstream communities floodcontrol for downstream infrastructure and property ownersor recreational considerations Study results indicate thatcontinuous in-stream flow and turbidity monitoring can beused to quantify the potential of outflow management toreduce reservoir sedimentation Along with the collectionof hydrodynamic and sediment data within reservoirsthese data can help calibrate and validate models thatbetter quantify spatial patterns of sediment depositionand resuspension under varying hydrologic and manage-ment scenarios Study results indicate that depending onthe specific reservoir characteristics reservoir outflow

Copyright copy 2012 John Wiley amp Sons Ltd

management can help preserve water supplies especiallyas sedimentation continues to usurp storage capacityAlthough the approach presented can evaluate the potentialof altered reservoir management to preserve storagemanagement decisions need to consider potential effectschanging reservoir operations on downstream flood controland aquatic ecosystems

REFERENCES

Barfield DB 2010 Eastern Kansas Water Supply presentation given atthe 2010 Kansas Field Conference June 2 2010 Available on the Webat httpwwwksdagovincludesdocument_centerdwrPresentationsBarfield_KGSFieldTour2010pdf

Carswell WJ Hart RJ 1985 Transit losses and travel times for reservoirreleases during drought conditions along the Neosho River fromCouncil Grove Lake to Iola east-central Kansas US GeologicalSurvey Water-Resources Investigations Report 85ndash4003 40

Chung SW Hipsey MR Imberger J 2009 Modeling the propagation ofturbid density inflows into a stratified lake Daecheong ReservoirKorea Environmental Modelling and Software 24 1467ndash1482

Cohn TA Gilroy EJ 1991 Estimating loads from periodic records USGeological Survey Branch of Systems Analysis Technical Memo9101 81

Cole TM Wells SA 2008 CE-QUAL-W2 A two-dimensional laterallyaveraged hydrodynamic and water-quality model 36 userrsquos manualUS Army Corps of Engineers Waterways Experiment Station 125

Droppo IG Ongley ED 1994 Floccuation of suspended-sediment inrivers of Southeastern Canada Water Research 28(8) 1799ndash1809

Duan N 1983 Smearing estimatemdasha nonparametric retransformationmethod Journal of the American Statistical Asssociation 78 605ndash610

Effler SW Prestigiacomo AR Peng F Bulygina KB 2006 Resolution ofturbidity patterns from runoff events in a water supply reservoir and theadvantages of in situ beam attenuation measurements Lake andReservoir Management 22(1) 79ndash93

Evans JK Gottgens JF Gill WM Mackey SD 2000 Sediment yieldscontrolled by intrabasinal storage and sediment conveyance over theinterval 1842-1994 Chagrin River Northeast Ohio USA Journal ofSoil and Water Conservation 55 264ndash70

Fan J Morris GL 1992a Reservoir sedimentation I Delta and densitycurrent deposits Journal of Hydraulic Engineering 118 (3) 354ndash69

Fan J Morris GL 1992b Reservoir sedimentation II Reservoir desiltationand long-term storage capacity Journal of Hydraulic Engineering118 (3) 370ndash84

Gelda RK Effler SW 2007 Modeling turbidity in a water supply reservoiradvancements and issues Journal of Environmental Engineering 133(2)139ndash148

Guy HP 1969 Laboratory theory and methods for sediment analysis USGeological Survey Techniques of Water-Resources Investigations book5 chap C1 58

Hach Company 2005 SOLITAX sc turbidity and suspended solids usermanual Available on the Web at httpwwwhachcomfmmimghachCODEDOC0235403232_3ED_8864|1

Helsel DR Hirsch RM 1992 Statistical methods in water resourcesElsevier New York 522

Hydrol Process 27 1426ndash1439 (2013)

0

20

40

60

80

100

120

140

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

2000000

Pre

cipi

tati

on i

n ce

ntim

eter

s

Stre

amfl

ow i

n th

ousa

nds

of m

3 an

dse

dim

ent

load

in

met

ric

tons

IncomingstreamflowIncomingsedimentOutgoingsedimentPrecipitation

2007 2008 2009 2010

929

886

858

853

Sediment trapping efficiency

Figure 7 Annual precipitation streamflow and sediment transport to andfrom John Redmond Reservoir February 2007 to September 2010

1435CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

inflows were smaller in 2010 than those in 2008 and 2009water was released more rapidly after sediment-ladeninflows in 2010 in part because of maintenance activitiesthat necessitated lower reservoir levels (T Lyons USACEoral communication 2010)

Sediment trapping efficiency relative to variation inreservoir management

Among the 48 eventrelease pairs with greater than37 000 metric tons of incoming sediment sedimenttrapping efficiency varied from 48 to 97 Relations

A

40

50

60

70

80

90

100

316 318 320 322 324 326

Flow-weighted reservoir elevation in meters

Linear fit

Top of conservation pool Top of flood pool

60

70

80

90

100

60 70

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

g ef

fici

ency

in

perc

ent

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

gef

fici

ency

in

pres

ent

Regression-predicted sep

C

Figure 8 Regression relations between turbidity-computed sediment trappinand (C) regression-predicted estimates of sedim

Copyright copy 2012 John Wiley amp Sons Ltd

established between sediment trapping efficiency flow-weighted reservoir elevation (Figure 8A) and flow-weighted residence time (Figure 8B) indicated that alarger proportion of incoming sediment is transportedthrough John Redmond Reservoir when the reservoir ismaintained at lower levels and when residence times areminimized (Table II) However these relations were alsomore variable during these conditions In additionpredicted sediment trapping efficiencies were nonlinearwith respect to the primary explanatory variables(reservoir elevation FWSCs and flow-weighted residencetime) single linear regressions typically overpredictedeventrelease pairs with small trapping efficiencies andunderpredicted eventrelease pairs with larger sedimenttrapping efficiencies To minimize bias in predictionsof sediment trapping efficiency separate linear regres-sions were identified for eventrelease pairs above andbelow a reservoir elevation threshold of 3185 m Theresulting predictions were similar to single regressionequations in terms of adjusted R2 and error but residualswere distributed more evenly throughout the range ofvaluesAmong eventrelease pairs in which reservoir elevation

was maintained lower than 3185m the variability inthe sediment trapping efficiency was best explained byflow-weighted reservoir elevation (EL) and by the FWSCof the inflow event (Figure 8C) Among eventrelease pairsin which reservoir elevation was maintained higher than3185 m the variability in sediment trapping efficiency

40

50

60

70

80

90

100

5 15 25 35 45

Flow-weighted residence time of event in days

B

80 90 100

Tur

bidi

ty-c

ompu

ted

sedi

men

t tra

ppin

gef

fici

ency

in

perc

ent

diment trapping efficiency in ercent

g efficiency and (A) flow-weighted reservoir elevation (B) residence timeent trapping efficiency for eventrelease pairs

Hydrol Process 27 1426ndash1439 (2013)

Table II Summary of sediment loading and reservoir characteristics during delineated inflows and releases from John RedmondReservoir

Range of flow-weighted averagereservoir elevations (ft abovemean sea level)

No eventrelease pairs

Turbidity-computedload in (metric tons)

Turbidity-computedload out

(metric tons)

Turbidity-computedtrapping efficiency

()

Regression-predictedsediment trappingefficiency ()

All releases 3161ndash3234 48 5 240 000 595 000 886 8873161ndash3173 8 708 000 179 000 747 7463173ndash3179 10 1 101 000 159 000 856 8623179ndash3197 8 892 000 88 000 901 8923197ndash3210 11 1 201 000 84 000 930 9253210ndash3234 11 1 337 000 85 000 936 945

Table III Maximum discharges and approximate travel times ofreleases to flood control points downstream from John Redmond

Reservoirdaggera

Flood controlend point

Approximate travel timefrom outflow gates (h)daggerb

Maximumdischarge (m3s)

Neosho River atBurlington Kansas

2 396

Neosho River atIola Kansas

24 510

Neosho River atChanute Kansas

36 510

Neosho River atParsons Kansas

60 481

Neosho River atCommerce Oklahoma

84 623

a In addition to flood control end points the water control manual specifiesthat outgoing discharges do not rise or fall by more than 566m3s every 3h the manual also includes the consideration of conditions at otherreservoirs in the basin (USACE 1996)b Data from USACE (1996)

326s

1436 C LEE AND G FOSTER

was best explained by flow-weighted hydraulic residencetime (RES) and by the FWSC of the inflow event(Figure 8C) All explanatory variables were significantlyrelated to sediment trapping efficiency (P valuelt 005) andthe regression equations chosen resulted in the largestadjusted R2 the smallest RMSE values and the smallestprediction error sum of squares values of other potentialcombinations of independent variables The varianceinflation factors among independent variables were lessthan 12 indicating that multicollinearity among incomingFWSC values and flow-weighted reservoir elevations didnot inflate or adversely affect regression estimates (Helseland Hirsch 1992) In addition to decreased sedimenttrapping efficiency during low reservoir levels and residencetimes more sediment was trapped when incoming sedimentconcentrations were larger (as indicated by larger FWSCvalues) potentially because of increased flocculation (andthus larger effective grain size and fall velocity Droppo andOngley 1994)Although sediment trapping predictions were variable

for individual eventrelease pairs they were muchmore accurate with respect to turbidity-computed resultsfor multiple event releases when grouped by flow-weighted reservoir elevation (Table II) Results suggestthat (i) reductions in trapping efficiency consistently areobserved when reservoir elevations are maintained nearconservation pool levels and (ii) although regression-derived estimates of sediment trapping may be inaccuratefor individual eventrelease pairs this method can producerelatively accurate long-term estimates of sedimenttrapping efficiency with respect to factors that can beaffected by altered reservoir management

315

316

317

318

319

320

321

322

323

324

325

11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Ele

vati

on a

bove

mea

n se

a le

vel

in m

eter

Observed reservoir level

Altered outflow scenario

Flood-control pool elevation

Conservationpool

elevation

Example period (fig 10)

Figure 9 Observed and altered elevation at John Redmond ReservoirFebruary 2007 through September 2010

Potential of altered reservoir management to reducesediment trapping

The USACE water control manual for John RedmondReservoir identifies specific flood control end points thatmust not be exceeded to ensure that reservoir outflows donot contribute to flood-related damages to crops andstructures (Table III) Any changes to reservoirmanagementmust also meet these end points to fulfil the mission forwhich John Redmond Reservoir was constructed Tosimulate the potential effect of changes in reservoir outflowmanagement on sediment accumulation in John RedmondReservoir an idealized and altered outflow management

Copyright copy 2012 John Wiley amp Sons Ltd

scenario was constructed which continued to meet theseend points while also preserving storage for drinking wateragricultural use and industrial use (Figure 9) This scenariois lsquoidealizedrsquo in that it benefits from the knowledge ofincoming and downstream flows whereas in practicereservoir operators rely on weather forecasts and modellingto ensure consistent water supplies and to preventdownstream flooding Because of the inability to access

Hydrol Process 27 1426ndash1439 (2013)

1437CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

historical rainfall forecasts or models that predict flows toand from John Redmond Reservoir results from thisscenario are presented as the maximum potential reductionin sediment trapping that could be achieved within currentoperational plansRelative to observed reservoir management the altered

scenario purposefully minimized reservoir elevation andresidence time through larger more rapid releases ofwater after periods of high inflows (Figure 9) To partiallycompensate for uncertainties in weather forecasts andpredicted streamflows the altered management scenariowas somewhat more conservative compared with watercontrol plan restrictions in that (i) outflows were notincreased bymore than 850m3sday and (ii) outflows werenever reduced by more than 566m3sday (other than whenthe reservoir was actuallymanaged in this fashion) After theconstruction of the altered scenario reservoir releases wereredelineated and matched to inflows as done with observeddata Flow-weighted reservoir elevations residence timesand FWSCs for incoming events were recomputed toestimate the effect of the altered management scenario onsediment trapping efficiency (using regressions developedwith observed values)Although data and models were not available to test the

uncertainty of forecasted flow conditions an exampleperiod (Figures 9 and 10) in July 2007 is highlighted toillustrate how the consideration of sediment trapping withinexisting reservoir operational plans could preserve reservoirstorage During late June and early July 2007 heavy rainsupstream and downstream from John Redmond Reservoirraised reservoir levels and caused flooding downstreamfrom the reservoir (see the Neosho River at Parsons as anexample Figure 10) These flows raised reservoir levelswell into the flood pool where it was held until 16 July2007 when John Redmond Reservoir gates began releasingmore water (as indicated by the observed Burlingtonstreamflow record Figure 10) Historical weather forecastsfrom the National Weather Service for Iola Kansas(between Burlington and Parsons National Weather

0

500

1000

1500

2000

2500

3000

3500

4000

4500

315

316

317

318

319

320

321

322

323

324

325

627 77 717 727 86 816

Stre

amfl

ow i

n cu

bic

mee

ters

per

sec

ond

Res

ervo

ir e

leva

tion

abo

ve m

ean

sea

leve

l in

met

ers

Observed reservoir elevation

Alternate reservoir elevation

Observed flow at Burlington KS

Alternate flow at Burlington KS

Observed flow at Parsons KS

Alternate flow at Parsons KS

Figure 10 Comparison of observed reservoir elevations and downstreamflow conditions relative to the altered reservoir management scenario June

to August 2007

Copyright copy 2012 John Wiley amp Sons Ltd

service written communication 2011) projected con-sistent (20ndash50) chances of thunderstorms throughoutJuly Compared with observed reservoir managementthe altered outflow scenario began discharging wateron 5 July 2007 while keeping downstream flows belowmaximum levels indicated in John Redmond Reservoircontrol manual (Table III) Two eventrelease pairs weredelineated during this period modelled residence timesbased on observed reservoir levels were 72 and 26 daysMore rapid release of water from John RedmondReservoir through the alternative scenario reduced theseto 66 and 16 days respectively Sediment trappingfor these periods is projected to have decreased from971 to 893 and from 957 to 822 Althoughthis example illustrates that consideration of sedimenttrapping within reservoir operational plans can reducesediment trapping uncertainty of weather forecasts andrainfall runoff models will often limit the ability topreserve reservoir storage within existing operationlimitsFrom 2007 to 2010 the altered management scenario

decreased the average reservoir elevation from 3178 to3173 m and decreased the average daily residence timesfrom 296 to 269 days Forty-six releases with more than37 000 tons of incoming sediment were delineated under thealtered scenario (compared with 48 observed releases)corresponding to 51 million metric tons of incomingsediment (compared with 53 million metric tons computedwith observed data Table IV) The average reservoirelevations for eventrelease pairs decreased from 3194 to3186 m and for residence times from 199 to 131 daysUnder the altered management scenario regression-predicted sediment trapping efficiency was reduced by39 passing approximately 180 000 additional metric tonsof sediment through the reservoir Estimates indicate thatwithin existing operational constraints altered reservoirmanagement has a maximum potential of decreasingsediment trapping by approximately 45 000 metric tonsper year or approximately 3 of the annual load of 141million metric tons transported during the study periodAn annual reduction of 45 000 tons of sediment from

John Redmond Reservoir would preserve approximately74 000m3 of storage in the reservoir per year This rate isequivalent to 15 of the designed reservoir sedimentationrate and equivalent to 81 of the observed sedimentationrate in the conservation pool given the average bulk densityestimates of Juracek (2010) As more reservoir storage islost to deposited sediments the residence time of sediment-laden inflows will continue to decrease and reservoirmanagement is likely to become a more effective alternativeto reducing reservoir sedimentation If existing reservoiroperational plans were adapted to accommodate watersupply as well as flood control uses of the reservoir alteredreservoir management might further reduce sedimentaccumulationAlthough continuous flow and sediment data have proven

useful in determining how short-term variability inhydrology and reservoir management affect sedimentationimproved understanding of in-reservoir processes is needed

Hydrol Process 27 1426ndash1439 (2013)

Table IV Sediment transport to and from John Redmond Reservoir during delineated inflows and releases under the alteredmanagement scenario

Range of flow-weighted averagereservoir elevations (meters abovemean sea level)

No releaseevents

Turbidity-computedload in (metric tons)

Regression-predictedload out (metric tons)

Regression-predicted sedimenttrapping efficiency ()

All releases 46 5 126 000 778 000 8483161ndash3173 17 1 813 000 426 000 7653173ndash3179 7 855 000 151 000 8243179ndash3197 7 690 000 76 000 8903197ndash3210 10 992 000 94 000 9053210ndash3234 5 775 000 31 000 960

1438 C LEE AND G FOSTER

to better characterize how sediment moves through isdeposited within and is resuspended from reservoirs duringvarious hydrologic and outflow management scenarios Forexample although the maintenance of low-reservoir levelsmay reduce the total amount of sedimentation in thereservoir it could change the predominant location ofsediment deposition from the flood to the conservation poolpossibly decreasing storage where it is most needed Anexpanded collection of turbidity data within reservoirscan improve understanding of in-reservoir processes (Effleret al 2006) and could better calibrate two- or three-dimensional reservoir models In addition while increasedsediment transport downstream from the reservoir underaltered management plans would still be less than thehistorical pre-impoundment conditions investigationswould need to ascertain potential effects on infrastructureand aquatic life Any potential changes to reservoirmanagement also would need to fully evaluate the degreeto which altered management plans could increase the riskof downstream flooding However study results indicatethat despite limited in-reservoir data the coupling ofcontinuous streamflow and turbidity data with reservoirmodelling can effectively project the degree to whichchanges in reservoir management can affect sedimentationin the reservoir

CONCLUSIONS

Rapid sediment accumulation in large reservoirs willincreasingly threaten communities reliant on reservoirstorage for municipal agricultural and industrial usesDecisions will need to be made about which reservoiruses will be prioritized such as maintenance of watersupplies for nearby and downstream communities floodcontrol for downstream infrastructure and property ownersor recreational considerations Study results indicate thatcontinuous in-stream flow and turbidity monitoring can beused to quantify the potential of outflow management toreduce reservoir sedimentation Along with the collectionof hydrodynamic and sediment data within reservoirsthese data can help calibrate and validate models thatbetter quantify spatial patterns of sediment depositionand resuspension under varying hydrologic and manage-ment scenarios Study results indicate that depending onthe specific reservoir characteristics reservoir outflow

Copyright copy 2012 John Wiley amp Sons Ltd

management can help preserve water supplies especiallyas sedimentation continues to usurp storage capacityAlthough the approach presented can evaluate the potentialof altered reservoir management to preserve storagemanagement decisions need to consider potential effectschanging reservoir operations on downstream flood controland aquatic ecosystems

REFERENCES

Barfield DB 2010 Eastern Kansas Water Supply presentation given atthe 2010 Kansas Field Conference June 2 2010 Available on the Webat httpwwwksdagovincludesdocument_centerdwrPresentationsBarfield_KGSFieldTour2010pdf

Carswell WJ Hart RJ 1985 Transit losses and travel times for reservoirreleases during drought conditions along the Neosho River fromCouncil Grove Lake to Iola east-central Kansas US GeologicalSurvey Water-Resources Investigations Report 85ndash4003 40

Chung SW Hipsey MR Imberger J 2009 Modeling the propagation ofturbid density inflows into a stratified lake Daecheong ReservoirKorea Environmental Modelling and Software 24 1467ndash1482

Cohn TA Gilroy EJ 1991 Estimating loads from periodic records USGeological Survey Branch of Systems Analysis Technical Memo9101 81

Cole TM Wells SA 2008 CE-QUAL-W2 A two-dimensional laterallyaveraged hydrodynamic and water-quality model 36 userrsquos manualUS Army Corps of Engineers Waterways Experiment Station 125

Droppo IG Ongley ED 1994 Floccuation of suspended-sediment inrivers of Southeastern Canada Water Research 28(8) 1799ndash1809

Duan N 1983 Smearing estimatemdasha nonparametric retransformationmethod Journal of the American Statistical Asssociation 78 605ndash610

Effler SW Prestigiacomo AR Peng F Bulygina KB 2006 Resolution ofturbidity patterns from runoff events in a water supply reservoir and theadvantages of in situ beam attenuation measurements Lake andReservoir Management 22(1) 79ndash93

Evans JK Gottgens JF Gill WM Mackey SD 2000 Sediment yieldscontrolled by intrabasinal storage and sediment conveyance over theinterval 1842-1994 Chagrin River Northeast Ohio USA Journal ofSoil and Water Conservation 55 264ndash70

Fan J Morris GL 1992a Reservoir sedimentation I Delta and densitycurrent deposits Journal of Hydraulic Engineering 118 (3) 354ndash69

Fan J Morris GL 1992b Reservoir sedimentation II Reservoir desiltationand long-term storage capacity Journal of Hydraulic Engineering118 (3) 370ndash84

Gelda RK Effler SW 2007 Modeling turbidity in a water supply reservoiradvancements and issues Journal of Environmental Engineering 133(2)139ndash148

Guy HP 1969 Laboratory theory and methods for sediment analysis USGeological Survey Techniques of Water-Resources Investigations book5 chap C1 58

Hach Company 2005 SOLITAX sc turbidity and suspended solids usermanual Available on the Web at httpwwwhachcomfmmimghachCODEDOC0235403232_3ED_8864|1

Helsel DR Hirsch RM 1992 Statistical methods in water resourcesElsevier New York 522

Hydrol Process 27 1426ndash1439 (2013)

Table II Summary of sediment loading and reservoir characteristics during delineated inflows and releases from John RedmondReservoir

Range of flow-weighted averagereservoir elevations (ft abovemean sea level)

No eventrelease pairs

Turbidity-computedload in (metric tons)

Turbidity-computedload out

(metric tons)

Turbidity-computedtrapping efficiency

()

Regression-predictedsediment trappingefficiency ()

All releases 3161ndash3234 48 5 240 000 595 000 886 8873161ndash3173 8 708 000 179 000 747 7463173ndash3179 10 1 101 000 159 000 856 8623179ndash3197 8 892 000 88 000 901 8923197ndash3210 11 1 201 000 84 000 930 9253210ndash3234 11 1 337 000 85 000 936 945

Table III Maximum discharges and approximate travel times ofreleases to flood control points downstream from John Redmond

Reservoirdaggera

Flood controlend point

Approximate travel timefrom outflow gates (h)daggerb

Maximumdischarge (m3s)

Neosho River atBurlington Kansas

2 396

Neosho River atIola Kansas

24 510

Neosho River atChanute Kansas

36 510

Neosho River atParsons Kansas

60 481

Neosho River atCommerce Oklahoma

84 623

a In addition to flood control end points the water control manual specifiesthat outgoing discharges do not rise or fall by more than 566m3s every 3h the manual also includes the consideration of conditions at otherreservoirs in the basin (USACE 1996)b Data from USACE (1996)

326s

1436 C LEE AND G FOSTER

was best explained by flow-weighted hydraulic residencetime (RES) and by the FWSC of the inflow event(Figure 8C) All explanatory variables were significantlyrelated to sediment trapping efficiency (P valuelt 005) andthe regression equations chosen resulted in the largestadjusted R2 the smallest RMSE values and the smallestprediction error sum of squares values of other potentialcombinations of independent variables The varianceinflation factors among independent variables were lessthan 12 indicating that multicollinearity among incomingFWSC values and flow-weighted reservoir elevations didnot inflate or adversely affect regression estimates (Helseland Hirsch 1992) In addition to decreased sedimenttrapping efficiency during low reservoir levels and residencetimes more sediment was trapped when incoming sedimentconcentrations were larger (as indicated by larger FWSCvalues) potentially because of increased flocculation (andthus larger effective grain size and fall velocity Droppo andOngley 1994)Although sediment trapping predictions were variable

for individual eventrelease pairs they were muchmore accurate with respect to turbidity-computed resultsfor multiple event releases when grouped by flow-weighted reservoir elevation (Table II) Results suggestthat (i) reductions in trapping efficiency consistently areobserved when reservoir elevations are maintained nearconservation pool levels and (ii) although regression-derived estimates of sediment trapping may be inaccuratefor individual eventrelease pairs this method can producerelatively accurate long-term estimates of sedimenttrapping efficiency with respect to factors that can beaffected by altered reservoir management

315

316

317

318

319

320

321

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324

325

11807 61707 111407 41208 9908 2609 7609 12309 5210 92910

Ele

vati

on a

bove

mea

n se

a le

vel

in m

eter

Observed reservoir level

Altered outflow scenario

Flood-control pool elevation

Conservationpool

elevation

Example period (fig 10)

Figure 9 Observed and altered elevation at John Redmond ReservoirFebruary 2007 through September 2010

Potential of altered reservoir management to reducesediment trapping

The USACE water control manual for John RedmondReservoir identifies specific flood control end points thatmust not be exceeded to ensure that reservoir outflows donot contribute to flood-related damages to crops andstructures (Table III) Any changes to reservoirmanagementmust also meet these end points to fulfil the mission forwhich John Redmond Reservoir was constructed Tosimulate the potential effect of changes in reservoir outflowmanagement on sediment accumulation in John RedmondReservoir an idealized and altered outflow management

Copyright copy 2012 John Wiley amp Sons Ltd

scenario was constructed which continued to meet theseend points while also preserving storage for drinking wateragricultural use and industrial use (Figure 9) This scenariois lsquoidealizedrsquo in that it benefits from the knowledge ofincoming and downstream flows whereas in practicereservoir operators rely on weather forecasts and modellingto ensure consistent water supplies and to preventdownstream flooding Because of the inability to access

Hydrol Process 27 1426ndash1439 (2013)

1437CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

historical rainfall forecasts or models that predict flows toand from John Redmond Reservoir results from thisscenario are presented as the maximum potential reductionin sediment trapping that could be achieved within currentoperational plansRelative to observed reservoir management the altered

scenario purposefully minimized reservoir elevation andresidence time through larger more rapid releases ofwater after periods of high inflows (Figure 9) To partiallycompensate for uncertainties in weather forecasts andpredicted streamflows the altered management scenariowas somewhat more conservative compared with watercontrol plan restrictions in that (i) outflows were notincreased bymore than 850m3sday and (ii) outflows werenever reduced by more than 566m3sday (other than whenthe reservoir was actuallymanaged in this fashion) After theconstruction of the altered scenario reservoir releases wereredelineated and matched to inflows as done with observeddata Flow-weighted reservoir elevations residence timesand FWSCs for incoming events were recomputed toestimate the effect of the altered management scenario onsediment trapping efficiency (using regressions developedwith observed values)Although data and models were not available to test the

uncertainty of forecasted flow conditions an exampleperiod (Figures 9 and 10) in July 2007 is highlighted toillustrate how the consideration of sediment trapping withinexisting reservoir operational plans could preserve reservoirstorage During late June and early July 2007 heavy rainsupstream and downstream from John Redmond Reservoirraised reservoir levels and caused flooding downstreamfrom the reservoir (see the Neosho River at Parsons as anexample Figure 10) These flows raised reservoir levelswell into the flood pool where it was held until 16 July2007 when John Redmond Reservoir gates began releasingmore water (as indicated by the observed Burlingtonstreamflow record Figure 10) Historical weather forecastsfrom the National Weather Service for Iola Kansas(between Burlington and Parsons National Weather

0

500

1000

1500

2000

2500

3000

3500

4000

4500

315

316

317

318

319

320

321

322

323

324

325

627 77 717 727 86 816

Stre

amfl

ow i

n cu

bic

mee

ters

per

sec

ond

Res

ervo

ir e

leva

tion

abo

ve m

ean

sea

leve

l in

met

ers

Observed reservoir elevation

Alternate reservoir elevation

Observed flow at Burlington KS

Alternate flow at Burlington KS

Observed flow at Parsons KS

Alternate flow at Parsons KS

Figure 10 Comparison of observed reservoir elevations and downstreamflow conditions relative to the altered reservoir management scenario June

to August 2007

Copyright copy 2012 John Wiley amp Sons Ltd

service written communication 2011) projected con-sistent (20ndash50) chances of thunderstorms throughoutJuly Compared with observed reservoir managementthe altered outflow scenario began discharging wateron 5 July 2007 while keeping downstream flows belowmaximum levels indicated in John Redmond Reservoircontrol manual (Table III) Two eventrelease pairs weredelineated during this period modelled residence timesbased on observed reservoir levels were 72 and 26 daysMore rapid release of water from John RedmondReservoir through the alternative scenario reduced theseto 66 and 16 days respectively Sediment trappingfor these periods is projected to have decreased from971 to 893 and from 957 to 822 Althoughthis example illustrates that consideration of sedimenttrapping within reservoir operational plans can reducesediment trapping uncertainty of weather forecasts andrainfall runoff models will often limit the ability topreserve reservoir storage within existing operationlimitsFrom 2007 to 2010 the altered management scenario

decreased the average reservoir elevation from 3178 to3173 m and decreased the average daily residence timesfrom 296 to 269 days Forty-six releases with more than37 000 tons of incoming sediment were delineated under thealtered scenario (compared with 48 observed releases)corresponding to 51 million metric tons of incomingsediment (compared with 53 million metric tons computedwith observed data Table IV) The average reservoirelevations for eventrelease pairs decreased from 3194 to3186 m and for residence times from 199 to 131 daysUnder the altered management scenario regression-predicted sediment trapping efficiency was reduced by39 passing approximately 180 000 additional metric tonsof sediment through the reservoir Estimates indicate thatwithin existing operational constraints altered reservoirmanagement has a maximum potential of decreasingsediment trapping by approximately 45 000 metric tonsper year or approximately 3 of the annual load of 141million metric tons transported during the study periodAn annual reduction of 45 000 tons of sediment from

John Redmond Reservoir would preserve approximately74 000m3 of storage in the reservoir per year This rate isequivalent to 15 of the designed reservoir sedimentationrate and equivalent to 81 of the observed sedimentationrate in the conservation pool given the average bulk densityestimates of Juracek (2010) As more reservoir storage islost to deposited sediments the residence time of sediment-laden inflows will continue to decrease and reservoirmanagement is likely to become a more effective alternativeto reducing reservoir sedimentation If existing reservoiroperational plans were adapted to accommodate watersupply as well as flood control uses of the reservoir alteredreservoir management might further reduce sedimentaccumulationAlthough continuous flow and sediment data have proven

useful in determining how short-term variability inhydrology and reservoir management affect sedimentationimproved understanding of in-reservoir processes is needed

Hydrol Process 27 1426ndash1439 (2013)

Table IV Sediment transport to and from John Redmond Reservoir during delineated inflows and releases under the alteredmanagement scenario

Range of flow-weighted averagereservoir elevations (meters abovemean sea level)

No releaseevents

Turbidity-computedload in (metric tons)

Regression-predictedload out (metric tons)

Regression-predicted sedimenttrapping efficiency ()

All releases 46 5 126 000 778 000 8483161ndash3173 17 1 813 000 426 000 7653173ndash3179 7 855 000 151 000 8243179ndash3197 7 690 000 76 000 8903197ndash3210 10 992 000 94 000 9053210ndash3234 5 775 000 31 000 960

1438 C LEE AND G FOSTER

to better characterize how sediment moves through isdeposited within and is resuspended from reservoirs duringvarious hydrologic and outflow management scenarios Forexample although the maintenance of low-reservoir levelsmay reduce the total amount of sedimentation in thereservoir it could change the predominant location ofsediment deposition from the flood to the conservation poolpossibly decreasing storage where it is most needed Anexpanded collection of turbidity data within reservoirscan improve understanding of in-reservoir processes (Effleret al 2006) and could better calibrate two- or three-dimensional reservoir models In addition while increasedsediment transport downstream from the reservoir underaltered management plans would still be less than thehistorical pre-impoundment conditions investigationswould need to ascertain potential effects on infrastructureand aquatic life Any potential changes to reservoirmanagement also would need to fully evaluate the degreeto which altered management plans could increase the riskof downstream flooding However study results indicatethat despite limited in-reservoir data the coupling ofcontinuous streamflow and turbidity data with reservoirmodelling can effectively project the degree to whichchanges in reservoir management can affect sedimentationin the reservoir

CONCLUSIONS

Rapid sediment accumulation in large reservoirs willincreasingly threaten communities reliant on reservoirstorage for municipal agricultural and industrial usesDecisions will need to be made about which reservoiruses will be prioritized such as maintenance of watersupplies for nearby and downstream communities floodcontrol for downstream infrastructure and property ownersor recreational considerations Study results indicate thatcontinuous in-stream flow and turbidity monitoring can beused to quantify the potential of outflow management toreduce reservoir sedimentation Along with the collectionof hydrodynamic and sediment data within reservoirsthese data can help calibrate and validate models thatbetter quantify spatial patterns of sediment depositionand resuspension under varying hydrologic and manage-ment scenarios Study results indicate that depending onthe specific reservoir characteristics reservoir outflow

Copyright copy 2012 John Wiley amp Sons Ltd

management can help preserve water supplies especiallyas sedimentation continues to usurp storage capacityAlthough the approach presented can evaluate the potentialof altered reservoir management to preserve storagemanagement decisions need to consider potential effectschanging reservoir operations on downstream flood controland aquatic ecosystems

REFERENCES

Barfield DB 2010 Eastern Kansas Water Supply presentation given atthe 2010 Kansas Field Conference June 2 2010 Available on the Webat httpwwwksdagovincludesdocument_centerdwrPresentationsBarfield_KGSFieldTour2010pdf

Carswell WJ Hart RJ 1985 Transit losses and travel times for reservoirreleases during drought conditions along the Neosho River fromCouncil Grove Lake to Iola east-central Kansas US GeologicalSurvey Water-Resources Investigations Report 85ndash4003 40

Chung SW Hipsey MR Imberger J 2009 Modeling the propagation ofturbid density inflows into a stratified lake Daecheong ReservoirKorea Environmental Modelling and Software 24 1467ndash1482

Cohn TA Gilroy EJ 1991 Estimating loads from periodic records USGeological Survey Branch of Systems Analysis Technical Memo9101 81

Cole TM Wells SA 2008 CE-QUAL-W2 A two-dimensional laterallyaveraged hydrodynamic and water-quality model 36 userrsquos manualUS Army Corps of Engineers Waterways Experiment Station 125

Droppo IG Ongley ED 1994 Floccuation of suspended-sediment inrivers of Southeastern Canada Water Research 28(8) 1799ndash1809

Duan N 1983 Smearing estimatemdasha nonparametric retransformationmethod Journal of the American Statistical Asssociation 78 605ndash610

Effler SW Prestigiacomo AR Peng F Bulygina KB 2006 Resolution ofturbidity patterns from runoff events in a water supply reservoir and theadvantages of in situ beam attenuation measurements Lake andReservoir Management 22(1) 79ndash93

Evans JK Gottgens JF Gill WM Mackey SD 2000 Sediment yieldscontrolled by intrabasinal storage and sediment conveyance over theinterval 1842-1994 Chagrin River Northeast Ohio USA Journal ofSoil and Water Conservation 55 264ndash70

Fan J Morris GL 1992a Reservoir sedimentation I Delta and densitycurrent deposits Journal of Hydraulic Engineering 118 (3) 354ndash69

Fan J Morris GL 1992b Reservoir sedimentation II Reservoir desiltationand long-term storage capacity Journal of Hydraulic Engineering118 (3) 370ndash84

Gelda RK Effler SW 2007 Modeling turbidity in a water supply reservoiradvancements and issues Journal of Environmental Engineering 133(2)139ndash148

Guy HP 1969 Laboratory theory and methods for sediment analysis USGeological Survey Techniques of Water-Resources Investigations book5 chap C1 58

Hach Company 2005 SOLITAX sc turbidity and suspended solids usermanual Available on the Web at httpwwwhachcomfmmimghachCODEDOC0235403232_3ED_8864|1

Helsel DR Hirsch RM 1992 Statistical methods in water resourcesElsevier New York 522

Hydrol Process 27 1426ndash1439 (2013)

1437CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

historical rainfall forecasts or models that predict flows toand from John Redmond Reservoir results from thisscenario are presented as the maximum potential reductionin sediment trapping that could be achieved within currentoperational plansRelative to observed reservoir management the altered

scenario purposefully minimized reservoir elevation andresidence time through larger more rapid releases ofwater after periods of high inflows (Figure 9) To partiallycompensate for uncertainties in weather forecasts andpredicted streamflows the altered management scenariowas somewhat more conservative compared with watercontrol plan restrictions in that (i) outflows were notincreased bymore than 850m3sday and (ii) outflows werenever reduced by more than 566m3sday (other than whenthe reservoir was actuallymanaged in this fashion) After theconstruction of the altered scenario reservoir releases wereredelineated and matched to inflows as done with observeddata Flow-weighted reservoir elevations residence timesand FWSCs for incoming events were recomputed toestimate the effect of the altered management scenario onsediment trapping efficiency (using regressions developedwith observed values)Although data and models were not available to test the

uncertainty of forecasted flow conditions an exampleperiod (Figures 9 and 10) in July 2007 is highlighted toillustrate how the consideration of sediment trapping withinexisting reservoir operational plans could preserve reservoirstorage During late June and early July 2007 heavy rainsupstream and downstream from John Redmond Reservoirraised reservoir levels and caused flooding downstreamfrom the reservoir (see the Neosho River at Parsons as anexample Figure 10) These flows raised reservoir levelswell into the flood pool where it was held until 16 July2007 when John Redmond Reservoir gates began releasingmore water (as indicated by the observed Burlingtonstreamflow record Figure 10) Historical weather forecastsfrom the National Weather Service for Iola Kansas(between Burlington and Parsons National Weather

0

500

1000

1500

2000

2500

3000

3500

4000

4500

315

316

317

318

319

320

321

322

323

324

325

627 77 717 727 86 816

Stre

amfl

ow i

n cu

bic

mee

ters

per

sec

ond

Res

ervo

ir e

leva

tion

abo

ve m

ean

sea

leve

l in

met

ers

Observed reservoir elevation

Alternate reservoir elevation

Observed flow at Burlington KS

Alternate flow at Burlington KS

Observed flow at Parsons KS

Alternate flow at Parsons KS

Figure 10 Comparison of observed reservoir elevations and downstreamflow conditions relative to the altered reservoir management scenario June

to August 2007

Copyright copy 2012 John Wiley amp Sons Ltd

service written communication 2011) projected con-sistent (20ndash50) chances of thunderstorms throughoutJuly Compared with observed reservoir managementthe altered outflow scenario began discharging wateron 5 July 2007 while keeping downstream flows belowmaximum levels indicated in John Redmond Reservoircontrol manual (Table III) Two eventrelease pairs weredelineated during this period modelled residence timesbased on observed reservoir levels were 72 and 26 daysMore rapid release of water from John RedmondReservoir through the alternative scenario reduced theseto 66 and 16 days respectively Sediment trappingfor these periods is projected to have decreased from971 to 893 and from 957 to 822 Althoughthis example illustrates that consideration of sedimenttrapping within reservoir operational plans can reducesediment trapping uncertainty of weather forecasts andrainfall runoff models will often limit the ability topreserve reservoir storage within existing operationlimitsFrom 2007 to 2010 the altered management scenario

decreased the average reservoir elevation from 3178 to3173 m and decreased the average daily residence timesfrom 296 to 269 days Forty-six releases with more than37 000 tons of incoming sediment were delineated under thealtered scenario (compared with 48 observed releases)corresponding to 51 million metric tons of incomingsediment (compared with 53 million metric tons computedwith observed data Table IV) The average reservoirelevations for eventrelease pairs decreased from 3194 to3186 m and for residence times from 199 to 131 daysUnder the altered management scenario regression-predicted sediment trapping efficiency was reduced by39 passing approximately 180 000 additional metric tonsof sediment through the reservoir Estimates indicate thatwithin existing operational constraints altered reservoirmanagement has a maximum potential of decreasingsediment trapping by approximately 45 000 metric tonsper year or approximately 3 of the annual load of 141million metric tons transported during the study periodAn annual reduction of 45 000 tons of sediment from

John Redmond Reservoir would preserve approximately74 000m3 of storage in the reservoir per year This rate isequivalent to 15 of the designed reservoir sedimentationrate and equivalent to 81 of the observed sedimentationrate in the conservation pool given the average bulk densityestimates of Juracek (2010) As more reservoir storage islost to deposited sediments the residence time of sediment-laden inflows will continue to decrease and reservoirmanagement is likely to become a more effective alternativeto reducing reservoir sedimentation If existing reservoiroperational plans were adapted to accommodate watersupply as well as flood control uses of the reservoir alteredreservoir management might further reduce sedimentaccumulationAlthough continuous flow and sediment data have proven

useful in determining how short-term variability inhydrology and reservoir management affect sedimentationimproved understanding of in-reservoir processes is needed

Hydrol Process 27 1426ndash1439 (2013)

Table IV Sediment transport to and from John Redmond Reservoir during delineated inflows and releases under the alteredmanagement scenario

Range of flow-weighted averagereservoir elevations (meters abovemean sea level)

No releaseevents

Turbidity-computedload in (metric tons)

Regression-predictedload out (metric tons)

Regression-predicted sedimenttrapping efficiency ()

All releases 46 5 126 000 778 000 8483161ndash3173 17 1 813 000 426 000 7653173ndash3179 7 855 000 151 000 8243179ndash3197 7 690 000 76 000 8903197ndash3210 10 992 000 94 000 9053210ndash3234 5 775 000 31 000 960

1438 C LEE AND G FOSTER

to better characterize how sediment moves through isdeposited within and is resuspended from reservoirs duringvarious hydrologic and outflow management scenarios Forexample although the maintenance of low-reservoir levelsmay reduce the total amount of sedimentation in thereservoir it could change the predominant location ofsediment deposition from the flood to the conservation poolpossibly decreasing storage where it is most needed Anexpanded collection of turbidity data within reservoirscan improve understanding of in-reservoir processes (Effleret al 2006) and could better calibrate two- or three-dimensional reservoir models In addition while increasedsediment transport downstream from the reservoir underaltered management plans would still be less than thehistorical pre-impoundment conditions investigationswould need to ascertain potential effects on infrastructureand aquatic life Any potential changes to reservoirmanagement also would need to fully evaluate the degreeto which altered management plans could increase the riskof downstream flooding However study results indicatethat despite limited in-reservoir data the coupling ofcontinuous streamflow and turbidity data with reservoirmodelling can effectively project the degree to whichchanges in reservoir management can affect sedimentationin the reservoir

CONCLUSIONS

Rapid sediment accumulation in large reservoirs willincreasingly threaten communities reliant on reservoirstorage for municipal agricultural and industrial usesDecisions will need to be made about which reservoiruses will be prioritized such as maintenance of watersupplies for nearby and downstream communities floodcontrol for downstream infrastructure and property ownersor recreational considerations Study results indicate thatcontinuous in-stream flow and turbidity monitoring can beused to quantify the potential of outflow management toreduce reservoir sedimentation Along with the collectionof hydrodynamic and sediment data within reservoirsthese data can help calibrate and validate models thatbetter quantify spatial patterns of sediment depositionand resuspension under varying hydrologic and manage-ment scenarios Study results indicate that depending onthe specific reservoir characteristics reservoir outflow

Copyright copy 2012 John Wiley amp Sons Ltd

management can help preserve water supplies especiallyas sedimentation continues to usurp storage capacityAlthough the approach presented can evaluate the potentialof altered reservoir management to preserve storagemanagement decisions need to consider potential effectschanging reservoir operations on downstream flood controland aquatic ecosystems

REFERENCES

Barfield DB 2010 Eastern Kansas Water Supply presentation given atthe 2010 Kansas Field Conference June 2 2010 Available on the Webat httpwwwksdagovincludesdocument_centerdwrPresentationsBarfield_KGSFieldTour2010pdf

Carswell WJ Hart RJ 1985 Transit losses and travel times for reservoirreleases during drought conditions along the Neosho River fromCouncil Grove Lake to Iola east-central Kansas US GeologicalSurvey Water-Resources Investigations Report 85ndash4003 40

Chung SW Hipsey MR Imberger J 2009 Modeling the propagation ofturbid density inflows into a stratified lake Daecheong ReservoirKorea Environmental Modelling and Software 24 1467ndash1482

Cohn TA Gilroy EJ 1991 Estimating loads from periodic records USGeological Survey Branch of Systems Analysis Technical Memo9101 81

Cole TM Wells SA 2008 CE-QUAL-W2 A two-dimensional laterallyaveraged hydrodynamic and water-quality model 36 userrsquos manualUS Army Corps of Engineers Waterways Experiment Station 125

Droppo IG Ongley ED 1994 Floccuation of suspended-sediment inrivers of Southeastern Canada Water Research 28(8) 1799ndash1809

Duan N 1983 Smearing estimatemdasha nonparametric retransformationmethod Journal of the American Statistical Asssociation 78 605ndash610

Effler SW Prestigiacomo AR Peng F Bulygina KB 2006 Resolution ofturbidity patterns from runoff events in a water supply reservoir and theadvantages of in situ beam attenuation measurements Lake andReservoir Management 22(1) 79ndash93

Evans JK Gottgens JF Gill WM Mackey SD 2000 Sediment yieldscontrolled by intrabasinal storage and sediment conveyance over theinterval 1842-1994 Chagrin River Northeast Ohio USA Journal ofSoil and Water Conservation 55 264ndash70

Fan J Morris GL 1992a Reservoir sedimentation I Delta and densitycurrent deposits Journal of Hydraulic Engineering 118 (3) 354ndash69

Fan J Morris GL 1992b Reservoir sedimentation II Reservoir desiltationand long-term storage capacity Journal of Hydraulic Engineering118 (3) 370ndash84

Gelda RK Effler SW 2007 Modeling turbidity in a water supply reservoiradvancements and issues Journal of Environmental Engineering 133(2)139ndash148

Guy HP 1969 Laboratory theory and methods for sediment analysis USGeological Survey Techniques of Water-Resources Investigations book5 chap C1 58

Hach Company 2005 SOLITAX sc turbidity and suspended solids usermanual Available on the Web at httpwwwhachcomfmmimghachCODEDOC0235403232_3ED_8864|1

Helsel DR Hirsch RM 1992 Statistical methods in water resourcesElsevier New York 522

Hydrol Process 27 1426ndash1439 (2013)

Table IV Sediment transport to and from John Redmond Reservoir during delineated inflows and releases under the alteredmanagement scenario

Range of flow-weighted averagereservoir elevations (meters abovemean sea level)

No releaseevents

Turbidity-computedload in (metric tons)

Regression-predictedload out (metric tons)

Regression-predicted sedimenttrapping efficiency ()

All releases 46 5 126 000 778 000 8483161ndash3173 17 1 813 000 426 000 7653173ndash3179 7 855 000 151 000 8243179ndash3197 7 690 000 76 000 8903197ndash3210 10 992 000 94 000 9053210ndash3234 5 775 000 31 000 960

1438 C LEE AND G FOSTER

to better characterize how sediment moves through isdeposited within and is resuspended from reservoirs duringvarious hydrologic and outflow management scenarios Forexample although the maintenance of low-reservoir levelsmay reduce the total amount of sedimentation in thereservoir it could change the predominant location ofsediment deposition from the flood to the conservation poolpossibly decreasing storage where it is most needed Anexpanded collection of turbidity data within reservoirscan improve understanding of in-reservoir processes (Effleret al 2006) and could better calibrate two- or three-dimensional reservoir models In addition while increasedsediment transport downstream from the reservoir underaltered management plans would still be less than thehistorical pre-impoundment conditions investigationswould need to ascertain potential effects on infrastructureand aquatic life Any potential changes to reservoirmanagement also would need to fully evaluate the degreeto which altered management plans could increase the riskof downstream flooding However study results indicatethat despite limited in-reservoir data the coupling ofcontinuous streamflow and turbidity data with reservoirmodelling can effectively project the degree to whichchanges in reservoir management can affect sedimentationin the reservoir

CONCLUSIONS

Rapid sediment accumulation in large reservoirs willincreasingly threaten communities reliant on reservoirstorage for municipal agricultural and industrial usesDecisions will need to be made about which reservoiruses will be prioritized such as maintenance of watersupplies for nearby and downstream communities floodcontrol for downstream infrastructure and property ownersor recreational considerations Study results indicate thatcontinuous in-stream flow and turbidity monitoring can beused to quantify the potential of outflow management toreduce reservoir sedimentation Along with the collectionof hydrodynamic and sediment data within reservoirsthese data can help calibrate and validate models thatbetter quantify spatial patterns of sediment depositionand resuspension under varying hydrologic and manage-ment scenarios Study results indicate that depending onthe specific reservoir characteristics reservoir outflow

Copyright copy 2012 John Wiley amp Sons Ltd

management can help preserve water supplies especiallyas sedimentation continues to usurp storage capacityAlthough the approach presented can evaluate the potentialof altered reservoir management to preserve storagemanagement decisions need to consider potential effectschanging reservoir operations on downstream flood controland aquatic ecosystems

REFERENCES

Barfield DB 2010 Eastern Kansas Water Supply presentation given atthe 2010 Kansas Field Conference June 2 2010 Available on the Webat httpwwwksdagovincludesdocument_centerdwrPresentationsBarfield_KGSFieldTour2010pdf

Carswell WJ Hart RJ 1985 Transit losses and travel times for reservoirreleases during drought conditions along the Neosho River fromCouncil Grove Lake to Iola east-central Kansas US GeologicalSurvey Water-Resources Investigations Report 85ndash4003 40

Chung SW Hipsey MR Imberger J 2009 Modeling the propagation ofturbid density inflows into a stratified lake Daecheong ReservoirKorea Environmental Modelling and Software 24 1467ndash1482

Cohn TA Gilroy EJ 1991 Estimating loads from periodic records USGeological Survey Branch of Systems Analysis Technical Memo9101 81

Cole TM Wells SA 2008 CE-QUAL-W2 A two-dimensional laterallyaveraged hydrodynamic and water-quality model 36 userrsquos manualUS Army Corps of Engineers Waterways Experiment Station 125

Droppo IG Ongley ED 1994 Floccuation of suspended-sediment inrivers of Southeastern Canada Water Research 28(8) 1799ndash1809

Duan N 1983 Smearing estimatemdasha nonparametric retransformationmethod Journal of the American Statistical Asssociation 78 605ndash610

Effler SW Prestigiacomo AR Peng F Bulygina KB 2006 Resolution ofturbidity patterns from runoff events in a water supply reservoir and theadvantages of in situ beam attenuation measurements Lake andReservoir Management 22(1) 79ndash93

Evans JK Gottgens JF Gill WM Mackey SD 2000 Sediment yieldscontrolled by intrabasinal storage and sediment conveyance over theinterval 1842-1994 Chagrin River Northeast Ohio USA Journal ofSoil and Water Conservation 55 264ndash70

Fan J Morris GL 1992a Reservoir sedimentation I Delta and densitycurrent deposits Journal of Hydraulic Engineering 118 (3) 354ndash69

Fan J Morris GL 1992b Reservoir sedimentation II Reservoir desiltationand long-term storage capacity Journal of Hydraulic Engineering118 (3) 370ndash84

Gelda RK Effler SW 2007 Modeling turbidity in a water supply reservoiradvancements and issues Journal of Environmental Engineering 133(2)139ndash148

Guy HP 1969 Laboratory theory and methods for sediment analysis USGeological Survey Techniques of Water-Resources Investigations book5 chap C1 58

Hach Company 2005 SOLITAX sc turbidity and suspended solids usermanual Available on the Web at httpwwwhachcomfmmimghachCODEDOC0235403232_3ED_8864|1

Helsel DR Hirsch RM 1992 Statistical methods in water resourcesElsevier New York 522

Hydrol Process 27 1426ndash1439 (2013)

1439CONTINUOUS TURBIDITY TO ASSESS RESERVOIR SEDIMENTATION

Hem JD 1985 Study and interpretation of the chemical characteristics ofnatural water Third Edition US Geological Survey Water-SupplyPaper 2254 263

Homer CC Huang L Yang BW Coan M 2004 Development of a 2001National Landcover Database for the United States PhotogrammetricEngineering and Remote Sensing 70 (7) 829ndash40

Jordan PR Hart RJ 1985 Transit losses and travel times for water-supplyreleases from Marion Lake during drought conditions CottonwoodRiver east-central Kansas US Geological Survey Water-ResourcesInvestigations Report 85ndash4263 41

Juracek KE 2010 Sedimentation sediment quality and upstreamchannel stability John Redmond Reservoir east-central Kansas1964ndash2009 US Geological Survey Scientific Investigations Report2010ndash5191 34

Kansas Biological Survey 2010 Bathymetry survey of John RedmondReservoir Coffey County Kansas 23

Kansas Water Office 2008 Sedimentation in our reservoirs causes andsolutions 143

Kansas Water Office 2010 John Redmond Reservoir fact sheet availableon the Web at httpwwwkwoorgreservoirsReservoirFactSheetsRpt_John_Redmond_2010pdf

Lee CJ Rasmussen PP Ziegler AC 2008 Characterization ofsuspended-sediment loading to and from John Redmond Reservoir2007-2008 US Geological Survey Scientific Investigations Report2008-5123 25

Morris GL Fan J 1998 Reservoir sedimentation handbook McGrawHill New York

Morris GL Annandale G Hotchkiss R 2008 Reservoir Sedimentation InMarcelo Garcia ed American Society of Civil Engineers Manual 110Chapter 12 579ndash612

National Weather Service 2010a Climate-Radar Data Inventories forEmporia KS Available on the Web at httpwww4ncdcnoaagovcgindashwinwwcgidllwwDI ~ StnSrch ~ StnID~ 20007525

National Weather Service 2010b Daily precipitation data from NeoshoRapids KS Available on the Web at httpwww4ncdcnoaagovcgindashwinwwcgidllwwDI ~ StnSrch ~ StnID~ 20007533

Nolan KM Gray JR Glysson DG 2005 Introduction to suspended-sediment sampling US Geological Survey Scientific InvestigationsReport 2005-5077 CD-ROM

Palmieri A Shah F Annandale GW Dinar A 2003 Reservoirconservation volume I the RESCON approach The World Bank101

Perry CA Wolock DM Artman JA 2004 Estimates of flow durationmean flow and peak-discharge frequency values for Kansas streamlocations US Geological Survey Scientific Investigations Report2004ndash5033 219

Copyright copy 2012 John Wiley amp Sons Ltd

Rasmussen PP Gray JR Glysson GD Ziegler AC 2009 Guidelinesand procedures for computing time-series suspended-sedimentconcentrations and loads from in-stream turbidity-sensor andstreamflow data US Geological Survey Techniques and Methodsbook 3 chap C4 57 p

Runkel RL Crawford CG Cohn TA 2004 Load estimator (LOADEST)A FORTRAN program for estimating constituent loads in streams andrivers US Geological Survey Techniques and Methods book 4 chapA5 75 p

Simotildees FJM Yang CT 2008 GSTARS computer models and theirapplications part II applications International Journal of SedimentResearch 23(4) 299ndash315

Sullivan AB Rounds SA Sobieszczyk S Bragg HM 2007 Modelinghydrodynamics water temperature and suspended sediment in DetroitLake Oregon US Geological Survey Scientific Investigations Report2007ndash5008 40

Trimble SW 1999 Decreased rates of alluvial sediment storage in theCoon Creek Basin Wisconsin 1975-93 Science 285 1244ndash1246

Turnipseed DP Sauer VB 2010 Discharge measurements at gagingstations US Geological Survey Techniques and Methods book 3chap A8 87 Available at httppubsusgsgovtmtm3-a8

US Army Corps of Engineers 1996 Water control manual for JohnRedmond Dam and Reservoir Neosho River Kansas US Army Corpsof Engineers Southwestern Division Dallas Texas 172

US Army Corps of Engineers 2002 Supplement to the finalenvironmental impact statement prepared for the reallocation of watersupply storage project John Redmond Lake Kansas Available on theWeb httpwwwkwoorgreports_publicationsReportsrpt_JRrealloc_study_DSEIS_main_122006_kwpdf

US Army Corps of Engineers 2010 John Redmond Lake ReservoirData Available on the Web httpwwwswtndashwcusacearmymilJOHNlakepagehtml

US Department of Agriculture 1994 State soil geographic (STATSGO)database for Kansas Available on the Web at httpwwwkansasgisorgcatalogcatalogcfm

Wagner RJ Boulger RW Jr Oblinger CJ Smith BA 2006 Guidelinesand standard procedures for con-tinuous water-quality monitorsmdashStation operation record computation and data reporting USGeological Survey Techniques and Methods 1ndashD3 51

Wetzel RG 2001 Limnology Lake and river ecosystems AcademicPress San Diego CA

White R 2001 Evacuation of sediments from reservoirs Thomas TelfordPublishing London

Yang CT Simotildees FJM 2008 GSTARS computer models and theirapplications part I theoretical development International Journal ofSediment Research 23(3) 197ndash211

Hydrol Process 27 1426ndash1439 (2013)