Journal of Hydrology 462–463 (2012) 4–14

Contents lists available at SciVerse ScienceDirect

Journal of Hydrology

journal homepage: www.elsevier .com/locate / jhydrol

Effects of evapotranspiration on baseflow in a tropical headwater catchment

Daniel Cadol a,⇑, Stephanie Kampf b, Ellen Wohl a

a Department of Geosciences, Colorado State University, Fort Collins, CO 80523-1482, USAb Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USA

a r t i c l e i n f o s u m m a r y

Article history:Available online 11 May 2012

Keywords:Diel cyclesEvapotranspirationBaseflowTropical forest hydrology

0022-1694/$ - see front matter � 2012 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.jhydrol.2012.04.060

⇑ Corresponding author. Tel.: +1 970 420 9656.E-mail address: dcadol@umces.edu (D. Cadol).

Diel cycles in stream discharge during baseflow periods in a headwater stream in La Selva Biological Sta-tion, Costa Rica, a tropical wet forest site, appear to be associated with groundwater withdrawal by theforest for evapotranspiration (ET). Analysis of the cycles indicates a strong correlation of stage changewith ET demand, similar to the variation found in riparian water table elevation by previous researchers.Links between daily forest ET demand cycles and stream discharge cycles have been reported in temper-ate humid and semi-arid regions, but the frequent flood hydrographs of the wet tropics tend to obscurethis daily signal. This study modifies and combines two established empirical methods for analyzing thediel ET signal in streamflow which lead to estimates of riparian ET derived from groundwater (ETG) athourly time scales and spatial extent of the riparian area. The model has a direct dependence on the esti-mate of specific yield, a difficult to constrain parameter, which we estimate from previously publishedsoil analyses. For the six baseflow periods analyzed, the model estimates groundwater ET losses rangingfrom 1.8 to 3.9 mm/day within the riparian area. These estimates are 52–81% of the total ET estimatedwith the Penman–Monteith equation (ETPM). The signal of ETG in the stream lags ETPM by 1.5–3 h, withapparent peak decay and signal duration lengthening during propagation. Model results indicate thatthe area of the riparian zone that influences streamflow by means of ET withdrawal increases with streamstage and ranges from 2.5% to 6.6% of the total basin area. Variations in the rate of change of nightlystream stage recovery suggest possible variations in the relative importance of subsurface hydraulicproperties. At high stages, the rate of stream stage recovery from ET losses decreases throughout thenight, whereas at low stages the rate of stream stage recovery increases throughout the night. Futurework with numerical models could explore mechanistic controls on these empirically-derived recoveryfunctions.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction

Forests affect stream function through multiple mechanismsincluding evapotranspiration (ET) (Link et al., 2005), delivery ofinstream wood (Benda and Sias, 2003), control of sediment delivery(Piégay et al., 2004), temperature control/shading (Gregory et al.,1991), and allochthonous nutrient delivery (Fisher and Likens,1973; Naiman et al., 2005). The focus of this study is ET, a funda-mental component of water budgets that exerts influence onstream discharge across multiple temporal scales. At the annualscale, a significant percentage of rainfall is exported as ET ratherthan runoff from a broad range of environments including thehumid tropics (Leigh, 1975). For example, between 54% and 66%of annual rainfall at La Selva Biological Station, Costa Rica, wasexported as ET during the years 1998–2000 (Loescher et al.,2005). At the daily scale, the diurnal rise, peak, and fall in ET

ll rights reserved.

demand combined with low nocturnal demand have the potentialto cause diel cycles (i.e., cycles with daily or 24-h periodicity) ingroundwater level (White, 1932; Troxell, 1936; Gribovszki et al.,2008; Loheide, 2008) and stream discharge (Reigner, 1966; Bondet al., 2002; Gribovszki et al., 2010). Although ET water sourcescan also include soil moisture, intercepted water, and surface water,the diel influence of forest ET on streamflow is derived from thegroundwater component.

Forest ET reductions in streamflow are mediated first throughsap flow and then through groundwater flow (Granier et al.,1996; Bond et al., 2002; Wondzell et al., 2007; Gribovszki et al.,2008). Sap flow transfers subsurface water to leaves and thenceto the atmosphere, and diel cycles in sap flow have been observedat numerous study sites in the neotropical region (Granier et al.,1996; Andrade et al., 1998; Meinzer et al., 2001; O’Brien et al.,2004). Likewise, there is a long history of researchers in semi-aridand temperate zone sites using fluctuations in groundwater levelsto estimate riparian ET or the portion of ET derived from below thewater table (White, 1932; Troxell, 1936; Reigner, 1966; Gribovszkiet al., 2008, 2010; Loheide, 2008). In areas where roots typically

D. Cadol et al. / Journal of Hydrology 462–463 (2012) 4–14 5

extend to the water table, such as riparian areas, trees can accesseither soil water or groundwater, with the relative importance ofgroundwater likely increasing during rainless periods when soilmoisture is depleted. In the extreme case, where all water is de-rived from below the water table, the volume of water lost to theaquifer or stream due to riparian forest transpiration should equalthe volume of water transpired by the forest over an area of influ-ence adjacent to the stream.

White (1932) and Troxell (1936) demonstrated that peak ET de-mand correlates with the maximum rate of decline of ripariangroundwater or streamflow, rather than minimum level or flow,developing a technique to approximate groundwater recharge tothe riparian zone by examining hydrograph behavior at night whenET is near zero. By subtracting the groundwater recharge compo-nent from the change in aquifer storage, which is found as thechange in water table elevation multiplied by the specific yieldfrom the aquifer, White (1932) was the first to estimate sub-dailyET rates from diel cycles in water level. While these earlierresearchers assumed a constant rate of groundwater recharge intothe riparian aquifer, Loheide (2008) noted that the hydraulic gradi-ent of groundwater flow into the riparian zone will actually varythroughout each cycle. This gradient is altered by forest wateruse lowering the water table and reduces the transfer rate of sub-surface water to the stream (Rowe and Pearce, 1994; Dye et al.,2001). While the White method gives sub-daily estimates of ETrate, another technique developed by Reigner (1966) yields dailyestimates of total ET derived from groundwater in riparian zone.The observed discharge is subtracted from the estimated baseflowof upgradient groundwater into the riparian zone, found by linkingthe nightly discharge peaks. Bond et al. (2002) built on this work toestimate the aerial extent of direct tree–groundwater connectionby scaling up locally measured ET rates to match daily stream dis-charge losses during dry summer periods in Oregon.

There are several difficulties in the actual estimation of ET fromstream stage or groundwater elevation data. First, knowledge ofthe readily available specific yield is required (Loheide, 2008). Thisreadily available specific yield is the fraction of water that can real-istically be drained over daily time scales and may differ from thetraditionally defined specific yield. Detailed knowledge of specificyield throughout a site is rare, so a single estimate is typically useddespite the direct dependence of calculated ET on specific yield.Furthermore, both the speed with which water withdrawal by veg-etation will become apparent in the stream and the rate at whichstreamflow recovers each night will depend on the transmissivityof the aquifer and the resultant groundwater flow rates.

Exploration of the connections between diel flow cycles and ETcould be conducted using a physically-based model of subsurfaceflow coupled with channel flow and ET. However, such a model ofriparian zone ET effects on discharge would require a map of ripar-ian zone boundaries, subsurface hydraulic properties, vegetationdistribution, potential ET, boundary conditions along adjacent hill-slopes, and ideally, spatial distributions of hydraulic head through-out the domain (Gribovszki et al., 2008; Szilágyi et al., 2008). Inmany cases, including this study, a model of this complexity isnot feasible based on available data (Kirchner, 2009). Hillslope in-puts to the riparian zone are typically unknown, as are the subsur-face hydraulic properties and the ET dynamics. Nevertheless, whenclear diel cycles are present in a discharge signal, a simpler spatiallylumped model may represent the key features of the riparian zoneET process. Streamflow monitoring is typically easier than sapflowor groundwater flow monitoring, and the streamflow signal inte-grates the riparian ET response throughout the watershed, enablingriparian area estimation. By monitoring flow cycles during base-flow, it may be possible to approximate certain ET characteristics,such as the amount of groundwater used by the forest. Althoughthe hydrological effects of logging have been well studied (Hibbert,

1967; Bosch and Hewlett, 1982; Bruijnzeel, 1990; Stednick, 1996),it is less clear how subtler impacts, such as climate variation orinvasive species introduction, might alter stream–forest interac-tions. Collecting ET data to explore these long-term changes canbe expensive and is hindered by limited historical records. Esti-mates of groundwater-sourced ET derived from high quality streamstage data, such as in this study, have the potential to increase thepopulation of records useful to the study of ET.

In this study, we hypothesize that observed diel cycles in stream-flow at La Selva Biological Station, Costa Rica are caused by ET with-drawal during the day and lateral groundwater replenishment atnight. To our knowledge, the link between ET and streamflow inthe humid tropics has not previously been quantified, perhaps be-cause of the confounding influence of the frequent precipitationevents. Therefore, we present an approach modified from previousstudies of groundwater level fluctuations (White, 1932; Troxell,1936; Loheide, 2008) for analyzing the ET signal in stream gage re-cords, using empirical relationships between stage and nightly flowrecovery to estimate the amount of water diverted from streamflowto ET (i.e., groundwater-sourced ET, ETG) at hourly time scales. As areference, we model ET using the Penman–Monteith equation,parameterized with values derived from previous research at thestudy site (Loescher et al., 2005). Derived ETG values are then com-bined with daily riparian water loss estimates found by subtractingobserved discharge from estimated baseflow (Reigner, 1966; Bondet al., 2002) to estimate riparian area, a difficult parameter to mea-sure in tropical forests where the continuous, dense canopy coverprevents visual delineation and inhibits topographic analysis.

2. Study site

La Selva Biological Station is a 16 km2 preserve operated by theOrganization for Tropical Studies (Fig. 1). The forest is classified astropical wet forest in the Holdridge system (Hartshorn and Peralta,1988), and is located at the transition from the Central VolcanicCordillera to the Caribbean coastal plain, with elevations rangingfrom 30 to 150 m. To the south, La Selva is bordered by BraulioCarillo National Park, which extends to the volcanic summits andelevations exceeding 3000 m. Along with a portion of the nationalpark, La Selva forms a peninsula of preserved rainforest thatextends to the lowlands and is surrounded by cleared land. Pri-mary forest at La Selva has a mean leaf area index (LAI) value of6.00 with a standard error of the mean of 0.32, a sampled rangeof 1.2–12.9, and no significant variation between wet and dry sea-sons (Clark et al., 2008).

Mean annual precipitation from 1963 to 2008 was 4370 mm,with the driest month on average being March with 168 mm,and the wettest months being July and December with 533 mmand 458 mm, respectively (Organization for Tropical Studies,2010). There is relatively little annual variation in precipitationat La Selva, either in total rainfall (4370 ± 700 mm, mean ± stan-dard deviation for period of record, 1963–2009) or distribution(minimum monthly precipitation is 103 ± 61 mm, and fallsbetween February and April 93% of years; maximum monthly pre-cipitation is 734 ± 196 mm, and falls between May and August 60%of years and between November and December 30% of years).Mean annual temperature is 26 �C. Monthly average temperaturefluctuates by <5 �C, whereas there is typically a daily temperaturefluctuation of >10 �C. Hurricanes seldom reach the area, but in-tense rains are generated from November to January by the estab-lishment of a cold front and polar trough that penetrates the airmass over the Caribbean Sea to as low as 10�N (Janzen, 1983).

We installed a gage to monitor the flow of El Surá, one of theprinciple streams of La Selva, at the final bedrock macro-step be-fore the stream reaches the Río Sarapiquí floodplain at an elevation

Fig. 1. Location of study site in Costa Rica.

6 D. Cadol et al. / Journal of Hydrology 462–463 (2012) 4–14

of approximately 55 m. Drainage area at the gage is 3.4 km2, andthe basin supports a mix of old-growth and 30 + year-old second-growth forest. There is a very limited floodplain at the gage site,extending 2–9 m on either side of the channel before transitioninginto valley sides with slopes of 10�-30�. Although inter-basingroundwater flow has been documented to be a major source ofwater in some watersheds at La Selva, solute contents of samplestaken at our gage site indicate that less than 1% of baseflow is frominter-basin groundwater transfer (Genereux et al., 2002). Stream-flow is flashy (Fig. 2), and baseflow periods are rare because ofthe high frequency of rainfall events. Stream water temperatureranged from 22 to 26 �C, with baseflow generally warmer, andstorm flow generally cooler.

The bedrock at La Selva consists of a sequence of andesitic lavaflows originating from the upslope volcanoes (Alvarado, 1990, in

Fig. 2. Stream discharge in El Surá and rainfall. The highest directly measured flows werfunction). Rainfall is on a logarithmic scale to emphasize dry periods. Periods for which

Kleber et al., 2007). Clay-rich saprolite is developed in the bedrock,several meters thick on most ridges, with residual soils typicallyabout 1 m thick above the saprolite (Sollins et al., 1994). The soilsin the narrow V-shaped valleys such as our study site are generallythicker and have been classified as Typic Humitropepts (Sollinset al., 1994). Dry bulk density of the A horizon is 570 kg/m3 andbulk density of the B horizon is 840 kg/m3 (Sollins et al., 1994).From these bulk density values, we estimate a mean saturatedwater content (hS) of 0.78 in the A horizon and 0.68 in the B hori-zon. Water content at �33 kPa (hfc) is 0.47 in A and 0.42 in B, andwater content at �1.5 MPa (hwp) is 0.37 in A and 0.33 in B (Sollinset al., 1994). Measured clay content is 42% in the A horizon and 64%in the B horizon, and actual content is likely to be higher due to dif-ficulty dispersing the clay aggregates that give the soil a welldeveloped structure (Strickland et al., 1988; Sollins et al., 1988).

e 1 m3/s; discharge values over 1 m3/s are extrapolations of the rating curve (powerETG was estimated are marked with arrows.

D. Cadol et al. / Journal of Hydrology 462–463 (2012) 4–14 7

The soils at La Selva have extensive macropore networks as a resultof aggregation, root decay, and burrowing (Sollins and Radulovich,1988), contributing to the low bulk densities and high estimatedporosities. Macropores (pores draining between 0 and �3 kPa)comprised 13% of the total porosity of 0.62 m3/m3 in one alluvialsoil near our study site (Radulovich et al., 1989). Because of strongaggregation and abundant macropores, the soils drain freely inspite of their high clay content, and yet retain large amounts ofplant-available water, characteristics typical of tropical volcanicsoils (Sollins et al., 1994).

3. Methods

3.1. Data collection and processing

From November 21, 2007 to June 15, 2009, stage was continu-ously monitored at a stable cross section created by a footbridgecrossing of El Surá using a vented pressure sensor (LevelTroll700,InSitu Inc., Fort Collins, CO) with a factory reported accuracy of50 Pa (�5 mm of water) at 15 �C and resolution of 65 Pa(�0.5 mm of water) set on a 10-min recording interval. Dischargewas measured using a salt slug dilution technique for nine flowsranging from 0.17 to 0.67 m3/s, and two more flows of 1.1 and1.25 m3/s were measured using a velocity meter and cross sectionsurvey at the adjacent bridge, enabling establishment of a stage–discharge relationship (Fig. 2). The stage data were converted todischarge by fitting a power function to these measurements. Dis-charge was then averaged to a 30-min interval to match the mete-orological data.

Meteorological data were collected by the staff of La Selva Bio-logical Station at a tower in a clearing approximately 2 km fromthe stream gage. The variables precipitation (mm), average temper-ature (�C), average relative humidity (%), vector average wind speed(m/s), average solar radiation (SR, units of lmol s�1 m�2 for all timeperiods, and additionally in units of W/m2 from June 2008 onward),average photosynthetically available radiation (PAR, units oflmol s�1 m�2), and maximum PAR were recorded at 30 min inter-vals. Vapor pressure deficit (VPD, units of kPa) was calculated fromtemperature and relative humidity.

In this flashy stream, diel flow patterns are easily masked byrainfall runoff hydrographs (Fig. 2), so we limited the analyses totimes with little precipitation. We identified 12 time periods rang-ing from 2 to 17 days that exhibited distinct diel flow cycles, (Fig. 3).Within these identified baseflow periods, the daily flow cycle wassuperimposed on a falling hydrograph. In order to isolate the daily

Fig. 3. Stream stage during identified baseflow periods. Dates in the legend indicatestart date for each period. ETG and AR were modeled for the six numbered periods.This order, from highest stage to lowest, will be maintained in subsequent graphs.

cycle, we detrended the baseflow data by subtracting a fitted powerfunction for each baseflow period (Table 1).

To establish an independent estimate of ET at our site, we usedthe Penman–Monteith equation

ETPM ¼DðRn � GÞ þ qaCpDga

qwkv Dþ c 1þ ga=gbð Þ½ � ð1Þ

where D is the slope of the saturated vapor pressure–temperaturerelationship (kPa K�1) calculated as a function of temperature; Rn

is the net radiation, and G is ground heat flux (MJ m�2 h�2), bothcalculated according to the ASCE standardized reference evapo-transpiration equation (ASCE, 2005); qa is the density of air(1.220 kg m�3); Cp is the specific heat capacity of air(0.00100 MJ kg�1 K�1), D is VPD (kPa); ga is the aerodynamic con-ductance (m/s) calculated as a linear function of wind speed ob-tained from a 3 year study of canopy energy flux in primaryforests at La Selva (Loescher, 2002; Loescher et al., 2005); qw isthe density of water (1000 kg m�3); kv is the latent heat of vapori-zation (MJ kg�1) calculated as a function of temperature; c is thepsychrometric constant (kPa K�1), and gb is the bulk canopy con-ductance (m/s) calculated as a function of Rn and D according tothe method described by Martin et al. (1997) with parameters ob-tained from the previously sited study by Loescher (2002). Becausewind speed at the meteorological station is likely to differ system-atically from the wind speed measured at the canopy tower used byLoescher et al. (2005), the sensitivity of ETPM to this variable wasevaluated. For comparison, the standardized reference evapotrans-piration from a tall (0.5 m) well watered crop was calculated forthe given meteorological data (ASCE, 2005), as was ET using thePriestly–Taylor equation (Priestly and Taylor, 1972).

3.2. Modeling ET from streamflow

We modified and adapted the White (1932) empirical model toestimate ET from streamflow and applied it to six of the twelvebaseflow periods that showed diel flow cycles: those starting 11/30/07, 2/3/08, 3/29/08, 5/2/08, 3/28/09, and 5/1/09. The other sixwere excluded from this component of the analysis due to shortduration (<6 days) or the occurrence of measurable rainfallP1 mm on any day in the period. As a foundation for the model,the daily cycles are conceptualized as fluctuations in storage ofgroundwater in the riparian zone, or more precisely the zone adja-cent to the stream where forest ET draws on groundwater (i.e.,zone of root–groundwater contact), using the equation

dSR

dt¼ �VETG þ GW � Q ð2Þ

where dSR/dt is the time derivative of riparian groundwater storage,VETG is the volumetric rate of groundwater derived evapotranspira-tion across the entire riparian area, GW is groundwater input fromupgradient, and Q is groundwater discharge to the stream (all inunits of volume per time). This differs slightly from the groundwa-ter model proposed by White (1932) due to the addition of a termfor streamflow. Converting the change of storage into a change ofwater table level yields

dhgw

dtSyAR ¼ �VETG þ GW � Q ð3Þ

where Sy is the specific yield of the aquifer, hgw is the effective aver-age height of the water table in the riparian zone and AR is the areaof the riparian zone. If the riparian water table is tightly coupledwith stream stage, then we can replace hgw with stage, d. This is akey assumption of our model, which will neglect any differencesin water table elevation between the stream and adjacent riparianzone. The abundant macropores in the tropical soils of our studysite increase the likelihood that this assumption is valid because

Table 1Best fit power function parameters of the falling hydrograph trend for each baseflow period.

Start Date 11/30/2007 1/9/2008 2/3/2008 2/28/2008 3/29/2008 5/2/2008 9/12/2008 10/25/2008 2/17/2009 3/19/2009 3/28/2009 5/1/2009

Days 7 12 7 17 7 7 2 2 3 7 12 6yo

1 0.475 0.541 0.368 0.325 0.285 0.294 0.396 0.417 0.501 0.490 0.433 0.330a1 �0.0559 �0.0796 �0.0217 �0.0106 �0.0275 �0.0236 �0.0417 �0.0402 �0.0597 �0.0482 �0.0403 �0.0196b1 0.827 0.722 0.822 0.456 0.931 0.559 1.093 0.684 1.041 1.010 0.695 0.901

1 Best fit power function parameters (y = yo + axb) with x in units of weeks and y in meters.

Fig. 4. Calculation of discharge ‘lost’ to the stream due to ET. Lost streamflow is thearea between the curves.

8 D. Cadol et al. / Journal of Hydrology 462–463 (2012) 4–14

macropores enable relatively rapid transfer of water between thestream and riparian zone. Ten days of water level data from a pie-zometer near our gage also show no measurable elevation differ-ence (±1 cm) during periods at least 24 h after a rainfall event.Similarly, Reigner (1966) considered a groundwater well near hisstudy stream in Pennsylvania, USA, to be essentially a stream stageindicator. However, we did not have continuous groundwater leveldata available for the period of analysis, resulting in uncertainty inthe relationship between stream and groundwater level.

Detrending the stage data to eliminate the background hydro-graph decay has the effect of eliminating Q and some part of GW,so that

ddd

dtSyAR ¼ �VETG þ GWd ð4Þ

where dd is the detrended stage, and GWd is the portion of ground-water recharge that varies daily as a result of cyclic changes inhydraulic gradient caused by local lowering of the water table byET withdrawal. If there were no ET influence, we would expectddd/dt and GWd to both equal zero. As stage and riparian water tableelevation vary cyclically each day, the hydraulic gradient betweenthe regional groundwater and the riparian zone will also fluctuate.Therefore, GWd will be a function of detrended stage (dd), as previ-ously proposed by Loheide (2008). This is different from the originalWhite method, which assumes a constant value of groundwater re-charge. The form of the function should reflect groundwaterdynamics controlled by hydraulic gradient and subsurface hydraulicproperties. Dividing through by SyAR gives

ddd

dt¼ � VETG

SyARþ GWd

SyARð5Þ

where the left side of the equation is the observed rate of streamstage change, the first term on the right is rate of stage changedue to ET, and the second term on the right is rate of stage changedue to lateral groundwater replenishment. Estimating GWd/SyAR asan empirical function of dd, which we abbreviate as f(dd), is nowpossible. We do not explicitly find Sy or AR at this point in the anal-ysis, but simply lump them into the empirical function. For each ofthe six baseflow periods, the nocturnal variation of stage recoveryrates was found (between roughly 10:00 pm and 7:00 am), whenET effects were presumed to be negligible and the first term onthe right side of equation (5) is �0. The nocturnal period was se-lected based on breaks in slope of the ddd/dt trend through time(e.g., Fig. 5). Plotting measured ddd/dt (which is �GWd/SyAR at night)against dd and fitting a curve to these data gives f(dd), GWd/SyAR as afunction of dd. We smoothed the dd data series prior to this analysisby averaging across all days in the baseflow period being considered(i.e., found average dd at 1:00 am, 1:30 am, etc.). The averagingcauses some information loss because this function will not be uni-versal, even within a site, but should vary through time as a result ofits dependence on changing subsurface hydrologic conditions suchas water table elevation. However, the averaging procedure reducednoise in the signal and can be partially justified because each base-flow period had consistent curve shapes within the period that weredistinct from the curves produced during other periods.

An estimate of the rate of groundwater-sourced ET (ETG, units oflength per time) can then be found by solving Eq. (5) for VETG/AR,the total volumetric rate of groundwater derived ET in the riparianzone divided by the riparian area.

ETG ¼VETG

AR¼ Sy f ðddÞ �

ddd

dt

� �ð6Þ

From this equation we then estimated the area over which ETaffects groundwater, after the method described by Reigner(1966) and Bond et al. (2002). For each of the six periods for whichETG was found, the average daily volumetric ET (i.e., average of VETG

integrated over each day) is expected to equal the water ‘lost’ inthe streamflow each day (Qlost). Lost water was calculated by find-ing the cumulative difference between the observed cyclic hydro-graph and a hydrograph constructed by linking each daily peakdischarge with straight lines, and dividing the sum by the numberof cycles thus bounded (Fig. 4). This method results in a minimumestimate of riparian water loss because it is possible that flow didnot fully recover to its ET-exclusive value before the ET demandfrom the following day began to lower the flow again (Reigner,1966). Thus the equation for calculation of AR is

AR ¼Q lostR 24 hrs

0 Sy f ðddÞ � ddddt

� � ð7Þ

where the denominator is ETG integrated across each day, in whichthe term f(dd) is again the empirical function for the recovery rate ofthe riparian water table found independently for each period ofanalysis, and AR is a minimum estimate of riparian area.

Groundwater-based estimates of ET are sensitive to uncertaintyin the value of Sy because ETG and Sy are linearly related (Gribovszkiet al., 2008). We used a value of 0.32, but tested values ranging from0.1 to 0.58. The minimum value tested was the relative volume ofmacropores measured in a nearby soil (Radulovich et al., 1989),while the maximum is the difference between hS of the B horizonin alluvial soils at the field site (Sollins et al., 1994) and the esti-mated residual water content (hR) of 0.1 for a clay rich aquifer.The maximum value is the traditional definition of specific yield,but is likely an overestimate of the yield during fluctuations withshorter durations that the draining time. Previous researchers havedefined a ‘readily available specific yield’ for use with groundwater

D. Cadol et al. / Journal of Hydrology 462–463 (2012) 4–14 9

fluctuation estimates of ET (Meyboom, 1964; Loheide et al., 2005;Gribovszki et al., 2008). Meyboom (1964) suggested using 50% ofhS–hR, while Loheide et al. (2005) estimated values of Sy for a varietyof sediment textures ranging from 0.38 for coarse sand to 0.012 forsilty clay loam from numerical simulations. The soil texture at ourstudy site, however, does not match any of these simulations,though it is described as draining freely like sand (Sollins et al.,1994). At our site, the numerical simulation for sandy soil (Loheideet al., 2005), 50% of hS–hR for the B horizon (Meyboom, 1964; Sollinset al., 1994), and hS–hfc (Sollins et al., 1994), all yield Sy values closeto 0.32, so we use this as a representative estimate of readily avail-able specific yield for the B horizon of the soil at our site. The B hori-zon typically extends deeper than 1.2 m below the surface (Sollinset al., 1994), but we do not know how deep nor do we have data fordeeper portions of the aquifer. Sy is likely to change with depth, butour simple model does not account for this.

Model results were compared with the estimate of ET foundwith the Penman–Monteith equation (ETPM). To identify time lagsbetween ETPM and the stream derived ETG we calculated Pearsoncorrelation coefficients (r) between the two at temporal offsetsthat increased by 30 min increments. We identified lag times be-tween ET demand and stream response as the temporal offset thatled to r values closest to 1.

Fig. 5. Period-averaged trends in dd and ddd/dt. (A) Period 1, March 28–April 8,2009, the highest flow period, showing linear drop in the stage recovery rate (ddd/dt) at night (10:00 pm to 7:00 am). (B) Period 6, March 29–April 4, 2008, the lowestflow period, showing linear rise in the stage recovery rate (ddd/dt) at night(10:00 pm to 7:00 am).

4. Results

The estimates of the water table recovery rate (i.e., the empiricalfunction f(dd)) showed great variability, and even changes in thesign of the slope, depending on the average stage of the baseflowperiod (Table 2). At relatively high stream stages, ddd/dt decreasedat a nearly constant rate between 10:00 pm and 7:00 am (Fig. 5A).For example, during the 12-day period that began on March 28,2009, with an average stage of 0.4 m, the relationship betweenthe nocturnal values of ddd/dt and dd had the form of a curve thatwas well fit by both a quadratic function and a rational functionwithin the range of observed values (Fig. 6B). We used the quadraticfunction, but a reanalysis using the rational function resulted innegligible differences in ETG estimates. The trend during this base-flow period was for the rate of lateral groundwater replenishmentto decrease throughout the night. At the lowest stream stages,however, the relationship between ddd/dt and dd was nearlyreversed. During the 7-day period that began March 29, 2008, withan average stage of 0.27 m, as water level rose from 10:00 pm to7:00 am, the rate of rise increased (Fig. 5B). The relationshipbetween the nocturnal values of ddd/dt and dd had a linear form(Fig. 6B). Half-hourly data from individual days yield f(dd) functionswith widely variable R2 values, apparently dependent on flow per-iod. For example, during the period beginning March 28, 2009 dailyf(dd) functions have regressions with R2 between 0.84 and 0.92,while the other high flow period beginning November 28, 2007has daily regressions with R2 between 0.17 and 0.43 (Fig. 6A). Therewere some subtle trends in the nocturnal relationship between

Table 2Groundwater recovery functions, f(dd).

Start date Function form Slope Equation f(dd) 1,2

11/30/2007 Quadratic Negative y = �35x2 � 0.14x + 0.000283/28/2009 Quadratic Negative y = �25x2 � 0.11x + 0.000582/3/2008 Quadratic Negative y = �17x2 � 0.12x + 0.00055/1/2009 Quadratic Horizontal y = �39.4x2 + 0.0092x + 0.00045/2/2008 Quadratic Positive y = �39.6x2 + 0.15x + 0.00063/29/2008 Linear Positive y = 0.0817x + 0.0005

1 x is detrended depth (dd) in units of m, and y is water table recovery rate (GWd)in units of m/h.

2 Equations are plotted in Fig. 6B.

ddd/dt and dd over the course of the baseflow periods, particularlythose with large drops in stage. Over the course of the period begin-ning November 30, 2007, the daily ddd/dt vs. dd plots trendedslightly toward the March 28, 2009 period functions, although therewas a great deal of overlap between days, but within the periodbeginning March 28, 2009 there was no trend (Fig. 6A).

Average ETG in the riparian zone for the baseflow periods calcu-lated with Eq. (6) and Sy = 0.32 ranged from 1.8 to 3.9 mm/day,while ETPM calculated with Eq. (1) ranged from 3.6 to 5.1 (Table3, Fig. 7). The minimum estimate of Sy (0.1) yielded ETG from 0.6to 1.2 mm/day while the maximum estimate of Sy (0.58) yieldedETG from 3.2 to 7.9 mm/day (Table 3). The ratio ETG/ETPM, whichcan be thought of as the fraction of ET derived from groundwater,ranged from 0.52 to 0.81 for Sy = 0.32, with the lowest value occur-ring during the period with highest stage and lowest net radiation(Table 3, Fig. 7). For all but this highest flow baseflow period, thedifference between ETPM and ETG could be eliminated by using a va-lue of Sy of �0.4 (Table 3).

About 51% of the variation in daily ETG is explained by ETPM,with ETG being 72% of ETPM on average (Fig. 8A). The relationshipdoes not vary systematically with stage. Using a value of Sy of0.44 would give the regression line a slope of 1. Daily averageVPD correlates strongly with daily ETPM, but less well with dailyETG (Fig. 8B). For both daily data and baseflow period averageddata, ETPM has a linear correlation with VPD, while ETG appears toinitially rise as VPD rises, but to plateau at a limit of approximately4 mm/day (Figs. 7B and 8B).

Fig. 6. Empirical relationships between detrended depth (dd) and nightly stage recovery rate (ddd/dt). (A) Half-hourly data for six nights in November/December 2007 andseven nights in March/April 2009, the two highest baseflow periods. (B) Average relationships for each baseflow period analyzed, arranged from highest flow (dark) to lowestflow (light), plotted with derived f(dd) functions.

Table 3Baseflow period flow and ET characteristics.

Start date 11/30/2007

1/9/2008

2/3/2008

2/28/2008

3/29/2008

5/2/2008

9/12/2008

10/25/2008

2/17/2009

3/19/2009

3/28/2009

5/1/2009

Days 7 12 7 17 7 7 2 2 3 7 12 6Ave. Qloss (m3/day) 395 570 438 414 320 371 499 371 428 473 545 353Ave. Q (m3/day) 35,670 42,830 19,170 13,450 8890 9700 24,950 27,870 46,690 40,940 26,280 14,310Proportion of Q ‘lost’ 0.011 0.013 0.023 0.031 0.036 0.038 0.020 0.013 0.009 0.012 0.021 0.025Ave. stage, d (m) 0.44 0.47 0.36 0.31 0.27 0.28 0.39 0.41 0.49 0.47 0.40 0.32Cycle amplit. (mm) 4.6 6.2 6.2 7.1 7.5 7.4 5.9 5.7 4.5 5.3 6.4 6.2Ave. SR

(lmol s�1 m�2)380 406 473 499 541 476 441 425 507 430 486 394

Ave. VPD (kPa) 0.36 0.39 0.51 0.53 0.57 0.50 0.48 0.40 0.50 0.43 0.57 0.40ETPM to ETG lag (h) 2 3 3 2.5 1.5 2.5ETPM (mm/day) 3.6 4.5 5.1 4.6 5.0 3.9ETG (mm/day) 1.8 3.4 3.8 3.7 3.9 2.8ETG – min Sy (mm/day) 0.6 1.1 1.2 1.1 1.2 0.9ETG – max Sy (mm/day) 3.2 6.2 6.8 6.6 7.1 5.2ETPM � ETG (mm/day) 1.8 1.1 1.3 0.9 1.1 1.1ETG:ETPM ratio 0.49 0.76 0.74 0.79 0.78 0.72Sy to match ETG and

ETPM

0.65 0.42 0.43 0.40 0.41 0.44

Ripar. area ETG (km2) 0.22 0.13 0.09 0.10 0.14 0.12Ripar. area (% of basin) 6.6 3.8 2.5 3.0 4.1 3.6Ripar. area ETPM (km2) 0.11 0.10 0.06 0.08 0.11 0.09

10 D. Cadol et al. / Journal of Hydrology 462–463 (2012) 4–14

Diurnal variation in ETG lagged ETPM, had a lower peak, andexhibited a more attenuated signal (Fig. 9). The ASCE standard ref-erence ET is very similar to ETPM, but dips negative at night (Fig. 9).Lag times between ETPM and ETG ranged from 1.5 to 3 h, and show aweak correlation with stage (Fig. 10). Discharge lost to the streamranged from 320 to 570 m3/day (Table 3). These values of Qloss rep-resent 1–4% of the daily discharge. The estimated riparian area (Ta-ble 3), calculated with Eq. (6), correlates positively with theaverage stage of the period being considered (Fig. 10). The rangeof values of AR, 0.09–0.22 km2, represents �2.5–6.6% of the totaldrainage basin area. Digitizing the valley bottom based on slope

angles of a 10-m resolution digital elevation model derived fromLiDAR yielded an upstream valley bottom area of 0.22 km2, equiv-alent to the maximum modeled value of AR.

Estimates of ETPM were insensitive to ga. Varying ga by a factor of3 led to ±3% variation in the total daily ET rate. Uncertainty in thefunction for gb is more influential, with a factor of 3 variation in gb

leading to ±40% variation in daily ET. Nonetheless, the trends inhourly ETPM found using Eq. (1) match very closely with standard-ized reference ET found with the ASCE equation (ASCE, 2005)(Fig. 9), suggesting this is a reasonable estimate. Additionally, ETPM

varies as 72–78% of the estimates of ET using the Priestly–Taylor

Fig. 7. Average ET values for the six baseflow periods showing ETPM, ETG forSy = 0.32, the difference between ETPM and ETG, and the ratio ETG/ETPM (on lowergraphs). (A) and (C) ET variation with VPD. (B) and (D) ET variation with averagestream stage.

D. Cadol et al. / Journal of Hydrology 462–463 (2012) 4–14 11

equation (Priestly and Taylor, 1972) at half hour time steps, thesame range as found by Shuttleworth et al. (1984) and Granieret al. (1996) for dry canopy conditions without water stress intropical forests.

5. Discussion

5.1. ET rates and riparian zone water loss

Groundwater-sourced ET rates (ETG) calculated with Eq. (5) ran-ged from 1.8 to 3.9 mm/day, similar to the 3.8 mm/day maximummeasured from sap flow by Granier et al. (1996) for stands inFrench Guyana during dry canopy conditions. Shuttleworth et al.(1984) found total ET rates of 1.75–4.22 mm/day in the Amazonbasin, again in dry canopy conditions. Estimates of 5.2–6.3 mm/day full year average ET at La Selva for the years 1998–2000(Loescher et al., 2005) are higher than our estimates of ETG andgenerally higher than our estimates of ETPM. This is may be due

Fig. 8. (A) Relationship between ETG and ETPM. Lines connect subsequent days to demonsETG and ETPM with vapor pressure deficit. Baseflow periods are arranged from highest st

to the importance of intercepted rainfall to total ET, which is equiv-alent to 17–18% of bulk precipitation according to Loescher et al.(2005). Wet canopy ET rates could be expected to exceed the dryconditions prevalent during the baseflow periods analyzed here,raising the yearly average.

The estimates of ET water loss for El Surá calculated with themodified Reigner method represent 1–4% of total baseflow in the12 study periods. This is similar to the loss of 1–6% of flow thatBond et al. (2002) observed at their study site in western Oregon.The values of riparian area of 0.09–0.22 km2 that we calculatedrepresent 2.5–6.6% of the drainage basin of El Surá. In contrast,Bond et al. (2002) found that only 0.1–0.3% of their 1-km2 studybasin contributed to ET losses to the stream, or roughly 0.01–0.03 km2. The order of magnitude difference in the portion of thebasin contributing to water withdrawal could reflect differencesin riparian zone characteristics such as width, transmissivity, orpossibly shallower rooting depths by the 40-year old second-growth trees and shrubs in Oregon. In general, tropical trees haveslightly deeper roots than temperate conifers (Canadell et al.,1996), although data from tropical sites are very scarce and highlyvariable. We do not have data on average root depth of ripariantrees at La Selva, although observations of the root wads of toppledtrees lead us to suppose that many large individuals have relativelyshallow roots in spite of heights that can exceed 50 m (Hartshornand Hammel, 1994). The observed decrease in riparian area withdeclining stage is also consistent with the findings of Bond et al.(2002). As water table drops, we expect that some plants that weremarginally connected to the groundwater will lose contact. Thisdoes not suggest that these plants will cease to transpire, butrather that they will obtain their water from sources other thanthe groundwater that affects streamflow on a daily time scale, suchas soil water (Ehleringer and Dawson, 1992; Meinzer et al., 1999).

5.2. Lag times between atmospheric demand and streamflow cycles

Lag time between estimated ETPM and ETG is likely to reflect acombination of effects along the pathway from leaf to streamincluding sap flow lag, groundwater flow lag, and streamflowlag. Sap flow in the trunk has been shown to lag branch sap flowand ET demand by 2–4 h in an Oregon conifer forest (Martin etal., 1997), although sap flow tends to track climatic indicatorsmore closely in broad-leaf trees (Dye and Olbrich, 1993; Hinkleyet al., 1994; Granier et al., 1996), suggesting that sap flow lag maybe a minor contributor to the observed lag in this study. Theshorter lags during high flow periods (Fig. 9) could result fromhigher groundwater velocities in the upper soil horizons andgreater stream depth and flow velocity. Another component of

trate lack of temporal trends. Dotted line is 1:1 relationship. (B) The relationship ofage (dark) to lowest stage (light).

Fig. 9. ET variation across cycles calculated from ASCE reference ET (thin black), Penman–Monteith equation (ETPM) (thin gray), and modified White method (ETG) (thickblack). (A) First 7 days of baseflow period beginning March 28, 2009, a high flow period. (B) Baseflow period beginning March 29, 2008, a low flow period.

12 D. Cadol et al. / Journal of Hydrology 462–463 (2012) 4–14

the possible streamflow lag and signal decay could be the resultof flow at our gage integrating an ET signal that is distributedalong the length of the stream and through the riparian zone.Reach-averaged flow depth for the 50-m-long reach above ourgage ranged from 0.2 to 0.3 m for measured baseflow conditions.Nearly identical values were found at sites 500 m and 1800 m up-stream. Shallow water wave celerity at these depths ranges from1.4 to 1.7 m/s, which would take 0.5–0.6 h to pass down the�3 km distance from headwaters to gage. In contrast, reach-aver-aged flow velocity in the 50 m above the gage was roughly 0.2 m/s, and at this speed a signal would take �4 h to pass from head-waters to gage. If flow velocity controls the ET signal travel speedin the stream, it is possible that stage at the gage is affected bynegative interference in the signal as it travels downstream asmodeled by Wondzell et al. (2007). Lower stage periods wouldbe expected to have greater signal destruction. Although lag timesincrease as stage decreases (Fig. 10), peak signal decay is fairlyconstant (Fig. 9). Likewise, we do not see greater discrepanciesbetween daily ETPM and ETG as stage drops (Fig. 7A). Rather, theratio of ETG to ETPM appears fairly constant, and has an anoma-lously low value for the highest stage (starting November 30,2007) when signal destruction due to flow lag would be expectedto be at a minimum. Thus we do not believe signal interference isdriving the lag, although it may become important in higher or-der streams. The November 2007 outlier may be the result ofhigher residual soil moisture levels during this wetter season pro-viding an alternative source of water for the riparian trees.

5.3. Empirical model evaluation

The nocturnal relationship between dd and ddd/dt at our sitevaries widely with time and stage (Figs. 5 and 6B), with the rateof recovery sometimes increasing as stage rises through the night,sometimes decreasing, and sometimes staying nearly constant(Table 2, Fig. 6B). The variation in trends of the dd–ddd/dt relation-ship may reflect variation in subsurface flow dynamics. In thehigher flow periods, when the rate of lateral groundwater replen-ishment decreased throughout the night, the trend may reflectthe decreasing head gradient between the regional groundwaterand the riparian groundwater as the depressed riparian water tablerecovers. The trend of the lower flow periods, when the rate of lat-eral groundwater replenishment increased throughout the night, is

more difficult to explain, although it has some features in commonwith the transmissivity feedback reported elsewhere during rain-fall events (Bishop et al., 1990) and snowmelt (Kendall et al.,1999). In these cases, the researchers observed increased subsur-face flow rates as the saturation level rose because of hydraulicconductivity values that were greater near the surface. It is possi-ble that the active bioturbation at La Selva, with numerous shallowroot casts and large subterranean insect nests and burrows (Clark,1990), contributes to higher conductivity rates nearer to the sur-face. However, the processes contributing to the rates of ground-water recovery are complex, and the dd–ddd/dt relationship mayalso be controlled by lag times (Wondzell et al., 2007) or relativehydraulic gradients between riparian zone groundwater and thestream (Reigner, 1966). We do not have sufficient information toattribute the nature of the recovery function to any one mecha-nism. Future research using this method of ET signal analysis couldrelate the empirically-derived groundwater recovery function tonumerical modeling to examine the significance and cause of thesetrends.

The estimate of specific yield has the potential to be a signifi-cant source of error in our analysis, and the observation of varyinggroundwater recovery characteristics suggest Sy may vary withdepth. If near-surface horizons have greater Sy, then our underesti-mation of Sy may explain the relatively large discrepancy betweenETG and ETPM for the highest stage baseflow period (Fig. 7).

The assumption that stream stage reflects water table eleva-tion may in part explain the tendency of our method to produceestimates below ETPM as well as the attenuation of the cyclic sig-nal. A limitation of this study is the lack of continuous groundwa-ter monitoring in the study area; however, we attempted a short-term test of the connection between stage and groundwater ele-vation by simultaneously monitoring water level in a well 6 mfrom the stream in the small floodplain near the study site andstage in the stream adjacent to the well for 10 days in November2009. During rainfall events there was as much as a 0.008 m/mslope in water elevation from the well to the stream, but betweenrainfall events the stage and water table elevation were withinthe measurement error of one another (±1 cm). In lower orderstreams, we would expect the coupling between groundwaterand surface water to be even stronger, suggesting that futureapplications of this method might have greater success in smallerbasins.

Fig. 10. Plots of the variation of lag time between ETPM and ETG and area of forest–groundwater interaction (riparian area) with average stream stage. Data points arefrom the six modeled baseflow periods.

D. Cadol et al. / Journal of Hydrology 462–463 (2012) 4–14 13

6. Conclusions

Riparian ET creates a diurnal signal in streamflow during base-flow recession, which accounts for 1–4% of daily average discharge.The signal lags behind reference ET by 1–3 h, with the lag increas-ing at lower stream stage, when less of the riparian zone is con-nected to the stream. This lag is similar to the lag times found inother settings (Bond et al., 2002; Gribovszki et al., 2008). The effec-tive area of the riparian zone contributing to the stream ET signalranges from 0.09 to 0.22 km2, the upper value matching the totalarea of valley bottoms measured from a 10-m digital elevationmodel.

Our model builds on past efforts to estimate ET from groundwa-ter and gives some idea of certain features of groundwater dynam-ics using only streamflow cycles as measured with an easilyemplaced pressure transducer. Groundwater-derived ET rates inthe riparian zone (ETG) are roughly 75% of the estimated total ETrates (ETPM). The method appears promising and demonstrates away to estimate historical changes in ET with only streamflowdata. It is applicable independent of meteorological data, or if bothstreamflow and humidity data are available it provides a means totrack variation in the relationship between VPD and ET, which mayin turn reflect land use or forest composition variation.

The dynamics of the nightly flow recovery derived from theempirical model vary consistently with stage and may give someindication of subsurface characteristics. At high stages, when theupper portion of the subsurface is active, groundwater recoveryrates decrease through the night. At these high stages, the ET signalis imposed on the baseflow recession, making the hydraulic gradient

highest at the beginning of the night and lowest at the end of thenight. In contrast, at low stages, when only the lower portion ofthe subsurface is active, nighttime groundwater recovery rates in-crease with rising stage until ET commences. This shift in thegroundwater recovery function at lower stream stages could reflecta transition from hydraulic gradient dominance to transmissivitydominance in the groundwater recovery function or could be an ef-fect of streamflow lag in the integration of the distributed ET signal atthe gage site. Future work could test these inferences about riparianzone groundwater dynamics using more detailed measurementsand through comparison of empirically-derived groundwater recov-ery functions with physically-based models of riparian zone flowdynamics.

Acknowledgments

We would like to thank Beth Cadol, who assisted in gage instal-lation and data collection. Thanks to Minor Hidalgo and Chip Smallfor additional flow measurement data used in calibrating thestage–discharge relationship. This research would not have beenpossible without the support of La Selva Biological Station andtheir world-class staff. Thanks to station director Deedra McClearnand lab manager Bérnal Matarrita for access to La Selva’s detailedmeteorological data. This manuscript benefitted greatly from theinsightful comments of three anonymous reviewers. Primary fund-ing for this research came from National Science Foundation GrantEAR-0808255, with supplemental funding from a Geological Soci-ety of America student research grant.

References

Alvarado, G., 1990. Characterísticas geológicas de la Estación Biológica La Selva,Costa Rica. Technología en Marcha 10, 11–22.

Andrade, J.L., Meinzer, F.C., Goldstein, G., Holbrook, N.M., Cavelier, J., Jackson, P.,Silvera, K., 1998. Regulation of water flux through trunks, branches, and leavesin trees of a lowland tropical forest. Oecologia 115 (4), 463–471.

ASCE, 2005. The ASCE standardized reference evapotranspiration equation. In:Allen, R.G. et al. (Eds.), Task Committee on Standardization of ReferenceEvapotranspiration. Environmental and Water Resources Institute of theAmerican Society of Civil Engineers, p. 59.

Benda, L.E., Sias, J.C., 2003. A quantitative framework for evaluating the massbalance of in-stream organic debris. For. Ecol. Manage. 172 (1), 1–16.

Bishop, K.H., Grip, H., O’Neill, A., 1990. The origins of acid runoff in a hillslope duringstorm events. J. Hydrol. 116, 35–61.

Bond, B.J., Jones, J.A., Moore, G., Phillips, N., Post, D., McDonnell, J.J., 2002. The zoneof vegetation influence on baseflow revealed by diel patterns of streamflow andvegetation water use in a headwater basin. Hydrol. Process. 16, 1671–1677.

Bosch, J.M., Hewlett, J.D., 1982. A review of catchment experiments to determinethe effect of vegetation changes on water yield and evapotranspiration. J.Hydrol. 55, 3–23.

Bruijnzeel, L.A., 1990. Hydrology of Moist Tropical Forests and Effects of Conversion:A State of Knowledge. UNESCO, Paris, and Vrije Universiteit, Amsterdam, TheNetherlands, p. 226.

Canadell, J., Jackson, R.B., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schultze, E.-D.,1996. Maximum rooting depth of vegetation types at the global scale. Oecologia108, 583–595.

Clark, D.B., 1990. The role of disturbance in the regeneration of neotropical moistforests. In: Bawa, K.S., Hadley, M. (Eds.), Reproductive Ecology of Tropical ForestPlants. UNESCO, Paris, pp. 291–315.

Clark, D.B., Olivas, P.C., Oberbauer, S.F., Clark, D.A., Ryan, M.G., 2008. First directlandscape-scale measurement of tropical rain forest leaf area index, a key driverof global primary productivity. Ecol. Lett. 11, 163–172.

Dye, P., Moses, G., Vilakazi, P., Ndlela, R., Royappen, M., 2001. Comparative wateruse of wattle thickets and indigenous plant communities at riparian sites in theWestern Cape and KwaZulu-Natal. Water SA 27, 529–538.

Dye, P.J., Olbrich, B.W., 1993. Estimating transpiration from 6-year-old Eucalyptusgrandis trees: development of a canopy conductance model and comparisonwith independent sap flux measurements. Plant Cell Environ. 16, 45–53.

Ehleringer, J.R., Dawson, T.E., 1992. Water uptake by plants: perspectives fromstable isotope composition. Plant Cell Environ. 15, 1073–1082.

Fisher, S.G., Likens, G.E., 1973. Energy flow in Bear Brook, New Hampshire: anintegrative approach to stream ecosystem metabolism. Ecol. Monogr. 43, 421–439.

Genereux, D.P., Wood, S.J., Pringle, C.M., 2002. Chemical tracing of interbasingroundwater transfer in the lowland rainforest of Costa Rica. J. Hydrol. 258 (1–4), 163–178.

14 D. Cadol et al. / Journal of Hydrology 462–463 (2012) 4–14

Granier, A., Huc, R., Barigah, S.T., 1996. Transpiration of natural rain forest and itsdependence on climatic factors. Agric. For. Meteorol. 78, 19–29.

Gregory, S.V., Swanson, F.J., McKee, W.A., Cummins, K.W., 1991. An ecosystemperspective of riparian zones. Bioscience 41 (8), 11.

Gribovszki, Z., Kalicz, P., Szilágyi, J., Kucsara, M., 2008. Riparian zoneevapotranspiration estimation from diurnal groundwater level fluctuations. J.Hydrol. 349, 6–17.

Gribovszki, Z., Szilágyi, J., Kalicz, P., 2010. Diurnal fluctuations in shallowgroundwater levels and streamflow rates and their interpretation – a review.J. Hydrol. 385, 371–383.

Hartshorn, G.S., Hammel, B.E., 1994. Vegetation types and floristic patterns. In:McDade, L.A., Bawa, K.S., Hespenheide, H.A., Hartshorn, G.S. (Eds.), La Selva:Ecology and Natural History of a Neotropical Rain Forest. University of ChicagoPress, Chicago, pp. 73–89.

Hartshorn, G.S., Peralta, R., 1988. Preliminary description of primary forests alongthe La Selva–Volcan Barva altitudinal transect, Costa Rica. In: Almeda, F.,Pringle, C. (Eds.), Tropical Rainforests: Diversity and Conservation. CaliforniaAcademy of Science, San Francisco, pp. 281–295.

Hibbert, A.R., 1967. Forest treatment effects on water yield. In: Sopper, W.E., Lull,H.W. (Eds.), International Symposium of Forest Hydrology, Pennsylvania,September 1965. Pergamon, Oxford, p. 813.

Hinckley, T.M., Brooks, J.R., Cermák, J., Ceulemans, R., Kucera, J., Meinzer, F.C.,Roberts, D.A., 1994. Water flux in a hybrid poplar stand. Tree Physiol. 14, 1005–1018.

Janzen, D.H., 1983. Costa Rican Natural History. University of Chicago Press.Kendall, K.A., Shanley, J.B., McDonnell, J.J., 1999. A hydrometric and geochemical

approach to test the transmissivity feedback hypothesis during snowmelt. J.Hydrol. 219, 188–205.

Kirchner, J.W., 2009. Catchments as simple dynamical systems: catchmentcharacterization, rainfall–runoff modeling, and doing hydrology backward.Water Resour. Res. 45, W02429.

Kleber, M., Schwendenmann, L., Veldkamp, E., Rösner, J., Jahn, R., 2007. Halloysiteversus gibbsite: silicon cycling as a pedogenetic process in two lowlandneotropical rain forest soils of La Selva Costa Rica. Geoderma 138 (1–2), 1–11.

Leigh, E.G., 1975. Structure and Climate in Tropical Rain Forest. Annu. Rev. Ecol.Syst. 6, 67–86.

Link, T.E., Flerchinger, G.N., Unsworth, M., Marks, D., 2005. Water relations in anold-growth douglas fir stand. In: Jong, C.d., Collins, D., Ranzi, R. (Eds.), Climateand Hydrology in Mountain Areas. John Wiley and Sons, Chichester, England,pp. 147–159.

Loescher, H.W., 2002. Dissertation: Ecosystem-level Responses of Carbon andEnergy from a Tropical Wet Forest in Costa Rica, University of Florida, p. 91.

Loescher, H.W., Gholz, H.L., Jacobs, J.M., Oberbauer, S.F., 2005. Energy dynamics andmodeled evapotranspiration from a wet tropical forest in Costa Rica. J. Hydrol.315, 274–294.

Loheide, S.P., 2008. A method for estimating subdaily evapotranspiration of shallowgroundwater using diurnal water table fluctuations. Ecohydrology 1, 59–66.

Loheide, S.P., Butler, J.J., Gorelick, S.M., 2005. Estimation of groundwaterconsumption by phreatophytes using diurnal water table fluctuations: Asaturated–unsaturated flow assessment. Water Resour. Res. 41, W07030.

Martin, T.A., Brown, K.J., Cermák, J., Ceulemans, R., Kucera, J., Meinzer, F.C., Rombold,J.S., Sprugel, D.G., Hinckley, T.M., 1997. Crown conductance and tree and standtranspiration in a second-growth Abies amabilis forest. Can. J. For. Res. 27, 797–808.

Meinzer, F.C., Andrade, J.L., Goldstein, G., Holbrook, N.M., Cavelier, J., Wright, S.J.,1999. Partitioning of soil water among canopy trees in a seasonally dry tropicalforest. Oecologia 121 (3), 293–301.

Meinzer, F.C., Goldstein, G., Andrade, J.L., 2001. Regulation of water flux throughtropical forest canopy trees: Do universal rules apply? Tree Physiol. 21, 19–26.

Meyboom, P., 1964. Three observations on streamflow depletion by phreatophytes.J. Hydrol. 2, 248–261.

Naiman, R.J., Décamps, H., McClain, M.E., 2005. Riparia: ecology, conservation, andmanagement of streamside communities. Elsevier, Amsterdam.

O’Brien, J.J., Oberbauer, S.F., Clark, D.B., 2004. Whole tree xylem sap flow responsesto multiple environmental variables in a wet tropical forest. Plant Cell Environ.27, 551–567.

Organization for Tropical Studies, 2010. La Selva Daily Rainfall, 1963–2009. In:Clark, D.A. (Ed.), <http://www.ots.ac.cr/meteoro/default.php?pestacion=2>.

Piégay, H., Walling, D.E., Landon, N., He, Q., Liébault, F., Petiot, R., 2004.Contemporary changes in sediment yield in an alpine mountain basin due toafforestation (the upper Drôme in France). Catena 55, 183–212.

Priestly, C.H.B., Taylor, R.J., 1972. On the assessment of surface heat flux andevaporation using large scale parameters. Mon. Weath Rev. 100, 81–92.

Radulovich, R., Solorzano, E., Sollins, P., 1989. Soil macropore size distribution fromwater breakthrough curves. Soil Sci. Soc. Am. J. 53, 556–559.

Reigner, I.C., 1966. A method of estimating streamflow loss by evapotranspirationfrom the riparian zone. Forest Science 12 (2), 130–139.

Rowe, L.K., Pearce, A.J., 1994. Hydrology and related changes after harvesting nativeforest catchments and establishing Pinus radiata plantations. Part 2. The nativeforest water balance and changes in streamflow after harvesting. Hydrol.Process. 8, 291–297.

Shuttleworth, W.J., Gash, J.H., Lloyd, C.R., Moore, C.J., Roberts, J., Marques Filho, A.,Fisch, G., Silva Filho, V., Goes Ribeiro, M., Molion, L., Abreu Sa, L., Nobre, C.A.,Cambral, O.M.R., Patel, S.R., Moraes, J.C., 1984. Eddy correlation measurementsof energy partition for Amazonian forest. Quarterly Journal of the RoyalMeteorological Society 110 (466), 1143–1162.

Sollins, P., Radulovich, R., 1988. Effects of soil physical structure on solute transportin a weathered tropical soil. Soil Sci. Soc. Am. J. 52, 1168–1173.

Sollins, P., Robertson, G.P., Uehara, G., 1988. Nutrient mobility in variable- andpermanent-charge soils. Biogeochemistry 6, 181–199.3.

Sollins, P., Sancho, M., Mata, R., Sanford, R.L., 1994. Soils and soil process research.In: McDade, L.A., Bawa, K.S., Hespenheide, H.A., Hartshorn, G.S. (Eds.), La Selva:Ecology and Natural History of a Neotropical Rain Forest. University of ChicagoPress, Chicago, pp. 34–53.

Stednick, J.D., 1996. Monitoring the effects of timber harvest on annual water yield.J. Hydrol. 176, 79–95.

Strickland, T.C., Kerele, E.A., Sollins, P., Schimel, D.S., 1988. Aggregation andaggregate stability in forest and range soils. Soil Sci. Soc. Am. J. 52, 829–833.

Szilágyi, J., Gribovszki, Z., Kalicz, P., Kucsara, M., 2008. On diurnal riparian zonegroundwater-level and streamflow fluctuations. J. Hydrol. 349, 1–5.

Troxell, H.C., 1936. The diurnal fluctuation in the ground-water and flow of theSanta Ana river and its meaning. Trans. Am. Geophys. Union 17 (4), 496–504.

White, W.N., 1932. Method of Estimating Groundwater Supplies based on Dischargeby Plants and Evaporation from Soil – Results of Investigation in EscalanteValley, Utah. Water Supply Paper 659-A, US Geological Survey Tech. Rep.

Wondzell, S.M., Gooseff, M.N., McGlynn, B.L., 2007. Flow velocity and the hydrologicbehavior of streams during baseflow. Geophys. Res. Lett. 34, L24404.