1/59 HL Distributed Hydrologic Modeling Mike Smith Victor Koren, Seann Reed, Ziya Zhang, Fekadu...

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HL Distributed Hydrologic Modeling

Mike Smith

Victor Koren, Seann Reed, Ziya Zhang, Fekadu Moreda, Fan Lei, Zhengtao Cui, Dongjun Seo, Shuzheng Cong,

John Schaake

DSSTFeb 24, 2006

2/59

Overview

• Today: – Goals, expectations, applicability– R&D

• Next Call– Development Strategy– Implementation– RFC experiences

3/59

Goals and Expectations

• Potential– History

• Lumped modeling took years and is a good example• We’re first to do operational forecasting

– Expectations• ‘As good or better than lumped’• Limited experience with calibration• May not yet show (statistical) improvement in all cases due to errors

and insufficient spatial variability of precipitation and basin features… but is proper future direction!

– New capabilities• Gridded water balance values and variables e.g., soil moisture• Flash Flood e.g., statistical distributed• Land Use Land cover changes

4/59

Expectations: Effect of Data Errors and Modeling Scale

Relative Sub-basin Scale A/Ak

1 10 100

10

15

20

25

30

0

5Re

lativ

e e

rro

r, E

k , %

(lumped) (distributed)

Noise 0% 25% 50% 75%

Data errors (noise) may mask the benefits of fine scale modeling. In some cases, they may make the results worse than lumped simulations.

Sim

ulat

ion

erro

r c

ompa

red

to fu

lly d

istr

ibut

ed

‘Truth’ is simulation from 100 sub-basin model

clean data

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Rationale

• Scientific motivation– Finer scales > better results– Data availability

• Field requests

• NOAA Water Resources Program

• NIDIS


6/59

Applicability

• Distributed models applicable everywhere

• Issues– Data availability and quality needed to realize

benefits– Parameterization– Calibration


7/59

Measures of Improvement

• Hydrographs at points (DMIP 1)– Guidance from RFC

• Spatial – Runoff– Soil moisture– Point to grid


8/59

HL R&D Strategy

• Conduct in-house work• Collaborate with partners

– U. Arizona, Penn St. University– DMIP 1, 2– ETL

• Work closely with RFC prototypes– ABRFC, WGRFC: DMS 1.0– MARFC, CBRFC: in-house

• Publish results• NAS Review of AHPS Science

Goal: produce models, tools, guidelines to improve field office operations

9/59

R&D Topics

1. Parameterization/calibration (with U. Arizona and Penn State U.)

2. Soil Moisture 3. Flash Flood Modeling: statistical distributed model,

other4. Snow (Snow-17 and energy budget models in HL-

RDHM)5. DMIP 26. Data assimilation (DJ Seo)7. Links to FLDWAV8. Impacts of spatial variability of precipitation9. Data issues

10/59

1. Distributed ModelParameterization-Calibration

• Explore STATSGO data as its has national coverage (available in CAP)

• Explore SSURGO fine scale soils data for initial SAC model parameters (deliverable: parameter data sets in CAP)

• Investigate auto-calibration techniques– HL: Simplified Line Search (SLS) with Koren’s initial

SAC estimates.– U. Arizona: Multi-objective techniques with HL-RDHM

and Koren’s initial SAC parameters.

• Continue expert-manual calibration

Strategy for Sacramento Model

11/59

Measured data at outlet(discharge, top width,

cross-section)

Spatially variable basin properties(slope, area, drainage density)

Observed outlet

hydrographs

Channel routing

parametersat outlets

Variable channelrouting

parameters

Geomorpho-logical

relations

Fitting curveparameteradjustment

Variable basin properties(STATSGO 1x1 km grids:

Soil texture, Hydrologic SoilGroup, Land cover/use)

Outletcalibratedparameters

Variablemodel

parameters

Area averageparameters

Transform.relationships

Rescaled variable

parameters

Channel routing parameters

Water balance parameters

Scale adjusted

parameters

Observed outlet

hydrograph

Lumped,Semi-lumped

calibration

Observed outlet

hydrograph

HL-RDHM Parameterization - Calibration Steps

LumpedHourlyCalb.

Spatial data

1. Distributed Model Parameterization/Calibration

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HL: Mod STATSGO SAC parms.

HL: SSURGOSAC parms

HL Lumped auto calibration using SCE and SLS

Gridded ParmValues

U. Arizona:ParameterUncertainty

HL: Climate SAC Parm adjustment(large area runs)

U. Az: Multi-objective Optimization of 1) HL-RDHM adj. factors, 2) gridparameters

Parameterization and Calibration R&D StrategyCombine Improved a priori parameter estimates with Auto-calibration techniquesa priori parameter estimates auto-calibration techniques

HL Dist autocalibration of HL-RDHM adj.factors: SCE, SLS

HL: STATSGO SAC parms. (in CAP at RFCs)

SCE: Shuffled Complex Evolution SLS: Simplified Line Search

HL Dist autocalibration of HL-RDHM grid parms: SLS

1

2

3

Red

uce

unce

rtai

nty

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Polygon – a soil map unit; it represents an area dominated by one to three kinds of soil

Components – are different kinds of soil. Components are each separate soils with individual properties and are grouped together for simplicity's sake when characterizing the map unit.

Horizons – are layers of soil that are approximately parallel to the surface. Up to six horizons may be recorded for each soil component.

Polygon

Components

Horizons

Description of SSURGO data*

Soils Data for SAC ParametersSoils Data for SAC Parameters* The Penn State Cooperative Extension, Geospatial Technology Program (GTP) Land Analysis Lab


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Demonstration of scale difference between polygons in STATSGO and SSURGO

SSURGO

STATSGO

Soils Data for SAC ParametersSoils Data for SAC Parameters1. Distributed Model Parameterization/Calibration

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2 km Grid Connectivity for

Distributed Channel Routing

Oklahoma

Arkansas

Basin Locations and Land CoverBasin Locations and Land Cover

1

2

3

5

4

6

8

7

10 9

111

2

3

5

4

6

8

7

10 9

11

12

Results of SSURGO and STATSGO Parameters for Distributed Modeling


16/59

Rm: Modified correlation

coefficient. It is calculated by reducing normal correlation coefficient by the ratio of the standard deviations of the observed and simulated hydrographs.

Comparison of Rm for whole time series of 11 basins

0.4

0.6

0.8

1

0.4 0.6 0.8 1

Rm (SSURGO, Overall)

Rm

(S

TA

TS

GO

, Ove

rall)

• Overall Rm--SSURGO-based > Rm--STATSGO-based for most basins

• More physically-based representation of the soil layers!

• More detailed spatial variability


1. Distributed Model ParameterizationCalibration

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Hydrograph Comparison__ Observed flow

__ SSURGO-based

__ STATSGO-based

Cave Cave SpringsSprings

STATSGO


SSURGO


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20

30

40

50

60

70

0 2000 4000 6000 8000 10000

Number of function evaluations

Mu

lti-

scale

OF

Min (SCE)

0

1

2

0 2000 4000 6000 8000 10000

Number of function evaluations

Dis

tan

ce

SCE soil

SLS soil

37.5

38

38.5

39

39.5

0 1 2 3

Relative distance

MS

OF

SCE SLS

Comparison of SCE and SLS calibration processes

1) SLS needs less function evaluations, but it leads to similar result;

2) SLS stops much faster and closer to the start point (a priori parameters);

3) On some basins, SCE misses the nearest ‘best’ solution.

4) SLS in AB-OPT

Dis

tanc

e fr

om s

tart

ing

para

met

ers


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HL-RDHM Kinematic Wave Solution

• Uses implicit finite difference solution technique• Need Q vs. A for each cell to implement

distributed routing– Derive relationship at outlet using observed data– Extrapolate upstream using empirical/theoretical

relationships

• Two methods are available in HL-RDHM– ‘Rating curve’ method : parameters a and b in Q =

aAb estimated based on empirical relationship– ‘Channel shape’ method: parameters estimated from

estimates of slope, roughness, approximate channel shape, and Chezy-Manning equation


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2. Solve for and using streamflow measurement data:

Channel Width () and Shape () Parameter Estimation

B H

B A x 1 1[( ) ]

1. Assume relationship between top width and depth:

0

1

2

3

4

5

6

-60 -40 -20 0 20 40 60

Distance (m)

Dep

th (m

)

B

H0

100

200

300

400

500

600

700

800

900

2 52 102 152

Top Width, B (m)

Cro

ss S

ect

ion

Flo

w A

rea,

Ax

(m2)

Illinois River at Watts, OK

Example cross section = 36.6, = 0.6)

HB 1


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Estimate Upstream Parameters Using Relationships from Geomorphology

1. Channel ModelParameterization

1. Distributed Model ParameterizationCalibration

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Probabilistic Channel Routing Parameters

• Basic concepts– Discharge – cross-section relationship obeys multiscale

lognormal bivariate Gaussian distribution– The scale dependence of hydraulic geometry is a result of the

asymmetry in channel cross-section (CS)

• Application– Define CS geometry as a function of scale from site

measurements– Define channel planform geometry as a function of scale– Define floodplain CS geometry as a function of scale from DEM– Monte-Carlo simulations to fit to multiscale lognormal model

1. Parameterization1. Distributed Model Parameterization/Calibration

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DERIVED PROBABILISTIC HG: Accounting for the variability of channel and floodplain shapes.

Exp{E[lnCA|lnQ]}

Exp{E[lnV|lnQ]}

1 1 2 2exp{E[ln | ln ]} exp{ E [ln | ln ] E [ln | ln ]}, whereV Q p V Q p V Q 1 1 1 2 2 2 1 2

1 2

and(ln ) [ (ln ) (1 ) (ln )] (1 ) (ln ) [ (ln ) (1 ) (ln )]

,

p w Q w Q w Q p w Q w Q w Q

marginal PDFs of marginal PDFs of dischargedischarge


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Probabilistic Channel Routing Parameters: BLUO2 Hydrographs

With flood plain

Without flood plain

Observed


25/59

2. Distributed Modeling and Soil Moisture

• Use for calibration, verification of models• New products and services

– NCRFC: WFO request– OHRFC: initialize MM5– NIDIS– NOAA Water Resources

2. Soil Moisture

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UZTWC UZFWC

LZ

TW

C

LZ

FS

C

LZ

FP

C

UZTWC UZFWC

LZ

TW

C

LZ

FS

C

LZ

FP

C

SMC1

SMC3

SMC4

SMC5

SMC2

Sacramento Model Storages

Sacramento Model Storages

Physically-basedSoil Layers andSoil Moisture

Modified Sacramento Soil Moisture Accounting Model

In each grid and in each time step, transform conceptual soil water content to physically-based

water content

Modified Sacramento Soil Moisture Accounting Model

Gridded precipitation, temperature

CONUS scale 4km gridded soil moisture products using SAC and Snow-17

(developed for Frozen Ground) 2. Soil Moisture

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Soil temperature

Soil moisture

Computed and observed soilMoisture and temperature: Valdai, Russia, 1972-1978

Validation of Modified Sacramento Model2. Soil Moisture

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Validation of Modified Sacramento Model

Comparison of observed, non-frozen ground, and frozen ground simulations: Root River, MN

observed

Frozen ground

Non frozen ground

2. Soil Moisture

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Modified SAC

• Publications– Koren, 2005. “Physically-Based Parameterization of Frozen Ground Effects:

Sensitivity to Soil Properties” VIIth IAHS Scientific Assembly, Session 7.2, Brazil, April.

– Koren, 2003. Parameterization of Soil Moisture-Heat Transfer Processes for Conceptual Hydrological Models”, paper EAE03-A-06486 HS18-1TU1P-0390, AGU-EGU, Nice, France, April.

– Mitchell, K., Koren, others, 2002. “Reducing near-surface cool/moist biases over snowpack and early spring wet soils in NCEP ETA model forecasts via land surface model upgrades”, Paper J1.1, 16th AMS Hydrology Conference, Orlando, Florida, January

– Koren et al., 1999. “A parameterization of snowpack and frozen ground intended for NCEP weather and climate models”, J. Geophysical Research, 104, D16, 19,569-19,585.

– Koren, et al., 1999. “Validation of a snow-frozen ground parameterization of the ETA model”, 14th Conference on Hydrology, 10-15 January 1999, Dallas, TX, by the AMS, Boston MA, pp. 410-413.

– http://www.nws.noaa.gov/oh/hrl/frzgrd/index.html

2. Soil Moisture

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NOAA Water Resources Program:Prototype Products

• Initial efforts focus on CONUS soil moisture

Soil moisture (m3/m3)

HL-RDHM soil moisture for April 5m 2002 12z

2. Soil Moisture

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HL-RDHM MOSAIC

Source: Moreda et al., 2005.

0

0.5

1

0 2000 4000 6000

Area km2

Cor

r.

0

0.5

1

0 2000 4000 6000

Area km2

Corr

.

Lower 30cmUpper 10cm

Comparison of Soil Moisture Estimates

HL-RDHM:HigherCorrelation

2. Soil Moisture

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Forecastedfrequencies

A Statistical-Distributed Model for Flash Flood Forecasting at Ungauged Locations

HistoricalReal-time

simulated historical

peaks (Qsp)

Simulated peaks distribution (Qsp) (unique for each

cell)

Archived

QPE

Initial hydro model states

StatisticalPost-processor

Distributed hydrologic model (HL-

RDHM)

Distributed hydrologic model (HL-

RDHM)

Real-time

QPE/QPF

Max forecasted

peaks

Why a frequency- based approach?

Frequency grids provide a well-understood historical context for characterizing flood severity; values relate to engineering design criteria for culverts, detention ponds, etc.

Computation of frequencies using model-based statistical distributions can inherently correct for model biases

Next step to define requirements for prototype

3. FlashFlood

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14 UTC 15 UTC

16 UTC 17 UTC

Statistical Distributed Flash Flood Modeling-Example Forecasted Frequency Grids Available at 4 Times on

1/4/1998

In these examples, frequencies are derived from routed flows, demonstrating the capability to forecast floods in locations downstream of where the rainfall occurred.

3. FlashFlood

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Eldon (795 km2)

Dutch (105 km2)

Implicitstatistical adjustment

0

200

400

600

800

1000

1/4/98 0:00 1/4/98 12:00 1/5/98 0:00 1/5/98 12:00 1/6/98 0:00 1/6/98 12:00 1/7/98 0:00 1/7/98 12:00

Date

Flo

w (

CM

S)

0

10

20

30

40

50

Simulated flow

Observed flow

QPF - 1/4/1998 3:00:00 PM UTC

Adjusted fcst peak

Fcst Time

0

100

200

300

400

500

600

1/4/98 0:00 1/4/98 12:00 1/5/98 0:00 1/5/98 12:00 1/6/98 0:00 1/6/98 12:00

Date

Flo

w (

CM

S)

0

20

40

60

80

100

Simulated flow

Observed flow

QPF - 1/4/1998 3:00:00 PM UTC

Adjusted fcst peak

Forecast time

~11 hr lead time

~1 hr lead time

Statistical Distributed Flash Flood Modeling -Example Forecast Grid and Corresponding Forecast Hydrographs for 1/4/1998 15z

3. Flash Flood

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spatial scale

mod

elin

g ca

pabi

lity

Where does Site Specific fit?

RFC

WFO

In this domain:-Statistical Distributed-Distributed-Site Specific with snowVar, routing

Site Specific, FFG, other

Perception of Modeling Trends

3. FlashFlood

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Transition from Snow-17 to Energy Budget Model for RFC Operations: HL Activities

New data for Snow-17

(wind speed, etc)

Time

% M

odel

Use

0

100

Today Today + (?) years

Snow-17 at RFCs

Energy Budget ModelCalb-O

FS biases

Distributed

Snow-17

Snow-17 M

ODs based

On Snodas

Sensitivity of Energy

Budget model to data

errors

Use of Snodas

Output in Snow-17

HLActivities

Use of Snodas

Output in runoff

models

4. Distributed modeling and snow

37/59

Distributed Snow-17

• Strategy: use distributed Snow-17 as a step in the migration to energy budget modeling: what can we learn?

• Snow-17now in HL-RDHM• Tested in MARFC area and over CONUS

(delivered historical data)• Further testing in DMIP 2• Gridded Snow-17 parameters for CONUS under

review (could be delivered in CAP)• Related work: data needs for energy budget

snow models


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Current approach SNOW-17 model within HL-RDHM

• SNOW-17 model is run at each pixel• Gridded precipitation from multi-sensor products are

provided at each pixel• Gridded temperature inputs are provided by using

DEM and regional temperature lapse rate • The area depletion curve is removed because of

distributed approach• Other parameters are studied either to replace them

with physical properties or relate them to these properties, e.g., SCF.


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SAC-SMA or CONT-API

Channel routing

SNOW -17

P, T & ET

surface runoff

Rain + melt

Flows and State variables

base flowhillslope routing

Gridded (or small basin) structure

Independent snow and rainfall-runoff models for each grid cell

Hillslope routing of runoff Channel routing (kinematic &

Muskingum-Cunge)

HL-RDHM

Features:


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Parameterization of Distributed Snow-17

Min Melt Factor

Max Melt Factor

Derived from:1. Aspect2. Forest Type3. Forest Cover, %4. Anderson’s rec’s.

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Snow Cover Simulation

Energy-budget model assimilated Distributed Snow-17

December 12, 2003 12Z

…Case Study

Snow cover obtained from energy-budget and Snow-17 model qualitatively agree well


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Flow simulation during snow periods(using lumped API model parms in each grid)

0

200

400

600

1101200200 1201200220 0101200316 0201200312 0304200308 0404200304

Flo

w m

3/s

0

20

40

60

80

100

120

140

Sn

ow

Wat

er E

qu

ival

ent

(mm

)

Hyd_obs Hyd_simul swe

0

50

100

1101200200 1201200220 0101200316 0201200312 0304200308 0404200304

Sn

ow

co

ver

%


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5. DMIP 2

–HL distributed model is worthy of implementation: we need to improve it for RFC use in all geographic regions

–Partial funding from Water Resources

–Much outside interest–HMT collaboration

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DMIP 2 Science Questions• Confirm basic DMIP 1 conclusions with a longer validation period and more test

basins• Improve our understanding of distributed model accuracy for small, interior

point simulations: flash flood scenarios• Evaluate new forcing data sets (e.g., HMT)• Evaluate the performance of distributed models in prediction mode • Use available soil moisture data to evaluate the physics of distributed models • Improve our understanding of the way routing schemes contribute to the

success of distributed models • Continue to gain insights into the interplay among spatial variability in rainfall,

physiographic features, and basin response, specifically in mountainous basins

• Improve our understanding of scale/data issues in mountainous area hydrology• Improve our ability to characterize simulation and forecast uncertainty in

different hydrologic regimes• Investigate data density/quality needs in mountainous areas (Georgakakos et

al., 1999; Tsintikidis, et al., 2002)

5. DMIP 2

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Distributed Model Intercomparison Project (DMIP)

Nevada

California

Texas

Oklahoma

Arkansas

MissouriKansas

Elk River

Illinois River

Blue River

AmericanRiver

CarsonRiver

Additional Tests in DMIP 1 Basins1. Routing2. Soil Moisture3. Lumped and Distributed4. Prediction Mode

Tests with Complex Hydrology1. Snow, Rain/snow events2. Soil Moisture3. Lumped and Distributed4. Data Requirements in mtn West

Phase 2 Scope

HMT

5. DMIP 2

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5. DMIP 2

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DMIP 2 & HMT-West

Research to Operations1. Basic precip

and temp data (gage only gridded)

2. Basic data enhanced by HMT observations:

-Network Density 1

-Network Density 2

-Network Density 3

Analyses,conclusions,recommendations fordata and tools for RFCs

Distributed model simulations– USGS

– HL-RDHM

– USBR

– Others

“What new data types are becoming available? What densities of observations are needed? Which models/approaches work best In mountainous areas?”

5. DMIP 2

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DMIP 2: Potential Participants • Witold Krajewski• Praveen Kumar• Mario DiLuzio, ARS, TAES• Sandra Garcia (Spain)• Eldho T. Iype (India)• John McHenry, BAMS• Konstantine Georgakakos• Ken Mitchell (NCEP)• Hilaire F. De Smedt (Belgium)• HL• Vincent Fortin, Canada• Robert Wallace, USACE,

Vicksburg• Murugesu Sivapalan, U. Illinois• Hoshin Gupta, U. Arizona

• Thian Gan, (Can.) • Newsha Ajami (Soroosh)• Vazken Andreassian (Fra)• George Leavesley (USGS)• Kuniyoshi Takeuchi (Japan)• Vieux and Associates• John England (USBR)• Andrew Wood, Dennis

Lettenmaier, U. Washington• Martyn Clarke• South Florida Water Mngt.

District• David Tarboton, Utah St. U.• David Hartley, NW Hydraulic

Consultants

5. DMIP 2

Names in red have officially registered

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Basic DMIP 2 Schedule

• Feb. 1, 2006: all data for Ok. basins available

• July 1, 2006: all basic data for western basins available

• Feb 1, 2007: Ok. simulations due from participants

• July 1, 2007: basic simulations for western basins due from participants

5. DMIP 2

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6. Data Assimilation for Distributed Modeling

• Needed since manual OFS ‘run-time mods’ will be nearly impossible

• Strategy based on Variational Assimilation developed and tested for lumped SAC model

• Initial work in progress

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WTTO2 in ABRFC WTTO2 channel network

Initial simulation

Assimilation period:streamflow, PE, precip

6. Data Assimilation

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Comparison of Unadjusted and 4DVAR-Adjusted Model States (WTTO2)

6. Data Assimilation

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Channel Routing and Flood Mapping Of Tar River below Rocky Mount

Rainfall Data

Tarboro

DistributedModel of TarRiver Basin

rain

de

pth

Tarboro

Estuary Model

RockyMount

7. Distributed Modeling and Links toFloodWave

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floodwave

Inflow hydrographFrom HL-RDHM 1

2

In this example,HL-RDHM provides:1. Upstream inflow hydrograph.at 1. 2. 5 lateral inflow hydrographs to floodwave between cross sections 1 and 2.

HL-RDHM grid

Floodwavelateral inflow reaches

7. Distributed Modeling and FloodWave

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Hydrographs at Greenville, Tar River

0

50

100

150

200

250

300

350

400

Date

Initial Simulation of Tar River using HL-RMS (no Flood Wave). No calibration.After the warm up period, the simulation is good. Uses only Victor’s a prioriparameters.

SAC-SMA ‘warm-up’

observed simulated

7. Distributed Modeling and FloodWave: Example

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8. Impact of Spatial Variability

• Question: how much spatial variability in precipitation and basin features is needed to warrant use of a distributed model?

• Goal: provide guidance/tools to RFCs to help guide implementation of distributed models, i.e., which basins will show most ‘bang for the buck’?

• Initial tests completed after DMIP 1: trends seen but no clear ‘thresholds’

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flow

time

output

input

precipitation at time tprecipitation at time t +t

precipitation at time t + 2t

8. Impact of Precipitation Spatial Variability

‘filter’

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’06 Funding HOSIP Stage

Topic AHPS WR 1 2 3 4DMIP 2 0 110

Parameterization:

SSURGO/STATSGO100 0

Regionalized SAC-Snow Parameters

30 0

Auto Calibration: Arizona 75 0

Auto Calibration: HL 0 0

Snow-17 and HL-RDHM 30 0

Large Area Simulation for WR products

0 31

Statistical Distributed 30 0

VAR for Distributed Modeling

0 0

Spatial Variability 0 0

DHM 2.0 AWIPS (HSEB) 200 ?

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Conclusions

• Distributed models are proper direction– Account for spatial variability:

• Parameterization• Calibration• Better results at outlets of some basins• Amenable to new data sources

– Scientifically supported flash flood modeling– New products and services

1/59 HL Distributed Hydrologic Modeling Mike Smith Victor Koren, Seann Reed, Ziya Zhang, Fekadu...

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