Distributed Hydrologic Modeling Presented by: Paul McKee and Mike Shultz West Gulf River Forecast Center DHM/HL-RDMH Workshop 2007 WGRFC Forecast Point

Distributed Hydrologic Modeling

Presented by:Presented by:Paul McKee and Mike ShultzPaul McKee and Mike Shultz

West Gulf River Forecast CenterWest Gulf River Forecast Center

DHM/HL-RDMH Workshop 2007

WGRFC Forecast Point Application and Results

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http://www.srh.noaa.gov/wgrfc

Overview

What’s the interest for WGRFC?What’s the interest for WGRFC? Testing Objectives and StrategyTesting Objectives and Strategy Model CalibratonModel Calibraton Operational ImplementationOperational Implementation Forecast Applications/ResultsForecast Applications/Results

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Overview and status

DevelopmentDevelopment and and calibrationcalibration of basin of basin modelsmodels 26 total basins26 total basins 25 available for operational forecasting25 available for operational forecasting 1 nested basin1 nested basin

Visual InspectionVisual Inspection and and qualitative analysisqualitative analysis for model comparisonfor model comparison

ImplementationImplementation into real-time river into real-time river forecast operationsforecast operations

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Distributed Modeling What’s the interest?

Research indicates the greatest improvement occurs Research indicates the greatest improvement occurs for basins with:for basins with: Non-uniform rainfall distributionsNon-uniform rainfall distributions Irregular shaped basins (Long and narrow)Irregular shaped basins (Long and narrow) Non-uniform soil type and land useNon-uniform soil type and land use Relatively large impervious areas which cause a rapid Relatively large impervious areas which cause a rapid

surface runoff responsesurface runoff response Increased accuracy of event timingIncreased accuracy of event timing Stream flow prediction at interior pointsStream flow prediction at interior points Distributed parameter inputs utilizes more data Distributed parameter inputs utilizes more data

complexity as availablecomplexity as available

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Basin Response TimesWGRFC Study Area

Hydrologic Response TimesHydrologic Response Times 6 hours or less 6 hours or less 20%20% 12 hours or less 12 hours or less 47%47% 18 hours or less 18 hours or less 65%65% 24 hours or less 24 hours or less 74%74%

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Application Objectives Test basin setup proceduresTest basin setup procedures Examine calibration strategiesExamine calibration strategies Evaluate simulations compared to Evaluate simulations compared to

lumped modellumped model Provide feedback to OH for prototypeProvide feedback to OH for prototype Assist with developing requirements for Assist with developing requirements for

an operational DHM (OSIP process)an operational DHM (OSIP process)

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Application StrategyBasin Development/ Setup

HeadwaterHeadwater Limitations with lumped modelLimitations with lumped model VAR basins to utilize SAC parameters VAR basins to utilize SAC parameters

estimated using ab_optestimated using ab_opt DiversityDiversity

size, shape, terrain, landuse/cover, soilssize, shape, terrain, landuse/cover, soils Varied time-to-peak responseVaried time-to-peak response (DA: 75-400mi2; peak times: 6-60hr)(DA: 75-400mi2; peak times: 6-60hr)

Interior stream gages Interior stream gages

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Basin CalibrationStrategy

Approach similar to lumped modelApproach similar to lumped model Manual “expert” process; parameter Manual “expert” process; parameter

estimation/ optimization tools unavailableestimation/ optimization tools unavailable Use ab_opt estimated SAC parameters Use ab_opt estimated SAC parameters

for scalar adjustmentsfor scalar adjustments Simulation comparisons:Simulation comparisons:

Apriori, ab_opt, “expert” calibration, lumped Apriori, ab_opt, “expert” calibration, lumped

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DHM Basin CalibrationConstraints/Limitations

Global scalar adjustment of parameter Global scalar adjustment of parameter grids; conserves relative diff. between grids; conserves relative diff. between grid cells grid cells

Lumped values only for PCTIM, Lumped values only for PCTIM, ADIMP, RIVA (grids unavailable)ADIMP, RIVA (grids unavailable)

Unknown effect of Unknown effect of aprioriapriori grid outliers grid outliers on calibration results (ie. sensitivity)on calibration results (ie. sensitivity)

Difficult to keep simple… build Difficult to keep simple… build complexity as neededcomplexity as needed

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Calibration Sensitivities?? Possible outliers in apriori param grids? Large relative differences of grid

values?

LZFPM apriori grid

QPE error, both location and amt.

Grid resolution vs. available data?

Outlier?

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DHM Calibrationearly tools and utility limitations

XDMS – 1XDMS – 1stst generation, display/ no generation, display/ no editing of parameter grids editing of parameter grids

Stat_q – text output, no graphicsStat_q – text output, no graphics Parameter Estimation/ Optimization Parameter Estimation/ Optimization

toolstools Enhances expert calib.Enhances expert calib. Automated parameter sensitivity anal.Automated parameter sensitivity anal. Graphics of statistical analyisisGraphics of statistical analyisis

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DHMOBS

LMP

GETT2: May 5, 2006S. Fork San Gabriel R.Georgetown

Integrating DHM into operationsForecast Mode

- Runs once per hour; no operational modifications applied

- View DMS and lumped simulations in operational forecast software… ensemble?

- No verification; qualitative analysis

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Basin StudiesGeologic Areas

Hill Country (S.C. TX)Hill Country (S.C. TX) Urban DevelopmentUrban Development Gulf Coastal PlainsGulf Coastal Plains Blackland Prairie (N. TX)Blackland Prairie (N. TX) Piney Woods (E. TX)Piney Woods (E. TX)

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Test Basins Locations

Replace with updated map

Blackland Prairie

Hill Country

Coastal Plains

Piney Woods

Urban

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DHM Test Basins Varied basin size, terrain, land-use/cover, soils

DA:

75 – 400 mi2

Peak times:

6 – 60 hr

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Basin CharacteristicsBasinBasin

DrainageDrainageArea (mi2)Area (mi2)

Avg HillslopeAvg Hillslope(m/m)(m/m)

Time to Peak Time to Peak (hrs)(hrs)

SOLT2SOLT2 336336 .0013.0013 8282GNVT2GNVT2 7878 .0050.0050 1616KNLT2KNLT2 349349 .0274.0274 77ATIT2ATIT2 326326 .0168.0168 99

HBMT2HBMT2 9595 .0010.0010 33MTPT2MTPT2 168168 .0009.0009 1717

Blackland

Hill Country

Gulf Coast

Piney Woods

Urban

Hill C./Urban

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Study Basin: KNLT2 Sandy Creek - Kingsland

semi-regular shape, fast response semi-regular shape, fast response steep slope (0.0274)steep slope (0.0274) drainage area: 346 mi2drainage area: 346 mi2 avg. time to peak: 7 hrsavg. time to peak: 7 hrs 2 interior stream gages2 interior stream gages

OXDT2 (147 mi2) OXDT2 (147 mi2) –Willow City–Willow City SNBT2 (155 mi2) SNBT2 (155 mi2) –Click–Click

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KNLT2KNLT2 time periodtime period RMSRMS(CMS)(CMS)

RR

DHMDHMAprioriApriori

10/1/97-12/31/0310/1/97-12/31/03 15.0815.08 0.770.77

DHMDHMCalibratedCalibrated

1/1/96 – 12/31/041/1/96 – 12/31/04 9.699.69 0.800.80

LumpedLumped(6 hr)(6 hr)

1/1/96 – 12/31/041/1/96 – 12/31/04 9.819.81 0.860.86

KNLT2 Calibration

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KNLT2: Apr 2004 TSinvestigating nested basins, interior points

DHMOBS

OXDT2

SNBT2

KNLT2

US

DS

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KNLT2: Apr 2004 WY

OBS

DHM

LMP

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KNLT2: May 2006 RT

OBS

DHM

LMP

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KNLT2: Mar 2007 RT

OBS

DHM

LMP

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ATIT2: Mar 29, 2006 RT Onion Creek – Austin

OBS

DHM

LMP

DA: 326 mi2 DA: 326 mi2 T2P: 9 hrT2P: 9 hr

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ATIT2: May 5, 2006 RT

OBS

DHM

LMP

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ATIT2: June 2006 RT

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ATIT2: Mar 2007 RT

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ATIT2: Apr 2007 RT

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ATIT2: May 2007 RT

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MTPT2: June 21, 2006 RTDA: 168 mi2 DA: 168 mi2 T2P: 17 hrT2P: 17 hr

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MTPT2: June 21, 2006 RT Tres Palacios R.- Midfield

DHMOBS

LMP


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HBMT2: June 2006 RT


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HBMT2: June 2006 RT

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GNVT2: Mar 2007 RT


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GNVT2: Apr 2007 RT

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GNVT2: May 2007 RT

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Study Basin: SOLT2 Pine Island Bayou – Sour Lake

Irregular shape, slow response Irregular shape, slow response Mild slope (0.0013)Mild slope (0.0013) Drainage area: 336 sq. mi.Drainage area: 336 sq. mi. Avg. time to peak: 48-60 hrsAvg. time to peak: 48-60 hrs

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SOLT2SOLT2 time periodtime period RMSRMS(CMS)(CMS)

RR

DHMDHMAprioriApriori

1/1/96-12/31/031/1/96-12/31/03 19.5519.55 0.860.86

DHMDHMCalibratedCalibrated

1/1/96-12/31/031/1/96-12/31/03 19.0719.07 0.860.86

LumpedLumped(6 hr)(6 hr)

10/1/00 – 9/30/0410/1/00 – 9/30/04 17.8217.82 0.910.91

SOLT2 Calibration

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SOLT2: Feb 2002 WY

DHM

OBS

LMP

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SOLT2: Jun 2004 TS

DHM

OBS

LMP

double peak

*notice 2 separate areas of heavy rainfall

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SOLT2: Nov 2003 TS

DHM

OBSLMP

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SOLT2: Mar 2006 RT

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SOLT2: Oct 2006 RT

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Study ConclusionsQuestions/Concerns of DHM at WGRFC

Difficult to calibrate peak flowsDifficult to calibrate peak flows Model errors and uncertainties tend to Model errors and uncertainties tend to

increase at smaller scalesincrease at smaller scales Does SAC model error compound for Does SAC model error compound for

each grid cell (diffused with lumped)?each grid cell (diffused with lumped)? Gridded data for all parameters may be Gridded data for all parameters may be

too much complexity (ie. zones?)too much complexity (ie. zones?) QPE most sensitive parameter… spatial QPE most sensitive parameter… spatial

and magnitude errors explain false and magnitude errors explain false peaks and compound peak flow errors peaks and compound peak flow errors

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Expected Effect of Data Errors and Modeling ScaleHow much is “too much” resolution and complexity?

Relative Sub-basin Scale A/Ak

1 10 100

10

15

20

25

30

0

5Rel

ativ

e er

ror,

Ek,

%

(lumped) (distributed)

Noise 0% 25% 50% 75%

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

Sim

ulat

ion

erro

r co

mpa

red

to fu

lly d

istri

bute

d

‘Truth’ is simulation from 100 sub-basin model

clean data

Graphic courtesy of Mike Smith, OHD

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Current Study ConclusionsBenefits of DHM at WGRFC

Timing of rising limbs well-simulated Timing of rising limbs well-simulated (variety of DAs and spatially distributed (variety of DAs and spatially distributed QPE)QPE)

Outperforms lumped model for irregulary Outperforms lumped model for irregulary shaped basinsshaped basins

Full utilization of gridded QPEFull utilization of gridded QPE Understanding model biases and Understanding model biases and

limitations useful for operational limitations useful for operational forecastingforecasting

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Model Application Spectrumhypothetical use within WGRFC operations

Lumped DHM

Influencing factors

basin type

basin response

basin shape

rainfall distribution

flow volume

headwater

fast

irregular

non-uniform

small

mainstem

slow

regular

uniform

large

model ensemble tool?

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DHM Study Summary Timing of rising limbs well-simulatedTiming of rising limbs well-simulated Generally performs as well or better

than lumped model for headwater basins.

DHM compliments the lumped model for ensemble forecasting.

Mainstem river basins have not been tested…

Operational DHM within OFS Operational DHM within OFS available… not presently setupavailable… not presently setup

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What next? Implement latest version of research Implement latest version of research

prototype for calibration improvements:prototype for calibration improvements: finer gridfiner grid auto-calibration processauto-calibration process

Setup downstream basins to test process Setup downstream basins to test process for combined segments.for combined segments.

Verify streamflow at interior pointsVerify streamflow at interior points Transition basins from research model to Transition basins from research model to

operational DHM within OFSoperational DHM within OFS

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A closer look Mike Shultz

CalibrationCalibration Urban/Coastal basins (HBMT2, GBHT2)Urban/Coastal basins (HBMT2, GBHT2) Effects of Tropical Storm AllisionEffects of Tropical Storm Allision Effects of wastewater effluent dischargesEffects of wastewater effluent discharges

Overview/ Observation summaryOverview/ Observation summary

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Brays Bayou at Houston (HBMT2)

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FCCalibration – a closer look

Greens Bayou at Houston (GBHT2)

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Statistical Analyses

================================================================================================================================================================================================== MULTI-YEAR STATISTICSMULTI-YEAR STATISTICS Abs.Abs. % % Obs. Sim. Obs. Sim. Obs. Sim. % RMS Nash-S. Modi.% % Obs. Sim. Obs. Sim. Obs. Sim. % RMS Nash-S. Modi. Bias Bias Qmean Qmean std std Cv Cv RMS (CMS) R r RmBias Bias Qmean Qmean std std Cv Cv RMS (CMS) R r Rm -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 13.476 59.115 3.683 4.179 18.88 25.94 5.126 6.207 521.6 19.21 0.674 -0.0356 0.49113.476 59.115 3.683 4.179 18.88 25.94 5.126 6.207 521.6 19.21 0.674 -0.0356 0.491

Best line fit: Qobs = A+B*Qsim: A--> 1.63 (cms) B--> 0.491Best line fit: Qobs = A+B*Qsim: A--> 1.63 (cms) B--> 0.491

================================================================================================================================================================================================== YEARLY STATISTICSYEARLY STATISTICS Absolute AbsoluteAbsolute Absolute % % Error Observed Simulated % RMS Nash-S.% % Error Observed Simulated % RMS Nash-S. Year Bias Bias (CMS) Qmean Qmean RMS (CMS) R rYear Bias Bias (CMS) Qmean Qmean RMS (CMS) R r ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1997 -3.316 46.22 1.876 4.060 3.926 132.1 5.362 0.773 0.5981997 -3.316 46.22 1.876 4.060 3.926 132.1 5.362 0.773 0.598 1998 -0.9055 46.30 1.854 4.004 3.967 211.1 8.453 0.840 0.6521998 -0.9055 46.30 1.854 4.004 3.967 211.1 8.453 0.840 0.652 1999 -1.798 50.78 1.041 2.051 2.014 136.5 2.799 0.802 0.6441999 -1.798 50.78 1.041 2.051 2.014 136.5 2.799 0.802 0.644 2000 14.16 58.75 1.460 2.486 2.838 315.8 7.850 0.827 0.1312000 14.16 58.75 1.460 2.486 2.838 315.8 7.850 0.827 0.131 2001 26.88 79.36 4.762 6.001 7.614 708.9 42.54 0.608 -0.3102001 26.88 79.36 4.762 6.001 7.614 708.9 42.54 0.608 -0.310 2002 20.38 56.00 2.303 4.113 4.951 360.2 14.82 0.787 0.3052002 20.38 56.00 2.303 4.113 4.951 360.2 14.82 0.787 0.305 2003 12.16 50.82 1.711 3.367 3.777 317.8 10.70 0.813 0.4192003 12.16 50.82 1.711 3.367 3.777 317.8 10.70 0.813 0.419

================================================================================================================================================================================================== MONTHLY STATISTICSMONTHLY STATISTICS Absolute AbsoluteAbsolute Absolute % % Error Observed Simulated % RMS Nash-S.% % Error Observed Simulated % RMS Nash-S. Month Bias Bias (CMS) Qmean Qmean RMS (CMS) R rMonth Bias Bias (CMS) Qmean Qmean RMS (CMS) R r ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 1 17.25 49.86 1.164 2.334 2.736 133.9 3.124 0.911 0.8021 17.25 49.86 1.164 2.334 2.736 133.9 3.124 0.911 0.802 2 7.216 46.73 1.075 2.301 2.467 120.2 2.767 0.805 0.6432 7.216 46.73 1.075 2.301 2.467 120.2 2.767 0.805 0.643 3 13.80 46.69 1.286 2.754 3.134 150.9 4.156 0.848 0.5083 13.80 46.69 1.286 2.754 3.134 150.9 4.156 0.848 0.508 4 2.671 37.74 0.917 2.430 2.495 171.5 4.167 0.954 0.7684 2.671 37.74 0.917 2.430 2.495 171.5 4.167 0.954 0.768 5 15.24 53.12 1.603 3.018 3.478 354.8 10.71 0.817 0.05045 15.24 53.12 1.603 3.018 3.478 354.8 10.71 0.817 0.0504 6 6.962 90.02 7.197 7.995 8.551 768.8 61.47 0.592 -0.3656 6.962 90.02 7.197 7.995 8.551 768.8 61.47 0.592 -0.365 7 26.90 59.05 1.312 2.222 2.820 131.9 2.932 0.902 0.7007 26.90 59.05 1.312 2.222 2.820 131.9 2.932 0.902 0.700 8 18.72 58.90 1.369 2.324 2.759 189.4 4.401 0.837 0.3788 18.72 58.90 1.369 2.324 2.759 189.4 4.401 0.837 0.378 9 12.22 61.42 3.057 4.978 5.587 188.2 9.369 0.831 0.6799 12.22 61.42 3.057 4.978 5.587 188.2 9.369 0.831 0.679 10 7.630 51.78 2.901 5.603 6.030 334.8 18.76 0.775 0.30510 7.630 51.78 2.901 5.603 6.030 334.8 18.76 0.775 0.305 11 21.31 53.25 2.620 4.920 5.969 308.5 15.18 0.842 0.42711 21.31 53.25 2.620 4.920 5.969 308.5 15.18 0.842 0.427 12 22.54 55.89 1.704 3.049 3.736 151.7 4.627 0.832 0.59712 22.54 55.89 1.704 3.049 3.736 151.7 4.627 0.832 0.597

Tropical Storm Allison (June, 2001)

Tropical Storm Allison (June, 2001)

R = 0.674Years: 1997 - 2003

Years: 1997 – 2000, 2002 - 2003

R = 0.773 – 0.840

R = 0.608

Months: January – May, July - December

R = 0.775 – 0.954

R = 0.592

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Geographic/ Topographic areasGeographic/ Topographic areas Model performance with various watershed Model performance with various watershed

characteristics (slope, soils, vegetation, etc.)characteristics (slope, soils, vegetation, etc.) Experience… what works… what doesn’tExperience… what works… what doesn’t Modeling/ Calibration limitationsModeling/ Calibration limitations

Detention pondsDetention ponds Unknown sources of inflowUnknown sources of inflow

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Questions?

Contacts for WGRFCContacts for WGRFC Bob CorbyBob Corby [email protected] Paul McKeePaul McKee [email protected] Mike ShultzMike Shultz [email protected]

mailto:[email protected]



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Extra slides

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Model Comparison Summary Lumped ModelLumped Model

Uses 6-hour Uses 6-hour time steptime step MAP computed; MAP computed;

assumes uniform rainfall assumes uniform rainfall across the basinacross the basin

Runoff applied to a unit Runoff applied to a unit hydrograph for the basinhydrograph for the basin

Uses single SAC-SMA Uses single SAC-SMA parameter across entire parameter across entire basinsbasins

Peak flow can be missed Peak flow can be missed at basins that crest in at basins that crest in less than 6 hoursless than 6 hours

Distributed ModelDistributed Model Uses 1-hour Uses 1-hour time steptime step Uses 4km x 4km gridsUses 4km x 4km grids Uses gridded QPEUses gridded QPE SAC-SMA parameters SAC-SMA parameters

estimated (i.e. soil type, estimated (i.e. soil type, vegetation type, land vegetation type, land use, slope, etc.) for each use, slope, etc.) for each grid cellgrid cell

Hydrologic simulations Hydrologic simulations computed using the computed using the kinematic wave kinematic wave techniquetechnique

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Understanding sources of error

Gridded data setsGridded data sets QPE spatial and magnitude errors QPE spatial and magnitude errors

neighboring grids

SAC param X 2X

QPE location Zerr Z

QPE amount 2A

Relative differences b/t grids

QPE mis-located where SAC param is half size

QPE over-est by double

error source

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Factors that Affect DHM Simulations

Quality of calibrationQuality of calibration QPE errors… location and amountsQPE errors… location and amounts Precipitation type (ie. no SNOW model)Precipitation type (ie. no SNOW model) Reservoirs/retention ponds Reservoirs/retention ponds Method for mainstem river routing; Method for mainstem river routing;

currently no different from lumpedcurrently no different from lumped Rating Curve accuracyRating Curve accuracy

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Documents

Distributed Hydrologic Modeling Presented by: Paul McKee and Mike Shultz West Gulf River Forecast Center DHM/HL-RDMH Workshop 2007 WGRFC Forecast Point