Study and development of a distributedhydrologic model, WetSpa, applied to the     DMIP2 basins in Oklahoma, USA          ...
How do we see reality?                                   Topography Landuse                     Soil texture              ...
WetSpa mthodology  Page 3
OutlinesDMIP2            • framework                 • testbasinsModel            • To basinsapplication                 •...
Science questions How applicable is the WetSpa model to the DMIP2 basins? What role does calibration play in realizing i...
OutlinesDMIP2            • framework                 • testbasinsModel            • To basinsapplication                 •...
DMIP2 project Initiated by the US HL-NWS of NOAA, 14 groups with 16 models participated, Designed to address model basi...
DMIP2 framework Model Run Periods:  Model run types:    a. Simulations with uncalibrated/initial parameters    b. Simula...
DMIP2 testbasins   Page 9
Radar-basedrainfall data(NEXRAD)•   160 radars across the US•   generate a one-hour    rainfall product•   with a nominal ...
OutlinesDMIP2              • framework                   • testbasinsModel              • To basinsapplication            ...
Introducing AM to evalute  model performanceFlow       Model bias      Correl. Coef.   Modified r   Nash-Sut Eff.   Agg. M...
WetSpa model resultsfor the parent basinsAM values and goodness of fit categories for the calibration period              ...
WetSpa model resultsfor the subbasins (1) AM values and goodness of fit categories for the calibration period AM values an...
WetSpa model results for the subbasins (2)Generally, in subbasin simulation, highflows are underestimated, whether or nott...
OutlinesDMIP2             • framework                  • testbasinsModel             • To basinsapplication               ...
PEST for fitting simulation to  observation (a schematic view)We wish to find those parameter values for which the model `...
Classic WetSpa Calibration• Parameter Estimation (PEST) Software    • Model Independent Parameter Estimator:      Minimize...
Proposed WetSpa Calibration methodology                                       Use multi search          Local search metho...
Model calibration methodologyBox Cox transformation to stabilize the variance                            after Box-Cox   Q...
Model calibration methodology    Obtaining uncorrelated errors                                                      Removi...
Defining new objective function •Converting model residuals (rt) to error terms (εt) that are homoskedastic and uncorrelat...
WetSpa model resultsmodel calibrated with PEST and ARIMA    Page 23
OutlinesDMIP2             • framework                  • testbasinsModel             • To basinsapplication               ...
Model prediction analysis     uncertainty of model predictionsKey predictions in the validation periods:1)   mean of low f...
Improving runoff predictionOur empirical equation to modify WetSpa model:               For the modified WetSpa model    P...
Improvingrunoffprediction   Page 27
Improving low flow prediction         Boussinesq approachThe aquifer dissipation coefficient (D) is replacing the baseflow...
Results of the modified WetSpafor subbasin prediction   Page 29
Conclusions (1) WetSpa is well suited for the DMIP2 basins. Uncalibrated WetSpa perform well         good for ungaged  m...
Conclusions (2) Calibration of the model for the parent basin is no guarantee for good   performance for the subbasins. ...
Recommendations Perform model applications to cases with a high diversity in  hydrological conditions, such as mountainou...
Recommendations For model evaluation and development, the probable error from  downscaling, and uncertainty in discharge ...
Publications of the thesis Safari, A. and De Smedt, F., Streamflow simulation using radar-based precipitation applied to ...
Thank you!Wednesday, December 05, 2012Page 35
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Study and development of a distributed hydrologic model, WetSpa, applied to the DMIP2 basins in Oklahoma, USA

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PhD thesis presentation, prepared for the public defense on 23rd Nov. 2012

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Study and development of a distributed hydrologic model, WetSpa, applied to the DMIP2 basins in Oklahoma, USA

  1. 1. Study and development of a distributedhydrologic model, WetSpa, applied to the DMIP2 basins in Oklahoma, USA Alireza Safari Promotor: Prof. Dr. Ir. F. De Smedt Department of Hydrology and Hydraulic Engineering 23 Nov 2012
  2. 2. How do we see reality? Topography Landuse Soil texture MODEL Input ↓ Syst em ↓ Out put Driving variables ↓ W et Spa ↓ Simulat ion result s Page 2
  3. 3. WetSpa mthodology Page 3
  4. 4. OutlinesDMIP2 • framework • testbasinsModel • To basinsapplication • To interior subbasinsModel • PEST program and its multi-search drivercalibration • Box-Cox transformation and ARIMA error modelWetSpa • Improving highflow predictionpredictionanalysis • Improving subbasin outflow prediction Page 4
  5. 5. Science questions How applicable is the WetSpa model to the DMIP2 basins? What role does calibration play in realizing improvements? Why the model generally tends to underestimate high flows, particularly major peaks? Is this a WetSpa model parameter estimation problem? Can maximization of model prediction for high flows make the calibrated model to bracket high flows, especially major peaks? Page 5
  6. 6. OutlinesDMIP2 • framework • testbasinsModel • To basinsapplication • To interior subbasinsModel • PEST program and its multi-search drivercalibration • Box-Cox transformation and ARIMA error modelWetSpa • Improving highflow predictionpredictionanalysis • Improving subbasin outflow prediction Page 6
  7. 7. DMIP2 project Initiated by the US HL-NWS of NOAA, 14 groups with 16 models participated, Designed to address model basin-interior processes, such as runoff and soil moisture Page 7
  8. 8. DMIP2 framework Model Run Periods:  Model run types: a. Simulations with uncalibrated/initial parameters b. Simulations with calibrated/optimized parameters Page 8
  9. 9. DMIP2 testbasins Page 9
  10. 10. Radar-basedrainfall data(NEXRAD)• 160 radars across the US• generate a one-hour rainfall product• with a nominal grid size of 4km*4km• for saving more space the data are stored in binary• we used a program (written in C) to convert them into ASCII files.• Using a fortran code hourly rainfall time series extracted. Page 10
  11. 11. OutlinesDMIP2 • framework • testbasinsModel • To basinsapplication • To interior subbasinsModel • PEST program and its multi-search drivercalibration • Box-Cox transformation and ARIMA error modelWetSpa • Improving highflow predictionpredictionanalysis • Improving subbasin outflow prediction Page 11
  12. 12. Introducing AM to evalute model performanceFlow Model bias Correl. Coef. Modified r Nash-Sut Eff. Agg. Measure Page 12
  13. 13. WetSpa model resultsfor the parent basinsAM values and goodness of fit categories for the calibration period Calibrated model performance Uncalibrated model performanceAM values and goodness of fit categories for the validation period Calibrated model performance Uncalibrated model performance Page 13
  14. 14. WetSpa model resultsfor the subbasins (1) AM values and goodness of fit categories for the calibration period AM values and goodness of fit categories for the validation period Page 14
  15. 15. WetSpa model results for the subbasins (2)Generally, in subbasin simulation, highflows are underestimated, whether or notthe model is calibrated. Page 15
  16. 16. OutlinesDMIP2 • framework • testbasinsModel • To basinsapplication • To interior subbasinsModel • PEST program and its multi-search drivercalibration • Box-Cox transformation and ARIMA error modelWetSpa • Improving highflow predictionpredictionanalysis • Improving subbasin outflow prediction Page 16
  17. 17. PEST for fitting simulation to observation (a schematic view)We wish to find those parameter values for which the model `best´ fits the data. Page 17
  18. 18. Classic WetSpa Calibration• Parameter Estimation (PEST) Software • Model Independent Parameter Estimator: Minimize the bias between observed and simulated flows by many runs as needed • PEST: works well in terms of saving time and efforts Page 18
  19. 19. Proposed WetSpa Calibration methodology Use multi search Local search method driver (PD_MS2)PEST Use Box-Cox transformation to stabilize error variance Least square method Use ARIMA error model to remove autocorrelation Page 19
  20. 20. Model calibration methodologyBox Cox transformation to stabilize the variance after Box-Cox Q: discharge transformation : transformation parameter Page 20
  21. 21. Model calibration methodology Obtaining uncorrelated errors Removing residuals autocorrelations by D=0.009 ARIMA`D´ test (Durbin and Watson, 1971) fordetecting autocorrelation: 0<D<4when D is close to 2, then the errors arewhite noise and uncorrelated. D=1.995 Page 21
  22. 22. Defining new objective function •Converting model residuals (rt) to error terms (εt) that are homoskedastic and uncorrelated using Box Cox and ARIMA error model Page 22
  23. 23. WetSpa model resultsmodel calibrated with PEST and ARIMA Page 23
  24. 24. OutlinesDMIP2 • framework • testbasinsModel • To basinsapplication • To interior subbasinsModel • PEST program and its multi-search drivercalibration • Box-Cox transformation and ARIMA error modelWetSpa • Improving highflow predictionpredictionanalysis • Improving subbasin outflow prediction Page 24
  25. 25. Model prediction analysis uncertainty of model predictionsKey predictions in the validation periods:1) mean of low flows2) mean of medium flows3) mean of high flows4) largest peak flow Page 25
  26. 26. Improving runoff predictionOur empirical equation to modify WetSpa model: For the modified WetSpa model Page 26
  27. 27. Improvingrunoffprediction Page 27
  28. 28. Improving low flow prediction Boussinesq approachThe aquifer dissipation coefficient (D) is replacing the baseflow recessioncoefficient (m6) in the original WetSpa model, and to be estimated by modelcalibration. Page 28
  29. 29. Results of the modified WetSpafor subbasin prediction Page 29
  30. 30. Conclusions (1) WetSpa is well suited for the DMIP2 basins. Uncalibrated WetSpa perform well good for ungaged modeling Calibration improves the model performance significantly. WetSpa forced with radar based rainfall data is able to reproduce streamflow Although, the calibrated WetSpa model performes well, but it remains inaccurate for high and low flows. Page 30
  31. 31. Conclusions (2) Calibration of the model for the parent basin is no guarantee for good performance for the subbasins. The modified WetSpa model is superior compared to the original WetSpa model. Page 31
  32. 32. Recommendations Perform model applications to cases with a high diversity in hydrological conditions, such as mountainous watersheds where snowmelt can cause flooding. Shorter time interval will improve the capability of the WetSpa model for subbasin simulations. If possible, use weather radar precipitation data as it enables to investigate finer time resolution for predicting flow in small subbasins. Page 32
  33. 33. Recommendations For model evaluation and development, the probable error from downscaling, and uncertainty in discharge data should be taken into account. Page 33
  34. 34. Publications of the thesis Safari, A. and De Smedt, F., Streamflow simulation using radar-based precipitation applied to the Illinois River basin in Oklahoma, USA; BALWOIS conference (2008); Ohrid, Republic of Macedonia. Safari, A., De Smedt, F., Moreda, F., WetSpa model application in the Distributed Model Intercomparison Project (DMIP2), Journal of Hydrology (2012), http://dx.doi.org/10.1016/j.jhydrol.2009.04.001 Michael B. Smith, Victor Koren, Fekadu Moreda,,.., and DMIP2 Participant, Results of the DMIP 2 Oklahoma experiments, Journal of Hydrology (2012), http://dx.doi.org/10.1016/j.jhydrol.2011.08.056 Safari, A. and De Smedt, F., Model Calibration and Predictive Analysis with ARIMA Error Model and PEST Program, Journal of Hydrological Engineering, (2012), in review Safari, A. and De Smedt, F., Improving WetSpa model to predict streamflows for gaged and ungaged catchments, Journal of Hydroinformatics (2012), under review Page 34
  35. 35. Thank you!Wednesday, December 05, 2012Page 35

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