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

1. Study and development of a distributed
hydrologic 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. How do we see reality?
Topography Landuse Soil texture
MODEL
Input ↓ Syst em ↓ Out put Driving variables ↓ W et Spa ↓ Simulat ion result s
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3. WetSpa mthodology
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4. Outlines
DMIP2 • framework
• testbasins
Model • To basins
application
• To interior subbasins
Model • PEST program and its multi-search driver
calibration
• Box-Cox transformation and ARIMA error model
WetSpa • Improving highflow prediction
prediction
analysis • Improving subbasin outflow prediction
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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?
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6. Outlines
DMIP2 • framework
• testbasins
Model • To basins
application
• To interior subbasins
Model • PEST program and its multi-search driver
calibration
• Box-Cox transformation and ARIMA error model
WetSpa • Improving highflow prediction
prediction
analysis • Improving subbasin outflow prediction
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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
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8. DMIP2 framework
 Model Run Periods:
 Model run types:
a. Simulations with uncalibrated/initial parameters
b. Simulations with calibrated/optimized parameters
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9. DMIP2 testbasins
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10. Radar-based
rainfall 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.
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11. Outlines
DMIP2 • framework
• testbasins
Model • To basins
application
• To interior subbasins
Model • PEST program and its multi-search driver
calibration
• Box-Cox transformation and ARIMA error model
WetSpa • Improving highflow prediction
prediction
analysis • Improving subbasin outflow prediction
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12. Introducing AM to evalute
model performance
Flow
Model bias Correl. Coef. Modified r Nash-Sut Eff. Agg. Measure
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13. WetSpa model results
for the parent basins
AM values and goodness of fit categories for the calibration period
Calibrated model performance
Uncalibrated model performance
AM values and goodness of fit categories for the validation period
Calibrated model performance
Uncalibrated model performance
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14. WetSpa model results
for 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
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15. WetSpa model
results
for the subbasins (2)
Generally, in subbasin simulation, high
flows are underestimated, whether or not
the model is calibrated.
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16. Outlines
DMIP2 • framework
• testbasins
Model • To basins
application
• To interior subbasins
Model • PEST program and its multi-search driver
calibration
• Box-Cox transformation and ARIMA error model
WetSpa • Improving highflow prediction
prediction
analysis • Improving subbasin outflow prediction
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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.
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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
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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
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20. Model calibration methodology
Box Cox transformation to stabilize the variance
after Box-Cox Q: discharge
transformation : transformation
parameter
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21. Model calibration methodology
Obtaining uncorrelated errors
Removing residuals
autocorrelations by
D=0.009 ARIMA
`D´ test (Durbin and Watson, 1971) for
detecting autocorrelation:
0<D<4
when D is close to 2, then the errors are
white noise and uncorrelated.
D=1.995
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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
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23. WetSpa model results
model calibrated with PEST and ARIMA
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24. Outlines
DMIP2 • framework
• testbasins
Model • To basins
application
• To interior subbasins
Model • PEST program and its multi-search driver
calibration
• Box-Cox transformation and ARIMA error model
WetSpa • Improving highflow prediction
prediction
analysis • Improving subbasin outflow prediction
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25. Model prediction analysis
uncertainty of model predictions
Key predictions in the validation periods:
1) mean of low flows
2) mean of medium flows
3) mean of high flows
4) largest peak flow
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26. Improving runoff prediction
Our empirical equation to modify WetSpa model:
For the modified WetSpa model
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27. Improving
runoff
prediction
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28. Improving low flow prediction
Boussinesq approach
The aquifer dissipation coefficient (D) is replacing the baseflow recession
coefficient (m6) in the original WetSpa model, and to be estimated by model
calibration.
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29. Results of the modified WetSpa
for subbasin prediction
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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.
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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.
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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.
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33. Recommendations
 For model evaluation and development, the probable error from
downscaling, and uncertainty in discharge data should be taken
into account.
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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
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35. Thank you!
Wednesday, December 05, 2012
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