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

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How do we see reality?

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Input ↓ System ↓ Output Driving variables ↓ WetSpa ↓ Simulation results

Topography Landuse Soil texture

MODEL

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WetSpa mthodology

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Outlines

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DMIP2 • framework

• testbasins

Model application

• To basins

• To interior subbasins

Model calibration

• PEST program and its multi-search driver

• Box-Cox transformation and ARIMA error model

WetSpa prediction analysis

• Improving highflow prediction

• Improving subbasin outflow prediction

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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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Outlines

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DMIP2• framework

• testbasins

Model application• To basins

• To interior subbasins

Model calibration• PEST program and its multi-search driver

• Box-Cox transformation and ARIMA error model

WetSpa prediction analysis• Improving highflow prediction

• Improving subbasin outflow prediction

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DMIP2 project

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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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DMIP2 framework

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Model run types:

a. Simulations with uncalibrated/initial parameters

b. Simulations with calibrated/optimized parameters

Model Run Periods:

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DMIP2 testbasins

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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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Outlines

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DMIP2• framework

• testbasins

Model application• To basins

• To interior subbasins

Model calibration• PEST program and its multi-search driver

• Box-Cox transformation and ARIMA error model

WetSpa prediction analysis• Improving highflow prediction

• Improving subbasin outflow prediction

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Introducing AM to evalute model performance

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Flo

w

Model bias Correl. Coef. Modified r Nash-Sut Eff. Agg. Measure

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WetSpa model results for the parent basins

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AM values and goodness of fit categories for the calibration period

AM values and goodness of fit categories for the validation period

Uncalibrated model performanceCalibrated model performance

Uncalibrated model performance

Calibrated model performance

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WetSpa model results for the subbasins (1)

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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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WetSpa model results for the subbasins (2)

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Generally, in subbasin simulation, high flows are underestimated, whether or not the model is calibrated.

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Outlines

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DMIP2• framework

• testbasins

Model application• To basins

• To interior subbasins

Model calibration• PEST program and its multi-search driver

• Box-Cox transformation and ARIMA error model

WetSpa prediction analysis• Improving highflow prediction

• Improving subbasin outflow prediction

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PEST for fitting simulation to observation (a schematic view)

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We wish to find those parameter values for which the model `best´ fits the data.

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Classic WetSpa Calibration

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• 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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Proposed WetSpa Calibration methodology

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PEST

Local search methodUse multi search driver (PD_MS2)

Least square method

Use Box-Cox transformation to stabilize error variance

Use ARIMA error model to remove autocorrelation

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Model calibration methodologyBox Cox transformation to stabilize the variance

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after Box-Cox transformation

Q: dischargel: transformation parameter

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Model calibration methodologyObtaining uncorrelated errors

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Removing residuals autocorrelations by

ARIMA

`D´ test (Durbin and Watson, 1971) for detecting autocorrelation: 0 < D < 4when D is close to 2, then the errors are white noise and uncorrelated.

D=0.009

D=1.995

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Defining new objective function

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• Converting model residuals (rt) to error terms (εt) that are homoskedastic and uncorrelated using Box Cox and ARIMA error model

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WetSpa model resultsmodel calibrated with PEST and ARIMA

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Outlines

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DMIP2• framework

• testbasins

Model application• To basins

• To interior subbasins

Model calibration• PEST program and its multi-search driver

• Box-Cox transformation and ARIMA error model

WetSpa prediction analysis• Improving highflow prediction

• Improving subbasin outflow prediction

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Model prediction analysisuncertainty of model predictions

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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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Improving runoff prediction

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Our empirical equation to modify WetSpa model:

For the modified WetSpa model

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Improving runoff prediction

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Improving low flow prediction

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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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Results of the modified WetSpa for subbasin prediction

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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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Conclusions (2)

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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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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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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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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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Wednesday, April 12, 2023

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T h a n k y o u !