Comparative evaluation of daily streamflow prediction by ANN and SWAT models in two karst watersheds in central south Texas

This work compares the accuracy of streamflow estimated by a data-driven artificial neural network (ANN) and the physically based soil and water assessment tool (SWAT). The models were applied in two small watersheds, one highly urbanized and the other primarily covered with evergreen forest and shr...

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Bibliographic Details
Published in:Hydrology Research 2023-12, Vol.54 (12), p.1606-1624
Main Authors: Mei, Xiaohan, Smith, Patricia K., Li, Jing
Format: Article
Language:English
Subjects:
artificial neural network (ann)
Artificial neural networks
Autoregressive models
Coniferous forests
Groundwater
Groundwater discharge
Groundwater recharge
Historical structures
Hydrologic data
Hydrologic models
Hydrology
Karst
karst watersheds
measurement and model uncertainty
Modelling
Neural networks
rainfall-runoff modeling
Simulation
soil and water assessment tool (swat)
Soil water
Stream discharge
Stream flow
Streamflow data
Streamflow forecasting
streamflow prediction
Surface water
Water discharge
Water flow
Watersheds
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Summary:This work compares the accuracy of streamflow estimated by a data-driven artificial neural network (ANN) and the physically based soil and water assessment tool (SWAT). The models were applied in two small watersheds, one highly urbanized and the other primarily covered with evergreen forest and shrubs, in the San Antonio region of central south Texas, where karst geologic features are prevalent. Both models predicted daily streamflow in the urbanized watershed very well, with the ANN and SWAT having the Nash–Sutcliffe coefficient of efficiency (NSE) values of 0.76 and 0.72 in the validation period, respectively. However, both models predicted streamflow poorly in the nonurban watershed. The NSE values of the ANNs significantly improved when a time series autoregressive model structure using historical streamflow data was implemented in the nonurban watershed. The SWAT model only achieved trivial performance improvement after using the SWAT-CUP SUFI-2 calibration procedure. This result suggests that an ANN model may be more suitable for short-term streamflow forecasting in watersheds heavily affected by karst features where the complex processes of rapid groundwater recharge and discharge strongly influence surface water flow.
ISSN:0029-1277
1998-9563
2224-7955
DOI:10.2166/nh.2023.229