Effect of Calibration and Validation Decisions on Streamflow Modeling for a Heterogeneous and Low Runoff–Producing River Basin in India
Publication: Journal of Hydrologic Engineering
Volume 24, Issue 7
Abstract
Spatial heterogeneity of a river basin increases the predicting uncertainty of streamflow using hydrological models, and for such river basins calibration and prediction become a challenge. For a large and low flow–producing river basin like the Pennar basin of India, single-site calibration may ignore spatial heterogeneity, which leads to the use of a multiple-site calibration approach. The present study used Soil and Water Assessment Tool (SWAT) model to develop a real-world numerical model for predicting streamflow in a large, low runoff–producing river basin and evaluated the performance of the model under single-site and multiple-site calibration approaches. Under multiple-site approach, the large basin was divided into smaller subbasins, and the calibrated parameters were sequentially applied to the entire basin, and so local conditions were incorporated very effectively into the calibration process. The results demonstrated the superiority of a multiple-site calibration over a single-site calibration approach in predicting streamflow. The overall improvement of model performance in predicting streamflow using multiple-site approach over single-site approach reached as high as 133% during calibration and 140% during validation, as observed at the basin outlet (Chennur station). It was also observed that, within the basin, precipitation contributed only 15.8% to surface runoff, 2.6% to total aquifer recharge, and 69.3% to evapotranspiration. Uncertainty analysis indicated that more than 64% of the observed streamflow was bracketed by a 95% prediction uncertainty (PPU) band under the multiple-site approach. Thus, the multiple-site calibration approach demonstrated its ability to improve model performance by capturing the heterogeneity of a low runoff–producing basin like Pennar.
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Acknowledgments
The authors acknowledge the financial assistance provided by the Indian Council of Agricultural Research as a project grant under the National Innovations in Climate Resilient Agriculture (NICRA). The authors are also thankful to all the organizations mentioned in this paper for providing the required data. The authors thank the anonymous reviewers for their efforts to improve the quality of the manuscript.
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Received: Jan 30, 2018
Accepted: Jan 9, 2019
Published online: Apr 30, 2019
Published in print: Jul 1, 2019
Discussion open until: Sep 30, 2019
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