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Water 2018, 10(7), 876; https://doi.org/10.3390/w10070876

Impact Assessment of Rainfall-Runoff Simulations on the Flow Duration Curve of the Upper Indus River—A Comparison of Data-Driven and Hydrologic Models

1
Faculty of Civil and Environmental Engineering, University of Engineering & Technology Taxila, Rawalpindi 47080, Pakistan
2
Department of Civil Engineering, Bannu Campus, University of Engineering & Technology Peshawar, Peshawar 25000, Pakistan
3
College of Engineering, Civil Engineering Department, Qassim University, Al-Mulida 51431, Saudi Arabia
*
Author to whom correspondence should be addressed.
Received: 29 May 2018 / Revised: 23 June 2018 / Accepted: 28 June 2018 / Published: 30 June 2018
(This article belongs to the Section Water Resources Management and Governance)
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Abstract

As a major component of the hydrologic cycle, rainfall runoff plays a key role in water resources management and sustainable development. Conceptual models of the rainfall-runoff process are governed by parameters that can rarely be directly determined for use in distributed models, but should be either inferred through good judgment or calibrated against the historical record. Artificial neural network (ANN) models require comparatively fewer such parameters, but their accuracy needs to be checked. This paper compares a Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) conceptual model and an ANN model based on the conjugate gradient method for streamflow prediction. Daily precipitation, temperature, and streamflow data of the Upper Indus River for a period of 20 years (1985–2004) are used as input for calibrating in the case of the HEC-HMS, and for training in case of the ANN. Ten years of data (2005–2014) are used to validate the HEC-HMS model and test the ANN. The performance of the models is assessed using different statistical indicators such as the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R2). The results show good predictions for streamflow in the case of both HEC-HMS and ANN models. A parametric study is conducted using Monte Carlo analysis and finds that the most important parameters for HEC-HMS models are the storage coefficient and the time of concentration; while for ANN models, input combinations are the most important. This study investigates the sensitivity of these parameters, which can be used to determine preliminary estimation ranges of their values for future modeling. Finally, evaluating the impact of the simulated streamflow’s accuracy on the flow duration curve shows that the curve is significantly affected by any streamflow simulation inaccuracy. View Full-Text
Keywords: artificial neural network; flow duration curves; HEC-HMS; Monte Carlo Analysis; River Indus; sensitivity analysis; streamflow artificial neural network; flow duration curves; HEC-HMS; Monte Carlo Analysis; River Indus; sensitivity analysis; streamflow
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Rauf, A.-U.; Ghumman, A.R. Impact Assessment of Rainfall-Runoff Simulations on the Flow Duration Curve of the Upper Indus River—A Comparison of Data-Driven and Hydrologic Models. Water 2018, 10, 876.

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