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

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 (R 2). 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.

Evaluating the Impacts of Pumping on Aquifer Depletion in Arid Regions Using MODFLOW, ANFIS and ANN

“…The NSE value was found to be 0.9. This value of NSE lies in the range of "very good" according to the criteria adopted by Rauf and Ghumman [54]. Furthermore, our results are similar to those of Mohanty et al [55] and Lyons et al [16], although the hydraulic conductivity and specific storage were the most sensitive parameters in the calibration phase of their groundwater hydraulic model, but adjustment of the boundary condition was most crucial issue in our case.…”

“…(2) where GWL is the variable representing the groundwater level, o is the observed value of GWL, p represents the predicted value, and n denotes the total number of data points. As per the criteria adopted by Rauf and Ghumman [54], values from 0.75 to 1.0 of NSE can be categorized as 'very good', 0.65 to 0.75 can be considered 'good', 0.5 to 0.65 as 'satisfactory', and the values between 0.4 and 0.5 represent an 'acceptable' performance of the model.…”

“…The positive values of MBE represent overestimated predictions whereas negative values are for underestimated predictions [54].…”

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“…HEC-HMS, known for being a semi-distributed conceptual hydrological model, simulates discharge hydrographs. The required inputs include: daily precipitation and physiographic information of the watershed to simulate time series of discharge as output [18]. The model architecture consists of a watershed model, meteorological model, control specifications, and input data (time series data) [18].…”

“…The required inputs include: daily precipitation and physiographic information of the watershed to simulate time series of discharge as output [18]. The model architecture consists of a watershed model, meteorological model, control specifications, and input data (time series data) [18]. Except for the soil moisture accounting model, all other hydrologic models used in HMS are event-based.…”