Abstract
Different modeling concepts, a simple (black-box) to a fully distributed modeling (white-box), were used to develop a rainfall-runoff model based on the watershed characteristics to estimate runoff at the watershed outlet. A conceptual (grey-box) model is usually a balance between the black-box and white-box model. In this study, three grey-box models were developed by varying model structures for a karst watershed. The performance of the grey-box models was evaluated and compared with a semi-distributed type (white-box) model that was developed using the Soil and Water Assessment Tool in a previous study. The evaluation was carried out using goodness-of-fit statistics and extreme flow analysis using WETSPRO (Water Engineering Time Series Processing tool). Nash-Sutcliffe efficiencies (NSE) of the grey-box models were from 0.39 to 0.77 in the calibration period and from 0.30 to 0.61 in the validation period. However, the white-box model performed better in terms of NSE but has a higher bias. The best grey-box model performed better in simulating extreme flow, whereas the white-box (SWAT) model adequately simulated daily flows.
 Asian Australas. J. Biosci. Biotechnol. 2021, 6 (1), 26-39
Highlights
Rainfall-runoff models are often used to predict the behavior of a natural hydrological system (Abdollahi et al, 2017; Amin et al, 2017; Cislaghi et al, 2020)
The performance of the selected grey-box model was compared with a semi-distributed SWAT model
The performance of the models was evaluated in terms of goodness-of-fit statistics and extreme flow analysis
Summary
Rainfall-runoff models are often used to predict the behavior of a natural hydrological system (Abdollahi et al, 2017; Amin et al, 2017; Cislaghi et al, 2020). In white-box models, physical processes are represented using many equations to establish a relation between the input and output of the system. The prediction of a white-box model is relatively accurate and reliable, but it requires a powerful computing system and much time to simulate the results. It requires a large amount of information and field data about the system to represent the processes accurately (Biftu & Gan, 2001; Chen et al, 2016; Praskievicz & Chang, 2009; Wang et al, 2010; Yang et al, 2014). As black-box models do not include any physical process, they often provide inaccurate results especially when used for extrapolation or used for a dynamic system (Vaze et al, 2011; Willems, 2015)
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