Title of retracted article: Revisiting Systems Type Black-Box Rainfall-Runoff Models for Flow Forecasting Application

Often we tend to spend huge amount of time and resources to setup and use complex hydrological models for simple goal of flow estimation. Running complex models becomes even more difficult when the amount of available data is scarce as we usually face in many parts of Africa. The aim of this study is to evaluate and revitalize the systems type black box model against complex hydrological models for easy flow estimation application. Six systems type black box models, the Simple Linear Model (SLM), Non-Parametric Simple Linear Model (NP-SLM), Linear Perturbation Model (LPM), Non-Parametric Linear Perturbation Model (NP-LPM) and Linearly Varying Gain Factor Model (LVGFM), a non-linear black box type artificial Neural Network model (ANN) are compared with three complex hydrological models of those under SMAR, HBV and SWAT. The models are compared based on daily rainfall and stream flow data (1980-2000) on Gilgel Abbay watershed. Event-based analysis was also conducted using 100 selected runoff events. In terms of the event rainfall-runoff relationship, it was indicated that the event runoff is largely a function of the amount of rainfall. The event rainfall-runoff relationships explained as much as 62% for the wet periods without the integration of the evaporating demands. Although rainfall intensity, duration and catchment characteristics play a role, in this watershed, rainfall amount affects substantial part of the runoff response consolidating that a simple rainfall-runoff relationship can describe the runoff in this watershed. Comparison of systems type black box and complex hydrological models in the study area indicates that the LPM and the ANN models perform better than the complex hydrological models such as SMARG, HBV and SWAT in terms of R2 and Nash Sutcliffe Efficiency (NSE) criteria. This confirms that simpler models (that take only rainfall as input) can surpass their complex counterparts in performance for continuous simulation and reproducing the Corresponding author. REACTED D. T. Mengistu et al. 66 hydrographs or flow estimation. There is a strong justification, therefore, for the claim that increasing the model complexity, thereby increasing the number of parameters, does not necessarily enhance the model performance. It is suggested that, in practical hydrology, the simpler models, may still play a significant role as effective simulation tools, and countries with scarce hydrological data should revitalize application of such systems type black box modelling schemes that depend only on rainfall and runoff data sets which could be easily available.

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