Streamflow simulation methods for ungauged and poorly gauged watersheds

Abstract

Abstract. Rainfall-runoff modelling procedures for ungauged and poorly gauged watersheds are developed in this study. A well established hydrological model, the UBC watershed model, is selected and applied in five different river basins located in Canada, Cyprus and Pakistan. Catchments from cold, temperate, continental and semiarid climate zones are included to demonstrate the develop procedures. Two methodologies for streamflow modelling are proposed and analysed. The first method uses the UBC watershed model with a universal set of parameters for water allocation and flow routing, and precipitation gradients estimated from the available annual precipitation data as well as from regional information on the distribution of orographic precipitation. This method is proposed for watersheds without streamflow gauge data and limited meteorological station data. The second hybrid method proposes the coupling of UBC watershed model with artificial neural networks (ANNs) and is intended for use in poorly gauged watersheds which have limited streamflow measurements. The two proposed methods have been applied to five mountainous watersheds with largely varying climatic, physiographic and hydrological characteristics. The evaluation of the applied methods is based on combination of graphical results, statistical evaluation metrics, and normalized goodness-of-fit statistics. The results show that the first method satisfactorily simulates the observed hydrograph assuming that the basins are ungauged. When limited streamflow measurements are available, the coupling of ANNs with the regional non-calibrated UBC flow model components is considered a successful alternative method over the conventional calibration of a hydrological model based on the employed evaluation criteria for streamflow modelling and flood frequency estimation.