OMPARISON AND SELECTION OF CONCEPTUAL RAINFALL-RUNOFF MODELS FOR SELECTED CATCHMENTS IN ABBAY RIVER BASIN

Abstract

Conceptual mathematical models are useful tools for rainfall runoff modeling of river basin. Thus, this study attempts to identify relationships between suitable conceptual rainfall-runoff model structures and catchment types, and demonstrates an objective procedure for selection of model structures for Gilgel Abbay, Dedessa and Neshe catchments. In this study four conceptual model structures have been tested on three catchments in Abbay River Basin. All the model structures are combinations of the water balance and routing components since they are conceptual models. Three catchments were selected to cover a range of hydrological types based on known average catchment characteristics. The important points in objectively selecting model structures for discharge simulation are proposed as: performance in calibration and validation, trade-off between high, mean and low flow performance and FDC, and model complexity (here represented as the number of parameters). Although, the results in this study provide no detail evidence of relationships between catchment type and preferred model structure with greater certainty, the study indicates that out of the four model structures, the ARNO model has greater performance in Gilgel Abbay catchment, SMAR has fairly good performance value in Dedessa catchment and HBV has good performance index in Neshe catchment. Eventhough, all model structures have poor performance index in low flow simulation, all models relatively suitable for runoff simulation under mean and high flow for selected catchments. The biased occurred between simulated and observed runoff may be due to in adequacy of model structure, human activities done, incorrect estimation of parameters especially in case of manual optimization , if there is an interflow between catchments, the quality of data and the absence of any substantial, consistent , or coherent relationship in the data used to calibrate the models.