Regionalization of runoff models derived by genetic programming

Abstract

The aim of this study is to assess the potential of hydrological models derived by genetic programming (GP) to estimate runoff at ungauged catchments by regionalization. A set of 176 catchments from the MOPEX (Model Parameter Estimation Experiment) project was used for our analysis. Runoff models for each catchment were derived by genetic programming (hereafter GP models). A comparison of efficiency was made between GP models and three conceptual models (SAC-SMA, BTOPMC, GR4J). The efficiency of the GP models was in general comparable with that of the SAC-SMA and BTOPMC models but slightly lower (up to 10% for calibration and 15% in validation) than for the GR4J model. The relationship between the efficiency of the GP models and catchment descriptors (CDs) was investigated. From 13 available CDs the aridity index and mean catchment elevation explained most of the variation in the efficiency of the GP models. The runoff for each catchment was then estimated considering GP models from single or multiple physically similar catchments (donors). Better results were obtained with multiple donor catchments. Increasing the number of CDs used for quantification of physical similarity improves the efficiency of the GP models in runoff simulation. The best regionalization results were obtained with 6 CDs together with 6 donors. Our results show that transfer of the GP models is possible and leads to satisfactory results when applied at physically similar catchments. The GP models can be therefore used as an alternative for runoff modelling at ungauged catchments if similar gauged catchments can be identified and successfully simulated.