Estimation of flow in ungauged catchments by coupling a hydrological model and neural networks: case study

The prediction of hydrological variables for ungauged basins is still a big challenge. Regionalization is the most widely used method to date, which relates parameters of watershed models to catchment characteristics. Relating catchment characteristics to watershed model parameters is too difficult for distributed hydrological models, due to the heterogeneous nature of catchments. A regional model was proposed by coupling a Kohonen neural network (KNN) and distributed Water Balance Simulation Model (WaSiM-ETH) to estimate flow in ungauged basin. KNN was used to delineate a hydrological homogeneous group based on predefined physical characteristics of catchments and WaSiM-ETH was applied to generate daily stream flow. Twenty-six subcatchments of the Blue Nile River basin, Ethiopia, were grouped into five hydrological homogenous groups, each with its own full set of optimized WaSiM-ETH parameters. In the regional model, the KNN assigned the ungauged catchment into one of the five hydrological homogenous groups. The whole set of optimized WaSiM parameters from the homogeneous group (which the ungauged river belongs to) were transferred to the ungauged river and WaSiM-ETH was used to compute the flow for this ungauged river. The regional model generally overestimated the low flow. In general, the results for validation subcatchments showed the regional model is satisfactory in transferring information from data-rich to data-poor catchments.