A priori parameter estimates for a distributed, grid-based Xinanjiang model using geographically based information

SummaryAn improved form of spatially distributed Grid-Xinanjiang model (GXM), which integrates features of a well-tested conceptual rainfall–runoff model and a physically based flow routing model, has been proposed for simulating hydrologic processes and forecasting flood events in watersheds. The digital elevation model (DEM) is utilized in the GXM to derive computational flow direction, routing sequencing, and hillslope and channel slopes. The processes in the model include canopy interception, direct channel precipitation, evapotranspiration, as well as runoff generation via a saturation excess mechanism. A two-step finite difference solution of the diffusion wave approximation of the St. Venant equations with second-order accuracy is used in the model to simulate the flow routed along the hillslope and channel on a cell basis with consideration of upstream inflow and flow partition to the channels. A physically, empirically based approach using geographically based information such as topography, soil data and land use/land cover data is employed for estimating spatially varied parameters. GXM is applied at a 1-km grid scale to a nested watershed located in Anhui province, China. The parent Tunxi watershed, with a drainage area of 2692.7 km2, contains five internal points with available observed streamflow data, allowing us to evaluate model’s ability to simulate the hydrologic processes within the watershed. Calibration and verification of the proposed GXM are carried out for both daily and hourly time scales using daily rainfall–runoff data and hourly streamflow data. Model performance is assessed by comparing simulated and observed flows at the watershed outlet and interior gauging stations. Initial tests indicate that the parameter estimation approach is efficient and the developed model can satisfactorily simulate not only the streamflow at the parent watershed outlet, but also the flood hydrograph at the interior gauging points without model recalibration. The impact of spatial and temporal variability in rainfall and basin characteristics on the prediction of interior hydrologic processes is investigated as well through the use of the cell-based model.

► A distributed GXM model is proposed for predicting rainfall–runoff responses. ► The spatially varied parameters are derived a priori from geographic information. ► We examine the ability of the model to predict interior hydrologic processes. ► Rainfall variability and basin characteristics affect the interior predictions.