Simulation Experiment of TOPMODEL to the Binggou Catchment, Heihe River Basin, Northwest China

This paper describes a distributed modeling approach applied to stimulate the stream flow on a 30.06 km2 Binggou watershed on the North Slope of the Tuolai Mountains in Heihe Basin, Northwest China. The simulation process has assembled a modeling system centered on TOPMODEL, simulation of saturated excess runoff was mainly based upon topography, the other components deemed relevant to the hydrologic processes in the basin, however, were simulated not only upon the topography but also the Geographic Information System (GIS) data. Precipitation was spatially interpolated from five rain gauges using linear interpolation on Delauney triangles and scaling by annual rainfall surface to represent orographic effects. The model included the following components: 1) reference evapotranspiration estimation by following Maident D.R.(1993); 2) interception and throughfall, an unsaturated zone soil layer that delayed water inputs to the saturated zone and provided infiltration excess runoff generation capability，and a kinematic wave channel routing component. Procedures were developed to generate model input files from digital elevation model and land resource inventory GIS data. Model elements are subwatersheds automatically extracted based upon the channel network generated from the Digital Elevation Model (DEM) and a specified stream order threshold. Model element parameters are linked to GIS information averaged over each subwatershed. Totally 9 subwatersheds were subdivided, and the model was calibrated using an interactive calibratioing package with the Gauss-Marquardt method. The calibration uses scale multipliers to retain GIS landcover derived relative differences between parameters across subwatersheds. Model parameters were first calibrated for one year then independently tested there for the following years. The calibration used precipitation measured at this small watershed while the validation exercised the precipitation interpolation methodology. The results indicate that Streamflow estimates are sensitive to uncertainty in the precipitation due to variability and orographic effects, and this precipitation uncertainty takes prodominate role among all the sources