Impact Of Precipitation Variability On Hydrological Processes

Precipitation is one of the most important components in the hydrologic cycles and the most remarkable uncertainties in streamflow simulation. In this dissertation, the Shiguanhe River basin is selected as the study region, and the effect of information entropy of rainfall and spatio-temporal resolution of precipitation are studied.According to the principle and methodology of information entropy, the information entropies of the 4 rainfall gages in the Huangnizhuang watershed are studied. Then the independent information and common information among those gages is obtained so as to propose the best combination of rain-gages. The rainfall gages used in winter are more representative, those gages should be taken into consideration firstly in the optimum distribution of rainfall gages. The selection of rainfall gages recorded in five kinds of time intervals differ from each other, so rainfall network should be subject to revision.The digital elevation drainage network model (DEDNM) is used to preprocess the digital elevation model (DEM) data and to derive the watershed boundary, flow vector and drainage network automatically. On the basis of the digital drainage network and the Xinanjiang model, a grid-based distributed conceptual hydrological model is established to simulate the runoff processes with the distributed rainfall input.The step-wise interpolation method is used to interpolate the observed rain-gage data to the grid-based precipitation at the spatial resolution of 30s and is further up-scaled spatially and temporally. Then the impact of the spatio-temporal scale of precipitation on hydrological simulation is investigated by the grid-based hydrological model using the static parameter method and the dynamic parameter method. The numerical simulation shows that streamflow simulation is very sensitive to the temporal resolution of precipitation data;the coarser the temporal resolution, the worse the flood simulation performance;temporal aggregation of precipitation data may lead to the lost of rainfall information, therefore streamflow simulation should use the precipitation data at finer temporal resolutions as much as possible. Furthermore, the spatial resolution of precipitation data has unobvious effects on streamflow simulation. Analyses on the spatial distribution of the aggregated precipitation show that the precipitation aggregation method adopted in this study has more obvious impacts on areal precipitation than on the location of the maximum precipitation intensity.