Study On Fine-scale Estimating Climatic Model For The Spatial Distribution Of Precipitation Over The Rugged Terrain In Yangtze

The paper firstly built two theoretical models to estimate respectively the precipitation background field and the precipitation increment caused by orographic lifting. And then, a fine-scale mixed models was established combining with the statistical methods, based on that, the spatial distribution of precipitation over the rugged terrain in Yangtze river basin was estimated with1km×1km resolution on GIS integrated the ground-based station data, digital elevation model (DEM) and NCEP reanalysis data. This paper obtained the following conclusions:1. The precipitation background field theoretical model can provide the distribution of annual and monthly precipitation background field quantity in Yangtze river basin. The annual and monthly precipitation background field distribution could describe the climate distribution regular pattern and characteristics of precipitation. So this theoretical model was considered practicable and dependable.2. The simulation result of correction factor for aspect could finely describe topographic characteristics of windward slopes and leeward slope. The annual and monthly precipitation increment quantity caused by orographic lifting could describe the topoclimate distribution regular pattern and characteristics of precipitation and could characterize the effect of moisture and topography conditions.3. Combined with statistical method, the model coefficient was estimated. Ultimately, the fine-scale mixed models was established to estimate the spatial distribution of precipitation over the rugged terrain in Yangtze river basin which could reflect the comprehensive influence of meteorological elements including water vapour, wind speed and wind direction. Getting rid of the individual stations which errors was caused by data precision, the annual and monthly relative errors were all below20%. So the mixed model based on physical mechanism has reliable theory and results.4. To explain the quality of the mixed model, this paper built a regression model based on the correction factor of slope and aspect. The corrected regression model could not indicate the physical connection between water vapor transmission and convergence with precipitation. Because of the variation of direction of water vapor transmission in each season, the difference of spatio-temporal distribution of precipitation with windward slope and leeward slope was not obvious. So the mixture model was better than corrected regression model in the simulation of terrain precipitation. But owing to the limitation of the physical equation form and the space precision(horizontal and vertical space) of meteorological data, the simulation results of mixture model also has some deficiency. For instance, because of the coupling calculation with NCEP data, the accuracy of altitude factor was lower, therefore, the influence of elevation to precipitation was less remarkable than the corrected regression model. So it was necessary to further perfect model, explore new solutions and new data, and improve the simulation accuracy.5. In the model, DEM data provided the intensification terrain factors. GIS software provided an efficient work platform for the grid coupling of terrain factors and meteorological factors. It also provided a powerful tool for the visual mapping of simulated results. The function of GIS technology and GIS data was powerful in the refining estimation of climate elements and visualization. In addition, GIS technology and GIS data needed to be better further development and utilization.