Simulating the Spatial-temporal Variability of Precipitation Response to Climate Change Scenarios in Great Lyon
Global climate has changed and will continue to change from some scientific evidences, so we need to improve our understanding of the global climate system to assess the possible impact of a climate change on hydrological processes. Hydrological impacts of climate change are frequently assessed by offline forcing of a hydrological model with climatic scenarios from Global Circulation Models (GCMs). However, GCMs, which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale in time and space. Therefore, it is necessary that the output from a GCMs has to be downscaled to obtain the relevant information to hydrologic studies. In this paper, the regional climate model TYN SC2.0 has been applied to the Great Lyon region to investigate the impact of future climate changes on the variability of spatial and temporal. The climate data-set TYN SC 2.0 of 0.5°×0.5° spatial resolution from Tyndall Centre is used, which include 8 climate change scenarios combinations of four emissions scenarios (A1F1, A2, B1, B2) and two global climate models (GCMs-CGCM2, HadCM3), covering 50% of the range of uncertainty in global warming in the 21st century published by the Intergovernmental Panel on Climate Change. For the spatial-temporal tendency analysis, the historical data include the precipitation from 1986 to 2005 in daily time step based on 30 gauges in Great Lyon area. Characteristics of precipitation including annual mean precipitation, rainfall intensity and spatial distribution from different climate model simulations are analyzed. Based on the spatial correlation statistical analyses methods of Arcgis, the spatial scale was downscaled to 30 rain gauges, and the rainfall spatial distribution was analyzed. A linear regression analysis is performed on the monthly scale data and max rain intensity of 1 h to assess the correlation of such a relation. Because of the good correlation, we anticipated the change tendency of max rain intensity in the future, and the contrary tendencies between annual precipitation and max rain intensity are abtained.
spatial - temporal precipitation distribution climate change downscaling
Too Tao Bernard Chocat Liu Suiqing Xin Kunlun
State key laboratory of Pollution Control and Resources Reuse, Tongji University, Shanghai,200092, C U. R. G. C. Hydrology Urbane, I. N. S. A. de Lyon, 69621, Villeurbanne Cedex, France
国际会议
郑州
英文
316-324
2009-10-20（万方平台首次上网日期，不代表论文的发表时间）