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Downscaling Monsoon Rainfall over River Godavari Basin under Different Climate-Change Scenarios

机译:不同气候变化情景下戈达瓦里河流域季风降雨的降尺度

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摘要

Evaluating the impact of climate change at river basin level has become essential for proper management of the water resources. In the present study, Godavari River basin in India is taken as study area to project the monthly monsoon precipitation using statistical downscaling. The downscaling method used is a regression based downscaling termed as fuzzy clustering with multiple regression. Among the atmospheric variables simulated by global circulation/climate model (GCM) mean sea level pressure, specific humidity and 500 hPa geopotential height are used as predictors. 1 degrees x 1 degrees gridded rainfall data over Godavari river basin are collected from India Meteorological Department (IMD). A statistical relationship is established between the predictors and predictand (monsoon rainfall) to project the monsoon rainfall for the future using the Canadian Earth System Model (CanESM2) over IMD grid points under the Representative Concentration Pathways 2.6, 4.5 and 8.5 (RCP 2.6, 4.5, 8.5) scenarios of Fifth Coupled Model Inter-Comparison Project (CMIP 5). Downscaling procedure is applied to all 25 IMD grid points over the basin to find out the spatial distribution of monsoon rainfall for the future scenarios. For 2.6 and 4.5 scenarios results show an increasing trend. For scenario 8.5 rainfall showed a mixed trend with rainfall decreasing in the first thirty years of prediction and then increasing gradually over the next sixty years.
机译:评估气候变化在流域层面的影响对于适当管理水资源至关重要。在本研究中,以印度的戈达瓦里河流域为研究区域,使用统计缩减法预测了季风降水。所使用的缩减方法是基于回归的缩减,称为具有多重回归的模糊聚类。在全球循环/气候模式(GCM)模拟的大气变量中,平均海平面压力,比湿度和500 hPa地势高度被用作预测因子。从印度气象局(IMD)收集了Godavari流域的1度x 1度栅格化降雨数据。在预测因子和预测因子(季风降雨)之间建立统计关系,以使用加拿大地球系统模型(CanESM2)在代表浓度路径2.6、4.5和8.5(RCP 2.6、4.5)下使用IMD网格点预测未来的季风降雨,8.5)第五个耦合模型相互比较项目(CMIP 5)的场景。对流域内的所有25个IMD网格点都进行了降尺度处理,以找出未来情景下的季风降雨的空间分布。对于2.6和4.5场景,结果显示出增加的趋势。对于情景8.5,降雨显示出混合趋势,在预测的前30年中降雨减少,然后在接下来的60年中逐渐增加。

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