甘肃省是全国极端干旱的省份之一,干旱是影响甘肃省人民生活和社会经济生产的主要因素。一直以来,对甘肃省干旱状况的研究层出不穷。本文主要利用Ohlsson提出的Social Water Scarcity Index(SWSI)评估了甘肃省的社会水资源压力状况。SWS...甘肃省是全国极端干旱的省份之一,干旱是影响甘肃省人民生活和社会经济生产的主要因素。一直以来,对甘肃省干旱状况的研究层出不穷。本文主要利用Ohlsson提出的Social Water Scarcity Index(SWSI)评估了甘肃省的社会水资源压力状况。SWSI是由传统的水文水资源稀缺指数(HWSI)与人类发展指数(HDI)运算得来的。文中用《中国统计年鉴》、《甘肃省统计年鉴》和《中国水资源公报》提供的数据评估了甘肃省2000—2009年的社会水资源压力状况并与全国的平均状况做出了对比。结果显示:从2000年到2009年甘肃省的社会水资源压力呈明显的减小趋势,这说明甘肃省的干旱状况得到了明显的改观;但与全国的平均水平相比,甘肃省SWSI是全国平均水平的3～4倍,这说明与全国相比甘肃省还是严重的干旱省份。展开更多
This research is an attempt to develop a new GIS index to investigate the surface water susceptibility to pollution (SWSi). In this index, the Weighted Linear Combination techniques within GIS environment was used to ...This research is an attempt to develop a new GIS index to investigate the surface water susceptibility to pollution (SWSi). In this index, the Weighted Linear Combination techniques within GIS environment was used to calculate the overall surface water susceptibility to pollution scores based on using 6 factors. The model includes 3 natural factors: gradient slope, distance to surface water and soil. Also, it includes 3 man-made activities: urban, agriculture and roads. Each factor was given the appropriate weight and ratings and then the final index was calculated using GIS techniques. The final results showed that the study area (1235 km2) could be classified into low susceptibility with an area of 250 km2 (20.2%), moderate susceptibility with an area of 815.5 km2 (66%), high susceptibility with an area of 166.2 km2 (13.5%) and very high susceptibility with an area of 3.3 km2 (0.3%).展开更多
文摘甘肃省是全国极端干旱的省份之一,干旱是影响甘肃省人民生活和社会经济生产的主要因素。一直以来,对甘肃省干旱状况的研究层出不穷。本文主要利用Ohlsson提出的Social Water Scarcity Index(SWSI)评估了甘肃省的社会水资源压力状况。SWSI是由传统的水文水资源稀缺指数(HWSI)与人类发展指数(HDI)运算得来的。文中用《中国统计年鉴》、《甘肃省统计年鉴》和《中国水资源公报》提供的数据评估了甘肃省2000—2009年的社会水资源压力状况并与全国的平均状况做出了对比。结果显示:从2000年到2009年甘肃省的社会水资源压力呈明显的减小趋势,这说明甘肃省的干旱状况得到了明显的改观;但与全国的平均水平相比,甘肃省SWSI是全国平均水平的3～4倍,这说明与全国相比甘肃省还是严重的干旱省份。
文摘This research is an attempt to develop a new GIS index to investigate the surface water susceptibility to pollution (SWSi). In this index, the Weighted Linear Combination techniques within GIS environment was used to calculate the overall surface water susceptibility to pollution scores based on using 6 factors. The model includes 3 natural factors: gradient slope, distance to surface water and soil. Also, it includes 3 man-made activities: urban, agriculture and roads. Each factor was given the appropriate weight and ratings and then the final index was calculated using GIS techniques. The final results showed that the study area (1235 km2) could be classified into low susceptibility with an area of 250 km2 (20.2%), moderate susceptibility with an area of 815.5 km2 (66%), high susceptibility with an area of 166.2 km2 (13.5%) and very high susceptibility with an area of 3.3 km2 (0.3%).
