Evaluation and risk identification of water resources carrying capacity in Guizhou Province based on combined weights-TOPSIS model
Based on the 2010-2020 water resources bulletin and statistical yearbook data and other relevant data,taking Guizhou Province as the research object,16 indicators were selected from the four aspects of water resources,society,economy,and ecology to construct a water resources carrying capacity evaluation index system.The TOPSIS model with combined weight was used to conduct a water resources carrying capacity evaluation analysis for the 88 counties in the study area.The results show that:1)The change range of county-level water resources carrying capacity in Guizhou Province in 2020 was 0.329～0.716,and the overall difference in water resources carrying capacity was significant.The water resources carrying capacity in 2020 has been significantly improved compared with that in 2010.In addition,the water resources carrying capacity in the central Guizhou region is lower,while the water resources carrying capacity in the surrounding regions is higher,and the water resources carrying capacity in the Qian'an region is the highest.2)Using ArcGIS,the global Moran index analysis was conducted on the water resources carrying capacity index of Guizhou Province's counties.The Moran index of Guizhou Province's county-level water resources carrying capacity was 0.515,and the z value was significantly greater than the critical value of 2.58,and the p value was less than the significance level.Guizhou Province's water resources carrying capacity index shows a spatial agglomeration trend.3)Through the geographic detector analysis,it can be found that the single factor with the greatest explanatory power for water resources carrying capacity is per capita water resources holding;the result of the influence degree of double factor interaction on water resources carrying capacity shows that per capita water resources holding is the dominant factor of water resources carrying capacity.
Water resources carrying capacitycombined weights-TOPSIS modelhot spot analysisgeographical detectorspatio-temporal distribution revision
NETL
NSTL
万方数据
