| Drought is one of the most destructive natural disasters.With the acceleration of the global warming and drying trend,the intensity and frequency of global drought events have increased to a certain extent.Southwest my country's karst area is the largest and most fully developed area of contiguous exposed carbonates among the three major karst concentrated distribution areas in the world.The long-term strong karstification has resulted in the formation of a surface and underground double-layered space structure,with shallow soil layers,sub-distributed soils and a small amount of soil,and the ecological environment is extremely fragile.Coupled with the uneven distribution of precipitation in space and time,large surface infiltration coefficient,deep groundwater table and other characteristics,drought events often occur.Therefore,the research on drought in the southwest karst area is of great significance to the sustainable development of the local ecological environment and the optimal management of water resources.Traditional drought monitoring is to collect meteorological data from meteorological stations to calculate the drought index.This kind of "point" station-type drought research is not only difficult to collect data,complicated work,coupled with the shortcomings of scarcity and uneven distribution of stations,it is difficult to be continuous in time and space.With the development of remote sensing technology and the emergence of products with high temporal and spatial resolution,the shortcomings of traditional methods have been well compensated,so that the research on drought based on remote sensing has gradually become a hot spot.There are many remote sensing drought indices,and the data and methods required for the calculation of different indices are different,resulting in different applicable areas.In this paper,MODIS products are used as the main data source,and the five most commonly used remote sensing drought index types are selected,and then the widely used remote sensing drought indices are selected from each type.Drought Severity Index(DSI)in karst areas.And this index is used to study the drought in the southwest karst area.The temporal and spatial distribution characteristics of DSI and its calculated components were analyzed by Theil-Sen Median trend analysis and Mann-Kendall test;the effects of temperature,precipitation,wind speed,sunshine hours,and relative humidity on DSI were analyzed by simple correlation analysis.The influence of the underlying surface environment on DSI.The main findings are as follows:(1)Examining the applicability of the five remote sensing indices in the southwestern karst area shows that: the Drought Severity Index(DSI)and the Standardized Precipitation Evapotranspiration Index(SPEI)of the selected 24 stations have a significant correlation ratio of 62.5 %,and it also restores the 4 dry months in the southwest karst region well in space.It shows that DSI can not only better characterize the meteorological drought in the southwest karst region,but also effectively invert its agricultural drought.(2)From 2001 to 2020,the multi-year average of DSI in the southwest karst area was 0,indicating that the overall dry and wet conditions in the southwest karst area were in a normal state during the study period.Its arid areas are mainly concentrated in the northwest of Guizhou,the northwest and southeast of Yunnan,and the south of Guangxi.The interannual variation showed an increasing trend,with a slope of0.0554 and an R2 of 0.8631,indicating that the drought in the southwestern karst area was alleviated during the study period.(3)The dry and wet changes in the southwest karst area have obvious seasonality,the overall winter is the driest,and the summer is the wettest.The driest in Guizhou and Guangxi occurred in winter,and the driest in Yunnan occurred in spring;the wettest in Guizhou and Guangxi occurred in summer,and the wettest in Yunnan occurred in autumn.The maximum monthly average value of the overall DSI in the southwest karst region is August,and the minimum value is March.Among them,the maximum value in Guizhou Province is in July,and the minimum value is in March;the maximum value in Yunnan Province is in September,and the minimum value is in March;the maximum value in Guangxi Zhuang Autonomous Region is in August,and the minimum value is in February.(4)During the study period,the drought frequency in the southwest karst area showed a decreasing trend,indicating that the occurrence of drought events was decreasing;the average CV of the multi-year drought frequency was 0.65,indicating that the fluctuation of drought events was high,especially in Guangxi.(5)The correlation between climate factors and DSI is in descending order:temperature(80.47%)> wind speed(77.56%)> precipitation(75.5%)> relative humidity(66.24%)> sunshine hours(-54.21%).It shows that except the sunshine hours and DSI which are negatively correlated,other factors are positively correlated,and the temperature has a great influence on the dry and wet changes in the southwest karst area.(6)Non-karst areas have better vegetation growth environment and stronge r water retention capacity than karst areas.Therefore,under the same condition s,non-karst areas are wetter than karst areas.Different karst landform types ar e ranked from large to small according to the annual mean value of drought fr equency: Karst middle and high mountain area > karst canyon area > rifted ba sin area > Fengcong depression area > Fenglin plain area > karst plateau area> karst trough valley area;the multi-year mean values of drought frequency a t different altitude gradients are in descending order: 5000 m ?>3000?3500m>3500?4000m>2500?3000>1500?2000m>0?500m>4500?5000m>4000?4500m>1000?1500m>500?1000m;different land use types are from large to small according t o the drought frequency The order is: construction land>shrub>arable land>gras sland>coniferous forest>mixed forest>broad-leaved forest.(7)Try to use geographic detectors to detect the explanatory power of each factor on DSI,taking 2010 as an example.The explanatory power of a single factor for DSI is ranked in descending order: sunshine hours(0.27)> relative humidity(0.232)>DEM(0.216)> precipitation(0.155)> karst landform(0.154)> wind speed(0.095)>slope(0.048)> air temperature(0.041)> roughness(0.024)> land use type(0.023);the influence of each factor on DSI is not independent of each other,nor is it a simple superposition,but the interaction is significant,the interaction is enhanced or nonlinear Enhancement effect;the risk detector shows that air temperature,precipitation,sunshine hours,relative humidity,DEM,and karst landform have a greater impact on the spatial distribution of DSI,while wind speed,slope,roughness and land use type have less impact on DSI. |
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