Spatial and temporal evolution of the service value of ice and snow ecosystem in Heilongjiang Province
Heilongjiang Province is a typical cold region in northern China,the climate change is complicated by the influence of cold air masses in winter,rich in ice and snow resources,the ice and snow economy is devel-oping rapidly in Heilongjiang Province and the distribution of ice and snow has a significant impact on the social and economic development of the region. On the basis of the previous ecosystem services,the ice and snow eco-system services in Heilongjiang Province are divided into three primary services:supply services,regulation ser-vices,and cultural services,and further divided into eight secondary services:freshwater resources,climate reg-ulation,runoff regulation,farmland drought control,soil entropy conservation,air humidification,atmospheric purification,and ice and snow culture. On the basis of MODIS,statistical data and weather station data,the spa-tiotemporal evolution of ice and snow ecosystem service value in Heilongjiang Province from 2012 to 2022 was studied. The results show that the snow cover area in Heilongjiang Province showed an increase trend in 10 years,from 108×104 km2 in 2012 to 115×104 km2 in 2022,among which Harbin changed the most,with the change of 18.08×104 km2. Suihua City has changed a little,and the amount of change was 2.30×104 km2. Af-fected by global climate warming,the total value of ice and snow ecosystem services in Heilongjiang Province shows a trend of the first,and then increasing,decreasing from 3874.11×108 RMB yuan in 2012 to 3041.65× 108 RMB yuan in 2017,and then increasing to 5501.92×108 RMB yuan in 2022,showing the characteristics of high in north and south and low in east and west. The spatial distribution of ecosystem service value is basically consistent with the distribution of snow cover area. Among the first-level services,regulation services accounted for the largest proportion of total ecosystem services,reaching 74.11％,and supply services accounted for the smallest proportion,10.25％,cultural services are in the middle,accounting for 15.64％ of the total. Climate regulation services accounted for the largest proportion,38.46％,and the air humidification service was the smallest,with 0.18％. In the past ten years,except for the reduction of soil moisture preservation and farmland drought control,the value of other services has increased to varying degrees. The ecological contribution of Daxing'anling Prefecture,Heihe City,Jiamusi City and Shuangyashan City is 20.45％,18.81％,14.97％ and 10.21％,respectively,which are the main contribution factors. It is mainly located in the eastern and northern regions,which is consistent with the spatial distribution characteristics of ice and snow value. At present,the re-search on ecosystem services is mainly oriented to natural ecosystems such as woodlands and wetlands,and the value equivalent method is used to study the value equivalent method,compared with the functional method,the value equivalent method is simpler to evaluate the value of ecosystem services,but the accuracy is lower. Ice and snow ecosystems are always mentioned,but the spatiotemporal evolution of ice and snow ecosystem servic-es has not been studied. In this paper,the ice and snow ecosystem in Heilongjiang Province was taken as the re-search object,and the functional quantity method and ecological contribution method were combined with the winter snowfall,snow cover area and snow depth in Heilongjiang Province to estimate the service value of ice and snow ecosystem in Heilongjiang Province from 2012 to 2022,and to explore its spatiotemporal evolution characteristics,so as to improve people's understanding of ice and snow services and promote the integration of ice and snow economy and traditional economic development,in order to provide reference for the development and utilization of ice and snow resources and related policies in Heilongjiang Province.
Heilongjiang Provinceice and snowecosystemservice valuespatial and temporal evolutionecological contribution
万方数据
维普
