The driving force of water resource stress change based on the STIRPAT model:take Zhangye City as a case study

A prominent contradiction between supply and demand of water resources has restricted local development in social and economic aspects of Zhangye City,located in a typical arid region of China.Our study quantified the Water Resource Stress Index(WRSI)from 2003 to 2017 and examined the factors of population,urbanization level,GDP per capita,Engel coefficient,and water consumption per unit of GDP by using the extended stochastic impact by regression on population,affluence and technology(STIRPAT)model to find the key factors that impact WRSI of Zhangye City to relieve the pressure on water resources.The ridge regression method is applied to improve this model to eliminate multicollinearity problems.The WRSI system was developed from the following three aspects:water resources utilization(WR),regional economic development water use(WU),and water environment stress(WE).Results show that the WRSI index has fallen from 0.81(2003)to 0.17(2017),with an average annual decreased rate of 9.8%.Moreover,the absolute values of normalized coefficients demonstrate that the Engel coefficient has the largest positive contribution to increase WRSI with an elastic coefficient of 0.2709,followed by water consumption per unit of GDP and population with elastic coefficients of 0.0971 and 0.0387,respectively.In contrast,the urbanization level and GDP per capita can decrease WRSI by−0.2449 and−0.089,respectively.The decline of WRSI was attributed to water-saving society construction which included the improvement of water saving technology and the adjustment of agricultural planting structures.Furthermore,this study demonstrated the feasibility of evaluating the driving forces affecting WRSI by using the STIRPAT model and ridge regression analysis.

Spatiotemporal dynamics of population density in China using nighttime light and geographic weighted regression method

The distribution and dynamic changes of regional or national population data with long time series are very important for regional planning,resource allocation,government decision-making,disaster assessment,ecological protection,and other sustainability research.However,the existing population datasets such as LandScan and WorldPop all provide data from 2000 with limited time series,while GHS-POP only utilizes land use data with limited accuracy.In view of the limited remote sensing images of long time series,it is necessary to combine existing multi-source remote sensing data for population spatialization research.In this research,we developed a nighttime light desaturation index(NTLDI).Through the cross-sensor calibration model based on an autoencoder convolutional neural network,the NTLDl was calibrated with the same period Visible Infrared Imaging Radiometer Suite Day/Night Band(VIRS-DNB)data.Then,the geographically weighted regression method is used to determine the population density of China from 1990 to 2020 based on the long time series NTL.Furthermore,the change characteristics and the driving factors of China's population spatial distribution are analyzed.The large-scale,long-term population spatialization results obtained in this study are of great significance in government planning and decision-making,disaster assessment,resource allocation,and other aspects.