Multi-scenario dynamic prediction of ecological risk assessment in an arid area of northwest China

Abstract

Ecological security is essential for the development of environmental health and human society. However, the driving mechanisms and spatial non-stationarity of ecological risks are still unknown, particularly in arid areas. Thus, this study constructed an ecological risk index (ERI) research framework in arid areas based on land use and socio-economic data from 1980 to 2020 to investigate the spatiotemporal evolution trend, driving mechanisms, and spatial non-stationarity of ecological risk and predict the ecological risk under business-as-usual, economic development, and ecological protection scenarios using a patch-generating land use simulation (PLUS) model. The results show that: (1) cropland and build-up land were expanding, whereas forestland and grassland water bodies were shrinking. Compared with the two development stages from 1980 to 2000 and from 2000 to 2020, cropland increased by 4.73% and 51.52%, respectively, whereas build-up land increased by 12.64% and 106.52%, respectively, indicating rapid development and drastic changes in the past 20 years. (2) With the change in landscape diversity, the regional ecological risks showed an increasing trend, whereas the future ecological risks showed an increasing trend because of the cumulative effect of landscape pattern changes caused by human activities. (3) Moreover, dramatic changes in ecological risk, particularly in areas of intense human activity in build-up land, agricultural land, artificial oases, and mountainous areas. Corresponding to the analysis of driving mechanisms, population, GDP, distance to government, water, and primary roads; have a significant effect on ecological risk, whereas DEM has direct and indirect effect paths. Ultimately, the proposed analysis framework can present the spatiotemporal changes of ecological risks and their influencing mechanisms, which can help develop sustainable development policies.

Keywords

• Ecological risk assessment
• Driving factors
• Scenario simulations
• Arid areas