Evaluation and Prediction of Ecosystem Health in Gansu Section of Yellow River Basin Based on PSR Model
[Objective]The health level of ecosystems in Gansu section of the Yellow River basin was evaluated,in order to provide a theoretical reference and decision-making basis for the ecological protection and high-quality development of the river basin.[Methods]Based on panel data from 2011 to 2021,the PSR model was used to build an ecosystem health evaluation index system in Gansu section of the Yellow River basin.The entropy method,comprehensive index method,and GM-ARIMA prediction model were used to comprehensively evaluate and predict the health level of ecosystems in the river basin.[Results]① From 2011 to 2021,the comprehensive index of urban and state ecosystems showed an upward trend.In the important water supply ecological functional area of Gannan Yellow River,the health level of the Gannan ecosystem was"inferior",and the health level of the Linxia ecosystem had changed from"inferior"to"poor".In the Loess Plateau,the health level of the Lanzhou ecosystem had changed from"medium"to"good",and the health level of the Tianshui,Dingxi,and Pingliang ecosystems had changed from"inferior"to"poor".The health level of Baiyin and Qingyang ecosystems was"inferior".In the sandstorm comprehensive preventions and control area,the health level of the Wuwei ecosystem was"inferior".② The comprehensive ecological environmental index from 2022 to 2031 is predicted to increase,but the ecosystem health level will not change significantly.[Conclusion]The overall health level of ecosystems in the Gansu section of the Yellow River basin is not high.Current ecological protection and environmental improvement measures have not fully solved the challenges faced by the ecological environment in the region.In the future,it will be necessary to further strengthen supervision and management to improve the health level of ecosystems in the region.
Gansu section of Yellow River basinecosystem healthpressure-state-response modleevaluation and prediction
国家科技期刊平台
NSTL
万方数据
