Early Warning Method for Regional Water Resources Carrying Capacity Based on the Logical Curve and Aggregate Warning Index

Menglu Chen, Juliang Jin, Shaowei Ning, Yuliang Zhou and Parmeshwar Udmale
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Menglu Chen: School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
Juliang Jin: School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
Shaowei Ning: School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
Yuliang Zhou: School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
Parmeshwar Udmale: Department of Development and Sustainability, Asian Institute of Technology, Pathumthani 12120, Thailand

IJERPH, 2020, vol. 17, issue 7, 1-15

Abstract: The sustainable utilization of water resources is a significant factor in the development of the national economy and society. Regional water resources carrying capacity (RWRCC) is an appropriate method for evaluating the balance in such utilization. In this paper, we combined time difference correlation analysis and set pair analysis firstly to identify the early warning sign index (EWSI) for RWRCC, and warning limits were determined using a logical curve. Analytic hierarchy process based on the accelerating genetic algorithm (AGA-AHP) method was used to improve the KLR model by determining weights objectively. We took advantage of the new improved model to build the aggregate warning index (AWI). Then, according to the corresponding relationship between EWSI and AWI, the early warning system for regional water resources carrying capacity (EWS-RWRCC) was established, and a case study was carried out in Anhui Province. The results showed there are eight effective EWSI obtained through the early warning analysis process of RWRCC in Anhui Province, among which the repetitive use rate of industrial water and average daily coefficient have a greater impact on AWI. Basically, the EWS-RWRCC can describe RWRCC changes in Anhui Province. From 2006 to 2014, more than half the signal lights in Anhui Province were yellow and orange, which indicated a poor state. It has been proved that the constraints of population, GDP growth and water supply capacity on the utilization of water resources in the future will be further tightened, which should be considered for future monitoring and early warning. The early warning method we used here can be widely applied into other fields; the results will enhance monitoring capacity and scientifically guide regional water resources management.

Keywords: regional water resources carrying capacity; early warning; logical curve; KLR model; AGA-AHP; Anhui Province (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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