Assessment of water resource security in karst area of Guizhou Province, China

Liying Liu

doi:10.1038/s41598-021-87066-5

Abstract

This paper presents the assessment of water resource security in the Guizhou karst area, China. A mean impact value and back-propagation (MIV-BP) neural network was used to understand the influencing factors. Thirty-one indices involving five aspects, the water quality subsystem, water quantity subsystem, engineering water shortage subsystem, water resource vulnerability subsystem, and water resource carrying capacity subsystem, were selected to establish an evaluation index of water resource security. In addition, a genetic algorithm and back-propagation (GA-BP) neural network was constructed to assess the water resource security of Guizhou Province from 2001 to 2015. The results show that water resource security in Guizhou was at a moderate warning level from 2001 to 2006 and a critical safety level from 2007 to 2015, except in 2011 when a moderate warning level was reached. For protection and management of water resources in a karst area, the modes of development and utilization of water resources must be thoroughly understood, along with the impact of engineering water shortage. These results are a meaningful contribution to regional ecological restoration and socio-economic development and can promote better practices for future planning.

Highlights

The concept of water resource security emerged in the late 1990s; Water has been widely regarded as the most essential natural resource[1]
Some water resource security assessment was emerged by considering climate change and human activity
Solving the problem of engineering water shortage is key to ensure water resource security in the karst area. It can be seen from the subsystems of the indices sorted by the absolute mean impact value (MIV) that the engineering water shortage subsystem had the greatest impact on water resource security in the karst area, which is the main reason to promote its transformation

Summary

Introduction

The concept of water resource security emerged in the late 1990s; Water has been widely regarded as the most essential natural resource[1]. Some water resource security assessment was emerged by considering climate change and human activity. Climate simulations project a strong increase in temperature and a decrease of precipitation in many karst regions in the world over the decades Despite this potentially bleak future, few studies quantify the impact of climate change on karst water resources[14]. With climate change and human activities, water resource systems are facing greater complexity and uncertainty, and water environment systems are extremely vulnerable to impacts and damage, especially in karst areas. Several recent studies have illustrated the value of ANN models in the assessments of the quality of the aquatic environment[21,22], water quality[23,24], land ecological security[25], carrying capacity of the aquatic environment[26], and karst groundwater m anagement[27]. GA-BP have been widely used in various fields, achieved good results, and become important intelligent algorithms[28,29], and there is no exception in the water science field

Findings

Discussion

Conclusion