Studying the evolutions, differences, and water security impacts of water demands under shared socioeconomic pathways: A SEMs-bootstrap-ANN approach applied to Sichuan Province

Li Mo; Sijing Lou; Yongqiang Wang; Zixuan Liu; Pingan Ren

doi:10.1016/j.jenvman.2023.119455

Studying the evolutions, differences, and water security impacts of water demands under shared socioeconomic pathways: A SEMs-bootstrap-ANN approach applied to Sichuan Province

J Environ Manage. 2024 Jan 1:349:119455. doi: 10.1016/j.jenvman.2023.119455. Epub 2023 Oct 31.

Authors

Li Mo¹, Sijing Lou², Yongqiang Wang³, Zixuan Liu⁴, Pingan Ren⁵

Affiliations

¹ School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan, 430074, China; Institute of Water Resources and Hydropower, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address: moli@hust.edu.cn.
² School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan, 430074, China; Institute of Water Resources and Hydropower, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address: sijing_lou@hust.edu.cn.
³ Institute of Comprehensive Utilization of Water Resources, Changjiang River Scientific Research Institute of Changjiang Water Resource Commission, Wuhan, Hubei, 430074, China. Electronic address: wangyq@mail.crsri.cn.
⁴ School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan, 430074, China; Institute of Water Resources and Hydropower, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address: zixuan_liu@hust.edu.cn.
⁵ School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan, 430074, China; Institute of Water Resources and Hydropower, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address: rpa@hust.edu.cn.

PMID: 37918238
DOI: 10.1016/j.jenvman.2023.119455

Abstract

In this study, a SEMs-bootstrap-ANN method was presented for constructing prediction intervals (PIs) of water demand under shared socioeconomic pathways (SSPs). The primary objective was to examine the evolution, disparities, and impacts on water security. Initially, a bootstrap algorithm and an artificial neural network (ANN) were combined to form a bootstrap-ANN model, which determined the centres and widths of the PIs at a specified significance level by estimating the distributions of prediction values and errors. The water demand factors in SSPs were projected using socioeconomic models like Cobb-Douglas, based on the narratives of the International Institute for Applied Systems Analysis (IIASA). By incorporating these factors into the bootstrap-ANN model, the study obtained the temporal changes of water demand PIs in SSPs, while quantifying the differences and water security implications using the interval difference index (IDI) and surface water exploration index (SWEI). The case study focused on Sichuan province, and the model performance was evaluated via the evaluation indices and cross-validation. The results demonstrated five key findings. Firstly, the proposed method showed a greater PICP of 0.985, slightly larger PIRAW of 9.83%, and higher MAIS than other methods in the historical dataset, indicating a small disadvantage in width in return for better accuracy and overall performance. Secondly, the reliability of the results in the SSP period was supported by the PIRAWs (mostly within 15%), the cross errors (approximately 5%), and their performance in 2021 (the PIs in SSP2 almost covered all true values). Thirdly, the total water demands in all SSPs within Sichuan Province exhibited a consistent upward trajectory, with SSP5 displaying the highest increase of 44-63% compared to current water usage. Fourthly, among the four SSPs, the most substantial disparities were observed between SSP5 and SSP3, reaching a maximum difference of 32%. Conversely, the disparities between SSP2 and SSP1 fluctuated around zero, transitioning from negative to positive trends. Notably, from an environmental perspective, SSP1 was considered preferable to SSP2. Lastly, the SWEIs, which reflected water security conditions in Sichuan Province under the four SSPs, ranked in the following order: SSP3, SSP1, SSP2, and SSP5, indicating a progressively worsening situation. Despite not reaching stress thresholds even during dry years until 2100, the water security conditions could deteriorate by 28-46% compared to historical extremes and by 3-16% compared to extended extremes in dry years.

Keywords: Climate change; Shared socioeconomic pathways; Surface water exploration index; Water demand forecast; Water security.

MeSH terms

China
Climate Change
Neural Networks, Computer
Reproducibility of Results
Socioeconomic Factors
Water Supply*
Water*

Substances

Water