Assessing the Impacts of Climate and Land Use Change on Water Conservation in the Three-River Headstreams Region of China Based on the Integration of the InVEST Model and Machine Learning

Xinyue Xie, Min Peng, Linglei Zhang, Min Chen, Jia Li and Youcai Tuo ()
Additional contact information
Xinyue Xie: State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China
Min Peng: Gansu Province Water Resources and Hydropower Survey Design Institute Co., Ltd., Lanzhou 730000, China
Linglei Zhang: State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China
Min Chen: State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China
Jia Li: State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China
Youcai Tuo: State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China

Land, 2024, vol. 13, issue 3, 1-33

Abstract: The Three-River Headstreams Region (TRHR) serves as the cradle of China’s three major rivers—the Yangtze, Yellow, and Lancang—rendering its water conservation (WC) capacity quintessentially significant for Asian water resource security. This study integrated the InVEST model and random forest model to holistically elucidate the spatiotemporal characteristics and factors influencing WC in the TRHR from 1980 to 2018. The results revealed that the WC growth rate was 5.65 mm/10a in the TRHR during the study period, yet pronounced regional disparities were observed among different basins, especially with the Lancang River Basin (LRB), which exhibited a decrease at a rate of 5.08 mm per decade despite having the highest WC. Through Theil–Sen trend analysis, the Mann–Kendall abrupt change test, and the cumulative deviation method, meteorological, vegetative, and land use abrupt changes in approximately 2000 were identified as the primary drivers for the abrupt surge in WC within the TRHR. Furthermore, precipitation and the aridity index were the core feature variables affecting WC. However, a positive transition in land use patterns post-2000 was also revealed, and its favorable effect on WC was not as significant as the abrupt climatic changes. This study offers new perspectives on managing multidimensional spatiotemporal data and contributes to laying the groundwork for machine learning applications in water conservation. Additionally, it potentially provides useful references for decision-making processes related to ecosystem security.

Keywords: InVEST model; random forest; regional disparities; spatial–temporal analysis; water conservation capacity (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/3/352/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/3/352/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:3:p:352-:d:1353996

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().