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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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xinyue Xie & Min Peng & Linglei Zhang & Min Chen & Jia Li & Youcai Tuo, 2024. "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," Land, MDPI, vol. 13(3), pages 1-33, March.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:3:p:352-:d:1353996
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    References listed on IDEAS

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    1. Meng Zhao & Geruo A & Yanlan Liu & Alexandra G. Konings, 2022. "Evapotranspiration frequently increases during droughts," Nature Climate Change, Nature, vol. 12(11), pages 1024-1030, November.
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