{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T08:06:02Z","timestamp":1772784362917,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41200881"],"award-info":[{"award-number":["41200881"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As a vital part of the geo-environment and water cycle, ecosystem health and human development are dependent on water resources. Water supply and demand are influenced significantly by land use and cover change (LUCC) which shapes the surface ecosystems by altering their structure and function. Under future climate change scenarios, LUCC may greatly impact regional water balance, yet the impact is still not well understood. Therefore, examining the spatial relationship between LUCC and water yield services is crucial for optimizing land resources and informing sustainable development policies. In this study, we focused on the Hanjiang River Basin and used the patch-generating land use simulation (PLUS) model, coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to assess water yield services under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. For the first time, we considered the impact of future changes in socio-economic and water use indicators on water demand using correction factors and ARIMA projections. The relationship between water supply and demand was explored using this approach, and LUCC\u2019s effects on this balance are also discussed. Results indicate that: (1) The patterns of LUCC are similar for the three scenarios from 2030 to 2050, with varying levels of decrease for cropland and significant growth of built-up areas, with increases of 6.77% to 19.65% (SSP119), 7.66% to 22.65% (SSP245), and 15.88% to 46.69% (SSP585), respectively, in the three scenarios relative to 2020; (2) The future supply and demand trends for the three scenarios of produced water services are similar, and the overall supply and demand risks are all on a downward trend. Water demand continues to decline, and by 2050, the water demand of the 3 scenarios will decrease by 96.275\u00d7108t, 81.210\u00d7108t, and 84.13\u00d7108t relative to 2020, respectively; while supply decreases from 2030 to 2040 and rises from 2040 to 2050; (3) Both water supply and demand distributions exhibit spatial correlation, and the distribution of hotspots is similar. The water supply and demand are well-matched, with an overall supply-demand ratio greater than 1.5; (4) LUCC can either increase or decrease water yield. Built-up land provides more water supply compared to other land types, while forest land has the lowest average water supply. Limiting land use type conversions can enhance the water supply.<\/jats:p>","DOI":"10.3390\/rs16224136","type":"journal-article","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T06:27:39Z","timestamp":1730874459000},"page":"4136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Quantifying the Effect of Land Use and Land Cover Changes on Spatial-Temporal Dynamics of Water in Hanjiang River Basin"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9253-2838","authenticated-orcid":false,"given":"Hao","family":"Xi","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Yanbin","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Heng","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1167-6730","authenticated-orcid":false,"given":"Xiaopan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,6]]},"reference":[{"key":"ref_1","first-page":"267","article-title":"Geological remote sensing","volume":"64","author":"Bishop","year":"2018","journal-title":"Int. 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