Optimizing land-use structure in the Jinghe River Basin under the goal of efficient water resource utilization

doi:10.1016/j.jaridl.2026.11.004

Journal of Arid Land

2026, Vol. 18

Issue (11): 1919-1939 DOI: 10.1016/j.jaridl.2026.11.004

Research article

Optimizing land-use structure in the Jinghe River Basin under the goal of efficient water resource utilization

ZHENG Dan, PEI Tingting^*(

), CHENG Hong, CHEN Ying, XIE Baopeng

College of Management, Gansu Agricultural University, Lanzhou 730070, China

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Abstract

The efficient use of limited water resources remains a critical challenge in arid and semi-arid areas, where land-use structure exerts strong control over eco-hydrological processes. The Jinghe River Basin, located in the central Loess Plateau of China, is a typical semi-arid watershed that has undergone pronounced land-use transitions under the Grain-for-Green Program. This study developed a sub-basin- scale analytical framework to optimize the land-use structure in the Jinghe River Basin to improve water-use efficiency. Multi-source remote sensing data (2002-2022) were used to classify the dominant land-use structures based on K-means clustering, and to quantify the key hydrological indicators, including the runoff coefficient, evapotranspiration ratio, and water yield. The eco-hydrological thresholds and trade-off-synergy relationships were identified through boundary-line and correlation analyses, and were then incorporated into the land-use optimization model that was solved using LINGO and spatially simulated in R v.4.2.2 software. The results showed that the basin can be classified into grassland-dominated, cropland- dominated, and mixed cropland-forestland-grassland sub-basins with distinct hydrological responses. Moreover, the optimization results based on these boundary-line thresholds indicated structure-specific optimal land-use proportions for water-use efficiency: the grassland proportion was approximately 0.85 in grassland-dominated sub-basins, and the cropland proportion was approximately 0.63 in cropland-dominated sub-basins. In mixed cropland-forestland-grassland sub-basins, the optimal cropland and grassland proportions were approximately 0.33 and 0.41, respectively, with a composite utilization index of 0.35. These thresholds represented the balance points among the runoff regulation, evapotranspiration control, and water-yielding capacity under limited water resources in the region. By explicitly linking the land-use configuration with the hydrological efficiency through threshold-based constraints, this study provides a data-efficient operational framework for coordinated land-water management in the Loess Plateau, as well as other semi-arid river basins.

Key words： land-use optimization water-use efficiency K-means clustering eco-hydrological thresholds boundary line analysis Loess Plateau

Received: 13 November 2025 Published: 30 November 2026

Corresponding Authors: ^*PEI Tingting (E-mail: peitt@gsau.edu.cn)

About author: Author contributions

Conceptualization: PEI Tingting; Data curation: ZHENG Dan, CHENG Hong, CHEN Ying, XIE Baopeng; Formal analysis: ZHENG Dan, CHENG Hong; Funding acquisition: PEI Tingting; Investigation: CHENG Hong; Methodology: ZHENG Dan, PEI Tingting; Project administration: PEI Tingting, CHEN Ying; Resources: PEI Tingting; Software: ZHENG Dan; Supervision: ZHENG Dan, CHENG Hong; Validation: ZHENG Dan, Visualization: ZHENG Dan, CHENG Hong, CHEN Ying, XIE Baopeng; Writing - original draft preparation: ZHENG Dan; Writing - review and editing: ZHENG Dan, PEI Tingting, CHEN Ying, XIE Baopeng. All authors approved the manuscript.

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Cite this article:

ZHENG Dan, PEI Tingting, CHENG Hong, CHEN Ying, XIE Baopeng. Optimizing land-use structure in the Jinghe River Basin under the goal of efficient water resource utilization. Journal of Arid Land, 2026, 18(11): 1919-1939.

URL:

http://jal.xjegi.com/10.1016/j.jaridl.2026.11.004 OR http://jal.xjegi.com/Y2026/V18/I11/1919

Fig. 1 Spatial distribution of land use in the Jinghe River Basin in 2022

Table 1 Main datasets and their descriptions

Fig. 2 Delineation of 29 sub-basins in the Jinghe River Basin overlaid on the digital elevation model (DEM). Sub-basin numbers are labeled within each sub-basin.

Table 2 Biophysical parameters used in the model

Fig. 3 Schematic illustration of the conversion of the boundary line thresholds into linear constraints for land-use optimization

Fig. 4 Radar charts of cluster centers for the three land-use structure types identified by K-means clustering. (a), grassland-dominated type; (b), cropland-dominated type; (c), mixed cropland-forestland-grassland type.

Fig. 5 Spatial distribution of different land-use structure types in the Jinghe River Basin from 2002 to 2022. (a1-e1), grassland-dominated type; (a2-e2), cropland-dominated type; (a3-e3), mixed cropland-forestland- grassland type. Sub-basin numbers are labeled within each sub-basin.

Fig. 6 Spatial distributions of multi-year average runoff coefficient (RC; a), evapotranspiration ratio (ETR; b), and water yield (WY; c) in the Jinghe River Basin during 2002-2022. Sub-basin numbers are labeled within each sub-basin.

Fig. 7 Spatial variations in trends of RC (a), ETR (b), and WY (c) in the Jinghe River Basin from 2002 to 2022. Sub-basin numbers are labeled within each sub-basin.

Fig. 8 Constraint effects of grassland-dominated structure (a-c) and cropland-dominated structure (d-f) on RC, ETR, and WY. In this figure, WY represents water yield volume rather than water yield depth.

Fig. 9 Constraint effects of cropland proportion (a-c), forestland proportion (d-f), grassland proportion (g-i), and the composite utilization index (j-l) on the RC, ETR, and WY in mixed cropland-forestland-grassland sub-basins. In this figure, WY represents water yield volume rather than water yield depth.

Fig. 10 Correlation coefficients among the hydrological indicators under different land-use structures. The dendrogram on the left represents hierarchical clustering based on correlation distances among hydrological indicators. 'C', 'G', and 'CFG' represent cropland-dominated, grassland-dominated, and mixed cropland-forestland- grassland structures, respectively.

Fig. 11 Comparison of actual (a), simulated (b), and optimized (c) land-use patterns in the Jinghe River Basin. Sub-basin numbers are labeled within each sub-basin.

Fig. 12 Spatially explicit optimization of the land-use structure in the Jinghe River Basin. Sub-basin numbers are labeled within each sub-basin.

Table 3 Summary of observed hydrological characteristics at four gauging stations in the Jinghe River Basin


[1]	Anderson M C, Hain C, Wardlow B, et al. 2011. Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. Journal of Climate, 24(8): 2025-2044.

[2]	Banjara M, Bhusal A, Ghimire A B, et al. 2024. Impact of land use and land cover change on hydrological processes in urban watersheds: analysis and forecasting for flood risk management. Geosciences, 14(2): 40, doi: 10.3390/geosciences14020040.

[3]	Chawanda C J, Nkwasa A, Thiery W, et al. 2024. Combined impacts of climate and land-use change on future water resources in Africa. Hydrology and Earth System Sciences, 28(1): 117-138.

[4]	Chen N, Wang R. 2024. Spatial differences in the response of runoff to climate and land use changes in the Yiluo River Basin. Journal of Resources and Ecology, 15(5): 1299-1310.

[5]	Chong Y H, Zeng Q M. 2024. Long-term ground deformation monitoring and quantitative interpretation in Shanghai using multi-platform TS-InSAR, PCA, and K-means clustering. Remote Sensing, 16(22): 4188, doi: 10.3390/rs16224188.

[6]	Conrad A C, März M, Chakrabarti R. 2019. Adapted subsurface water solutions across continents. Journal of Water Security, 5: jws2019006, doi: 10.15544/jws.2019.006.

[7]	Das P C, Esraz-Ul-Zannat M. 2022. Assessing the impacts of land use-land cover changes on direct surface runoff: a remote sensing approach in Khulna City. Water Science and Technology, 85(10): 3122-3144.

[8]	de Fleury M, Kergoat L, Grippa M. 2023. Hydrological regime of Sahelian small waterbodies from combined Sentinel-2 MSI and Sentinel-3 Synthetic Aperture Radar Altimeter data. Hydrology and Earth System Sciences, 27(11): 2189-2204.

[9]	Deng L Z, Fei K, Sun T Y, et al. 2019. Characteristics of runoff processes and nitrogen loss via surface flow and interflow from weathered granite slopes of Southeast China. Journal of Mountain Science, 16(5): 1048-1064.

[10]	Dey P, Mishra A. 2017. Separating the impacts of climate change and human activities on streamflow: A review of methodologies and critical assumptions. Journal of Hydrology, 548: 278-290.

[11]	Ding Y, Feng H H, Zou B. 2022. Remote sensing-based estimation on hydrological response to land use and cover change. Forests, 13(11): 1749, doi: 10.3390/f13111749.

[12]	Dong K N, Wang H W, Luo K, et al. 2024. The use of an optimized grey multi-objective programming-PLUS model for multi-scenario simulation of land use in the Weigan-Kuche River Oasis, China. Land, 13(6): 802, doi: 10.3390/land13060802.

[13]	Gomes E, Inácio M, Bogdzevič K, et al. 2021. Future land-use changes and its impacts on terrestrial ecosystem services: a review. Science of The Total Environment, 781: 146716, doi: 10.1016/j.scitotenv.2021.146716.

[14]	Gu H L, Liu S X, Huan C Y, et al. 2025. Multi-objective land use optimization based on NSGA-II and PLUS models: balancing economic development and carbon neutrality goals. Land, 14(8): 1585, doi: 10.3390/land14081585.

[15]	Gu X H, Zhang Q, Li J F, et al. 2019. Impact of urbanization on nonstationarity of annual and seasonal precipitation extremes in China. Journal of Hydrology, 575: 638-655.

[16]	Guan R H, Huang W, Li Y, et al. 2023. Can land use/land cover optimization improve the environmental flow guarantee in regulated basins? Journal of Hydrology, 626: 130309, doi: 10.1016/j.jhydrol.2023.130309.

[17]	Guo S C, Tian L, Chen S Y, et al. 2024. Analysis of effects of vegetation cover and elevation on water yield in an alpine basin of the Qilian Mountains in Northwest China by integrating the WRF-Hydro and Budyko framework. Journal of Hydrology, 629: 130580, doi: 10.1016/j.jhydrol.2023.130580.

[18]	Gyamfi C, Ndambuki J M, Salim R W. 2016. Hydrological responses to land use/cover changes in the Olifants Basin, South Africa. Water, 8(12): 588, doi: 10.3390/w8120588.

[19]	Hao R F, Yu D Y, Wu J G, et al. 2016. Constraint line methods and the applications in ecology. Chinese Journal of Plant Ecology, 40(10): 1100-1109. (in Chinese)

[20]	Hu J Y, Wu Y P, Wang L J, et al. 2021. Impacts of land-use conversions on the water cycle in a typical watershed in the southern Chinese Loess Plateau. Journal of Hydrology, 593: 125741, doi: 10.1016/j.jhydrol.2020.125741.

[21]	Hwang H T, Park Y J, Berg S J, et al. 2025. A dynamic meshing scheme for integrated hydrologic modeling to represent evolving landscapes. Science of The Total Environment, 976: 179129, doi: 10.1016/j.scitotenv.2025.179129.

[22]	IPCC (Intergovernmental Panel on Climate Change). 2022. Climate Change 2022:Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge and New York: Cambridge University Press.

[23]	Kang W D, Ni F Q, Deng Y, et al. 2023. Response of blue and green water to climate and land use changes: a study in the Wujiang River Basin, China. Transactions of the Chinese Society of Agricultural Engineering, 39(19): 131-140. (in Chinese)

[24]	Li G, Zhang F M, Jing Y S, et al. 2017. Response of evapotranspiration to changes in land use and land cover and climate in China during 2001-2013. Science of The Total Environment, 596-597: 256-265.

[25]	Li Z J, Huang B S, Qiu J, et al. 2021. Analysis on evolution characteristics of ecological flow of Hanjiang River under changing environment. Water Resources Protection, 37(5): 22-29. (in Chinese)

[26]	Liaqat M U, Mohamed M M, Chowdhury R, et al. 2021. Impact of land use/land cover changes on groundwater resources in Al Ain region of the United Arab Emirates using remote sensing and GIS techniques. Groundwater for Sustainable Development, 14: 100587, doi: 10.1016/j.gsd.2021.100587.

[27]	Lin R L, Zhuo W S, Gao Y K, et al. 2020. Assessment of ecohydrological regulation service in the Beijiang River Basin based on SWAT model. Water Resources Protection, 36(6): 131-136. (in Chinese)

[28]	Liu H F, Yan H C, Guan M F. 2025. Evaluating the effects of topography and land use change on hydrological signatures: a comparative study of two adjacent watersheds. Hydrology and Earth System Sciences, 29(8): 2109-2132.

[29]	Lü Y H, Zhang L W, Feng X M, et al. 2015. Recent ecological transitions in China: greening, browning and influential factors. Scientific Reports, 5: 8732, doi: 10.1038/srep08732.

[30]	Luan C X, Liu R Z. 2025. Integrated ecological and environmental spatial soft constraint mechanism: urban multi-objective land use optimization under multiple scenarios. Journal of Environmental Management, 385: 125661, doi: 10.1016/j.jenvman.2025.125661.

[31]	Luo Q, Zhang J, Bao Y J, et al. 2025. Multi-scenario simulation of land use optimization based on ecosystem services and ecological security patterns in the Liaohe River Basin. Frontiers in Environmental Science, 13: 1647039, doi: 10.3389/fenvs.2025.1647039.

[32]	Lyu J Q, Yang Y H, Yin S S, et al. 2024. Effects of environmental changes on flood patterns in the Jing River Basin: a case study from the Loess Plateau, China. Land, 13(12): 2053, doi: 10.3390/land13122053.

[33]	Maxwell C O, Oonge Z I, Odira P M A, et al. 2024. Water-energy nexus-based optimization of the water supply infrastructure in a dryland urban setting. Water, 16(21): 3073, doi: 10.3390/w16213073.

[34]	Omer A, Ma Z G, Zheng Z Y, et al. 2020. Natural and anthropogenic influences on the recent droughts in Yellow River Basin, China. Science of The Total Environment, 704: 135428, doi: 10.1016/j.scitotenv.2019.135428.

[35]	Otamendi U, Maiza M, Olaizola I G, et al. 2024. Integrated water resource management in the Segura Hydrographic Basin: an artificial intelligence approach. Journal of Environmental Management, 370: 122526, doi: 10.1016/j.jenvman.2024.122526.

[36]	Pearson K. 1895. VII. Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58(347-352): 240-242.

[37]	Shi X L, Yang Z Y, Yan D H, et al. 2014. On hydrological response to land-use/cover change in Luanhe River Basin. Advances in Water Science, 25(1): 21-27. (in Chinese)

[38]	Shiferaw N, Habte L, Waleed M. 2025. Land use dynamics and their impact on hydrology and water quality of a river catchment: A comprehensive analysis and future scenario. Environmental Science and Pollution Research, 32(7): 4124-4136.

[39]	Tajbakhsh S M, Memarian H, Kheyrkhah A. 2018. A GIS-based integrative approach for land use optimization in a semi-arid watershed. Global Journal of Environmental Science and Management, 4(1): 31-46.

[40]	Tang H, Halike A, Yao K X, et al. 2024. Ecosystem service valuation and multi-scenario simulation in the Ebinur Lake Basin using a coupled GMOP-PLUS model. Scientific Reports, 14: 5071, doi: 10.1038/s41598-024-55763-6.

[41]	Tang J, Song P H, Hu X J, et al. 2023. Coupled effects of land use and climate change on water supply in SSP-RCP scenarios: A case study of the Ganjiang River Basin, China. Ecological Indicators, 154: 110745, doi: 10.1016/j.ecolind.2023.110745.

[42]	Tang Z X, Zhou Z X, Wang D, et al. 2022. Impact of vegetation restoration on ecosystem services in the Loess Plateau, a case study in the Jinghe Watershed, China. Ecological Indicators, 142: 109183, doi: 10.1016/j.ecolind.2022.109183.

[43]	Teklay A, Dile Y T, Setegn S G, et al. 2019. Evaluation of static and dynamic land use data for watershed hydrologic process simulation: A case study in Gummara watershed, Ethiopia. CATENA, 172: 65-75.

[44]	Tezel A Ç, Akpınar A, Bor A, et al. 2025. Exploring hydrological response to land use/land cover change using the SWAT+ model in the İznik Lake Watershed, Türkiye. Water, 17(13): 1924, doi: 10.3390/w17131924.

[45]	Tong Y P, Wang Y Q, Zhou J X, et al. 2024. Dataset of soil hydraulic parameters in the Yellow River Basin based on in situ deep sampling. Scientific Data, 11: 740, doi: 10.1038/s41597-024-03576-7.

[46]	Toosi A S, Batelaan O, Shanafield M, et al. 2025. Land use‐land cover and hydrological modeling: A review. Wiley Interdisciplinary Reviews: Water, 12(2): e70013, doi: 10.1002/wat2.70013.

[47]	Verburg P H, Tabeau A, Hatna E. 2013. Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis: a study for land use in Europe. Journal of Environmental Management, 127: 132-144.

[48]	Wan Z G, Ding W G, Pu X T, et al. 2023. Analysis of the spatial-temporal variation and driving factors of water yield in Qilian Mountain National Park. Journal of Soil and Water Conservation, 37(6): 161-169. (in Chinese)

[49]	Wang B Y, Oguchi T, Zhang L. 2019a. Simulating impact of over-grazing on grassland degradation using GIS: a case study in the Heihe River Basin, China. Abstracts of the International Cartographic Association, 1: 15-20.

[50]	Wang S, Zhang Q, Yang T, et al. 2019b. River health assessment: Proposing a comprehensive model based on physical habitat, chemical condition and biotic structure. Ecological Indicators, 103: 446-460.

[51]	Wang Z, Zhong A Y, Li Q Z. 2024. Optimization of land use structure based on the coupling of GMOP and PLUS models: A case study of Lvliang City, China. Land, 13(8): 1335, doi: 10.3390/land13081335.

[52]	Wei X N, Long A H, Yin Z L, et al. 2022. Simulation of response of glacier runoff to climate change in the Hotan River Basin. Water Resources Protection, 38(4): 137-144. (in Chinese)

[53]	Worku T, Khare D, Tripathi S K. 2017. Modeling runoff-sediment response to land use/land cover changes using integrated GIS and SWAT model in the Beressa watershed. Environmental Earth Sciences, 76: 550, doi: 10.1007/s12665-017-6883-3.

[54]	Xin S J, Li Z G, Chen N, et al. 2024. The contribution of multi-objective land use optimization to reducing ecological risk: A case study of the Lanzhou-Xining urban agglomeration. Ecological Indicators, 168: 112604, doi: 10.1016/j.ecolind.2024.112604.

[55]	Xu C, Jiang Y N, Su Z H, et al. 2022. Assessing the impacts of Grain-for-Green Programme on ecosystem services in Jinghe River basin, China. Ecological Indicators, 137: 108757, doi: 10.1016/j.ecolind.2022.108757.

[56]	Yan F P, Shangguan W, Zhang J, et al. 2020. Depth-to-bedrock map of China at a spatial resolution of 100 meters. Scientific Data, 7: 2, doi: 10.1038/s41597-019-0345-6.

[57]	Yan R, Zhang X P, Yan S J, et al. 2018. Spatial patterns of hydrological responses to land use/cover change in a catchment on the Loess Plateau, China. Ecological Indicators, 92: 151-160.

[58]	Ye Z G, Yin S, Cao Y, et al. 2024. AI-driven optimization of agricultural water management for enhanced sustainability. Scientific Reports, 14: 25721, doi: 10.1038/s41598-024-76915-8.

[59]	Yellow River Conservancy Commission of the Ministry of Water Resources. 2020. Hydrological Yearbook of the Yellow River Basin (2006-2020). (in Chinese).

[60]	Yuan Z, Xu J J, Wang Y Q. 2019. Historical and future changes of blue water and green water resources in the Yangtze River source region, China. Theoretical and Applied Climatology, 138(1-2): 1035-1047.

[61]	Zang C F. 2017. Spatial and temporal variability of blue/green water flows in typical meteorological years in an inland river basin in China. Journal of Water and Climate Change, 8(1): 165-176.

[62]	Zhang H H, Zeng Y N, Jin X B, et al. 2016. Simulating multi-objective land use optimization allocation using Multi-agent system-A case study in Changsha, China. Ecological Modelling, 320: 334-347.

[63]	Zhang P P, Zhang L X, Hao Y, et al. 2024. Food-energy-water nexus optimization brings substantial reduction of urban resource consumption and greenhouse gas emissions. PNAS Nexus, 3(2): pgae028, doi: 10.1093/pnasnexus/pgae028.

[64]	Zhang S L, Wu G, Zhang H J, et al. 2021. Variation characteristics of runoff coefficients of Jinghe River Basin during 1966-2005. Water Resources and Power, 39(5): 33-36. (in Chinese)

[65]	Zhang X, Duan Y W, Duan J P, et al. 2022. A daily drought index based on evapotranspiration and its application in regional drought analyses. Science China Earth Sciences, 65(2): 317-336.

[66]	Zhang Y S, Habudula H. 2009. Study on land resource optimal allocation method based on LINGO 11.0. Environmental Science and Management, 34(4): 157-159. (in Chinese)

[67]	Zhao A Z, Zhu X F, Liu X F, et al. 2016. Impacts of land use change and climate variability on green and blue water resources in the Weihe River Basin of northwest China. CATENA, 137: 318-327.

[68]	Zhao X E, Fang K Y, Chen F, et al. 2023. Reconstructed Jing River streamflow from western China: A 399-year perspective for hydrological changes in the Loess Plateau. Journal of Hydrology, 621: 129573, doi: 10.1016/j.jhydrol.2023.129573.

[69]	Zheng Y W, Wang Z F, Li S J, et al. 2025. Analysis of spatiotemporal dynamic evolution characteristics and driving forces of land use/cover change in the Weihe River Basin in the past 30 years. Environmental Science, 46(11): 7106-7118. (in Chinese)

[70]	Zhou M, Cai Y L, Guan X L, et al. 2015. A hybrid inexact optimization model for land-use allocation of China. Chinese Geographical Science, 25(1): 62-73.

[71]	Zhou W Z. 2003. A study on available water capacity of main soil types in China based on GIS. MSc Thesis. Nanjing: Nanjing Agricultural University. (in Chinese)

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