Please wait a minute...
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
Download: HTML     PDF(1901KB)
Export: BibTeX | EndNote (RIS)      

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.

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
Data type Dataset Time range Original spatial resolution Data source
Topography Digital Elevation Model (DEM) 2000 30 m Geospatial Data Cloud (http://www.gscloud.cn)
Land use China Land Cover Dataset (CLCD) 2002-2022 30 m Wuhan University (https://irsip.whu.edu.cn/resv2/CLCD.php)
Hydrology National Surface Runoff Distribution Dataset (China) 2002-2022 1 km Geographic Remote Sensing Ecological Network Platform (http://www.gisrs.cn)
Climate China Regional Actual Evapotranspiration Dataset 2002-2022 500 m Geographic Data Sharing Infrastructure, Global Resources Data Cloud
(http://www.gis5g.com)
Climate National Annual Potential Evapotranspiration Dataset 2002-2022 500 m Geographic Data Sharing Infrastructure, Global Resources Data Cloud
(http://www.gis5g.com)
Climate Annual precipitation dataset for China 2002-2022 1 km Geographic Data Sharing Infrastructure, Global Resources Data Cloud
(http://www.gis5g.com)
Soil China Soil Dataset based on Harmonized World Soil Database (HWSD) (v1.1) 2009 250 m National Tibetan Plateau Data Center/
Third Pole Environment Data Center (https://data.tpdc.ac.cn)
Population LandScan Global 2002-2022 1 km Oak Ridge National Laboratory
(https://landscan.ornl.gov)
Accessibility Distance to rivers, settlements,
and roads
2002, 2007, 2012, 2017, and 2022 1 km Resource and Environmental Science Data Platform (https://www.resdc.cn)
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.
Land-use type lucode lulc_veg root_depth (mm) kc
Cropland 1 1 500 0.60
Forestland 2 1 3000 0.90
Shrubland 3 1 2000 0.85
Grassland 4 1 500 0.75
Water body 5 0 1 1.00
Bare land 7 0 1 0.30
Impervious surface 8 0 1 0.50
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.
Station Drainage area
(km2)
Mean annual discharge (m3/s) Annual runoff volume (×108 m3) Runoff depth
(mm)
Peak flow
(m3/s)
Peak date
Yangjiaping 14,124 25.4 8.046 57.0 277 24 August
Yuluoping 19,019 10.1 3.208 16.9 441 6 August
Zhangjiashan 43,216 38.7 12.230 28.3 655 17 August
Jingcun 40,281 47.1 14.910 37.0 544 6 August
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)
[1] WU Han, LI Jilong, CUI Tianjiao, WANG Xianle, HE Shan. Loess shoulder-line extraction based on bidimensional empirical mode decomposition and edge detection[J]. Journal of Arid Land, 2026, 18(11): 1940-1960.
[2] MU Mengyu, GUAN Chao, ZHAO Chenguang, SONG Xinyang, WANG Jiazheng, ZHAO Changming. Precipitation dominates the mortality of Caragana korshinskii on the Loess Plateau with a threshold response[J]. Journal of Arid Land, 2026, 18(10): 1813-1830.
[3] WANG Jincheng, JING Mingbo, GUO Xiaopeng, CHANG Sijing, DUAN Chunyan, SONG Xi, QIAN Li, QIN Xuexue, SHI Shengli. Structural and functional responses of soil microbial communities to petroleum pollution in the eastern Gansu Province on the Loess Plateau, China[J]. Journal of Arid Land, 2025, 17(9): 1314-1340.
[4] XI Ruiyun, PEI Tingting, CHEN Ying, XIE Baopeng, HOU Li, WANG Wen. Spatiotemporal dynamic and drivers of ecological environmental quality on the Chinese Loess Plateau: Insights from kRSEI model and climate-human interaction analysis[J]. Journal of Arid Land, 2025, 17(7): 958-978.
[5] CHE Cunwei, ZHANG Mingjun, XIAO Shengchun, YANG Wanmin, WANG Shengjie, WANG Zhilan, SUN Meiling. Radial growth of Platycladus orientalis Linn. and its growth resilience after extreme droughts along a precipitation gradient[J]. Journal of Arid Land, 2025, 17(3): 381-393.
[6] SHAN Rui, TIAN Peng, LU Ang, FAN Junjian, GUO Xiaoxue, ZHAO Yanbo, MU Xingmin, ZHAO Guangju. Soil erosion and sediment connectivity variations in the Hantaichuan Watershed, northern Loess Plateau, China from 1995 to 2020[J]. Journal of Arid Land, 2025, 17(12): 1761-1784.
[7] SUN Hui, ZHAO Yunge, GAO Liqian, XU Mingxiang. Reasonable grazing may balance the conflict between grassland utilization and soil conservation in the semi-arid hilly areas, China[J]. Journal of Arid Land, 2024, 16(8): 1130-1146.
[8] LU Rui, ZHANG Mingjun, ZHANG Yu, QIANG Yuquan, CHE Cunwei, SUN Meiling, WANG Shengjie. Estimation of evapotranspiration from artificial forest in mountainous areas of western Loess Plateau based on HYDRUS-1D model[J]. Journal of Arid Land, 2024, 16(12): 1664-1685.
[9] YING Chunye, LI Chenglong, LI Lanxing, ZHOU Chang. Combined effects of polymer SH and ryegrass on the water-holding characteristics of loess[J]. Journal of Arid Land, 2024, 16(12): 1686-1700.
[10] LI Qing, LI Dan, WANG Sheng, WANG Jinfeng, WANG Rende, FU Gang, YUAN Yixiao, ZHENG Zhenhua. Spatiotemporal patterns and driving factors of soil protection in the wind-water erosion area of Chinese Loess Plateau[J]. Journal of Arid Land, 2024, 16(11): 1522-1540.
[11] MEN Huan, DING Hua, DENG Yahong, MU Huandong, HE Nainan, SUN Pushuo, LI Zhixu, LIU Yan. Rock mechanical characteristics and landscape evolutionary mechanism of the slit-type Danxia landform on the Chinese Loess Plateau[J]. Journal of Arid Land, 2024, 16(10): 1327-1343.
[12] WANG Jing, WEI Yulu, PENG Biao, LIU Siqi, LI Jianfeng. Spatiotemporal variations in ecosystem services and their trade-offs and synergies against the background of the gully control and land consolidation project on the Loess Plateau, China[J]. Journal of Arid Land, 2024, 16(1): 131-145.
[13] MA Xinxin, ZHAO Yunge, YANG Kai, MING Jiao, QIAO Yu, XU Mingxiang, PAN Xinghui. Long-term light grazing does not change soil organic carbon stability and stock in biocrust layer in the hilly regions of drylands[J]. Journal of Arid Land, 2023, 15(8): 940-959.
[14] ZHANG Yixin, LI Peng, XU Guoce, MIN Zhiqiang, LI Qingshun, LI Zhanbin, WANG Bin, CHEN Yiting. Temporal and spatial variation characteristics of extreme precipitation on the Loess Plateau of China facing the precipitation process[J]. Journal of Arid Land, 2023, 15(4): 439-459.
[15] SUN Liquan, GUO Huili, CHEN Ziyu, YIN Ziming, FENG Hao, WU Shufang, Kadambot H M SIDDIQUE. Check dam extraction from remote sensing images using deep learning and geospatial analysis: A case study in the Yanhe River Basin of the Loess Plateau, China[J]. Journal of Arid Land, 2023, 15(1): 34-51.