A basin-scale water budget calibration method for sustainable water management: A case study in the Loess Plateau, China

Abstract

Accurate water budget closure is critical for sustainable water resource management facing increased pressures from climate change and human activities. Although error reduction methods for individual water balance components have advanced, persistent biases remain due to the independent development of datasets, impacting basin scale water budget balance. In this research, we analyzed the mathematical origin of the bias between water budget components and developed a new basin-scale water balance calibration method that redistributes errors across components while enforcing water balance constraints. Validation confirms systematic improvements, with reduced RMSE (Precipitation: -2.29 mm/month; ET: -1.34 mm/month) and increased R² against in situ observations. Applied to the Jinghe River Basin (2000−2019), the calibrated data reveal declining precipitation (-1.70 mm/year) and evapotranspiration (-1.84 mm/year) alongside slightly increasing runoff (0.20 mm/year in basin depth), signaling a drying trend. Land cover changes—marked by cropland loss (-3,497 km²) and forest (+720 km²) and grassland (+2,776 km²) expansion—reflect improved water consumption requirements by ecosystem, raising concerns for water retention and ecosystem stability. The method is particularly effective for ungauged basins with sparse ground data and underscores the need for integrated land-water management to enhance long-term resilience.

Keywords

Basin water balance / Revegetation / Loess plateau / Evapotranspiration

Declaration of generative AI in scientific writing

During the preparation of this work the author(s) used DeepSeek in order to revise the language to be more concise. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Data availability statement

The remote sensing data used in this research are openly accessed. The calibration method of this research has been developed as API and open accessed through this website: http://etwatch.cn/html/API/Basin Water Balance Closure.

CRediT authorship contribution statement

Zonghan Ma: Writing - original draft, Validation, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. Bingfang Wu: Writing - review & editing, Supervision, Funding acquisition, Conceptualization. Nana Yan: Writing - review & editing. Weiwei Zhu: Writing - review & editing, Funding acquisition. Mengxiao Li: Writing - review & editing, Validation, Methodology. Hongwei Zeng: Writing - review & editing. Yixuan Wang: Writing - review & editing. Peilin Song: Writing - review & editing. Qiquan Yang: Writing - review & editing. Qingcheng Pan: Writing - review & editing.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Bingfang Wu is an Editorial Board Member for this journal and was not involved in the editorial review or the decision to publish this article.

Acknowledgements

This research was financially supported by the National Key Research and Development Program of China (Grants No. 2024YFF0810500 and 2022YFD1900802), the National Natural Scientific Foundations of China (Grants No. 41991232, 42301016 and 42571034), and the Hainan Provincial Natural Science Foundation of China (Grant No. 424QN354).

References

[1] Abolafia-Rosenzweig R., Pan M., Zeng J.L., Livneh B., 2021. Remotely sensed ensembles of the terrestrial water budget over major global river basins: an assessment of three closure techniques. Remote Sens. Environ. 252, 112191. doi: 10.1016/j.rse.2020.112191.

[2] Aires F., 2014. Combining datasets of satellite-retrieved products. Part I: methodology and water budget closure. J. Hydrometeorol. 15 (4), 1677-1691. doi: 10.1175/JHM-D-13-0148.1.

[3] An Q., Liu L., Cheng Y., Liu J., Staal A., Huang G., 2025. Unraveling the role of moisture recycling in water consumption across different ecosystems in China. J. Hydrol. 662, 134020. doi: 10.1016/j.jhydrol.2025.134020.

[4] Beck H.E., Vergopolan N., Pan M., Levizzani V., van Dijk A.I.J.M., Weedon G.P., Brocca L., Pappenberger F., Huffman G.J., Wood E.F., 2017. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol. Earth Syst. Sci. 21 (12), 6201-6217. doi: 10.5194/hess-21-6201-2017.

[5] Belete M., Deng J.S., Wang K., Zhou M.M., Zhu E.Y., Shifaw E., Bayissa Y., 2020. Evaluation of satellite rainfall products for modeling water yield over the source region of Blue Nile Basin. Sci. Total Environ. 708, 134834. doi: 10.1016/j.scitotenv.2019.134834.

[6] Bennour A., Jia L., Menenti M., Zheng C.L., Zeng Y.L., Barnieh B.A., Jiang M., 2022. Calibration and validation of SWAT model by using hydrological remote sensing observables in the Lake Chad basin. Remote Sens. 14 (6), 1511. doi: 10.3390/rs14061511.

[7] Chen J.M., Liu J., 2020. Evolution of evapotranspiration models using thermal and shortwave remote sensing data. Remote Sens. Environ. 237, 111594. doi: 10.1016/j.rse.2019.111594.

[8] Chen S., Xiong L., Ma Q., Kim J.-S., Chen J., Xu C.-Y., 2020. Improving daily spatial precipitation estimates by merging gauge observation with multiple satellite-based precipitation products based on the geographically weighted ridge regression method. J. Hydrol. 589, 125156. doi: 10.1016/j.jhydrol.2020.125156.

[9] Cruz-Roa A.F., Olaya-Marin E.J., Barrios M.I., 2017. Ground and satellite based assessment of meteorological droughts: the Coello River basin case study. Int. J. Appl. Earth Obs. Geoinf. 62, 114-121. doi: 10.1016/j.jag.2017.06.005.

[10] Del Giudice D., Muenich R.L., Kalcic M.M., Bosch N.S., Scavia D., Michalak A.M., 2018. On the practical usefulness of least squares for assessing uncertainty in hydrologic and water quality predictions. Environ. Model. Softw. 105, 286-295. doi: 10.1016/j.envsoft.2018.03.009.

[11] Dorigo W., Dietrich S., Aires F., Brocca L., Carter S., Cretaux J.-F., Dunkerley D., Enomoto H., Forsberg R., Güntner A., 2021. Closing the water cycle from observations across scales: where do we stand? Bull. Amer. Meteorol. Soc. 102 (10), E1897- E1935. doi: 10.1175/BAMS-D-19-0316.1.

[12] Feng T., Zheng H., Wei W., Wang P., Bi H., Zhang J., Wei T., Wang R., Wang L., 2025. Natural forests accelerate soil hydrological processes and enhance water-holding capacities compared to planted forests after long-term restoration. Water Resour. Res. 61 (9), e2025WR040857. doi: 10.1029/2025WR040857.

[13] Feng X., Fu B., Piao S., Wang S., Ciais P., Zeng Z., Lü Y., Zeng Y., Li Y., Jiang X., 2016. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang. 6 (11), 1019-1022. doi: 10.1038/nclimate3092.

[14] Feng X., Fu B., Zhang Y., Pan N., Zeng Z., Tian H., Lyu Y., Chen Y., Ciais P., Wang Y., Zhang L., Cheng L., Maestre F.T., Fernández-Martínez M., Sardans J., Peñuelas J., 2021. Recent leveling off of vegetation greenness and primary production reveals the increasing soil water limitations on the greening. Earth Sci. Bull. 66 (14), 1462-1471. doi: 10.1016/j.scib.2021.02.023.

[15] Fu Z., Ciais P., Wigneron J.-P., Gentine P., Feldman A.F., Makowski D., Viovy N., Kemanian A.R., Goll D.S., Stoy P.C., Prentice I.C., Yakir D., Liu L., Ma H., Li X., Huang Y., Yu K., Zhu P., Li X., Zhu Z., Lian J., Smith W.K., 2024. Global critical soil moisture thresholds of plant water stress. Nat. Commun. 15 (1), 4826. doi: 10.1038/s41467-024-49244-7.

[16] Guo J., Su X., Singh V.P., Jin J., 2016. Impacts of climate and land use/cover change on streamflow using SWAT and a separation method for the Xiying River basin in Northwestern China. Water 8 (5), 192. doi: 10.3390/w8050192.

[17] Han E., Crow W.T., Hain C.R., Anderson M.C., 2015. On the use of a water balance to evaluate interannual terrestrial ET variability. J. Hydrometeorol. 16 (3), 1102-1108. doi: 10.1175/JHM-D-14-0175.1.

[18] Hirschi M., Seneviratne S.I., 2017. Basin-scale water-balance dataset (BSWB): an update. Earth Syst. Sci. Data 9 (1), 251-258. doi: 10.5194/essd-9-251-2017.

[19] Hoek van Dijke, A.J., Herold M., Mallick K., Benedict I., Machwitz M., Schlerf M., Pranindita A., Theeuwen J.J., Teuling A.J., 2022. Shifts in regional water availability due to global tree restoration. Nat. Geosci. 15 (5), 363-368. doi: 10.1038/s41561-022-00935-0.

[20] Hu X., Shi L., Lin G., Lin L., 2021. Comparison of physical-based, data-driven and hybrid modeling approaches for evapotranspiration estimation. J. Hydrol. 601, 126592. doi: 10.1016/j.jhydrol.2021.126592.

[21] Humphrey V., Gudmundsson L., 2019. GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth Syst. Sci. Data 11 (3), 1153-1170. doi: 10.5194/essd-11-1153-2019.

[22] Jiao W., Wang L., Smith W.K., Chang Q., Wang H., D’Odorico P., 2021. Observed increasing water constraint on vegetation growth over the last three decades. Nat. Commun. 12 (1), 3777. doi: 10.1038/s41467-021-24016-9.

[23] Kendall M.G., 1955. Rank Correlation Methods. Hafner Publishing Co.

[24] Kerns B.W., Chen S.S., 2020. A 20-year climatology of Madden-Julian oscillation convection: large-scale precipitation tracking from TRMM-GPM rainfall. J Geophys. Res.- Atmos. 125 (7), e2019JD032142. doi: 10.1029/2019JD032142.

[25] Khorchani M., Nadal-Romero E., Lasanta T., Tague C., 2021. Natural revegetation and afforestation in abandoned cropland areas: hydrological trends and changes in Mediterranean mountains. Hydrol. Process. 35 (5), e14191. doi: 10.1002/hyp.14191.

[26] Lawson C.L., Hanson R.J., 1995. Solving Least Squares Problems. SIAM.

[27] Li C., Fu B., Wang S., Stringer L.C., Wang Y., Li Z., Liu Y., Zhou W., 2021. Drivers and impacts of changes in China’s drylands. Nat. Rev. Earth Environ. 2, 858-873. doi: 10.1038/s43017-021-00226-z.

[28] Li D.L., Min X.X., Xu J.T., Xue J., Shi Z., 2022. Assessment of three gridded satellite-based precipitation products and their performance variabilities during typhoons over Zhejiang, southeastern China. J. Hydrol. 610, 127985. doi: 10.1016/j.jhydrol.2022.127985.

[29] Li J., Mao X., Li M., 2017. Modeling hydrological processes in oasis of Heihe River Basin by landscape unit-based conceptual models integrated with FEFLOW and GIS. Agric. Water Manage. 179, 338-351. doi: 10.1016/j.agwat.2016.09.007.

[30] Li Y., Ge J., Wu H., Tang R., Cheng Y., Liu X., Wang Y., Zhao W., Gao C., Duan S.-B., 2025. Amplified local cooling effect of forestation in warming Europe. Nat. Commun. 16 (1), 8412. doi: 10.1038/s41467-025-63556-2.

[31] Liu J., Yang H., Gosling S.N., Kummu M., Flörke M., Pfister S., Hanasaki N., Wada Y., Zhang X., Zheng C., 2017. Water scarcity assessments in the past, present, and future. Earths Future 5 (6), 545-559. doi: 10.1002/2016EF000518.

[32] Liu J., Zhao X., Yang H., Liu Q., Xiao H., Cheng G., 2018. Assessing China’s “developing a water-saving society ” policy at a river basin level: a structural decomposition analysis approach. J. Clean. Prod. 190, 799-808. doi: 10.1016/j.jclepro.2018.04.194.

[33] Liu M., Zhou X., Huang G., Li Y., 2024. The increasing water stress projected for China could shift the agriculture and manufacturing industry geographically. Commun. Earth Environ. 5 (1), 396. doi: 10.1038/s43247-024-01560-y.

[34] Liu S., Ward S.E., Wilby A., Manning P., Gong M., Davies J., Killick R., Quinton J.N., Bardgett R.D., 2025. Multiple targeted grassland restoration interventions enhance ecosystem service multifunctionality. Nat. Commun. 16 (1), 3971. doi: 10.1038/s41467-025-59157-8.

[35] Luthcke S.B., Sabaka T.J., Loomis B.D., Arendt A.A., McCarthy J.J., Camp J., 2013. Antarctica, Greenland and Gulf of Alaska land-ice evolution from an iterated GRACE global mascon solution. J. Glaciol. 59 (216), 613-631. doi: 10.3189/2013JoG12J147.

[36] Ma Z., Wu B., Yan N., Zhu W., Xu J., 2021a. Coupling water and carbon processes to estimate field-scale maize evapotranspiration with Sentinel-2 data. Agric. For. Meteorol. 306, 108421. doi: 10.1016/j.agrformet.2021.108421.

[37] Ma Z., Wu B., Yan N., Zhu W., Zeng H., Xu J., 2021b. Spatial allocation method from coarse evapotranspiration data to agricultural fields by quantifying variations in crop cover and soil moisture. Remote Sens. 13 (3), 343. doi: 10.3390/rs13030343.

[38] Ma Z., Yan N., Wu B., Stein A., Zhu W., Zeng H.,2019. Variation in actual evapotranspiration following changes in climate and vegetation cover during an ecological restoration period ( 2000-2015) in the Loess Plateau, China. Sci. Total Environ. 689, 534-545. doi: 10.1016/j.scitotenv.2019.06.155.

[39] Mann H.B., 1945. Nonparametric tests against trend. Econometrica 13 (3), 245-259. doi: 10.2307/1907187.

[40] Martens B., Gonzalez Miralles D., Lievens H., Van Der Schalie R., De Jeu R.A., Fernández-Prieto D., Beck H.E., Dorigo W., Verhoest N., 2017. GLEAM v3: satellitebased land evaporation and root-zone soil moisture. Geosci. Model Dev. 10 (5), 1903-1925. doi: 10.5194/gmd-10-1903-2017.

[41] McKinney D.C., 1999. Modeling water resources management at the basin level: review and future directions. System-Wide Initiative on Water Management (SWIM).

[42] McNamara I., Baez-Villanueva O.M., Zomorodian A., Ayyad S., Zambrano-Bigiarini M., Zaroug M., Mersha A., Nauditt A., Mbuliro M., Wamala S., Ribbe L., 2021. How well do gridded precipitation and actual evapotranspiration products represent the key water balance components in the Nile Basin? J. Hydrol. Reg. Stud. 37, 100884. doi: 10.1016/j.ejrh.2021.100884.

[43] Moreira A.A., Ruhoff A.L., Roberti D.R., Souza V.d.A., da Rocha H.R., de Paiva R.C.D., 2019. Assessment of terrestrial water balance using remote sensing data in South America. J. Hydrol. 575, 131-147. doi: 10.1016/j.jhydrol.2019.05.021.

[44] Mtewele Z.F., Xu X.Y., Jia G.S., 2021. Heterogeneous trends of precipitation extremes in recent two decades over East Africa. J. Meteorol. Res. 35 (6), 1057-1073. doi: 10.1007/s13351-021-1028-8.

[45] Mu Q., Zhao M., Running S.W., 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115 ( 8), 1781-1800. doi: 10.1016/j.rse.2011.02.019.

[46] Munier S., Aires F., Schlaffer S., Prigent C., Papa F., Maisongrande P., Pan M., 2014. Combining data sets of satellite-retrieved products for basin-scale water balance study: 2. Evaluation on the Mississippi Basin and closure correction model. J. Geophys. Res. Atmos. 119 (21), 12100-12116. doi: 10.1002/2014JD021953.

[47] Musie M., Sen S., Srivastava P., 2019. Comparison and evaluation of gridded precipitation datasets for streamflow simulation in data scarce watersheds of Ethiopia. J. Hydrol. 579, 124168. doi: 10.1016/j.jhydrol.2019.124168.

[48] Neitsch S.L., Arnold J.G., Kiniry J.R., Williams J.R., King K.W., 2002. Soil and water assessment tool theoretical documentation, version 2000. TWRI Report TR- 191. Texas Water Resource Institute, College Station, Texas. Nicholson, S.E., Klotter, D., Zhou, L., Hua, W., 2019. Validation of satellite precipitation estimates over the Congo Basin. J. Hydrometeorol. 20 (4), 631-656. doi: 10.1175/JHM-D-18-0118.1.

[49] Oliveira P.T.S., Nearing M.A., Moran M.S., Goodrich D.C., Wendland E., Gupta H.V., 2014. Trends in water balance components across the Brazilian Cerrado. Water Resour. Res. 50 (9), 7100-7114. doi: 10.1002/2013WR015202.

[50] Pan M., Sahoo A.K., Troy T.J., Vinukollu R.K., Sheffield J., Wood E.F., 2012. Multisource estimation of long-term terrestrial water budget for major global river basins. J. Clim. 25 (9), 3191-3206. doi: 10.1175/JCLI-D-11-00300.1.

[51] Pan M., Wood E.F., 2006. Data assimilation for estimating the terrestrial water budget using a constrained ensemble Kalman filter. J. Hydrometeorol. 7 (3), 534-547. doi: 10.1175/JHM495.1.

[52] Pellet V., Aires F., Papa F., Munier S., Decharme B., 2020. Long-term total water storage change from a satellite water cycle reconstruction over large southern Asian basins. Hydrol. Earth Syst. Sci. 24 (6), 3033-3055. doi: 10.5194/hess-24-3033-2020.

[53] Pellet V., Aires F., Yamazaki D., Zhou X., Paris A., 2022. A first continuous and distributed satellite-based mapping of river discharge over the Amazon. J. Hydrol. 614, 128481. doi: 10.1016/j.jhydrol.2022.128481.

[54] Peng H., Jia Y., Niu C., Gong J., Hao C., Gou S., 2015. Eco-hydrological simulation of soil and water conservation in the Jinghe River basin in the Loess Plateau, China. J. Hydro-environ. Res. 9 (3), 452-464. doi: 10.1016/j.jher.2014.09.003.

[55] Pereira L.S., Allen R.G., Smith M., Raes D., 2015. Crop evapotranspiration estimation with FAO56: past and future. Agric. Water Manage. 147, 4-20. doi: 10.1016/j.agwat.2014.07.031.

[56] Pradhan R.K., Markonis Y., Godoy M.R.V., Villalba-Pradas A., Andreadis K.M., Nikolopoulos E.I., Papalexiou S.M., Rahim A., Tapiador F.J., Hanel M., 2022. Review of GPM IMERG performance: a global perspective. Remote Sens. Environ. 268, 112754. doi: 10.1016/j.rse.2021.112754.

[57] Rigden A.J., Salvucci G.D., Entekhabi D., Short Gianotti D.J., 2018. Partitioning evapotranspiration over the continental United States using weather station data. Geophys. Res. Lett. 45 (18), 9605-9613. doi: 10.1029/2018GL079121.

[58] Rodell M., Beaudoing H.K., L’ecuyer T., Olson W.S., Famiglietti J.S., Houser P.R., Adler R., Bosilovich M.G., Clayson C.A., Chambers D., 2015. The observed state of the water cycle in the early twenty-first century. J. Clim. 28 (21), 8289-8318. doi: 10.1175/JCLI-D-14-00555.1.

[59] Safeeq M., Bart R.R., Pelak N.F., Singh C.K., Dralle D.N., Hartsough P., Wagenbrenner J.W., 2021. How realistic are water-balance closure assumptions? A demonstration from the southern sierra critical zone observatory and kings river experimental watersheds. Hydrol. Process. 35 (5), e14199. doi: 10.1002/hyp.14199.

[60] Sahoo A.K., Pan M., Troy T.J., Vinukollu R.K., Sheffield J., Wood E.F., 2011. Reconciling the global terrestrial water budget using satellite remote sensing. Remote Sens. Environ. 115 ( 8), 1850-1865. doi: 10.1016/j.rse.2011.03.009.

[61] Schoups G., Nasseri M., 2021. GRACEfully closing the water balance: a data-driven probabilistic approach applied to river basins in Iran. Water Resour. Res. 57 (6), e2020WR029071. doi: 10.1029/2020WR029071.

[62] Skofronick-Jackson G., Petersen W.A., Berg W., Kidd C., Stocker E.F., Kirschbaum D.B., Kakar R., Braun S.A., Huffman G.J., Iguchi T., Kirstetter P.E., Kummerow C., Meneghini R., Oki R., Olson W.S., Takayabu Y.N., Furukawa K., Wilheit T., 2017. The global precipitation measurement (GPM) mission for science and society. Bull. Amer. Meteor. Soc. 98 (8), 1679-1695. doi: 10.1175/BAMS-D-15-00306.1.

[63] Sorooshian S., Hsu K.L., Gao X., Gupta H.V., Imam B., Braithwaite D., 2000. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc. 81 (9), 2035-2046. doi:10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2.

[64] Stoffelen A., 1998. Toward the true near-surface wind speed: error modeling and calibration using triple collocation. J. Geophys. Res. 103 (C4), 7755-7766. doi: 10.1029/97JC03180.

[65] Tan X., Liu B., Tan X., Chen X., 2022. Long-term water imbalances of watersheds resulting from biases in hydroclimatic data sets for water budget analyses. Water Resour. Res. 58 (3), e2021WR031209. doi: 10.1029/2021WR031209.

[66] Tapley B.D., Bettadpur S., Ries J.C., Thompson P.F., Watkins M.M., 2004. GRACE measurements of mass variability in the earth system. Science 305 (5683), 503-505. doi: 10.1126/science.1099192.

[67] Tian B.R., Chen H., Wang J.L., Xu C.Y., 2021. Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang River catchment. Hydrol. Res. 52 (5), 1048-1065. doi: 10.2166/nh.2021.188.

[68] Vishwakarma B.D., Devaraju B., Sneeuw N., 2018. What is the spatial resolution of GRACE satellite products for hydrology? Remote Sens. 10 (6), 852. doi: 10.3390/rs10060852.

[69] Wiese D.N., Landerer F.W., Watkins M.M., 2016. Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resour. Res. 52 (9), 7490-7502. doi: 10.1002/2016WR019344.

[70] Wong J.S., Zhang X., Gharari S., Shrestha R.R., Wheater H.S., Famiglietti J.S., 2021. Assessing water balance closure using multiple data assimilation and remote sensingbased datasets for Canada. J. Hydrometeorol. 22 (6), 1569-1589. doi: 10.1175/JHM-D-20-0131.1.

[71] Wu B., Fu Z., Fu B., Yan C., Zeng H., Zhao W., 2024. Dynamics of land cover changes and driving forces in China’s drylands since the 1970s. Land Use Policy 140, 107097. doi: 10.1016/j.landusepol.2024.107097.

[72] Wu B., Ma Z., Feng X., Zeng H., Gou S., Li H., Huang J., Fu B., 2025. Coordinating red line policies with water consumption caps in China’s drylands. Sci. Bull. 70 (13), 2039-2042. doi: 10.1016/j.scib.2025.02.021.

[73] Wu B., Zeng H., Zhu W., Yan N., Ma Z., 2021. Enhancing China’s three red lines strategy with water consumption limitations. Sci. Bull. 66 (20), 2057-2060. doi: 10.1016/j.scib.2021.06.012.

[74] Wu B., Zhu W., Yan N., Xing Q., Xu J., Ma Z., Wang L., 2020. Regional actual evapotranspiration estimation with land and meteorological variables derived from multisource satellite data. Remot. Sens. 12 (2), 332. doi: 10.3390/rs12020332.

[75] Xie Z.J., Yao Y.J., Zhang X.T., Liang S.L., Fisher J.B., Chen J.Q., Jia K., Shang K., Yang J.M., Yu R.Y., Guo X.Z., Liu L., Ning J., Zhang L.L., 2022. The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: algorithm development and preliminary validation. J. Hydrol. 610, 127990. doi: 10.1016/j.jhydrol.2022.127990.

[76] Xu C., Jiang Y., Su Z., Liu Y., Lyu J., 2022. Assessing the impacts of Grain-for-Green Programme on ecosystem services in Jinghe River basin, China. Ecol. Indic. 137, 108757. doi: 10.1016/j.ecolind.2022.108757.

[77] Yan Y., Wu H., Gu G.J., Huang Z.J., Alfieri L.Z., Li X.M., Nanding N.G., Pan X.S., Tang Q.H.,2020. Climatology and interannual variability of floods during the TRMM era ( 1998-2013). J. Clim. 33 (8), 3289-3305. doi: 10.1175/JCLI-D-19-0415.1.

[78] You Y.L., Wang N.Y., Kubota T., Aonashi K., Shige S., Kachi M., Kummerow C., Randel D., Ferraro R., Braun S., Takayabu Y., 2020. Comparison of TRMM microwave imager rainfall datasets from NASA and JAXA. J. Hydrometeorol. 21 (3), 377-397 10.1175%2Fjhm-d-19-0022.1.

[79] Yu Y., Yu P., Wang Y., Tu X., Zhang X., Zhang S., Xu L., Wang X., Liu Z., Wang B., 2020. Dependence of annual runoff coefficients on basin size and other properties in a climate transition zone from semi-humid to arid and semi-arid on the Loess Plateau, China. J. Hydrol. 591, 125727. doi: 10.1016/j.jhydrol.2020.125727.

[80] Yu Y., Yu P., Wang Y., Wan Y., Wang B., Han X., Tu X., Li J., Xu L., Wang X., Liu Z., 2023. Natural revegetation has dominated annual runoff reduction since the Grain for green program began in the Jing River Basin, Northwest China. J. Hydrol. 625, 129978. doi: 10.1016/j.jhydrol.2023.129978.

[81] Yuan S.S., Zhu L.Y., Quiring S.M., 2021. Comparison of two multisatellite algorithms for estimation of tropical cyclone precipitation in the United States and Mexico: TMPA and IMERG. J. Hydrometeorol. 22 (4), 923-939. doi: 10.1175/JHM-D-19-0296.1.

[82] Zeng H., Elnashar A., Wu B., Zhang M., Zhu W., Tian F., Ma Z., 2022. A framework for separating natural and anthropogenic contributions to evapotranspiration of humanmanaged land covers in watersheds based on machine learning. Sci. Total Environ. 823, 153726. doi: 10.1016/j.scitotenv.2022.153726.

[83] Zhang G., Tian G., Cai D., Bai R., Tong J., 2021. Merging radar and rain gauge data by using spatial-temporal local weighted linear regression kriging for quantitative precipitation estimation. J. Hydrol. 601, 126612. doi: 10.1016/j.jhydrol.2021.126612.

[84] Zhang G., Xu T., Yin W., Bateni S.M., Jun C., Kim D., Liu S., Xu Z., Ming W., Wang J., 2024. A machine learning downscaling framework based on a physically constrained sliding window technique for improving resolution of global water storage anomaly. Remot. Sens. Environ. 313, 114359. doi: 10.1016/j.rse.2024.114359.

[85] Zhang M., Delgado-Baquerizo M., Li G., Isbell F., Wang Y., Hautier Y., Wang Y., Xiao Y., Cai J., Pan X., 2023. Experimental impacts of grazing on grassland biodiversity and function are explained by aridity. Nat. Commun. 14 (1), 5040. doi: 10.1038/s41467-023-40809-6.

[86] Zhang Y., Kong D., Gan R., Chiew F.H., McVicar T.R., Zhang Q., Yang Y., 2019. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017. Remote Sens. Environ. 222, 165-182. doi: 10.1016/j.rse.2018.12.031.

[87] Zhang Y., Pan M., Sheffield J., Siemann A.L., Fisher C.K., Liang M., Beck H.E., Wanders N., MacCracken R.F., Houser P.R., Zhou T., Lettenmaier D.P., Pinker R.T., Bytheway J., Kummerow C.D., Wood E.F., 2018. A Climate Data Record (CDR) for the global terrestrial water budget: 1984-2010. Hydrol. Earth Syst. Sci. 22 (1), 241-263. doi: 10.5194/hess-22-241-2018.

[88] Zhao Y., Xu K., Dong N., Wang H., 2022. Optimally integrating multi-source products for improving long series precipitation precision by using machine learning methods. J. Hydrol. 609, 127707. doi: 10.1016/j.jhydrol.2022.127707.

[89] Zhou Z., Majeed Y., Diverres Naranjo G., Gambacorta E.M.T., 2021. Assessment for crop water stress with infrared thermal imagery in precision agriculture: a review and future prospects for deep learning applications. Comp. Electron. Agric. 182, 106019. doi: 10.1016/j.compag.2021.106019.