Articles | Volume 29, issue 18
https://doi.org/10.5194/hess-29-4607-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-4607-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Sep 2025
Research article |
| 24 Sep 2025
| 24 Sep 2025
A novel method for correcting water budget components and reducing their uncertainties by optimally distributing the imbalance residual without full closure
Zengliang Luo
Hubei Key Laboratory of Regional Ecology and Environmental Change, State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China
Hanjia Fu
Hubei Key Laboratory of Regional Ecology and Environmental Change, State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China
Quanxi Shao
CORRESPONDING AUTHOR
CSIRO Data61, Australian Resources Research Centre, 26 Dick Perry Avenue, Kensington, WA 6155, Australia
Wenwen Dong
Hubei Key Laboratory of Regional Ecology and Environmental Change, State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China
Xi Chen
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Xiangyi Ding
CORRESPONDING AUTHOR
Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Lunche Wang
Hubei Key Laboratory of Regional Ecology and Environmental Change, State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China
Xihui Gu
Hubei Key Laboratory of Regional Ecology and Environmental Change, State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
Ranjan Sarukkalige
School of Civil and Mechanical Engineering, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
Heqing Huang
Hubei Key Laboratory of Regional Ecology and Environmental Change, State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
HUN-REN Balaton Limnological Research Institute, Tihany, Hungary
Related authors
No articles found.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
Short summary
Short summary
Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Ling Zou, Lars Hoffmann, Sabine Griessbach, Reinhold Spang, and Lunche Wang
Atmos. Chem. Phys., 21, 10457–10475, https://doi.org/10.5194/acp-21-10457-2021, https://doi.org/10.5194/acp-21-10457-2021, 2021
Short summary
Short summary
Ice clouds in the lowermost stratosphere (SICs) have important impacts on the radiation budget and climate change. We quantified the occurrence of SICs over North America and analysed its relations with convective systems and gravity waves to investigate potential formation mechanisms of SICs. Deep convection is proved to be the primary factor linked to the occurrence of SICs over North America.
Cited articles
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015, Sci. Data, 5, 1–12, https://doi.org/10.1038/sdata.2017.191, 2018.
Abhishek, Kinouchi, T., Abolafia-Rosenzweig, R., and Ito, M.: Water budget closure in the Upper Chao Phraya River basin, Thailand using multisource data, Remote Sens., 14, 173, https://doi.org/10.3390/rs14010173, 2021.
Abolafia-Rosenzweig, R., Pan, M., Zeng, J., and Livneh, B.: Remotely sensed ensembles of the terrestrial water budget over major global river basins: an assessment of three closure techniques, Remote Sens. Environ., 252, 112191, https://doi.org/10.1016/j.rse.2020.112191, 2021.
Aires, F.: Combining datasets of satellite-retrieved products. Part I: methodology and water budget closure, J. Hydrometeorol., 15, 1677–1691, https://doi.org/10.1175/JHM-D-13-0148.1, 2014.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration – Guidelines for computing crop water requirements, FAO Irrig. Drain. Pap., 56, 300 pp., D05109, https://doi.org/10.4060/cd6621en, 1998.
Aziz, K. M. A., and Rashwan, K. S.: Comparison of different resolutions of six free online DEMs with GPS elevation data on a new 6th of October City, Egypt, Arab. J. Geosci., 15, 1585, https://doi.org/10.1007/s12517-022-10845-5, 2022.
Bai, X., Wu, X., and Wang, P.: Blending long-term satellite-based precipitation data with gauge observations for drought monitoring: considering effects of different gauge densities, J. Hydrol., 577, 124007, https://doi.org/10.1016/j.jhydrol.2019.124007, 2019.
Bai, X., Wang, P., He, Y., Zhang, Z., and Wu, X.: Assessing the accuracy and drought utility of long-term satellite-based precipitation estimation products using the triple collocation approach, J. Hydrol., 603, 127098, https://doi.org/10.1016/j.jhydrol.2021.127098, 2021.
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., and Wood, E. F.: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, 2017.
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., van Dijk, A. I. J. M., Huffman, G. J., Adler, R. F., and Wood, E. F.: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, 2019a.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Van Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019b.
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., and Ziese, M.: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, 2013.
Boergens, E., Güntner, A., Sips, M., Schwatke, C., and Dobslaw, H.: Interannual variations of terrestrial water storage in the East African Rift region, Hydrol. Earth Syst. Sci., 28, 4733–4754, https://doi.org/10.5194/hess-28-4733-2024, 2024.
Bormann, H.: Impact of spatial data resolution on simulated catchment water balances and model performance of the multi-scale TOPLATS model, Hydrol. Earth Syst. Sci., 10, 165–179, https://doi.org/10.5194/hess-10-165-2006, 2006.
Bouaziz, L., Weerts, A., Schellekens, J., Sprokkereef, E., Stam, J., Savenije, H., and Hrachowitz, M.: Redressing the balance: quantifying net intercatchment groundwater flows, Hydrol. Earth Syst. Sci., 22, 6415–6434, https://doi.org/10.5194/hess-22-6415-2018, 2018.
Burek, P. and Smilovic, M.: The use of GRDC gauging stations for calibrating large-scale hydrological models, Earth Syst. Sci. Data, 15, 5617–5629, https://doi.org/10.5194/essd-15-5617-2023, 2023.
Chen, J., Tapley, B. D., Rodell, M., Seo, K. W., Wilson, C. R., Scanlon, B. R., and Pokhrel, Y.: Basin-scale river runoff estimation from GRACE gravity satellites, climate models, and in situ observations: a case study in the Amazon basin, Water Resour. Res., 56, e2020WR028032, https://doi.org/10.1029/2020WR028032, 2020a.
Chen, S., Liu, B., Tan, X., and Wu, Y.: Inter-comparison of spatiotemporal features of precipitation extremes within six daily precipitation products, Clim. Dynam., 54, 1057–1076, https://doi.org/10.1007/s00382-019-05045-z, 2020b.
Chen, X., Su, Z., Ma, Y., Trigo, I. F., and Gentine, P.: Remote sensing of global daily evapotranspiration based on a surface energy balance method and reanalysis data, J. Geophys. Res.-Atmos., 126, e2020JD032873, https://doi.org/10.1029/2020JD032873, 2021.
Clarke, R. T.: Uncertainty in the estimation of mean annual flood due to rating-curve indefinition, J. Hydrol., 222, 185–190, https://doi.org/10.1016/S0022-1694(99)00097-9, 1999.
Crosbie, R. S., Pollock, D. W., Mpelasoka, F. S., Barron, O. V., Charles, S. P., and Donn, M. J.: Changes in Köppen-Geiger climate types under a future climate for Australia: hydrological implications, Hydrol. Earth Syst. Sci., 16, 3341–3349, https://doi.org/10.5194/hess-16-3341-2012, 2012.
Cui, W., Dong, X., Xi, B., Feng, Z. H. E., and Fan, J.: Can the GPM IMERG final product accurately represent MCSs' precipitation characteristics over the central and eastern United States?, J. Hydrometeorol., 21, 39–57, https://doi.org/10.1175/JHM-D-19-0123.1, 2020.
Dagan, G., Stier, P., and Watson-Parris, D.: Analysis of the atmospheric water budget for elucidating the spatial scale of precipitation changes under climate change, Geophys. Res. Lett., 46, 10504–10511, https://doi.org/10.1029/2019GL084776, 2019.
Dastjerdi, P. A., Ghomlaghi, A., and Nasseri, M.: A new approach to ensemble precipitation estimation: coupling satellite hydrological products with backward water balance models in large-scale, J. Hydrol., 629, 130564, https://doi.org/10.1016/j.jhydrol.2023.130564, 2024.
Du, H., Zeng, S., Liu, X., and Xia, J.: An improved Budyko framework model incorporating water-carbon relationship for estimating evapotranspiration under climate and vegetation changes, Ecol. Indic., 169, 112887, https://doi.org/10.1016/j.ecolind.2024.112887, 2024.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions “Should we apply bias correction to global and regional climate model data?”, Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012.
Esquivel-Arriaga, G., Huber-Sannwald, E., Reyes-Gómez, V. M., Bravo-Peña, L. C., Dávila-Ortiz, R., Martínez-Tagüeña, N., and Velázquez-Zapata, J. A.: Performance evaluation of global precipitation datasets in northern Mexico drylands, J. Appl. Meteorol. Climatol., 63, 1545–1558, https://doi.org/10.1175/JAMC-D-23-0227.1, 2024.
Famiglietti, J. S., Lo, M., Ho, S. L., Bethune, J., Anderson, K. J., Syed, T. H., Swenson, S. C., De Linage, C. R., and Rodell, M.: Satellites measure recent rates of groundwater depletion in California's Central Valley, Geophys. Res. Lett., 38, L03403, https://doi.org/10.1029/2010GL046442, 2011.
Gao, H., Tang, Q., Ferguson, C. R., Wood, E. F., and Lettenmaier, D. P.: Estimating the water budget of major US river basins via remote sensing, Int. J. Remote Sens., 31, 3955–3978, https://doi.org/10.1080/01431161.2010.483488, 2010.
Guo, W., Hong, F., Yang, H., Huang, L., Ma, Y., Zhou, H., and Wang, H.: Quantitative evaluation of runoff variation and its driving forces based on multi-scale separation framework, J. Hydrol. Reg. Stud., 43, 101183, https://doi.org/10.1016/j.ejrh.2022.101183, 2022.
Hansford, M. R., Plink-Björklund, P., and Jones, E. R.: Global quantitative analyses of river discharge variability and hydrograph shape with respect to climate types, Earth-Sci. Rev., 200, 102977, https://doi.org/10.1016/j.earscirev.2019.102977, 2020.
Hao, X., Zhang, S., Li, W., Duan, W., Fang, G., Zhang, Y., and Guo, B.: The uncertainty of Penman-Monteith method and the energy balance closure problem, J. Geophys. Res.-Atmos., 123, 7433–7443, https://doi.org/10.1029/2018JD028371, 2018.
He, Q., Fok, H. S., Ferreira, V., Tenzer, R., Ma, Z., and Zhou, H.: Three-dimensional Budyko framework incorporating terrestrial water storage: unraveling water-energy dynamics, vegetation, and ocean-atmosphere interactions, Sci. Total Environ., 904, 166380, https://doi.org/10.1016/j.scitotenv.2023.166380, 2023.
Hsu, K.-L., Gao, X., Sorooshian, S., and Gupta, H. V.: Precipitation estimation from remotely sensed information using artificial neural networks, J. Appl. Meteorol. Climatol., 36, 1176–1190, https://doi.org/10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2, 1997.
Hua, D., Hao, X., Zhang, Y., and Qin, J.: Uncertainty assessment of potential evapotranspiration in arid areas, as estimated by the Penman-Monteith method, J. Arid Land, 12, 166–180, https://doi.org/10.1007/s40333-020-0093-7, 2020.
Huang, C., Hu, J., Chen, S., Zhang, A., Liang, Z., Tong, X., Xiao, L., Min, C., and Zhang, Z.: How well can IMERG products capture typhoon extreme precipitation events over southern China?, Remote Sens., 11, 70, https://doi.org/10.3390/rs11010070, 2019.
Huang, P., Wang, G., Guo, L., Mello, C. R., Li, K., Ma, J., and Sun, S.: Most global gauging stations present biased estimations of total catchment discharge, Geophys. Res. Lett., 50, e2023GL104253, https://doi.org/10.1029/2023GL104253, 2023.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., and Yoo, S.-H.: NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG), Algorithm theoretical basis document (ATBD) version 4, 26, 2020–2005, 2015.
Jacobs, A. F. G. and De Bruin, H. A. R.: Makkink's equation for evapotranspiration applied to unstressed maize, Hydrol. Process., 12, 1063–1066, https://doi.org/10.1002/(SICI)1099-1085(19980615)12:7<1063::AID-HYP640>3.0.CO;2-2, 1998.
Jiménez, C., Martens, B., Miralles, D. M., Fisher, J. B., Beck, H. E., and Fernández-Prieto, D.: Exploring the merging of the global land evaporation WACMOS-ET products based on local tower measurements, Hydrol. Earth Syst. Sci., 22, 4513–4533, https://doi.org/10.5194/hess-22-4513-2018, 2018.
Jian, J., Ryu, D., Costelloe, J. F., and Su, C. H.: Towards reliable hydrological model calibrations with river level measurements, in: 21st International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand, Gold Coast, Australia, 29 November–4 December 2015, 2325–2331, 2015.
Kaprom, C., Williams, J. A., Mehrotra, R., Ophaphaibun, C., and Sriwongsitanon, N.: A comprehensive evaluation of the accuracy of satellite-based precipitation estimates over Thailand, J. Hydrol. Reg. Stud., 59, 102380, https://doi.org/10.1016/j.ejrh.2025.102380, 2025.
Koster, R. D. and Suarez, M. J.: A simple framework for examining the interannual variability of land surface moisture fluxes, J. Climate, 12, 1911–1917, https://doi.org/10.1175/1520-0442(1999)012<1911:ASFFET>2.0.CO;2, 1999.
Krabbenhoft, C. A., Allen, G. H., Lin, P., Godsey, S. E., Allen, D. C., Burrows, R. M., DelVecchia, A. G., Fritz, K. M., Shanafield, M., Burgin, A. J., Zimmer, M. A., Datry, T., Dodds, W. K., Jones, C. N., Mims, M. C., Franklin, C., Hammond, J. C., Zipper, S., Ward, A. S., Costigan, K. H., Beck, H. E., and Olden, J. D.: Assessing placement bias of the global river gauge network, Nat. Sustain., 5, 586–592, https://doi.org/10.1038/s41893-022-00873-0, 2022.
Landerer, F. W., and Swenson, S. C.: Accuracy of scaled GRACE terrestrial water storage estimates, Water Resour. Res., 48, W04531, https://doi.org/10.1029/2011WR011453, 2012.
Landerer, F. W., Flechtner, F. M., Save, H., Webb, F. H., Bandikova, T., Bertiger, W. I., Bettadpur, S. V., Byun, S. H., Dahle, C., Dobslaw, H., Fahnestock, E., Harvey, N., Kang, Z., Kruizinga, G. L. H., Loomis, B. D., McCullough, C., Murböck, M., Nagel, P., Paik, M., Pie, N., Poole, S., Strekalov, D., Tamisiea, M. E., Wang, F., Watkins, M. M., Wen, H.-Y., Wiese, D. N., and Yuan, D. N.: Extending the global mass change data record: GRACE Follow-On instrument and science data performance, Geophys. Res. Lett., 47, e2020GL088306, https://doi.org/10.1029/2020GL088306, 2020.
Lehmann, F., Vishwakarma, B. D., and Bamber, J.: How well are we able to close the water budget at the global scale?, Hydrol. Earth Syst. Sci., 26, 35–54, https://doi.org/10.5194/hess-26-35-2022, 2022.
Levison, J., Larocque, M., Ouellet, M. A., Ferland, O., and Poirier, C.: Long-term trends in groundwater recharge and discharge in a fractured bedrock aquifer–past and future conditions, Can. Water Resour. J., 41, 500–514, https://doi.org/10.1080/07011784.2015.1037795, 2016.
Li, L., Dai, Y., Wei, Z., Wei, S., Zhang, Y., Wei, N., and Li, Q.: Enforcing water balance in multitask deep learning models for hydrological forecasting, J. Hydrometeorol., 25, 89–103, https://doi.org/10.1175/JHM-D-23-0073.1, 2024.
Liu, J., Zhang, Q., Singh, V. P., and Shi, P.: Contribution of multiple climatic variables and human activities to streamflow changes across China, J. Hydrol., 545, 145–162, https://doi.org/10.1016/j.jhydrol.2016.12.016, 2017.
Liu, X., Yang, K., Ferreira, V. G., and Bai, P.: Hydrologic model calibration with remote sensing data products in global large basins, Water Resour. Res., 58, e2022WR032929, https://doi.org/10.1029/2022WR032929, 2022.
Lockhoff, M., Zolina, O., Simmer, C., and Schulz, J.: Evaluation of satellite-retrieved extreme precipitation over Europe using gauge observations, J. Climate, 27, 607–623, https://doi.org/10.1175/JCLI-D-13-00194.1, 2014.
Loomis, B. D., Rachlin, K. E., Wiese, D. N., Landerer, F. W., and Luthcke, S. B.: Replacing GRACE/GRACE-FO with satellite laser ranging: Impacts on Antarctic Ice Sheet mass change, Geophys. Res. Lett., 47, e2019GL085488, https://doi.org/10.1029/2019GL085488, 2020.
Lute, A. C., and Abatzoglou, J. T.: Role of extreme snowfall events in interannual variability of snowfall accumulation in the western United States, Water Resour. Res., 50, 2874–2888, https://doi.org/10.1002/2013WR014465, 2014.
Luo, Z., Gao, Z., Wang, L., Wang, S., and Wang, L.: A method for balancing the terrestrial water budget and improving the estimation of individual budget components, Agric. For. Meteorol., 341, 109667, https://doi.org/10.1016/j.agrformet.2023.109667, 2023a.
Luo, Z., Li, H., Zhang, S., Wang, L., Wang, S., and Wang, L.: A novel two-step method for enforcing water budget closure and an intercomparison of budget closure correction methods based on satellite hydrological products, Water Resour. Res., 59, e2022WR032176, https://doi.org/10.1029/2022WR032176, 2023b.
Luo, Z., Yu, H., Liu, H., and Chen, J.: Assessing the water budget closure accuracy of satellite/reanalysis-based hydrological data products over mainland China, Remote Sens., 15, 5230, https://doi.org/10.3390/rs15215230, 2023c.
Lv, M., Ma, Z., Yuan, X., Lv, M., Li, M., and Zheng, Z.: Water budget closure based on GRACE measurements and reconstructed evapotranspiration using GLDAS and water use data for two large densely-populated mid-latitude basins, J. Hydrol., 547, 585–599, https://doi.org/10.1016/j.jhydrol.2017.02.027, 2017.
Majid, R., and Ardalan, E. S.: Performance of the Gravity Recovery and Climate Experiment (GRACE) method in monitoring groundwater-level changes in local-scale study regions within Iran, Hydrogeol. J., 27, 2497–2509, https://doi.org/10.1007/s10040-019-02007-x, 2019.
Masunaga, H., Schröder, M., Furuzawa, F. A., Kummerow, C., Rustemeier, E., and Schneider, U.: Inter-product biases in global precipitation extremes, Environ. Res. Lett., 14, 125016, https://doi.org/10.1088/1748-9326/ab5da9, 2019.
McMahon, T. A., Finlayson, B. L., and Peel, M. C.: Historical developments of models for estimating evaporation using standard meteorological data, WIREs Water, 3, 788–818, https://doi.org/10.1002/wat2.1172, 2016.
Mehrnegar, N., Schumacher, M., Jagdhuber, T., and Forootan, E.: Making the best use of GRACE, GRACE-FO and SMAP data through a constrained Bayesian data-model integration, Water Resour. Res., 59, e2023WR034544, https://doi.org/10.1029/2023WR034544, 2023.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Mourad, R., Schoups, G., Bastiaanssen, W., and Kumar, D. N.: Expert-based prior uncertainty analysis of gridded water balance components: application to the irrigated Hindon River Basin, India, J. Hydrol. Reg. Stud., 55, 2214–5818, https://doi.org/10.1016/j.ejrh.2024.101935, 2024.
Mu, D., Yan, H., Feng, W., and Peng, P.: GRACE leakage error correction with regularization technique: case studies in Greenland and Antarctica, Geophys. J. Int., 208, 1775–1786, https://doi.org/10.1093/gji/ggw494, 2017.
Mueller, B., Seneviratne, S. I., Jimenez, C., Corti, T., Hirschi, M., Balsamo, G., Ciais, P., Dirmeyer, P., Fisher, J. B., Guo, Z., Jung, M., Maignan, F., McCabe, M. F., Reichle, R., Reichstein, M., Rodell, M., Sheffield, J., Teuling, A. J., Wang, K., Wood, E. F., and Zhang, Y.: Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations, Geophys. Res. Lett., 38, L06402, https://doi.org/10.1029/2010GL046230, 2011.
Mueller, D. S.: Field evaluation of boat-mounted acoustic Doppler instruments used to measure streamflow, in: Proceedings of the IEEE/OES Seventh Working Conference on Current Measurement Technology, IEEE, 30–34, https://doi.org/10.1109/CCM.2003.1194278, 2003.
Munier, S. and Aires, F.: A new global method of satellite dataset merging and quality characterization constrained by the terrestrial water budget, Remote Sens. Environ., 205, 119–130, https://doi.org/10.1016/j.rse.2017.11.008, 2018.
Nassaj, B. N., Zohrabi, N., Shahbazi, A. N., and Fathian, H.: Evaluating the performance of eight global gridded precipitation datasets across Iran, Dyn. Atmos. Oceans, 98, 101297, https://doi.org/10.1016/j.dynatmoce.2022.101297, 2022.
Palharini, R. S. A., Vila, D. A., Rodrigues, D. T., Quispe, D. P., Palharini, R. C., de Siqueira, R. A., and de Sousa Afonso, J. M.: Assessment of the extreme precipitation by satellite estimates over South America, Remote Sens., 12, 2085, https://doi.org/10.3390/rs12132085, 2020.
Pan, M., Sahoo, A. K., Troy, T. J., Vinukollu, R. K., Sheffield, J., and Wood, E. F.: Multisource estimation of long-term terrestrial water budget for major global river basins, J. Climate, 25, 3191–3206, https://doi.org/10.1175/JCLI-D-11-00300.1, 2012.
Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, H., Arora, V. K., Haverd, V., Jain, A. K., Kato, E., Lienert, S., Lombardozzi, D., Nabel, J. E. M. S., Ottlé, C., Poulter, B., Zaehle, S., and Running, S. W.: Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling, Hydrol. Earth Syst. Sci., 24, 1485–1509, https://doi.org/10.5194/hess-24-1485-2020, 2020.
Papacharalampous, G., Tyralis, H., Markonis, Y., Máca, P., and Hanel, M.: Features of the Earth's seasonal hydroclimate: characterizations and comparisons across the Köppen–Geiger climates and across continents, Prog. Earth Planet. Sci., 10, 46, https://doi.org/10.1186/s40645-023-00574-y, 2023.
Park, J. and Choi, M.: Estimation of evapotranspiration from ground-based meteorological data and global land data assimilation system (GLDAS), Stoch. Environ. Res. Risk Assess., 29, 1963–1992, https://doi.org/10.1007/s00477-014-1004-2, 2015.
Pellet, V., Aires, F., Papa, F., Munier, S., and Decharme, B.: Long-term total water storage change from a Satellite Water Cycle reconstruction over large southern Asian basins, Hydrol. Earth Syst. Sci., 24, 3033–3055, https://doi.org/10.5194/hess-24-3033-2020, 2020.
Peltier, W. R., Drummond, R., and Roy, K.: Comment on “Ocean mass from GRACE and glacial isostatic adjustment” by DP Chambers et al., J. Geophys. Res.-Sol. Ea., 117, B11, https://doi.org/10.1029/2011JB008967, 2012.
Petković, V., and Kummerow, C. D.: Understanding the sources of satellite passive microwave rainfall retrieval systematic errors over land, J. Appl. Meteorol. Climatol., 56, 597–614, https://doi.org/10.1175/JAMC-D-16-0174.1, 2017.
Potter, N. J., Zhang, L., Milly, P. C. D., McMahon, T. A., and Jakeman, A. J.: Effects of rainfall seasonality and soil moisture capacity on mean annual water balance for Australian catchments, Water Resour. Res., 41, 6, https://doi.org/10.1029/2004WR003697, 2005.
Reager, J. T., Thomas, B. F., and Famiglietti, J. S.: River basin flood potential inferred using GRACE gravity observations at several months lead time, Nat. Geosci., 7, 588–592, https://doi.org/10.1038/ngeo2203, 2014.
Resende, T. C., Longuevergne, L., Gurdak, J. J., Leblanc, M., Favreau, G., Ansems, N., Van der Gun, J., Gaye, C. B., and Aureli, A.: Assessment of the impacts of climate variability on total water storage across Africa: implications for groundwater resources management, Hydrogeol. J., 27, 493–512, https://doi.org/10.1007/s10040-018-1864-5, 2019.
Rodell, M., Velicogna, I., and Famiglietti, J. S.: Satellite-based estimates of groundwater depletion in India, Nature, 460, 999–1002, https://doi.org/10.1038/nature08238, 2009.
Ruhoff, A., de Andrade, B. C., Laipelt, L., Fleischmann, A. S., Siqueira, V. A., Moreira, A. A., Barbedo, R., Cyganski, G. L., Fernandez, G. M. R., Brêda, J. P. L. F., Paiva, R. C. D., Meller, A., Teixeira, A. A., Araújo, A. A., Fuckner, M. A., and Biggs, T.: Global evapotranspiration datasets assessment using water balance in South America, Remote Sens., 14, 2526, https://doi.org/10.3390/rs14112526, 2022.
Sadeghi, M., Akbari Asanjan, A., Faridzad, M., Afzali Gorooh, V., Nguyen, P., Hsu, K., Sorooshian, S., and Braithwaite, D.: Evaluation of PERSIANN-CDR constructed using GPCP V2.2 and V2.3 and a comparison with TRMM 3B42 V7 and CPC unified gauge-based analysis in global scale, Remote Sens., 11, 2755, https://doi.org/10.3390/rs11232755, 2019.
Sahoo, A. K., Pan, M., Troy, T. J., Vinukollu, R. K., Sheffield, J., and Wood, E. F.: Reconciling the global terrestrial water budget using satellite remote sensing, Remote Sens. Environ., 115, 1850–1865, https://doi.org/10.1016/j.rse.2011.03.009, 2011.
Sankarasubramanian, A. and Vogel, R. M.: Annual hydroclimatology of the United States, Water Resour. Res., 38, 19-1–19-12, https://doi.org/10.1029/2001WR000619, 2002.
Schiavo, M.: The role of different sources of uncertainty on the stochastic quantification of subsurface discharges in heterogeneous aquifers, J. Hydrol., 617, 128930, https://doi.org/10.1016/j.jhydrol.2022.128930, 2023.
Schneider, U., Fuchs, T., Meyer-Christoffer, A., and Rudolf, B.: Global precipitation analysis products of the GPCC, Global Precipitation Climatology Centre (GPCC), DWD, Internet Publikation, 112, 3819–3837, 2008.
Senan, S., Thomas, J., Vema, V. K., Jainet, P. J., Nizar, S., Sivan, S., and Sudheer, K. P.: A study of the influence of rainfall datasets' spatial resolution on stream simulation in Chaliyar River Basin, India, J. Water Clim. Change, 13, 4234–4254, https://doi.org/10.2166/wcc.2022.273, 2022.
Shamsudduha, M., Taylor, R. G., Jones, D., Longuevergne, L., Owor, M., and Tindimugaya, C.: Recent changes in terrestrial water storage in the Upper Nile Basin: an evaluation of commonly used gridded GRACE products, Hydrol. Earth Syst. Sci., 21, 4533–4549, https://doi.org/10.5194/hess-21-4533-2017, 2017.
Shiklomanov, A. I., Yakovleva, T. I., Lammers, R. B., Karasev, I. P., Vörösmarty, C. J., and Linder, E.: Cold region river discharge uncertainty – Estimates from large Russian rivers, J. Hydrol., 326, 231–256, https://doi.org/10.1016/j.jhydrol.2005.10.037, 2006.
Su, Y. and Zhang, S.: Optimizing Parameters in the Common Land Model by Using Gravity Recovery and Climate Experiment Satellite Observations, Land, 13, 508, https://doi.org/10.3390/land13040508, 2024.
Swenson, S. and Wahr, J.: Post-processing removal of correlated errors in GRACE data, Geophys. Res. Lett., 33, L08402, https://doi.org/10.1029/2005GL025285, 2006.
Tan, X., Liu, B., Tan, X., and Chen, X.: Long-term water imbalances of watersheds resulting from biases in hydroclimatic data sets for water budget analyses, Water Resour. Res., 58, e2021WR031209, https://doi.org/10.1029/2021WR031209, 2022.
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., and Watkins, M. M.: GRACE measurements of mass variability in the Earth system, Science, 305, 503–505, https://doi.org/10.1126/science.1099192, 2004.
Trenberth, K. E., Dai, A., Van Der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R., and Sheffield, J.: Global warming and changes in drought, Nat. Clim. Change, 4, 17–22, https://doi.org/10.1038/nclimate2067, 2014.
Wang, L., Wang, J., Li, M., Wang, L., Li, X., and Zhu, L.: Response of terrestrial water storage and its change to climate change in the endorheic Tibetan Plateau, J. Hydrol., 612, 128231, https://doi.org/10.1016/j.jhydrol.2022.128231, 2022.
Wang, S., McKenney, D. W., Shang, J., and Li, J.: A national-scale assessment of long-term water budget closures for Canada's watersheds, J. Geophys. Res. Atmos., 119, 8712–8725, https://doi.org/10.1002/2014JD021951, 2014.
Wang, S., Huang, J., Yang, D., Pavlic, G., and Li, J.: Long-term water budget imbalances and error sources for cold region drainage basins, Hydrol. Process., 29, 2125–2136, https://doi.org/10.1002/hyp.10343, 2015.
Wang, Z., Zhong, R., Lai, C., and Chen, J.: Evaluation of the GPM IMERG satellite-based precipitation products and the hydrological utility, Atmos. Res., 196, 151–163, https://doi.org/10.1016/j.atmosres.2017.06.020, 2017.
Watkins, M. M., Wiese, D. N., Yuan, D. N., Boening, C., and Landerer, F. W.: Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res.-Sol. Ea., 120, 2648–2671, https://doi.org/10.1002/2014JB011547, 2015.
Wong, J. S., Zhang, X., Gharari, S., Shrestha, R. R., Wheater, H. S., and Famiglietti, J. S.: Assessing water balance closure using multiple data assimilation–and remote sensing–based datasets for Canada, J. Hydrometeorol., 22, 1569–1589, https://doi.org/10.1175/JHM-D-20-0131.1, 2021.
Wu, C., Yeh, P. J. F., Hu, B. X., and Huang, G.: Controlling factors of errors in the predicted annual and monthly evaporation from the Budyko framework, Adv. Water Resour., 121, 432–445, https://doi.org/10.1016/j.advwatres.2018.09.013, 2018.
Wu, Z., Zhang, Y., Sun, Z., Lin, Q., and He, H.: Improvement of a combination of TMPA (or IMERG) and ground-based precipitation and application to a typical region of the East China Plain, Sci. Total Environ., 640, 1165–1175, https://doi.org/10.1016/j.scitotenv.2018.05.272, 2018.
Xu, T., Guo, Z., Xia, Y., Ferreira, V. G., Liu, S., Wang, K., Yao, Y., Zhang, X., and Zhao, C.: Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States, J. Hydrol., 578, 124105, https://doi.org/10.1016/j.jhydrol.2019.124105, 2019.
Yao, Y., Liang, S., Li, X., Hong, Y., Fisher, J. B., Zhang, N., Chen, J., Cheng, J., Zhao, S., Zhang, X., Jiang, B., Sun, L., Jia, K., Wang, K., Chen, Y., Mu, Q., and Feng, F.: Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations, J. Geophys. Res.-Atmos., 119, 4521–4545, https://doi.org/10.1002/2013JD020864, 2014.
Yeh, P. J. F., Swenson, S. C., Famiglietti, J. S., and Rodell, M.: Remote sensing of groundwater storage changes in Illinois using the Gravity Recovery and Climate Experiment (GRACE), Water Resour. Res., 42, W12203, https://doi.org/10.1029/2006WR005374, 2006.
Zhang, D., Liu, X., Zhang, Q., Liang, K., and Liu, C.: Investigation of factors affecting intra-annual variability of evapotranspiration and streamflow under different climate conditions, J. Hydrol., 543, 759–769, https://doi.org/10.1016/j.jhydrol.2016.10.047, 2016.
Zhang, L., Potter, N., Hickel, K., Zhang, Y., and Shao, Q.: Water balance modeling over variable time scales based on the Budyko framework–Model development and testing, J. Hydrol., 360, 117–131, https://doi.org/10.1016/j.jhydrol.2008.07.021, 2008.
Zhang, Y., Pan, M., and Wood, E. F.: On creating global gridded terrestrial water budget estimates from satellite remote sensing, in: Remote Sensing and Water Resources, edited by: Gebremichael, M., Springer, 59–78, https://doi.org/10.1007/978-3-319-32449-4_4, 2016.
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., and Wood, E. F.: A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010, Hydrol. Earth Syst. Sci., 22, 241–263, https://doi.org/10.5194/hess-22-241-2018, 2018.
Zhou, L., Cao, Y., Shi, C., Liang, H., and Fan, L.: Quantifying the Atmospheric Water Balance Closure over Mainland China Using Ground-Based, Satellite, and Reanalysis Datasets, Atmosphere, 15, 497, https://doi.org/10.3390/atmos15040497, 2024.
Short summary
Existing correction methods may introduce large errors, and more seriously cause unrealistic negative values in P, ET and Q in up to 10 % of cases. A novel IWE-Res method is proposed to improve the accuracy and consistency of corrected satellite-based water budget component data. In most river basins (except cold regions), the best correction is achieved by adjusting 40 % to 90 % of the total water imbalance error.
Existing correction methods may introduce large errors, and more seriously cause unrealistic...
