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Research Article

Assessing robustness in global hydrological predictions by comparing modelling and Earth observations

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 2357-2372 | Received 05 Aug 2022, Accepted 14 Sep 2023, Published online: 06 Nov 2023

References

  • Alfieri, L., et al., 2023. GloFAS – global ensemble streamflow forecasting and flood early warning. Hydrology and Earth System Sciences, 17 (3), 1161–1175. doi:10.5194/hess-17-1161-2013
  • Andersson, J.C.M., et al., 2017. Process refinements improve a hydrological model concept applied to the Niger River basin. Hydrological Processes, 31 (25), 4540–4554. doi:10.1002/hyp.11376
  • Archfield, S.A., et al., 2015. Accelerating advances in continental domain hydrologic modeling. Water Resources Research, 51 (12), 10078–10091. doi:10.1002/2015WR017498
  • Arheimer, B., et al., 2020. Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation. Hydrology and Earth System Sciences, 24 (2), 535–559. doi:10.5194/hess-24-535-2020
  • Arnell, N.W., 1999. A simple water balance model for the simulation of streamflow over a large geographic domain. Journal of Hydrology, 217 (3–4), 314–335. doi:10.1016/S0022-1694(99)00023-2
  • Best, M.J., et al., 2011. The Joint UK Land Environment Simulator (JULES), model description – part 1: energy and water fluxes. Geosciences Model Development, 4 (3), 677–699. doi:10.5194/GMD-4-677-2011
  • Beven, K., 2006. A manifesto for the equifinality thesis. Journal of Hydrology, 320 (1), 18–36. doi:10.1016/j.jhydrol.2005.07.007
  • Bierkens, M.F.P., et al., 2015. Hyper-resolution global hydrological modelling: what is next? Hydrological Processes, 29 (2), 310–320. doi:10.1002/HYP.10391
  • Bierkens, M.F.P., Sutanudjaja, E.H., and Wanders, N., 2021. Large-scale sensitivities of groundwater and surface water to groundwater withdrawal. Hydrology and Earth System Sciences, 25 (11), 5859–5878. doi:10.5194/hess-25-5859-2021
  • Blöschl, G., et al., 2019. Twenty-three unsolved problems in hydrology (UPH) – a community perspective. Hydrological Sciences Journal, 64 (10), 1141–1158. doi:10.1080/02626667.2019.1620507
  • Cáceres, D., et al., 2020. Assessing global water mass transfers from continents to oceans over the period 1948–2016. Hydrology and Earth System Sicences, 24 (10), 4831–4851. doi:10.5194/HESS-24-4831-2020
  • Colwell, R.K., 1974. Predictability, constancy, and contingency of periodic phenomena. Ecology, 55 (5), 1148–1153. doi:10.2307/1940366
  • Cornwell, E., Molotch, N.P., and McPhee, J., 2016. Spatio-temporal variability of snow water equivalent in the extra-tropical Andes Cordillera from distributed energy balance modelling and remotely sensed snow cover. Hydrology and Earth System Sciences, 20 (1), 411–430. doi:10.5194/HESS-20-411-2016
  • Crochemore, L., et al., 2020. Lessons learnt from checking the quality of openly accessible river flow data worldwide. Hydrological Sciences Journal, 65 (5), 699–711. doi:10.1080/02626667.2019.1659509
  • De Graaf, I.E.M., et al., 2019. Environmental flow limits to global groundwater pumping. Nature, 574 (7776), 90–94. doi:10.1038/s41586-019-1594-4
  • Demirel, M.C., et al., 2018. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model. Hydrology and Earth System Sciences, 22 (2), 1299–1315. doi:10.5194/hess-22-1299-2018
  • Dile, Y.T., et al., 2020. Evaluating satellite-based evapotranspiration estimates for hydrological applications in data-scarce regions: a case in Ethiopia. Science of the Total Environment, 743, 140702. doi:10.1016/J.SCITOTENV.2020.140702
  • Döll, P., et al., 2014. Global-scale assessment of groundwater depletion and related groundwater abstractions: combining hydrological modelling with information from well observations and GRACE satellites. Water Resources Research, 50 (7), 5698–5720. doi:10.1002/2014WR015595
  • Döll, P., et al., 2018. Risks for the global freshwater system at 1.5 °C and 2 °C global warming. Environmental Research Letters, 13 (4), 044038. doi:10.1088/1748-9326/AAB792
  • Döll, P., Kaspar, F., and Lehner, B., 2003. A global hydrological model for deriving water availability indicators: Model tuning and validation. Journal of Hydrology, 270 (1–2), 105–134. doi:10.1016/S0022-1694(02)00283-4
  • Donnelly, C., Andersson, J.C.M., and Arheimer, B., 2016. Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe. Hydrological Sciences Journal, 61 (2), 255–273. doi:10.1080/02626667.2015.1027710
  • Dorigo, W., et al., 2017. ESA CCI soil moisture for improved earth system understanding: state-of-the art and future directions. Remote Sensing of Environment, 203, 185–215. doi:10.1016/j.rse.2017.07.001
  • Dorigo, W.A., et al., 2010. Error characterisation of global active and passive microwave soil moisture datasets. Hydrology and Earth System Sciences, 14 (12), 2605–2616. doi:10.5194/hess-14-2605-2010
  • Dozier, J., 1989. Spectral signature of alpine snow cover from the Landsat thematic mapper. Remote Sensing of Environment, 28, 9–22. doi:10.1016/0034-4257(89)90101-6
  • Faisol, A., et al., 2020. An evaluation of MODIS global evapotranspiration product (MOD16A2) as terrestrial evapotranspiration in East Java - Indonesia. IOP Conference Series: Earth and Environmental Science, 485 (1), 012002. doi:10.1088/1755-1315/485/1/012002
  • Fayad, A., et al., 2017. Snow hydrology in Mediterranean mountain regions: a review. Journal of Hydrology, 551, 374–396. doi:10.1016/j.jhydrol.2017.05.063
  • Friedl, M.A., et al., 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83 (1), 287–302. doi:10.1016/S0034-4257(02)00078-0
  • Gädeke, A., et al., 2020. Performance evaluation of global hydrological models in six large Pan-Arctic watersheds. Climate Change, 163 (3), 1329–1351. doi:10.1007/s10584-020-02892-2
  • Gascoin, S., et al., 2015. A snow cover climatology for the Pyrenees from MODIS snow products. Hydrology and Earth System Sicences, 19 (5), 2337–2351. doi:10.5194/HESS-19-2337-2015
  • GCEW, 2023. The What, Why and how of the world water crisis: global commission on the economics of water phase 1 review and findings. Paris: Global Commission on the Economics of Water.
  • Gudmundsson, L., et al., 2021. Globally observed trends in mean and extreme river flow attributed to climate change. Science, 371 (6534), 1159–1162. doi:10.1126/science.aba3996
  • Guimberteau, M., et al., 2012. Discharge simulation in the sub-basins of the Amazon using ORCHIDEE forced by new datasets. Hydrology and Earth System Sicences, 16 (3), 911–935. doi:10.5194/HESS-16-911-2012
  • Gupta, H.V., et al., 2009. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. Journal of Hydrology, 377 (1), 80–91. doi:10.1016/j.jhydrol.2009.08.003
  • Hall, D.K., et al., 2002. MODIS snow-cover products. Remote Sensing of Environment, 83 (1), 181–194. doi:10.1016/S0034-4257(02)00095-0
  • Hall, D.K. and Riggs, G.A., 2017. Accuracy assessment of the MODIS snow products. Hydrological Processes, 21 (12), 1534–1547. doi:10.1002/hyp.6715
  • Han, P., et al., 2019. Improved understanding of snowmelt runoff from the headwaters of China’s Yangtze River using remotely sensed snow products and hydrological modelling. Remote Sensing of Environment, 224, 44–59. doi:10.1016/J.RSE.2019.01.041
  • Han, S.-C., et al., 2005. Improved estimation of terrestrial water storage changes from GRACE. Geophysical Research Letters, 32 (7), 7. doi:10.1029/2005GL022382
  • Hancock, S., et al., 2013. Evaluating global snow water equivalent products for testing land surface models. Remote Sensing of Environment, 128, 107–117. doi:10.1016/J.RSE.2012.10.004
  • Hargreaves, G.H. and Samani, Z.A., 1982. Estimating Potential Evapotranspiration. Journal of the Irrigation and Drainage Division, 108 (3), 225–230. doi:10.1061/JRCEA4.0001390
  • Harrigan, S., et al., 2023. Daily ensemble river discharge reforecasts and real-time forecasts from the operational global flood awareness system. Hydrology and Earth System Sciences, 27 (1), 1–19. doi:10.5194/hess-27-1-2023
  • Harris, M. and Frigg, R., 2023. Climate models and robustness analysis – part I: core concepts and premises. In: G. Pellegrino and M. Di Paola, eds. Handbook of Philosophy of Climate Change. Switzerland: Springer Cham. doi:10.1007/978-3-030-16960-2_146-1
  • Her, Y. and Chaubey, I., 2015. Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty. Hydrological Processes, 29 (19), 4220–4237. doi:10.1002/hyp.10487
  • Hoekstra, A.Y., et al., 2012. Global monthly water scarcity: blue water footprints versus blue water availability. PLoS ONE, 7 (2), e32688. doi:10.1371/journal.pone.0032688
  • Hu, G., Jia, L., and Menenti, M., 2015. Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe for 2011. Remote Sensing of Environment, 156, 510–526. doi:10.1016/J.RSE.2014.10.017
  • Huang, C., et al., 2018. Detecting, extracting, and monitoring surface water from space using optical sensors: a review. Review of Geophysics, 56 (2), 333–360. doi:10.1029/2018RG000598
  • Jensen, M.E. and Haise, H.R., 1963. Estimating evapotranspiration from solar radiation. Proceedings of the American Society of Civil Engineers, Journal of the Irrigation and Drainage Division, 89 (4), 15–41. doi:10.1061/JRCEA4.0000287
  • Jin, Y., et al., 2003. Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 1. Algorithm performance. Journal of Geophysical Research: Atmospheres, 108, D5. doi:10.1029/2002JD002803
  • Katul, G.G., et al., 2012. Evapotranspiration: a process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system. Review of Geophysics, 50 (3). doi:10.1029/2011RG000366
  • Kim, H.W., et al., 2012. Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia. KSCE Journal of Civil Engineering, 16 (2), 229–238. doi:10.1007/s12205-012-0006-1
  • Kirchner, J.W., 2006. Getting the right answers for the right reasons: linking measurements, analyses, and models to advance the science of hydrology. Water Resources Research, 42 (3). doi:10.1029/2005WR004362
  • Konapala, G., et al., 2020. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nature Communication, 11 (1), 3044. doi:10.1038/s41467-020-16757-w
  • Krysanova, V., et al., 2017. Intercomparison of regional-scale hydrological models in the present and future climate for 12 large river basins worldwide - A synthesis. Environmental Research Letters, 12 (10), 105002. doi:10.1088/1748-9326/aa8359
  • Krysanova, V., et al., 2018. How the performance of hydrological models relates to credibility of projections under climate change. Hydrological Sciences Journal, 63 (5), 696–720. doi:10.1080/02626667.2018.1446214
  • Krysanova, V., et al., 2020. How evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change. Climate Change, 163 (3), 1353–1377. doi:10.1007/s10584-020-02840-0
  • Landerer, F.W. and Swenson, S.C., 2012. Accuracy of scaled GRACE terrestrial water storage estimates. Water Resources Research, 48 (4), 4. doi:10.1029/2011WR011453
  • Lee, T.E., et al., 2006. The NPOESS VIIRS day/night visible sensor. Bulletin American Meteorological Society, 87 (2), 191–200. doi:10.1175/BAMS-87-2-191
  • Li, Y., et al., 2018. Hydrologic model calibration using remotely sensed soil moisture and discharge measurements: the impact on predictions at gauged and ungauged locations. Journal of Hydrology, 557, 897–909. doi:10.1016/j.jhydrol.2018.01.013
  • Liang, X., et al., 1994. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. Journal of Geophysical Research, 99 (D7), 14415–14428. doi:10.1029/94JD00483
  • Lindström, G., et al., 2010. Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrology Research, 41 (3–4), 295–319. doi:10.2166/nh.2010.007
  • Lloyd, E., 2015. Model robustness as a confirmatory virtue: the case of climate science. Studies in History and Philosophy of Science, 49, 58–68. doi:10.1016/j.shpsa.2014.12.002
  • Luojus, K., et al., 2016. Assessing global satellite-based snow water equivalent datasets in ESA SnowPEx project. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, China, 5284–5287. doi:10.1109/IGARSS.2016.7730376
  • Ma, H., et al., 2019. Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: a comprehensive assessment using global ground-based observations. Remote Sensing of Environment, 231, 111215. doi:10.1016/j.rse.2019.111215
  • MacDonald, M.K., et al., 2018. Impacts of 1.5 and 2.0 °C warming on Pan-Arctic River discharge into the Hudson Bay complex through 2070. Geophysical Research Letters, 45 (15), 7561–7570. doi:10.1029/2018GL079147
  • Marchane, A., et al., 2015. Assessment of daily MODIS snow cover products to monitor snow cover dynamics over the Moroccan Atlas mountain range. Remote Sensing of Environment, 160, 72–86. doi:10.1016/J.RSE.2015.01.002
  • Marchane, A., et al., 2017. Climate change impacts on surface water resources in the Rheraya catchment (High Atlas, Morocco). Hydrological Sciences Journal, 62 (6), 979–995. doi:10.1080/02626667.2017.1283042
  • Michel, D., et al., 2016. The WACMOS-ET project – part 1: tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms. Hydrology and Earth System Sciences, 20 (2), 803–822. doi:10.5194/hess-20-803-2016
  • Miralles, D., et al., 2016. The WACMOS-ET project-part 2: evaluation of global terrestrial evaporation data sets. Hydrology and Earth System Sciences, 20 (2), 823–842. doi:10.5194/hess-20-823-2016
  • Montanari, M., et al., 2008. Calibration and sequential updating of a coupled hydrologic-hydraulic model using remote sensing-derived water stages. Hydrology and Earth System Sciences, 5 (6), 3213–3245. doi:10.5194/hessd-5-3213-2008
  • Monteith, J.L., 1965. Evaporation and environment. Symposia of the Society for Experimental Biology, 19, 205–234.
  • Mortimer, C., et al., 2020. Evaluation of long-term northern hemisphere snow water equivalent products. The Cryosphere, 14 (5), 1579–1594. doi:10.5194/tc-14-1579-2020
  • Mu, Q., et al., 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111 (4), 519–536. doi:10.1016/j.rse.2007.04.015
  • Mu, Q., Zhao, M., and Running, S.W., 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115 (8), 1781–1800. doi:10.1016/j.rse.2011.02.019
  • Muñoz, E., et al., 2014. Identifiability analysis: towards constrained equifinality and reduced uncertainty in a conceptual model. Hydrological Sciences Journal, 59 (9), 1690–1703. doi:10.1080/02626667.2014.892205
  • Myneni, R.B., et al., 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83 (1), 214–231. doi:10.1016/S0034-4257(02)00074-3
  • Notarnicola, C., 2020. Hotspots of snow cover changes in global mountain regions over 2000–2018. Remote Sensing of Environment, 243, 111781. doi:10.1016/j.rse.2020.111781
  • Notarnicola, C., et al., 2013. Snow cover maps from MODIS images at 250 m resolution, part 2. Validation, 5 (4), 1568–1587. doi:10.3390/rs5041568
  • Notarnicola, C., Angiulli, M., and Posa, F., 2008. Soil moisture retrieval from remotely sensed data: neural network approach versus bayesian method. IEEE Transactions on Geoscience and Remote Sensing, 46 (2), 547–557. doi:10.1109/TGRS.2007.909951
  • O’Loughlin, R., 2021. Robustness reasoning in climate model comparisons. Studies in History and Philosophy of Science, 85, 34–43. doi:10.1016/j.shpsa.2020.12.005
  • Paca, V.H., et al., 2019. The spatial variability of actual evapotranspiration across the Amazon River Basin based on remote sensing products validated with flux towers. Ecological Processes, 8 (1), 1–20. doi:10.1186/s13717-019-0158-8
  • Parajka, J., et al., 2012. MODIS snow cover mapping accuracy in a small mountain catchment - Comparison between open and forest sites. Hydrological and Earth System Sciences, 16 (7), 2365–2377. doi:10.5194/HESS-16-2365-2012
  • Pechlivanidis, I.G. and Arheimer, B., 2015. Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case. Hydrology and Earth System Sciences, 19 (11), 4559–4579. doi:10.5194/hess-19-4559-2015
  • Penman, H.L., 1948. Natural evaporation from open water, bare soil, and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 193 (1032), 120–145. doi:10.1098/rspa.1948.0037
  • Pimentel, R., et al., 2018. Validating improved-MODIS products from spectral mixture-Landsat snow cover maps in a mountain region in southern Spain. PIAHS. doi:10.5194/piahs-380-67-2018
  • Pimentel, R., et al., 2023. Which potential evapotranspiration formula to use in hydrological modelling world-wide? Water Resources Research, 59 (5), e2022WR033447. doi:10.1029/2022WR033447
  • Pimentel, R., Herrero, J., and Polo, M.J., 2017a. Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography. Hydrology and Earth System Sciences, 21 (2), 805–820. doi:10.5194/hess-21-805-2017
  • Pimentel, R., Herrero, J., and Polo, M.J., 2017b. Quantifying snow cover distribution in semiarid regions combining satellite and terrestrial imagery. Remote Sensing, 9 (10), 995. doi:10.3390/rs9100995
  • Pokhrel, Y.N., Fan, Y., and Miguez-Macho, G., 2014. Potential hydrologic changes in the Amazon by the end of the 21st century and the groundwater buffer. Environmental Research Letters, 9 (8), 84004. doi:10.1088/1748-9326/9/8/084004
  • Polo, M.J., et al., 2020. Mountain hydrology in the Mediterranean region, water resources in the Mediterranean region. Elsevier, 51–75. doi:10.1016/B978-0-12-818086-0.00003-0
  • Priestley, C.H.B. and Taylor, R.J., 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100 (2), 81–92. doi:10.1175/1520-0493(1972)100<0081:otaosh>2.3.CO;2
  • Pulliainen, J.T., Grandell, J., and Hallikainen, M.T., 1999. HUT snow emission model and its applicability to snow water equivalent retrieval. IEEE Transactions on Geoscience and Remote Sensing, 37 (3), 1378–1390. doi:10.1109/36.763302
  • Rajib, M.A., Merwade, V., and Yu, Z., 2016. Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture. Journal of Hydrology, 536, 192–207. doi:10.1016/j.jhydrol.2016.02.037
  • Rakovec, O., et al., 2016. Multiscale and multivariate evaluation of water fluxes and states over European River Basins. Journal of Hydrometeorology, 17 (1), 287–307. doi:10.1175/JHM-D-15-0054.1
  • Rittger, K., Painter, T.H., and Dozier, J., 2013. Assessment of methods for mapping snow cover from MODIS. Advances in Water Resources, 51, 367–380. doi:10.1016/J.ADVWATRES.2012.03.002
  • Rockström, J., et al., 2023. Safe and just earth system boundaries. Nature, 619 (7968), 102–111. doi:10.1038/s41586-023-06083-8
  • Rößler, S., et al., 2021. Remote sensing of snow cover variability and its influence on the runoff of Sápmi’s rivers. Geosciences, 11 (3), 130. doi:10.3390/geosciences11030130
  • Rost, S., et al., 2008. Agricultural green and blue water consumption and its influence on the global water system. Water Resources Research, 44 (9), 9405. doi:10.1029/2007WR006331
  • Ruhoff, A.L., et al., 2013. Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin. Hydrological Sciences Journal, 58 (8), 1658–1676. doi:10.1080/02626667.2013.837578
  • Salomon, J.G., et al., 2006. Validation of the MODIS bidirectional reflectance distribution function and albedo retrievals using combined observations from the aqua and terra platforms. IEEE Transactions on Geoscience and Remote Sensing, 44 (6), 1555–1565. doi:10.1109/TGRS.2006.871564
  • Samuelsson, P., et al., 2011. The Rossby centre regional climate model RCA3: model description and performance. Tellus Atmosphere, 63 (1), 4–23. doi:10.1111/j.1600-0870.2010.00478.x
  • Santos, L., Andersson, J.C.M., and Arheimer, B., 2022. Evaluation of parameter sensitivity of a rainfall-runoff model over a global catchment set. Hydrological Sciences Journal, 67 (3), 342–357. doi:10.1080/02626667.2022.2035388
  • Schaaf, C.B., et al., 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83 (1), 135–148. doi:10.1016/S0034-4257(02)00091-3
  • Schupbach, J.N., 2018. Robustness analysis as explanatory reasoning. The British Journal for the Philosophy of Science, 69 (1), 275–300. doi:10.1093/bjps/axw008
  • Sood, A. and Smakhtin, V., 2015. Global hydrological models: a review. Hydrological Sciences Journal, 60 (4), 549–565. doi:10.1080/02626667.2014.950580
  • Takala, M., et al., 2011. Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sensing of Environment, 115 (12), 3517–3529. doi:10.1016/j.rse.2011.08.014
  • Tang, Q., et al., 2007. The influence of precipitation variability and partial irrigation within grid cells on a hydrological simulation. Journal of Hydrometeorology, 8 (3), 499–512. doi:10.1175/JHM589.1
  • Thornthwaite, C.W., 1948. An approach toward a rational classification of climate. Geographical Review, 38 (1), 55–94. doi:10.2307/210739
  • Van Loon, A.F., et al., 2016. Drought in a human-modified world: reframing drought definitions, understanding, and analysis approaches. Hydrology and Earth System Sciences, 20 (9), 3631–3650. doi:10.5194/hess-20-3631-2016
  • Velpuri, N.M., et al., 2013. A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: using point and gridded FLUXNET and water balance ET. Remote Sensing of Environment, 1 (39), 35–49. doi:10.1016/j.rse.2013.07.013
  • Vörösmarty, C.J., et al., 2000. Global water resources: vulnerability from climate change and population growth. Science, 289 (5477), 284–288. doi:10.1126/science.289.5477.284
  • Wada, Y., Wisser, D., and Bierkens, M.F., 2014. Global modeling of widthdrawal, allocation and consumptive use of surface water and groundwater resources. Earth System Dynamics, 5 (1), 15–40. doi:10.5194/esd-5-15-2014
  • Wahr, J., Molenaar, M., and Bryan, F., 1998. Time variability of the earth’s gravity field: hydrological and oceanic effects and their possible detection using GRACE. Journal of Geophysical Research: Solid Earth, 103 (B12), 30205–30229. doi:10.1029/98JB02844
  • Widén-Nilsson, E., Halldin, S., and Xu, C., 2007. Global water-balance modelling with WASMOD-M: parameter estimation and regionalisation. Journal of Hydrology, 340 (1), 105–118. doi:10.1016/j.jhydrol.2007.04.002
  • Wigneron, J.-P., et al., 2017. Modelling the passive microwave signature from land surfaces: a review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. Remote Sensing of Environment, 192, 238–262. doi:10.1016/j.rse.2017.01.024
  • Xu, T., et al., 2019. Evaluation of twelve evapotranspiration products from machine learning, remote sensing, and land surface models over conterminous United States. Journal of Hydrology, 578, 124105. doi:10.1016/J.JHYDROL.2019.124105
  • Xu, X., Li, J., and Tolson, B.A., 2014. Progress in integrating remote sensing data and hydrologic modelling. Progress in Physical. Geography: Earth and Environment, 38 (4), 464–498. doi:10.1177/0309133314536583
  • Yang, J., et al., 2015. Evaluation of snow products over the Tibetan Plateau. Hydrological Processes, 29 (15), 3247–3260. doi:10.1002/HYP.10427
  • Zeng, J., et al., 2015. Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in situ observations. Remote Sensing of Environment, 163, 91–110. doi:10.1016/j.rse.2015.03.008
  • Zhu, W., et al., 2022. Multi-scale evaluation of global evapotranspiration products derived from remote sensing images: accuracy and uncertainty. Journal of Hydrology, 611, 127982. doi:10.1016/J.JHYDROL.2022.127982