Multi-model hydrological reference dataset over continental Europe and an African basin
Status PubMed-not-MEDLINE Jazyk angličtina Země Velká Británie, Anglie Médium electronic
Typ dokumentu časopisecké články
PubMed
39289384
PubMed Central
PMC11408525
DOI
10.1038/s41597-024-03825-9
PII: 10.1038/s41597-024-03825-9
Knihovny.cz E-zdroje
- Publikační typ
- časopisecké články MeSH
Although Essential Climate Variables (ECVs) have been widely adopted as important metrics for guiding scientific and policy decisions, the Earth Observation (EO) and Land Surface and Hydrologic Model (LSM/HM) communities have yet to treat terrestrial ECVs in an integrated manner. To develop consistent terrestrial ECVs at regional and continental scales, greater collaboration between EO and LSM/HM communities is needed. An essential first step is assessing the LSM/HM simulation uncertainty. To that end, we introduce a new hydrological reference dataset that comprises a range of 19 existing LSM/HM simulations that represent the current state-of-the-art of our LSM/HMs. Simulations are provided on a daily time step, covering Europe, notably the Rhine and Po river basins, alongside the Tugela river basin in Africa, and are uniformly formatted to allow comparisons across simulations. Furthermore, simulations are comprehensively validated with discharge, evapotranspiration, soil moisture and total water storage anomaly observations. Our dataset provides valuable information to support policy development and serves as a benchmark for generating consistent terrestrial ECVs through the integration of EO products.
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Bojinski, S. et al. The concept of essential climate variables in support of climate research, applications, and policy. Bulletin of the American Meteorological Society95, 1431–1443, 10.1175/BAMS-D-13-00047.1 (2014).
Global Climate Observing System (GCOS). About Essential Climate Variables. https://gcos.wmo.int/en/essential-climate-variables/about Acessed 2023-12-04 (2023).
Global Climate Observing System (GCOS). Essential Climate Variables. https://gcos.wmo.int/en/essential-climate-variables Acessed 2023-12-04 (2023).
Dee, D. et al. Toward a consistent reanalysis of the climate system. Bulletin of the American Meteorological Society95, 1235–1248, 10.1175/BAMS-D-13-00043.1 (2014).
Balmaseda, M. A. et al. The ocean reanalyses intercomparison project (ora-ip). Journal of Operational Oceanography8, s80–s97, 10.1080/1755876X.2015.1022329 (2015).
Barker Schaaf, C. et al. Terrestrial essential climate variables for climate change assessment, mitigation and adaptation. Tech. Rep., Food and Agriculture Organization of the United Nations (FAO), Rome (2008).
Baatz, R. et al. Reanalysis in earth system science: Toward terrestrial ecosystem reanalysis. Reviews of Geophysics59, e2020RG000715, 10.1029/2020RG000715 (2021).
Baldocchi, D., Dralle, D., Jiang, C. & Ryu, Y. How much water is evaporated across california? a multiyear assessment using a biophysical model forced with satellite remote sensing data. Water Resources Research55, 2722–2741, 10.1029/2018WR023884 (2019).
Zink, M., Kumar, R., Cuntz, M. & Samaniego, L. A high-resolution dataset of water fluxes and states for germany accounting for parametric uncertainty. Hydrology and Earth System Sciences21, 1769–1790, 10.5194/hess-21-1769-2017 (2017).
Gou, J. et al. Cnrd v1. 0: a high-quality natural runoff dataset for hydrological and climate studies in china. Bulletin of the American Meteorological Society 1–57 10.1175/BAMS-D-20-0094.1 (2021).
Shrestha, P. et al. Towards improved simulations of disruptive reservoirs in global hydrological modelling. Water Resources Research (2023). Submited. In revision (minor).
Dickinson, R. E. et al. The community land model and its climate statistics as a component of the community climate system model. Journal of Climate19, 2302–2324, 10.1175/JCLI3742.1 (2006).
Collins, W. D. et al. The community climate system model version 3 (ccsm3). Journal of Climate19, 2122–2143, 10.1175/JCLI3761.1 (2006).
Oleson, K. W. et al. Improvements to the community land model and their impact on the hydrological cycle. Journal of Geophysical Research: Biogeosciences11310.1029/2007JG000563 (2008).
Naz, B. S., Kollet, S. J., Franssen, H. J. H., Montzka, C. & Kurtz, W. A 3 km spatially and temporally consistent european daily soil moisture reanalysis from 2000 to 2015. Scientific Data710.1038/s41597-020-0450-6 (2020). PubMed PMC
Lawrence, P. J. & Chase, T. N. Representing a new modis consistent land surface in the community land model (clm 3.0). Journal of Geophysical Research: Biogeosciences112, 10.1029/2006JG000168 (2007).
Thornton, P. E. & Zimmermann, N. E. An improved canopy integration scheme for a land surface model with prognostic canopy structure. Journal of Climate20, 3902–3923, 10.1175/JCLI4222.1 (2007).
Lawrence, D. M., Thornton, P. E., Oleson, K. W. & Bonan, G. B. The partitioning of evapotranspiration into transpiration, soil evaporation, and canopy evaporation in a gcm: Impacts on land–atmosphere interaction. Journal of Hydrometeorology8, 862–880, 10.1175/JHM596.1 (2007).
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E. & Gulden, L. E. A simple topmodel-based runoff parameterization (simtop) for use in global climate models. Journal of Geophysical Research: Atmospheres110, 10.1029/2005JD006111 (2005).
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., Gulden, L. E. & Su, H. Development of a simple groundwater model for use in climate models and evaluation with gravity recovery and climate experiment data. Journal of Geophysical Research: Atmospheres112, 10.1029/2006JD007522 (2007).
Niu, G.-Y. & Yang, Z.-L. Effects of frozen soil on snowmelt runoff and soil water storage at a continental scale. Journal of Hydrometeorology7, 937–952, 10.1175/JHM538.1 (2006).
Naz, B. S. et al. Improving soil moisture and runoff simulations at 3 km over europe using land surface data assimilation. Hydrology and Earth System Sciences23, 277–301, 10.5194/hess-23-277-2019 (2019).
LIU, J.-G., JIA, B.-H., XIE, Z.-H. & SHI, C.-X. Improving the simulation of terrestrial water storage anomalies over china using a bayesian model averaging ensemble approach. Atmospheric and Oceanic Science Letters11, 322–329, 10.1080/16742834.2018.1484656 (2018).
Lawrence, D. M. et al. Parameterization improvements and functional and structural advances in version 4 of the community land model. Journal of Advances in Modeling Earth Systems3, 10.1029/2011MS00045 (2011).
David, O. et al. A software engineering perspective on environmental modeling framework design: The object modeling system. Environmental Modelling & Software39, 201–213, 10.1016/j.envsoft.2012.03.006 (2013).
Tubini, N. & Rigon, R. Implementing the water, heat and transport model in geoframe (whetgeo-1d v.1.0): algorithms, informatics, design patterns, open science features, and 1d deployment. Geoscientific Model Development15, 75–104, 10.5194/gmd-15-75-2022 (2022).
Formetta, G., Antonello, A., Franceschi, S., David, O. & Rigon, R. Hydrological modelling with components: A gis-based open-source framework. Environmental Modelling & Software55, 190–200, 10.1016/j.envsoft.2014.01.019 (2014).
Serafin, F., David, O., Carlson, J. R., Green, T. R. & Rigon, R. Bridging technology transfer boundaries: Integrated cloud services deliver results of nonlinear process models as surrogate model ensembles. Environmental Modelling & Software146, 105231, 10.1016/j.envsoft.2021.105231 (2021).
Formetta, G., Mantilla, R., Franceschi, S., Antonello, A. & Rigon, R. The jgrass-newage system for forecasting and managing the hydrological budgets at the basin scale: models of flow generation and propagation/routing. Geoscientific Model Development4, 943–955, 10.5194/gmd-4-943-2011 (2011).
Formetta, G., Kampf, S. K., David, O. & Rigon, R. Snow water equivalent modeling components in newage-jgrass. Geoscientific Model Development7, 725–736, 10.5194/gmd-7-725-2014 (2014).
Bottazzi, M. et al. Comparing evapotranspiration estimates from the geoframe-prospero model with penman–monteith and priestley-taylor approaches under different climate conditions. Water13, 10.3390/w13091221 (2021).
Abera, W., Antonello, A., Franceschi, S., Formetta, G. & Rigon, R.The uDig Spatial Toolbox for hydro-geomorphic analysis, chap. 2, 1–19 (British Society of Geomorphology, 2014).
Bancheri, M. et al. The design, deployment, and testing of kriging models in geoframe with sik-0.9.8. Geoscientific Model Development11, 2189–2207, 10.5194/gmd-11-2189-2018 (2018).
Abera, W., Formetta, G., Brocca, L. & Rigon, R. Modeling the water budget of the upper blue nile basin using the jgrass-newage model system and satellite data. Hydrology and Earth System Sciences21, 3145–3165, 10.5194/hess-21-3145-2017 (2017).
Rigon, R. et al. Hess opinions: Participatory digital earth twin hydrology systems (darths) for everyone – a blueprint for hydrologists. Hydrology and Earth System Sciences26, 4773–4800, 10.5194/hess-26-4773-2022 (2022).
Azimi, S. et al. On understanding mountainous carbonate basins of the mediterranean using parsimonious modeling solutions. Hydrology and Earth System Sciences27, 4485–4503, 10.5194/hess-27-4485-2023 (2023).
Samaniego, L., Kumar, R. & Attinger, S. Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resources Research46, 10.1029/2008WR007327 (2010).
Kumar, R., Samaniego, L. & Attinger, S. Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations. Water Resources Research49, 360–379, 10.1029/2012WR012195 (2013).
Rakovec, O., Kumar, R., Attinger, S. & Samaniego, L. Improving the realism of hydrologic model functioning through multivariate parameter estimation. Water Resources Research52, 7779–7792, 10.1002/2016WR019430 (2016).
Thober, S. et al. The multiscale routing model mrm v1.0: simple river routing at resolutions from 1 to 50 km. Geoscientific Model Development12, 10.5194/gmd-12-2501-2019 (2019).
Kumar, R., Livneh, B. & Samaniego, L. Toward computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme. Water Resources Research49, 5700–5714, 10.1002/wrcr.20431 (2013).
Thober, S. et al. Multi-model ensemble projections of european river floods and high flows at 1.5, 2, and 3 degrees global warming. Environmental Research Letters13, 014003, 10.1088/1748-9326/aa9e35 (2018).
Samaniego, L. et al. Anthropogenic warming exacerbates european soil moisture droughts. Nature Climate Change5, 1117–1121, 10.1038/s41558-018-0138-5 (2018).
Marx, A. et al. Climate change alters low flows in europe under global warming of 1.5, 2, and 3 °c. Hydrology and Earth System Sciences22, 1017–1032, 10.5194/hess-22-1017-2018 (2018).
Wanders, N. et al. Development and evaluation of a pan-european multimodel seasonal hydrological forecasting system. Journal of Hydrometeorology20, 99–115, 10.1175/JHM-D-18-0040.1 (2019).
Samaniego, L. et al. Hydrological forecasts and projections for improved decision-making in the water sector in europe. Bulletin of the American Meteorological Society100, 2451–2472, 10.1175/BAMS-D-17-0274.1 (2019).
Zink, M. et al. The German drought monitor. Environmental Research Letters11, 074002, 10.1088/1748-9326/11/7/074002 (2016).
Samaniego, L. et al. mhm-ufz/mhm: v5.13.1 10.5281/zenodo.8279545 (2023).
Pohl, F. et al. Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for icos sites. Scientific Data10, 281, 10.1038/s41597-023-02192-1 (2023). PubMed PMC
Kollet, S. J. & Maxwell, R. M. Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model. Advances in Water Resources29, 945–958, 10.1016/j.advwatres.2005.08.006 (2006).
Maxwell, R. M. A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic modeling. Advances in Water Resources53, 109–117, 10.1016/j.advwatres.2012.10.001 (2013).
Dai, Y. et al. The common land model. Bulletin of the American Meteorological Society84, 1013–1024, 10.1175/BAMS-84-8-1013 (2003).
Maxwell, R. M., Condon, L. E. & Kollet, S. J. A high-resolution simulation of groundwater and surface water over most of the continental us with the integrated hydrologic model parflow v3. Geoscientific Model Development8, 923–937, 10.5194/gmd-8-923-2015 (2015).
Kuffour, B. N. O. et al. Simulating coupled surface–subsurface flows with parflow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model. Geoscientific Model Development13, 1373–1397, 10.5194/gmd-13-1373-2020 (2020).
Belleflamme, A. et al. Hydrological forecasting at impact scale: the integrated parflow hydrological model at 0.6 km for climate resilient water resource management over germany. Frontiers in Water5, 10.3389/frwa.2023.1183642 (2023).
van Beek, R. & Bierkens, M. F. The global hydrological model pcr-globwb: Conceptualization, parameterization and verification. Tech. Rep., Department of Physical Geography, Utrecht University, Utrecht, the Netherlands Acessed 2023-11-28 (2009).
Sutanudjaja, E. H. et al. Pcr-globwb 2: a 5 arcmin global hydrological and water resources model. Geoscientific Model Development11, 2429–2453, 10.5194/gmd-11-2429-2018 (2018).
Wada, Y., Wisser, D. & Bierkens, M. F. Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources. Earth System Dynamics5, 15–40, 10.5194/esd-5-15-2014 (2014).
van Beek, L. P. H., Wada, Y. & Bierkens, M. F. P. Global monthly water stress: 1. water balance and water availability. Water Resources Research47, 10.1029/2010WR009791 (2011).
Wada, Y. et al. Global monthly water stress: 2. water demand and severity of water stress. Water Resources Research47, 10.1029/2010WR009792 (2011).
van Beek, L. P., Eikelboom, T., van Vliet, M. T. & Bierkens, M. F. A physically based model of global freshwater surface temperature. Water Resources Research48, 10.1029/2012WR011819 (2012).
Sutanudjaja, E., Van Beek, L., De Jong, S., Van Geer, F. & Bierkens, M. Calibrating a large-extent high-resolution coupled groundwater-land surface model using soil moisture and discharge data. Water Resources Research50, 687–705, 10.1002/2013WR013807 (2014).
de Graaf, I. D., Sutanudjaja, E., Van Beek, L. & Bierkens, M. A high-resolution global-scale groundwater model. Hydrology and Earth System Sciences19, 823–837, 10.5194/hess-19-823-2015 (2015).
Van Vliet, M. et al. Multi-model assessment of global hydropower and cooling water discharge potential under climate change. Global Environmental Change40, 156–170, 10.1016/j.gfloenvcha.2016.07.007 (2016).
de Graaf, I. E., Gleeson, T., Van Beek, L., Sutanudjaja, E. H. & Bierkens, M. F. Environmental flow limits to global groundwater pumping. Nature574, 90–94, 10.1038/s41586-019-1594-4 (2019). PubMed
Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X. & Bierkens, M. F. Globgm v1. 0: a parallel implementation of a 30 arcsec pcr-globwb-modflow global-scale groundwater model. Geoscientific Model Development Discussions2022, 1–27, 10.5194/gmd-2022-226 (2022).
Hoch, J. M., Sutanudjaja, E. H., Wanders, N., Van Beek, R. L. & Bierkens, M. F. Hyper-resolution pcr-globwb: opportunities and challenges from refining model spatial resolution to 1 km over the european continent. Hydrology and Earth System Sciences27, 1383–1401, 10.5194/hess-27-1383-2023 (2023).
Francés, F., Vélez, J. I. & Vélez, J. J. Split-parameter structure for the automatic calibration of distributed hydrological models. Journal of Hydrology332, 226–240, 10.1016/j.jhydrol.2006.06.032 (2007).
Vélez, J. J., Puricelli, M., López Unzu, F. & Francés, F. Parameter extrapolation to ungauged basins with a hydrological distributed model in a regional framework. Hydrology and Earth System Sciences13, 229–246, 10.5194/hess-13-229-200910.5194/hessd-4-909-2007 (2009).
Bussi, G., Francés, F., Horel, E., López-Tarazón, J. A. & Batalla, R. J. Modelling the impact of climate change on sediment yield in a highly erodible mediterranean catchment. Journal of Soils and Sediments14, 1921–1937, 10.1007/s11368-014-0956-7 (2014).
Ruiz-Villanueva, V., Bussi, G., Francés, F. & Bréthaut, C. Climate change impacts on discharges of the rhone river in lyon by the end of the 21st century: model results and implications. Regional Environmental Change15, 505–515, 10.1007/s10113-014-0707-8 (2015).
Siswanto, S. Y. & Francés, F. How land use/land cover changes can affect water, flooding and sedimentation in a tropical watershed: a case study using distributed modeling in the upper citarum watershed, indonesia. Environmental Earth Sciences78, 10.1007/s12665-019-8561-0 (2019).
Puertes, C. et al. Improving the modelling and understanding of carbon-nitrogen-water interactions in a semiarid mediterranean oak forest. Ecological Modelling420, 108976, 10.1016/j.ecolmodel.2020.108976 (2020).
Pool, S. et al. From flood to drip irrigation under climate change: Impacts on evapotranspiration and groundwater recharge in the mediterranean region of valencia (spain). Earth’s Future910.1029/2020EF001859 (2021).
Echeverría, C. et al. Assessment of remotely sensed near-surface soil moisture for distributed eco-hydrological model implementation. Water11, 2613–2619, 10.3390/w11122613 (2019).
Gomis-Cebolla, J., Garcia-Arias, A., Perpinyà-Vallès, M. & Francés, F. Evaluation of sentinel-1, smap and smos surface soil moisture products for distributed eco-hydrological modelling in mediterranean forest basins. Journal of Hydrology608, 127569, 10.1016/j.jhydrol.2022.127569 (2022).
Gasper, F. et al. Implementation and scaling of the fully coupled terrestrial systems modeling platform (terrsysmp v1. 0) in a massively parallel supercomputing environment–a case study on juqueen (ibm blue gene/q). Geoscientific model development7, 2531–2543, 10.5194/gmd-7-2531-2014 (2014).
Shrestha, P., Sulis, M., Masbou, M., Kollet, S. & Simmer, C. A scale-consistent terrestrial systems modeling platform based on cosmo, clm, and parflow. Monthly weather review142, 3466–3483, 10.1175/MWR-D-14-00029.1 (2014).
Baldauf, M. et al. Operational convective-scale numerical weather prediction with the cosmo model: Description and sensitivities. Monthly Weather Review139, 3887–3905, 10.1175/MWR-D-10-05013.1 (2011).
Valcke, S. The oasis3 coupler: A european climate modelling community software. Geoscientific Model Development6, 373–388, 10.5194/gmd-6-373-2013 (2013).
Furusho-Percot, C. et al. Pan-european groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation. Scientific data6, 320, 10.1038/s41597-019-0328-7 (2019). PubMed PMC
Li, F., Kurtz, W., Hung, C. P., Vereecken, H. & Hendricks Franssen, H.-J. Water table depth assimilation in integrated terrestrial system models at the larger catchment scale. Frontiers in water5, 1150999, 10.3389/frwa.2023.1150999 (2023).
Hartick, C., Furusho-Percot, C., Goergen, K. & Kollet, S. An interannual probabilistic assessment of subsurface water storage over europe using a fully coupled terrestrial model. Water Resources Research57, e2020WR027828, 10.1029/2020WR027828 (2021).
van Verseveld, W. J. et al. Wflow_sbm v0.7.3, a spatially distributed hydrologic model: from global data to local applications. Geoscientific Model Development17, 3199–3234, 10.5194/gmd-17-3199-2024 (2024).
Vertessy, R. A. & Elsenbeer, H. Distributed modeling of storm flow generation in an amazonian rain forest catchment: Effects of model parameterization. Water Resources Research35, 2173–2187, 10.1029/1999WR900051 (1999).
Eilander, D. et al. Hydromt: Automated and reproducible model building and analysis. journal of open source software. Journal of Open Source Software8, 2173–2187, 10.21105/joss.04897 (2023).
Eilander, D. et al. A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models. Hydrology and Earth System Sciences25, 5287–5313, 10.5194/hess-25-5287-2021 (2021).
Imhoff, R. O., van Verseveld, W. J., van Osnabrugge, B. & Weerts, A. H. Scaling Point-Scale (Pedo)transfer Functions to Seamless Large-Domain Parameter Estimates for High-Resolution Distributed Hydrologic Modeling: An Example for the Rhine River. Water Resources Research56, e2019WR026807, 10.1029/2019WR026807 (2020).
López López, P. et al. Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations. Hydrology and Earth System Sciences20, 3059–3076, 10.5194/hess-20-3059-2016 (2016).
Rusli, S., Bense, V., Taufiq, A. & Weerts, A. Quantifying basin-scale changes in groundwater storage using grace and one-way coupled hydrological and groundwater flow model in the data-scarce bandung groundwater basin, indonesia. Groundwater for Sustainable Development22, 100953, 10.1016/j.gsd.2023.100953 (2023).
van der Laan, E., Hazenberg, P. & Weerts, A. H. Simulation of long-term storage dynamics of headwater reservoirs across the globe using public cloud computing infrastructure. Science of The Total Environment931, 172678, 10.1016/j.scitotenv.2024.172678 (2024). PubMed
Aerts, J. P. et al. Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model. Hydrology and Earth System Sciences26, 4407–4430, 10.5194/hess-26-4407-2022 (2022).
Priestley, C. H. B. & Taylor, R. J. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly weather review100, 81–92, 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2 (1972).
Hargreaves, G. H. & Samani, Z. A. Reference crop evapotranspiration from temperature. Applied engineering in agriculture1, 96–99, 10.13031/2013.26773 (1985).
Allen, R. G. et al. Crop evapotranspiration-guidelines for computing crop water requirements - fao irrigation and drainage paper 56. Tech. Rep., Food and Agriculture Organization of the United Nations (FAO) (1998).
de Bruin, H. D., Trigo, I., Bosveld, F. & Meirink, J. A thermodynamically based model for actual evapotranspiration of an extensive grass field close to fao reference, suitable for remote sensing application. Journal of Hydrometeorology17, 1373–1382, 10.1175/JHM-D-15-0006.1 (2016).
Haylock, M. et al. A european daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres113, 10.1029/2008JD010201 (2008).
Hofstra, N., Haylock, M., New, M. & Jones, P. D. Testing E-OBS European high-resolution gridded data set of daily precipitation and surface temperature. Journal of Geophysical Research: Atmospheres114, 10.1029/2009JD011799 (2009).
Klein Tank, A. et al. Daily dataset of 20th-century surface air temperature and precipitation series for the european climate assessment. International Journal of Climatology: A Journal of the Royal Meteorological Society22, 1441–1453, 10.1002/joc.773 (2002).
Klok, E. & Klein Tank, A. Updated and extended european dataset of daily climate observations. International Journal of Climatology: A Journal of the Royal Meteorological Society29, 1182–1191, 10.1002/joc.1779 (2009).
Thiemig, V. et al. Emo-5: a high-resolution multi-variable gridded meteorological dataset for europe. Earth System Science Data14, 3249–3272, 10.5194/essd-14-3249-2022 (2022).
Hersbach, H. et al. The era5 global reanalysis. Quarterly Journal of the Royal Meteorological Society146, 1999–2049, 10.1002/qj.3803 (2020).
Dee, D. P. et al. The era-interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the royal meteorological society137, 553–597, 10.1002/qj.828 (2011).
Owens, R. G. & Hewson, T. D. Ecmwf forecast user guide. Tech. Rep., European Centre for Medium-Range Weather Forecasts (ECMWF) 10.21957/m1cs7h (2018).
Urraca, R. et al. Evaluation of global horizontal irradiance estimates from era5 and cosmo-rea6 reanalyses using ground and satellite-based data. Solar Energy164, 339–354, 10.1016/j.solener.2018.02.059 (2018).
Centro Funzional Regione autonoma Valle D’Aosta. Discharge and meteorological dataset. https://presidi2.regione.vda.it/str_dataview Acessed 2023-07-28 (2023).
The Global Runoff Data Centre. GRDC discharge dataset. https://www.bafg.de/GRDC/ Acessed 2023-07-17 (2023).
Klingler, C., Schulz, K. & Herrnegger, M. Lamah-ce: Large-sample data for hydrology and environmental sciences for central europe. Earth System Science Data13, 4529–4565, 10.5194/essd-13-4529-2021 (2021).
Coxon, G. et al. Camels-gb: hydrometeorological time series and landscape attributes for 671 catchments in great britain. Earth System Science Data12, 2459–2483, 10.5194/essd-12-2459-2020 (2020).
Höge, M. et al. Camels-ch: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic switzerland. Earth System Science Data Discussions2023, 1–46, 10.5194/essd-2023-127 (2023).
Dati Ambientali Emilia-Romagna. DAER discharge dataset. https://webbook.arpae.it/indicatore/Portata-dei-fiumi-00001/?id=46803a8c-c127-11e2-9a51-11c9866a0f33 Acessed 2023-06-26 (2023).
Department Water and Sanitation of the Republic of South Africa (DWS). DWS discharge dataset. https://www.dws.gov.za/ Acessed 2020-04-14 (2023).
L’Agenzia Regionale per la Protezione Ambientale del Lombardia. Discharge dataset. https://idro.arpalombardia.it/it/map/sidro/ Acessed 2023-07-28 (2023).
L’Agenzia Regionale per la Protezione Ambientale del Piemonte. Discharge and meteorological dataset. https://www.arpa.piemonte.it/rischi_naturali/snippets_arpa_graphs/map_meteoweb/?rete=stazione_meteorologica Acessed 2023-07-28 (2023).
Pastorello, G. et al. The fluxnet2015 dataset and the oneflux processing pipeline for eddy covariance data. Scientific data7, 225, 10.1038/s41597-020-0534-3 (2020). PubMed PMC
International Soil Moisture Network. ISMN soil moisture dataset. https://ismn.earth/ Acessed 2023-07-25 (2023).
Dorigo, W. et al. The international soil moisture network: serving earth system science for over a decade. Hydrology and earth system sciences25, 5749–5804, 10.5194/hess-25-5749-2021 (2021).
Landerer, F. W. & Swenson, S. Accuracy of scaled grace terrestrial water storage estimates. Water resources research4810.1029/2011WR011453 (2012).
Landerer, F. JPL TELLUS GRACE Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.0 version 04 Acessed 2023-07-24 10.5067/TELND-3AJ64 (2021).
Landerer, F. JPL TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.1 version 04 Acessed 2023-07-24 10.5067/GFLND-3J614 (2023).
Wiese, D. N., Yuan, D.-N., Boening, C., Landerer, F. W. & Watkins, M. M. JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height Coastal Resolution Improvement (CRI) Filtered Release 06.1 Version 03 Acessed 2023-08-23, 10.5067/TEMSC-3JC63 (2023).
Landerer, F. CSR TELLUS GRACE Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.0 version 04 Acessed 2023-07-24, 10.5067/TELND-3AC64 (2021).
Landerer, F. CSR TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.1 version 04 Acessed 2023-07-24, 10.5067/GFLND-3C614 (2023).
Landerer, F. GFZ TELLUS GRACE Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.0 version 04 Acessed 2023-07-24, 10.5067/TELND-3AG64 (2021).
Landerer, F. GFZ TELLUS GRACE-FO Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.1 version 04 Acessed 2023-07-24, 10.5067/GFLND-3G614 (2023).
Humphrey, V., Rodell, M. & Eicker, A. Using satellite-based terrestrial water storage data: A review. Surveys in Geophysics44, 1489–1517, 10.1007/s10712-022-09754-9 (2023). PubMed PMC
Gupta, H. V. & Kling, H. On typical range, sensitivity, and normalization of mean squared error and nash-sutcliffe efficiency type metrics. Water Resources Research4710.1029/2011WR010962 (2011).
Oleson, K. et al. CLM3.5. https://github.com/HPSCTerrSys/CLM3.5 (2023).
Formetta, G., Bancheri, M., Tubini, N., Andreis, D. & Morlot, M. GEOframe: Components Development. https://github.com/geoframecomponents (2023).
Smith, S. et al. parflow. https://github.com/parflow/parflow (2023).
Sutanudjaja, E. H. et al. PCR-GLOBWB model. https://github.com/UU-Hydro/PCR-GLOBWB_model Acessed 2023-07-23 (2023).
Universitat Politècnica de València. TETIS: Conceptual and distributed hydrological model V9.1. http://lluvia.dihma.upv.es/ES/software/software.html (1995).
Keller, J. et al. TSMP. https://github.com/HPSCTerrSys/TSMP (2023).
van Verseveld, W. et al. Wflow.jl (v0.7.3). 10.5281/zenodo.10495638 (2024).
Droppers, B. 4dHydro WP2 benchmark. https://codebase.helmholtz.cloud/4dhydro/wp2/benchmark Acessed 2024-06-08 (2024).
Food and Agriculture Organization of the United Nation (FAO). Digital Soil Map of the World (DSMW). https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/446ed430-8383-11db-b9b2-000d939bc5d8 (2007).
Friedl, M. A. et al. Global land cover mapping from modis: algorithms and early results. Remote sensing of Environment83, 287–302, 10.1016/S0034-4257(02)00078-0 (2002).
Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PloS One12, e0169748, 10.1371/journal.pone.0169748 (2017). PubMed PMC
Danielson, J. J. et al. Global multi-resolution terrain elevation data 2010 (gmted2010) 10.3133/ofr20111073 (2011).
Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos, Transactions American Geophysical Union89, 93–94, 10.1029/2008EO100001 (2008).
European Space Agency (ESA), Universit Catholique de Louvain. Global Land Cover Map for 2009. http://due.esrin.esa.int/files/Globcover2009_V2.3_Global_.zip (2009).
Tucker, C. J., Pinzon, J. E. & Brown, M. E. Global inventory modeling and mapping studies (gimms). http://iridl.ldeo.columbia.edu/SOURCES/.UMD/.GLCF/.GIMMS/.NDVIg/.global/ (2004).
Hartmann, J. & Moosdorf, N. Global Lithological Map Database v1.0 (gridded to 0.5 degree spatial resolution) (2012). Supplement to: Hartmann, Jens; Moosdorf, Nils (2012): The new global lithological map database GLiM: A representation of rock properties at the Earth surface. Geochemistry, Geophysics, Geosystems, 13, Q12004, 10.1029/2012GC004370.
Zhang, Y. & Schaap, M. G. Weighted recalibration of the rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (rosetta3). Journal of Hydrology547, 39–53, 10.1016/j.jhydrol.2017.01.004 (2017).
European Environment Agency (EEA). Copernicus Land Monitoring Service 2018: Corine Land Cover (CLC) 2018, version 2020_20u1 10.2909/960998c1-1870-4e82-8051-6485205ebbac (2018).
Duscher, K. et al. The gis layers of the” international hydrogeological map of europe 1: 1,500,000” in a vector format. Hydrogeology journal23, 1867, 10.1007/s10040-015-1296-4 (2015).
Gesch, D. B., Verdin, K. L. & Greenlee, S. K. New land surface digital elevation model covers the earth. Eos, Transactions American Geophysical Union80, 69–70, 10.1029/99EO00050 (1999).
Verdin, K. & Greenlee, S. Development of continental scale digital elevation models and extraction of hydrographic features. In Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, New Mexico, 21–26 (National Center for Geographic Information and Analysis, Santa Barbara, California, 1996).
Loveland, T. R. et al. Development of a global land cover characteristics database and igbp discover from 1 km avhrr data. International journal of remote sensing21, 1303–1330, 10.1080/014311600210191 (2000).
Gleeson, T., Moosdorf, N., Hartmann, J. & van Beek, L. V. A glimpse beneath earth’s surface: Global hydrogeology maps (glhymps) of permeability and porosity. Geophysical Research Letters41, 3891–3898, 10.1002/2014GL059856 (2014).
Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of hydrology296, 1–22, 10.1016/j.jhydrol.2004.03.028 (2004).
Portmann, F. T., Siebert, S. & Döll, P. Mirca2000—global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. Global biogeochemical cycles2410.1029/2008GB003435 (2010).
Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river- flow management. Frontiers in Ecology and the Environment9, 494–502, 10.1890/100125 (2011).
Food and Agriculture Organization of the United Nation (FAO). Soil Map of the World (DSMW). https://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/ (1988).
Yamazaki, D. et al. MERIT Hydro: A high-resolution global hydrography map based on latest topography dataset. Water Resources Research55, 5053–5073, 10.1029/2019WR024873 (2019).
Buchhorn, M. et al. Copernicus Global Land Service: Land cover 100m, epoch 2015, globe (version v2.0.2) 10.5281/zenodo.3243509 (2019).
Myneni, R. B., Knyazikhin, Y. & Park, T. MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006, 10.5067/MODIS/MCD15A3H.006 (2015).
Messager, M. L., Lehner, B., Grill, G., Nedeva, L. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications7, 10.1038/ncomms13603 (2016). PubMed PMC
RGI Consortium. Randolph glacier inventory - A dataset of global glacier outlines: Version 6.0. https://nsidc.org/data/nsidc-0770/versions/6 (2017).
Raup, B. H. et al. The GLIMS geospatial glacier database: A new tool for studying glacier change. Global and Planetary Change56, 101–110, 10.1016/j.gloplacha.2006.07.018 (2007).
Fischer, M., Huss, M., Barboux, C. & Hoelzle, M. The new Swiss glacier inventory SGI2010: Relevance of using high-resolution source data in areas dominated by very small glaciers. Arctic, Antarctic, and Alpine Research46, 933–945, 10.1657/1938-4246-46.4.933 (2007).
Kollet, S. et al. FZJ hydrological variables data collection for the ESA 4DHydro project. https://datapub.fz-juelich.de/slts/4DHydro/ (2023).
Azimi, S. & Rigon, R. GEOframe 4DHydro simulations 10.17605/OSF.IO/7NY3V (2023).
Rakovec, O. & Samaniego, L. 4DHydro mHM Tier 1 simulations 10.48758/ufz.14386 (2023).
Droppers, B., Wanders, N. & Bierkens, M. PCR-GLOBWB community reference output - 4dHydro working package 2 10.24416/UU01-YA6SFX (2023).
Cortes-Torres, N., de León Pérez, D. & Frances, F. TETIS 4DHydro simulations Po 10.5281/zenodo.10245905 (2023).
Cortes-Torres, N., de León Pérez, D. & Frances, F. TETIS 4DHydro simulations Tugela 10.5281/zenodo.10246521 (2023).
Weerts, A. H. & Imhoff, R. O. 4DHydro Benchmark Dataset wflow_sbm 10.4121/6d391c3d-d3d7-4fa2-ae0e-4584776ac3e4.v1 (2023).