Goodrich, D. et al. The usda-ars experimental watershed network: Evolution, lessons learned, societal benefits, and moving forward. Water Resources Research 57, e2019WR026473 (2021).
Google Scholar
Likens, G. E. The watershed-ecosystem approach. Hydrological Processes 35, e13977, https://doi.org/10.1002/hyp.13977 (2021).
Google Scholar
Goodman, K. J., Parker, S. M., Edmonds, J. W. & Zeglin, L. H. Expanding the scale of aquatic sciences: the role of the national ecological observatory network (neon). Freshwater Science 34, 377–385 (2015).
Google Scholar
Kovács, G. Proposal to construct a coordinating matrix for comparative hydrology. Hydrological Sciences Journal 29, 435–443 (1984).
Google Scholar
Falkenmark, M. & Chapman, T. Comparative hydrology: An ecological approach to land and water resources (Unesco, 1989).
Andreassian, V., Hall, A., Chahinian, N. & Schaake, J. Introduction and synthesis: Why should hydrologists work on a large number of basin data sets? In Andreassian, V., Hall, A., Chahinian, N. & Schaake, J. (eds.) Large sample basin experiments for hydrological model parameterization: results of the model parameter experiment–MOPEX, vol. IAHS Publ. 307, 1–5 (Wallingford: IAHS Press, 2006).
Blöschl, G. et al. Twenty-three unsolved problems in hydrology (uph)–a community perspective. Hydrological sciences journal 64, 1141–1158 (2019).
Google Scholar
Gupta, H. V. et al. Large-sample hydrology: a need to balance depth with breadth. Hydrology and Earth System Sciences 18, 463–477 (2014).
Google Scholar
Stahl, K. et al. Streamflow trends in europe: evidence from a dataset of near-natural catchments. Hydrology and Earth System Sciences 14, 2367–2382, https://doi.org/10.5194/hess-14-2367-2010 (2010).
Google Scholar
Gudmundsson, L., Seneviratne, S. I. & Zhang, X. Anthropogenic climate change detected in european renewable freshwater resources. Nature Climate Change 7, 813–816 (2017).
Google Scholar
Gudmundsson, L., Leonard, M., Do, H. X., Westra, S. & Seneviratne, S. I. Observed trends in global indicators of mean and extreme streamflow. Geophysical Research Letters 46, 756–766, https://doi.org/10.1029/2018GL079725 (2019).
Google Scholar
Gudmundsson, L. et al. Globally observed trends in mean and extreme river flow attributed to climate change. Science 371, 1159–1162, https://doi.org/10.1126/science.aba3996 (2021).
Google Scholar
Kratzert, F. et al. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrology and Earth System Sciences 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019 (2019).
Google Scholar
Kratzert, F. et al. Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research 55, 11344–11354, https://doi.org/10.1029/2019WR026065 (2019).
Google Scholar
Ghiggi, G., Humphrey, V., Seneviratne, S. I. & Gudmundsson, L. Grun: an observation-based global gridded runoff dataset from 1902 to 2014. Earth System Science Data 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019 (2019).
Google Scholar
Ghiggi, G., Humphrey, V., Seneviratne, S. I. & Gudmundsson, L. G-run ensemble: A multi-forcing observation-based global runoff reanalysis. Water Resources Research 57, e2020WR028787, https://doi.org/10.1029/2020WR028787 (2021).
Google Scholar
Addor, N. et al. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges. Hydrological Sciences Journal 65, 712–725 (2020).
Google Scholar
Schaake, J., Cong, S. & Duan, Q. The US MOPEX data set. In Andreassian, V., Hall, A., Chahinian, N. & Schaake, J. (eds.) Large sample basin experiments for hydrological model parameterization: results of the model parameter experiment–MOPEX, vol. IAHS Publ. 307, 9–28 (Wallingford: IAHS Press, 2006).
Fowler, K. J., Acharya, S. C., Addor, N., Chou, C. & Peel, M. C. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in australia. Earth System Science Data 13, 3847–3867 (2021).
Google Scholar
Klingler, C., Schulz, K. & Herrnegger, M. LamaH-CE: Large-sample data for hydrology and environmental sciences for central europe. Earth System Science Data 13, 4529–4565 (2021).
Google Scholar
Chagas, V. B. et al. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in brazil. Earth System Science Data 12, 2075–2096 (2020).
Google Scholar
Arsenault, R. et al. A comprehensive, multisource database for hydrometeorological modeling of 14,425 north american watersheds. Scientific Data 7, 1–12 (2020).
Google Scholar
Hao, Z. et al. CCAM: China catchment attributes and meteorology dataset. Earth System Science Data 13, 5591–5616 (2021).
Google Scholar
Alvarez-Garreton, C. et al. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies–chile dataset. Hydrology and Earth System Sciences 22, 5817–5846 (2018).
Google Scholar
Kuentz, A., Arheimer, B., Hundecha, Y. & Wagener, T. Understanding hydrologic variability across europe through catchment classification. Hydrology and Earth System Sciences 21, 2863–2879 (2017).
Google Scholar
Coxon, G. et al. CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in great britain. Earth System Science Data 12, 2459–2483 (2020).
Google Scholar
Newman, A. et al. Development of a large-sample watershed-scale hydrometeorological data set for the contiguous usa: data set characteristics and assessment of regional variability in hydrologic model performance. Hydrology and Earth System Sciences 19, 209–223 (2015).
Google Scholar
Addor, N., Newman, A. J., Mizukami, N. & Clark, M. P. The CAMELS data set: catchment attributes and meteorology for large-sample studies. Hydrology and Earth System Sciences 21, 5293–5313 (2017).
Google Scholar
Do, H. X., Gudmundsson, L., Leonard, M. & Westra, S. The global streamflow indices and metadata archive (gsim)–part 1: The production of a daily streamflow archive and metadata. Earth System Science Data 10, 765–785 (2018).
Google Scholar
Gudmundsson, L., Do, H. X., Leonard, M. & Westra, S. The global streamflow indices and metadata archive (GSIM)–part 2: Quality control, time-series indices and homogeneity assessment. Earth System Science Data 10, 787–804 (2018).
Google Scholar
Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Scientific data 6, 1–15, https://doi.org/10.1038/s41597-019-0300-6 (2019).
Google Scholar
Beck, H. E. et al. Global-scale regionalization of hydrologic model parameters. Water Resources Research 52, 3599–3622 (2016).
Google Scholar
Beck, H. E. et al. Global fully distributed parameter regionalization based on observed streamflow from 4,229 headwater catchments. Journal of Geophysical Research: Atmospheres 125, e2019JD031485 (2020).
Google Scholar
Blöschl, G. et al. Changing climate both increases and decreases european river floods. Nature 573, 108–111 (2019).
Google Scholar
Wilkinson, M. D. et al. The fair guiding principles for scientific data management and stewardship. Scientific data 3, 1–9 (2016).
Google Scholar
Metzger, M. J. et al. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Global Ecology and Biogeography 22, 630–638 (2013).
Google Scholar
Muñoz-Sabater, J. et al. Era5-land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13, 4349–4383 (2021).
Google Scholar
Lehner, B. Hydroatlas version 1.0 data download. Figshare https://doi.org/10.6084/m9.figshare.9890531.v1 (2022).
Gorelick, N. et al. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment https://doi.org/10.1016/j.rse.2017.06.031 (2017).
Google Scholar
Kratzert, F. et al. Caravan – A global community dataset for large-sample hydrology (Version 1.0), Zenodo, https://doi.org/10.5281/ZENODO.7540792 (2022).
Muñoz Sabater, J. et al. Era5-land hourly data from 1981 to present. ECMWF https://doi.org/10.24381/cds.e2161bac (2021).
Lehner, B., Linke, S. & Thieme, M. Hydroatlas version 1.0. Figshare https://doi.org/10.6084/m9.figshare.9890531.v1 (2019).
Fowler, K., Acharya, S. C., Addor, N., Chou, C. & Peel, M. CAMELS-AUS v1: Hydrometeorological time series and landscape attributes for 222 catchments in australia. PANGAEA https://doi.org/10.1594/PANGAEA.921850 (2020).
Chagas, V. B. P. et al. CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in brazil. Zenodo https://doi.org/10.5281/zenodo.3964745 (2020).
Alvarez-Garreton, C. et al. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – chile dataset. PANGAEA https://doi.org/10.1594/PANGAEA.894885 (2018).
Coxon, G. et al. Catchment attributes and hydro-meteorological timeseries for 671 catchments across great britain (CAMELS-GB). NERC Environmental Information Data Centre https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9 (2020).
Klingler, C., Kratzert, F., Schulz, K. & Herrnegger, M. LamaH-CE: Large-sample data for hydrology and environmental sciences for central europe. Zenodo https://doi.org/10.5281/zenodo.5153305 (2021).
Newman, A. et al. A large-sample watershed-scale hydrometeorological dataset for the contiguous usa. UCAR/NCAR – GDEX https://doi.org/10.5065/D6MW2F4D (2014).
McMillan, H. K., Westerberg, I. K. & Krueger, T. Hydrological data uncertainty and its implications. Wiley Interdisciplinary Reviews: Water 5, e1319 (2018).
Beven, K. Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication. Hydrological Sciences Journal 61, 1652–1665 (2016).
Google Scholar
Colliander, A. et al. Validation of smap surface soil moisture products with core validation sites. Remote Sensing of Environment 191, 215–231 (2017).
Google Scholar
Habib, E. & Krajewski, W. F. Uncertainty analysis of the trmm ground-validation radar-rainfall products: Application to the teflun-b field campaign. Journal of applied meteorology 41, 558–572 (2002).
Google Scholar
Kumar, S. V., Dirmeyer, P. A., Peters-Lidard, C. D., Bindlish, R. & Bolten, J. Information theoretic evaluation of satellite soil moisture retrievals. Remote Sensing of Environment 204, 392–400 (2018).
Google Scholar
Nearing, G. S. et al. Nonparametric triple collocation. Water Resources Research 53, 5516–5530 (2017).
Google Scholar
Alemohammad, S. H., McColl, K. A., Konings, A. G., Entekhabi, D. & Stoffelen, A. Characterization of precipitation product errors across the united states using multiplicative triple collocation. Hydrology and Earth System Sciences 19, 3489–3503 (2015).
Google Scholar
McMillan, H., Jackson, B., Clark, M., Kavetski, D. & Woods, R. Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models. Journal of Hydrology 400, 83–94 (2011).
Google Scholar
Domeneghetti, A., Castellarin, A. & Brath, A. Assessing rating-curve uncertainty and its effects on hydraulic model calibration. Hydrology and Earth System Sciences 16, 1191–1202 (2012).
Google Scholar
Koch, J. Caravan extension Denmark – Danish dataset for large-sample hydrology. Zenodo https://doi.org/10.5281/zenodo.6762361 (2022).
Knoben, W. J. M., Woods, R. A. & Freer, J. E. A quantitative hydrological climate classification evaluated with independent streamflow data. Water Resources Research 54, 5088–5109, https://doi.org/10.1029/2018WR022913 (2018).
Google Scholar
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