Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1559-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-1559-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
25 Mar 2024
Data description paper |
| 25 Mar 2024
| 25 Mar 2024
A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies
Ziyun Yin
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, China
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, China
International Research Center for Big Data for Sustainable Development Goals, Beijing, China
Southwest United Graduate School, Kunming, Yunnan, China
Ryan Riggs
Department of Geography, Texas A&M University, College Station, Texas, USA
George H. Allen
Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
Xiangyong Lei
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, China
Ziyan Zheng
Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
Siyu Cai
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
Related authors
Kaihao Zheng, Peirong Lin, and Ziyun Yin
Earth Syst. Sci. Data, 16, 3873–3891, https://doi.org/10.5194/essd-16-3873-2024, https://doi.org/10.5194/essd-16-3873-2024, 2024
Short summary
Short summary
We develop a globally applicable thresholding scheme for DEM-based floodplain delineation to improve the representation of spatial heterogeneity. It involves a stepwise approach to estimate the basin-level floodplain hydraulic geometry parameters that best respect the scaling law while approximating the global hydrodynamic flood maps. A ~90 m resolution global floodplain map, the Spatial Heterogeneity Improved Floodplain by Terrain analysis (SHIFT), is delineated with demonstrated superiority.
Peyman Saemian, Omid Elmi, Molly Stroud, Ryan Riggs, Benjamin M. Kitambo, Fabrice Papa, George H. Allen, and Mohammad J. Tourian
Earth Syst. Sci. Data, 17, 2063–2085, https://doi.org/10.5194/essd-17-2063-2025, https://doi.org/10.5194/essd-17-2063-2025, 2025
Short summary
Short summary
Our study addresses the need for better river discharge data, crucial for water management, by expanding global gauge networks with satellite data. We used satellite altimetry to estimate river discharge for over 8700 stations worldwide, filling gaps in existing records. Our data set, SAEM, supports a better understanding of water systems, helping to manage water resources more effectively, especially in regions with limited monitoring infrastructure.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
Short summary
Short summary
Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Zhen Zhang, Benjamin Poulter, Joe R. Melton, William J. Riley, George H. Allen, David J. Beerling, Philippe Bousquet, Josep G. Canadell, Etienne Fluet-Chouinard, Philippe Ciais, Nicola Gedney, Peter O. Hopcroft, Akihiko Ito, Robert B. Jackson, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Sara H. Knox, Tingting Li, Xin Li, Xiangyu Liu, Kyle McDonald, Gavin McNicol, Paul A. Miller, Jurek Müller, Prabir K. Patra, Changhui Peng, Shushi Peng, Zhangcai Qin, Ryan M. Riggs, Marielle Saunois, Qing Sun, Hanqin Tian, Xiaoming Xu, Yuanzhi Yao, Yi Xi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Biogeosciences, 22, 305–321, https://doi.org/10.5194/bg-22-305-2025, https://doi.org/10.5194/bg-22-305-2025, 2025
Short summary
Short summary
This study assesses global methane emissions from wetlands between 2000 and 2020 using multiple models. We found that wetland emissions increased by 6–7 Tg CH4 yr-1 in the 2010s compared to the 2000s. Rising temperatures primarily drove this increase, while changes in precipitation and CO2 levels also played roles. Our findings highlight the importance of wetlands in the global methane budget and the need for continuous monitoring to understand their impact on climate change.
Kaihao Zheng, Peirong Lin, and Ziyun Yin
Earth Syst. Sci. Data, 16, 3873–3891, https://doi.org/10.5194/essd-16-3873-2024, https://doi.org/10.5194/essd-16-3873-2024, 2024
Short summary
Short summary
We develop a globally applicable thresholding scheme for DEM-based floodplain delineation to improve the representation of spatial heterogeneity. It involves a stepwise approach to estimate the basin-level floodplain hydraulic geometry parameters that best respect the scaling law while approximating the global hydrodynamic flood maps. A ~90 m resolution global floodplain map, the Spatial Heterogeneity Improved Floodplain by Terrain analysis (SHIFT), is delineated with demonstrated superiority.
Md Safat Sikder, Jida Wang, George H. Allen, Yongwei Sheng, Dai Yamazaki, Chunqiao Song, Meng Ding, Jean-François Crétaux, and Tamlin M. Pavelsky
Earth Syst. Sci. Data, 15, 3483–3511, https://doi.org/10.5194/essd-15-3483-2023, https://doi.org/10.5194/essd-15-3483-2023, 2023
Short summary
Short summary
We introduce Lake-TopoCat to reveal detailed lake hydrography information. It contains the location of lake outlets, the boundary of lake catchments, and a wide suite of attributes that depict detailed lake drainage relationships. It was constructed using lake boundaries from a global lake dataset, with the help of high-resolution hydrography data. This database may facilitate a variety of applications including water quality, agriculture and fisheries, and integrated lake–river modeling.
Songyan Yu, Hong Xuan Do, Albert I. J. M. van Dijk, Nick R. Bond, Peirong Lin, and Mark J. Kennard
Hydrol. Earth Syst. Sci., 24, 5279–5295, https://doi.org/10.5194/hess-24-5279-2020, https://doi.org/10.5194/hess-24-5279-2020, 2020
Short summary
Short summary
There is a growing interest globally in the spatial distribution and temporal dynamics of intermittently flowing streams and rivers. We developed an approach to quantify catchment-wide flow intermittency over long time frames. Modelled patterns of flow intermittency in eastern Australia revealed highly dynamic behaviour in space and time. The developed approach is transferable to other parts of the world and can inform hydro-ecological understanding and management of intermittent streams.
Cited articles
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark, M. P.: A ranking of hydrologicalsignatures based on their predictability in space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018.
Addor, N., Do, H. X., Alvarez-Garreton, C., Coxon, G., Fowler, K., and Mendoza, P. A.: Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges, Hydrolog. Sci. J., 65, 712–725, https://doi.org/10.1080/02626667.2019.1683182, 2020.
Aerts, J. P. M., Hut, R. W., van de Giesen, N. C., Drost, N., van Verseveld, W. J., Weerts, A. H., and Hazenberg, P.: Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model, Hydrol. Earth Syst. Sci., 26, 4407–4430, https://doi.org/10.5194/hess-26-4407-2022, 2022.
AghaKouchak, A., Chiang, F., Huning, L. S., Love, C. A., Mallakpour, I., Mazdiyasni, O., Moftakhari, H., Papalexiou, S. M., Ragno, E., and Sadegh, M.: Climate Extremes and Compound Hazards in a Warming World, Annu. Rev. Earth Pl. Sc., 48, 519–548, https://doi.org/10.1146/annurev-earth-071719-055228, 2020.
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018.
Anuario de Aforos Digital – datos.gob.esm: Spain Anuario de Aforos 2022 Anuario de Aforos, Anuario de Aforos Digital – datos.gob.esm [data set], http://datos.gob.es/es/catalogo/e00125801-anuario-de-aforos/resource/4836b826-e7fd-4a41-950c-89b4eaea0279 (last access: 4 February 2024), 2022.
Arsenault, R., Brissette, F., Martel, J.-L., Troin, M., Lévesque, G., Davidson-Chaput, J., Gonzalez, M. C., Ameli, A., and Poulin, A.: A comprehensive, multisource database for hydrometeorological modeling of 14 425 North American watersheds, Scientific Data, 7, 243, https://doi.org/10.1038/s41597-020-00583-2, 2020.
Australian Bureau of Meteorology: Australian Bureau of Meteorology waterdata, Australian Bureau of Meteorology [data set], http://www.bom.gov.au/waterdata/ (last access: 29 October 2023), 2022.
Banasik, K. and Hejduk, L.: Long-term changes in runoff from a small agricultural catchment, Soil Water Res., 7, 64–72, https://doi.org/10.1007/s10661-006-0769-2, 2012.
Beck, H. E., van Dijk, A. I., De Roo, A., Miralles, D. G., McVicar, T. R., Schellekens, J., and Bruijnzeel, L. A.: Global-scale regionalization of hydrologic model parameters, Water Resour. Res., 52, 3599–3622, https://doi.org/10.1002/2015WR018247, 2016.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., and de Roo, A.: MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21, 589–615, https://doi.org/10.5194/hess-21-589-2017, 2017.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Van Dijk, A. I., 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, 2019.
Belvederesi, C., Zaghloul, M. S., Achari, G., Gupta, A., and Hassan, Q. K.: Modelling river flow in cold and ungauged regions: A review of the purposes, methods, and challenges, Environ. Rev., 30, 159–173, https://doi.org/10.1139/er-2021-0043, 2022.
Benke, K. K., Lowell, K. E., and Hamilton, A. J.: Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model, Math. Comput. Model., 47, 1134–1149, https://doi.org/10.1016/j.mcm.2007.05.017, 2008.
Beven, K. J. and Alcock, R. E.: Modelling everything everywhere: a new approach to decision-making for water management under uncertainty, Freshwater Biol., 57, 124–132, https://doi.org/10.1111/j.1365-2427.2011.02592.x, 2012.
Bourdin, D. R., Fleming, S. W., and Stull, R. B.: Streamflow modelling: a primer on applications, approaches and challenges, Atmos. Ocean, 50, 507–536, https://doi.org/10.1080/07055900.2012.734276, 2012.
Brazil National Water Agency: National water and sanitation agency (ANA) Agência Nac Águas E Saneam. Básico ANA, Brazil National Water Agency [data set], https://www.snirh.gov.br/hidroweb/serieshistoricas (last access: 5 July 2023), 2022.
Brugnara, Y., Good, E., Squintu, A. A., van der Schrier, G., and Brönnimann, S.: The EUSTACE global land station daily air temperature dataset, Geosci. Data J., 6, 189–204, https://doi.org/10.1002/gdj3.81, 2019.
Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L., and Karger, D. N.: Global climate-related predictors at kilometer resolution for the past and future, Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, 2022.
Brunner, M. I., Slater, L., Tallaksen, L. M., and Clark, M.: Challenges in modeling and predicting floods and droughts: A review, WIRes Water, 8, e1520, https://doi.org/10.1002/wat2.1520, 2021.
Burges, S. J.: Streamflow prediction: capabilities, opportunities, and challenges, Hydrologic Sciences: Taking Stock and Looking Ahead, 5, 101–134, 1998.
Canada National Water Data Archive: National water data archive HYDAT, Canada National Water Data Archive [data set], https://www.canada.ca/en/environment-climate-change/services/water-overview/quantity/monitoring/survey/data-products-services/national-archive-hydat.html (last access: 5 July 2023), 2022.
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.
Chen, X., Jiang, L., Luo, Y., and Liu, J.: A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022), Earth Syst. Sci. Data, 15, 4463–4479, https://doi.org/10.5194/essd-15-4463-2023, 2023.
Chile Center for Climate and Resilience Research: Center for climate and resilience research CR2 Explorator, Chile Center for Climate and Resilience Research [data set], https://explorador.cr2.cl/ (last access: 5 July 2023), 2022.
Cho, K. and Kim, Y.: Improving streamflow prediction in the WRF-Hydro model with LSTM networks, J. Hydrol., 605, 127297, https://doi.org/10.1016/j.jhydrol.2021.127297, 2022.
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J., Tang, G., Gharari, S., Freer, J. E., Whitfield, P. H., and Shook, K. R.: The abuse of popular performance metrics in hydrologic modeling, Water Resour. Res., 57, e2020WR029001, https://doi.org/10.1029/2020WR029001, 2021.
Claverie, M., Matthews, J. L., Vermote, E. F., and Justice, C. O.: A 30+ year AVHRR LAI and FAPAR climate data record: Algorithm description and validation, Remote Sens.-Basel, 8, 263, https://doi.org/10.3390/rs8030263, 2016.
Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, 2020.
Delaigue, O., Brigode, P., Andréassian, V., Perrin, C., Etchevers, P., Soubeyroux, J. M., Janet, B., and Addor, N.: CAMELS-FR: A large sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking, IAHS-2022 Scientific Assembly, May 2022, Montpellier, France, hal-03687235, https://doi.org/10.5194/egusphere-egu21-13349, 2022.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata, Earth Syst. Sci. Data, 10, 765–785, https://doi.org/10.5194/essd-10-765-2018, 2018.
Du, Z., Yu, L., Chen, X., Li, X., Peng, D., Zheng, S., Hao, P., Yang, J., Guo, H., and Gong, P.: An Operational Assessment Framework for Near Real-time Cropland Dynamics: Toward Sustainable Cropland Use in Mid-Spine Belt of Beautiful China, Journal of Remote Sensing, 3, 0065, https://doi.org/10.34133/remotesensing.0065, 2023.
Duan, Q., Schaake, J., Andréassian, V., Franks, S., Goteti, G., Gupta, H., Gusev, Y., Habets, F., Hall, A., and Hay, L.: Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops, J. Hydrol., 320, 3–17, https://doi.org/10.1016/j.jhydrol.2005.07.031, 2006.
Fang, Y., Huang, Y., Qu, B., Zhang, X., Zhang, T., and Xia, D.: Estimating the Routing Parameter of the Xin'anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning, Remote Sens.-Basel, 14, 4609, https://doi.org/10.3390/rs14184609, 2022.
Fowler, K. J. A., Acharya, S. C., Addor, N., Chou, C., and Peel, M. C.: CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia, Earth Syst. Sci. Data, 13, 3847–3867, https://doi.org/10.5194/essd-13-3847-2021, 2021.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V006, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, https://doi.org/10.1016/J.RSE.2009.08.016, 2010.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., and Reichle, R.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Global Modeling and Assimilation Office (GMAO): inst3_3d_asm_Cp: MERRA-2 3D IAU State, Meteorology Instantaneous 3-hourly (p-coord, 0.625x0.5L42), version 5.12.4, Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), Greenbelt, MD, USA, https://doi.org/10.5067/VJAFPLI1CSIV, 2015.
Gudmundsson, L., Do, H. X., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment, Earth Syst. Sci. Data, 10, 787–804, https://doi.org/10.5194/essd-10-787-2018, 2018.
Gupta, H. V., Perrin, C., Blöschl, G., Montanari, A., Kumar, R., Clark, M., and Andréassian, V.: Large-sample hydrology: a need to balance depth with breadth, Hydrol. Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/hess-18-463-2014, 2014.
Hao, Z., Jin, J., Xia, R., Tian, S., Yang, W., Liu, Q., Zhu, M., Ma, T., Jing, C., and Zhang, Y.: CCAM: China Catchment Attributes and Meteorology dataset, Earth Syst. Sci. Data, 13, 5591–5616, https://doi.org/10.5194/essd-13-5591-2021, 2021.
Henck, A. C., Montgomery, D. R., Huntington, K. W., and Liang, C.: Monsoon control of effective discharge, Yunnan and Tibet, Geology, 38, 975–978, https://doi.org/10.1130/G31444.1, 2010.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee,D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hrachowitz, M., Savenije, H., Blöschl, G., McDonnell, J., Sivapalan, M., Pomeroy, J., Arheimer, B., Blume, T., Clark, M., and Ehret, U.: A decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1198–1255, https://doi.org/10.1080/02626667.2013.803183, 2013.
Hu, D., Cao, S., Chen, S., Deng, L., and Feng, N.: Monitoring spatial patterns and changes of surface net radiation in urban and suburban areas using satellite remote-sensing data, Int. J. Remote Sens., 38, 1043–1061, https://doi.org/10.1080/01431161.2016.1275875, 2017.
Huang, Y.: High spatiotemporal resolution mapping of global urban change from 1985 to 2015, figshare [data set], https://doi.org/10.6084/m9.figshare.11513178.v1, 2020.
Immerzeel, W. and Droogers, P.: Calibration of a distributed hydrological model based on satellite evapotranspiration, J. Hydrol., 349, 411–424, https://doi.org/10.1016/j.jhydrol.2007.11.017, 2008.
India Water Resources Information System: India Water Resources Information System [data set], https://indiawris.gov.in/wris/#/RiverMonitoring (last access: 5 July 2023), 2022.
Kauffeldt, A., Halldin, S., Rodhe, A., Xu, C.-Y., and Westerberg, I. K.: Disinformative data in large-scale hydrological modelling, Hydrol. Earth Syst. Sci., 17, 2845–2857, https://doi.org/10.5194/hess-17-2845-2013, 2013.
Klingler, C., Schulz, K., and Herrnegger, M.: LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, 2021.
Kovács, G.: Proposal to construct a coordinating matrix for comparative hydrology, Hydrolog. Sci. J., 29, 435–443, https://doi.org/10.1080/02626668409490961, 1984.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019.
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., and Nevo, S.: Caravan-A global community dataset for large-sample hydrology, Scientific Data, 10, 61, https://doi.org/10.1038/s41597-023-01975-w, 2023.
Lehner, B., Messager, M. L., Korver, M. C., and Linke, S.: Global hydro-environmental lake characteristics at high spatial resolution, Scientific Data, 9, 351, https://doi.org/10.1038/s41597-022-01425-z, 2022.
Li, B., Rodell, M., Kumar, S., Beaudoing, H. K., Getirana, A., Zaitchik, B. F., de Goncalves, L. G., Cossetin, C., Bhanja, S., and Mukherjee, A.: Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges, Water Resour. Res., 55, 7564–7586, https://doi.org/10.1029/2018WR024618, 2019.
Lin, P., Rajib, M. A., Yang, Z. L., Somos-Valenzuela, M., Merwade, V., Maidment, D. R., Wang, Y., and Chen, L.: Spatiotemporal evaluation of simulated evapotranspiration and streamflow over Texas using the WRF-Hydro-RAPID modeling framework, J. Am. Water Resour. As., 54, 40–54, https://doi.org/10.1111/1752-1688.12585, 2018.
Lin, P. R., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., and Wood, E. F.: Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches, Water Resour. Res., 55, 6499–6516, https://doi.org/10.1029/2019wr025287, 2019.
Lin, P. R., Pan, M., Wood, E. F., Yamazaki, D., and Allen, G. H.: A new vector-based global river network dataset accounting for variable drainage density, Sci. Data, 8, 28, https://doi.org/10.1038/s41597-021-00819-9, 2021.
Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V., Maxwell, S., and Moidu, H.: Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution, Scientific Data, 6, 283, https://doi.org/10.1038/s41597-019-0300-6, 2019.
Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., Lin, P., Gong, K., Ziegler, A. D., and Chen, A.: High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015, Nature Sustainability, 3, 564–570, https://doi.org/10.1038/s41893-020-0521-x, 2020.
Lu, J., Wang, G., Chen, T., Li, S., Hagan, D. F. T., Kattel, G., Peng, J., Jiang, T., and Su, B.: A harmonized global land evaporation dataset from model-based products covering 1980–2017, Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, 2021.
Lucas-Borja, M. E., Carrà, B. G., Nunes, J. P., Bernard-Jannin, L., Zema, D. A., and Zimbone, S. M.: Impacts of land-use and climate changes on surface runoff in a tropical forest watershed (Brazil), Hydrolog. Sci. J., 65, 1956–1973, https://doi.org/10.1016/j.catena.2006.04.015, 2020.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Merchant, C. J., Paul, F., Popp, T., Ablain, M., Bontemps, S., Defourny, P., Hollmann, R., Lavergne, T., Laeng, A., de Leeuw, G., Mittaz, J., Poulsen, C., Povey, A. C., Reuter, M., Sathyendranath, S., Sandven, S., Sofieva, V. F., and Wagner, W.: Uncertainty information in climate data records from Earth observation, Earth Syst. Sci. Data, 9, 511–527, https://doi.org/10.5194/essd-9-511-2017, 2017.
Ministry of Land, Infrastructure, Transport and Tourism: Japanese Water Information System, Ministry of Land, Infrastructure, Transport and Tourism [data set], http://www.river.go.jp/ (last access: 5 July 2023), 2022.
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.
Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.e2161bac, 2019.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Nandi, S. and Reddy, M. J.: An integrated approach to streamflow estimation and flood inundation mapping using VIC, RAPID and LISFLOOD-FP, J. Hydrol., 610, 127842, https://doi.org/10.1016/j.jhydrol.2022.127842, 2022.
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: 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, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015.
Niraula, R., Meixner, T., and Norman, L. M.: Determining the importance of model calibration for forecasting absolute/relative changes in streamflow from LULC and climate changes, J. Hydrol., 522, 439–451, https://doi.org/10.1016/j.jhydrol.2015.01.007, 2015.
Qu, S., Wang, L., Lin, A., Zhu, H., and Yuan, M.: What drives the vegetation restoration in Yangtze River basin, China: climate change or anthropogenic factors?, Ecol. Indic., 90, 438–450, https://doi.org/10.1016/j.ecolind.2018.03.029, 2018.
Razavi, T. and Coulibaly, P.: Streamflow prediction in ungauged basins: review of regionalization methods, J. Hydrol. Eng., 18, 958–975, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000690, 2013.
Ren, K., Fang, W., Qu, J., Zhang, X., and Shi, X.: Comparison of eight filter-based feature selection methods for monthly streamflow forecasting–three case studies on CAMELS data sets, J. Hydrol., 586, 124897, https://doi.org/10.1016/j.jhydrol.2020.124897, 2020.
Riggs, R. M., Allen, G. H., Wang, J., Pavelsky, T. M., Gleason, C. J., David, C. H., and Durand, M.: Extending global river gauge records using satellite observations, Environ. Res. Lett., 18, 064027, https://doi.org/10.1088/1748-9326/acd407, 2023.
Schaake, J., Cong, S., and Duan, Q.: U. S. Mopex Data Set, United States, https://www.osti.gov/servlets/purl/899413 (last access: 21 February 2024), 2006.
Schmidt, A. H., Montgomery, D. R., Huntington, K. W., and Liang, C.: The question of communist land degradation: new evidence from local erosion and basin-wide sediment yield in Southwest China and Southeast Tibet, Ann. Assoc. Am. Geogr., 101, 477–496, https://doi.org/10.1080/00045608.2011.560059, 2011.
Schreiner-McGraw, A. P. and Ajami, H.: Impact of uncertainty in precipitation forcing data sets on the hydrologic budget of an integrated hydrologic model in mountainous terrain, Water Resour. Res., 56, e2020WR027639, https://doi.org/10.1029/2020WR027639, 2020.
Singer, M. B., Asfaw, D. T., Rosolem, R., Cuthbert, M. O., Miralles, D. G., MacLeod, D., Quichimbo, E. A., and Michaelides, K.: Hourly potential evapotranspiration at 0.1 resolution for the global land surface from 1981–present, Scientific Data, 8, 224, https://doi.org/10.1038/s41597-021-01003-9, 2021.
Sörensson, A. A. and Ruscica, R. C.: Intercomparison and uncertainty assessment of nine evapotranspiration estimates over South America, Water Resour. Res., 54, 2891–2908, https://doi.org/10.1002/2017WR021682, 2018.
Tang, G., Clark, M., and Papalexiou, S.: EM-Earth: The Ensemble Meteorological Dataset for Planet Earth, Federated Research Data Repository, https://doi.org/10.20383/102.0547, 2022a.
Tang, G., Clark, M. P., and Papalexiou, S. M.: EM-Earth: The ensemble meteorological dataset for planet Earth, B. Am. Meteorol. Soc., 103, E996–E1018, https://doi.org/10.1175/BAMS-D-21-0106.1, 2022b.
Tang, G., Clark, M. P., Knoben, W. J. M., Liu, H., Gharari, S., Arnal, L., Beck, H. E., Wood, A. W., Newman, A. J., and Papalexiou, S. M.: The impact of meteorological forcing uncertainty on hydrological modeling: A global analysis of cryosphere basins, Water Resour. Res., 59, e2022WR033767, https://doi.org/10.1029/2022WR033767, 2023.
Thackeray, C. W., Hall, A., Norris, J., and Chen, D.: Constraining the increased frequency of global precipitation extremes under warming, Nat. Clim. Change, 12, 441–448, https://doi.org/10.1038/s41558-022-01329-1, 2022.
Thailand Royal Irrigation Department: RID River Discharge Data, Thailand Royal Irrigation Department [data set], http://hydro.iis.u-tokyo.ac.jp/GAME-T/GAIN-T/routine/rid-river/disc_d.html (last access: 5 July 2023), 2022.
The Global Runoff Data Centre: The global runoff data centre GRDC Data Portal, The Global Runoff Data Centre [data set], https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser, last access date: 27 October 2023, 2022.
Ukhurebor, K. E., Azi, S. O., Aigbe, U. O., Onyancha, R. B., and Emegha, J. O.: Analyzing the uncertainties between reanalysis meteorological data and ground measured meteorological data, Measurement, 165, 108110, https://doi.org/10.1016/j.measurement.2020.108110, 2020.
U.S. Geological Survey: Gages Through the Ages, U.S. Geological Survey [data set], https://labs.waterdata.usgs.gov/visualizations/gages-through-the-ages (last access: 5 July 2023), 2019.
Velpuri, N. M. and Senay, G. B.: Analysis of long-term trends (1950–2009) in precipitation, runoff and runoff coefficient in major urban watersheds in the United States, Environ. Res. Lett., 8, 024020, https://doi.org/10.1088/1748-9326/8/2/024020, 2013.
Vermote, E.: NOAA CDR Program. NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5 [LAI], NOAA National Centers for Environmental Information, https://doi.org/10.7289/V5TT4P69, 2019.
Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs dataset for bridging attributes and geolocations, Earth Syst. Sci. Data, 14, 1869–1899, https://doi.org/10.5194/essd-14-1869-2022, 2022.
Water Systems Analysis Group: R-ArcticNET, Water Systems Analysis Group [data set], http://www.r-arcticnet.sr.unh.edu/v4.0/AllData/index.html (last access: 5 July 2023), 2022.
Wilby, R. L. and Dessai, S.: Robust adaptation to climate change, Weather, 65, 180–185, https://doi.org/10.1002/wea.543, 2010.
Xiong, J., Yin, J., Guo, S., He, S., and Chen, J.: Annual runoff coefficient variation in a changing environment: A global perspective, Environ. Res. Lett., 17, 064006, https://doi.org/10.1088/1748-9326/ac62ad, 2022.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853, https://doi.org/10.1002/2017GL072874, 2017.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and Pavelsky, T. M.: MERIT Hydro: a high-resolution global hydrography map based on latest topography dataset, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019WR024873, 2019.
Yang, L., Yang, Y., Villarini, G., Li, X., Hu, H., Wang, L., Blöschl, G., and Tian, F.: Climate more important for Chinese flood changes than reservoirs and land use, Geophys. Res. Lett., 48, e2021GL093061, https://doi.org/10.1029/2021GL093061, 2021.
Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1 (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.8090704, 2023a.
Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1 (1.3), Zenodo [data set], https://doi.org/10.5281/zenodo.10433905, 2023b.
Zaitchik, B. F., Rodell, M., and Reichle, R. H.: Assimilation of GRACE terrestrial water storage data into a land surface model: Results for the Mississippi River basin, J. Hydrometeorol., 9, 535–548, https://doi.org/10.1175/2007jhm951.1, 2008.
Zhang, J., Wang, T., and Ge, J.: Assessing vegetation cover dynamics induced by policy-driven ecological restoration and implication to soil erosion in southern China, PLoS One, 10, e0131352, https://doi.org/10.1371/journal.pone.0131352, 2015.
Zhang, J., Lin, P., Gao, S., and Fang, Z.: Understanding the re-infiltration process to simulating streamflow in North Central Texas using the WRF-hydro modeling system, J. Hydrol., 587, 124902, https://doi.org/10.1016/j.jhydrol.2020.124902, 2020.
Zhang, S., Zhou, L., Zhang, L., Yang, Y., Wei, Z., Zhou, S., Yang, D., Yang, X., Wu, X., and Zhang, Y.: Reconciling disagreement on global river flood changes in a warming climate, Nat. Clim. Change, 12, 1160–1167, https://doi.org/10.1038/s41558-022-01539-7, 2022.
Zhang, Y. and Liang, S.: Changes in forest biomass and linkage to climate and forest disturbances over Northeastern China, Global Change Biol., 20, 2596–2606, https://doi.org/10.1111/gcb.12588, 2014.
Zhang, Y., Zheng, H., Zhang, X., Leung, L. R., Liu, C., Zheng, C., Guo, Y., Chiew, F. H., Post, D., and Kong, D.: Future global streamflow declines are probably more severe than previously estimated, Nat. Water, 1, 261–271, https://doi.org/10.1038/s44221-023-00030-7, 2023.
Short summary
Large-sample hydrology (LSH) datasets have been the backbone of hydrological model parameter estimation and data-driven machine learning models for hydrological processes. This study complements existing LSH studies by creating a dataset with improved sample coverage, uncertainty estimates, and dynamic descriptions of human activities, which are all crucial to hydrological understanding and modeling.
Large-sample hydrology (LSH) datasets have been the backbone of hydrological model parameter...
Altmetrics
Final-revised paper
Preprint