Articles | Volume 17, issue 5
https://doi.org/10.5194/essd-17-2063-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2063-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 May 2025
Data description paper |
| 16 May 2025
| 16 May 2025
Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM)
Peyman Saemian
CORRESPONDING AUTHOR
Institute of Geodesy, University of Stuttgart, Stuttgart, Germany
Omid Elmi
Institute of Geodesy, University of Stuttgart, Stuttgart, Germany
Molly Stroud
Department of Geosciences, Virginia Polytechnic Institute and State University, Virginia, USA
Ryan Riggs
Department of Geography, Texas A&M University, College Station, Texas, USA
Benjamin M. Kitambo
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES/CNRS/IRD/UT3, Toulouse, France
Fabrice Papa
Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES/CNRS/IRD/UT3, Toulouse, France
George H. Allen
Department of Geosciences, Virginia Polytechnic Institute and State University, Virginia, USA
Mohammad J. Tourian
Institute of Geodesy, University of Stuttgart, Stuttgart, Germany
Related authors
Mohammad J. Tourian, Omid Elmi, Yasin Shafaghi, Sajedeh Behnia, Peyman Saemian, Ron Schlesinger, and Nico Sneeuw
Earth Syst. Sci. Data, 14, 2463–2486, https://doi.org/10.5194/essd-14-2463-2022, https://doi.org/10.5194/essd-14-2463-2022, 2022
Short summary
Short summary
HydroSat as a global water cycle database provides estimates of and uncertainty in geometric quantities of the water cycle: (1) surface water extent of lakes and rivers, (2) water level time series of lakes and rivers, (3) terrestrial water storage anomaly, (4) water storage anomaly of lakes and reservoirs, and (5) river discharge estimates for large and small rivers.
Anne Springer, Gabriëlle De Lannoy, Matthew Rodell, Yorck Ewerdwalbesloh, Helena Gerdener, Mehdi Khaki, Bailing Li, Fupeng Li, Maike Schumacher, Natthachet Tangdamrongsub, Mohammad J. Tourian, Wanshu Nie, and Jürgen Kusche
EGUsphere, https://doi.org/10.5194/egusphere-2025-2058, https://doi.org/10.5194/egusphere-2025-2058, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
The GRACE and GRACE Follow-On satellites monitor changes in Earth's water storage by observing gravity variations. By integrating these observations into hydrological models through data assimilation, estimates of groundwater, soil moisture, and hydrological trends are improved, helping to monitor droughts, floods, and human water use. This review highlights recent advances in GRACE data assimilation, identifies key challenges, and discusses future directions with upcoming satellite missions.
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.
Howlader Mohammad Mehedi Hasan, Petra Döll, Seyed-Mohammad Hosseini-Moghari, Fabrice Papa, and Andreas Güntner
Hydrol. Earth Syst. Sci., 29, 567–596, https://doi.org/10.5194/hess-29-567-2025, https://doi.org/10.5194/hess-29-567-2025, 2025
Short summary
Short summary
We calibrate a global hydrological model using multiple observations to analyse the benefits and trade-offs of multi-variable calibration. We found such an approach to be very important for understanding the real-world system. However, some observations are very essential to the system, in particular, streamflow. We also showed uncertainties in the calibration results, which are often useful for making informed decisions. We emphasize considering observation uncertainty in model calibration.
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.
Ziyun Yin, Peirong Lin, Ryan Riggs, George H. Allen, Xiangyong Lei, Ziyan Zheng, and Siyu Cai
Earth Syst. Sci. Data, 16, 1559–1587, https://doi.org/10.5194/essd-16-1559-2024, https://doi.org/10.5194/essd-16-1559-2024, 2024
Short summary
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.
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.
Benjamin M. Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, and Sly Wongchuig
Earth Syst. Sci. Data, 15, 2957–2982, https://doi.org/10.5194/essd-15-2957-2023, https://doi.org/10.5194/essd-15-2957-2023, 2023
Short summary
Short summary
The surface water storage (SWS) in the Congo River basin (CB) remains unknown. In this study, the multi-satellite and hypsometric curve approaches are used to estimate SWS in the CB over 1992–2015. The results provide monthly SWS characterized by strong variability with an annual mean amplitude of ~101 ± 23 km3. The evaluation of SWS against independent datasets performed well. This SWS dataset contributes to the better understanding of the Congo basin’s surface hydrology using remote sensing.
Mohammad J. Tourian, Omid Elmi, Yasin Shafaghi, Sajedeh Behnia, Peyman Saemian, Ron Schlesinger, and Nico Sneeuw
Earth Syst. Sci. Data, 14, 2463–2486, https://doi.org/10.5194/essd-14-2463-2022, https://doi.org/10.5194/essd-14-2463-2022, 2022
Short summary
Short summary
HydroSat as a global water cycle database provides estimates of and uncertainty in geometric quantities of the water cycle: (1) surface water extent of lakes and rivers, (2) water level time series of lakes and rivers, (3) terrestrial water storage anomaly, (4) water storage anomaly of lakes and reservoirs, and (5) river discharge estimates for large and small rivers.
Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Stephane Calmant, Ayan Santos Fleischmann, Frederic Frappart, Melanie Becker, Mohammad J. Tourian, Catherine Prigent, and Johary Andriambeloson
Hydrol. Earth Syst. Sci., 26, 1857–1882, https://doi.org/10.5194/hess-26-1857-2022, https://doi.org/10.5194/hess-26-1857-2022, 2022
Short summary
Short summary
This study presents a better characterization of surface hydrology variability in the Congo River basin, the second largest river system in the world. We jointly use a large record of in situ and satellite-derived observations to monitor the spatial distribution and different timings of the Congo River basin's annual flood dynamic, including its peculiar bimodal pattern.
Adam Hastie, Ronny Lauerwald, Philippe Ciais, Fabrice Papa, and Pierre Regnier
Earth Syst. Dynam., 12, 37–62, https://doi.org/10.5194/esd-12-37-2021, https://doi.org/10.5194/esd-12-37-2021, 2021
Short summary
Short summary
We used a model of the Congo Basin to investigate the transfer of carbon (C) from land (vegetation and soils) to inland waters. We estimate that leaching of C to inland waters, emissions of CO2 from the water surface, and the export of C to the coast have all increased over the last century, driven by increasing atmospheric CO2 levels and climate change. We predict that these trends may continue through the 21st century and call for long-term monitoring of these fluxes.
Cited articles
Alsdorf, D. E., Rodríguez, E., and Lettenmaier, D. P.: Measuring surface water from space, Rev. Geophys., 45, RG2002, https://doi.org/10.1029/2006RG000197, 2007. a
Altenau, E. H., Pavelsky, T. M., Durand, M. T., Yang, X., Frasson, R. P. d. M., and Bendezu, L.: The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A global river network for satellite data products, Water Resour. Res., 57, e2021WR030054, https://doi.org/10.1029/2021WR030054, 2021. a
ArcticNET: R-ArcticNET, University of New Hampshire, R-ArcticNET Version 4.0, ArcticNET [data set], https://www.r-arcticnet.sr.unh.edu/v4.0/AllData/index.html, last access: May 2021. a
Australian Bureau of Meteorology: Australian Hydrological Geospatial Fabric – Water Data Online, Australian Bureau of Meteorology [data set], https://www.bom.gov.au/waterdata/, last access: September 2021. a
Behling, R., Roessner, S., Foerster, S., Saemian, P., Tourian, M. J., Portele, T. C., and Lorenz, C.: Interrelations of vegetation growth and water scarcity in Iran revealed by satellite time series, Sci. Rep., 12, 20784, https://doi.org/10.1038/s41598-022-24712-6, 2022. a
Berry, P., Garlick, J., Freeman, J., and Mathers, E.: Global inland water monitoring from multi-mission altimetry, Geophys. Res. Lett., 32, L16401, https://doi.org/10.1029/2005GL022814, 2005. a
Bhaduri, A., Bogardi, J., Siddiqi, A., Voigt, H., Vörösmarty, C., Pahl-Wostl, C., Bunn, S. E., Shrivastava, P., Lawford, R., Foster, S., Kremer, H., Renaud, F. G., Bruns, A., and Osuna, V. R.: Achieving sustainable development goals from a water perspective, Frontiers in Environmental Science, 4, 64, https://doi.org/10.3389/fenvs.2016.00064, 2016. a
Birkett, C. M.: Contribution of the TOPEX NASA radar altimeter to the global monitoring of large rivers and wetlands, Water Resour. Res., 34, 1223–1239, https://doi.org/10.1029/98WR00124, 1998. a
Birkinshaw, S., Moore, P., Kilsby, C., O'donnell, G., Hardy, A. J., and Berry, P.: Daily discharge estimation at ungauged river sites using remote sensing, Hydrol. Process., 28, 1043–1054, 2014. a
Birkinshaw, S. J., O'donnell, G., Moore, P., Kilsby, C., Fowler, H., and Berry, P.: Using satellite altimetry data to augment flow estimation techniques on the Mekong River, Hydrol. Process., 24, 3811–3825, 2010. a
Boergens, E., Dettmering, D., and Seitz, F.: Observing water level extremes in the Mekong River Basin: The benefit of long-repeat orbit missions in a multi-mission satellite altimetry approach, J. Hydrol., 570, 463–472, 2019. a
Brazil National Water Agency: HidroWeb – Séries Históricas, Brazil National Water Agency [data set], https://www.snirh.gov.br/hidroweb/serieshistoricas, last access: September 2021. a
Canada National Water Data Archive: HYDAT – Environment and Climate Change Canada, 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: October 2021. a
Cartwright, D. and Edden, A. C.: Corrected tables of tidal harmonics, Geophys. J. Int., 33, 253–264, 1973. a
Cerbelaud, A., David, C. H., Biancamaria, S., Wade, J., Tom, M., Prata de Moraes Frasson, R., and Blumstein, D.: Peak flow event durations in the Mississippi River basin and implications for temporal sampling of rivers, Geophys. Res. Lett., 51, e2024GL109220, https://doi.org/10.1029/2024GL109220, 2024. a
Chile Center for Climate and Resilience Research: CR2 Explorer, Chile Center for Climate and Resilience Research [data set], https://explorador.cr2.cl/, last access: September 2021. a
Coss, S., Durand, M., Lettenmaier, D., Yi, Y., Jia, Y., Guo, Q., Tuozzolo, S., Shum, C. K.; Allen, G. H., Calmant, S., and Pavelsky, T.: PRESWOT_HYDRO_GRRATS_L2_VIRTUAL_STATION_HEIGHTS_V2. Ver. 2. PO.DAAC, CA, USA [data set], https://doi.org/10.5067/PSGRA-SA2V2, 2019a. a
Coss, S., Durand, M., Lettenmaier, D., Yi, Y., Jia, Y., Guo, Q., Tuozzolo, S., Shun, C. K., Allen, G. H., Calmant, S., and Pavelsky, T.: PRESWOT_HYDRO_GRRATS_L2_DAILY_VIRTUAL_STATION_HEIGHTS_V2. Ver. 2. PO.DAAC, CA, USA [data set], https://doi.org/10.5067/PSGRA-DA2V2, 2019b. a
Da Silva, J. S., Calmant, S., Seyler, F., Rotunno Filho, O. C., Cochonneau, G., and Mansur, W. J.: Water levels in the Amazon basin derived from the ERS 2 and ENVISAT radar altimetry missions, Remote Sens. Environ., 114, 2160–2181, https://doi.org/10.1016/j.rse.2010.04.020, 2010. a, b
DGFI-TUM (Deutsches Geodätisches Forschungsinstitut der Technischen Universität München): Database for Hydrological Time Series of Inland Waters (DAHITI), DGFI-TUM [data set], https://dahiti.dgfi.tum.de/en/products/water-level-altimetry/, last access: February 2025. a
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. a
Döll, P., Douville, H., Güntner, A., Müller Schmied, H., and Wada, Y.: Modelling freshwater resources at the global scale: challenges and prospects, Surv. Geophys., 37, 195–221, 2016. a
Dudgeon, D., Arthington, A. H., Gessner, M. O., Kawabata, Z.-I., Knowler, D. J., Lévêque, C., Naiman, R. J., Prieur-Richard, A.-H., Soto, D., Stiassny, M. L. J., and Sullivan, C. A.: Freshwater biodiversity: importance, threats, status and conservation challenges, Biol. Rev., 81, 163–182, https://doi.org/10.1017/S1464793105006950, 2006. a
Elmi, O. and Tourian, M. J.: Retrieving time series of river water extent from global inland water data sets, J. Hydrol., 617, 128880, https://doi.org/10.1016/j.jhydrol.2022.128880, 2023. a, b
Elmi, O., Tourian, M. J., and Sneeuw, N.: River discharge estimation using channel width from satellite imagery, in: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 727–730, IEEE, https://doi.org/10.1109/IGARSS.2015.7325867, 2015. a
Feng, D., Gleason, C. J., Lin, P., Yang, X., Pan, M., and Ishitsuka, Y.: Recent changes to Arctic river discharge, Nat. Commun., 12, 6917, https://doi.org/10.1038/s41467-021-27228-1, 2021. a
Frappart, F., Calmant, S., Cauhopé, M., Seyler, F., and Cazenave, A.: Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin, Remote Sens. Environ., 100, 252–264, 2006. a
Frappart, F., Papa, F., Marieu, V., Malbeteau, Y., Jordy, F., Calmant, S., Durand, F., and Bala, S.: Preliminary assessment of SARAL/AltiKa observations over the Ganges-Brahmaputra and Irrawaddy Rivers, Mar. Geod., 38, 568–580, 2015. a
Gal, L., Zakharova, E., Biancamaria, S., Paris, A., Kitambo, B., Lefebve, J., and Boussaroque, M.: ESA River Discharge Climate Change Initiative (RD_cci): Altimetry-based River Discharge product, v1.0, ESA, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/44c930e1388f40728884fbdf7e28c109, 2024. a, b
Garrick, D. E., Hall, J. W., Dobson, A., Damania, R., Grafton, R. Q., Hope, R., Hepburn, C., Bark, R., Boltz, F., De Stefano, L., O'Donnell, E., Matthews, N., and Money, A.: Valuing water for sustainable development, Science, 358, 1003–1005, 2017. a
Gharari, S. and Razavi, S.: A review and synthesis of hysteresis in hydrology and hydrological modeling: Memory, path-dependency, or missing physics?, J. Hydrol., 566, 500–519, 2018. a
GRDC: The Global Runoff Data Centre, GRDC [data set], https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser, last access: September 2021. a
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. a, b
LEGOS/CTOH and CNES: Hydroweb.Next – Water Level Time Series from Satellite Altimetry, LEGOS/CTOH and CNES [data set], https://hydroweb.next.theia-land.fr/, last access: February 2025. a
India-WRIS: India Water Resources Information System, India-WRIS [data set], https://indiawris.gov.in/wris/#/RiverMonitoring, last access: June 2021. a
Institute of Geodesy, University of Stuttgart: HydroSat, Institute of Geodesy, University of Stuttgart [data set], https://hydrosat.gis.uni-stuttgart.de/php/index.php, last access: February 2025. a
Japanese Water Information System: 2022 Ministry of Land, Infrastructure, Transport and Tourism, Japanese Water Information System [data set], https://www.mlit.go.jp/en/, last access: September 2021. a
Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006. a
Kitambo, B., Papa, F., Paris, A., Tshimanga, R. M., Calmant, S., Fleischmann, A. S., Frappart, F., Becker, M., Tourian, M. J., Prigent, C., and Andriambeloson, J.: A combined use of in situ and satellite-derived observations to characterize surface hydrology and its variability in the Congo River basin, Hydrol. Earth Syst. Sci., 26, 1857–1882, https://doi.org/10.5194/hess-26-1857-2022, 2022. a, b, c
Kitambo, B. M., Papa, F., Paris, A., Tshimanga, R. M., Frappart, F., Calmant, S., Elmi, O., Fleischmann, A. S., Becker, M., Tourian, M. J., Jucá Oliveira, R. A., and Wongchuig, S.: A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015, Earth Syst. Sci. Data, 15, 2957–2982, https://doi.org/10.5194/essd-15-2957-2023, 2023. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, 2012. a
Komjathy, A. and Born, G. H.: GPS-based ionospheric corrections for single frequency radar altimetry, J. Atmos. Sol.-Terr. Phy., 61, 1197–1203, 1999. a
Kouraev, A. V., Zakharova, E. A., Samain, O., Mognard, N. M., and Cazenave, A.: Ob'river discharge from TOPEX/Poseidon satellite altimetry (1992–2002), Remote Sens. Environ., 93, 238–245, 2004. a
Leopold, L. B. and Maddock, T.: The he hydraulic geometry of stream channels and some physiographic implications (USGS Professional Paper 252), U.S. Geological Survey, https://pubs.er.usgs.gov/publication/pp252 (last access: 12 May 2025), 1953. a
Lin, P., Feng, D., Gleason, C. J., Pan, M., Brinkerhoff, C. B., Yang, X., Beck, H. E., and de Moraes Frasson, R. P.: Inversion of river discharge from remotely sensed river widths: A critical assessment at three-thousand global river gauges, Remote Sens. Environ., 287, 113489, https://doi.org/10.1016/j.rse.2023.113489, 2023. a, b, c, d
Lopes, R. H., Reid, I., and Hobson, P. R.: The two-dimensional Kolmogorov–Smirnov test, Brunel University Research Archive, https://bura.brunel.ac.uk/handle/2438/1166 (last access: 12 May 2025), 2007. a
NASA JPL (Jet Propulsion Laboratory) PO.DAAC: Pre-SWOT Hydrology GRRATS Level 2 Virtual Station Heights Version 2, NASA [data set], https://doi.org/10.5067/PSGRA-SA2V2, last access: 1 February 2025. a
Nielsen, K., Zakharova, E., Tarpanelli, A., Andersen, O. B., and Benveniste, J.: River levels from multi mission altimetry, a statistical approach, Remote Sens. Environ., 270, 112876, https://doi.org/10.1016/j.rse.2021.112876, 2022. a
Normandin, C., Frappart, F., Diepkilé, A. T., Marieu, V., Mougin, E., Blarel, F., Lubac, B., Braquet, N., and Ba, A.: Evolution of the performances of radar altimetry missions from ERS-2 to Sentinel-3A over the Inner Niger Delta, Remote Sensing, 10, 833, https://doi.org/10.3390/rs10060833, 2018. a
Pail, R., Fecher, T., Barnes, D., Factor, J., Holmes, S., Gruber, T., and Zingerle, P.: Short note: the experimental geopotential model XGM2016, J. Geodesy, 92, 443–451, 2018. a
Papa, F., Durand, F., Rossow, W., Rahman, A., and Bala, S.: Seasonal and Interannual Variations of the Ganges-Brahmaputra River Discharge, 1993–2008 from satellite altimeters, J. Geophys. Res., 115, C12013, https://doi.org/10.1029/2009JC006075, 2010a. a
Papa, F., Durand, F., Rossow, W. B., Rahman, A., and Bala, S. K.: Satellite altimeter-derived monthly discharge of the Ganga-Brahmaputra River and its seasonal to interannual variations from 1993 to 2008, J. Geophys. Res.-Atmos., 115, C12013, https://doi.org/10.1029/2009JC006075, 2010b. a
Papa, F., Bala, S. K., Pandey, R. K., Durand, F., Gopalakrishna, V., Rahman, A., and Rossow, W. B.: Ganga-Brahmaputra river discharge from Jason-2 radar altimetry: an update to the long-term satellite-derived estimates of continental freshwater forcing flux into the Bay of Bengal, J. Geophys. Res.-Oceans, 117, C11021, https://doi.org/10.1029/2012JC008158, 2012. a
Paris, A., Dias de Paiva, R., Santos da Silva, J., Medeiros Moreira, D., Calmant, S., Garambois, P.-A., Collischonn, W., Bonnet, M.-P., and Seyler, F.: Stage-discharge rating curves based on satellite altimetry and modeled discharge in the Amazon basin, Water Resour. Res., 52, 3787–3814, 2016. a
Pavelsky, T. M.: Using width-based rating curves from spatially discontinuous satellite imagery to monitor river discharge, Hydrol. Process., 28, 3035–3040, 2014. a
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution mapping of global surface water and its long-term changes, Nature, 540, 418–422, 2016. a
Riggs, R., Moulds, S., Wortmann, M., Slater, L., and Allen, G.: RivRetrieve: Retrieve Global River Gauge Data, CRAN [code], https://doi.org/10.32614/CRAN.package.RivRetrieve, 2024 (code available at: https://github.com/Ryan-Riggs/RivRetrieve, last access: February 2025). a, b
Saemian, P.: Analyzing and characterizing spaceborne observation of water storage variation: Past, present, future (Doctoral dissertation, University of Stuttgart), OPUS, https://doi.org/10.18419/opus-13923, 2024. a
Saemian, P., Elmi, O., Vishwakarma, B., Tourian, M., and Sneeuw, N.: Analyzing the Lake Urmia restoration progress using ground-based and spaceborne observations, Sci. Total Environ., 739, 139857, https://doi.org/10.1016/j.scitotenv.2020.139857, 2020. a
Saemian, P., Tourian, M. J., AghaKouchak, A., Madani, K., and Sneeuw, N.: How much water did Iran lose over the last two decades?, Journal of Hydrology: Regional Studies, 41, 101095, https://doi.org/10.1016/j.ejrh.2022.101095, 2022. a
Saemian, P., Elmi, O., Stroud, M., Riggs, R., Kitambo, B. M., Papa, F., Allen, G. H., and Tourian, M. J.: Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM), DaRUS [data set], https://doi.org/10.18419/darus-4475, 2024. a, b
Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell, N. W., Clark, D. B., Dankers, R., Eisner, S., Fekete, B. M., Colón-González, F. J., Gosling, S. N., Kim, H., Liu, X., Masaki, Y., Portmann, F. T., Satoh, Y., Stacke, T., Tang, Q., Wada, Y., Wisser, D., Albrecht, T., Frieler, K., Piontek, F., Warszawski, L., and Kabat, P.: Multimodel assessment of water scarcity under climate change, P. Natl. Acad. Sci., 111, 3245–3250, 2014. a
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, Annals of the Association of American Geographers, 101, 477–496, https://doi.org/10.1080/00045608.2011.560059, 2011. a, b
Schwatke, C., Dettmering, D., Bosch, W., and Seitz, F.: DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry, Hydrol. Earth Syst. Sci., 19, 4345–4364, https://doi.org/10.5194/hess-19-4345-2015, 2015. a
Shapiro, S. S. and Wilk, M. B.: An analysis of variance test for normality (complete samples), Biometrika, 52, 591–611, 1965. a
Spain Anuario de Aforos: 2022 Anuario de Aforos – Anuario de Aforos Digital – datos.gob.es [data set], http://datos.gob.es/es/catalogo/e00125801-anuario-de-aforos/resource/4836b826-e7fd-4a41-950c-89b4eaea0279, last access: 1 September 2021). a
Tang, Q., Gao, H., Lu, H., and Lettenmaier, D. P.: Remote sensing: hydrology, Prog. Phys. Geogr., 33, 490–509, 2009. a
Tarpanelli, A., Santi, E., Tourian, M. J., Filippucci, P., Amarnath, G., and Brocca, L.: Daily river discharge estimates by merging satellite optical sensors and radar altimetry through artificial neural network, IEEE T. Geosci. Remote Sens., 57, 329–341, 2018. a
Tarpanelli, A., Paris, A., Sichangi, A. W., OLoughlin, F., and Papa, F.: Water resources in Africa: the role of earth observation data and hydrodynamic modeling to derive river discharge, Surv. Geophys., 44, 97–122, 2023. a
Thailand Royal Irrigation Department: 2022 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: September 2021. a
Tourian, M., Tarpanelli, A., Elmi, O., Qin, T., Brocca, L., Moramarco, T., and Sneeuw, N.: Spatiotemporal densification of river water level time series by multimission satellite altimetry, Water Resour. Res., 52, 1140–1159, 2016. a
Tourian, M., Schwatke, C., and Sneeuw, N.: River discharge estimation at daily resolution from satellite altimetry over an entire river basin, J. Hydrol., 546, 230–247, 2017. a
Tourian, M. J., Elmi, O., Shafaghi, Y., Behnia, S., Saemian, P., Schlesinger, R., and Sneeuw, N.: HydroSat: geometric quantities of the global water cycle from geodetic satellites, Earth Syst. Sci. Data, 14, 2463–2486, https://doi.org/10.5194/essd-14-2463-2022, 2022. a, b, c
U.S. Geological Survey (USGS): Current Water Data for the Nation, U.S. Geological Survey [data set], https://waterdata.usgs.gov/nwis/rt, last access: September 2021. a
Vörösmarty, C. J., Lévêque, C., and Revenga, C.: Chapter 7: Fresh Water, in: Ecosystems and Human Well-being: Current State and Trends (Millennium Ecosystem Assessment, edited by: Hassan, R., Scholes, R., and Ash, N., Vol. 1, 165–207, Island Press, Washington, DC, https://www.millenniumassessment.org/documents/document.276.aspx.pdf (last access: 13 May 2025), 2005. a
Wahr, J. M.: Deformation induced by polar motion, J. Geophys. Res.-Sol. Ea., 90, 9363–9368, 1985. a
Zingerle, P., Pail, R., Gruber, T., and Oikonomidou, X.: The combined global gravity field model XGM2019e, J. Geodesy, 94, 66, https://doi.org/10.1007/s00190-020-01398-0, 2020. a
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.
Our study addresses the need for better river discharge data, crucial for water management, by...
Altmetrics
Final-revised paper
Preprint