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.
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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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Hydrol. Earth Syst. Sci., 24, 3033–3055, https://doi.org/10.5194/hess-24-3033-2020, https://doi.org/10.5194/hess-24-3033-2020, 2020
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Seyed-Mohammad Hosseini-Moghari, Shahab Araghinejad, Mohammad J. Tourian, Kumars Ebrahimi, and Petra Döll
Hydrol. Earth Syst. Sci., 24, 1939–1956, https://doi.org/10.5194/hess-24-1939-2020, https://doi.org/10.5194/hess-24-1939-2020, 2020
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Nizar Abou Zaki, Ali Torabi Haghighi, Pekka M. Rossi, Mohammad J. Tourian, and Bjørn Klove
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-471, https://doi.org/10.5194/hess-2018-471, 2018
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Groundwater is considered a main source of fresh water in semi-arid climatic zones, especially for agricultural usage. This study compares in-situ groundwater volume variation measurements with GRACE derived water mass data. The study concludes the possibility of using GRACE data to monitor groundwater depletion in catchments that lack measured data. GRACE data can here help in drawing general conclusions for integrated water resources management, and sustainable usage of this resources.
Xiangyu Luo, Hong-Yi Li, L. Ruby Leung, Teklu K. Tesfa, Augusto Getirana, Fabrice Papa, and Laura L. Hess
Geosci. Model Dev., 10, 1233–1259, https://doi.org/10.5194/gmd-10-1233-2017, https://doi.org/10.5194/gmd-10-1233-2017, 2017
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This study shows that alleviating vegetation-caused biases in DEM data, refining channel cross-sectional geometry and Manning roughness coefficients, as well as accounting for backwater effects can effectively improve the modeling of streamflow, river stages and flood extent in the Amazon Basin. The obtained understanding could be helpful to hydrological modeling in basins with evident inundation, which has important implications for improving land–atmosphere interactions in Earth system models.
Related subject area
Domain: ESSD – Land | Subject: Geophysics and geodesy
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A high-quality Data Set for seismological studies in the East Anatolian Fault Zone, Türkiye
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Eric Buchta, Mirko Scheinert, Matt A. King, Terry Wilson, Achraf Koulali, Peter J. Clarke, Demián Gómez, Eric Kendrick, Christoph Knöfel, and Peter Busch
Earth Syst. Sci. Data, 17, 1761–1780, https://doi.org/10.5194/essd-17-1761-2025, https://doi.org/10.5194/essd-17-1761-2025, 2025
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Geodetic GPS measurements in Antarctica have been used to track bedrock displacement, which is vital for understanding geodynamic processes such as plate motion and glacial isostatic adjustment. However, the potential of GPS data has been limited by its partially fragmented availability and unreliable metadata. A new dataset, which spans the period from 1995 to 2021, offers consistently processed coordinate time series for 286 GPS sites and promises to enhance future geodynamic research.
Tim Enzlberger Jensen, Bjørnar Dale, Andreas Stokholm, René Forsberg, Alexandre Bresson, Nassim Zahzam, Alexis Bonnin, and Yannick Bidel
Earth Syst. Sci. Data, 17, 1667–1684, https://doi.org/10.5194/essd-17-1667-2025, https://doi.org/10.5194/essd-17-1667-2025, 2025
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The availability of data from two airborne gravity campaigns using sensors based on both classical and quantum technology is presented. Data are made available by the European Space Agency as raw, intermediate, and final data products. Here “raw” refers to the sensor output, while “final” refers to the along-track gravity estimates. This makes the data relevant for users interested in applications ranging from data processing and quantum studies to geophysical studies using gravity observations.
Christoph Dahle, Eva Boergens, Ingo Sasgen, Thorben Döhne, Sven Reißland, Henryk Dobslaw, Volker Klemann, Michael Murböck, Rolf König, Robert Dill, Mike Sips, Ulrike Sylla, Andreas Groh, Martin Horwath, and Frank Flechtner
Earth Syst. Sci. Data, 17, 611–631, https://doi.org/10.5194/essd-17-611-2025, https://doi.org/10.5194/essd-17-611-2025, 2025
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GRACE and GRACE-FO are unique observing systems to quantify mass changes at the Earth’s surface from space. Time series of these mass changes are of high value for various applications, e.g., in hydrology, glaciology, and oceanography. GravIS (Gravity Information Service) provides easy access to user-friendly, regularly updated mass anomaly products. The portal visualizes and describes these data, aiming to highlight their significance for understanding changes in the climate system.
Hui Gao, Xinming Wu, Xiaoming Sun, Mingcai Hou, Hang Gao, Guangyu Wang, and Hanlin Sheng
Earth Syst. Sci. Data, 17, 595–609, https://doi.org/10.5194/essd-17-595-2025, https://doi.org/10.5194/essd-17-595-2025, 2025
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We propose three strategies for field seismic data curation, knowledge-guided synthesization, and generative adversarial network (GAN)-based generation to construct a massive-scale, feature-rich, and high-realism benchmark dataset of seismic facies and evaluate its effectiveness in training a deep-learning model for automatic seismic facies classification.
Cesare Comina, Guido Maria Adinolfi, Carlo Bertok, Andrea Bertea, Vittorio Giraud, and Pierluigi Pieruccini
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-413, https://doi.org/10.5194/essd-2024-413, 2024
Revised manuscript accepted for ESSD
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Estimates of earthquake ground motions rely on evaluating soil and rock profiles, with shear wave velocity (Vs) as a key factor. Uncertainty in Vs impacts seismic hazard predictions. Stochastic procedures model this uncertainty, but must be calibrated using detailed geological data and Vs databases. This paper provides a new Vs profile database for Piedmont (NW Italy), integrating geological modeling and geophysical data, supporting similar studies in other regions.
Andrea Rovida, Mario Locati, Andrea Antonucci, and Romano Camassi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-467, https://doi.org/10.5194/essd-2024-467, 2024
Revised manuscript accepted for ESSD
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ASMI, the Italian Archive of Historical Earthquake Data, is a data collection that provides seismological data on more than 6600 earthquakes that occurred in Italy and surrounding between 461 BC and today, based on more than 460 data sources. ASMI distributes in diverse ways and formats earthquake parameters, sets of macroseismic intensity, together with the bibliographical reference of the data source and, if possible the data source itself.
Leonardo Colavitti, Dino Bindi, Gabriele Tarchini, Davide Scafidi, Matteo Picozzi, and Daniele Spallarossa
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-448, https://doi.org/10.5194/essd-2024-448, 2024
Revised manuscript accepted for ESSD
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This work describes a dataset of 5 years of earthquakes with magnitude range 2.0–5.5 from January 2019 along the East Anatolian Fault, Türkiye. All events were located using the Non-Linear Location algorithm, providing reliable horizontal locations and depths. The distributed product includes Fourier Amplitude Spectra, Peak Ground Acceleration and Peak Ground Velocity; we strongly believe that the creation of high-quality, open-source datasets is crucial for any seismological investigation.
Fanny Lehmann, Filippo Gatti, Michaël Bertin, and Didier Clouteau
Earth Syst. Sci. Data, 16, 3949–3972, https://doi.org/10.5194/essd-16-3949-2024, https://doi.org/10.5194/essd-16-3949-2024, 2024
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Numerical simulations are a promising approach to characterizing the intensity of ground motion in the presence of geological uncertainties. However, the computational cost of 3D simulations can limit their usability. We present the first database of seismic-induced ground motion generated by an earthquake simulator for a collection of 30 000 heterogeneous geologies. The HEMEWS-3D dataset can be helpful for geophysicists, seismologists, and machine learning scientists, among others.
Daniela Famiani, Fabrizio Cara, Giuseppe Di Giulio, Giovanna Cultrera, Francesca Pacor, Sara Lovati, Gaetano Riccio, Maurizio Vassallo, Giulio Brunelli, Antonio Costanzo, Antonella Bobbio, Marta Pischiutta, Rodolfo Puglia, Marco Massa, Rocco Cogliano, Salomon Hailemikael, Alessia Mercuri, Giuliano Milana, Luca Minarelli, Alessandro Di Filippo, Lucia Nardone, Simone Marzorati, Chiara Ladina, Debora Pantaleo, Carlo Calamita, Maria Grazia Ciaccio, Antonio Fodarella, Stefania Pucillo, Giuliana Mele, Carla Bottari, Gaetano De Luca, Luigi Falco, Antonino Memmolo, Giulia Sgattoni, and Gabriele Tarabusi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-162, https://doi.org/10.5194/essd-2024-162, 2024
Revised manuscript accepted for ESSD
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This paper describes data and preliminary analyses made by the INGV emergency task force EMERSITO, devoted to site effects and seismic microzonation studies, following the November 9th, 2022, MW 5.5 earthquake (Adriatic sea, Italy). Considering the earthquake affected area, EMERSITO deployed a temporary seismic network of 11 stations (net code 6N) sampling different geological units in the urban area of Ancona, the regional capital of Marche, and operated from November 2022 to February 2023.
Guangyu Wang, Xinming Wu, and Wen Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-131, https://doi.org/10.5194/essd-2024-131, 2024
Revised manuscript accepted for ESSD
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Seismic paleochannel interpretation is essential for hydrocarbon exploration and paleoclimate studies but remains labor-intensive. Deep learning (DL) is promising to automate it but hindered by the lack of labeled data. We propose a workflow to simulate various channels and realistic seismic volumes, yielding the largest 3D seismic dataset with diverse channel labels. Its effectiveness is proven by field applications. The dataset, codes and DL models are released to advance further research.
Hao Zhou, Lijun Zheng, Yaozong Li, Xiang Guo, Zebing Zhou, and Zhicai Luo
Earth Syst. Sci. Data, 16, 3261–3281, https://doi.org/10.5194/essd-16-3261-2024, https://doi.org/10.5194/essd-16-3261-2024, 2024
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The satellite gravimetry mission Gravity Recovery and Climate Experiment (GRACE) and its follower GRACE-FO play a vital role in monitoring mass transportation on Earth. Based on the latest observation data derived from GRACE and GRACE-FO and an updated data processing chain, a new monthly temporal gravity field series, HUST-Grace2024, was determined.
Francesca Pace, Andrea Vergnano, Alberto Godio, Gerardo Romano, Luigi Capozzoli, Ilaria Baneschi, Marco Doveri, and Alessandro Santilano
Earth Syst. Sci. Data, 16, 3171–3192, https://doi.org/10.5194/essd-16-3171-2024, https://doi.org/10.5194/essd-16-3171-2024, 2024
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We present the geophysical data set acquired close to Ny-Ålesund (Svalbard islands) for the characterization of glacial and hydrological processes and features. The data have been organized in a repository that includes both raw and processed (filtered) data and some representative results of 2D models of the subsurface. This data set can foster multidisciplinary scientific collaborations among many disciplines: hydrology, glaciology, climatology, geology, geomorphology, etc.
Dino Bindi, Riccardo Zaccarelli, Angelo Strollo, Domenico Di Giacomo, Andres Heinloo, Peter Evans, Fabrice Cotton, and Frederik Tilmann
Earth Syst. Sci. Data, 16, 1733–1745, https://doi.org/10.5194/essd-16-1733-2024, https://doi.org/10.5194/essd-16-1733-2024, 2024
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The size of an earthquake is often described by a single number called the magnitude. Among the possible magnitude scales, the seismic moment (Mw) and the radiated energy (Me) scales are based on physical parameters describing the rupture process. Since these two magnitude scales provide complementary information that can be used for seismic hazard assessment and for seismic risk mitigation, we complement the Mw catalog disseminated by the GEOFON Data Centre with Me values.
Neda Darbeheshti, Martin Lasser, Ulrich Meyer, Daniel Arnold, and Adrian Jäggi
Earth Syst. Sci. Data, 16, 1589–1599, https://doi.org/10.5194/essd-16-1589-2024, https://doi.org/10.5194/essd-16-1589-2024, 2024
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This paper discusses strategies to improve the GRACE gravity field monthly solutions computed at the Astronomical Institute of the University of Bern. We updated the input observations and background models, as well as improving processing strategies in terms of instrument data screening and instrument parameterization.
Athina Peidou, Donald F. Argus, Felix W. Landerer, David N. Wiese, and Matthias Ellmer
Earth Syst. Sci. Data, 16, 1317–1332, https://doi.org/10.5194/essd-16-1317-2024, https://doi.org/10.5194/essd-16-1317-2024, 2024
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This study recommends a framework for preparing and processing vertical land displacements derived from GPS positioning for future integration with Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) measurements. We derive GPS estimates that only reflect surface mass signals and evaluate them against GRACE (and GRACE-FO). We also quantify uncertainty of GPS vertical land displacement estimates using various uncertainty quantification methods.
Lavinia Tunini, Andrea Magrin, Giuliana Rossi, and David Zuliani
Earth Syst. Sci. Data, 16, 1083–1106, https://doi.org/10.5194/essd-16-1083-2024, https://doi.org/10.5194/essd-16-1083-2024, 2024
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This study presents 20-year time series of more than 350 GNSS stations located in NE Italy and surroundings, together with the outgoing velocities. An overview of the input data, station information, data processing and solution quality is provided. The documented dataset constitutes a crucial and complete source of information about the deformation of an active but slowly converging margin over the last 2 decades, also contributing to the regional seismic hazard assessment of NE Italy.
Wenbin Tang, Ji Zhou, Jin Ma, Ziwei Wang, Lirong Ding, Xiaodong Zhang, and Xu Zhang
Earth Syst. Sci. Data, 16, 387–419, https://doi.org/10.5194/essd-16-387-2024, https://doi.org/10.5194/essd-16-387-2024, 2024
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This paper reported a daily 1 km all-weather land surface temperature (LST) dataset for Chinese land mass and surrounding areas – TRIMS LST. The results of a comprehensive evaluation show that TRIMS LST has the following special features: the longest time coverage in its class, high image quality, and good accuracy. TRIMS LST has already been released to the scientific community, and a series of its applications have been reported by the literature.
Arno Zang, Peter Niemz, Sebastian von Specht, Günter Zimmermann, Claus Milkereit, Katrin Plenkers, and Gerd Klee
Earth Syst. Sci. Data, 16, 295–310, https://doi.org/10.5194/essd-16-295-2024, https://doi.org/10.5194/essd-16-295-2024, 2024
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We present experimental data collected in 2015 at Äspö Hard Rock Laboratory. We created six cracks in a rock mass by injecting water into a borehole. The cracks were monitored using special sensors to study how the water affected the rock. The goal of the experiment was to figure out how to create a system for generating heat from the rock that is better than what has been done before. The data collected from this experiment are important for future research into generating energy from rocks.
Quetzalcoatl Rodríguez-Pérez and F. Ramón Zúñiga
Earth Syst. Sci. Data, 15, 4781–4801, https://doi.org/10.5194/essd-15-4781-2023, https://doi.org/10.5194/essd-15-4781-2023, 2023
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We present a comprehensive catalog of focal mechanisms for earthquakes in Mexico and neighboring areas spanning February 1928 to July 2022. The catalog comprises a wide range of earthquake magnitudes and depths and includes data from diverse geological environments. We collected and revised focal mechanism data from various sources and methods. The catalog is a valuable resource for future studies on earthquake source mechanisms, tectonics, and seismic hazard in the region.
Iman R. Kivi, Auregan Boyet, Haiqing Wu, Linus Walter, Sara Hanson-Hedgecock, Francesco Parisio, and Victor Vilarrasa
Earth Syst. Sci. Data, 15, 3163–3182, https://doi.org/10.5194/essd-15-3163-2023, https://doi.org/10.5194/essd-15-3163-2023, 2023
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Induced seismicity has posed significant challenges to secure deployment of geo-energy projects. Through a review of published documents, we present a worldwide, multi-physical database of injection-induced seismicity. The database contains information about in situ rock, tectonic and geologic characteristics, operational parameters, and seismicity for various subsurface energy-related activities. The data allow for an improved understanding and management of injection-induced seismicity.
Claudia Finger, Marco P. Roth, Marco Dietl, Aileen Gotowik, Nina Engels, Rebecca M. Harrington, Brigitte Knapmeyer-Endrun, Klaus Reicherter, Thomas Oswald, Thomas Reinsch, and Erik H. Saenger
Earth Syst. Sci. Data, 15, 2655–2666, https://doi.org/10.5194/essd-15-2655-2023, https://doi.org/10.5194/essd-15-2655-2023, 2023
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Passive seismic analyses are a key technology for geothermal projects. The Lower Rhine Embayment, at the western border of North Rhine-Westphalia in Germany, is a geologically complex region with high potential for geothermal exploitation. Here, we report on a passive seismic dataset recorded with 48 seismic stations and a total extent of 20 km. We demonstrate that the network design allows for the application of state-of-the-art seismological methods.
Jia-Hao Li, Zhao-Liang Li, Xiangyang Liu, and Si-Bo Duan
Earth Syst. Sci. Data, 15, 2189–2212, https://doi.org/10.5194/essd-15-2189-2023, https://doi.org/10.5194/essd-15-2189-2023, 2023
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The Advanced Very High Resolution Radiometer (AVHRR) is the only sensor that has the advantages of frequent revisits (twice per day), relatively high spatial resolution (4 km at the nadir), global coverage, and easy access prior to 2000. This study developed a global historical twice-daily LST product for 1981–2021 based on AVHRR GAC data. The product is suitable for detecting and analyzing climate changes over the past 4 decades.
Konstantinos Michailos, György Hetényi, Matteo Scarponi, Josip Stipčević, Irene Bianchi, Luciana Bonatto, Wojciech Czuba, Massimo Di Bona, Aladino Govoni, Katrin Hannemann, Tomasz Janik, Dániel Kalmár, Rainer Kind, Frederik Link, Francesco Pio Lucente, Stephen Monna, Caterina Montuori, Stefan Mroczek, Anne Paul, Claudia Piromallo, Jaroslava Plomerová, Julia Rewers, Simone Salimbeni, Frederik Tilmann, Piotr Środa, Jérôme Vergne, and the AlpArray-PACASE Working Group
Earth Syst. Sci. Data, 15, 2117–2138, https://doi.org/10.5194/essd-15-2117-2023, https://doi.org/10.5194/essd-15-2117-2023, 2023
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We examine the spatial variability of the crustal thickness beneath the broader European Alpine region by using teleseismic earthquake information (receiver functions) on a large amount of seismic waveform data. We compile a new Moho depth map of the broader European Alps and make our results freely available. We anticipate that our results can potentially provide helpful hints for interdisciplinary imaging and numerical modeling studies.
Muhammad Rizwan Asif, Nikolaj Foged, Thue Bording, Jakob Juul Larsen, and Anders Vest Christiansen
Earth Syst. Sci. Data, 15, 1389–1401, https://doi.org/10.5194/essd-15-1389-2023, https://doi.org/10.5194/essd-15-1389-2023, 2023
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To apply a deep learning (DL) algorithm to electromagnetic (EM) methods, subsurface resistivity models and/or the corresponding EM responses are often required. To date, there are no standardized EM datasets, which hinders the progress and evolution of DL methods due to data inconsistency. Therefore, we present a large-scale physics-driven model database of geologically plausible and EM-resolvable subsurface models to incorporate consistency and reliability into DL applications for EM methods.
Médéric Gravelle, Guy Wöppelmann, Kevin Gobron, Zuheir Altamimi, Mikaël Guichard, Thomas Herring, and Paul Rebischung
Earth Syst. Sci. Data, 15, 497–509, https://doi.org/10.5194/essd-15-497-2023, https://doi.org/10.5194/essd-15-497-2023, 2023
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We produced a reanalysis of GNSS data near tide gauges worldwide within the International GNSS Service. It implements advances in data modelling and corrections, extending the record length by about 7 years. A 28 % reduction in station velocity uncertainties is achieved over the previous solution. These estimates of vertical land motion at the coast supplement data from satellite altimetry or tide gauges for an improved understanding of sea level changes and their impacts along coastal areas.
Michal Kruszewski, Gerd Klee, Thomas Niederhuber, and Oliver Heidbach
Earth Syst. Sci. Data, 14, 5367–5385, https://doi.org/10.5194/essd-14-5367-2022, https://doi.org/10.5194/essd-14-5367-2022, 2022
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The authors assemble an in situ stress magnitude and orientation database based on 429 hydrofracturing tests that were carried out in six coal mines and two coal bed methane boreholes between 1986 and 1995 within the greater Ruhr region (Germany). Our study summarises the results of the extensive in situ stress test campaign and assigns quality to each data record using the established quality ranking schemes of the World Stress Map project.
Andrea Rovida, Andrea Antonucci, and Mario Locati
Earth Syst. Sci. Data, 14, 5213–5231, https://doi.org/10.5194/essd-14-5213-2022, https://doi.org/10.5194/essd-14-5213-2022, 2022
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EPICA is the 1000–1899 catalogue compiled for the European Seismic Hazard Model 2020 and contains 5703 earthquakes with Mw ≥ 4.0. It relies on the data of the European Archive of Historical Earthquake Data (AHEAD), both macroseismic intensities from historical seismological studies and parameters from regional catalogues. For each earthquake, the most representative datasets were selected and processed in order to derive harmonised parameters, both from intensity data and parametric catalogues.
Suqin Zhang, Changhua Fu, Jianjun Wang, Guohao Zhu, Chuanhua Chen, Shaopeng He, Pengkun Guo, and Guoping Chang
Earth Syst. Sci. Data, 14, 5195–5212, https://doi.org/10.5194/essd-14-5195-2022, https://doi.org/10.5194/essd-14-5195-2022, 2022
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The Sheshan observatory has nearly 150 years of observation history, and its observation data have important scientific value. However, with time, these precious historical data face the risk of damage and loss. We have carried out a series of rescues on the historical data of the Sheshan observatory. New historical datasets were released, including the quality-controlled absolute hourly mean values of three components (D, H, and Z) from 1933 to 2019.
Guoyu Li, Wei Ma, Fei Wang, Huijun Jin, Alexander Fedorov, Dun Chen, Gang Wu, Yapeng Cao, Yu Zhou, Yanhu Mu, Yuncheng Mao, Jun Zhang, Kai Gao, Xiaoying Jin, Ruixia He, Xinyu Li, and Yan Li
Earth Syst. Sci. Data, 14, 5093–5110, https://doi.org/10.5194/essd-14-5093-2022, https://doi.org/10.5194/essd-14-5093-2022, 2022
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A permafrost monitoring network was established along the China–Russia crude oil pipeline (CRCOP) route at the eastern flank of the northern Da Xing'anling Mountains in Northeast China. The resulting datasets fill the gaps in the spatial coverage of mid-latitude mountain permafrost databases. Results show that permafrost warming has been extensively observed along the CRCOP route, and local disturbances triggered by the CRCOPs have resulted in significant permafrost thawing.
Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
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This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Xiaoli Chang, Huijun Jin, Ruixia He, Yanlin Zhang, Xiaoying Li, Xiaoying Jin, and Guoyu Li
Earth Syst. Sci. Data, 14, 3947–3959, https://doi.org/10.5194/essd-14-3947-2022, https://doi.org/10.5194/essd-14-3947-2022, 2022
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Based on 10-year observations of ground temperatures in seven deep boreholes in Gen’he, Mangui, and Yituli’he, a wide range of mean annual ground temperatures at the depth of 20 m (−2.83 to −0.49 ℃) and that of annual maximum thawing depth (about 1.1 to 7.0 m) have been revealed. This study demonstrates that most trajectories of permafrost changes in Northeast China are ground warming and permafrost degradation, except that the shallow permafrost is cooling in Yituli’he.
Alice C. Frémand, Julien A. Bodart, Tom A. Jordan, Fausto Ferraccioli, Carl Robinson, Hugh F. J. Corr, Helen J. Peat, Robert G. Bingham, and David G. Vaughan
Earth Syst. Sci. Data, 14, 3379–3410, https://doi.org/10.5194/essd-14-3379-2022, https://doi.org/10.5194/essd-14-3379-2022, 2022
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This paper presents the release of large swaths of airborne geophysical data (including gravity, magnetics, and radar) acquired between 1994 and 2020 over Antarctica by the British Antarctic Survey. These include a total of 64 datasets from 24 different surveys, amounting to >30 % of coverage over the Antarctic Ice Sheet. This paper discusses how these data were acquired and processed and presents the methods used to standardize and publish the data in an interactive and reproducible manner.
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...
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