Articles | Volume 14, issue 5
https://doi.org/10.5194/essd-14-2463-2022
© Author(s) 2022. 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-14-2463-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
25 May 2022
Data description paper |
| 25 May 2022
| 25 May 2022
HydroSat: geometric quantities of the global water cycle from geodetic satellites
Mohammad J. Tourian
CORRESPONDING AUTHOR
Institute of Geodesy (GIS), University of Stuttgart, Stuttgart, Germany
Omid Elmi
Institute of Geodesy (GIS), University of Stuttgart, Stuttgart, Germany
Yasin Shafaghi
GAF AG, Munich, Germany
Sajedeh Behnia
Institute of Geodesy (GIS), University of Stuttgart, Stuttgart, Germany
Peyman Saemian
Institute of Geodesy (GIS), University of Stuttgart, Stuttgart, Germany
Ron Schlesinger
Institute of Geodesy (GIS), University of Stuttgart, Stuttgart, Germany
Nico Sneeuw
Institute of Geodesy (GIS), University of Stuttgart, Stuttgart, Germany
Related authors
Peyman Saemian, Omid Elmi, Molly Stroud, Ryan Riggs, Benjamin M. Kitambo, Fabrice Papa, George H. Allen, and Mohammad J. Tourian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-406, https://doi.org/10.5194/essd-2024-406, 2024
Preprint under review for ESSD
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 8,700 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.
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.
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.
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
Short summary
Short summary
This paper uses a multi-objective approach for calibrating the WGHM model to determine the role of human water use and climate variations in the recent loss of water storage in Lake Urmia basin, Iran. We found that even without human water use Lake Urmia would not have recovered from the significant loss of lake water volume caused by the drought year 2008.
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
Preprint withdrawn
Short summary
Short summary
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.
Peyman Saemian, Omid Elmi, Molly Stroud, Ryan Riggs, Benjamin M. Kitambo, Fabrice Papa, George H. Allen, and Mohammad J. Tourian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-406, https://doi.org/10.5194/essd-2024-406, 2024
Preprint under review for ESSD
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 8,700 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.
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.
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.
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
Short summary
Short summary
This paper uses a multi-objective approach for calibrating the WGHM model to determine the role of human water use and climate variations in the recent loss of water storage in Lake Urmia basin, Iran. We found that even without human water use Lake Urmia would not have recovered from the significant loss of lake water volume caused by the drought year 2008.
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
Preprint withdrawn
Short summary
Short summary
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.
Related subject area
Geosciences – Geodesy
The cooperative IGS RT-GIMs: a reliable estimation of the global ionospheric electron content distribution in real time
RECOG RL01: correcting GRACE total water storage estimates for global lakes/reservoirs and earthquakes
Open access to regional geoid models: the International Service for the Geoid
GOCO06s – a satellite-only global gravity field model
Description of the multi-approach gravity field models from Swarm GPS data
ICGEM – 15 years of successful collection and distribution of global gravitational models, associated services, and future plans
Qi Liu, Manuel Hernández-Pajares, Heng Yang, Enric Monte-Moreno, David Roma-Dollase, Alberto García-Rigo, Zishen Li, Ningbo Wang, Denis Laurichesse, Alexis Blot, Qile Zhao, Qiang Zhang, André Hauschild, Loukis Agrotis, Martin Schmitz, Gerhard Wübbena, Andrea Stürze, Andrzej Krankowski, Stefan Schaer, Joachim Feltens, Attila Komjathy, and Reza Ghoddousi-Fard
Earth Syst. Sci. Data, 13, 4567–4582, https://doi.org/10.5194/essd-13-4567-2021, https://doi.org/10.5194/essd-13-4567-2021, 2021
Short summary
Short summary
The upper part of the atmosphere, the ionosphere, is partially ionized, and it is being crossed by many multi-frequency signals of the Global Navigation Satellite System (GNSS) satellites. This unique source of data can be acquired in real time from hundreds of permanent GNSS receivers. The real-time processing providing the distribution of the ionospheric free electrons (Global Ionospheric Maps) can be done as well in real time. We present their updated real-time assessment and combination.
Simon Deggim, Annette Eicker, Lennart Schawohl, Helena Gerdener, Kerstin Schulze, Olga Engels, Jürgen Kusche, Anita T. Saraswati, Tonie van Dam, Laura Ellenbeck, Denise Dettmering, Christian Schwatke, Stefan Mayr, Igor Klein, and Laurent Longuevergne
Earth Syst. Sci. Data, 13, 2227–2244, https://doi.org/10.5194/essd-13-2227-2021, https://doi.org/10.5194/essd-13-2227-2021, 2021
Short summary
Short summary
GRACE provides us with global changes of terrestrial water storage. However, the data have a low spatial resolution, and localized storage changes in lakes/reservoirs or mass change due to earthquakes causes leakage effects. The correction product RECOG RL01 presented in this paper accounts for these effects. Its application allows for improving calibration/assimilation of GRACE into hydrological models and better drought detection in earthquake-affected areas.
Mirko Reguzzoni, Daniela Carrion, Carlo Iapige De Gaetani, Alberta Albertella, Lorenzo Rossi, Giovanna Sona, Khulan Batsukh, Juan Fernando Toro Herrera, Kirsten Elger, Riccardo Barzaghi, and Fernando Sansó
Earth Syst. Sci. Data, 13, 1653–1666, https://doi.org/10.5194/essd-13-1653-2021, https://doi.org/10.5194/essd-13-1653-2021, 2021
Short summary
Short summary
The International Service for the Geoid provides free access to a repository of geoid models. The most important ones are freely available to perform analyses on the evolution of the geoid computation research field. Furthermore, the ISG performs research taking advantage of its archive and organizes specific training courses on geoid determination. This paper aims at describing the service and showing the added value of the archive of geoid models for the scientific community and technicians.
Andreas Kvas, Jan Martin Brockmann, Sandro Krauss, Till Schubert, Thomas Gruber, Ulrich Meyer, Torsten Mayer-Gürr, Wolf-Dieter Schuh, Adrian Jäggi, and Roland Pail
Earth Syst. Sci. Data, 13, 99–118, https://doi.org/10.5194/essd-13-99-2021, https://doi.org/10.5194/essd-13-99-2021, 2021
Short summary
Short summary
Earth's gravity field provides invaluable insights into the state and changing nature of our planet. GOCO06s combines over 1 billion measurements from 19 satellites to produce a global gravity field model. The combination of different observation principles allows us to exploit the strengths of each satellite mission and provide a high-quality data set for Earth and climate sciences.
João Teixeira da Encarnação, Pieter Visser, Daniel Arnold, Aleš Bezdek, Eelco Doornbos, Matthias Ellmer, Junyi Guo, Jose van den IJssel, Elisabetta Iorfida, Adrian Jäggi, Jaroslav Klokocník, Sandro Krauss, Xinyuan Mao, Torsten Mayer-Gürr, Ulrich Meyer, Josef Sebera, C. K. Shum, Chaoyang Zhang, Yu Zhang, and Christoph Dahle
Earth Syst. Sci. Data, 12, 1385–1417, https://doi.org/10.5194/essd-12-1385-2020, https://doi.org/10.5194/essd-12-1385-2020, 2020
Short summary
Short summary
Although not the primary mission of the Swarm three-satellite constellation, the sensors on these satellites are accurate enough to measure the melting and accumulation of Earth’s ice reservoirs, precipitation cycles, floods, and droughts, amongst others. Swarm sees these changes well compared to the dedicated GRACE satellites at spatial scales of roughly 1500 km. Swarm confirms most GRACE observations, such as the large ice melting in Greenland and the wet and dry seasons in the Amazon.
E. Sinem Ince, Franz Barthelmes, Sven Reißland, Kirsten Elger, Christoph Förste, Frank Flechtner, and Harald Schuh
Earth Syst. Sci. Data, 11, 647–674, https://doi.org/10.5194/essd-11-647-2019, https://doi.org/10.5194/essd-11-647-2019, 2019
Short summary
Short summary
ICGEM is a non-profit scientific service that contributes to any research area in which the use of gravity information is essential. ICGEM offers the largest collection of global gravity field models, interactive calculation and visualisation services and delivers high-quality datasets to researchers and students in geodesy, geophysics, glaciology, hydrology, oceanography, and climatology and most importantly general public. Static, temporal, and topographic gravity field models are available.
Cited articles
A, G., Wahr, J., and Zhong, S.: Computations of the viscoelastic response of a
3-D compressible Earth to surface loading: an application to Glacial
Isostatic Adjustment in Antarctica and Canada, Geophys. J. Int., 192, 557–572,
https://doi.org/10.1093/gji/ggs030, 2012. a
Abileah, R., Vignudelli, S., and Scozzari, A.: A completely remote sensing
approach to monitoring reservoirs water volume, Int. Water Technol. J., 1,
63–77, 2011. a
Allen, G. H. and Pavelsky, T. M.: Global extent of rivers and streams, Science,
361, 585–588, https://doi.org/10.1126/science.aat0636, 2018. a
Alsdorf, D. and Lettenmaier, D. P.: Tracking Fresh Water from Space, Science,
301, 1491–1494, https://doi.org/10.1126/science.1089802, 2003. a, b
Alsdorf, D. E., Rodriguez, E., and Lettenmaier, D. P.: Measuring surface water
from space, Rev. Geophys., 45, RG2002, https://doi.org/10.1029/2006RG000197, 2007. a, b
Berner, E. K. and Berner, R. A.: Global environment: water, air, and
geochemical cycles, Princeton University Press, ISBN 9780691136783, 2012. a
Berry, P. A. M., Garlick, J. D., Freeman, J. A., and Mathers, E. L.: Global
inland water monitoring from multi-mission altimetery, Geophys. Res.
Lett., 32, L16401, https://doi.org/10.1029/2005GL022814, 2005. a
Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M.: The SWOT mission and
its capabilities for land hydrology, Surv. Geophys., 37, 307–337,
https://doi.org/10.1007/s10712-015-9346-y, 2016. a, b
Birkett, C. M.: The global remote sensing of lakes, wetlands and rivers for
hydrological and climate research, in: Geoscience and Remote Sensing
Symposium, 1995, IGARSS '95, Quantitative Remote Sensing for Science and
Applications, International, 3, 1979–1981,
https://doi.org/10.1109/IGARSS.1995.524084, 1995. a
Birkinshaw, S. J., O'Donnell, G. M., Moore, P., Kilsby, C. G., Fowler, H. J.,
and Berry, P. A. M.: Using satellite altimetry data to augment flow
estimation techniques on the Mekong River, Hydrol. Process., 24,
3811–3825, https://doi.org/10.1002/hyp.7811, 2010. a
Bjerklie, D. M., Lawrence Dingman, S., Vörösmarty, C. J., Bolster, C. H.,
and Congalton, R. G.: Evaluating the potential for measuring river discharge
from space, J. Hydrol., 278, 17–38,
https://doi.org/10.1016/S0022-1694(03)00129-X, 2003. a
Boergens, E., Buhl, S., Dettmering, D., Klüppelberg, C., and Seitz, F.:
Combination of multi-mission altimetry data along the Mekong River with
spatio-temporal kriging, J. Geodesy, 91, 519–534,
https://doi.org/10.1007/s00190-016-0980-z, 2017. a
Boergens, E., Dobslaw, H., Dill, R., Thomas, M., Dahle, C., Murböck, M.,
and Flechtner, F.: Modelling spatial covariances for terrestrial water
storage variations verified with synthetic GRACE-FO data, Int.
J. Geomath., 11, 24, https://doi.org/10.1007/s13137-020-00160-0, 2020. a
Bosch, W., Dettmering, D., and Schwatke, C.: Multi-mission cross-calibration of
satellite altimeters: Constructing a long-term data record for global and
regional sea level change studies, Remote Sensing, 6, 2255–2281,
https://doi.org/10.3390/rs6032255, 2014. a
Busker, T., de Roo, A., Gelati, E., Schwatke, C., Adamovic, M., Bisselink, B., Pekel, J.-F., and Cottam, A.: A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry, Hydrol. Earth Syst. Sci., 23, 669–690, https://doi.org/10.5194/hess-23-669-2019, 2019. a
Chen, J., Wilson, C., Blankenship, D., and Tapley, B.: Accelerated Antarctic
ice loss from satellite gravity measurements, Nat. Geosci., 2, 859–862,
https://doi.org/10.1038/ngeo694, 2009. a
Chen, J., Wilson, C., and Tapley, B.: Contribution of ice sheet and mountain
glacier melt to recent sea level rise, Nat. Geosci., 6, 549–552,
https://doi.org/10.1038/ngeo1829, 2013a. a, b
Cheng, M., Tapley, B. D., and Ries, J. C.: Deceleration in the Earth's
oblateness, J. Geophys. Res.-Sol. Ea., 118, 740–747,
https://doi.org/10.1002/jgrb.50058, 2013. a
Coss, S., Durand, M., Yi, Y., Jia, Y., Guo, Q., Tuozzolo, S., Shum, C. K., Allen, G. H., Calmant, S., and Pavelsky, T.: Global River Radar Altimetry Time Series (GRRATS): new river elevation earth science data records for the hydrologic community, Earth Syst. Sci. Data, 12, 137–150, https://doi.org/10.5194/essd-12-137-2020, 2020. a, b
Crétaux, J.-F., Calmant, S., Romanovski, V., Shabunin, A., Lyard, F.,
Bergé-Nguyen, M., Cazenave, A., Hernandez, F., and Perosanz, F.: An
absolute calibration site for radar altimeters in the continental domain:
Lake Issykkul in Central Asia, J. Geodesy, 83, 723–735,
https://doi.org/10.1007/s00190-008-0289-7, 2009. a
Crétaux, J.-F., Jelinski, W., Calmant, S., Kouraev, A., Vuglinski, V.,
Bergé-Nguyen, M., Gennero, M., Nino, F., Rio, R. A. D., Cazenave, A., and
Maisongrande, P.: SOLS: A lake database to monitor in the Near Real Time
water level and storage variations from remote sensing data, Adv.
Space Res., 47, 1497–1507, https://doi.org/10.1016/j.asr.2011.01.004, 2011. a, b, c
Crétaux, J.-F., Berge-Nguyen, M., Calmant, S., Romanovski, V. V., Meyssignac,
B., Perosanz, F., Tashbaeva, S., Arsen, A., Fund, F., Martignago, N., Bonnefond, P., Laurain, O., Morrow, R., and Maisongrande, P.:
Calibration of Envisat radar altimeter over Lake Issykkul, Adv. Space
Res., 51, 1523–1541, https://doi.org/10.1016/j.asr.2012.06.039, 2013. a
Dickinson, R. E.: Modeling Evapotranspiration for Three-Dimensional Global Climate Models, in: Climate Processes and Climate Sensitivity, edited by: Hansen, J. E. and Takahashi, T., https://doi.org/10.1029/GM029p0058, 1984. a
Döll, P., Trautmann, T., Gerten, D., Schmied, H. M., Ostberg, S., Saaed,
F., and Schleussner, C.-F.: Risks for the global freshwater system at 1.5 ∘C
and 2 ∘C global warming, Environ. Res. Lett., 13, 044038,
https://doi.org/10.1088/1748-9326/aab792, 2018. a
Donchyts, G., Baart, F., Winsemius, H., Gorelick, N., Kwadijk, J., and Van
De Giesen, N.: Earth's surface water change over the past 30 years, Nat.
Clim. Change, 6, 810–813, https://doi.org/10.1038/nclimate3111, 2016. a
Durand, M., Gleason, C. J., Garambois, P. A., Bjerklie, D., Smith, L. C., Roux, H.,
Rodriguez, E., Bates, P. D., Pavelsky, T. M., Monnier, J., Chen, X., Di Baldassarre, G., Fiset, J.-M., Flipo, N., Frasson, R. P. d. M., Fulton, J., Goutal, N., Hossain, F., Humphries, E., Minear, J. T., Mukolwe, M. M., Neal, J. C., Ricci, S., Sanders, B. F., Schumann, G., Schubert, J. E., Vilmin, L.: An
intercomparison of remote sensing river discharge estimation algorithms from
measurements of river height, width, and slope, Water Resour. Res., 52,
4527–4549, https://doi.org/10.1002/2015WR018434, 2016. a
Elmi, O.: Dynamic water masks from optical satellite imagery,
Verlag der Bayerischen Akademie der Wissenschaften, München, ISBN 978-3-7696-5246-8,
https://doi.org/10.18419/opus-10597, 2019. a, b
Elmi, O., Tourian, M. J., and Sneeuw, N.: Dynamic river masks from
multi-temporal satellite imagery: An automatic algorithm using graph cuts
optimization, Remote Sensing, 8, 1005, https://doi.org/10.3390/rs8121005, 2016. a, b, c
Elmi, O., Tourian, M. J., Bárdossy, A., and Sneeuw, N.: Spaceborne River
Discharge From a Nonparametric Stochastic Quantile Mapping Function, Water
Resour. Res., 57, e2021WR030277,
https://doi.org/10.1029/2021WR030277, 2021. a, b, c
European Space Agency: RA-2 Geophysical Data Record. Version 3.0, ESA [data set], https://doi.org/10.5270/EN1-ajb696a, 2018. a, b
Famiglietti, J.: Rallying Around Our Known Unknowns: What We Don’t Know Will
Hurt Us, Water 50/50, June 28, https://aquadoc.typepad.com/waterwired/2012/06/jay-famiglietti-rallying-around-our-known-unknowns-what-we-dont-know-will-hurt-us.html (last access: 21 May 2022), 2012. a
Famiglietti, J. S.: Remote Sensing of Terrestrial Water Storage, Soil Moisture and Surface Waters, in: The State of the Planet: Frontiers and Challenges in Geophysics, edited by: Sparks, R. and Hawkesworth, C., American Geophysical Union (AGU), https://doi.org/10.1029/150GM16, 2004. a
Fernandes, M. J., Lázaro, C., Nunes, A. L., and Scharroo, R.: Atmospheric
Corrections for Altimetry Studies over Inland Water, Remote Sensing, 6,
4952–4997, https://doi.org/10.3390/rs6064952, 2014. a
Förste, C., Bruinsma, S. L., Flechtner, F., Marty, J., Lemoine, J.-M.,
Dahle, C., Abrikosov, O., Neumayer, H., Biancale, R., Barthelmes, F., and Balmino, G.:
A preliminary update of the Direct approach GOCE Processing and a new release
of EIGEN-6C, in: AGU Fall meeting abstracts, vol. 2012, pp. G31B–0923, https://gfzpublic.gfz-potsdam.de/pubman/faces/ViewItemFullPage.jsp?itemId=item_246439_1 (last access: 21 May 2022), 2012. a
Fu, L.-L. and Cazenave, A.: Satellite Altimetry and Earth Sciences: A
Handbook of Techniques and Applications, International Geophysical
series, ISBN 9780080516585, 2001. a
Gao, H., Birkett, C., and Lettenmaier, D. P.: Global monitoring of large
reservoir storage from satellite remote sensing, Water Resour. Res.,
48, W09504, https://doi.org/10.1029/2012WR012063, 2012. a
Gleason, C. J. and Durand, M. T.: Remote sensing of river discharge: A review
and a framing for the discipline, Remote Sensing, 12, 1107,
https://doi.org/10.3390/rs12071107, 2020. a
Jacob, T., Wahr, J., Pfeffer, W. T., and Swenson, S.: Recent contributions of
glaciers and ice caps to sea level rise, Nature, 482, 514–518,
https://doi.org/10.1038/nature10847, 2012. a
Jiang, D., Wang, J., Huang, Y., Zhou, K., Ding, X., and Fu, J.: The review of
GRACE data applications in terrestrial hydrology monitoring, Adv.
Meteorol., 2014, 725131, https://doi.org/10.1155/2014/725131, 2014. a
Khandelwal, A., Karpatne, A., Marlier, M. E., Kim, J., Lettenmaier, D. P., and
Kumar, V.: An approach for global monitoring of surface water extent
variations in reservoirs using MODIS data, Remote Sens. Environ.,
202, 113–128, https://doi.org/10.1016/j.rse.2017.05.039, 2017. a
Klein, I., Gessner, U., Dietz, A. J., and Kuenzer, C.: Global WaterPack – A 250 m resolution dataset revealing the daily dynamics of global inland water
bodies, Remote Sens. Environ., 198, 345–362,
https://doi.org/10.1016/j.rse.2017.06.045, 2017. a
Klein, I., Mayr, S., Gessner, U., Hirner, A., and Kuenzer, C.: Water and
hydropower reservoirs: High temporal resolution time series derived from
MODIS data to characterize seasonality and variability, Remote Sens.
Environ., 253, 112207, https://doi.org/10.1016/j.rse.2020.112207, 2021. 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, https://doi.org/10.1016/j.rse.2004.07.007,
2004. a
Kvas, A., Behzadpour, S., Ellmer, M., Klinger, B., Strasser, S., Zehentner, N.,
and Mayer-Gürr, T.: ITSG-Grace2018: Overview and evaluation of a new
GRACE-only gravity field time series, J. Geophys. Res.-Sol.
Ea., 124, 9332–9344, https://doi.org/10.1029/2019JB017415, 2019. a
Landerer, F.: CSR TELLUS GRACE-FO Level-3 Monthly Land
Water-Equivalent-Thickness Surface Mass Anomaly Release 6.0 version 03, NASA [data set],
https://doi.org/10.5067/GFLND-3AC63,
2020a. a, b
Landerer, F.: CSR TELLUS GRACE Level-3 Monthly Land Water-Equivalent-Thickness
Surface Mass Anomaly Release 6.0 version 03, NASA [data set], https://doi.org/10.5067/TELND-3AC63, 2020b. a
Landerer, F.: GFZ TELLUS GRACE Level-3 Monthly Land Water-Equivalent-Thickness
Surface Mass Anomaly Release 6.0 version 03, NASA [data set], https://doi.org/10.5067/TELND-3AG63, 2020c. a
Landerer, F.: JPL TELLUS GRACE-FO Level-3 Monthly Land
Water-Equivalent-Thickness Surface Mass Anomaly Release 6.0 version 03, NASA [data set],
https://doi.org/10.5067/GFLND-3AJ63,
2020d. a
Landerer, F.: JPL TELLUS GRACE Level-3 Monthly Land Water Equivalent Thickness
Surface Mass Anomaly Release 6.0 version 03, NASA [data set], https://doi.org/10.5067/TELND-3AJ63, 2020e. a
Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial water
storage estimates, Water Resour. Res., 48, W04531, https://doi.org/10.1029/2011WR011453,
2012. a
Lettenmaier, D. P.: Observations of the Global Water Cycle – Global Monitoring
Networks, Encyclopedia of Hydrological Sciences, Print ISBN 9780471491033, Online ISBN 9780470848944,
https://doi.org/10.1002/0470848944.hsa181, 2006. a
Li, J., Chen, J., Li, Z., Wang, S.-Y., and Hu, X.: Ellipsoidal correction in
GRACE surface mass change estimation, J. Geophys. Res.-Sol.
Ear., 122, 9437–9460, https://doi.org/10.1002/2017JB014033, 2017. a
Li, X., Long, D., Huang, Q., Han, P., Zhao, F., and Wada, Y.: High-temporal-resolution water level and storage change data sets for lakes on the Tibetan Plateau during 2000–2017 using multiple altimetric missions and Landsat-derived lake shoreline positions, Earth Syst. Sci. Data, 11, 1603–1627, https://doi.org/10.5194/essd-11-1603-2019, 2019. a
Li, Y., Gao, H., Zhao, G., and Tseng, K.-H.: A high-resolution bathymetry
dataset for global reservoirs using multi-source satellite imagery and
altimetry, Remote Sens. Environ., 244, 111831,
https://doi.org/10.1016/j.rse.2020.111831, 2020. a, b
Long, D., Scanlon, B. R., Longuevergne, L., Sun, A. Y., Fernando, D. N., and
Save, H.: GRACE satellite monitoring of large depletion in water storage in
response to the 2011 drought in Texas, Geophys. Res. Lett., 40,
3395–3401, https://doi.org/10.1002/grl.50655, 2013. a
Lorenz, C., Devaraju, B., Tourian, M. J., Riegger, J., Kunstmann, H., and
Sneeuw, N.: Large-scale runoff from landmasses: a global assessment of the
closure of the hydrological and atmospheric water balances, J.
Hydrometeorol., 15, 2111–2139, https://doi.org/10.1175/JHM-D-13-0157.1, 2014. a
Lorenz, C., Tourian, M. J., Devaraju, B., Sneeuw, N., and Kunstmann, H.:
Basin-scale runoff prediction: An Ensemble Kalman Filter framework based on
global hydrometeorological data sets, Water Resour. Res., 51,
8450–8475, https://doi.org/10.1002/2014WR016794, 2015. a, b
Lvovitch, M.: The global water balance, Eos Trans. AGU, 54, 28–53, https://doi.org/10.1029/EO054i001p00028, 1973. a
Markert, K. N., Pulla, S. T., Lee, H., Markert, A. M., Anderson, E. R., Okeowo,
M. A., and Limaye, A. S.: AltEx: An open source web application and toolkit
for accessing and exploring altimetry datasets, Environ. Modell.
Softw., 117, 164–175, https://doi.org/10.1016/j.envsoft.2019.03.021, 2019. a, b
Mayer-Gürr, T., Behzadpour, S., Kvas, A., Ellmer, M., Klinger, B.,
Strasser, S., and Zehentner, N.: ITSG-Grace2018: Monthly, Daily and Static
Gravity Field Solutions from GRACE, ICGEM [data set], https://doi.org/10.5880/ICGEM.2018.003, 2018. a
NASA JPL: NASA Shuttle Radar Topography Mission Water Body Data Shapefiles &
Raster Files, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MEaSUREs/SRTM/SRTMSWBD.003, 2013. a
Okeowo, M. A., Lee, H., Hossain, F., and Getirana, A.: Automated generation of
lakes and reservoirs water elevation changes from satellite radar altimetry,
IEEE J. Sel. Top. Appl., 10, 3465–3481, https://doi.org/10.1109/JSTARS.2017.2684081, 2017. a
Pail, R., Bingham, R., Braitenberg, C., et al.: Observing
Mass Transport to Understand Global Change and and to benefit Society:
Science and User Needs-An international multidisciplinary initiative for
IUGG, ISBN 978-3-7696-8599-2, 2015. 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, https://doi.org/10.1007/s00190-017-1070-6, 2018. 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., 115, C12013, https://doi.org/10.1029/2009JC006075,
2010a. a
Papa, F., Prigent, C., Aires, F., Jimenez, C., Rossow, W. B., and Matthews, E.:
Interannual variability of surface water extent at the global scale,
1993–2004, J. Geophys. Res.-Atmos., 115, D12111,
https://doi.org/10.1029/2009JD012674, 2010b. a
Pavlis, N. K., Holmes, S. A., Kenyon, S. C., and Factor, J. K.: The
development and evaluation of the Earth Gravitational Model 2008 (EGM2008),
J. Geophys. Res.-Sol. Ea., 117, B04406,
https://doi.org/10.1029/2011JB008916, 2012. 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, https://doi.org/10.1038/nature20584, 2016 (data available at: https://global-surface-water.appspot.com/, last access: 18 May 2022). a, b, c, d
Pickens, A. H., Hansen, M. C., Hancher, M., Stehman, S. V., Tyukavina, A.,
Potapov, P., Marroquin, B., and Sherani, Z.: Mapping and sampling to
characterize global inland water dynamics from 1999 to 2018 with full Landsat
time-series, Remote Sens. Environ., 243, 111792,
https://doi.org/10.1016/j.rse.2020.111792, 2020. a
Rauch, H. E., Tung, F., and Striebel, C. T.: Maximum likelihood estimates of
linear dynamic systems, AIAA J., 3, 1445–1450, https://doi.org/10.2514/3.3166,
1965. a
Reager, J., Thomas, B., and Famiglietti, J.: River basin flood potential
inferred using GRACE gravity observations at several months lead time,
Nat. Geosci., 7, 588–592, https://doi.org/10.1038/ngeo2203, 2014. a
Riegger, J., Tourian, M. J., Devaraju, B., and Sneeuw, N.: Analysis of
grace uncertainties by hydrological and hydro-meteorological
observations, Journal of Geodynamics, 59, 16–27,
https://doi.org/10.1016/j.jog.2012.02.001, 2012. a, b
Rodell, M. and Famiglietti, J.: Detectability of variations in continental
water storage from satellite observation of time dependent gravity field,
Water Resour. Res., 35, 2705–2723, https://doi.org/10.1029/1999WR900141, 1999. a
Rodell, M., Chen, J., Kato, H., Famiglietti, J., Nigro, J., and Wilson, C.:
Estimating groundwater storage changes in the Mississippi River basin
(USA) using GRACE, Hydrogeol. J., 15, 159–166,
https://doi.org/10.1007/s10040-006-0103-7, 2006. a
Rodell, M., Beaudoing, H. K., L’Ecuyer, T. S., Olson, W. S., Famiglietti, J. S.,
Houser, P. R., Adler, R., Bosilovich, M. G., Clayson, C. A., Chambers, D.,
Clark, E., Fetzer, E. J., Gao, X., Gu, G., Hilburn, K., Huffman, G. J., Lettenmaier, D. P., Liu, W. T., Robertson, F. R., Schlosser, C. A., Sheffield, J., and Wood, E. F.: The observed state of the water cycle in the early twenty-first
century, J. Climate, 28, 8289–8318, https://doi.org/10.1175/JCLI-D-14-00555.1,
2015. a, b
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
Save, H.: CSR GRACE and GRACE-FO RL06 Mascon Solutions v02. 2020, University of Texas [data set], https://doi.org/10.15781/cgq9-nh24, 2020. a
Save, H., Bettadpur, S., and Tapley, B. D.: High-resolution CSR GRACE RL05
mascons, J. Geophys. Res.-Sol. Ea., 121, 7547–7569,
https://doi.org/10.1002/2016JB013007, 2016. a
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, 2015a. a, b, c, d
Schwatke, C., Scherer, D., and Dettmering, D.: Automated extraction of
consistent time-variable water surfaces of lakes and reservoirs based on
landsat and sentinel-2, Remote Sensing, 11, 1010, https://doi.org/10.3390/rs11091010,
2019. a
Schwatke, C., Dettmering, D., and Seitz, F.: Volume Variations of Small Inland
Water Bodies from a Combination of Satellite Altimetry and Optical Imagery,
Remote Sensing, 12, 1606, https://doi.org/10.3390/rs12101606, 2020. a, b
Sneeuw, N., Lorenz, C., Devaraju, B., Tourian, M. J., Riegger, J., Kunstmann,
H., and Bárdossy, A.: Estimating Runoff Using Hydro-Geodetic
Approaches, Surv. Geophys., 35, 1333–1359,
https://doi.org/10.1007/s10712-014-9300-4, 2014. a
Strassberg, G., Scanlon, B., and Rodell, M.: Comparison of seasonal terrestrial
water storage variations from GRACE with groundwater-level measurements
from the High Plains Aquifer (USA), Geophys. Res. Lett., 34,
L14402, https://doi.org/10.1029/2007GL030139, 2007. a, b
Stuurman, C. and Pottier, C.: Level 2 KaRIn high rate river single pass vector
product, Surface Water and Ocean Topography (SWOT) Project SWOT Product
Description, https://podaac-tools.jpl.nasa.gov/drive/files/misc/web/misc/swot_mission_docs/pdd/D-56411_SWOT_Product_Description_L2_HR_PIXC_20200810.pdf (last access: 21 May 2022), 2020. a
Swenson, S., Chamber, D., and Wahr, J.: Estimating geocenter variations from a
combination of grace and ocean model output, J.
Geophys. Res., 113, B08410, https://doi.org/10.1029/2007JB005338, 2007. a
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., and Watkins, M. M.:
The Gravity Recovery and Climate Expriment: Mission overview and
early results, Geophys. Res. Lett., 31, L09607,
https://doi.org/10.1029/2004GL019920, 2004. a, b
Tapley, B. D., Watkins, M. M., Flechtner, F., Reigber, C., Bettadpur, S., Rodell, M., Sasgen, I., Famiglietti, J. S., Landerer, F. W., Chambers, D. P., Reager, J. T., Gardner, A. S., Save, H., Ivins, E. R., Swenson, S. C., Boening, C., Dahle, C., Wiese, D. N., Dobslaw, H., Tamisiea, M. E., and Velicogna, I.: Contributions of GRACE to understanding climate change, Nat.
Clim. Change, 9, 358–369, https://doi.org/10.1038/s41558-019-0456-2, 2019. a
Thomas, A. C., Reager, J. T., Famiglietti, J. S., and Rodell, M.: A GRACE-based
water storage deficit approach for hydrological drought characterization,
Geophys. Res. Lett., 41, 1537–1545, https://doi.org/10.1002/2014GL059323,
2014. a
Tourian, M., Elmi, O., Shafaghi, Y., Behnia, S., Saemian, P., Schlesinger, R.,
and Sneeuw, N.: HydroSat: a repository of global water cycle products from
spaceborne geodetic sensors, GFZ Data Services [data set],
https://doi.org/10.5880/fidgeo.2021.017, 2021. a, b
Tourian, M. J.: Application of spaceborne geodetic sensors for hydrology, ISBN 978-3-7696-5132-4,
https://doi.org/10.18419/opus-3929, 2013. a, b
Tourian, M. J., Sneeuw, N., and Bárdossy, A.: A quantile function approach
to discharge estimation from satellite altimetry (ENVISAT), Water Resour.
Res., 49, 4174–4186, https://doi.org/10.1002/wrcr.20348, 2013. a
Tourian, M. J., 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, https://doi.org/10.1002/2015WR017654, 2016. a, b, c, d
Tourian, M. J., Schwatke, C., and Sneeuw, N.: River discharge estimation at daily
resolution from satellite altimetry over an entire river basin, J.
Hydrol., 546, 230–247, https://doi.org/10.1016/j.jhydrol.2017.01.009,
2017a. a, b
Tourian, M. J., Elmi, O., Mohammadnejad, A., and Sneeuw, N.: Estimating river
depth from SWOT-Type observables obtained by satellite altimetry and imagery,
Water, 9, 753, https://doi.org/10.3390/w9100753, 2017b. a
Vishwakarma, B. D., Horwath, M., Devaraju, B., Groh, A., and Sneeuw, N.: A
data-driven approach for repairing the hydrological catchment signal damage
due to filtering of GRACE products, Water Resour. Res., 53, 9824–9844,
https://doi.org/10.1002/2017WR021150, 2017. a
Vörösmarty, C., Fekete, B. M., Meybeck, M., and Lammers, R. B.: Global
system of rivers: Its role in organizing continental land mass and defining
land-to-ocean linkages, Global Biogeochem. Cy., 14, 599–621,
https://doi.org/10.1029/1999GB900092, 2000. a
Wahr, J., Molenaar, M., and Bryan, F.: The Time-Varibility of the Earth's
gravity field: Hydrological and oceanic effects and their possible detection
using GRACE, J. Geophys. Res., 103, 30205–30230,
https://doi.org/10.1029/98JB02844, 1998. a
Wang, H., Chu, Y., Huang, Z., Hwang, C., and Chao, N.: Robust, long-term lake
level change from multiple satellite altimeters in Tibet: Observing the rapid
rise of Ngangzi Co over a new wetland, Remote Sensing, 11, 558,
https://doi.org/10.3390/rs11050558, 2019. a
Wang, J., Song, C., Reager, J. T., Yao, F., Famiglietti, J. S., Sheng, Y.,
MacDonald, G. M., Brun, F., Müller Schmied, H., Marston, R. A., and Wada, Y.: Recent
global decline in endorheic basin water storages, Nat. Geosci., 11,
926–932, https://doi.org/10.1038/s41561-018-0265-7, 2018. a
Wiese, D. N., Yuan, D.-N., Boening, C., Landerer, F. W., and Watkins, M. M.:
JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water
Height Coastal Resolution Improvement (CRI) Filtered Release 06 Version 02, NASA [data set],
https://doi.org/10.5067/TEMSC-3JC62, 2020. a
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.
a
Yang, K., Yao, F., Wang, J., Luo, J., Shen, Z., Wang, C., and Song, C.: Recent
dynamics of alpine lakes on the endorheic Changtang Plateau from
multi-mission satellite data, J. Hydrol., 552, 633–645,
https://doi.org/10.1016/j.jhydrol.2017.07.024, 2017. a
Yao, F., Wang, J., Yang, K., Wang, C., Walter, B. A., and Crétaux, J.-F.:
Lake storage variation on the endorheic Tibetan Plateau and its attribution
to climate change since the new millennium, Environ. Res. Lett.,
13, 064011, https://doi.org/10.1088/1748-9326/aab5d3, 2018. a
Yao, F., Wang, J., Wang, C., and Crétaux, J.-F.: Constructing long-term
high-frequency time series of global lake and reservoir areas using Landsat
imagery, Remote Sens. Environ., 232, 111210,
https://doi.org/10.1016/j.rse.2019.111210, 2019. a
Yeh, P., Swenson, S., Famiglietti, J., and Rodell, M.: Remote sensing of
groundwater storage changes in Illinois using the Gravity Recovery and
Climate Experiment (GRACE), Water Resour. Res., 42, W12203,
https://doi.org/10.1029/2006WR005374, 2006. a
Zhang, S. and Gao, H.: A novel algorithm for monitoring reservoirs under
all-weather conditions at a high temporal resolution through passive
microwave remote sensing, Geophys. Res. Lett., 43, 8052–8059,
https://doi.org/10.1002/2016GL069560, 2016. a
Zhao, G. and Gao, H.: Automatic correction of contaminated images for
assessment of reservoir surface area dynamics, Geophys. Res. Lett.,
45, 6092–6099, https://doi.org/10.1029/2018GL078343, 2018. a
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.
HydroSat as a global water cycle database provides estimates of and uncertainty in geometric...
Altmetrics
Final-revised paper
Preprint