Articles | Volume 27, issue 13
https://doi.org/10.5194/hess-27-2413-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-2413-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jul 2023
Research article |
| 04 Jul 2023
| 04 Jul 2023
A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry
Geodesy and Geoinformatics, HafenCity University Hamburg, Hamburg, Germany
Annette Eicker
Geodesy and Geoinformatics, HafenCity University Hamburg, Hamburg, Germany
Laura Jensen
Geodesy and Geoinformatics, HafenCity University Hamburg, Hamburg, Germany
Andreas Güntner
GFZ German Research Centre for Geosciences, Helmholtz Centre Potsdam, Potsdam, Germany
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
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Daniel Rasche, Cosimo Brogi, Markus Köhli, David McJannet, Jannis Weimar, Martin Schrön, Theresa Blume, and Andreas Güntner
EGUsphere, https://doi.org/10.5194/egusphere-2026-1495, https://doi.org/10.5194/egusphere-2026-1495, 2026
This preprint is open for discussion and under review for Geoscientific Instrumentation, Methods and Data Systems (GI).
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Cosmic-Ray Neutron Sensing is a passive technique to estimate soil moisture, plant traits and snow at the field scale by using instruments sensitive to different neutron energy ranges. For devices sensitive to lower energies, a generalised assessment of the signal response under varying environmental conditions is currently missing. Our simulation-based study highlights the differing signal response of different instruments to environmental variables and provides a baseline for further research.
Roland Hohensinn, Junyang Gou, Ulrich Meyer, Vincent Humphrey, Wouter Dorigo, Eva Boergens, Benedikt Soja, Alexander Gruber, Laura Jensen, Annette Eicker, Michael Rast, and Andreas Güntner
EGUsphere, https://doi.org/10.5194/egusphere-2026-975, https://doi.org/10.5194/egusphere-2026-975, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This study provides a statistically traceable, uncertainty-aware assessment of global terrestrial and groundwater storage trends based on 21.5 years of GRACE/-FO observations using the Global Gravity-based Groundwater Product (G3P). By accounting for long-range memory and interannual variability, we isolate robust trends from climate variability and noise. The analysis refines continental budgets and identifies emerging groundwater gains alongside widespread persistent decline.
Andreas Güntner, Ehsan Sharifi, Julian Haas, Eva Boergens, Feifei Cao, Christoph Dahle, Neda Darbeheshti, Henryk Dobslaw, Inés Dussaillant, Wouter Dorigo, Frank Flechtner, Adrian Jäggi, Miriam Kosmale, Martin Lasser, Kari Luojus, Ulrich Meyer, Adam Pasik, Wolfgang Preimersberger, Claudia Ruz Vargas, and Michael Zemp
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-797, https://doi.org/10.5194/essd-2025-797, 2026
Preprint under review for ESSD
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We provide a data set that illustrates how the amount of water that is stored in the subsurface as groundwater varies in time over the continents of the Earth. A main source of the data are observations with satellites that weigh the changing amount of water by its mass attraction effect. The data allow for assessing how groundwater as the most important freshwater resource for mankind and ecosystems is affected by climate variability, climate change and withdrawal by human activities.
Ehsan Sharifi, Julian Haas, Eva Boergens, Henryk Dobslaw, and Andreas Güntner
Hydrol. Earth Syst. Sci., 29, 6985–6998, https://doi.org/10.5194/hess-29-6985-2025, https://doi.org/10.5194/hess-29-6985-2025, 2025
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This study presents a method to make the spatial resolution of global Water Storage Compartments (WSCs) compatible with terrestrial water storage (TWS) data from Gravity Recovery And Climate Experiment (GRACE) missions. The method compares the spatial structure of the WSCs and TWS by considering the correlation between neighboring grid cells. An isotropic Gaussian filter with an optimal filter width of 250 km is found to be the most suitable, ensuring compatibility for consistent comparison with GRACE data in hydrological applications.
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.
Eva Boergens, Andreas Güntner, Mike Sips, Christian Schwatke, and Henryk Dobslaw
Hydrol. Earth Syst. Sci., 28, 4733–4754, https://doi.org/10.5194/hess-28-4733-2024, https://doi.org/10.5194/hess-28-4733-2024, 2024
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The satellites GRACE and GRACE-FO observe continental terrestrial water storage (TWS) changes. With over 20 years of data, we can look into long-term variations in the East Africa Rift region. We focus on analysing the interannual TWS variations compared to meteorological data and observations of the water storage compartments. We found strong influences of natural precipitation variability and human actions over Lake Victoria's water level.
Daniel Rasche, Theresa Blume, and Andreas Güntner
SOIL, 10, 655–677, https://doi.org/10.5194/soil-10-655-2024, https://doi.org/10.5194/soil-10-655-2024, 2024
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Soil moisture measurements at the field scale are highly beneficial for numerous (soil) hydrological applications. Cosmic-ray neutron sensing (CRNS) allows for the non-invasive monitoring of field-scale soil moisture across several hectares but only for the first few tens of centimetres of the soil. In this study, we modify and test a simple modeling approach to extrapolate CRNS-derived surface soil moisture information down to 450 cm depth and compare calibrated and uncalibrated model results.
Petra Döll, Howlader Mohammad Mehedi Hasan, Kerstin Schulze, Helena Gerdener, Lara Börger, Somayeh Shadkam, Sebastian Ackermann, Seyed-Mohammad Hosseini-Moghari, Hannes Müller Schmied, Andreas Güntner, and Jürgen Kusche
Hydrol. Earth Syst. Sci., 28, 2259–2295, https://doi.org/10.5194/hess-28-2259-2024, https://doi.org/10.5194/hess-28-2259-2024, 2024
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Currently, global hydrological models do not benefit from observations of model output variables to reduce and quantify model output uncertainty. For the Mississippi River basin, we explored three approaches for using both streamflow and total water storage anomaly observations to adjust the parameter sets in a global hydrological model. We developed a method for considering the observation uncertainties to quantify the uncertainty of model output and provide recommendations.
Daniel Rasche, Jannis Weimar, Martin Schrön, Markus Köhli, Markus Morgner, Andreas Güntner, and Theresa Blume
Hydrol. Earth Syst. Sci., 27, 3059–3082, https://doi.org/10.5194/hess-27-3059-2023, https://doi.org/10.5194/hess-27-3059-2023, 2023
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We introduce passive downhole cosmic-ray neutron sensing (d-CRNS) as an approach for the non-invasive estimation of soil moisture in deeper layers of the unsaturated zone which exceed the observational window of above-ground CRNS applications. Neutron transport simulations are used to derive mathematical descriptions and transfer functions, while experimental measurements in an existing groundwater observation well illustrate the feasibility and applicability of the approach.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
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Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Maik Heistermann, Heye Bogena, Till Francke, Andreas Güntner, Jannis Jakobi, Daniel Rasche, Martin Schrön, Veronika Döpper, Benjamin Fersch, Jannis Groh, Amol Patil, Thomas Pütz, Marvin Reich, Steffen Zacharias, Carmen Zengerle, and Sascha Oswald
Earth Syst. Sci. Data, 14, 2501–2519, https://doi.org/10.5194/essd-14-2501-2022, https://doi.org/10.5194/essd-14-2501-2022, 2022
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This paper presents a dense network of cosmic-ray neutron sensing (CRNS) to measure spatio-temporal soil moisture patterns during a 2-month campaign in the Wüstebach headwater catchment in Germany. Stationary, mobile, and airborne CRNS technology monitored the root-zone water dynamics as well as spatial heterogeneity in the 0.4 km2 area. The 15 CRNS stations were supported by a hydrogravimeter, biomass sampling, and a wireless soil sensor network to facilitate holistic hydrological analysis.
Andreas Wieser, Andreas Güntner, Peter Dietrich, Jan Handwerker, Dina Khordakova, Uta Ködel, Martin Kohler, Hannes Mollenhauer, Bernhard Mühr, Erik Nixdorf, Marvin Reich, Christian Rolf, Martin Schrön, Claudia Schütze, and Ute Weber
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-131, https://doi.org/10.5194/hess-2022-131, 2022
Preprint withdrawn
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We present an event-triggered observation concept which covers the entire process chain from heavy precipitation to flooding at the catchment scale. It combines flexible and mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
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Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Tina Trautmann, Sujan Koirala, Nuno Carvalhais, Andreas Güntner, and Martin Jung
Hydrol. Earth Syst. Sci., 26, 1089–1109, https://doi.org/10.5194/hess-26-1089-2022, https://doi.org/10.5194/hess-26-1089-2022, 2022
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We assess the effect of how vegetation is defined in a global hydrological model on the composition of total water storage (TWS). We compare two experiments, one with globally uniform and one with vegetation parameters that vary in space and time. While both experiments are constrained against observational data, we found a drastic change in the partitioning of TWS, highlighting the important role of the interaction between groundwater–soil moisture–vegetation in understanding TWS variations.
Daniel Rasche, Markus Köhli, Martin Schrön, Theresa Blume, and Andreas Güntner
Hydrol. Earth Syst. Sci., 25, 6547–6566, https://doi.org/10.5194/hess-25-6547-2021, https://doi.org/10.5194/hess-25-6547-2021, 2021
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Cosmic-ray neutron sensing provides areal average soil moisture measurements. We investigated how distinct differences in spatial soil moisture patterns influence the soil moisture estimates and present two approaches to improve the estimate of soil moisture close to the instrument by reducing the influence of soil moisture further afield. Additionally, we show that the heterogeneity of soil moisture can be assessed based on the relationship of different neutron energies.
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
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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.
Cited articles
Abelen, S.: Signals of Weather Extremes in Soil Moisture and
Terrestrial Water Storage from Multi-Sensor Earth Observations
and Hydrological Modeling, PhD Thesis, Technische Universität
München, München, https://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:91-diss-20160627-1295206-1-4 (last access: 23 June 2023), 2016. a
Abelen, S., Seitz, F., Abarca-del Rio, R., and Güntner, A.: Droughts and
Floods in the La Plata Basin in Soil Moisture Data and GRACE,
Remote Sensing, 7, 7324–7349, https://doi.org/10.3390/rs70607324, 2015. a
Al Bitar, A. and Mahmoodi, A.: Algorithm Theoretical Basis Document
(ATBD) for the SMOS Level 4 Root Zone Soil Moisture, v30_01, Zenodo, https://doi.org/10.5281/ZENODO.4298572, 2020. a
Al Bitar, A., Kerr, Y., Merlin, O., Cabot, F., and Wigneron, J.-P.: Global
drought index from SMOS soil moisture, in: IEEE International
Geoscience and Remote Sensing Symposium, IGARSS 2013.
Melbourne, Australia, 21–26 July 2013, 2013. a
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E.,
Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin,
T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3
daily soil moisture and brightness temperature maps, Earth System Science
Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017. a
Bauer-Marschallinger, B., Paulik, C., Hochstöger, S., Mistelbauer, T.,
Modanesi, S., Ciabatta, L., Massari, C., Brocca, L., and Wagner, W.: Soil
Moisture from Fusion of Scatterometer and SAR: Closing the Scale
Gap with Temporal Filtering, Remote Sensing, 10, 1030,
https://doi.org/10.3390/rs10071030, 2018. a
Baur, O., Kuhn, M., and Featherstone, W.: GRACE-derived ice-mass variations
over Greenland by accounting for leakage effects, J. Geophys.
Res.-Sol. Ea., 114, B06407, https://doi.org/10.1029/2008JB006239, 2009. a
Bergmann, I. and Dobslaw, H.: Short-term transport variability of the
Antarctic Circumpolar Current from satellite gravity observations, J. Geophys. Res.-Oceans, 117, C05044, https://doi.org/10.1029/2012JC007872, 2012. a
Bonin, J. A. and Chambers, D. P.: Evaluation of high-frequency oceanographic
signal in GRACE data: Implications for de-aliasing, Geophys. Res. Lett., 38, L17608,
https://doi.org/10.1029/2011GL048881, 2011. a
Box, G., Jenkins, G., and Reinsel, G.: Time Series Analysis: Forecasting
and Control, vol. 3, Prentice Hall, New Jersey, USA, ISBN 9780130607744, 1994. a
Camps, A., Park, H., Pablos, M., Foti, G., Gommenginger, C. P., Liu, P.-W., and
Judge, J.: Sensitivity of GNSS-R Spaceborne Observations to Soil
Moisture and Vegetation, IEEE J. Sel. Top. Appl., 9, 4730–4742,
https://doi.org/10.1109/JSTARS.2016.2588467, 2016. a
Carranza, C. D. U., van der Ploeg, M. J., and Torfs, P. J. J. F.: Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values, Hydrol. Earth Syst. Sci., 22, 2255–2267, https://doi.org/10.5194/hess-22-2255-2018, 2018. a
CATDS: CATDS-PDC L3SM Filtered – 1 day global map of soil moisture values from SMOS satellite, CATDS (CNES, IFREMER, CESBIO) [data set], https://doi.org/10.12770/9cef422f-ed3f-4090-9556-b2e895ba2ca8, 2022a. a
CATDS: CATDS-PDC L4SM RZSM – 1 day global map of root zone soil moisture values from SMOS satellite, CATDS (CNES, IFREMER, CESBIO) [data set], https://doi.org/10.12770/316e77af-cb72-4312-96a3-3011cc5068d4, 2022b. a
Cheng, M. and Ries, J.: The unexpected signal in GRACE estimates of
C20, J. Geodesy, 91, 897–914,
https://doi.org/10.1007/s00190-016-0995-5, 2017. a
Chew, C. C. and Small, E. E.: Soil Moisture Sensing Using Spaceborne
GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP
Soil Moisture, Geophys. Res. Lett., 45, 4049–4057,
https://doi.org/10.1029/2018GL077905, 2018. a
Cui, C., Xu, J., Zeng, J., Chen, K.-S., Bai, X., Lu, H., Chen, Q., and Zhao,
T.: Soil Moisture Mapping from Satellites: An Intercomparison of
SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network
Regions at Different Spatial Scales, Remote Sensing, 10, 33,
https://doi.org/10.3390/rs10010033, 2017. a
De Lannoy, G. J. M. and Reichle, R. H.: Global Assimilation of Multiangle
and Multipolarization SMOS Brightness Temperature Observations into
the GEOS-5 Catchment Land Surface Model for Soil Moisture
Estimation, J. Hydrometeorol., 17, 669–691,
https://doi.org/10.1175/JHM-D-15-0037.1, 2016. a
Dobslaw, H., Bergmann-Wolf, I., Dill, R., Poropat, L., Thomas, M., Dahle, C.,
Esselborn, S., König, R., and Flechtner, F.: A new high-resolution model of
non-tidal atmosphere and ocean mass variability for de-aliasing of satellite
gravity observations: AOD1B RL06, Geophys. J. Int., 211,
263–269, https://doi.org/10.1093/gji/ggx302, 2017. a
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi,
M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D.,
Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C.,
van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.:
ESA CCI Soil Moisture for improved Earth system understanding:
State-of-the art and future directions, Remote Sens. Environ., 203,
185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017. a, b
Dorigo, W., Dietrich, S., Aires, F., Brocca, L., Carter, S., Cretaux, J.-F.,
Dunkerley, D., Enomoto, H., Forsberg, R., Güntner, A., Hegglin, M. I.,
Hollmann, R., Hurst, D. F., Johannessen, J. A., Kummerow, C., Lee, T.,
Luojus, K., Looser, U., Miralles, D. G., Pellet, V., Recknagel, T., Vargas,
C. R., Schneider, U., Schoeneich, P., Schröder, M., Tapper, N., Vuglinsky,
V., Wagner, W., Yu, L., Zappa, L., Zemp, M., and Aich, V.: Closing the
Water Cycle from Observations across Scales: Where Do We
Stand?, B. Am. Meteorol. Soc., 102, E1897–E1935,
https://doi.org/10.1175/BAMS-D-19-0316.1, 2021a. a
Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021b. a
Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu, R. A. M., and Naeimi, V.: Error characterisation of global active and passive microwave soil moisture datasets, Hydrol. Earth Syst. Sci., 14, 2605–2616, https://doi.org/10.5194/hess-14-2605-2010, 2010. a
Dorigo, W., Preimesberger, W., Moesinger, L., Pasik, A., Scanlon, T., Hahn, S., Van der Schalie, R., Van der Vliet, M., De Jeu, R., Kidd, R., Rodriguez-Fernandez, N., and Hirschi, M.: ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 07.1 data collection, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/ea3eb0714dc6402b905fe9f7ee50dbbc, 2023. a
Eicker, A., Jensen, L., Wöhnke, V., Dobslaw, H., Kvas, A., Mayer-Gürr, T.,
and Dill, R.: Daily GRACE satellite data evaluate short-term
hydro-meteorological fluxes from global atmospheric reanalyses, Sci.
Rep., 10, 4504, https://doi.org/10.1038/s41598-020-61166-0, 2020. a, b, c
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T.,
Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J.,
Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C.,
Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman,
S. W., Tsang, L., and Van Zyl, J.: The Soil Moisture Active Passive
(SMAP) Mission, P. IEEE, 98, 704–716,
https://doi.org/10.1109/JPROC.2010.2043918, 2010. a, b
Entekhabi, D., Yueh, S., O'Neill, P. E., Kellogg, K. H., Allen, A., Bindlish,
R., Brown, M., Chan, S., Colliander, A., Crow, W. T., Das, N., De Lannoy, G., Dunbar, R. S., Edelstein, W. N., Entin, J. K., Escobar, V., Goodman, S. D., Jackson, T. J., Jai, B., Johnson, J., Kim, E., Kim, S., Kimball, J., Koster, R. D., Leon, A., McDonald, K. C., Moghaddam, M., Mohammed, P., Moran, S., Njoku, E. G., Piepmeier, J. R., Reichle, R., Rogez, F., Shi, J., Spencer, M. W., Thurman, S. W., Tsang, L., Van Zyl, J., Weiss, B., and West, R.: SMAP Handbook: Soil Moisture Active Passive:
Mapping Soil Moistureand Freeze/Thaw from Space, Pasadena, USA, https://asf.alaska.edu/wp-content/uploads/2019/03/smap_handbook.pdf (last access: 23 June 2023),
2014. a
Escorihuela, M., Chanzy, A., Wigneron, J., and Kerr, Y.: Effective soil
moisture sampling depth of L-band radiometry: A case study, Remote
Sens. Environ., 114, 995–1001, https://doi.org/10.1016/j.rse.2009.12.011,
2010. a
Gouweleeuw, B. T., Kvas, A., Gruber, C., Gain, A. K., Mayer-Gürr, T., Flechtner, F., and Güntner, A.: Daily GRACE gravity field solutions track major flood events in the Ganges–Brahmaputra Delta, Hydrol. Earth Syst. Sci., 22, 2867–2880, https://doi.org/10.5194/hess-22-2867-2018, 2018. a, b
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019. a, b, c
Güntner, A., Stuck, J., Werth, S., Döll, P., Verzano, K., and Merz, B.: A
global analysis of temporal and spatial variations in continental water
storage, Water Resour.
Res., 43, W05416, https://doi.org/10.1029/2006WR005247, 2007. a
Güntner, A., Reich, M., Mikolaj, M., Creutzfeldt, B., Schroeder, S., and Wziontek, H.: Landscape-scale water balance monitoring with an iGrav superconducting gravimeter in a field enclosure, Hydrol. Earth Syst. Sci., 21, 3167–3182, https://doi.org/10.5194/hess-21-3167-2017, 2017. a
H SAF: Scatterometer Root Zone Soil Moisture (RZSM) Data Record 10km resolution – Multimission, EUMETSAT SAF on Support to Operational Hydrology and Water Management [data set], https://doi.org/10.15770/EUM_SAF_H_0008,
2020. a
Karthikeyan, L., Pan, M., Wanders, N., Kumar, D. N., and Wood, E. F.: Four
decades of microwave satellite soil moisture observations: Part 2.
Product validation and inter-satellite comparisons, Adv. Water
Resour., 109, 236–252, https://doi.org/10.1016/j.advwatres.2017.09.010, 2017. a, b
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin,
J., Escorihuela, M. J., Font, J., Reul, N., Gruhier, C., Juglea, S. E.,
Drinkwater, M. R., Hahne, A., Martín-Neira, M., and Mecklenburg, S.: The
SMOS Mission: New Tool for Monitoring Key Elements of the
Global Water Cycle, P. IEEE, 98, 666–687,
https://doi.org/10.1109/JPROC.2010.2043032, 2010. a, b
Kim, H. and Lakshmi, V.: Use of Cyclone Global Navigation Satellite
System (CyGNSS) Observations for Estimation of Soil Moisture,
Geophys. Res. Lett., 45, 8272–8282, https://doi.org/10.1029/2018GL078923,
2018. a
Kim, S., Dong, J., and Sharma, A.: A Triple Collocation-Based
Comparison of Three L-Band Soil Moisture Datasets, SMAP,
SMOS-IC, and SMOS, Over Varied Climates and Land Covers,
Frontiers in Water, 3, 693172, https://doi.org/10.3389/frwa.2021.693172, 2021. a
Kurtenbach, E., Eicker, A., Mayer-Gürr, T., Holschneider, M., Hayn, M.,
Fuhrmann, M., and Kusche, J.: Improved daily GRACE gravity field solutions
using a Kalman smoother, J. Geodyn., 59–60, 39–48,
https://doi.org/10.1016/j.jog.2012.02.006, 2012. 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, b
Lambeck, K.: Geophysical geodesy: the slow deformations of the earth
Lambeck, Oxford [England], Clarendon Press, New York, Oxford University
Press, ISBN 9780198544371, 1988. a
Landerer, F. W., Flechtner, F. M., Save, H., Webb, F. H., Bandikova, T.,
Bertiger, W. I., Bettadpur, S. V., Byun, S. H., Dahle, C., Dobslaw, H.,
Fahnestock, E., Harvey, N., Kang, Z., Kruizinga, G. L. H., Loomis, B. D.,
McCullough, C., Murböck, M., Nagel, P., Paik, M., Pie, N., Poole, S.,
Strekalov, D., Tamisiea, M. E., Wang, F., Watkins, M. M., Wen, H., Wiese,
D. N., and Yuan, D.: Extending the Global Mass Change Data Record:
GRACE Follow‐On Instrument and Science Data Performance,
Geophys. Res. Lett., 47, e2020GL088306, https://doi.org/10.1029/2020GL088306, 2020. a
Lei, F., Crow, W., Shen, H., Parinussa, R., and Holmes, T.: The Impact of
Local Acquisition Time on the Accuracy of Microwave Surface
Soil Moisture Retrievals over the Contiguous United States,
Remote Sensing, 7, 13448–13465, https://doi.org/10.3390/rs71013448, 2015. a
Longuevergne, L., Wilson, C. R., Scanlon, B. R., and Crétaux, J. F.: GRACE water storage estimates for the Middle East and other regions with significant reservoir and lake storage, Hydrol. Earth Syst. Sci., 17, 4817–4830, https://doi.org/10.5194/hess-17-4817-2013, 2013. a
Mayer-Gürr, T., Behzadpour, S., Ellmer, M., Kvas, A., Klinger, B., Strasser, S., and Zehentner, N.: ITSG-Grace2018 – Monthly, Daily and Static Gravity Field Solutions from GRACE, GFZ Data Services [data set], https://doi.org/10.5880/ICGEM.2018.003, 2018. a
Mayer-Gürr, T., Behzadpour, S., Eicker, A., Ellmer, M., Koch, B., Krauss, S.,
Pock, C., Rieser, D., Strasser, S., Süsser-Rechberger, B., Zehentner, N.,
and Kvas, A.: GROOPS: A software toolkit for gravity field recovery and
GNSS processing, Comput. Geosci., 155, 104864,
https://doi.org/10.1016/j.cageo.2021.104864, 2021. a
Montzka, C., Bogena, H., Zreda, M., Monerris, A., Morrison, R., Muddu, S., and
Vereecken, H.: Validation of Spaceborne and Modelled Surface Soil
Moisture Products with Cosmic-Ray Neutron Probes, Remote Sensing,
9, 103, https://doi.org/10.3390/rs9020103, 2017. a, b
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., and Chaubell,
M. J.: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil
Moisture, Version 8, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/OMHVSRGFX38O, 2021. a, b
Owe, M., de Jeu, R., and Walker, J.: A methodology for surface soil moisture
and vegetation optical depth retrieval using the microwave polarization
difference index, IEEE T. Geosci. Remote S., 39,
1643–1654, https://doi.org/10.1109/36.942542, 2001. a
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of
satellite-derived global land surface moisture, J. Geophys.
Res.-Sol. Ea., 113, 687, https://doi.org/10.1029/2007JF000769, 2008. a
Peltier, W. R., Argus, D. F., and Drummond, R.: Comment on “An Assessment
of the ICE-6G_C (VM5a) Glacial Isostatic Adjustment Model”
by Purcell et al., J. Geophys. Res.-Sol. Ea., 123,
2019–2028, https://doi.org/10.1002/2016JB013844, 2017. a
Purkhauser, A. F., Siemes, C., and Pail, R.: Consistent quantification of the
impact of key mission design parameters on the performance of next-generation
gravity missions, Geophys. J. Int., 221, 1190–1210,
https://doi.org/10.1093/gji/ggaa070, 2020. a
Reichle, R., De Lannoy, G., Koster, R., Crow, W., Kimball, J., and Liu, Q.:
SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone
Soil Moisture Geophysical Data, Version 6, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/08S1A6811J0U, 2021. a, b
Robinson, D. A., Campbell, C. S., Hopmans, J. W., Hornbuckle, B. K., Jones,
S. B., Knight, R., Ogden, F., Selker, J., and Wendroth, O.: Soil Moisture
Measurement for Ecological and Hydrological Watershed-Scale
Observatories: A Review, Vadose Zone J., 7, 358,
https://doi.org/10.2136/vzj2007.0143, 2008. a
Sadeghi, M., Babaeian, E., Tuller, M., and Jones, S. B.: The optical trapezoid
model: A novel approach to remote sensing of soil moisture applied to
Sentinel-2 and Landsat-8 observations, Remote Sens. Environ.,
198, 52–68, https://doi.org/10.1016/j.rse.2017.05.041, 2017. a
Schmeer, M., Schmidt, M., Bosch, W., and Seitz, F.: Separation of mass signals
within GRACE monthly gravity field models by means of empirical orthogonal
functions, J. Geodyn., 59–60, 124–132,
https://doi.org/10.1016/j.jog.2012.03.001, 2012. a
Shiklomanov, I. A.: World fresh water resources, in: Water in Crisis: A
Guide to the Word's Fresh Water Resources, edited by: Gleick, P. H.,
Oxford University Press, New York, USA, ISBN 9780195076288, 1993. a
Sun, Y., Riva, R., and Ditmar, P.: Optimizing estimates of annual variations
and trends in geocenter motion and J2 from a combination of GRACE data
and geophysical models, J. Geophys. Res.-Sol. Ea., 121,
8352–8370, https://doi.org/10.1002/2016JB013073, 2016. a
Swenson, S., Chambers, D., and Wahr, J.: Estimating geocenter variations from a
combination of GRACE and ocean model output, J. Geophys.
Res.-Sol. Ea., 113, B08410, https://doi.org/10.1029/2007JB005338, 2008. a
Tangdamrongsub, N., Han, S.-C., Yeo, I.-Y., Dong, J., Steele-Dunne, S. C.,
Willgoose, G., and Walker, J. P.: Multivariate data assimilation of GRACE,
SMOS, SMAP measurements for improved regional soil moisture and
groundwater storage estimates, Adv. Water Resour., 135, 103477,
https://doi.org/10.1016/j.advwatres.2019.103477, 2020. a
Tapley, B. D., Bettadpur, S., Watkins, M., and Reigber, C.: The gravity
recovery and climate experiment: Mission overview and early results,
Geophys. Res. Lett., 31, L09607, https://doi.org/10.1029/2004GL019920, 2004. a
Tian, S., Renzullo, L. J., van Dijk, A. I. J. M., Tregoning, P., and Walker, J. P.: Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response, Hydrol. Earth Syst. Sci., 23, 1067–1081, https://doi.org/10.5194/hess-23-1067-2019, 2019. a
Wagner, W.: Soil moisture retrieval from ERS scatterometer data, in: Geowissenschaftliche Mitteilungen: Vol. 49, Veröffentlichung des
Institutes für Photogrammetrie und Fernerkundung, Inst. für Photogrammetrie u. Fernerkundung d. Techn. Univ., https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:3-619 (last access: 23 June 2023), 1998. a
Xiong, J., Guo, S., Abhishek, Li, J., and Yin, J.: A Novel Standardized
Drought and Flood Potential Index Based on Reconstructed Daily
GRACE Data, J. Hydrometeorol., 23, 1419–1438,
https://doi.org/10.1175/JHM-D-22-0011.1, 2022. a
Xu, L., Chen, N., Zhang, X., Moradkhani, H., Zhang, C., and Hu, C.: In-situ and
triple-collocation based evaluations of eight global root zone soil moisture
products, Remote Sens. Environ., 254, 112248,
https://doi.org/10.1016/j.rse.2020.112248, 2021a. a
Xu, X. and Frey, S.: Validation of SMOS, SMAP, and ESA CCI Soil
Moisture Over a Humid Region, IEEE J. Sel. Top.
Appl., 14, 10784–10793,
https://doi.org/10.1109/JSTARS.2021.3122068, 2021. a
Xu, Z.-g., Wu, Z.-y., He, H., Guo, X., and Zhang, Y.-l.: Comparison of soil
moisture at different depths for drought monitoring based on improved soil
moisture anomaly percentage index, Water Sci. Eng., 14,
171–183, https://doi.org/10.1016/j.wse.2021.08.008, 2021b.
a
Zhang, N., Quiring, S., Ochsner, T., and Ford, T.: Comparison of Three
Methods for Vertical Extrapolation of Soil Moisture in Oklahoma,
Vadose Zone J., 16, vzj2017.04.0085, https://doi.org/10.2136/vzj2017.04.0085,
2017. a
Short summary
Soil moisture (SM), a key variable of the global water cycle, is analyzed using two types of satellite observations; microwave sensors measure the top few centimeters and satellite gravimetry (GRACE) the full vertical water column. As SM can change very fast, non-standard daily GRACE data are applied for the first time for this analysis. Jointly analyzing these data gives insight into the SM dynamics at different soil depths, and time shifts indicate the infiltration time into deeper layers.
Soil moisture (SM), a key variable of the global water cycle, is analyzed using two types of...
