Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-6935-2022
© 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/gmd-15-6935-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Sep 2022
Model description paper |
| 15 Sep 2022
| 15 Sep 2022
Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation
Stefania Camici
CORRESPONDING AUTHOR
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Gabriele Giuliani
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Luca Brocca
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Christian Massari
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Angelica Tarpanelli
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Hassan Hashemi Farahani
Institute of Geodesy, University of Stuttgart, Geschwister-Scholl-Straße 24D, 70174 Stuttgart, Germany
Nico Sneeuw
Institute of Geodesy, University of Stuttgart, Geschwister-Scholl-Straße 24D, 70174 Stuttgart, Germany
Marco Restano
SERCO c/o ESA-ESRIN, Largo Galileo Galilei, Frascati, 00044, Italy
Jérôme Benveniste
European Space Agency, ESA-ESRIN, Largo Galileo Galilei, Frascati, 00044, Italy
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Jacopo Dari, Luca Brocca, Sara Modanesi, Christian Massari, Angelica Tarpanelli, Silvia Barbetta, Raphael Quast, Mariette Vreugdenhil, Vahid Freeman, Anaïs Barella-Ortiz, Pere Quintana-Seguí, David Bretreger, and Espen Volden
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Sara Modanesi, Christian Massari, Michel Bechtold, Hans Lievens, Angelica Tarpanelli, Luca Brocca, Luca Zappa, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 4685–4706, https://doi.org/10.5194/hess-26-4685-2022, https://doi.org/10.5194/hess-26-4685-2022, 2022
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Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
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This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
Paolo Filippucci, Luca Brocca, Raphael Quast, Luca Ciabatta, Carla Saltalippi, Wolfgang Wagner, and Angelica Tarpanelli
Hydrol. Earth Syst. Sci., 26, 2481–2497, https://doi.org/10.5194/hess-26-2481-2022, https://doi.org/10.5194/hess-26-2481-2022, 2022
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Christian Massari, Francesco Avanzi, Giulia Bruno, Simone Gabellani, Daniele Penna, and Stefania Camici
Hydrol. Earth Syst. Sci., 26, 1527–1543, https://doi.org/10.5194/hess-26-1527-2022, https://doi.org/10.5194/hess-26-1527-2022, 2022
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Droughts are a creeping disaster, meaning that their onset, duration and recovery are challenging to monitor and forecast. Here, we provide further evidence of an additional challenge of droughts, i.e. the fact that the deficit in water supply during droughts is generally much more than expected based on the observed decline in precipitation. At a European scale we explain this with enhanced evapotranspiration, sustained by higher atmospheric demand for moisture during such dry periods.
Martin Horwath, Benjamin D. Gutknecht, Anny Cazenave, Hindumathi Kulaiappan Palanisamy, Florence Marti, Ben Marzeion, Frank Paul, Raymond Le Bris, Anna E. Hogg, Inès Otosaka, Andrew Shepherd, Petra Döll, Denise Cáceres, Hannes Müller Schmied, Johnny A. Johannessen, Jan Even Øie Nilsen, Roshin P. Raj, René Forsberg, Louise Sandberg Sørensen, Valentina R. Barletta, Sebastian B. Simonsen, Per Knudsen, Ole Baltazar Andersen, Heidi Ranndal, Stine K. Rose, Christopher J. Merchant, Claire R. Macintosh, Karina von Schuckmann, Kristin Novotny, Andreas Groh, Marco Restano, and Jérôme Benveniste
Earth Syst. Sci. Data, 14, 411–447, https://doi.org/10.5194/essd-14-411-2022, https://doi.org/10.5194/essd-14-411-2022, 2022
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Global mean sea-level change observed from 1993 to 2016 (mean rate of 3.05 mm yr−1) matches the combined effect of changes in water density (thermal expansion) and ocean mass. Ocean-mass change has been assessed through the contributions from glaciers, ice sheets, and land water storage or directly from satellite data since 2003. Our budget assessments of linear trends and monthly anomalies utilise new datasets and uncertainty characterisations developed within ESA's Climate Change Initiative.
Florence Marti, Alejandro Blazquez, Benoit Meyssignac, Michaël Ablain, Anne Barnoud, Robin Fraudeau, Rémi Jugier, Jonathan Chenal, Gilles Larnicol, Julia Pfeffer, Marco Restano, and Jérôme Benveniste
Earth Syst. Sci. Data, 14, 229–249, https://doi.org/10.5194/essd-14-229-2022, https://doi.org/10.5194/essd-14-229-2022, 2022
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The Earth energy imbalance at the top of the atmosphere due to the increase in greenhouse gases and aerosol concentrations is responsible for the accumulation of energy in the climate system. With its high thermal inertia, the ocean accumulates most of this energy excess in the form of heat. The estimation of the global ocean heat content through space geodetic observations allows monitoring of the energy imbalance with realistic uncertainties to better understand the Earth’s warming climate.
Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, https://doi.org/10.5194/hess-25-6283-2021, 2021
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Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Land surface models are not able to correctly simulate irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by irrigation. We equipped a land surface model with an observation operator able to transform Sentinel-1 backscatter observations into realistic vegetation and soil states via data assimilation.
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The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
Daniele Masseroni, Stefania Camici, Alessio Cislaghi, Giorgio Vacchiano, Christian Massari, and Luca Brocca
Hydrol. Earth Syst. Sci., 25, 5589–5601, https://doi.org/10.5194/hess-25-5589-2021, https://doi.org/10.5194/hess-25-5589-2021, 2021
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We evaluate 63 years of changes in annual streamflow volume across Europe, using a data set of more than 3000 stations, with a special focus on the Mediterranean basin. The results show decreasing (increasing) volumes in the southern (northern) regions. These trends are strongly consistent with the changes in temperature and precipitation.
Denise Dettmering, Felix L. Müller, Julius Oelsmann, Marcello Passaro, Christian Schwatke, Marco Restano, Jérôme Benveniste, and Florian Seitz
Earth Syst. Sci. Data, 13, 3733–3753, https://doi.org/10.5194/essd-13-3733-2021, https://doi.org/10.5194/essd-13-3733-2021, 2021
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In this study, a new gridded altimetry-based regional sea level dataset for the North Sea is presented, named North SEAL. It is based on long-term multi-mission cross-calibrated altimetry data consistently preprocessed with coastal dedicated algorithms. On a 6–8 km wide triangular mesh, North SEAL provides time series of monthly sea level anomalies as well as sea level trends and amplitudes of the mean annual sea level cycle for the period 1995–2019 for various applications.
Maria Teresa Brunetti, Massimo Melillo, Stefano Luigi Gariano, Luca Ciabatta, Luca Brocca, Giriraj Amarnath, and Silvia Peruccacci
Hydrol. Earth Syst. Sci., 25, 3267–3279, https://doi.org/10.5194/hess-25-3267-2021, https://doi.org/10.5194/hess-25-3267-2021, 2021
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Satellite and rain gauge data are tested to predict landslides in India, where the annual toll of human lives and loss of property urgently demands the implementation of strategies to prevent geo-hydrological instability. For this purpose, we calculated empirical rainfall thresholds for landslide initiation. The validation of thresholds showed that satellite-based rainfall data perform better than ground-based data, and the best performance is obtained with an hourly temporal resolution.
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Short summary
This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And...
