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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Muhammad Usman Liaqat, Stefania Camici, Francesco Leopardi, Jaime Gaona, and Luca Brocca
EGUsphere, https://doi.org/10.5194/egusphere-2025-3659, https://doi.org/10.5194/egusphere-2025-3659, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Tracking land water storage helps examine extreme events and manage water resources. GRACE missions observe changes, but coarse resolution in space and time challenges effective management. New gravity mission called MAGIC can offers better accuracy. This study tested MAGIC’s potential to estimate precipitation using SM2RAIN approach. Results show SM2RAIN works well with frequent, accurate data, but deteriorates with noisy/sparse data, emphasising need for precise gravity missions.
Domenico De Santis, Silvia Barbetta, Sumit Sen, Viviana Maggioni, Farhad Bahmanpouri, Ashutosh Sharma, Ankit Agarwal, Sagar Gupta, Francesco Avanzi, and Christian Massari
EGUsphere, https://doi.org/10.5194/egusphere-2025-4933, https://doi.org/10.5194/egusphere-2025-4933, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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A conceptual, semi-distributed hydrological model was tailored to simulate high flows in monsoon-dominated, glacier-influenced and flood-prone Himalayan basins. Multi-data calibration using satellite-based glacier mass loss and evapotranspiration estimates improved process realism in data-scarce environments. The proposed modelling approach captured key streamflow features despite significant input uncertainties, proving to be a useful tool for exploring the local hydrological response dynamics.
Paolo Filippucci, Luca Brocca, Luca Ciabatta, Hamidreza Mosaffa, Francesco Avanzi, and Christian Massari
Earth Syst. Sci. Data, 17, 5221–5258, https://doi.org/10.5194/essd-17-5221-2025, https://doi.org/10.5194/essd-17-5221-2025, 2025
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Accurate rainfall data is essential, yet measuring daily precipitation worldwide is challenging. This research presents HYdroclimatic PERformance-enhanced Precipitation (HYPER-P), a dataset combining satellite, ground, and reanalysis data to estimate precipitation at a 1 km scale from 2000 to 2022. HYPER-P improves accuracy, especially in areas with few rain gauges. This dataset supports scientists and decision-makers in understanding and managing water resources more effectively.
Sofia Ortenzi, Lucio Di Matteo, Daniela Valigi, Marco Donnini, Marco Dionigi, Davide Fronzi, Josie Geris, Fabio Guadagnano, Ivan Marchesini, Paolo Filippucci, Francesco Avanzi, Daniele Penna, and Christian Massari
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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This study investigates groundwater–surface water interactions in the Ussita catchment (Italy), using hydrological, hydrochemical–isotopic, thermal drone, and satellite data. Results show that fractured limestone aquifers sustain streamflow, with snowmelt contributing ~20% to recharge. The integrated approach supports water management, climate adaptation, and ecosystem sustainability in Mediterranean mountain regions.
Xiangmei Liu, Peng Shen, Jiaqi Chen, David Andrew Barry, Christian Massari, Jiansheng Chen, Mingming Feng, Xi Zhang, Fenyan Ma, Fei Yang, and Haixia Jin
EGUsphere, https://doi.org/10.5194/egusphere-2025-4263, https://doi.org/10.5194/egusphere-2025-4263, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Our research in the Songnen Basin of Northeast China has revealed why numerous lakes remain unfrozen during winter. By studying Lake Chagan, we discovered that deep groundwater sustains these lakes, likely flowing through subterranean channels from the Tibetan Plateau. When earthquakes disrupt these conduits, water rises along fault lines to replenish the lakes. This finding challenges the theory that graben lakes primarily depend on local precipitation.
Jaime Gaona, Davide Bavera, Guido Fioravanti, Sebastian Hahn, Pietro Stradiotti, Paolo Filippucci, Stefania Camici, Luca Ciabatta, Hamidreza Mosaffa, Silvia Puca, Nicoletta Roberto, and Luca Brocca
Hydrol. Earth Syst. Sci., 29, 3865–3888, https://doi.org/10.5194/hess-29-3865-2025, https://doi.org/10.5194/hess-29-3865-2025, 2025
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Soil moisture is crucial for the water cycle since it is at the front line of drought. Satellite, model and in situ data help identify soil moisture stress but are challenged by data uncertainties. This study evaluates trends and data coherence of common active/passive microwave sensors and model-based soil moisture data against in situ stations across Europe from 2007 to 2022. Data reliability is increasing, but combining data types improves soil moisture monitoring capabilities.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabriëlle J. M. De Lannoy
EGUsphere, https://doi.org/10.5194/egusphere-2025-2550, https://doi.org/10.5194/egusphere-2025-2550, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, Jong Cheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Tan Jackson, and Hylke E. Beck
EGUsphere, https://doi.org/10.5194/egusphere-2024-4194, https://doi.org/10.5194/egusphere-2024-4194, 2025
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Our study evaluated 23 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG and JRA-3Q, demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.
Ling Zhang, Yanhua Xie, Xiufang Zhu, Qimin Ma, and Luca Brocca
Earth Syst. Sci. Data, 16, 5207–5226, https://doi.org/10.5194/essd-16-5207-2024, https://doi.org/10.5194/essd-16-5207-2024, 2024
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This study presented new annual maps of irrigated cropland in China from 2000 to 2020 (CIrrMap250). These maps were developed by integrating remote sensing data, irrigation statistics and surveys, and an irrigation suitability map. CIrrMap250 achieved high accuracy and outperformed currently available products. The new irrigation maps revealed a clear expansion of China’s irrigation area, with the majority (61%) occurring in the water-unsustainable regions facing severe to extreme water stress.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabrielle J. M. De Lannoy
EGUsphere, https://doi.org/10.2139/ssrn.4974019, https://doi.org/10.2139/ssrn.4974019, 2024
Preprint archived
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Jacopo Dari, Paolo Filippucci, and Luca Brocca
Hydrol. Earth Syst. Sci., 28, 2651–2659, https://doi.org/10.5194/hess-28-2651-2024, https://doi.org/10.5194/hess-28-2651-2024, 2024
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We have developed the first operational system (10 d latency) for estimating irrigation water use from accessible satellite and reanalysis data. As a proof of concept, the method has been implemented over an irrigated area fed by the Kakhovka Reservoir, in Ukraine, which collapsed on June 6, 2023. Estimates for the period 2015–2023 reveal that, as expected, the irrigation season of 2023 was characterized by the lowest amounts of irrigation.
Jérôme Benveniste, Salvatore Dinardo, Luciana Fenoglio-Marc, Christopher Buchhaupt, Michele Scagliola, Marcello Passaro, Karina Nielsen, Marco Restano, Américo Ambrózio, Giovanni Sabatino, Carla Orrù, and Beniamino Abis
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Søren Julsgaard Kragh, Jacopo Dari, Sara Modanesi, Christian Massari, Luca Brocca, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci., 28, 441–457, https://doi.org/10.5194/hess-28-441-2024, https://doi.org/10.5194/hess-28-441-2024, 2024
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This study provides a comparison of methodologies to quantify irrigation to enhance regional irrigation estimates. To evaluate the methodologies, we compared various approaches to quantify irrigation using soil moisture, evapotranspiration, or both within a novel baseline framework, together with irrigation estimates from other studies. We show that the synergy from using two equally important components in a joint approach within a baseline framework yields better irrigation estimates.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Y. Lin, C. Gao, X. Li, T. Zhang, J. Yu, Y. Rong, L. Li, X. Zhou, J. Cai, and N. Sneeuw
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 443–450, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-443-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-443-2023, 2023
Victor Rousseau, Robin Fraudeau, Matthew Hammond, Odilon Joël Houndegnonto, Michaël Ablain, Alejandro Blazquez, Fransisco Mir Calafat, Damien Desbruyères, Giuseppe Foti, William Llovel, Florence Marti, Benoît Meyssignac, Marco Restano, and Jérôme Benveniste
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-236, https://doi.org/10.5194/essd-2023-236, 2023
Preprint withdrawn
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The estimation of regional Ocean Heat Content (OHC) is crucial for climate analysis and future climate predictions. In our study, we accurately estimate regional OHC changes in the Atlantic Ocean using satellite and in situ data. Findings reveal significant warming in the Atlantic basin from 2002 to 2020 with a mean trend of 0.17W/m², representing 230 times the power of global nuclear plants. The product has also been successfully validated in the North Atlantic basin using in situ data.
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
Earth Syst. Sci. Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, https://doi.org/10.5194/essd-15-1555-2023, 2023
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Irrigation is the main source of global freshwater consumption. Despite this, a detailed knowledge of irrigation dynamics (i.e., timing, extent of irrigated areas, and amounts of water used) are generally lacking worldwide. Satellites represent a useful tool to fill this knowledge gap and monitor irrigation water from space. In this study, three regional-scale and high-resolution (1 and 6 km) products of irrigation amounts estimated by inverting the satellite soil moisture signals are presented.
Kunlong He, Wei Zhao, Luca Brocca, and Pere Quintana-Seguí
Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023, https://doi.org/10.5194/hess-27-169-2023, 2023
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In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for spatially downscaling the GPM daily precipitation product by exploiting the connection between surface soil moisture and precipitation according to the soil water balance equation. Based on this physical method, the spatial resolution of the daily precipitation product was downscaled to 1 km and the SMPD method shows good potential for the development of the high-resolution precipitation product.
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Niccolò Tubini, Concetta D'Amato, Olaf David, and Christian Massari
Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
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The
Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.
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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Given the crucial impact of irrigation practices on the water cycle, this study aims at estimating irrigation through the development of an innovative data assimilation system able to ingest high-resolution Sentinel-1 radar observations into the Noah-MP land surface model. The developed methodology has important implications for global water resource management and the comprehension of human impacts on the water cycle and identifies main challenges and outlooks for future research.
Angelica Tarpanelli, Alessandro C. Mondini, and Stefania Camici
Nat. Hazards Earth Syst. Sci., 22, 2473–2489, https://doi.org/10.5194/nhess-22-2473-2022, https://doi.org/10.5194/nhess-22-2473-2022, 2022
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We analysed 10 years of river discharge data from almost 2000 sites in Europe, and we extracted flood events, as proxies of flood inundations, based on the overpasses of Sentinel-1 and Sentinel-2 satellites to derive the percentage of potential inundation events that they were able to observe. Results show that on average 58 % of flood events are potentially observable by Sentinel-1 and only 28 % by Sentinel-2 due to the obstacle of cloud coverage.
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
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
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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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A high-resolution (1 km) rainfall product with 10–30 d temporal resolution was obtained starting from SM data from Sentinel-1. Good performances are achieved using observed data (gauge and radar) over the Po River Valley, Italy, as a benchmark. The comparison with a product characterized by lower spatial resolution (25 km) highlights areas where the high spatial resolution of Sentinel-1 has great benefits. Possible applications include water management, agriculture and index-based insurances.
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.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
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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.
Louise Mimeau, Yves Tramblay, Luca Brocca, Christian Massari, Stefania Camici, and Pascal Finaud-Guyot
Hydrol. Earth Syst. Sci., 25, 653–669, https://doi.org/10.5194/hess-25-653-2021, https://doi.org/10.5194/hess-25-653-2021, 2021
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Soil moisture is a key variable related to droughts and flood genesis, but little is known about the evolution of soil moisture under climate change. Here, using a simulation approach, we show that changes in soil moisture are driven by changes in precipitation intermittence rather than changes in precipitation intensity or in temperature.
Cited articles
Albergel, C., Rüdiger, C., Carrer, D., Calvet, J.-C., Fritz, N., Naeimi, V., Bartalis, Z., and Hasenauer, S.: An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France, Hydrol. Earth Syst. Sci., 13, 115–124, https://doi.org/10.5194/hess-13-115-2009, 2009.
Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T., and Siebert, S.: Development and testing of the WaterGAP 2 global model of water use and availability, Hydrolog. Sci. J., 48, 317–337, https://doi.org/10.1623/hysj.48.3.317.45290, 2003.
Alexander, J. S., Wilson, R. C., and Green, W. R.: A brief history and summary of the effects of river engineering and dams on the Mississippi River system and delta, US Department of the Interior, US Geological Survey, 53, https://doi.org/10.3133/cir1375, 2012.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration – guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper 56, FAO, Rome, 300, D05109, ISBN 92-5-104219-5, 1988.
Arabzadeh, A. and Behrangi, A.: Investigating Various Products of IMERG for Precipitation Retrieval Over Surfaces With and Without Snow and Ice Cover, Remote Sens.-Basel, 13, 2726, https://doi.org/10.3390/rs13142726, 2021.
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., vanden Hurk, B., Hirschi, M., and Betts, A. K.: A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the integrated forecast system, J. Hydrometeorol., 10, 623–643, https://doi.org/10.1175/2008JHM1068.1, 2009.
Barbarossa, V., Huijbregts, M. A., Beusen, A. H., Beck, H. E., King, H., and Schipper, A. M.: FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015, Sci. Data, 55, 180052, https://doi.org/10.1038/sdata.2018.52, 2018.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R., and Schellekens, J.: Global evaluation of runoff from 10 state-of-the-art hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903, https://doi.org/10.5194/hess-21-2881-2017, 2017.
Berghuijs, W. R., Woods, R. A., Hutton, C. J., and Sivapalan, M.: Dominant flood generating mechanisms across the United States, Geophys. Res. Lett., 43, 4382–4390, https://doi.org/10.1002/2016GL068070, 2016.
Bergström, S.: The HBV model, in: Computer models of watershed hydrology. Water Resources Publications, edited by: Singh, V. P., Highlands Ranch, CO, 443–476, ISBN 978-1-887201-74-2, 1995.
Berthet, L., Andréassian, V., Perrin, C., and Javelle, P.: How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments, Hydrol. Earth Syst. Sci., 13, 819–831, https://doi.org/10.5194/hess-13-819-2009, 2009.
Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H. H. G. (Eds.): Runoff predictions in ungauged basins: A synthesis across processes, places and scales, Cambridge University Press, Cambridge, ISBN 9781107028180, 2013.
Bober, W.: Introduction to Numerical and Analytical Methods with MATLAB for Engineers and Scientists, CRC Press, Inc., Boca Raton, FL, USA, https://doi.org/10.1201/b16030, 2013.
Botter, G., Peratoner, F., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A.: Signatures of large-scale soil moisture dynamics on streamflow statistics across U. S. Climate regimes, Water Resour. Res., 43, W11413, https://doi.org/10.1029/2007WR006162, 2007a.
Botter, G., Porporato, A., Daly, E., Rodriguez-Iturbe, I., and Rinaldo, A.: Probabilistic characterization of base flows in river basins: Roles of soil, vegetation, and geomorphology, Water Resour. Res., 43, W06404, https://doi.org/10.1029/2006WR005397, 2007b.
Brocca, L., Melone, F., and Moramarco, T.: On the estimation of antecedent wetness conditions in rainfall-runoff modelling, Hydrol. Process., 22, 629–642, https://doi.org/10.1002/hyp.6629, 2008.
Brocca, L., Melone, F., Moramarco, T., and Morbidelli, R.: Antecedent wetness conditions based on ERS scatterometer data, J. Hydrol., 364, 73–87, https://doi.org/10.1016/j.jhydrol.2008.10.007, 2009.
Brocca, L., Melone, F., and Moramarco, T.: Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting, Hydrol. Process., 25, 2801–2813, https://doi.org/10.1002/hyp.8042, 2011.
Brocca, L., Ciabatta, L., Massari, C., Camici, S., and Tarpanelli, A.: Soil moisture for hydrological applications: open questions and new opportunities, Water, 9, 140, https://doi.org/10.3390/w9020140, 2017.
Cai, X., Yang, Z. L., David, C. H., Niu, G. Y., and Rodell, M.: Hydrological evaluation of the Noah-MP land surface model for the Mississippi River Basin, J. Geophys. Res.-Atmos., 119, 23–38, https://doi.org/10.1002/2013JD020792, 2014.
Camici, S.: STREAM (SaTellite based Runoff Evaluation And Mapping) code (1.3), Zenodo [code], https://doi.org/10.5281/zenodo.4744984, 2021.
Cislaghi, A., Masseroni, D., Massari, C., Camici, S., and Brocca, L.: Combining a rainfall–runoff model and a regionalization approach for flood and water resource assessment in the western Po Valley, Italy, Hydrolog. Sci. J., 65, 348–370, https://doi.org/10.1080/02626667.2019.1690656, 2020.
Corradini, C., Morbidelli, R., Saltalippi, C., and Melone, F.: An adaptive model for flood forecasting on medium size basins, in: Applied Simulation and Modelling, edited by: Ubertini, L., IASTED Acta Press, Anaheim, CA, 555–559, ISBN 0-88986334-2, 2002.
Crochemore, L., Isberg, K., Pimentel, R., Pineda, L., Hasan, A., and Arheimer, B.: Lessons learnt from checking the quality of openly accessible river flow data worldwide, Hydrolog. Sci. J., 65, 699–711, https://doi.org/10.1080/02626667.2019.1659509, 2020.
Crow, W. T., Bindlish, R., and Jackson, T. J.: The added value of spaceborne passive microwave soil moisture retrievals for forecasting rainfall-runoff partitioning, Geophys. Res. Lett., 32, L18401, https://doi.org/10.1029/2005GL023543, 2005.
Döll, P., Kaspar, F., and Lehner, B.: A global hydrological model for deriving water availability indicators: Model tuning and validation, J. Hydrol., 270, 105–134, https://doi.org/10.1016/S0022-1694(02)00283-4, 2003.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, D., Hamer, P., 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.
Dyer, J.: Snow depth and streamflow relationships in large North American watersheds, J. Geophys. Res., 113, D18113, https://doi.org/10.1029/2008JD010031, 2008.
Eaton, J. W., Bateman, D., Hauberg, S., and Wehbring, R.: GNU Octave version 7.2.0 manual: a high-level interactive language for numerical computations, https://docs.octave.org/octave.pdf (last access: 26 August 2022), 2020.
Entekhabi, D., Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J., Goodman, S., Jackson, T., Johnson, J. T., Kimball, J., Piepmeier, J., Koster, R., Martin, N., McDonald, K., Moghaddam, M., Moran, M. S., Reichle, R., Shi, J., Spencer, M., Thurman, S., 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.
ESA CCI SM: Soil moisture data, http://www.esa-soilmoisture-cci.org/, last access: 26 August 2022.
Famiglietti, J. S. and Rodell, M.: Water in the balance, Science, 340, 1300–1301, https://doi.org/10.1126/science.1236460, 2013.
Famiglietti, J. S. and Wood, E. F.: Multiscale modeling of spatially variable water and energy balance processes, Water Resour. Res., 30, 3061–3078, https://doi.org/10.1029/94WR01498, 1994.
Fan, Y. and Van den Dool, H. A.: Global monthly land surface air temperature analysis for 1948–present, J. Geophys. Res.-Atmos., 113, D01103, https://doi.org/10.1029/2007JD008470, 2008.
Fekete, B. M., Looser, U., Pietroniro, A., and Robarts, R. D.: Rationale for monitoring discharge on the ground, J. Hydrometeorol., 13, 1977–1986, https://doi.org/10.1175/JHM-D-11-0126.1, 2012.
Feldman, A. D.: Hydrologic modeling system HEC-HMS, Technical reference manual, US Army Corps of Engineers, Hydrologic Engineering Center, 2000.
Georgakakos, K. P. and Baumer, O. W.: Measurement and utilization of onsite soil moisture data, J. Hydrol., 184, 131–152, https://doi.org/10.1016/0022-1694(95)02971-0, 1996.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019a.
Ghiggi, G., Seneviratne, S. I., Humphrey, V., and Gudmundsson, L.: GRUN: Global Runoff Reconstruction (GRUN_v1), https://doi.org/10.3929/ethz-b-000324386, 2019b.
Ghotbi, S., Wang, D., Singh, A., Blöschl, G., and Sivapalan, M.: A New Framework for Exploring Process Controls of Flow Duration Curves, Water Resour. Res., 56, e2019WR026083, https://doi.org/10.1029/2019WR026083, 2020.
Global Runoff Data Centre (GRDC): In situ river discharge data, https://www.bafg.de/GRDC/EN/Home/homepage_node.html, last access: 26 August 2022.
Gochis, D. J., Barlage, M., Dugger, A., FitzGerald, K., Karsten, L., McAllister, M., McCreight, J., Mills, J., RafieeiNasab, A., Read, L., Sampson, K., Yates, D., and Yu, W.: The WRF-Hydro modeling system technical description (Version 5.0), NCAR Technical Note, https://ral.ucar.edu/sites/default/files/public/WRF-HydroV5TechnicalDescription.pdf (last access: 26 August 2022), 2018.
Gudmundsson, L. and Seneviratne, S. I.: Observation-based gridded runoff estimates for Europe (E-RUN version 1.1), Earth Syst. Sci. Data, 8, 279–295, https://doi.org/10.5194/essd-8-279-2016, 2016.
Gudmundsson, L., Tallaksen, L. M., Stahl, K., Clark, D. B., Dumont, E., Hagemann, S., Bertrand, N., Gerten, D., Heinke, J., Hanasaki, N., Voss, F., and Koirala, S.: Comparing Large-Scale Hydrological Model Simulations to Observed Runoff Percentiles in Europe, J. Hydrometeorol., 13, 604–662, https://doi.org/10.1175/JHM-D-11-083.1, 2012a.
Gudmundsson, L., Wagener, T., Tallaksen, L. M., and Engeland, K.: Evaluation of nine large-scale hydrological models with respect to the seasonal runoff climatology in Europe, Water Resour. Res., 48, W11504, https://doi.org/10.1029/2011WR010911, 2012b.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Gupta, V. K., Waymire, E., and Wang, C. T.: A representation of an instantaneous unit hydrograph from geomorphology, Water Resour. Res., 16, 855–862, https://doi.org/10.1029/WR016i005p00855, 1980.
Haddeland, I., Heinke, J., Voß, F., Eisner, S., Chen, C., Hagemann, S., and Ludwig, F.: Effects of climate model radiation, humidity and wind estimates on hydrological simulations, Hydrol. Earth Syst. Sci., 16, 305–318, https://doi.org/10.5194/hess-16-305-2012, 2012.
Hanasaki, N., Kanae, S., Oki, T., Masuda, K., Motoya, K., Shirakawa, N., Shen, Y., and Tanaka, K.: An integrated model for the assessment of global water resources – Part 1: Model description and input meteorological forcing, Hydrol. Earth Syst. Sci., 12, 1007–1025, https://doi.org/10.5194/hess-12-1007-2008, 2008.
Hastie, T., Tibshirani, R., and Friedman, J. H.: The Elements of Statistical Learning – Data Mining, Inference, and Prediction, 2nd edn., Springer Series in Statistics, Springer, New York, http://www-stat.stanford.edu/~tibs/ElemStatLearn/ (last access: 5 July 2016), 2009.
Hong, Y., Adler, R. F., Hossain, F., Curtis, S., and Huffman, G. J.: A first approach to global runoff simulation using satellite rainfall estimation, Water Resour. Res., 43, W08502, https://doi.org/10.1029/2006WR005739, 2007.
Horton, R. E.: Hydrological approach to quantitative morphology, Geol. Soc. Am. Bull., 56, 275–370, 1945.
Houborg, R., Rodell, M., Li, B., Reichle, R., and Zaitchik, B. F.: Drought indicators based on model-assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations, Water Resour. Res., 48, W07525, https://doi.org/10.1029/2011WR011291, 2012.
Hu, G. R. and Li, X. Y.: Subsurface Flow, in: Observation and Measurement. Ecohydrology, edited by: Li, X. and Vereecken, H., Springer, Berlin, Heidelberg, 2018.
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G. J., Nelkin, E. J., Bowman, K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/jhm560.1, 2007.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Adler, R. F.: TRMM Version 7 3B42 and 3B43 Data Sets, NASA/GSFC, Greenbelt, MD, https://gpm.nasa.gov/sites/default/files/document_files/3B42_3B43_doc_V7.pdf (last access: 26 August 2022), 2014.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_ATBD_V06.pdf (last access: 26 August 2022), 2019.
Jäggi, A., Weigelt, M., Flechtner, F., Güntner, A., Mayer-Gürr, T., Martinis, S., Bruinsma, S., Flury, J., Bourgogne, S., Steffen, H., Meyer, U., Jean, Y., Sušnik, A., Grahsl, A., Arnold, D., Cann-Guthauser, K., Dach, R., Li, Z., Chen, Q., van Dam, T., Gruber, C., Poropat, L., Gouweleeuw, B., Kvas, A., Klinger, B., Lemoine, J-M., Biancale, R., Zwenzner, H., Bandikova, T., and Shabanloui, A.: European gravity service for improved emergency management (EGSIEM) – from concept to implementation, Geophys. J. Int., 218, 1572–1590, https://doi.org/10.1093/gji/ggz238, 2019.
Kauffeldt, A., Wetterhall, F., Pappenberger, F., Salamon, P., and Thielen, J.: Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level, Environ. Modell. Softw., 75, 68–76, https://doi.org/10.1016/j.envsoft.2015.09.009, 2016.
Kim, H., Watanabe, S., Chang, E. C., Yoshimura, K., Hirabayashi, J., Famiglietti, J., and Oki, T.: Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1), Data Integration and Analysis System (DIAS) [data set], https://doi.org/10.20783/DIAS.501, 2017.
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.
Klees, R., Revtova, E. A., Gunter, B. C., Ditmar, P., Oudman, E., Winsemius, H. C., and Savenije, H. H. G.: The design of an optimal filter for monthly GRACE gravity models, Geophys. J. Int., 175, 417–432, https://doi.org/10.1111/j.1365-246X.2008.03922.x, 2008.
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, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
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.
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.-Y., Wiese, D. N., and Yuan, D.-N.: 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.
Lehner, B., Reidy Liermann, C., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rodel, R., Sindorf, N., and Wisser, D.: High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011.
Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., and Arheimer, B.: Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales, Hydrol. Res., 41, 295–319, https://doi.org/10.2166/nh.2010.007, 2010.
Long, D., Longuevergne, L., and Scanlon, B. R.: Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites, Water Resour. Res., 50, 1131–1151, https://doi.org/10.1002/2013WR014581, 2014.
Lorenz, C., Kunstmann, H., Devaraju, B., Tourian, M. J., Sneeuw, N., and Riegger, J.: 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.
Luthcke, S. B., Sabaka, T. J., Loomis, B. D., Arendt, A. A., McCarthy, J. J., and Camp, J.: Antarctica, Greenland and Gulf of Alaska land-ice evolution from an iterated GRACE global mascon solution, J. Glaciol., 59, 613–631, https://doi.org/10.3189/2013JoG12J147, 2013.
Markstrom, S. L., Regan, R. S., Hay, L. E., Viger, R. J., Webb, R. M. T., Payn, R. A., and LaFontaine, J. H.: PRMS-IV, the precipitation-runoff modeling system, version 4, Techniques and Methods 6-B7, U. S. Geological Survey, 158 pp., https://doi.org/10.3133/tm6B7, 2015.
Massari, C., Brocca, L., Barbetta, S., Papathanasiou, C., Mimikou, M., and Moramarco, T.: Using globally available soil moisture indicators for flood modelling in Mediterranean catchments, Hydrol. Earth Syst. Sci., 18, 839–853, https://doi.org/10.5194/hess-18-839-2014, 2014.
Massari, C., Brocca, L., Tarpanelli, A., Hong, Y., Crow, W., Ciabatta, L., Camici, S., Barbetta, S., and Moramarco, T.: Global surface runoff estimation in near real time by using SMAP and GPM, poster at SMAP conference, 3rd Satellite Soil Moisture Validation and Application Workshop, New York, 210-22 September 2016, https://doi.org/10.13140/RG.2.2.34725.09448/2, 2016.
Massotti, L., Siemes, C., March, G., Haagmans, R., and Silvestrin, P.: Next generation gravity mission elements of the mass change and geoscience international constellation: From orbit selection to instrument and mission design, Remote Sens.-Basel, 13, 3935, https://doi.org/10.3390/rs13193935, 2021.
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015.
Merz, R. and Blöschl, G.: Regionalisation of catchment model parameters, J. Hydrol., 287, 95–123, https://doi.org/10.1016/j.jhydrol.2003.09.028, 2004.
Merz, R. and Blöschl, G.: A regional analysis of event runoff coefficients with respect to climate and catchment characteristics in Austria, Water Resour. Res., 45, W01405, https://doi.org/10.1029/2008WR007163, 2009.
Müller Schmied, H., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., Oki, T., Portmann, F. T., Reinecke, R., Riedel, C., Song, Q., Zhang, J., and Döll, P.: Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use, Hydrol. Earth Syst. Sci., 20, 2877–2898, https://doi.org/10.5194/hess-20-2877-2016, 2016.
Muneepeerakul, R., Azaele, S., Botter, G., Rinaldo, A., and Rodriguez-Iturbe, I.: Daily streamflow analysis based on a two-scaled gamma pulse model, Water Resour. Res., 46, W11546, https://doi.org/10.1029/2010WR009286, 2010.
Nash, J. E.: The form of the instantaneous unit hydrograph, IASH publication, 45, 114–121, 1957.
Natural Resources Conservation Service (NRCS): Urban hydrology for small watersheds, Tech. Release 55, 2nd Edn., U. S. Dep. of Agric., Washington, D. C., https://nationalstormwater.com/wp/wp-content/uploads/2020/07/Urban-Hydrology-for-Small-Watersheds-TR-55.pdf (last access: 26 August 2022), 1986.
NOAA PSL: Air temperature data, https://psl.noaa.gov/data/gridded/data.cpc.globaltemp.html, last access: 26 August 2022.
Noacco, V., Sarrazin, F., Pianosi, F., and Wagener, T.: Matlab/R workflows to assess critical choices in Global Sensitivity Analysis using the SAFE toolbox, MethodsX, 6, 2258–2280, https://doi.org/10.1016/j.mex.2019.09.033, 2019.
Oleson, K., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S., Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, J. F., Lawrence, P. J., Leung, L. R., Lipscomb,W., Muszala, S. P., Ricciuto, D. M., Sacks, W. J., Sun, Y., Tang, J., and Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model (CLM), no. NCAR/TN-503+STR, https://doi.org/10.5065/D6RR1W7M, 2013.
O'Neill, M. M. F., Tijerina, D. T., Condon, L. E., and Maxwell, R. M.: Assessment of the ParFlow–CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States, Geosci. Model Dev., 14, 7223–7254, https://doi.org/10.5194/gmd-14-7223-2021, 2021.
Orth, R. and Seneviratne, S. I.: Introduction of a simple-model-based land surface dataset for Europe, Environ. Res. Lett., 10, 044012, https://doi.org/10.1088/1748-9326/10/4/044012, 2015.
Pellet, V., Aires, F., Munier, S., Fernández Prieto, D., Jordá, G., Dorigo, W. A., Polcher, J., and Brocca, L.: Integrating multiple satellite observations into a coherent dataset to monitor the full water cycle – application to the Mediterranean region, Hydrol. Earth Syst. Sci., 23, 465–491, https://doi.org/10.5194/hess-23-465-2019, 2019.
Pianosi, F., Sarrazin, F., and Wagener, T.: A Matlab toolbox for Global Sensitivity Analysis, Environ. Modell. Softw., 70, 80–85, https://doi.org/10.1016/j.envsoft.2015.04.009, 2015.
Prudhomme, C., Giuntoli, I., Robinson, E. L., Clark, D. B., Arnell, N. W., Dankers, R., Fekete, B. M., Franssen, W., Dieter Gerten, Gosling, S. N., Hagemann, S., Hannah, D. M., Kim, H., Masaki, Y., Satoh, Y., Stacke, T., Wada, Y., and Wisser, D.: Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment, P. Natl. Acad. Sci. USA, 111, 3262–3267, 2014.
Rakovec, O., Kumar, R., Attinger, S., and Samaniego, L.: Improving the realism of hydrologic model functioning through multivariate parameter estimation, Water Resour. Res., 52, 7779–7792, https://doi.org/10.1002/2016WR019430, 2016.
Riegger, J. and Tourian, M. J.: Characterization of runoff-storage relationships by satellite gravimetry and remote sensing, Water Resour. Res., 50, 3444–3466, https://doi.org/10.1002/2013WR013847, 2014.
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 15twenty-first century, J. Climate, 28, 8289–8318, https://doi.org/10.1175/JCLI-D-14-00555.1, 2015.
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.: A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, 2017.
Schwanghart, W. and Kuhn, N. J.: TopoToolbox: A set of Matlab functions for topographic analysis, Environ. Modell. Softw., 25, 770–781, 2010.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture–climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
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.
Sobol, I. M.: Sensitivity analysis for non-linear mathematical models, Mathematical Modelling and Computational Experiments, 2, 407–414, 1993.
Solomatine, D. P. and Ostfeld, A.: Data-driven modelling: some past experiences and new approaches, J. Hydroinform., 10, 3–22, https://doi.org/10.2166/hydro.2008.015, 2008.
Sood, A. and Smakhtin, V.: Global hydrological models: a review, Hydrolog. Sci. J., 60, 549–565, https://doi.org/10.1080/02626667.2014.950580, 2015.
Strahler, A. N.: Hypsometric (area-altitude) analysis of erosional topography, Geol. Soc. Am. Bull., 63, 1117–1142, https://doi.org/10.1130/0016-7606(1952)63[1117:HAAOET]2.0.CO;2, 1952.
Tang, Y., Reed, P., Wagener, T., and van Werkhoven, K.: Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation, Hydrol. Earth Syst. Sci., 11, 793–817, https://doi.org/10.5194/hess-11-793-2007, 2007.
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.
Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., and De Roo, A.: Hydrological evaluation of satellite rainfall estimates over the Volta and Baro-Akobo Basin, J. Hydrol., 499, 324–338, https://doi.org/10.1016/j.jhydrol.2013.07.012, 2013.
Tijerina, D., Condon, L., FitzGerald, K., Dugger, A., O'Neill, M. M., Sampson, K., Gochis, D., and Maxwell, R.: Continental hydrologic intercomparison project, phase 1: A large-scale hydrologic model comparison over the continental United States, Water Resour. Res., 57, e2020WR028931, https://doi.org/10.1029/2020WR028931, 2021.
Tourian, M. J., Reager, J. T., and Sneeuw, N.: The total drainable water storage of the Amazon river basin: A first estimate using GRACE, Water Resour. Res., 54, 3290–3312, https://doi.org/10.1029/2017WR021674, 2018.
Tramblay, Y., Bouvier, C., Martin, C., Didon-Lescot, J. F., Todorovik, D., and Domergue, J. M.: Assessment of initial soil moisture conditions for event-based rainfall–runoff modelling, J. Hydrol., 387, 176–187, https://doi.org/10.1016/j.jhydrol.2010.04.006, 2010.
Tropical Rainfall Measuring Mission (TRMM): TRMM (TMPA) Rainfall Estimate L3 3 hour 0.25 degree x 0.25 degree V7, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/TRMM/TMPA/3H/7, 2011.
Troutman, B. M. and Karlinger, M. B.: Unit hydrograph approximation assuming linear flow through topologically random channel networks, Water Resour. Res., 21, 743–754, https://doi.org/10.1029/WR021i005p00743, 1985.
Van Beek, L. P. H. and Bierkens, M. F. P.: The global hydrological model PCR-GLOBWB: conceptualization, parameterization and verification, Utrecht University, Utrecht, the Netherlands, 1, 25–26, http://vanbeek.geo.uu.nl/suppinfo/vanbeekbierkens2009.pdf (last access: 26 August 2022), 2009.
Vishwakarma, B. D., Devaraju, B., and Sneeuw, N.: What is the spatial resolution of GRACE satellite products for hydrology?, Remote Sens.-Basel, 10, 852, https://doi.org/10.3390/rs10060852, 2018.
Vörösmarty, C., Askew, A., Grabs, W., Barry, R. G., Birkett, C., Döll, P., Goodison, B., Hall, A., Jenne, R., Kitaev, L., Landwehr, J., Keeler, M., Leavesley, G., Schaake, J., Strzepek, K., Sundarvel, S. S., Takeuchi, K., and Webster, F.: Global water data: A newly endangered species, Eos Trans. Am. Geophys. Union, 82, 54–58, https://doi.org/10.1029/01EO00031, 2001.
Vose, R. S., Applequist, S., Durre, I., Menne, M. J., Williams, C. N., Fenimore, C., Gleason, K., and Arndt, D.: Improved Historical Temperature and Precipitation on Time Series For U. S. Climate Divisions, J. Appl. Meteorol. Clim., 53, 1232–1251, https://doi.org/10.1175/JAMC-D-13-0248.1, 2014.
Wagner, W., Lemoine, G., and Rott, H.: A method for estimating soil moisture from ERS scatterometer and soil data, Remote Sens. Environ., 70, 191–207, https://doi.org/10.1016/S0034-4257(99)00036-X, 1999.
Wagner, W., Blöschl, G., Pampaloni, P., Calvet, J. C., Bizzarri, B., Wigneron, J. P., and Kerr, Y.: Operational readiness of microwave remote sensing of soil moisture for hydrologic applications, Hydrol. Res., 38, 1–20, https://doi.org/10.2166/nh.2007.029, 2007.
Wang, Y. H., Broxton, P., Fang, Y., Behrangi, A., Barlage, M., Zeng, X., and Niu, G. Y.: A wet-bulb temperature-based rain-snow partitioning scheme improves snowpack prediction over the drier western United States, Geophys. Res. Lett., 46, 13825–13835, https://doi.org/10.1029/2019GL085722, 2019.
Wisser, D., Fekete, B. M., Vörösmarty, C. J., and Schumann, A. H.: Reconstructing 20th century global hydrography: a contribution to the Global Terrestrial Network- Hydrology (GTN-H), Hydrol. Earth Syst. Sci., 14, 1–24, https://doi.org/10.5194/hess-14-1-2010, 2010.
Yi, S. and Sneeuw, N.: Filling the data gaps within GRACE missions using Singular Spectrum Analysis, J. Geophys. Res.-Sol. Ea., 126, e2020JB021227, https://doi.org/10.1029/2020JB021227, 2021.
Yokoo, Y. and Sivapalan, M.: Towards reconstruction of the flow duration curve: development of a conceptual framework with a physical basis, Hydrol. Earth Syst. Sci., 15, 2805–2819, https://doi.org/10.5194/hess-15-2805-2011, 2011.
Zhang, Y., Pan, M., Sheffield, J., Siemann, A. L., Fisher, C. K., Liang, M., Beck, H. E., Wanders, N., MacCracken, R. F., Houser, P. R., Zhou, T., Lettenmaier, D. P., Pinker, R. T., Bytheway, J., Kummerow, C. D., and Wood, E. F.: A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010, Hydrol. Earth Syst. Sci., 22, 241–263, https://doi.org/10.5194/hess-22-241-2018, 2018.
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...
