Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-4957-2023
© 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/gmd-16-4957-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
31 Aug 2023
Model evaluation paper |
| 31 Aug 2023
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Adam Pasik
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Alexander Gruber
CORRESPONDING AUTHOR
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Wolfgang Preimesberger
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Domenico De Santis
Research Institute for Geo-Hydrological Protection, National Research Council, Via della Madonna Alta 126, 06128 Perugia, Italy
Wouter Dorigo
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
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Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023, https://doi.org/10.5194/hess-27-4087-2023, 2023
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We explored different options for data assimilation (DA) of the remotely sensed leaf area index (LAI). We found strong biases between LAI predicted by Noah-MP and observations. LAI DA that does not take these biases into account can induce unphysical patterns in the resulting LAI and flux estimates and leads to large changes in the climatology of root zone soil moisture. We tested two bias-correction approaches and explored alternative solutions to treating bias in LAI DA.
Martin Hirschi, Bas Crezee, Pietro Stradiotti, Wouter Dorigo, and Sonia I. Seneviratne
EGUsphere, https://doi.org/10.5194/egusphere-2023-2499, https://doi.org/10.5194/egusphere-2023-2499, 2023
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Based on surface and root-zone soil moisture, we compare the ability of selected long-term reanalysis and merged remote-sensing products to represent major agroecological drought events. While all products capture the investigated droughts, they particularly show differences in the drought magnitudes. Globally, the diverse and regionally contradicting dry-season soil moisture trends of the products is an important factor governing their drought representation and monitoring capability.
Remi Madelon, Nemesio J. Rodríguez-Fernández, Hassan Bazzi, Nicolas Baghdadi, Clement Albergel, Wouter Dorigo, and Mehrez Zribi
Hydrol. Earth Syst. Sci., 27, 1221–1242, https://doi.org/10.5194/hess-27-1221-2023, https://doi.org/10.5194/hess-27-1221-2023, 2023
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We present an approach to estimate soil moisture (SM) at 1 km resolution using Sentinel-1 and Sentinel-3 satellites. The estimates were compared to other high-resolution (HR) datasets over Europe, northern Africa, Australia, and North America, showing good agreement. However, the discrepancies between the different HR datasets and their lower performances compared with in situ measurements and coarse-resolution datasets show the remaining challenges for large-scale HR SM mapping.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
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Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
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Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Matthias Forkel, Luisa Schmidt, Ruxandra-Maria Zotta, Wouter Dorigo, and Marta Yebra
Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, https://doi.org/10.5194/hess-27-39-2023, 2023
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The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.
Leander Moesinger, Ruxandra-Maria Zotta, Robin van der Schalie, Tracy Scanlon, Richard de Jeu, and Wouter Dorigo
Biogeosciences, 19, 5107–5123, https://doi.org/10.5194/bg-19-5107-2022, https://doi.org/10.5194/bg-19-5107-2022, 2022
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The standardized vegetation optical depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage, spans the past 3 decades and is derived from multiple spaceborne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data even if the data are not gap-free.
Robin van der Schalie, Mendy van der Vliet, Clément Albergel, Wouter Dorigo, Piotr Wolski, and Richard de Jeu
Hydrol. Earth Syst. Sci., 26, 3611–3627, https://doi.org/10.5194/hess-26-3611-2022, https://doi.org/10.5194/hess-26-3611-2022, 2022
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Climate data records of surface soil moisture, vegetation optical depth, and land surface temperature can be derived from passive microwave observations. The ability of these datasets to properly detect anomalies and extremes is very valuable in climate research and can especially help to improve our insight in complex regions where the current climate reanalysis datasets reach their limitations. Here, we present a case study over the Okavango Delta, where we focus on inter-annual variability.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
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Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
Stefan Schlaffer, Marco Chini, Wouter Dorigo, and Simon Plank
Hydrol. Earth Syst. Sci., 26, 841–860, https://doi.org/10.5194/hess-26-841-2022, https://doi.org/10.5194/hess-26-841-2022, 2022
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Prairie wetlands are important for biodiversity and water availability. Knowledge about their variability and spatial distribution is of great use in conservation and water resources management. In this study, we propose a novel approach for the classification of small water bodies from satellite radar images and apply it to our study area over 6 years. The retrieved dynamics show the different responses of small and large wetlands to dry and wet periods.
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.
Irene E. Teubner, Matthias Forkel, Benjamin Wild, Leander Mösinger, and Wouter Dorigo
Biogeosciences, 18, 3285–3308, https://doi.org/10.5194/bg-18-3285-2021, https://doi.org/10.5194/bg-18-3285-2021, 2021
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Vegetation optical depth (VOD), which contains information on vegetation water content and biomass, has been previously shown to be related to gross primary production (GPP). In this study, we analyzed the impact of adding temperature as model input and investigated if this can reduce the previously observed overestimation of VOD-derived GPP. In addition, we could show that the relationship between VOD and GPP largely holds true along a gradient of dry or wet conditions.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
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We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Kurt C. Solander, Brent D. Newman, Alessandro Carioca de Araujo, Holly R. Barnard, Z. Carter Berry, Damien Bonal, Mario Bretfeld, Benoit Burban, Luiz Antonio Candido, Rolando Célleri, Jeffery Q. Chambers, Bradley O. Christoffersen, Matteo Detto, Wouter A. Dorigo, Brent E. Ewers, Savio José Filgueiras Ferreira, Alexander Knohl, L. Ruby Leung, Nate G. McDowell, Gretchen R. Miller, Maria Terezinha Ferreira Monteiro, Georgianne W. Moore, Robinson Negron-Juarez, Scott R. Saleska, Christian Stiegler, Javier Tomasella, and Chonggang Xu
Hydrol. Earth Syst. Sci., 24, 2303–2322, https://doi.org/10.5194/hess-24-2303-2020, https://doi.org/10.5194/hess-24-2303-2020, 2020
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We evaluate the soil moisture response in the humid tropics to El Niño during the three most recent super El Niño events. Our estimates are compared to in situ soil moisture estimates that span five continents. We find the strongest and most consistent soil moisture decreases in the Amazon and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. Our results can be used to improve estimates of soil moisture in tropical ecohydrology models at multiple scales.
Angelika Xaver, Luca Zappa, Gerhard Rab, Isabella Pfeil, Mariette Vreugdenhil, Drew Hemment, and Wouter Arnoud Dorigo
Geosci. Instrum. Method. Data Syst., 9, 117–139, https://doi.org/10.5194/gi-9-117-2020, https://doi.org/10.5194/gi-9-117-2020, 2020
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Soil moisture plays a key role in the hydrological cycle and the climate system. Although soil moisture can be observed by the means of satellites, ground observations are still crucial for evaluating and improving these satellite products. In this study, we investigate the performance of a consumer low-cost soil moisture sensor in the lab and in the field. We demonstrate that this sensor can be used for scientific applications, for example to create a dataset valuable for satellite validation.
Miguel D. Mahecha, Fabian Gans, Gunnar Brandt, Rune Christiansen, Sarah E. Cornell, Normann Fomferra, Guido Kraemer, Jonas Peters, Paul Bodesheim, Gustau Camps-Valls, Jonathan F. Donges, Wouter Dorigo, Lina M. Estupinan-Suarez, Victor H. Gutierrez-Velez, Martin Gutwin, Martin Jung, Maria C. Londoño, Diego G. Miralles, Phillip Papastefanou, and Markus Reichstein
Earth Syst. Dynam., 11, 201–234, https://doi.org/10.5194/esd-11-201-2020, https://doi.org/10.5194/esd-11-201-2020, 2020
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The ever-growing availability of data streams on different subsystems of the Earth brings unprecedented scientific opportunities. However, researching a data-rich world brings novel challenges. We present the concept of
Earth system data cubesto study the complex dynamics of multiple climate and ecosystem variables across space and time. Using a series of example studies, we highlight the potential of effectively considering the full multivariate nature of processes in the Earth system.
Leander Moesinger, Wouter Dorigo, Richard de Jeu, Robin van der Schalie, Tracy Scanlon, Irene Teubner, and Matthias Forkel
Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, https://doi.org/10.5194/essd-12-177-2020, 2020
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Vegetation optical depth (VOD) is measured by satellites and is related to the density of vegetation and its water content. VOD has a wide range of uses, including drought, wildfire danger, biomass, and carbon stock monitoring. For the past 30 years there have been various VOD data sets derived from space-borne microwave sensors, but biases between them prohibit a combined use. We removed these biases and merged the data to create the global long-term VOD Climate Archive (VODCA).
Alexander Gruber, Tracy Scanlon, Robin van der Schalie, Wolfgang Wagner, and Wouter Dorigo
Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, https://doi.org/10.5194/essd-11-717-2019, 2019
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Soil moisture is a key variable in our Earth system. Knowledge of soil moisture and its dynamics across scales is vital for many applications such as the prediction of agricultural yields or irrigation demands, flood and drought monitoring, weather forecasting and climate modelling. To date, the ESA CCI SM products are the only consistent long-term multi-satellite soil moisture data sets available. This paper reviews the evolution of these products and their underlying merging methodology.
Felix Zaussinger, Wouter Dorigo, Alexander Gruber, Angelica Tarpanelli, Paolo Filippucci, and Luca Brocca
Hydrol. Earth Syst. Sci., 23, 897–923, https://doi.org/10.5194/hess-23-897-2019, https://doi.org/10.5194/hess-23-897-2019, 2019
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About 70 % of global freshwater is consumed by irrigation. Yet, policy-relevant estimates of irrigation water use (IWU) are virtually lacking at regional to global scales. To bridge this gap, we develop a method for quantifying IWU from a combination of state-of-the-art remotely sensed and modeled soil moisture products and apply it over the United States for the period 2013–2016. Overall, our estimates agree well with reference data on irrigated area and irrigation water withdrawals.
Victor Pellet, Filipe Aires, Simon Munier, Diego Fernández Prieto, Gabriel Jordá, Wouter Arnoud Dorigo, Jan Polcher, and Luca Brocca
Hydrol. Earth Syst. Sci., 23, 465–491, https://doi.org/10.5194/hess-23-465-2019, https://doi.org/10.5194/hess-23-465-2019, 2019
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This study is an effort for a better understanding and quantification of the water cycle and related processes in the Mediterranean region, by dealing with satellite products and their uncertainties. The aims of the paper are 3-fold: (1) developing methods with hydrological constraints to integrate all the datasets, (2) giving the full picture of the Mediterranean WC, and (3) building a model-independent database that can evaluate the numerous regional climate models (RCMs) for this region.
Matthias Forkel, Niels Andela, Sandy P. Harrison, Gitta Lasslop, Margreet van Marle, Emilio Chuvieco, Wouter Dorigo, Matthew Forrest, Stijn Hantson, Angelika Heil, Fang Li, Joe Melton, Stephen Sitch, Chao Yue, and Almut Arneth
Biogeosciences, 16, 57–76, https://doi.org/10.5194/bg-16-57-2019, https://doi.org/10.5194/bg-16-57-2019, 2019
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Weather, humans, and vegetation control the occurrence of fires. In this study we find that global fire–vegetation models underestimate the strong increase of burned area with higher previous-season plant productivity in comparison to satellite-derived relationships.
Luca Ciabatta, Christian Massari, Luca Brocca, Alexander Gruber, Christoph Reimer, Sebastian Hahn, Christoph Paulik, Wouter Dorigo, Richard Kidd, and Wolfgang Wagner
Earth Syst. Sci. Data, 10, 267–280, https://doi.org/10.5194/essd-10-267-2018, https://doi.org/10.5194/essd-10-267-2018, 2018
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In this study, rainfall is estimated starting from satellite soil moisture observation on a global scale, using the ESA CCI soil moisture datasets. The new obtained rainfall product has proven to correctly identify rainfall events, showing performance sometimes higher than those obtained by using classical rainfall estimation approaches.
Matthias Forkel, Wouter Dorigo, Gitta Lasslop, Irene Teubner, Emilio Chuvieco, and Kirsten Thonicke
Geosci. Model Dev., 10, 4443–4476, https://doi.org/10.5194/gmd-10-4443-2017, https://doi.org/10.5194/gmd-10-4443-2017, 2017
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Wildfires affect infrastructures, vegetation, and the atmosphere. However, it is unclear how fires should be accurately represented in global vegetation models. We introduce here a new flexible data-driven fire modelling approach that allows us to explore sensitivities of burned areas to satellite and climate datasets. Our results suggest combining observations with data-driven and process-oriented fire models to better understand the role of fires in the Earth system.
Clément Albergel, Simon Munier, Delphine Jennifer Leroux, Hélène Dewaele, David Fairbairn, Alina Lavinia Barbu, Emiliano Gelati, Wouter Dorigo, Stéphanie Faroux, Catherine Meurey, Patrick Le Moigne, Bertrand Decharme, Jean-Francois Mahfouf, and Jean-Christophe Calvet
Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, https://doi.org/10.5194/gmd-10-3889-2017, 2017
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LDAS-Monde, a global land data assimilation system, is applied over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. It is able to ingest information from satellite-derived surface soil moisture (SSM) and leaf area index (LAI) observations to constrain the ISBA land surface model coupled with the CTRIP continental hydrological system. Assimilation of SSM and LAI leads to a better representation of evapotranspiration and gross primary production.
Marko Scholze, Michael Buchwitz, Wouter Dorigo, Luis Guanter, and Shaun Quegan
Biogeosciences, 14, 3401–3429, https://doi.org/10.5194/bg-14-3401-2017, https://doi.org/10.5194/bg-14-3401-2017, 2017
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This paper briefly reviews data assimilation techniques in carbon cycle data assimilation and the requirements of data assimilation systems on observations. We provide a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation, focussing on relevant space-based observations.
Jaap Schellekens, Emanuel Dutra, Alberto Martínez-de la Torre, Gianpaolo Balsamo, Albert van Dijk, Frederiek Sperna Weiland, Marie Minvielle, Jean-Christophe Calvet, Bertrand Decharme, Stephanie Eisner, Gabriel Fink, Martina Flörke, Stefanie Peßenteiner, Rens van Beek, Jan Polcher, Hylke Beck, René Orth, Ben Calton, Sophia Burke, Wouter Dorigo, and Graham P. Weedon
Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, https://doi.org/10.5194/essd-9-389-2017, 2017
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The dataset combines the results of 10 global models that describe the global continental water cycle. The data can be used as input for water resources studies, flood frequency studies etc. at different scales from continental to medium-scale catchments. We compared the results with earth observation data and conclude that most uncertainties are found in snow-dominated regions and tropical rainforest and monsoon regions.
Brecht Martens, Diego G. Miralles, Hans Lievens, Robin van der Schalie, Richard A. M. de Jeu, Diego Fernández-Prieto, Hylke E. Beck, Wouter A. Dorigo, and Niko E. C. Verhoest
Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, https://doi.org/10.5194/gmd-10-1903-2017, 2017
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Terrestrial evaporation is a key component of the hydrological cycle and reliable data sets of this variable are of major importance. The Global Land Evaporation Amsterdam Model (GLEAM, www.GLEAM.eu) is a set of algorithms which estimates evaporation based on satellite observations. The third version of GLEAM, presented in this study, includes an improved parameterization of different model components. As a result, the accuracy of the GLEAM data sets has been improved upon previous versions.
Christina Papagiannopoulou, Diego G. Miralles, Stijn Decubber, Matthias Demuzere, Niko E. C. Verhoest, Wouter A. Dorigo, and Willem Waegeman
Geosci. Model Dev., 10, 1945–1960, https://doi.org/10.5194/gmd-10-1945-2017, https://doi.org/10.5194/gmd-10-1945-2017, 2017
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Global satellite observations provide a means to unravel the influence of climate on vegetation. Common statistical methods used to study the relationships between climate and vegetation are often too simplistic to capture the complexity of these relationships. Here, we present a novel causality framework that includes data fusion from various databases, time series decomposition, and machine learning techniques. Results highlight the highly non-linear nature of climate–vegetation interactions.
Markus Enenkel, Christoph Reimer, Wouter Dorigo, Wolfgang Wagner, Isabella Pfeil, Robert Parinussa, and Richard De Jeu
Hydrol. Earth Syst. Sci., 20, 4191–4208, https://doi.org/10.5194/hess-20-4191-2016, https://doi.org/10.5194/hess-20-4191-2016, 2016
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Soil moisture is a crucial variable for a variety of applications, ranging from weather forecasting and agricultural production to the monitoring of floods and droughts. Satellite observations are particularly important in regions where no in situ measurements are available. Our study presents a method to integrate global near-real-time satellite observations from different sensors into one harmonized, daily data set. A first validation shows good results on a global scale.
C. Szczypta, J.-C. Calvet, F. Maignan, W. Dorigo, F. Baret, and P. Ciais
Geosci. Model Dev., 7, 931–946, https://doi.org/10.5194/gmd-7-931-2014, https://doi.org/10.5194/gmd-7-931-2014, 2014
A. Loew, T. Stacke, W. Dorigo, R. de Jeu, and S. Hagemann
Hydrol. Earth Syst. Sci., 17, 3523–3542, https://doi.org/10.5194/hess-17-3523-2013, https://doi.org/10.5194/hess-17-3523-2013, 2013
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RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling
Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest
Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation
A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability
CREST-VEC: a framework towards more accurate and realistic flood simulation across scales
Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains
The eWaterCycle platform for open and FAIR hydrological collaboration
Evaluating the Atibaia River hydrology using JULES6.1
A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations
Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model
Evaluating a reservoir parametrization in the vector-based global routing model mizuRoute (v2.0.1) for Earth system model coupling
Improved runoff simulations for a highly varying soil depth and complex terrain watershed in the Loess Plateau with the Community Land Model version 5
GSTools v1.3: a toolbox for geostatistical modelling in Python
AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
Modeling of streamflow in a 30 km long reach spanning 5 years using OpenFOAM 5.x
Tree hydrodynamic modelling of the soil–plant–atmosphere continuum using FETCH3
Effects of dimensionality on the performance of hydrodynamic models for stratified lakes and reservoirs
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Lele Shu, Paul Ullrich, Xianghong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-128, https://doi.org/10.5194/gmd-2023-128, 2023
Revised manuscript accepted for GMD
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
EGUsphere, https://doi.org/10.5194/egusphere-2023-375, https://doi.org/10.5194/egusphere-2023-375, 2023
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We developed and validated an inter-grid cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Ciaran Harman and Esther Xu Fei
EGUsphere, https://doi.org/10.5194/egusphere-2022-1262, https://doi.org/10.5194/egusphere-2022-1262, 2022
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Over the last 10 years scientists have developed a new way of modeling how material is transported through complex systems, called StorAge Selection. Here we present some new code implementing this method that is easy to use, but also flexible and very accurate. We show that for cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other people's code to the right answer in an important way: it conserves mass.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
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A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
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This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
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We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
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We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
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Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
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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.
Ji Li, Daoxian Yuan, Fuxi Zhang, Jiao Liu, and Mingguo Ma
Geosci. Model Dev., 15, 6581–6600, https://doi.org/10.5194/gmd-15-6581-2022, https://doi.org/10.5194/gmd-15-6581-2022, 2022
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A new karst hydrological model (the QMG model) is developed to simulate and predict the floods in karst trough valley basins. Unlike the complex structure and parameters of current karst groundwater models, this model has a simple double-layered structure with few parameters and decreases the demand for modeling data in karst areas. The flood simulation results based on the QMG model of the Qingmuguan karst trough valley basin are satisfactory, indicating the suitability of the model simulation.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
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MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Zhi Li, Shang Gao, Mengye Chen, Jonathan Gourley, Naoki Mizukami, and Yang Hong
Geosci. Model Dev., 15, 6181–6196, https://doi.org/10.5194/gmd-15-6181-2022, https://doi.org/10.5194/gmd-15-6181-2022, 2022
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Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes, with streamflow routing being one of the most time-consuming parts. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme with a lake module that markedly speeds up streamflow prediction. Moreover, accuracy is improved and flood false alarms are mitigated.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
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Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
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With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev., 15, 5233–5240, https://doi.org/10.5194/gmd-15-5233-2022, https://doi.org/10.5194/gmd-15-5233-2022, 2022
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Land surface models allow us to understand and investigate the cause and effect of environmental process changes. Therefore, this type of model is increasingly used for hydrological assessments. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for the metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to simulate the basin hydrology.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
Short summary
Short summary
Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson
Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, https://doi.org/10.5194/gmd-15-4569-2022, 2022
Short summary
Short summary
Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, https://doi.org/10.5194/gmd-15-4275-2022, 2022
Short summary
Short summary
The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
Short summary
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Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Jiming Jin, Lei Wang, Jie Yang, Bingcheng Si, and Guo-Yue Niu
Geosci. Model Dev., 15, 3405–3416, https://doi.org/10.5194/gmd-15-3405-2022, https://doi.org/10.5194/gmd-15-3405-2022, 2022
Short summary
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This study aimed to improve runoff simulations and explore deep soil hydrological processes for a highly varying soil depth and complex terrain watershed in the Loess Plateau, China. The actual soil depths and river channels were incorporated into the model to better simulate the runoff in this watershed. The soil evaporation scheme was modified to better describe the evaporation processes. Our results showed that the model significantly improved the runoff simulations.
Sebastian Müller, Lennart Schüler, Alraune Zech, and Falk Heße
Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022, https://doi.org/10.5194/gmd-15-3161-2022, 2022
Short summary
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The GSTools package provides a Python-based platform for geoostatistical applications. Salient features of GSTools are its random field generation, its kriging capabilities and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige, ogs5py or scikit-gstat, and provides interfaces to meshio and PyVista. Four presented workflows showcase the abilities of GSTools.
Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho
Geosci. Model Dev., 15, 3021–3039, https://doi.org/10.5194/gmd-15-3021-2022, https://doi.org/10.5194/gmd-15-3021-2022, 2022
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The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
Yunxiang Chen, Jie Bao, Yilin Fang, William A. Perkins, Huiying Ren, Xuehang Song, Zhuoran Duan, Zhangshuan Hou, Xiaoliang He, and Timothy D. Scheibe
Geosci. Model Dev., 15, 2917–2947, https://doi.org/10.5194/gmd-15-2917-2022, https://doi.org/10.5194/gmd-15-2917-2022, 2022
Short summary
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Climate change affects river discharge variations that alter streamflow. By integrating multi-type survey data with a computational fluid dynamics tool, OpenFOAM, we show a workflow that enables accurate and efficient streamflow modeling at 30 km and 5-year scales. The model accuracy for water stage and depth average velocity is −16–9 cm and 0.71–0.83 in terms of mean error and correlation coefficients. This accuracy indicates the model's reliability for evaluating climate impact on rivers.
Marcela Silva, Ashley M. Matheny, Valentijn R. N. Pauwels, Dimetre Triadis, Justine E. Missik, Gil Bohrer, and Edoardo Daly
Geosci. Model Dev., 15, 2619–2634, https://doi.org/10.5194/gmd-15-2619-2022, https://doi.org/10.5194/gmd-15-2619-2022, 2022
Short summary
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Our study introduces FETCH3, a ready-to-use, open-access model that simulates the water fluxes across the soil, roots, and stem. To test the model capabilities, we tested it against exact solutions and a case study. The model presented considerably small errors when compared to the exact solutions and was able to correctly represent transpiration patterns when compared to experimental data. The results show that FETCH3 can correctly simulate above- and below-ground water transport.
Mayra Ishikawa, Wendy Gonzalez, Orides Golyjeswski, Gabriela Sales, J. Andreza Rigotti, Tobias Bleninger, Michael Mannich, and Andreas Lorke
Geosci. Model Dev., 15, 2197–2220, https://doi.org/10.5194/gmd-15-2197-2022, https://doi.org/10.5194/gmd-15-2197-2022, 2022
Short summary
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Reservoir hydrodynamics is often described in numerical models differing in dimensionality. 1D and 2D models assume homogeneity along the unresolved dimension. We compare the performance of models with 1 to 3 dimensions. All models presented reasonable results for seasonal temperature dynamics. Neglecting longitudinal transport resulted in the largest deviations in temperature. Flow velocity could only be reproduced by the 3D model. Results support the selection of models and their assessment.
Cited articles
Adeaem, Gößwein, B., Hahn, S., Preimesberger, W., and BM, B.: TUW-GEO/pyswi: v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7534919, 2023. a
Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008. a, b, c, d, e, f, g, h, i
Al Bitar, A. and Mahmoodi, A.: Algorithm Theoretical Basis Document (ATBD) for
the SMOS Level 4 Root Zone Soil Moisture (Version v30_01), Tech. Rep.,
https://doi.org/10.5281/zenodo.4298572, 2020. a
Alday, J. G., Camarero, J. J., Revilla, J., and Resco de Dios, V.: Similar
diurnal, seasonal and annual rhythms in radial root expansion across two
coexisting Mediterranean oak species, Tree Physiol., 40, 956–968,
https://doi.org/10.1093/treephys/tpaa041, 2020. a
Al-Yaari, A., Dayau, S., Chipeaux, C., Aluome, C., Kruszewski, A., Loustau, D.,
and Wigneron, J.-P.: The AQUI Soil Moisture Network for Satellite Microwave
Remote Sensing Validation in South-Western France, Remote Sensing, 10, 1839,
https://doi.org/10.3390/rs10111839, 2018. a
Baldwin, D., Manfreda, S., Keller, K., and Smithwick, E.: Predicting root zone
soil moisture with soil properties and satellite near-surface moisture data
across the conterminous United States, J. Hydrol., 546, 393–404,
https://doi.org/10.1016/j.jhydrol.2017.01.020, 2017. a
Bauer Marschallinger, B.: Algorithm Theoretical Basis Document,
CGLOPS1_ATBD_SWI1km-V1 l1.30, Tech. rep., Copernicus Global Land
Operations, 2022. 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
Beck, H. E., de Jeu, R. A. M., Schellekens, J., van Dijk, A. I. J. M., and
Bruijnzeel, L. A.: Improving Curve Number Based Storm Runoff Estimates Using
Soil Moisture Proxies, IEEE J. Sel. Top. Appl., 2, 250–259,
https://doi.org/10.1109/JSTARS.2009.2031227, 2009. a, b, c
Bell, J. E., Palecki, M. A., Baker, C. B., Collins, W. G., Lawrimore, J. H.,
Leeper, R. D., Hall, M. E., Kochendorfer, J., Meyers, T. P., Wilson, T., and
Diamond, H. J.: U.S. Climate Reference Network Soil Moisture and Temperature
Observations, J. Hydrometeorol., 14, 977–988,
https://doi.org/10.1175/JHM-D-12-0146.1, 2013. a
Beven, K.: On the concept of model structural error, Water Sci.
Technol., 52, 167–175, https://doi.org/10.2166/wst.2005.0165, 2005. a
Beyrich, F. and Adam, W.: Site and Data Report for the Lindenberg Reference
Site in CEOP – Phase 1, Tech. Rep. 230, Deutscher Wetterdienst, Offenbach am
Main, 2007. a
Biddoccu, M., Ferraris, S., Opsi, F., and Cavallo, E.: Long-term monitoring of
soil management effects on runoff and soil erosion in sloping vineyards in
Alto Monferrato (North–West Italy), Soil Till. Res., 155,
176–189, https://doi.org/10.1016/j.still.2015.07.005, 2016. a
Bircher, S., Skou, N., Jensen, K. H., Walker, J. P., and Rasmussen, L.: A soil moisture and temperature network for SMOS validation in Western Denmark, Hydrol. Earth Syst. Sci., 16, 1445–1463, https://doi.org/10.5194/hess-16-1445-2012, 2012. a
Blöschl, G., Blaschke, A. P., Broer, M., Bucher, C., Carr, G., Chen, X., Eder, A., Exner-Kittridge, M., Farnleitner, A., Flores-Orozco, A., Haas, P., Hogan, P., Kazemi Amiri, A., Oismüller, M., Parajka, J., Silasari, R., Stadler, P., Strauss, P., Vreugdenhil, M., Wagner, W., and Zessner, M.: The Hydrological Open Air Laboratory (HOAL) in Petzenkirchen: a hypothesis-driven observatory, Hydrol. Earth Syst. Sci., 20, 227–255, https://doi.org/10.5194/hess-20-227-2016, 2016. a
Bogena, H., Kunkel, R., Puetz, T., Vereecken, H., Krueger, E., Zacharias, S.,
Dietrich, P., Wollschlaeger, U., Kunstmann, H., Papen, H., Schmid, H. P.,
Munch, J. C., Priesack, E., Schwank, M., Bens, O., Brauer, A., Borg, E., and
Hajnsek, I.: TERENO – Long-term monitoring network for terrestrial
environmental research, Hydrol. Wasserbewirts., 56, 138–143,
2012. a
Bogena, H., Montzka, C., Huisman, J., Graf, A., Schmidt, M., Stockinger, M.,
von Hebel, C., Hendricks-Franssen, H., van der Kruk, J., Tappe, W., Lücke,
A., Baatz, R., Bol, R., Groh, J., Pütz, T., Jakobi, J., Kunkel, R., Sorg,
J., and Vereecken, H.: The TERENO-Rur Hydrological Observatory: A Multiscale
Multi-Compartment Research Platform for the Advancement of Hydrological
Science, Vadose Zone J., 17, 180055, https://doi.org/10.2136/vzj2018.03.0055,
2018. a
Bogena, H. R.: TERENO: German network of terrestrial environmental
observatories, Journal of Large-Scale Research Facilities, 2, A52–A52,
https://doi.org/10.17815/jlsrf-2-98, 2016. a
Bouaziz, L. J. E., Steele-Dunne, S. C., Schellekens, J., Weerts, A. H., Stam,
J., Sprokkereef, E., Winsemius, H. H. C., Savenije, H. H. G., and Hrachowitz,
M.: Improved Understanding of the Link Between Catchment-Scale Vegetation
Accessible Storage and Satellite-Derived Soil Water Index, Water Resour.
Res., 56, e2019WR026365, https://doi.org/10.1029/2019WR026365, 2020. a, b, c, d, e
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. a
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. a
Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W.,
Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J.,
Martin, C., and Bittelli, M.: Soil moisture estimation through ASCAT and
AMSR-E sensors: An intercomparison and validation study across Europe, Remote
Sens. Environ., 115, 3390–3408, https://doi.org/10.1016/j.rse.2011.08.003,
2011. a, b, c, d
C3S: Algorithm Theoretical Baseline Document (ATBD) – Soil Moisture Service
D1.SM.2-v3.0, Tech. Rep., EODC, https://doi.org/10.24381/cds.d7782f18, 2020. a, b
Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., and Piguet,
B.: In situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA
network, in: 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–28 July 2007,
1196–1199, https://doi.org/10.1109/IGARSS.2007.4423019, 2007. a
Calvet, J.-C., Fritz, N., Berne, C., Piguet, B., Maurel, W., and Meurey, C.: Deriving pedotransfer functions for soil quartz fraction in southern France from reverse modeling, SOIL, 2, 615–629, https://doi.org/10.5194/soil-2-615-2016, 2016. a
Canisius, F.: Calibration of Casselman, Ontario Soil Moisture Monitoring
Network, Tech. Rep., Agriculture and Agri-food Canada, 2011. a
Capello, G., Biddoccu, M., Ferraris, S., and Cavallo, E.: Effects of Tractor
Passes on Hydrological and Soil Erosion Processes in Tilled and Grassed
Vineyards, Water, 11, 2118, https://doi.org/10.3390/w11102118, 2019. a
Cappelaere, B., Descroix, L., Lebel, T., Boulain, N., Ramier, D., Laurent,
J.-P., Favreau, G., Boubkraoui, S., Boucher, M., Bouzou Moussa, I.,
Chaffard, V., Hiernaux, P., Issoufou, H., Le Breton, E., Mamadou, I.,
Nazoumou, Y., Oi, M., Ottlé, C., and Quantin, G.: The AMMA-CATCH experiment
in the cultivated Sahelian area of south-west Niger – Investigating water
cycle response to a fluctuating climate and changing environment, J.
Hydrol., 375, 34–51, https://doi.org/10.1016/j.jhydrol.2009.06.021, 2009. a
Cook, D. R.: Soil Water and Temperature System (SWATS) Instrument Handbook,
Tech. Rep., US Department of Energy, https://doi.org/10.2172/1251383,
2016a. a
Cook, D. R.: Soil Temperature and Moisture Profile (STAMP) System Handbook,
Tech. Rep., US Department of Energy, https://doi.org/10.2172/1332724,
2016b. a
Cook, D. R.: Surface Energy Balance System (SEBS) Instrument Handbook, Tech.
Rep., US Department of Energy, https://doi.org/10.2172/1004944, 2018. a
de Lange, R., Beck, R., van de Giesen, N., Friesen, J., de Wit, A., and Wagner,
W.: Scatterometer-Derived Soil Moisture Calibrated for Soil Texture With a
One-Dimensional Water-Flow Model, IEEE T. Geosci. Remote, 46, 4041–4049, https://doi.org/10.1109/TGRS.2008.2000796, 2008. a, b, c, d
Dente, L., Su, Z., and Wen, J.: Validation of SMOS Soil Moisture Products over
the Maqu and Twente Regions, Sensors, 12, 9965–9986,
https://doi.org/10.3390/s120809965, 2012. a, b, c
de Rosnay, P., Gruhier, C., Timouk, F., Baup, F., Mougin, E., Hiernaux, P.,
Kergoat, L., and LeDantec, V.: Multi-scale soil moisture measurements at the
Gourma meso-scale site in Mali, J. Hydrol., 375, 241–252,
https://doi.org/10.1016/j.jhydrol.2009.01.015, 2009. a
De Santis, D. and Biondi, D.: Error Propagation from Remotely Sensed Surface
Soil Moisture Into Soil Water Index Using an Exponential Filter, in: HIC
2018. 13th International Conference on Hydroinformatics, Palermo, Italy, 1–6 July 2018, edited by: Loggia,
G. L., Freni, G., Puleo, V., and Marchis, M. D., vol. 3 of EPiC Series
in Engineering, EasyChair, 520–525, https://doi.org/10.29007/kvhb, 2018. a, b, c, d, e, f
Dorigo, W., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A.,
Sanchis-Dufau, A., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.:
Global Automated Quality Control of In Situ Soil Moisture Data from the
International Soil Moisture Network, Vadose Zone J., 12, vzj2012.0097,
https://doi.org/10.2136/vzj2012.0097, 2013. 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
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, b
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, b
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: COMBINED Product, Version 05.3, Centre for Environmental Data
Analysis [data set],
https://catalogue.ceda.ac.uk/uuid/e43aead9947549078c2d108b2c3632b2 (last access: 28 August 2023),
2021c. a
Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T.: The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675–1698, https://doi.org/10.5194/hess-15-1675-2011, 2011. a
Famiglietti, J. S., Ryu, D., Berg, A. A., Rodell, M., and Jackson, T. J.: Field
observations of soil moisture variability across scales, Water Resour.
Res., 44, W01423, https://doi.org/10.1029/2006WR005804, 2008. a
Flammini, A., Corradini, C., Morbidelli, R., Saltalippi, C., Picciafuoco, T.,
and Giráldez, J. V.: Experimental Analyses of the Evaporation Dynamics in
Bare Soils under Natural Conditions, Water Resour. Manag., 32,
1153–1166, https://doi.org/10.1007/s11269-017-1860-x, 2018a. a
Flammini, A., Morbidelli, R., Saltalippi, C., Picciafuoco, T., Corradini, C.,
and Govindaraju, R. S.: Reassessment of a semi-analytical field-scale
infiltration model through experiments under natural rainfall events, J. Hydrol., 565, 835–845, https://doi.org/10.1016/j.jhydrol.2018.08.073,
2018b. a
Ford, T. W., Harris, E., and Quiring, S. M.: Estimating root zone soil moisture using near-surface observations from SMOS, Hydrol. Earth Syst. Sci., 18, 139–154, https://doi.org/10.5194/hess-18-139-2014, 2014. a, b, c, d
Fuchsberger, J., Kirchengast, G., and Kabas, T.: WegenerNet high-resolution weather and climate data from 2007 to 2020, Earth Syst. Sci. Data, 13, 1307–1334, https://doi.org/10.5194/essd-13-1307-2021, 2021. a
Galle, S., Grippa, M., Peugeot, C., Bouzou Moussa, I., Cappelaere,
B., Demarty, J., Mougin, E., Lebel, T., and Chaffard, V.: AMMA-CATCH
a Hydrological, Meteorological and Ecological Long Term Observatory on West
Africa: Some Recent Results, in: AGU Fall Meeting Abstracts, vol. 2015,
GC42A–01, 2015. a
GCOS: The Global Observing System for Climate: Implementation needs, World
Meteorological Organization, 214,
https://public.wmo.int/en/resources/library/global-observing-system-climate-implementation-needs (last access: 28 August 2023),
2016. a
González-Zamora, A., Sánchez, N., Pablos, M., and Martínez-Fernández, J.:
CCI soil moisture assessment with SMOS soil moisture and in situ data under
different environmental conditions and spatial scales in Spain, Remote
Sens. Environ., 225, 469–482, https://doi.org/10.1016/j.rse.2018.02.010, 2019. a
Grillakis, M. G., Koutroulis, A. G., Alexakis, D. D., Polykretis, C., and
Daliakopoulos, I. N.: Regionalizing Root-Zone Soil Moisture Estimates From
ESA CCI Soil Water Index Using Machine Learning and Information on Soil,
Vegetation, and Climate, Water Resour. Res., 57, e2020WR029249,
https://doi.org/10.1029/2020WR029249, 2021. a, b, c, d
Gruber, A., Dorigo, W., Crow, W., and Wagner, W.: Triple Collocation-Based
Merging of Satellite Soil Moisture Retrievals, IEEE T.
Geosci. Remote, 55, 6780–6792, https://doi.org/10.1109/TGRS.2017.2734070,
2017. 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
Gruber, A., De Lannoy, G., Albergel, C., Al-Yaari, A., Brocca, L., Calvet,
J.-C., Colliander, A., Cosh, M., Crow, W., Dorigo, W., Draper, C., Hirschi,
M., Kerr, Y., Konings, A., Lahoz, W., McColl, K., Montzka, C.,
Muñoz-Sabater, J., Peng, J., Reichle, R., Richaume, P., Rüdiger, C.,
Scanlon, T., van der Schalie, R., Wigneron, J.-P., and Wagner, W.:
Validation practices for satellite soil moisture retrievals: What are (the)
errors?, Remote Sens. Environ., 244, 111806,
https://doi.org/10.1016/j.rse.2020.111806, 2020. a
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. a
Hajdu, I., Yule, I., Bretherton, M., Singh, R., and Hedley, C.: Field
performance assessment and calibration of multi-depth AquaCheck
capacitance-based soil moisture probes under permanent pasture for hill
country soils, Agr. Water Manage., 217, 332–345,
https://doi.org/10.1016/j.agwat.2019.03.002, 2019. a
Hollinger, S. E. and Isard, S. A.: A Soil Moisture Climatology of Illinois,
J. Climate, 7, 822–833,
https://doi.org/10.1175/1520-0442(1994)007<0822:ASMCOI>2.0.CO;2, 1994. a
Ikonen, J., Vehviläinen, J., Rautiainen, K., Smolander, T., Lemmetyinen, J., Bircher, S., and Pulliainen, J.: The Sodankylä in situ soil moisture observation network: an example application of ESA CCI soil moisture product evaluation, Geosci. Instrum. Method. Data Syst., 5, 95–108, https://doi.org/10.5194/gi-5-95-2016, 2016. a
Ikonen, J., Smolander, T., Rautiainen, K., Cohen, J., Lemmetyinen, J.,
Salminen, M., and Pulliainen, J.: Spatially Distributed Evaluation of ESA CCI
Soil Moisture Products in a Northern Boreal Forest Environment, Geosciences,
8, 51, https://doi.org/10.3390/geosciences8020051, 2018. a
Jackson, T. J., Cosh, M. H., Bindlish, R., Starks, P. J., Bosch, D. D.,
Seyfried, M., Goodrich, D. C., Moran, M. S., and Du, J.: Validation of
Advanced Microwave Scanning Radiometer Soil Moisture Products, IEEE
T. Geosci. Remote, 48, 4256–4272,
https://doi.org/10.1109/TGRS.2010.2051035, 2010. a
Jensen, K. H. and Refsgaard, J. C.: HOBE: The Danish Hydrological Observatory,
Vadose Zone J., 17, 180059, https://doi.org/10.2136/vzj2018.03.0059, 2018. a
Jin, R., Li, X., Yan, B., Li, X., Luo, W., Ma, M., Guo, J., Kang, J., Zhu, Z.,
and Zhao, S.: A Nested Ecohydrological Wireless Sensor Network for Capturing
the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin,
China, IEEE Geosci. Remote S., 11, 2015–2019,
https://doi.org/10.1109/LGRS.2014.2319085, 2014. a
Kang, C. S., Kanniah, K. D., and Kerr, Y. H.: Calibration of SMOS Soil Moisture
Retrieval Algorithm: A Case of Tropical Site in Malaysia, IEEE T. Geosci. Remote, 57, 3827–3839,
https://doi.org/10.1109/TGRS.2018.2888535, 2019. a
Kang, J., Li, X., Jin, R., Ge, Y., Wang, J., and Wang, J.: Hybrid Optimal
Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of
the Heihe River Basin, China, Sensors, 14, 19095–19114,
https://doi.org/10.3390/s141019095, 2014. a
Kirchengast, G., Kabas, T., Leuprecht, A., Bichler, C., and Truhetz, H.:
WegenerNet: A Pioneering High-Resolution Network for Monitoring Weather and
Climate, B. Am. Meteorol. Soc., 95, 227–242,
https://doi.org/10.1175/BAMS-D-11-00161.1, 2014. a
Larson, K. M., Small, E. E., Gutmann, E. D., Bilich, A. L., Braun, J. J., and
Zavorotny, V. U.: Use of GPS receivers as a soil moisture network for water
cycle studies, Geophys. Res. Lett., 35, L24405, https://doi.org/10.1029/2008GL036013,
2008. a
Leavesley, G., David, O., Garen, D., Lea, J., Marron, J., Pagano, T., Perkins,
T., and Strobel, M.: A modeling framework for improved agricultural water
supply forecasting, in: AGU Fall Meeting Abstracts, vol. 1, San Francisco,
CA, USA, 2008. a
Lebel, T., Cappelaere, B., Galle, S., Hanan, N., Kergoat, L., Levis, S., Vieux,
B., Descroix, L., Gosset, M., Mougin, E., Peugeot, C., and Seguis, L.:
AMMA-CATCH studies in the Sahelian region of West-Africa: An overview,
J. Hydrol., 375, 3–13, https://doi.org/10.1016/j.jhydrol.2009.03.020,
2009. a
Leys, C., Ley, C., Klein, O., Bernard, P., and Licata, L.: Detecting outliers:
Do not use standard deviation around the mean, use absolute deviation around
the median, J. Exp. Soc. Psychol., 49, 764–766,
https://doi.org/10.1016/j.jesp.2013.03.013, 2013. a
L'Heureux, J.: 2011 Installation Report for AAFC‐ SAGES Soil Moisture
Stations in Kenaston, SK, Tech. Rep., Agriculture and Agri-food Canada, 2011. a
Loew, A., Dall'Amico, J. T., Schlenz, F., and Mauser, W.: The Upper Danube soil moisture validation site: Measurements and activities,
Earth Observation and Water Cycle Science, 674, 56, 2009. a
Mahmood, R. and Hubbard, K. G.: Relationship between soil moisture of near
surface and multiple depths of the root zone under heterogeneous land uses
and varying hydroclimatic conditions, Hydrol. Process., 21, 3449–3462,
https://doi.org/10.1002/hyp.6578, 2007. a, b, c, d
Manfreda, S., Brocca, L., Moramarco, T., Melone, F., and Sheffield, J.: A physically based approach for the estimation of root-zone soil moisture from surface measurements, Hydrol. Earth Syst. Sci., 18, 1199–1212, https://doi.org/10.5194/hess-18-1199-2014, 2014. a
Marczewski, W., Slominski, J., Slominska, E., Usowicz, B., Usowicz, J., Romanov, S., Maryskevych, O., Nastula, J., and Zawadzki, J.: Strategies for validating and directions for employing SMOS data, in the Cal-Val project SWEX (3275) for wetlands, Hydrol. Earth Syst. Sci. Discuss., 7, 7007–7057, https://doi.org/10.5194/hessd-7-7007-2010, 2010. a
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a
Mattar, C., Santamaría-Artigas, A., Durán-Alarcón, C., Olivera-Guerra, L.,
Fuster, R., and Borvarán, D.: LAB-net the first Chilean soil moisture
network for remote sensing applications, in: Quantitative Remote Sensing
Symposium (RAQRS), 22–26 September 2014, Torrent, Spain, P4.35, 22–26, 2014. a
Mattar, C., Santamaría-Artigas, A., Durán-Alarcón, C., Olivera-Guerra, L.,
Fuster, R., and Borvarán, D.: The LAB-Net Soil Moisture Network: Application
to Thermal Remote Sensing and Surface Energy Balance, Data, 1, 6,
https://doi.org/10.3390/data1010006, 2016. a
Moghaddam, M., Entekhabi, D., Goykhman, Y., Li, K., Liu, M., Mahajan, A.,
Nayyar, A., Shuman, D., and Teneketzis, D.: A Wireless Soil Moisture Smart
Sensor Web Using Physics-Based Optimal Control: Concept and Initial
Demonstrations, IEEE J. Sel. Top. Appl., 3, 522–535, https://doi.org/10.1109/JSTARS.2010.2052918, 2010. a
Moghaddam, M., Silva, A., Clewley, D., Akbar, R., Hussaini, S., Whitcomb, J.,
Devarakonda, R., Shrestha, R., Cook, R., Prakash, G., Santhana Vannan, S.,
and Boyer, A.: Soil Moisture Profiles and Temperature Data from SoilSCAPE
Sites, USA [data set], https://doi.org/10.3334/ORNLDAAC/1339, 2016. a
Morbidelli, R., Corradini, C., Saltalippi, C., Flammini, A., and Rossi, E.: Infiltration-soil moisture redistribution under natural conditions: experimental evidence as a guideline for realizing simulation models, Hydrol. Earth Syst. Sci., 15, 2937–2945, https://doi.org/10.5194/hess-15-2937-2011, 2011. a
Morbidelli, R., Saltalippi, C., Flammini, A., Rossi, E., and Corradini, C.:
Soil water content vertical profiles under natural conditions: matching of
experiments and simulations by a conceptual model, Hydrol. Process.,
28, 4732–4742, https://doi.org/10.1002/hyp.9973, 2014. a
Morbidelli, R., Saltalippi, C., Flammini, A., Cifrodelli, M., Picciafuoco, T.,
Corradini, C., and Govindaraju, R. S.: In situ measurements of soil saturated
hydraulic conductivity: Assessment of reliability through rainfall–runoff
experiments, Hydrol. Process., 31, 3084–3094, https://doi.org/10.1002/hyp.11247,
2017. a
Mougin, E., Hiernaux, P., Kergoat, L., Grippa, M., de Rosnay, P., Timouk, F.,
Le Dantec, V., Demarez, V., Lavenu, F., Arjounin, M., Lebel, T., Soumaguel,
N., Ceschia, E., Mougenot, B., Baup, F., Frappart, F., Frison, P., Gardelle,
J., Gruhier, C., Jarlan, L., Mangiarotti, S., Sanou, B., Tracol, Y.,
Guichard, F., Trichon, V., Diarra, L., Soumaré, A., Koité, M., Dembélé,
F., Lloyd, C., Hanan, N., Damesin, C., Delon, C., Serça, D., Galy-Lacaux,
C., Seghieri, J., Becerra, S., Dia, H., Gangneron, F., and Mazzega, P.: The
AMMA-CATCH Gourma observatory site in Mali: Relating climatic variations to
changes in vegetation, surface hydrology, fluxes and natural resources,
J. Hydrol., 375, 14–33, https://doi.org/10.1016/j.jhydrol.2009.06.045, 2009. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus
Climate Change Service (C3S) Climate Data Store (CDS) [data set],
https://doi.org/10.24381/cds.e2161bac, 2019. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to 1980, Copernicus Climate
Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac,
2021. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b, c
Musial, J., Dabrowska-Zielinska, K., Kiryla, W., Oleszczuk, R., Gnatowski, T.,
and Jaszczynski, J.: Derivation and validation of the high resolution
satellite soil moisture products: a case study of the Biebrza Sentinel-1
validation sites, Geoinformation Issues,
8, 37–53, https://doi.org/10.34867/gi.2016.4, 2016. a
Nguyen, H. H., Kim, H., and Choi, M.: Evaluation of the soil water content
using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated
region, Adv. Water Resour., 108, 125–138,
https://doi.org/10.1016/j.advwatres.2017.07.020, 2017. a
Ojo, E. R., Bullock, P. R., L'Heureux, J., Powers, J., McNairn, H., and
Pacheco, A.: Calibration and Evaluation of a Frequency Domain Reflectometry
Sensor for Real-Time Soil Moisture Monitoring, Vadose Zone J., 14,
vzj2014.08.0114, https://doi.org/10.2136/vzj2014.08.0114, 2015. a
Osenga, E. C., Arnott, J. C., Endsley, K. A., and Katzenberger, J. W.:
Bioclimatic and Soil Moisture Monitoring Across Elevation in a Mountain
Watershed: Opportunities for Research and Resource Management, Water
Resour. Res., 55, 2493–2503, https://doi.org/10.1029/2018WR023653, 2019. a
Osenga, E. C., Vano, J. A., and Arnott, J. C.: A community-supported weather
and soil moisture monitoring database of the Roaring Fork catchment of the
Colorado River Headwaters, Hydrol. Process., 35, e14081,
https://doi.org/10.1002/hyp.14081, 2021. a
Parinussa, R. M., Meesters, A. G. C. A., Liu, Y. Y., Dorigo, W., Wagner, W.,
and de Jeu, R. A. M.: Error Estimates for Near-Real-Time Satellite Soil
Moisture as Derived From the Land Parameter Retrieval Model, IEEE Geosci. Remote S., 8, 779–783, https://doi.org/10.1109/LGRS.2011.2114872,
2011. a
Pasik, A. J. and Preimesberger, W.: 2002–2020 Error-characterized Root-zone Soil Moisture (0–2 m) from C3S Surface Observations (1.6), TU Wien [data set], https://doi.org/10.48436/9gsg6-nn854, 2023. a
Pathe, C., Wagner, W., Sabel, D., Doubkova, M., and Basara, J.: Using ENVISAT
ASAR global mode data for surface soil moisture retrieval over Oklahoma, USA,
IEEE Trans. Geosci. Rem. Sens., 47, 468–480,
https://doi.org/10.1109/TGRS.2008.2004711, 2009. a
Pellarin, T., Calvet, J.-C., and Wagner, W.: Evaluation of ERS scatterometer
soil moisture products over a half-degree region in southwestern France,
Geophys. Res. Lett., 33, L17401, https://doi.org/10.1029/2006GL027231, 2006. a
Petropoulos, G. P. and McCalmont, J. P.: An Operational In Situ Soil Moisture
& Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network,
Sensors, 17, 1481, https://doi.org/10.3390/s17071481, 2017. a
Preimesberger, W., Scanlon, T., Su, C.-H., Gruber, A., and Dorigo, W.:
Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture
Multisatellite Climate Data Record, IEEE T. Geosci.
Remote, 59, 2845–2862, https://doi.org/10.1109/TGRS.2020.3012896, 2021. a
Raffelli, G., Previati, M., Canone, D., Gisolo, D., Bevilacqua, I., Capello,
G., Biddoccu, M., Cavallo, E., Deiana, R., Cassiani, G., and Ferraris, S.:
Local- and Plot-Scale Measurements of Soil Moisture: Time and Spatially
Resolved Field Techniques in Plain, Hill and Mountain Sites, Water, 9, 706,
https://doi.org/10.3390/w9090706, 2017. a
Reichle, R., DeLannoy, G., Koster, R. D., Crow, W. T., and Kimball, J.: SMAP L4
9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version
3, Tech. Rep., TU Vienna, https://doi.org/10.5067/B59DT1D5UMB4, 2017. a
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004. a, b
Rüdiger, C., Hancock, G., Hemakumara, H. M., Jacobs, B., Kalma, J. D.,
Martinez, C., Thyer, M., Walker, J. P., Wells, T., and Willgoose, G. R.:
Goulburn River experimental catchment data set, Water Resour. Res., 43, W10403,
https://doi.org/10.1029/2006WR005837, 2007. a
Schaefer, G. L., Cosh, M. H., and Jackson, T. J.: The USDA Natural Resources
Conservation Service Soil Climate Analysis Network (SCAN), J.
Atmos. Ocean. Tech., 24, 2073–2077,
https://doi.org/10.1175/2007JTECHA930.1, 2007. a
Schlenz, F., dall'Amico, J. T., Loew, A., and Mauser, W.: Uncertainty
Assessment of the SMOS Validation in the Upper Danube Catchment, IEEE
T. Geosci. Remote, 50, 1517–1529,
https://doi.org/10.1109/TGRS.2011.2171694, 2012. a
Shuman, D. I., Nayyar, A., Mahajan, A., Goykhman, Y., Li, K., Liu, M.,
Teneketzis, D., Moghaddam, M., and Entekhabi, D.: Measurement Scheduling for
Soil Moisture Sensing: From Physical Models to Optimal Control, P. IEEE, 98, 1918–1933, https://doi.org/10.1109/JPROC.2010.2052532, 2010. a
Smith, A. B., Walker, J. P., Western, A. W., Young, R. I., Ellett, K. M.,
Pipunic, R. C., Grayson, R. B., Siriwardena, L., Chiew, F. H. S., and
Richter, H.: The Murrumbidgee soil moisture monitoring network data set,
Water Resour. Res., 48, W07701, https://doi.org/10.1029/2012WR011976, 2012. a
Stefan, V.-G., Indrio, G., Escorihuela, M.-J., Quintana-Seguí, P., and Villar,
J. M.: High-Resolution SMAP-Derived Root-Zone Soil Moisture Using an
Exponential Filter Model Calibrated per Land Cover Type, Remote Sensing, 13, 1112,
https://doi.org/10.3390/rs13061112, 2021. a
Su, Z., Wen, J., Dente, L., van der Velde, R., Wang, L., Ma, Y., Yang, K., and Hu, Z.: The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products, Hydrol. Earth Syst. Sci., 15, 2303–2316, https://doi.org/10.5194/hess-15-2303-2011, 2011. a, b, c
Sure, A. and Dikshit, O.: Estimation of root zone soil moisture using passive
microwave remote sensing: A case study for rice and wheat crops for three
states in the Indo-Gangetic basin, J. Environ. Manage., 234,
75–89, https://doi.org/10.1016/j.jenvman.2018.12.109, 2019. a, b, c, d
Tagesson, T., Fensholt, R., Guiro, I., Rasmussen, M. O., Huber, S., Mbow, C.,
Garcia, M., Horion, S., Sandholt, I., Holm-Rasmussen, B., Göttsche, F. M.,
Ridler, M.-E., Olén, N., Lundegard Olsen, J., Ehammer, A., Madsen, M.,
Olesen, F. S., and Ardö, J.: Ecosystem properties of semiarid savanna
grassland in West Africa and its relationship with environmental variability,
Glob. Change Biol., 21, 250–264, https://doi.org/10.1111/gcb.12734, 2015. a
Taylor, J. R.: An Introduction to Error Analysis: The Study of Uncertainties in
Physical Measurements, 2nd edn., University Science Books, Sausalito, ISBN 9780935702750, 1997. a
Tobin, K. J., Torres, R., Crow, W. T., and Bennett, M. E.: Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS, Hydrol. Earth Syst. Sci., 21, 4403–4417, https://doi.org/10.5194/hess-21-4403-2017, 2017. a, b
Van Cleve, K., Chapin, F., Stuart, R., and W., R.: Bonanza Creek Long Term
Ecological Research Project Climate Database,
https://www.lter.uaf.edu/ (last access: 28 August 2023), 2015. a
van der Schalie, R., de Jeu, R., Kerr, Y., Wigneron, J.,
Rodríguez-Fernández, N., Al-Yaari, A., Parinussa, R., Mecklenburg, S., and
Drusch, M.: The merging of radiative transfer based surface soil moisture
data from SMOS and AMSR-E, Remote Sens. Environ., 189, 180–193,
https://doi.org/10.1016/j.rse.2016.11.026, 2017. a
Vreugdenhil, M., Dorigo, W., Broer, M., Haas, P., Eder, A., Hogan, P.,
Bloeschl, G., and Wagner, W.: Towards a high-density soil moisture network
for the validation of SMAP in Petzenkirchen, Austria, in: 2013 IEEE
International Geoscience and Remote Sensing Symposium – IGARSS, 21–26 July 2013, Melbourne, VIC, Australia,
1865–1868, https://doi.org/10.1109/IGARSS.2013.6723166, 2013. a
Vreugdenhil, M., Greimeister-Pfeil, I., Preimesberger, W., Camici, S., Dorigo,
W., Enenkel, M., van der Schalie, R., Steele-Dunne, S., and Wagner, W.:
Microwave remote sensing for agricultural drought monitoring: Recent
developments and challenges, Frontiers in Water, 4, 1045451,
https://doi.org/10.3389/frwa.2022.1045451, 2022. a
Wang, T., Franz, T. E., You, J., Shulski, M. D., and Ray, C.: Evaluating
controls of soil properties and climatic conditions on the use of an
exponential filter for converting near surface to root zone soil moisture
contents, J. Hydrol., 548, 683–696,
https://doi.org/10.1016/j.jhydrol.2017.03.055, 2017. a, b, c, d, e, f
Wigneron, J.-P., Dayan, S., Kruszewski, A., Aluome, C., AI-Yaari, M. G.-E. A.,
Fan, L., Guven, S., Chipeaux, C., Moisy, C., Guyon, D., and Loustau, D.: The
Aqui Network: Soil Moisture Sites in the “Les Landes” Forest and Graves
Vineyards (Bordeaux Aquitaine Region, France), in: IGARSS 2018–2018 IEEE
International Geoscience and Remote Sensing Symposium, 22–27 July 2018, Valencia, Spain, 3739–3742,
https://doi.org/10.1109/IGARSS.2018.8517392, 2018. a
Xaver, A., Zappa, L., Rab, G., Pfeil, I., Vreugdenhil, M., Hemment, D., and Dorigo, W. A.: Evaluating the suitability of the consumer low-cost Parrot Flower Power soil moisture sensor for scientific environmental applications, Geosci. Instrum. Method. Data Syst., 9, 117–139, https://doi.org/10.5194/gi-9-117-2020, 2020. a
Yang, K., Qin, J., Zhao, L., Chen, Y., Tang, W., Han, M., Lazhu, Chen, Z., Lv,
N., Ding, B., Wu, H., and Lin, C.: A Multiscale Soil Moisture and
Freeze–Thaw Monitoring Network on the Third Pole, B. Am.
Meteorol. Soc., 94, 1907–1916, https://doi.org/10.1175/BAMS-D-12-00203.1,
2013. a
Yang, Y., Bao, Z., Wu, H., Wang, G., Liu, C., Wang, J., and Zhang, J.: An
Exponential Filter Model-Based Root-Zone Soil Moisture Estimation Methodology
from Multiple Datasets, Remote Sensing, 14, 1785, https://doi.org/10.3390/rs14081785, 2022. a, b
Young, R., Walker, J., Yeoh, N., Smith, A.and Ellett, K., Merlin, O., and
Western, A.: Soil moisture and meteorological observations from the
murrumbidgee catchment, Tech. Rep., Department of Civil and Environmental
Engineering, The University of Melbourne, 2008. a
Zacharias, S., Bogena, H., Samaniego, L., Mauder, M., Fuß, R., Pütz, T.,
Frenzel, M., Schwank, M., Baessler, C., Butterbach-Bahl, K., Bens, O., Borg,
E., Brauer, A., Dietrich, P., Hajnsek, I., Helle, G., Kiese, R., Kunstmann,
H., Klotz, S., Munch, J. C., Papen, H., Priesack, E., Schmid, H. P.,
Steinbrecher, R., Rosenbaum, U., Teutsch, G., and Vereecken, H.: A Network of
Terrestrial Environmental Observatories in Germany, Vadose Zone J., 10,
955–973, https://doi.org/10.2136/vzj2010.0139, 2011. a
Zappa, L., Forkel, M., Xaver, A., and Dorigo, W.: Deriving Field Scale Soil
Moisture from Satellite Observations and Ground Measurements in a Hilly
Agricultural Region, Remote Sensing, 11, 2596, https://doi.org/10.3390/rs11222596, 2019. a
Zappa, L., Woods, M., Hemment, D., Xaver, A., and Dorigo, W.: Evaluation of
remotely sensed soil moisture products using crowdsourced measurements, in:
Eighth International Conference on Remote Sensing and Geoinformation of the
Environment (RSCy2020), edited by: Themistocleous, K., Papadavid, G.,
Michaelides, S., Ambrosia, V., and Hadjimitsis, D. G., vol. 11524, International Society for Optics and Photonics, SPIE,
115241U,
https://doi.org/10.1117/12.2571913, 2020. a
Zhao, T., Shi, J., Lv, L., Xu, H., Chen, D., Cui, Q., Jackson, T. J., Yan, G.,
Jia, L., Chen, L., Zhao, K., Zheng, X., Zhao, L., Zheng, C., Ji, D., Xiong,
C., Wang, T., Li, R., Pan, J., Wen, J., Yu, C., Zheng, Y., Jiang, L., Chai,
L., Lu, H., Yao, P., Ma, J., Lv, H., Wu, J., Zhao, W., Yang, N., Guo, P., Li,
Y., Hu, L., Geng, D., and Zhang, Z.: Soil moisture experiment in the Luan
River supporting new satellite mission opportunities, Remote Sens.
Environ., 240, 111 680, https://doi.org/10.1016/j.rse.2020.111680, 2020. a
Zheng, J., Zhao, T., Lü, H., Shi, J., Cosh, M. H., Ji, D., Jiang, L., Cui, Q.,
Lu, H., Yang, K., Wigneron, J.-P., Li, X., Zhu, Y., Hu, L., Peng, Z., Zeng,
Y., Wang, X., and Kang, C. S.: Assessment of 24 soil moisture datasets using
a new in situ network in the Shandian River Basin of China, Remote Sens.
Environ., 271, 112891, https://doi.org/10.1016/j.rse.2022.112891, 2022. a
Zreda, M., Desilets, D., Ferré, T. P. A., and Scott, R. L.: Measuring soil
moisture content non-invasively at intermediate spatial scale using
cosmic-ray neutrons, Geophys. Res. Lett., 35, L21402,
https://doi.org/10.1029/2008GL035655, 2008. a
Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012. a
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Short summary
We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate...
