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
| 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
Related authors
No articles found.
Martin Hirschi, Pietro Stradiotti, Bas Crezee, Wouter Dorigo, and Sonia I. Seneviratne
Hydrol. Earth Syst. Sci., 29, 397–425, https://doi.org/10.5194/hess-29-397-2025, https://doi.org/10.5194/hess-29-397-2025, 2025
Short summary
Short summary
We investigate the potential of long-term satellite and reanalysis products for characterising soil drying by analysing their 2000–2022 soil moisture trends and their representation of agroecological drought events of this period. Soil moisture trends are globally diverse and partly contradictory between products. This also affects the products' drought-detection capacity. Based on the best-estimate products, consistent soil drying is observed over more than 40 % of the land area covered.
Wolfgang Preimesberger, Pietro Stradiotti, and Wouter Dorigo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-610, https://doi.org/10.5194/essd-2024-610, 2025
Preprint under review for ESSD
Short summary
Short summary
We introduce the official ESA CCI Soil Moisture GAPFILLED climate data record. A univariate interpolation algorithm is applied to predict missing data points without relying on ancillary variables. The dataset includes gap-free uncertainty estimates for all predictions and was validated with independent in situ reference measurements. The data are recommended for applications, which require global long-term gap-free satellite soil moisture data.
Ruxandra-Maria Zotta, Leander Moesinger, Robin van der Schalie, Mariette Vreugdenhil, Wolfgang Preimesberger, Thomas Frederikse, Richard de Jeu, and Wouter Dorigo
Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, https://doi.org/10.5194/essd-16-4573-2024, 2024
Short summary
Short summary
VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA v2 comprises two products: VODCA CXKu, spanning 34 years of observations (1987–2021), suitable for monitoring upper canopy dynamics, and VODCA L (2010–2021), for above-ground biomass monitoring. VODCA v2 has lower noise levels than the previous product version and provides valuable insights into plant water dynamics and biomass changes, even in areas where optical data are limited.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, Luke Smallmann, Susan Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zähle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek El-Madany, Mirco Migliavacca, Marika Honkanen, Yann Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaetan Pique, Amanda Ojasalo, Shaun Quegan, Peter Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
EGUsphere, https://doi.org/10.5194/egusphere-2024-1534, https://doi.org/10.5194/egusphere-2024-1534, 2024
Short summary
Short summary
When it comes to climate change, the land surfaces are where the vast majority of impacts happen. The task of monitoring those across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us see what changes on our lands.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Related subject area
Hydrology
Selecting a conceptual hydrological model using Bayes' factors computed with replica-exchange Hamiltonian Monte Carlo and thermodynamic integration
The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling
Modelling rainfall with a Bartlett–Lewis process: pyBL (v1.0.0), a Python software package and an application with short records
Virtual Joint Field Campaign: a framework of synthetic landscapes to assess multiscale measurement methods of water storage
SERGHEI v2.0: introducing a performance-portable, high-performance, three-dimensional variably saturated subsurface flow solver (SERGHEI-RE)
The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features
Generalised drought index: a novel multi-scale daily approach for drought assessment
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
A reach-integrated hydraulic modelling approach for large-scale and real-time inundation mapping
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Prediction of hysteretic matric potential dynamics using artificial intelligence: application of autoencoder neural networks
LM4-SHARC v1.0: Resolving the Catchment-scale Soil-Hillslope Aquifer-River Continuum for the GFDL Earth System Modeling Framework
Regionalization in global hydrological models and its impact on runoff simulations: a case study using WaterGAP3 (v 1.0.0)
Wastewater matters: Incorporating wastewater reclamation into a process-based hydrological model (CWatM v1.08)
STORM v.2: A simple, stochastic rainfall model for exploring the impacts of climate and climate change at and near the land surface in gauged watersheds
Fluvial flood inundation and socio-economic impact model based on open data
RoGeR v3.0.5 – a process-based hydrological toolbox model in Python
Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments
An open-source refactoring of the Canadian Small Lakes Model for estimates of evaporation from medium-sized reservoirs
EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions
Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software
HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications
Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community
HydroFATE (v1): a high-resolution contaminant fate model for the global river system
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications
GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers
mesas.py v1.0: a flexible Python package for modeling solute transport and transit times using StorAge Selection functions
rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment
GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model
Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale
Geosci. Model Dev., 18, 1709–1736, https://doi.org/10.5194/gmd-18-1709-2025, https://doi.org/10.5194/gmd-18-1709-2025, 2025
Short summary
Short summary
Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. Bayes’ factor is a tool that can be used to compare models; however, it is very difficult to compute Bayes' factor numerically. In our paper, we explore and develop highly efficient algorithms for computing Bayes’ factor of hydrological systems, which will introduce this useful tool for selecting models into everyday hydrological practice.
Kerry L. Callaghan, Andrew D. Wickert, Richard Barnes, and Jacqueline Austermann
Geosci. Model Dev., 18, 1463–1486, https://doi.org/10.5194/gmd-18-1463-2025, https://doi.org/10.5194/gmd-18-1463-2025, 2025
Short summary
Short summary
We present the Water Table Model (WTM), a new model for simulating groundwater and lake levels at continental scales over millennia. The WTM enables long-term evaluations of water-table changes. As a proof of concept, we simulate the North American water table for the present and the Last Glacial Maximum (LGM), showing that North America held more groundwater and lake water during the LGM than it does today – enough to lower sea levels by 14.98 cm. The open-source code is available on GitHub.
Chi-Ling Wei, Pei-Chun Chen, Chien-Yu Tseng, Ting-Yu Dai, Yun-Ting Ho, Ching-Chun Chou, Christian Onof, and Li-Pen Wang
Geosci. Model Dev., 18, 1357–1373, https://doi.org/10.5194/gmd-18-1357-2025, https://doi.org/10.5194/gmd-18-1357-2025, 2025
Short summary
Short summary
pyBL is an open-source package for generating realistic rainfall time series based on the Bartlett–Lewis (BL) model. It can preserve not only standard but also extreme rainfall statistics across various timescales. Notably, compared to traditional frequency analysis methods, the BL model requires only half the record length (or even shorter) to achieve similar consistency in estimating sub-hourly rainfall extremes. This makes it a valuable tool for modelling rainfall extremes with short records.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not knowing the truth. We propose a virtual framework in which this truth is fully known and the sensor observations for cosmic ray neutron sensing, remote sensing, and hydrogravimetry are simulated. This allows for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Zhi Li, Gregor Rickert, Na Zheng, Zhibo Zhang, Ilhan Özgen-Xian, and Daniel Caviedes-Voullième
Geosci. Model Dev., 18, 547–562, https://doi.org/10.5194/gmd-18-547-2025, https://doi.org/10.5194/gmd-18-547-2025, 2025
Short summary
Short summary
We introduce SERGHEI-RE, a 3D subsurface flow simulator with performance-portable parallel computing capabilities. SERGHEI-RE performs effectively on various computational devices: from personal computers to advanced clusters. It allows users to solve flow equations with multiple numerical schemes, making it adaptable to various hydrological scenarios. Testing results show its accuracy and performance, confirming that SERGHEI-RE is a powerful tool for hydrological research.
Hannes Müller Schmied, Tim Trautmann, Sebastian Ackermann, Denise Cáceres, Martina Flörke, Helena Gerdener, Ellen Kynast, Thedini Asali Peiris, Leonie Schiebener, Maike Schumacher, and Petra Döll
Geosci. Model Dev., 17, 8817–8852, https://doi.org/10.5194/gmd-17-8817-2024, https://doi.org/10.5194/gmd-17-8817-2024, 2024
Short summary
Short summary
Assessing water availability and water use at the global scale is challenging but essential for a range of purposes. We describe the newest version of the global hydrological model WaterGAP, which has been used for numerous water resource assessments since 1996. We show the effects of new model features, as well as model evaluations, against water abstraction statistics and observed streamflow and water storage anomalies. The publicly available model output for several variants is described.
João António Martins Careto, Rita Margarida Cardoso, Ana Russo, Daniela Catarina André Lima, and Pedro Miguel Matos Soares
Geosci. Model Dev., 17, 8115–8139, https://doi.org/10.5194/gmd-17-8115-2024, https://doi.org/10.5194/gmd-17-8115-2024, 2024
Short summary
Short summary
This study proposes a new daily drought index, the generalised drought index (GDI). The GDI not only identifies the same events as established indices but is also capable of improving their results. The index is empirically based and easy to compute, not requiring fitting the data to a probability distribution. The GDI can detect flash droughts and longer-term events, making it a versatile tool for drought monitoring.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024, https://doi.org/10.5194/gmd-17-7751-2024, 2024
Short summary
Short summary
We develop an operational forecast system, Coastlines-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model has relatively low computational requirements, and results compare well with near-real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and wave predictions can improve in accuracy.
Robert Chlumsky, James R. Craig, and Bryan A. Tolson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-184, https://doi.org/10.5194/gmd-2024-184, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We aim to improve mapping of floods, and present a new method for hydraulic modelling that uses a combination of novel geospatial analysis and existing hydraulic modelling approaches. This method is wrapped into a modelling software called Blackbird. We compared Blackbird to two other existing options for flood mapping and found that the Blackbird model outperformed both. The Blackbird model has the potential to support real-time and large-scale flood mapping applications in the future.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
Short summary
Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Geosci. Model Dev., 17, 7083–7103, https://doi.org/10.5194/gmd-17-7083-2024, https://doi.org/10.5194/gmd-17-7083-2024, 2024
Short summary
Short summary
Geoscientists commonly use various potential evapotranpiration (PET) formulas for environmental studies, which can be prone to errors and sensitive to climate change. PyEt, a tested and open-source Python package, simplifies the application of 20 PET methods for both time series and gridded data, ensuring accurate and consistent PET estimations suitable for a wide range of environmental applications.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024, https://doi.org/10.5194/gmd-17-6949-2024, 2024
Short summary
Short summary
The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of the dynamic SWP.
Minki Hong, Nathaniel Chaney, Sergey Malyshev, Enrico Zorzetto, Anthony Preucil, and Elena Shevliakova
EGUsphere, https://doi.org/10.5194/egusphere-2024-2005, https://doi.org/10.5194/egusphere-2024-2005, 2024
Short summary
Short summary
This study aims to understand the significance of groundwater in resolving water-energy budgets in the context of Earth system processes. LM4-SHARC describes the hillslope groundwater using its emergent properties derived from streamflow observations and yields noticeable improvements in soil moisture/temperature and groundwater discharge predictions. The implication of the groundwater-mediated hydrologic interactions between hillslope and stream needs further exploration in the ESM community.
Jenny Kupzig, Nina Kupzig, and Martina Flörke
Geosci. Model Dev., 17, 6819–6846, https://doi.org/10.5194/gmd-17-6819-2024, https://doi.org/10.5194/gmd-17-6819-2024, 2024
Short summary
Short summary
Valid simulation results from global hydrological models (GHMs) are essential, e.g., to studying climate change impacts. Adapting GHMs to ungauged basins requires regionalization, enabling valid simulations. In this study, we highlight the impact of regionalization of GHMs on runoff simulations using an ensemble of regionalization methods for WaterGAP3. We have found that regionalization leads to temporally and spatially varying uncertainty, potentially reaching up to inter-model differences.
Dor Fridman, Mikhail Smilovic, Peter Burek, Sylvia Tramberend, and Taher Kahil
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-143, https://doi.org/10.5194/gmd-2024-143, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Global hydrological models are applied at high spatial resolutions to quantify water availability and evaluate water scarcity mitigation options. Yet they mostly oversee important local processes. This paper presents and demonstrates the inclusion of wastewater treatment and reclamation into a global hydrological model. As a result model performance is improved, and models are capable to utilize treated wastewater as an alternative water source.
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
Short summary
Short summary
STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
Short summary
Short summary
River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
Short summary
Short summary
The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
Short summary
Short summary
This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
Short summary
Short summary
This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
Short summary
Short summary
The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
Short summary
Short summary
Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
Short summary
Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
Short summary
Short summary
We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
Short summary
Short summary
The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
Short summary
Short summary
Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
Short summary
Short summary
We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
Short summary
Short summary
Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
Short summary
Short summary
We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
Short summary
Short summary
Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in 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 codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
Short summary
Short summary
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.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
Short summary
Short summary
This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(2537 KB) - Full-text XML
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...
