Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1325-2022
https://doi.org/10.5194/nhess-22-1325-2022
Research article
 | 
12 Apr 2022
Research article |  | 12 Apr 2022

Estimating soil moisture conditions for drought monitoring with random forests and a simple soil moisture accounting scheme

Yves Tramblay and Pere Quintana Seguí

Related authors

Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?
Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, Jonathan Gourley, and Antoinette Alias
Nat. Hazards Earth Syst. Sci., 24, 1163–1183, https://doi.org/10.5194/nhess-24-1163-2024,https://doi.org/10.5194/nhess-24-1163-2024, 2024
Short summary
Impact-based flood forecasting in the Greater Horn of Africa
Lorenzo Alfieri, Andrea Libertino, Lorenzo Campo, Francesco Dottori, Simone Gabellani, Tatiana Ghizzoni, Alessandro Masoero, Lauro Rossi, Roberto Rudari, Nicola Testa, Eva Trasforini, Ahmed Amdihun, Jully Ouma, Luca Rossi, Yves Tramblay, Huan Wu, and Marco Massabò
Nat. Hazards Earth Syst. Sci., 24, 199–224, https://doi.org/10.5194/nhess-24-199-2024,https://doi.org/10.5194/nhess-24-199-2024, 2024
Short summary
On the visual detection of non-natural records in streamflow time series: challenges and impacts
Laurent Strohmenger, Eric Sauquet, Claire Bernard, Jérémie Bonneau, Flora Branger, Amélie Bresson, Pierre Brigode, Rémy Buzier, Olivier Delaigue, Alexandre Devers, Guillaume Evin, Maïté Fournier, Shu-Chen Hsu, Sandra Lanini, Alban de Lavenne, Thibault Lemaitre-Basset, Claire Magand, Guilherme Mendoza Guimarães, Max Mentha, Simon Munier, Charles Perrin, Tristan Podechard, Léo Rouchy, Malak Sadki, Myriam Soutif-Bellenger, François Tilmant, Yves Tramblay, Anne-Lise Véron, Jean-Philippe Vidal, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 27, 3375–3391, https://doi.org/10.5194/hess-27-3375-2023,https://doi.org/10.5194/hess-27-3375-2023, 2023
Short summary
Changes in Mediterranean flood processes and seasonality
Yves Tramblay, Patrick Arnaud, Guillaume Artigue, Michel Lang, Emmanuel Paquet, Luc Neppel, and Eric Sauquet
Hydrol. Earth Syst. Sci., 27, 2973–2987, https://doi.org/10.5194/hess-27-2973-2023,https://doi.org/10.5194/hess-27-2973-2023, 2023
Short summary
ADHI: the African Database of Hydrometric Indices (1950–2018)
Yves Tramblay, Nathalie Rouché, Jean-Emmanuel Paturel, Gil Mahé, Jean-François Boyer, Ernest Amoussou, Ansoumana Bodian, Honoré Dacosta, Hamouda Dakhlaoui, Alain Dezetter, Denis Hughes, Lahoucine Hanich, Christophe Peugeot, Raphael Tshimanga, and Patrick Lachassagne
Earth Syst. Sci. Data, 13, 1547–1560, https://doi.org/10.5194/essd-13-1547-2021,https://doi.org/10.5194/essd-13-1547-2021, 2021
Short summary

Related subject area

Hydrological Hazards
Hyper-resolution flood hazard mapping at the national scale
Günter Blöschl, Andreas Buttinger-Kreuzhuber, Daniel Cornel, Julia Eisl, Michael Hofer, Markus Hollaus, Zsolt Horváth, Jürgen Komma, Artem Konev, Juraj Parajka, Norbert Pfeifer, Andreas Reithofer, José Salinas, Peter Valent, Roman Výleta, Jürgen Waser, Michael H. Wimmer, and Heinz Stiefelmeyer
Nat. Hazards Earth Syst. Sci., 24, 2071–2091, https://doi.org/10.5194/nhess-24-2071-2024,https://doi.org/10.5194/nhess-24-2071-2024, 2024
Short summary
Compound droughts under climate change in Switzerland
Christoph Nathanael von Matt, Regula Muelchi, Lukas Gudmundsson, and Olivia Martius
Nat. Hazards Earth Syst. Sci., 24, 1975–2001, https://doi.org/10.5194/nhess-24-1975-2024,https://doi.org/10.5194/nhess-24-1975-2024, 2024
Short summary
Brief communication: SWM – stochastic weather model for precipitation-related hazard assessments using ERA5-Land data
Melody Gwyneth Whitehead and Mark Stephen Bebbington
Nat. Hazards Earth Syst. Sci., 24, 1929–1935, https://doi.org/10.5194/nhess-24-1929-2024,https://doi.org/10.5194/nhess-24-1929-2024, 2024
Short summary
Text mining uncovers the unique dynamics of socio-economic impacts of the 2018–2022 multi-year drought in Germany
Jan Sodoge, Christian Kuhlicke, Miguel D. Mahecha, and Mariana Madruga de Brito
Nat. Hazards Earth Syst. Sci., 24, 1757–1777, https://doi.org/10.5194/nhess-24-1757-2024,https://doi.org/10.5194/nhess-24-1757-2024, 2024
Short summary
The value of multi-source data for improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0
Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini
Nat. Hazards Earth Syst. Sci., 24, 1681–1696, https://doi.org/10.5194/nhess-24-1681-2024,https://doi.org/10.5194/nhess-24-1681-2024, 2024
Short summary

Cited articles

Almendra-Martín, L., Martínez-Fernández, J., González-Zamora, Á., Benito-Verdugo, P., and Herrero-Jiménez, C. M.: Agricultural Drought Trends on the Iberian Peninsula: An Analysis Using Modeled and Reanalysis Soil Moisture Products, Atmosphere, 12, 236, https://doi.org/10.3390/atmos12020236, 2021. 
Anctil, F., Michel, C., Perrin, C., and Andréassian, V.: A soil moisture index as an auxiliary ANN input for stream flow forecasting, J. Hydrol., 286, 155–167, https://doi.org/10.1016/j.jhydrol.2003.09.006, 2004. 
Barella-Ortiz, A. and Quintana-Seguí, P.: Evaluation of drought representation and propagation in regional climate model simulations across Spain, Hydrol. Earth Syst. Sci., 23, 5111–5131, https://doi.org/10.5194/hess-23-5111-2019, 2019. 
Bauer-Marschallinger, B., Freeman, V., Cao, S., Paulik, C., Schaufler, S., Stachl, T., Modanesi, S., Massari, C., Ciabatta, L., Brocca, L., and Wagner, W.: Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles, IEEE T. Geosci. Remote, 57, 520–539, https://doi.org/10.1109/TGRS.2018.2858004, 2019. 
Blöschl, G. and Sivapalan, M.: Scale issues in hydrological modelling: A review, Hydrol. Process., 9, 251–290, https://doi.org/10.1002/hyp.3360090305, 1995. 
Download
Short summary
Monitoring soil moisture is important during droughts, but very few measurements are available. Consequently, land-surface models are essential tools for reproducing soil moisture dynamics. In this study, a hybrid approach allowed for regionalizing soil water content using a machine learning method. This approach proved to be efficient, compared to the use of soil property maps, to run a simple soil moisture accounting model, and therefore it can be applied in various regions.
Altmetrics
Final-revised paper
Preprint