Articles | Volume 22, issue 4
Nat. Hazards Earth Syst. Sci., 22, 1325–1334, 2022
https://doi.org/10.5194/nhess-22-1325-2022

Special issue: Hydrological cycle in the Mediterranean (ACP/AMT/GMD/HESS/NHESS/OS...

Nat. Hazards Earth Syst. Sci., 22, 1325–1334, 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

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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. 
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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.
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