Abstract
Soil moisture is a key variable for drought monitoring but soil moisture measurements networks are very scarce. Land-surface models can provide a valuable alternative to simulate soil moisture dynamics, but only a few countries have such modelling schemes implemented for monitoring soil moisture at high spatial resolution. In this study, a soil moisture accounting model (SMA) was regionalized over the Iberian Peninsula, taking as a reference the soil moisture simulated by a high-resolution land surface model. To estimate soil water holding capacity, the parameter required to run the SMA model, two approaches were compared: the direct estimation from European soil maps using pedotransfer functions, or an indirect estimation by a Machine Learning approach, Random Forests, using as predictors altitude, temperature, precipitation, evapotranspiration and land use. Results showed that the Random Forest model estimates are more robust, especially for estimating low soil moisture levels. Consequently, the proposed approach can provide an efficient way to simulate daily soil moisture and therefore monitor soil moisture droughts, in contexts where high-resolution soil maps are not available, as it relies on a set of covariates that can be reliably estimated from global databases.
Highlights
Soil moisture droughts have strong impacts on vegetation and agricultural production (Raymond et al, 2019; Tramblay et al, 2020; Vicente-Serrano et al, 2014; Pena-Gallardo et al, 2019)
A simple model allowing the monitoring of the soil saturation level was regionalized over the entire Iberian Peninsula, taking as a reference the soil moisture simulated by a high-resolution land surface model
Results have shown that the estimation by Random Forest is more robust notably to estimate low soil moisture levels
Summary
Soil moisture droughts have strong impacts on vegetation and agricultural production (Raymond et al, 2019; Tramblay et al, 2020; Vicente-Serrano et al, 2014; Pena-Gallardo et al, 2019). There is a growing interest for simple indicators to monitor drought events at short timescales that could be related to impacts (Li et al, 2020; Noguera et al, 2021). Soil moisture indicators could be more relevant than climatic ones to monitor potential impacts of droughts on agriculture and natural vegetation (Piedallu et al, 2013). Land-surface models (LSM) are valuable tools for a fine scale monitoring of drought events; their implementation requires accurate forcing data and computational resources (Almendra-Martín et al, 2021; Quintana-Seguí et al, 2019; Barella-Ortiz and Quintana-Seguí, 2019). Very few countries have land-surface schemes implemented at the national level to monitor droughts (Habets et al, 2008)
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