@article{fdi:010084639,
  title = {{E}stimating soil moisture conditions for drought monitoring with random forests and a simple soil moisture accounting scheme},
  author = {{T}ramblay, {Y}ves and {S}egui, {P}. {Q}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}oil moisture is a key variable for drought monitoring, but soil moisture measurements networks are very scarce. {L}and-surface models can provide a valuable alternative for simulating soil moisture dynamics, but only a few countries have such modelling schemes implemented for monitoring soil moisture at high spatial resolution. {I}n this study, a soil moisture accounting model ({SMA}) was regionalized over the {I}berian {P}eninsula, taking as a reference the soil moisture simulated by a high-resolution land-surface model. {T}o estimate the soil water holding capacity, the sole parameter required to run the {SMA} model, two approaches were compared: the direct estimation from {E}uropean soil maps using pedotransfer functions or an indirect estimation by a machine learning approach, random forests, using as predictors altitude, temperature, precipitation, potential evapotranspiration and land use. {R}esults showed that the random forest model estimates are more robust, especially for estimating low soil moisture levels. {C}onsequently, 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.},
  keywords = {{ESPAGNE} ; {PORTUGAL}},
  booktitle = {},
  journal = {{N}atural {H}azards and {E}arth {S}ystem {S}ciences},
  volume = {22},
  numero = {4},
  pages = {1325--1334},
  ISSN = {1561-8633},
  year = {2022},
  DOI = {10.5194/nhess-22-1325-2022},
  URL = {https://www.documentation.ird.fr/hor/fdi:010084639},
}