Random forest for spatialization of daily evapotranspiration (ET0) in watersheds in the Atlantic Forest

Environ Monit Assess. 2022 May 23;194(6):449. doi: 10.1007/s10661-022-10110-y.

Abstract

The importance of daily data on reference evapotranspiration (ET0) has increased in recent years due to its relevance in planning and decision making regarding irrigated agriculture, water production, and forest restoration. Facing the scarcity of this information measured in loco, the study of interpolation methods capable of representing ET0 becomes important. Therefore, this study aimed to evaluate the adequacy of the Random Forest (RF) method in the spatialization of ET0 in the watersheds of the Mid-South region of the Espírito Santo State, located within the Atlantic Forest biome, Brazil. From this study, it was found that the RF method is the most suitable one for ET0 spatialization when compared to the Angular distance weighting (ADW) and the inverse distance weighting (IDW) techniques. Also, the spatializations carried out by this method were transformed into databases in a grid format and made available online. Furthermore, the RF database was also compared to other ET0 grid databases, and it was concluded that the RF database also carried out a better performance than the other ones.

Keywords: ADW; IDW; Machine learning; Reference evapotranspiration; Spatial interpolation.

MeSH terms

  • Crops, Agricultural*
  • Ecosystem
  • Environmental Monitoring
  • Plant Transpiration*
  • Temperature