A machine learning-based analysis of actual evaporation predictors across different land covers in high-elevation drylands

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dc.article.number103471
dc.catalogadorvzp
dc.contributor.authorBoada Campos, Javiera Ignacia
dc.contributor.authorLobos Roco, Felipe Andrés
dc.contributor.authorAguirre Correa, Francisca
dc.contributor.authorSuárez Poch, Francisco Ignacio
dc.date.accessioned2026-01-07T16:00:11Z
dc.date.available2026-01-07T16:00:11Z
dc.date.issued2025
dc.description.abstractAccurate estimation of actual evaporation (E) is essential for sustainable ecosystem management in arid, highelevation regions, where water losses are dominated by evaporation processes. In such landscapes, characterized by endorheic basins and heterogeneous land covers including salt flats, vegetated surfaces, bare soils with shallow groundwater, and saline lagoons, E results from complex interactions among energy availability, water availability, and atmospheric dynamics. While recent research has highlighted machine learning (ML) algorithms as promising tools to predict E, the extent to which environmental predictors vary across land cover types remains underexplored. This study applies the Random Forest (RF) algorithm to identify the key predictors of E across evaporitic land covers in the Chilean Altiplano, using evaporation fluxes and environmental variables collected during multiple field campaigns. Predictor importance is assessed through the Shapley Additive Explanation (SHAP) method. Results reveal that the most significant predictors of E vary substantially with land cover: energy limitation governs salt flats and vegetated surfaces; bare soils are influenced by both energy and water availability; and mechanical turbulence dominates over open water. We further demonstrate that accurate E predictions can be achieved using a reduced set of variables, four for land surfaces and three for water bodies, supporting the development of cost-effective monitoring networks focused on key environmental predictors. These findings improve our understanding of land-atmosphere interactions in arid, high-elevation ecosystems and offer a scalable, data-driven approach to enhance water resource management in high-elevation drylands worldwide.
dc.fechaingreso.objetodigital2026-01-07
dc.fuente.origenORCID
dc.identifier.doi10.1016/J.ECOINF.2025.103471
dc.identifier.urihttps://doi.org/10.1016/J.ECOINF.2025.103471
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/107571
dc.information.autorucEscuela de Ingeniería; Boada Campos, Javiera Ignacia; S/I; 1064909
dc.information.autorucInstituto de Geografía; Lobos Roco, Felipe Andrés; S/I; 157192
dc.information.autorucEscuela de Ingeniería; Aguirre Correa, Francisca; 0000-0001-5346-4472; 245616
dc.information.autorucEscuela de Ingeniería; Suarez Poch, Francisco; 0000-0002-4394-957X; 15891
dc.nota.accesocontenido completo
dc.rightsacceso abierto
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectEvapotranspiration
dc.subjectRandom forest
dc.subjectShapley additive explanation (SHAP)
dc.subjectChilean Altiplano
dc.subjectArid regions
dc.subject.ddc550
dc.titleA machine learning-based analysis of actual evaporation predictors across different land covers in high-elevation drylands
dc.typeartículo
dc.volumen92
sipa.codpersvinculados1064909
sipa.codpersvinculados157192
sipa.codpersvinculados245616
sipa.codpersvinculados15891
sipa.trazabilidadORCID;2025-12-22
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