A framework for separating natural and anthropogenic contributions to evapotranspiration of human-managed land covers in watersheds based on machine learning

Actual EvapoTranspiration (ET) represents the water consumption in watersheds; distinguishing between natural and anthropogenic contributions to ET is essential for water conservation and ecological sustainability. This study proposed a framework to separate the contribution of natural and anthropogenic factors to ET of human-managed land cover types using the Random Forest Regressor (RFR). The steps include: (1) classify land cover into natural and human-managed land covers and then divide ET, meteorological, topographical, and geographical data into two parts corresponding to natural and human-managed land cover types; (2) construct a natural ET (ETₙ) prediction model using natural land cover types of ET, and the corresponding meteorological, topographical and geographical factors; (3) the constructed ETₙ prediction model is used to predict the ETₙ of human-managed land cover types using the corresponding meteorological, topographical and geographical data as inputs, and (4) derive the anthropogenic ET (ETₕ) by subtracting the natural ET from the total ET (ETₜ) for human-managed land cover types. Take 2017 as an example, ETₙ and ETₕ for rainfed agriculture, mosaic agriculture, irrigated agriculture, and settlement in Colorado, Blue Nile, and Heihe Basin were separated by the proposed framework, with R² and NSE of predicted ETₙ above 0.95 and RB within 1% for all three basins. In the semi-arid Colorado River Basin and arid Heihe Basin, human activities on human-managed land cover types tended to increase ET higher than humid Blue Nile Basin. The anthropogenic contribution to total water consumption is approaching 53.68%, 66.47%, and 6.14% for the four human-managed land cover types in Colorado River Basin, Heihe Bain and Blue Nile Basin, respectively. The framework provides strong support for the disturbance of water resources by different anthropogenic activities at the basin scale and the accurate estimation of the impact of human activities on ET to help achieve water-related sustainable development goals.