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

Hongwei Zeng; Abdelrazek Elnashar; Bingfang Wu; Miao Zhang; Weiwei Zhu; Fuyou Tian; Zonghan Ma

doi:10.1016/j.scitotenv.2022.153726

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

Sci Total Environ. 2022 Jun 1:823:153726. doi: 10.1016/j.scitotenv.2022.153726. Epub 2022 Feb 9.

Authors

Hongwei Zeng¹, Abdelrazek Elnashar², Bingfang Wu³, Miao Zhang⁴, Weiwei Zhu⁴, Fuyou Tian⁴, Zonghan Ma⁴

Affiliations

¹ State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
² State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Department of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza 12613, Egypt.
³ State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: wubf@aircas.ac.cn.
⁴ State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.

PMID: 35150693
DOI: 10.1016/j.scitotenv.2022.153726

Abstract

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 (ET_n) prediction model using natural land cover types of ET, and the corresponding meteorological, topographical and geographical factors; (3) the constructed ET_n prediction model is used to predict the ET_n of human-managed land cover types using the corresponding meteorological, topographical and geographical data as inputs, and (4) derive the anthropogenic ET (ET_h) by subtracting the natural ET from the total ET (ET_t) for human-managed land cover types. Take 2017 as an example, ET_n and ET_h 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 ET_n 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.

Keywords: Anthropogenic ET; ET separation; Natural ET; Random Forest Regressor.

MeSH terms

Agriculture
Conservation of Natural Resources*
Desert Climate
Humans
Humidity
Machine Learning
Rivers*
Water Resources*