A Framework for Separating Climate and Anthropogenic Contributions to Evapotranspiration Changes in Natural to Agricultural Regions of Watersheds Based on Machine Learning

Evapotranspiration is a crucial component of the water cycle and is significantly influenced by climate change and human activities. Agricultural expansion, as a major aspect of human activity, together with climate change, profoundly affects regional ET variations. This study proposes a quantification framework to assess the impacts of climate change (ETm) and agricultural development (ETh) on regional ET variations based on the Random Forest algorithm. The framework was applied in a large-scale agricultural expansion area in China, specifically, the Songhua River Basin. Meteorological, topographic, and ET remote sensing data for the years of 1980 and 2015 were selected. The Random Forest model effectively simulates ET in the natural areas (i.e., forest, grassland, marshland, and saline-alkali land) in the Songhua River Basin, with R2 values of around 0.99. The quantification results showed that climate change has altered ET by −8.9 to 24.9 mm and −3.4 to 29.7 mm, respectively, in the natural areas converted to irrigated and rainfed agricultural areas. Deducting the impact of climate change on the ET variation, the development of irrigated and rainfed agriculture resulted in increases of 2.9 mm to 55.9 mm and 0.9 mm to 53.4 mm in ET, respectively, compared to natural vegetation types. Finally, the Self-Organizing Map method was employed to explore the spatial heterogeneity of ETh and ETm. In the natural–agriculture areas, ETm is primarily influenced by moisture conditions. When moisture levels are adequate, energy conditions become the predominant factor. ETh is intricately linked not only to meteorological conditions but also to the types of original vegetation. This study provides theoretical support for quantifying the effects of climate change and farmland development on ET, and the findings have important implications for water resource management, productivity enhancement, and environmental protection as climate change and agricultural expansion persist.

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Journal
Remote Sensing
Published
2024-11-25
DOI
https://doi.org/10.3390/rs16234408
Citations
1
Primary Topic
Hydrological Forecasting Using AI
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article
Field-Weighted Citation Impact
0.21
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article

A Framework for Separating Climate and Anthropogenic Contributions to Evapotranspiration Changes in Natural to Agricultural Regions of Watersheds Based on Machine Learning

Peng Qi, Guangxin Zhang, Hongyan Li, Fengping Li et al.
1 citations
Remote Sensing
Hydrological Forecasting Using AI
0.21
article

A Framework for Separating Climate and Anthropogenic Contributions to Evapotranspiration Changes in Natural to Agricultural Regions of Watersheds Based on Machine Learning

Peng Qi, Guangxin Zhang, Hongyan Li, Fengping Li, Z.N. Liang
article en
1 citations

Abstract

Evapotranspiration is a crucial component of the water cycle and is significantly influenced by climate change and human activities. Agricultural expansion, as a major aspect of human activity, together with climate change, profoundly affects regional ET variations. This study proposes a quantification framework to assess the impacts of climate change (ETm) and agricultural development (ETh) on regional ET variations based on the Random Forest algorithm. The framework was applied in a large-scale agricultural expansion area in China, specifically, the Songhua River Basin. Meteorological, topographic, and ET remote sensing data for the years of 1980 and 2015 were selected. The Random Forest model effectively simulates ET in the natural areas (i.e., forest, grassland, marshland, and saline-alkali land) in the Songhua River Basin, with R2 values of around 0.99. The quantification results showed that climate change has altered ET by −8.9 to 24.9 mm and −3.4 to 29.7 mm, respectively, in the natural areas converted to irrigated and rainfed agricultural areas. Deducting the impact of climate change on the ET variation, the development of irrigated and rainfed agriculture resulted in increases of 2.9 mm to 55.9 mm and 0.9 mm to 53.4 mm in ET, respectively, compared to natural vegetation types. Finally, the Self-Organizing Map method was employed to explore the spatial heterogeneity of ETh and ETm. In the natural–agriculture areas, ETm is primarily influenced by moisture conditions. When moisture levels are adequate, energy conditions become the predominant factor. ETh is intricately linked not only to meteorological conditions but also to the types of original vegetation. This study provides theoretical support for quantifying the effects of climate change and farmland development on ET, and the findings have important implications for water resource management, productivity enhancement, and environmental protection as climate change and agricultural expansion persist.

Remote SensingVol. 16(23)
Jilin University (CN), Chinese Academy of Sciences (CN), Northeast Institute of Geography and Agroecology (CN)
Openalex Percentile: Top 48%
Hydrological Forecasting Using AI
0.21
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