Simulation and analysis of evapotranspiration from desert grasslands based on a random forest regression model
Abstract Evapotranspiration (ET) is a critical component of the water and energy cycles in desert grassland ecosystems. However, modeling ET in arid grasslands faces significant challenges due to data scarcity, high spatiotemporal heterogeneity, and complex interactions among climatic drivers. To ad...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-11056-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849234559980273664 |
|---|---|
| author | Haitao Chen Nishi Chu Aiqing Kang Wenchuan Wang Ji He |
| author_facet | Haitao Chen Nishi Chu Aiqing Kang Wenchuan Wang Ji He |
| author_sort | Haitao Chen |
| collection | DOAJ |
| description | Abstract Evapotranspiration (ET) is a critical component of the water and energy cycles in desert grassland ecosystems. However, modeling ET in arid grasslands faces significant challenges due to data scarcity, high spatiotemporal heterogeneity, and complex interactions among climatic drivers. To address these challenges, this study developed a Random Forest Regression (RF-R) model integrated with high-resolution PML-V2 ET data and CRU meteorological datasets (2001–2020) to simulate ET in China’s desert grasslands. The RF-R model achieved superior performance, with R² values of 0.953 (training) and 0.931 (testing), RMSE of 3.421 and 4.182 mm/month, and an average prediction bias of 11.815%. The comparative analysis between BPNN and SVR models confirms the robustness of RF-R estimates. Key climate factors were identified through multi-scale importance assessments: precipitation and wet-day frequency were the primary drivers, followed by cloud cover and diurnal temperature range. This study provides a reliable framework for ET simulation in data-scarce arid regions and supports targeted water management strategies for desert grassland restoration. |
| format | Article |
| id | doaj-art-bd9af488576d4d5bbc2245cf92460ff8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bd9af488576d4d5bbc2245cf92460ff82025-08-20T04:03:06ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-11056-0Simulation and analysis of evapotranspiration from desert grasslands based on a random forest regression modelHaitao Chen0Nishi Chu1Aiqing Kang2Wenchuan Wang3Ji He4College of Water Resources, North China University of Water Resources and Electric PowerCollege of Water Resources, North China University of Water Resources and Electric PowerChina Institute of Water Resources and HydropowerCollege of Water Resources, North China University of Water Resources and Electric PowerCollege of Water Resources, North China University of Water Resources and Electric PowerAbstract Evapotranspiration (ET) is a critical component of the water and energy cycles in desert grassland ecosystems. However, modeling ET in arid grasslands faces significant challenges due to data scarcity, high spatiotemporal heterogeneity, and complex interactions among climatic drivers. To address these challenges, this study developed a Random Forest Regression (RF-R) model integrated with high-resolution PML-V2 ET data and CRU meteorological datasets (2001–2020) to simulate ET in China’s desert grasslands. The RF-R model achieved superior performance, with R² values of 0.953 (training) and 0.931 (testing), RMSE of 3.421 and 4.182 mm/month, and an average prediction bias of 11.815%. The comparative analysis between BPNN and SVR models confirms the robustness of RF-R estimates. Key climate factors were identified through multi-scale importance assessments: precipitation and wet-day frequency were the primary drivers, followed by cloud cover and diurnal temperature range. This study provides a reliable framework for ET simulation in data-scarce arid regions and supports targeted water management strategies for desert grassland restoration.https://doi.org/10.1038/s41598-025-11056-0EvapotranspirationRandom forestPML-V2Desert grassland |
| spellingShingle | Haitao Chen Nishi Chu Aiqing Kang Wenchuan Wang Ji He Simulation and analysis of evapotranspiration from desert grasslands based on a random forest regression model Scientific Reports Evapotranspiration Random forest PML-V2 Desert grassland |
| title | Simulation and analysis of evapotranspiration from desert grasslands based on a random forest regression model |
| title_full | Simulation and analysis of evapotranspiration from desert grasslands based on a random forest regression model |
| title_fullStr | Simulation and analysis of evapotranspiration from desert grasslands based on a random forest regression model |
| title_full_unstemmed | Simulation and analysis of evapotranspiration from desert grasslands based on a random forest regression model |
| title_short | Simulation and analysis of evapotranspiration from desert grasslands based on a random forest regression model |
| title_sort | simulation and analysis of evapotranspiration from desert grasslands based on a random forest regression model |
| topic | Evapotranspiration Random forest PML-V2 Desert grassland |
| url | https://doi.org/10.1038/s41598-025-11056-0 |
| work_keys_str_mv | AT haitaochen simulationandanalysisofevapotranspirationfromdesertgrasslandsbasedonarandomforestregressionmodel AT nishichu simulationandanalysisofevapotranspirationfromdesertgrasslandsbasedonarandomforestregressionmodel AT aiqingkang simulationandanalysisofevapotranspirationfromdesertgrasslandsbasedonarandomforestregressionmodel AT wenchuanwang simulationandanalysisofevapotranspirationfromdesertgrasslandsbasedonarandomforestregressionmodel AT jihe simulationandanalysisofevapotranspirationfromdesertgrasslandsbasedonarandomforestregressionmodel |