Chen Haitao, Chu Nishi, Kang Aiqing, Wang Wenchuan, He Ji
College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
China Institute of Water Resources and Hydropower, Beijing, 100038, China.
Sci Rep. 2025 Jul 16;15(1):25760. doi: 10.1038/s41598-025-11056-0.
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.
蒸散（ET）是荒漠草原生态系统中水分和能量循环的关键组成部分。然而，由于数据稀缺、高时空异质性以及气候驱动因素之间的复杂相互作用，在干旱草原地区对蒸散进行建模面临重大挑战。为应对这些挑战，本研究开发了一种随机森林回归（RF-R）模型，该模型整合了高分辨率的PML-V2蒸散数据和CRU气象数据集（2001 - 2020年），以模拟中国荒漠草原的蒸散情况。RF-R模型表现优异，训练集的R²值为0.953，测试集的R²值为0.931，均方根误差分别为3.421和4.182毫米/月，平均预测偏差为11.815%。与BPNN和SVR模型的对比分析证实了RF-R估计的稳健性。通过多尺度重要性评估确定了关键气候因素：降水量和降水日频率是主要驱动因素，其次是云量和日较差。本研究为数据稀缺的干旱地区的蒸散模拟提供了一个可靠的框架，并为荒漠草原恢复的针对性水资源管理策略提供了支持。
