基于多种机器学习模型的西北地区蒸散发模拟与趋势分析

基于机器学习方法和多源数据构建高精度蒸散发（Evapotranspiration，ET）产品对研究气候变化背景下干旱、半干旱地区陆地水循环变化具有重要意义。本文利用西北地区12个草地通量站点与卫星遥感产品，基于随机森林、极端梯度提升、支持向量回归和人工神经网络4种机器学习方法构建ET估算模型，制作5 km分辨率ET产品，并分析ET的长期变化趋势。交叉验证结果表明，4种模型的均方根误差都低于0.57 mm·d^-1，R²高达0.73～0.88。SHAP （SHapley Additive exPlanation）可解释性分析表明，4种模型均将净辐射、植被和土壤湿度作为ET估算的重要因子，也能刻画出土壤偏干时土壤水分对ET的限制作用，有较好的物理解释性。多模型集合的ET结果相比单一机器学习模型以及现有遥感产品误差分别降低7％～20％和45％～70％。趋势分析结果显示，西北地区非裸地下垫面在2001—2018年间整体呈现ET增加趋势，平均速率为19 mm/（10 a）。在河套平原和内蒙古中部和东北部地区，ET的增长速率超过降水，这可能会进一步加剧这些地区的干旱化。

Modeling the evapotranspiration and its long-term trend over Northwest China using different machine learning models

Using machine learning models (MLMs) to develop high-accuracy evapotranspiration (ET) products is important for investigating the terrestrial hydrological changes in arid and semi-arid regions in the context global warming.Based on the 12 flux stations in Northwest China and multi-source observation datasets, we present a 5-km gridded ET product based on 4 MLMs including the random forest, the extreme gradient boosting, the support vector regression, and the artificial neural network, and analyze the long-term ET trend over Northwest China.The cross-validation results show that all the four models can simulate the daily ET reasonably well, with the root-mean-square error (RMSE) smaller than 0.57 mm·d^-1and the R² up to 0.73~0.88.Moreover, the Sharply additive explanations (SHAP) method reveals that all the models treat the net radiation, vegetation indexes and soil moisture as the most important predictors and capture the limitation effect of soil water on ET reasonably well, indicating a good physical interpretability of the 4 MLMs.No model always has superiority, and the ensemble mean of the 4 models shows a 7%-20% and 45%-70% smaller RMSE than the individual member and other ET products.The ensemble ET shows an increasing trend over the Northwest China during 2001-2018, with a mean increase of 19 mm/(10 a).In addition, the rate of growth of ET is greater than the rate of increase of precipitation in the Hetao region and the middle and northeastern parts of Inner Mongolia, suggesting an intensified drying trend in these regions.