Evapotranspiration (ET) links water, energy, and carbon balances, and its magnitude and patterns are changing due to climate and land use change in the southeastern U.S. Quantifying the environmental controls on ET is essential for developing reliable ecohydrological models for water resources management. Here, we synthesized eddy covariance data from 24 AmeriFlux sites distributed across the southeastern U.S., comprising 162 site-years of flux data representing six representative ecosystems including cropland vegetation mosaic (CVM), deciduous broadleaf forests (DBF), evergreen needle-leaf forests (ENF), grasslands (GRA), savannas (SAV), and wetlands (WET). Our objectives were to assess the daily, seasonal, and annual variability in ET and to develop practical predictive models for regional applications in ecosystem service analysis. We evaluated the response of ET to climatic and biotic forcings including potential evapotranspiration (PET), precipitation (P), and leaf area index (LAI), and compared the performance of these empirical ET models based and those developed using machine learning algorithms. Our results showed that the mean daily ET varied significantly, ranging from 1.36 mm d in GRA to 2.30 mm d in SAV, with a numerical order : GRA < DBF < ENF < WET < CVM < SAV. In this humid region, mean annual PET exceeded P in 16 out of the 24 flux sites. Using the Budyko framework, we showed that ENF had the highest evaporative efficiency (ET/P). PET and leaf area index (LAI) emerged as the most influential factors explaining ET variability. Artificial neural networks (ANN) and random forest (RF) models demonstrated superior capabilities in predicting monthly ET across sites over generalized additive modeling (GAM) and multiple linear regression (MLR) methods. The present study confirmed that the Southeast region is generally 'energy limited', implying that atmospheric demand along with vegetation information can be used to reliably estimate monthly and annual ET. Our study provides valuable insights into how ET of specific ecosystems is controlled by climatic and land surface drivers, enabling the development of reliable predictive models for regional extrapolation of flux measurements in water resource management in the humid southeastern U.S. region.

中文翻译：

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能源可用性和叶面积主导美国东南部生态系统蒸散的控制

蒸散量 (ET) 将水、能源和碳平衡联系在一起，由于美国东南部的气候和土地利用变化，其规模和模式正在发生变化。量化对 ET 的环境控制对于开发可靠的水资源管理生态水文学模型至关重要。在这里，我们综合了分布在美国东南部的 24 个 AmeriFlux 站点的涡度协方差数据，包括 162 个站点年的通量数据，代表了六种代表性生态系统，包括农田植被镶嵌（CVM）、落叶阔叶林（DBF）、常绿针叶林（ ENF）、草原（GRA）、稀树草原（SAV）和湿地（WET）。我们的目标是评估蒸散的每日、季节和年度变化，并为生态系统服务分析中的区域应用开发实用的预测模型。我们评估了蒸散对气候和生物强迫的响应，包括潜在蒸散量 (PET)、降水量 (P) 和叶面积指数 (LAI)，并比较了这些基于经验蒸散模型和使用机器学习算法开发的模型的性能。我们的结果表明，平均每日ET变化很大，从GRA的1.36 mm d到SAV的2.30 mm d，数字顺序为：GRA < DBF < ENF < WET < CVM < SAV。在这个潮湿地区，24 个通量地点中的 16 个地点的年平均 PET 超过了 P。使用 Budyko 框架，我们表明 ENF 具有最高的蒸发效率 (ET/P)。 PET 和叶面积指数 (LAI) 成为解释 ET 变异性的最有影响力的因素。人工神经网络 (ANN) 和随机森林 (RF) 模型在预测跨站点每月 ET 方面表现出优于广义加性建模 (GAM) 和多元线性回归 (MLR) 方法的能力。目前的研究证实，东南部地区普遍“能源有限”，这意味着大气需求和植被信息可以用来可靠地估计月度和年度蒸散量。我们的研究为特定生态系统的蒸散如何受气候和地表驱动因素控制提供了宝贵的见解，从而能够开发可靠的预测模型，用于美国潮湿的东南部地区水资源管理中通量测量的区域外推。