Evapotranspiration (ET) plays a critical role in water and energy budgets at regional to global scales. ET is composed of direct evaporation (E) and plant transpiration (T) where the latter is regulated via stomatal conductance (g_sc), which depends on a multitude of plant physiological processes and hydrometeorological forcings. In recent years, significant advances have been made toward estimating g_sc using a variety of models, ranging from relatively simple empirical models to more complex and data-intensive plant hydraulic models. Using machine learning (ML) and eddy covariance flux tower data of 642 site years across 84 sites distributed across 10 land covers globally, here we show that structural constraints inherent in current empirical and plant hydraulic models of g_sc limit their effectiveness for predicting ET. These constraints also prevent the models from fully utilizing the available hydrometeorological data at eddy covariance sites. Even if these g_sc models are calibrated locally, structural simplifications inherent in them limit their capability to accurately capture g_sc dynamics. In contrast, a ML approach, wherein the model structure is learned from the data, outperforms traditional models, thus highlighting that there still is significant room for improvement in the structure of traditional models for predicting ET. These results underscore the need to prioritize improvements in g_sc models for more accurate ET estimation. This, in turn, will help reduce uncertainties in the assessments of plants' role in regulating the Earth's climate.

中文翻译：

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当前气孔导度模型的结构限制妨碍了蒸散量的准确预测


蒸散量 (ET) 在区域到全球范围内的水和能源预算中发挥着至关重要的作用。 ET 由直接蒸发 ( E ) 和植物蒸腾 ( T ) 组成，其中后者通过气孔导度 ( g _sc ) 进行调节，这取决于多种植物生理过程和水文气象强迫。近年来，使用各种模型（从相对简单的经验模型到更复杂和数据密集型工厂水力模型）估算g _sc方面取得了重大进展。利用分布在全球 10 个土地覆盖范围的 84 个站点的 642 个站点年的机器学习 (ML) 和涡流协方差通量塔数据，我们在此表明​​ g _sc当前经验和工厂水力模型固有的结构约束限制了其预测蒸散的有效性。这些限制还阻止模型充分利用涡流协方差站点的可用水文气象数据。即使这些g _sc模型在本地进行了校准，它们固有的结构简化也限制了它们准确捕获g _sc动力学的能力。相比之下，从数据中学习模型结构的机器学习方法优于传统模型，这凸显出预测蒸散的传统模型的结构仍有很大的改进空间。这些结果强调需要优先改进g _sc模型，以实现更准确的 ET 估计。 反过来，这将有助于减少植物在调节地球气候方面的作用评估的不确定性。