Understanding causal relations is of utmost importance in hydrology and climate research for systems identification, prediction, and understanding systems behavior in a changing climate. Traditionally, researchers in hydrometeorology attempted to study causal questions by conducting controlled experiments using numerical models. This approach, however, in most cases of interest provides uncertain results because the models are approximate representation of the natural system. An alternative approach that has recently drawn significant attention in several fields is to infer causal relations from purely observational data. It possesses several traits to its utility particularly in hydrometeorology due to the rapid accumulation of in situ and remotely sensed data records. The first objective of this study is to present a brief description of four causal discovery methods (Granger causality, Transfer Entropy, graph‐based algorithms, and Convergent Cross Mapping) with special emphasis on the assumptions on which they are built. Second, using synthetic data generated from a hydrological model, we assess their performance in retrieving causal information taking into account sensitivity to sample size and presence of noise. Last, we use causal analysis to examine and formulate hypotheses on causal drivers of evapotranspiration in a shrubland region during summer and winter seasons. An interpretation of the hypotheses based on canopy seasonal dynamics and evapotranspiration processes is presented. It is hoped that the results presented here can be useful in guiding researchers studying hydrometeorological systems as to which causal method is most appropriate to the characteristics of the system under study.

中文翻译：

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水文气象系统因果发现方法的评价

在水文和气候研究中，了解因果关系对于系统识别，预测和理解气候变化中的系统行为至关重要。传统上，水文气象学研究人员试图通过使用数值模型进行受控实验来研究因果问题。但是，由于大多数模型都是自然系统的近似表示，因此在大多数情况下，这种方法都无法提供确定的结果。最近在多个领域引起广泛关注的另一种方法是从纯粹的观察数据推断因果关系。由于现场和遥感数据记录的迅速积累，它具有多种用途，特别是在水文气象学中。这项研究的第一个目的是简要介绍四种因果发现方法（格兰杰因果关系，转移熵，基于图的算法和收敛交叉映射），并特别强调它们所基于的假设。其次，使用从水文模型中产生的综合数据，我们考虑到对样本量的敏感性和噪声的存在，评估了它们在检索因果信息时的性能。最后，我们使用因果分析来检验和提出关于夏季和冬季灌木丛地区蒸散量的因果驱动因素的假设。提出了基于冠层季节动态和蒸散过程的假设解释。