Abstract Informational entropy can be used to elucidate some important relationships between precipitation, evapotranspiration (ET), and discharge over a range of spatial and temporal scales. Entropy does not suffer from any a priori assumptions of linearity or distributional characteristics. In a study of the Southeastern United States, entropy and mutual information were used to identify relationships between hydrologic variables over spatial scales from a few hundred to several thousand km2 and temporal scales from days to months. Two distinct ET data sets were compared- one based on a highly parameterized energy budget approach and the other based on a complex iterative solution and a modified Penman method. It was found that there was a considerable difference in uncertainty between the two methods and that, although they each contained some amount of explanatory information regarding streamflow the more complex approach contained the most shared information. Further, it was found that streamflow entropy, or uncertainty, increased with drainage area, indicating that larger basins have more uncertainty to be reduced, in that more information can possibly be gained through the knowledge of other variables. However, the knowledge of ET reduces a greater proportion of uncertainty in smaller basins (

中文翻译：

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蒸散在水流建模中的作用——一种使用熵的分析

摘要 信息熵可用于阐明降水、蒸散 (ET) 和流量之间在一系列空间和时间尺度上的一些重要关系。熵不受任何线性或分布特征的先验假设的影响。在美国东南部的一项研究中，熵和互信息被用于确定从几百到几千平方公里的空间尺度和从几天到几个月的时间尺度上的水文变量之间的关系。比较了两个不同的 ET 数据集——一个基于高度参数化的能量预算方法，另一个基于复杂的迭代解决方案和改进的 Penman 方法。发现两种方法在不确定性方面存在相当大的差异，并且，尽管它们每个都包含一些关于流的解释信息，但更复杂的方法包含最多的共享信息。此外，还发现流量熵或不确定性随着流域面积的增加而增加，这表明较大的盆地有更多的不确定性需要降低，因为通过了解其他变量可能获得更多信息。然而，ET 的知识减少了较小盆地中更大比例的不确定性（因为通过了解其他变量可能会获得更多信息。然而，ET 的知识减少了较小盆地中更大比例的不确定性（因为通过了解其他变量可能会获得更多信息。然而，ET 的知识减少了较小盆地中更大比例的不确定性（