This paper illustrates the advantages of a multilevel/hierarchical approach for predictive modeling, including flexibility of model formulation, explicitly accounting for hierarchical structure in the data, and the ability to predict the outcome of new cases. As a generalization of the classical approach, the multilevel modeling approach explicitly models the hierarchical structure in the data by considering both the within‐ and between‐group variances leading to a partial pooling of data across all levels in the hierarchy. The modeling framework provides means for incorporating variables at different spatiotemporal scales. The examples used in this paper illustrate the iterative process of model fitting and evaluation, a process that can lead to improved understanding of the system being studied.

Over the last 20 years, dew harvesting has evolved to fruition because of a better understanding of its physics, thermodynamics, and the radiative cooling process of condensing substrates. Although resultant yields are relatively small, dew positions itself as a viable water resources supplement because it occurs naturally and frequently in many locations globally, particularly in the absence of precipitation or when more traditional water sources are subject to depletion. Moreover, dew water is generally potable, especially in rural locations, where it is most beneficial. This review summarizes dew harvesting research achievements to date including formation processes, collection in various environments, prediction models, water quality, and applications. The paper concludes with outlining existing gaps and future research needs to improve the understanding and performance of dew harvesting in the context of adaptation to climate change.