Abstract
Drought is a costly natural disaster characterized by water shortages that impact water availability, agriculture, ecosystems, and the economy. The driving mechanisms of drought operate on a wide range of spatial scales, from the movement of soil water on a hillslope to global atmospheric circulation. Additionally, drought impacts vary across spatial scales, from drought induced crop stress on a specific agricultural field to widespread continental water shortages. As a result, multiscalar drought monitoring and early warning systems are needed to utilize observational data sets obtained at different spatial scales and to communicate drought impacts to various levels of decision-makers in government and industry. However, scaling must be employed to translate information across scales, either to fix incongruencies in the spatial scale of input data sets or to modify the model output scale. These scaling techniques have several challenges and limitations that hinder drought accuracy and interpretability, such as the Modifiable Areal Unit Problem (MAUP) and increased model uncertainty. This paper reviews the role of spatial scale in drought monitoring and early warning systems, the associated challenges, and techniques to minimize their impact. Finally, this review identifies several knowledge gaps and future directions.
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