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Water quality and groundwater dynamics in wetlands are strongly influenced by the spatiotemporal distribution of contaminant application, and variations and changes in climate, vegetation, and anthropogenic interventions in its neighborhood. For groundwater‐fed wetlands, this relevant neighborhood at least extends to the groundwater contributing area (GCA) boundary. In spite of its importance, understanding of the nature of GCA dynamics vis‐à‐vis meteorological variations remains largely understudied. This work attempts to map GCA of inland forested wetlands. Following that, two specific questions are answered: (a) Is GCA extent and its variation different than that of the topographic contributing area (TCA)? and (b) Is the temporal dynamics of GCA for different wetlands, all of which are experiencing very similar climatological forcing, similar? Our results show that GCAs for wetlands vary temporally, are much different in extent and shape than the TCA, and on an average are larger than the TCA. Although wetlands in the studied watershed experienced similar meteorological forcings, their covariation with forcings varied markedly. Majority of the wetlands registered an increase in GCA during dry period, but for a few the GCA decreased. This highlights the role of additional physical controls, other than meteorological forcings, on temporal dynamics of GCA. Notably, wetlands with larger TCA are found to generally have larger average GCA as well, thus indicating the dominant role of topography in determining the relative size of average GCA over the landscape. Our results provide a refined picture of the spatiotemporal patterns of GCA dynamics and the controls on it. The information will help improve the prediction of wet period dynamics, recharge, and contamination risk of groundwater‐fed wetlands.

 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.