Downscaling is the process of making a coarse-scale global climate model into a finer resolution in order to capture some of the localized detail that the coarse global models cannot resolve. There are two general approaches of downscaling: dynamical and statistical. Within those, many dynamical models have been developed by different institutions, and there are a number of statistical algorithms that have been developed over the years. Many past studies have evaluated the performance of these two broad approaches of downscaling with respect to climate variables (e.g., temperature, precipitation), but few have translated these evaluations to ecological metrics that managers use to make decisions in planning for climate change. This study uses maple syrup production as a case study for evaluating how these two downscaling techniques perform in terms of projecting changes in the tapping season

Water temperatures are warming in lakes and streams, resulting in the loss of many native fish. Given clear passage, coldwater stream fishes can take refuge upstream when larger streams become too warm. Likewise, many Midwestern lakes “thermally stratify” resulting in warmer waters on top of deeper, cooler waters. Many of these lakes are connected to threatened streams. To date, assessments of the effects of climate change on fish have mostly ignored lakes, and focused instead on streams. Because surface waters represent a network of habitats, an integrated assessment of stream and lake temperatures under climate change is necessary for decision-making. This work  informed the preservation of lake/stream linkages, prioritization restoration strategies, and stocking efforts for sport fish. This project employed state-of-the-science methods to model historical and future thermal habitat for nearly ten thousand lakes