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This work provides a flexible and scalable framework to assess the impacts of climate change on streamflow and stream temperature within the North Atlantic Landscape Conservation Cooperative (NALCC) region. This is accomplished through use of lumped parameter, physically-based, conceptual hydrologic and stream temperature models formulated in a hierarchical Bayesian framework. This allows for model predictions of streamflow and temperature at ungaged locations and a formal accounting of model estimate uncertainty at each location, something not previously achieved in these models. These environmental models will also link seamlessly with the land use and fish models. The final products of this project will provide:...
The Interactive Catchment Explorer (ICE) is a dynamic visualization interface for exploring catchment characteristics and environmental model predictions. ICE was created for resource managers and researchers to explore complex, multivariate environmental datasets and model results, to identify spatial patterns related to ecological conditions, and to prioritize locations for restoration or further study. ICE incorporates stream temperature and brook trout occurrence models for headwaters of the Northeast, including projections of the potential effects of climate change. ICE is part of the Spatial Hydro-Ecological Decision System (SHEDS).
Categories: Data,
Web Site;
Tags: Application,
Climate Impacts,
Data,
Eastern Brook Trout,
LCC Network Science Catalog,
AbstractWe analyzed the associations of catchment-scale and riparian-scale environmental factors with occurrence of Brook Trout Salvelinus fontinalis in Connecticut headwater stream segments with catchment areas of <15 km2 . A hierarchical Bayesian approach was applied to a statewide stream survey data set, in which Brook Trout detection probability was incorporated and statistical significance of environmental covariates was based on 95% credible intervals of estimated coefficients that did not overlap a value of zero. Forested land at the catchment scale was the most important covariate affecting Brook Trout occurrence; i.e., heavily forested catchments with corresponding low levels of developed and impervious...
Designed by scientists to simplify consistent data collection and management, the iPlover smartphone application gives trained resource managers an easy-to-use platform where they can collect and share data about coastal habitat utilization across a diverse community of field technicians, scientists, and managers. With the click of a button, users can contribute biological and geomorphological data to regional models designed to forecast the habitat outlook for piping plover, and other species that depend upon sandy beach habitat.iPlover app is available for iPhones and Androids on the USGS Mobile Application Directory. The app is free, but users must ask for and receive an approved login to use it. Training is...
Categories: Data;
Tags: Application,
Atlantic Coast,
Coastal Geomorphology,
LCC Network Science Catalog,
Piping Plover,
The impacts from climate change are increasing the possibility of vulnerable coastal species and habitats crossing critical thresholds that could spur rapid and possibly irreversible changes. For species of high conservation concern, improved knowledge of quantitative thresholds could greatly improve management. To meet this need, we synthesized information pertaining to biological responses as tipping points to sea level rise (SLR) and coastal storms for 45 fish, wildlife, and plant species along the U.S. Atlantic and Gulf Coasts and Caribbean through a literature review and expert elicitation. Although these species were selected based on their ecological, economic, and cultural importance, just over half (56%,...
ABSTRACTWater temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE D 0:59 °C), identified a clear warming trend (0.63 °C...
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