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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:...
Categories: Data; Tags: BIOSPHERE, BIOSPHERE, BIOSPHERE, Completed, DATA ANALYSIS AND VISUALIZATION, All tags...
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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 also link seamlessly with the land use and fish models. The goal for this project was to provide: 1) Estimates...
The cascade of uncertainty that underscores climate impact assessments of regional hydrology undermines their value for long-term water resources planning and management. This study presents a statistical framework that quantifies and propagates the uncertainties of hydrologic model response through projections of future streamflow under climate change. Different sources of hydrologic model uncertainty are accounted for using Bayesian modeling. The distribution of model residuals is formally characterized to quantify predictive skill, and Markov chain Monte Carlo sampling is used to infer the posterior distributions of both hydrologic and error model parameters. Parameter and residual error uncertainties are integrated...
    map background search result map search result map Using Dynamic Linear Modeling to Characterize Hydrologic Regimes and Detect Flow Modifications at Multiple Temporal Scales Using Dynamic Linear Modeling to Characterize Hydrologic Regimes and Detect Flow Modifications at Multiple Temporal Scales