Projecting Future Climate, Vegetation, and Hydrology in the Pacific Northwest Completed
In the Pacific Northwest, temperatures are projected to increase 2-15°F by 2100. Winters are expected to become wetter and summers could become drier. Snowpack will likely decrease substantially, and snowmelt runoff may occur earlier in the year. Wildfires are projected to become more frequent and severe, and forest types are expected to change from maritime evergreen to subtropical mixed-woodlands.
Because the impacts of climate change vary from place to place, regionally-specific climate projections are critical to help farmers, foresters, city planners, public utility providers, and fish and wildlife managers plan for how to best manage resources. However, the models that are used to project changes in climate are produced at the global scale and do not provide the degree of resolution necessary for generating meaningful projections about changing conditions for a given region.
To address this need, researchers first evaluated the ability of different global climate models to simulate observed patterns in the Northwest. The best-performing models were then downscaled – that is, transformed from a global scale to a regional scale. Researchers then used the downscaled models to project future changes in climate, vegetation, and hydrology in the region.
This project resulted in publically-available datasets that can be used to address place-based management questions in the Northwest and to develop strategies for reducing the impacts of climate change on the region’s ecosystems, agricultural systems, and built environments. For example, the results can help managers identify forests and grasslands that are most vulnerable to climate change, enabling them to prioritize investments to increase the resilience of these landscapes. Information on projected changes in climate, vegetation, and hydrology is vital as resource managers seek to plan for the impacts of changing conditions and develop effective management strategies.
- Source: USGS Sciencebase (id: 5006eb9de4b0abf7ce733f5c)
In the Pacific Northwest, temperatures are projected to increase 2-15°F by 2100. Winters are expected to become wetter and summers could become drier. Snowpack will likely decrease substantially, and snowmelt runoff may occur earlier in the year. Wildfires are projected to become more frequent and severe, and forest types are expected to change from maritime evergreen to subtropical mixed-woodlands.
Because the impacts of climate change vary from place to place, regionally-specific climate projections are critical to help farmers, foresters, city planners, public utility providers, and fish and wildlife managers plan for how to best manage resources. However, the models that are used to project changes in climate are produced at the global scale and do not provide the degree of resolution necessary for generating meaningful projections about changing conditions for a given region.
To address this need, researchers first evaluated the ability of different global climate models to simulate observed patterns in the Northwest. The best-performing models were then downscaled – that is, transformed from a global scale to a regional scale. Researchers then used the downscaled models to project future changes in climate, vegetation, and hydrology in the region.
This project resulted in publically-available datasets that can be used to address place-based management questions in the Northwest and to develop strategies for reducing the impacts of climate change on the region’s ecosystems, agricultural systems, and built environments. For example, the results can help managers identify forests and grasslands that are most vulnerable to climate change, enabling them to prioritize investments to increase the resilience of these landscapes. Information on projected changes in climate, vegetation, and hydrology is vital as resource managers seek to plan for the impacts of changing conditions and develop effective management strategies.
- Source: USGS Sciencebase (id: 5006eb9de4b0abf7ce733f5c)