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NF (Region 8) relied on information from The Nature<br />

Conservancy’s Climate Change Wizard (Table 2). The<br />

Chequamegon-Nicolet NF (Region 9) employed data<br />

from WICCI, and the Chugach NF (Region 10) utilized<br />

projections provided by the University of Alaska,<br />

Fairbanks (UAF) Scenarios Network for Alaska Planning<br />

Project (Figure 7).<br />

All the pilot assessments used air temperature change<br />

projections in their analyses and most pilots included<br />

projected changes to precipitation. These projections<br />

were obtained from the variety of publically available<br />

state or regional climate sources listed above. All<br />

projections of future climate are based on General<br />

Circulation Models (GCM). These models are<br />

mathematical representations of atmospheric and<br />

oceanic motion, physics, and chemistry, and employ<br />

different emission scenarios to yield predictions of<br />

temperature and precipitation change. The globalscale<br />

model outputs are very coarse, so data are<br />

often downscaled and used as inputs to macro-scale<br />

hydrologic models for use in regional and finer scale<br />

analysis, such as the WVA pilots. The accuracy of the<br />

data becomes more uncertain with each subsequent layer<br />

of modeling. The greatest certainty is associated with<br />

air temperature projections. Precipitation projections<br />

Projected Climatic Changes<br />

Anticipated Hydrologic<br />

Response<br />

Warmer air temperatures • Warmer water temperature in<br />

streams<br />

Changes in precipitation amounts<br />

and timing<br />

Less snowfall, earlier snowmelt,<br />

increased snowpack density<br />

Intensified storms, greater<br />

extremes of precipitation and wind<br />

• Altered timing and volume of<br />

runoff<br />

• Altered erosion rates<br />

• Higher winter flows<br />

• Lower summer flows<br />

• Earlier and smaller peak flows in<br />

spring<br />

• Greater likelihood of flooding<br />

• Increased erosion rates and<br />

sediment yields<br />

10 | ASSESSING THE VULNERABILITY OF WATERSHEDS TO CLIMATE CHANGE<br />

are highly variable, with even less certainty for derived<br />

attributes like snowmelt, runoff, and stream baseflows.<br />

Precise changes in hydrologic extremes, such as flood<br />

and drought frequency, cannot be credibly modeled at<br />

the stream reach scale at present.<br />

The WVA pilot experience points to the value of broader<br />

scale (e.g., Regional) vulnerability analyses in providing<br />

exposure data and recommending future climate<br />

scenarios to National Forests. Interpreting exposure<br />

data at a broad scale would be useful for several reasons.<br />

First, exposure data is not available at finer scales.<br />

Second, consistency among National Forests in selected<br />

emissions scenarios and modeling assumptions would<br />

allow comparisons of expected climate changes across<br />

National Forests.<br />

Evaluating Hydrologic Changes<br />

Using projections of future temperatures and other<br />

climatic changes, most pilot Forests then considered<br />

what specific hydrologic changes would result from<br />

projected climate changes, and how water resource<br />

values would be affected by these changes. This step<br />

was integrative. In addition to the obvious connection<br />

Potential Consequences to<br />

Watershed Resources<br />

• Decrease in coldwater aquatic<br />

habitats<br />

• Increases or decreases in<br />

availability of water supplies<br />

• Complex changes in water quality<br />

related to flow and sediment<br />

changes<br />

• Changes in the amounts, quality<br />

and distribution of aquatic and<br />

riparian habitats and biota<br />

• Changes in aquatic and riparian<br />

habitats<br />

• Increased damage to roads,<br />

campgrounds, and other facilities<br />

Table 3. Projected hydrologic changes relative to identified values (Helena NF). Adapted from Water, Climate change,<br />

and Forests GTR (Furniss et al. 2010).

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