Guillaume S. Mauger

Climate change predicted to shift wolverine distributions, connectivity, and dispersal corridors

Boreal species sensitive to the timing and duration of snow cover are particularly vulnerable to global climate change. Recent work has shown a link between wolverine (Gulo gulo) habitat and persistent spring snow cover through 15 May, the approximate end of the wolverines reproductive denning period. We modeled the distribution of snow cover within the Columbia, Upper Missouri, and Upper Colorado River Basins using a downscaled ensemble climate model. The ensemble model was based on the arithmetic mean of 10 global climate models (GCMs) that best fit historical climate trends and patterns within these three basins. Snow cover was estimated from resulting downscaled temperature and precipitation patterns using a hydrologic model. We bracketed our ensemble model predictions by analyzing warm (miroc 3.2) and cool (pcm1) downscaled GCMs. Because Moderate-Resolution Imaging Spectroradiometer (MODIS)-based snow cover relationships were analyzed at much finer grain than downscaled GCM output, we conducted a second analysis based on MODIS-based snow cover that persisted through 29 May, simulating the onset of spring two weeks earlier in the year. Based on the downscaled ensemble model, 67% of predicted spring snow cover will persist within the study area through 2030-2059, and 37% through 2070-2099. Estimated snow cover for the ensemble model during the period 2070- 2099 was similar to persistent MODIS snow cover through 29 May. Losses in snow cover were greatest at the southern periphery of the study area (Oregon, Utah, and New Mexico, USA) and least in British Columbia, Canada. Contiguous areas of spring snow cover become smaller and more isolated over time, but large (.1000 km 2 ) contiguous areas of wolverine habitat are predicted to persist within the study area throughout the 21st century for all projections. Areas that retain snow cover throughout the 21st century are British Columbia, north-central Washington, northwestern Montana, and the Greater Yellowstone Area. By the late 21st century, dispersal modeling indicates that habitat isolation at or above levels associated with genetic isolation of wolverine populations becomes widespread. Overall, we expect wolverine habitat to persist throughout the species range at least for the first half of the 21st century, but populations will likely become smaller and more isolated.

Comparing Large-Scale Hydrological Model Predictions with Observed Streamflow in the Pacific Northwest: Effects of Climate and Groundwater

Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse ( 1 /168) and fine ( 1 /1208) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In particular, the adequacy of VIC simulations in groundwater- versus runoff-dominated watersheds using a range of flow metrics relevant for water supply and aquatic habitat was examined. These flow metrics were 1) total annual streamflow; 2) total fall, winter, spring, and summer season streamflows; and 3) 5th, 25th, 50th, 75th, and 95th flow percentiles. The effect of climate on model performance was also evaluated by comparing the observed and simulated streamflow sensitivities to temperature and precipitation. Model performance was evaluated using four quantitative statistics: nonparametric rank correlation r, normalized Nash‐Sutcliffe efficiency NNSE, root-mean-square error RMSE, and percent bias PBIAS. The VIC model captured the sensitivity of streamflow for temperature better than for precipitation and was in poor agreement with the corresponding temperature and precipitation sensitivities derived from observed streamflow. The model was able to capture the hydrologic behavior of the study watersheds with reasonable accuracy. Both total streamflow and flow percentiles, however, are subject to strong systematic model bias. For example, summer streamflows were underpredicted (PBIAS 52 13%) in groundwater-dominated watersheds and overpredicted (PBIAS 5 48%) in runoff-dominated watersheds. Similarly, the 5th flow percentile was underpredicted (PBIAS 5 251%) in groundwater-dominated watersheds and overpredicted (PBIAS 5 19%) in runoff-dominated watersheds. These results provide a foundation for improving model parameterization and calibration in ungauged basins.

Impacts of Climate Change on Milk Production in the United States

Climate change is likely to affect milk production because of the sensitivity of dairy cows to excessive temperature and humidity. We use downscaled climate data and county-level dairy industry data to estimate milk production losses for Holstein dairy cows in the conterminous United States. On a national level, we estimate present-day production losses of 1.9 percent relative to baseline production and project that climate impacts could increase these losses to 6.3 percent by the end of the twenty-first century. Using present-day prices, this corresponds to annual losses of

Climate Change, Heavy Precipitation and Flood Risk in the Western United States

670 million per year today, rising to

An Overview of the Columbia Basin Climate Change Scenarios Project: Approach, Methods, and Summary of Key Results

2.2 billion per year by the end of the century. We also find that there is significant geographic variation in production losses and that regions currently experiencing the greatest heat-related impacts are also projected to experience the greatest additional losses with climate change. Specifically, statewide average estimates of end-of-century losses range from 0.4 percent in Washington to a 25 percent loss in annual milk production in Florida. Given that the majority of these losses occur in the summer months, this has the potential to significantly impact operations in hotter climates.

Regional Climate and Hydrologic Change in the Northern U.S. Rockies and Pacific Northwest: Internally Consistent Projections of Future Climate for Resource Management

Current flood management, including flood control structures, land use regulations, and insurance markets, is adapted to historic flood risks, often using data from the past 100 years. In places where climate change will increase the flood risk outside the historic exposure, current management practices may not be adequate and losses could become increasingly catastrophic. For planning purposes, communities require scenarios of likely future flood inundation, which requires modeling the combined effects of sea level rise and changing peak flows along the relevant rivers, which in turn are derived from climate models and downscaling methods. In many regions, including the western United States, extreme precipitation is projected to increase with climate change, and these changes would have substantial impacts on flood risk. Simulating the effects of climate change on extreme precipitation presents substantial modeling challenges due to the complex weather dynamics of these events. Downscaling methods are critical to adequately incorporate the effects of climate change on extreme events and to simulate the response of local flood risk to these changes at the spatial and temporal scales most relevant to assessing community-scale risks from flooding. Statistical and dynamical downscaling is discussed and the implications of these methods for flood risk projections is evaluated. A case study is presented that illustrates three primary pathways for climate change impacts on a flood plain (sea level rise, reduced snowpack and higher intensity precipitation extremes) and illustrates the importance of methodological choices.