The effects of current and future climate change on biological phenology, distribution, community composition, mortality, and extinction have been thoroughly analyzed. However, there has been little analysis of site-specific methods to preserve species and habitats faced with inevitable natural and anthropogenic climate change. The Nature Conservancy (TNC) is currently prioritizing parcels for protection within the Mount Hamilton region. This region contains oak woodlands, riparian habitats and rare and endangered species that are sensitive to the direct and indirect impacts of climate variation. Climate change predictions are based on a low to moderate emissions scenario and a global circulation model downscaled to a 40 kilometer horizontal resolution grid. Binary logistical regression is used to determine which climatic variables influence the distribution of habitat for six high priority species. Climate envelopes based on current and the predicted future climate are compared to determine the amount habitat lost due to climate change. Blue oak (Quercus douglasii) woodlands are the least impacted, but are still projected to lose up to 55% of their habitat. Bay Checkerspot Butterflies (Euphydryas editha bayensis) are the most impacted, and are projected to lose 100% of their habitat. Migration to areas of new habitat is restricted by elevation, low dispersal rates, and the lack of suitable habitat conditions. TNC and other organizations can help to avoid local extirpation of high priority species by preserving high elevation future habitat, migration corridors, and transplanting species upslope. With these efforts, the Mount Hamilton region could continue to support its rich biodiversity despite a rapidly changing climate.

Scientists predict that future climate change will effect both human and natural systems. Using two rainfall-runoff modeling methods, this analysis predicts the effects of climate change on the hydrology of upper Alameda Creek, a small drainage area in California’s Coast Range. I analyzed daily rainfall, temperature, and stream flow data collected from field gages for 8 years to develop a numerical predictive model. Using the Army Corps of Engineers Hec-HMS model and autoregressive statistical techniques, I minimized the difference between the predicted and the observed creek discharge. I then generated an altered temperature and precipitation regime based on a high-end climate change prediction downscaled to a 60 square mile grid. For upper Alameda Creek, annual precipitation is predicted to fall by 28.2% and annual temperature is predicted to increase by 5.2°C by 2100. The autoregressive model had the lowest error when compared to the observed data, and predicts a 22% decrease in total discharge and considerably smaller peak flows with climate change. The Hec-HMS model predicts a 46% reduction in total discharge and large reductions in peak flows with climate change. Reduced discharge and peak flows will have adverse impacts on downstream uses, including drinking water supplies for San Francisco, recreational uses at Sunol Regional Wilderness, and habitat for native rainbow trout, alluvial sycamore, California red-legged frog, California tiger salamander, and other rare and endangered species.