Han Guo

Recent drought, wildfires and rising temperatures in the western US highlight the urgency of increasing resiliency in overstocked forests. However, limited valuation information hinders the broader participation of beneficiaries in forest management. We assessed how historical disturbances in California's Central Sierra Nevada affected live biomass, forest water use and carbon uptake and estimated marginal values of these changes. On average, low‐severity wildfire caused greater declines in forest evapotranspiration (ET), gross primary productivity (GPP) and live biomass than did commercial thinning. Low‐severity wildfires represent proxies for prescribed burns and both function as biomass removal to alleviate overstocked conditions. Increases in potential runoff over 15 years post‐disturbance were valued at $108,000/km² for commercial thinning versus $234,000/km² for low‐severity wildfire, based on historical water prices. Respective declines in GPP were valued at −$305,000 and −$1,317,000/km², based on an average social cost of carbon. Considering biomass levels created by commercial thinning and low‐severity fire as more‐sustainable management baselines for overstocked forests, carbon uptake over 15 years post‐disturbance can be viewed as a benefit rather than loss. Realizing this benefit upon management re‐entry may require sequestering thinned material. High‐severity wildfire and clearcutting resulted in greater declines in ET and thus greater potential water benefits but also substantial declines in GPP and live carbon. These lessons from historical disturbances indicate what benefit ranges from fuels treatments can be expected from more‐sustainable management of mixed‐conifer forests and the importance of setting an appropriate baseline.

Climate change is rapidly modifying historic river flows and snowpack conditions in the Sierra Nevada in California and other seasonally snow-covered mountains. Statistical forecasting methods based on regressing summer flow against spring snow water equivalent, precipitation, and antecedent runoff are thus becoming increasingly inadequate for water-resources decision making, which can lead to missed opportunities in maximizing beneficial uses, including the value of hydropower resources. An enhanced forecasting method using a process-based model and spatially distributed wireless sensor data offers more accurate runoff forecasts. In this paper, we assessed the forecasting accuracy of these two forecasting methods by applying them to two tributaries within the North Fork Feather River basin in California. The result shows the enhanced forecasting method having better accuracy than the statistical model. In addition, a hydropower simulation showed a considerable increase in energy value with the enhanced forecasting informing reservoir operations. The investment analysis on applying this method shows an average internal rate of return of 31% across all scenarios, making this forecasting method an attractive way to better inform water-related decisions for hydropower generation in the context of climate change.