This study examines the air temperature/stream temperature relationship at a geographically diverse set of streams. We evaluate the general temperature relationships (both linear and nonlinear) that apply to these streams, and then examine how changes in stream temperature associated with climate variability or climate warming might affect dissolved oxygen levels. The majority of streams showed an increase in water temperature of about 0.6–0.8°C for every 1°C increase in air temperature, with very few streams displaying a linear 1:1 air/water temperature trend. For most of the streams, a nonlinear model produced a better fit than did a simple linear model. Understanding the relationship between air temperature and water temperature is important if people want to estimate how stream temperatures are likely to respond to anticipated future increases in surface air temperature. Surface water temperature in many streams will likely increase 2 to 3°C as air temperature increases 3 to 5°C. At sites with current...

Seasonal turbidity patterns and event-level hysteresis analysis of turbidity verses discharge in four 1 km² headwater catchments in California’s Sierra Nevada indicate localized in-channel sediment sources and seasonal accumulation-depletion patterns of stream sediments. Turbidity signals were analyzed for three years in order to look at the relationships between seasonal turbidity trends, event turbidity patterns, and precipitation type to stream sediment production and transport. Seasonal patterns showed more turbidity events associated with fall and early to mid- winter events than with peak snow-melt. No significant turbidity patterns emerged for periods of snow melt vs. rain. Single event hysteresis loops showed clockwise patterns were dominant suggesting local sediment sources. In successive discharge events, the largest turbidity spike was often associated with the first but not necessarily the largest discharge event-indicating seasonal depletion of local sediment stores. In multi-peaked discharge events, hysteresis loops shifted from clockwise to linear or random patterns suggesting that localized sediment stores are being used up and sufficient flow energy must be reached to start entraining the more consolidated bank/bed sediment or that dominant sediment sources may be shifting to less localized areas such as hill slopes. A conceptual model with phases of accumulation and transport is proposed.

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.