Snow-dominated mountain ecosystems are particularly sensitive to changes in climate. Warmer temperatures lead to earlier snow melt and alter seasonal timing of streamflow. Changes in the timing of water inputs will also alter seasonal soil wetting-drying patterns and soil-vegetation biogeochemical system processes that depend on moisture conditions. Characterizing soil moisture dynamics, however, is complicated by soil moisture dependence on multiple interacting controls that vary with scale, time and location. At plot to watershed scales, combinations of topography, vegetation and soil characteristics have been linked with soil moisture patterns and their organization in time. Soil moisture sampling directed at understanding how the system responds to climate variation must take into account these multiple controls. In this research, we develop a soil moisture (and vegetation water flux) sampling strategy that is explicitly designed to capture spatial heterogeneity that will likely be important in characterizing system responses to inter-annual climate variability. Our research site, the Sierra Critical Zone Observatory (CZO) is also located in rain-snow transition zone, and thus offers an ideal site for investigating how inter-annual climate variability will change the spatial distribution of snow melt and associated soil wetting-drying and plant water use. Prior sampling design is selected using physically-based, spatially distributed eco-hydrologic model and associated statistical analysis. We initially calibrate the model to reproduce existing soil moisture, sap-flow and streamflow data. We use the model to generate spatial-temporal patterns of snow, soil moisture and transpiration under historical and projected future climate. These patterns are then clustered to identify areas of hydrologic similarity, where similarity will be defined by inter-annual mean and variation of a suite of hydrologic indicators (e.g. seasonal trajectories of snowmelt, root-zone soil moisture storage, and evapotranspiration). We use both traditional clustering approaches and Empirical Orthogonal Function analysis to reduce the dimensionality of the system. Results from this study will demonstrate the utility of such a closely integrated measurement-modeling approach.
Son, K., Tague, C. (2009): An Optimized Soil Moisture Sampling Design to Represent the Impact of Climate Variability on Soil Moisture and Vegetation Water Use in Snow-Dominated Watersheds. Fall meeting, American Geophysical Union, December 2009. 90(52). Abstract H33D-0902. .