Warming temperatures in the western United States had lead to reduced snow accumulation and earlier snowmelt, altering the timing and magnitude of vegetation water use and productivity. Eco-hydrologic models are key tools used to estimate the magnitude and spatial pattern of these responses and to project future responses to climate change. These models, however, require field measurements in order to estimate model parameters, evaluate model uncertainty and error and where needed, refine model representations of specific processes. Mountain watersheds, however, have high spatial heterogeneity in atmospheric forcing, topography, vegetation and soil properties over relatively short spatial scales, and field measurements are limited by cost, feasibility and accessibility. Thus, a key challenge in these environments is to combine strategically designed field measurements with model parameterization and evaluation.
Our research site is the Southern Sierra Critical Zone Observatory (SSCZO) is situated at the rain-snow to the snow-dominated transition zone. We applied Regional Hydro-Ecologic Simulation System (RHESSys) at this site to develop a strategy for field data collection that is explicitly directed at evaluating and improving model estimates of spatial heterogeneity in ecohydrologic processes including soil moisture and transpiration. We used initial model estimates to identify clusters of eco-hydrologic similarity and then defined sampling sites based on these clusters. The collected data are used to improve the ecohydrologic predictions (soil moisture, transpiration and streamflow) and reduce the model predictive uncertainty. Model sensitivity analysis and comparisons with CZO-flux tower data and distributed soil moisture and sapflux data highlight the importance of adequate representation of micro-climate patterns as controls on summer moisture deficits, transpiration and net primary productivity for the SSCZO watersheds. To support improved representation of micro-climate forcing patterns in models, we have collected additional air temperature and relative humidity data in the SSCZO watersheds using twenty-three micro-climate sensors (HOBO). The collected data are used to refine the climate inputs in order to improve the ecohydrolgic predictions in the SSCZO watersheds.
Son, K., and Tague, C. (2012): Strategic sampling of microclimate, soil moisture and sapflux for improving ecohydrological model estimates in the California Sierra. Fall meeting, American Geophysical Union, December 2012. Abstract H31G-1208..