Mountain ecohydrologic processes are scale-dependent and are highly spatially heterogeneous. Mountain ecohydrology is also vulnerable to climate change. Ecohydrologic models are our main tools to evaluate how climate change will alter ecohydrologic processes including snow, soil moisture, evapotranspiration and carbon fluxes. Models require a variety of field data to estimate parameters and evaluate model uncertainty and error. Moreover, mountain watersheds have high spatial heterogeneity in atmospheric forcing, topography, vegetation and soil properties. However, field measurements in mountain environments are limited by cost, feasibility and accessibility. Therefore, a key challenge for modeling ecohydrologic processes in mountain watersheds is to develop a strategic sampling design for collecting ecohydrologic data along with a systematic framework for incorporating the collected data into models for model parameterization and evaluation. Our research site is the Southern Sierra Critical Zone Observatory (SSCZO). Located in a snow-rain transition zone, the SSCZO offers an ideal site for testing how climate change alters the ecohydrologic processes in Sierra watersheds. We have applied the Regional Hydro-Ecologic Simulation System (RHESSys), a physically-based spatially-distributed model of coupled carbon and nutrient cycling and hydrology. RHESSys was used to develop a strategy for soil moisture and sapflux data collection that was explicitly directed at evaluating and improving model estimates of ecohydrologic response to climate. We used model estimates of snow, soil moisture, and transpiration to define clusters of ecohydrologic similarity and then selected sampling locations based on these clusters. The field-sampled soil moisture data showed similar spatial pattern to model-based clustering, validating our sampling design. However, modeled microclimate, soil moisture, transpiration, and streamflow were notably different from observations at some locations. To improve model predictions at these locations, fine-scale representations of microclimate, topography, vegetation and soil were added to the model in stages and model estimates were progressively tested against field data. The results demonstrate how field data can be used to identify weaknesses in model parameterization and structure and to develop strategies for improving model estimates.
Son, K. and C. Tague. (2013): A framework for improving the predictions of ecohydrologic responses to climate change in Sierra Critical Zone Observatory watersheds. American Geophysical Union.
This Paper/Book acknowledges NSF CZO grant support.