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The importance of sub-watershed variability for predicting ecohydrologic responses to inter-annual climate variability and climate warming in California’s Sierra Nevada watersheds

  • Author(s): Son, Kyongho
  • Advisor(s): Tague, Christina
  • et al.
Abstract

This dissertation improves the understanding of how accounting for fine-scale variability of topography and soil is important for modeling ecohydrologic responses to climate change and variability in California’s Sierra Nevada watersheds. In the first chapter, I apply the Regional Hydrologic-Ecologic Simulation System (RHESSys) to eight small watersheds and investigate how the spatial resolution of digital elevation model (DEM) resolution affects the accuracy of modeling streamflow and model estimates of ecohydrologic responses to inter-annual climate variability. Modeled streamflow estimates become worse with DEM resolution coarser than 10m, the explanation lying in the corresponding reduction in the spatial variance of the wetness index. In the second chapter, I use Generalized Likelihood Uncertainty Estimation (GLUE) to investigate the effect of soil parameter uncertainty in modeling ecohydrologic estimates in the two small watersheds with different snow regimes. The predictive uncertainty of annual evapotranspiration and net primary production estimates for a transient snow watershed are larger than those for a snow-dominated watershed, but the predictive uncertainty in model estimates for daily streamflow is larger for the snow-dominated watershed. The effect of soil parameter uncertainty varies seasonally, between wet and dry years, and its effect on ecohydrologic estimates is often large relative to the effect of climate warming. In the third chapter, I investigate the different hydrologic responses to climate warming between a snow-dominated watershed and a transient snow watershed. The modeling results show that the snow-dominated watershed has greater sensitivity to climate warming than the transient snow watershed. In the both watersheds, leaf area index and wetness index are primary factors controlling spatial patterns of seasonal evapotranspiration under both of historical climate conditions and climate warming scenarios. Climate warming results in increased spatial variability in monthly evapotranspiration, especially in the summer period. In the last chapter, I develop a strategic sampling design for collecting soil moisture and sapflux data to capture watershed ecohydrologic responses to inter-annual climate variability in a transient snow watershed. The comparison of model-based calculated hydrological similarity indicators with measured values shows that spatial patterns of field-sampled soil moisture data are similar to those of model-based estimates. However, the model fails to capture the soil moisture and sapflux dynamics in the riparian zone site, and in a site where lateral subsurface flow may not follow surface topography. Future research will reduce these errors by the use of finer-scale representations of microclimate, topography, vegetation, and soil properties in the model.

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