Publication Date


Date of Final Oral Examination (Defense)


Type of Culminating Activity


Degree Title

Doctor of Philosophy in Geosciences



Major Advisor

Alejandro Flores, Ph.D.


James McNamara, Ph.D.


Hans-Peter Marshall, Ph.D.


Jodi Mead, Ph.D.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


In much of the world, water for agricultural, domestic, and hydroelectric power generation uses are derived from snow-dominated mountain basins. In these regions, water management requires accurate and timely knowledge of runoff generation by snowmelt. This information is used to plan reservoir releases for downstream users and is generated by models of biophysical processes associated with varying degrees of fidelity to physical processes and/or spatial heterogeneities. The large variability in the characteristic spatial and temporal scales of atmospheric forcings, land-surface water and energy balance, and groundwater flow contribute to significant uncertainties in resolved hydrologic states and fluxes. Underlying sources of uncertainty in these models include difficulties in parameterizing nonlinear or unresolved processes, associated uncertainties in meteorological forcing data and parameters, as well as the large variability in characteristic spatial and temporal scales of atmospheric forcing, surface energy balance, and subsurface hydrological processes. These sources of uncertainty can introduce systematic biases when performing integrated atmospheric and hydrologic modeling. Reconciling these discrepancies while maintaining computational tractability remains a fundamental challenge in hydrologic modeling. This work investigates and quantifies the impacts of discrepancies in scales between distributed meteorological forcing data and modeled land surface and subsurface water flow at hillslope scales. In particular, we are interested in assessing hydrologic state variables and fluxes such as snow water equivalent, discharge, and soil water storage. Also, this work includes the evaluation of the outputs of integrated hydrologic models against observations for a particular set of environmental forcing data (i.e., spatially distributed, semi-distributed, and uniform). We also include an investigation of how the external forcings impact the estimation of snow prognostic and diagnostic variables, primarily snow water equivalent, by performing a global sensitivity analysis. Results of this work suggest that topography (e.g., slope, aspect and valley bottoms) is the primary physiographic variable that describes variations spatial patterns of snow water equivalent and soil water storage when hillslope-scale models are driven by atmospheric forcing characterized by a range of spatial resolutions. At the same time, simulations performed with spatially distributed and semi-distributed meteorological forcings revealed interesting interrelationships between different forcing variables during the snowmelt process. Of particular significance were relationships found between longwave radiation and other atmospheric forcings. Our global sensitivity analyses work allowed us to quantify the strength of first and second order interactions between forcing variables and the snowmelt process. This work has important implications for the use of atmospheric data and integrated hydrologic models in remote and ungauged areas and provides key insights regarding which forcing variables, if measured more precisely, may afford the most significant improvements in snowmelt predictions. In particular, this work has potential ramifications for the selection of forcing datasets for integrated hydrologic modeling experiment as well as for the design and development of observing system simulation experiments (OSSEs) in complex and snow-dominated landscapes.



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