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Estimating the Spatial Distribution of Snow Water Equivalent and Simulated Snowmelt Runoff Modeling in Headwater Basins of the Semi-arid Southwest [electronic resource]

by Dressler, Kevin Andrew.

Abstract (Summary)
The spatial distribution of snowpack in relation to snow water equivalent (SWE) and covered extent is highly variable in time both seasonally and interannually. In order to assess basin water resources, SWE must be distributed to areal estimates. This spatially distributed SWE connects the point scale to the larger scale of the basin (i.e. macro-scale), requiring a combination approach of statistical interpolation techniques and snowpack extent constraint from remote sensing. This research connects those multiple spatial scales and applies the combined remote sensing and ground-based SWE products in a hydrologic model setting to aid in improving streamflow forecasting in the mountainous terrain of snowmelt-dominated basins, a current modeling gap. Four specific advancements were achieved: 1) a comprehensive assessment of spatial distribution techniques in interpolating point snow water equivalent (SWE) measurements at snow telemetry (SNOTEL) stations to the macro-scale was made and an optimal technique for distributing SWE on this scale was obtained; 2) differences between two major data sources of SWE (SNOTEL and snowcourse) were quantified for both point-scale variability and interpolated macro-scale variability to determine spatial and temporal differences in data sources for dry, average and wet years to better inform water resources management applications; 3) basin-scale estimates of ground-based SWE and snow covered area (SCA) from remote sensing were evaluated relative to equivalent fields calculated by a hydrologic model and the effect of assimilating the remote sensing products into the model were investigated; and 4) in the context of (3), improvements were made in macro-scale SCA estimates through both a canopy correction and a low pass statistical filter in an effort to correct for the relatively low resolution of remotely sensed estimates.
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School:The University of Arizona

School Location:USA - Arizona

Source Type:Master's Thesis

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