Publication Date

12-2013

Date of Final Oral Examination (Defense)

11-1-2013

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Geosciences

Department

Geosciences

Major Advisor

James P. McNamara, Ph.D.

Abstract

Deep percolation (DP) is estimated from a small study catchment in the semi arid rain-snow transition zone in the foothills north of Boise, ID. A water balance is performed at the catchment soil bedrock interface, where soil drainage is assumed to be partitioned into DP and streamflow. While stream flow is measured, soil drainage must be estimated. We model the snow dynamics and surface water inputs (SWI) to the soil (Chapter 3), and the soil dynamics and soil drainage to the soil-bedrock interface (Chapter 4). The high spatiotemporal dataset used in this modeling effort is presented for the 2011 water year, which includes weather, topographic, vegetation, and soils data (Chapter 1).

The image SNOw and mass BALance model is used to predict the distributed surface water inputs at a 2.5 m2 resolution. Southwest facing slopes receive smaller and more frequent SWI from mid winter snowmelt, while the northeast slope receives more SWI during the spring. Rain on snow events produce similar SWI between slopes. Turbulent fluxes dominated the snowpack energetics in four of the five rain-on-snow events. Advective fluxes are greater than 17% during the 2 rain-on-snow events in December and January. Net radiation fluxes dominate spring melt events. Variations in the method used to distribute precipitation may result in large differences in total precipitation to the basin.

The Soil Ecohydraulic Model is used to predict soil drainage at 57 points across the catchment. Soils on the southwest facing slope drain more often throughout the water year, but the northeast facing slope contributes a greater total magnitude of soil drainage. Peaks in catchment soil drainage and deep percolation coincide with rain on snow events. Deep percolation is estimated to be 272 mm ± 34 mm for the 2011 water year, which is 29% ±4% of the precipitation.

In summary, we provide a high temporal and spatial data set from a catchment in the rain snow transition zone in Chapter 2. This dataset provides a) soil, vegetation, and weather data to parameterize and drive hydrologic models, and b) snow and hydrologic response data to validate hydrologic models. The data is used to run a physically based snow accumulation and melt model, from which we obtain a high spatial and temporal resolution data set of surface water inputs to the catchment in Chapter 3. Chapter 4 estimates deep percolation from the catchment using the surface water input time series from Chapter 3.

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