Title: GIS Plan For A Study of Snow Water Equivalent (SWE) Distribution In The Rocky Mountains, Colorado.
Principle Investigator
Douglas M. Hultstrand
Department of Forest, Rangeland, and
Watershed Stewardship
Colorado State University
Fort Collins, CO 80251-1472
Date: 18 November 2003
ABSTRACT
This study outlines objectives and methods for applying geographic information system (GIS) techniques in a research effort to asses snow water equivalent (SWE) distribution within the Colorado Rocky Mountains. My objectives for this research are to use a GIS environment to analyze the spatial distribution of snowpack parameters. I believe that using GIS to incorporate metrological parameters, topography, slope, aspect, and vegetation with snow course, snow telemetry (SnoTel), and remote sensing measurements would develop a structure that could be used to better represent spatial distribution of SWE in mountain regions. To date, there has there has been no integration of snow course, SnoTel, and remote sensing SWE measurements. I will use historical snow course, SnoTel, and remote sensed data within a GIS environment to represent average SWE values. Derived SWE equations from historical data can be used in a GIS to spatially distribute SWE values with fewer point measurements. GIS will provide an integral part in all areas of my research, more specifically with snowcover and SWE spatial distribution.
Introduction
The bulk of western United States water resources arrive in the form of solid precipitation, and is naturally stored in mountain snowpacks at high elevations. Peak stream flow is a result of winter snowpack melt. Yearly water consumption in the west averages around 44% of renewable supplies, compared to only 4% for the rest of the country (Serreze et al., 1999). For this reason, understanding physical location and amount of water held in snowpack is of great importance for water supplies, power production, and flood control. SWE is the mass of water contained within a snowpack and is expressed as a unit of length, i.e. mm. In mountainous regions large variation of SWE and snow depth are due to variations in slope, aspect, elevation, exposure, and surface cover (Goodison et al., 1981).
The importance of snowpack properties such as SWE for the annual hydrograph have lead to the development of agencies that focus on the measurement and distribution of SWE in mountain regions. The three common agencies are the Natural Resource Conservation Service (NRCS), U.S. Department of Agriculture (USDA), and the National Operational Hydrologic Remote Sensing Center (NOHRSC). Each agency derives SWE from different methods: snow course field measurements, snowpack telemetry (SnoTel), and remote sensing of electromagnetic radiation (EMR).
Objectives
Specific objectives for my research
1) Assess snow course, SnoTel, and remote sensing measurements of SWE and compare similarities and differences between methods.
2) Evaluate biasing factors that may influence estimates of SWE, such as vegetation, topography, slope, aspect, and local climatology.
3) Combine snow course, SnoTel, and remote sensed measurements in GIS environment.
4) Spatial analysis on point measurements and interpolation across larger area.
5) Create SWE distribution layout for Colorado Rocky Mountains.
6) Derive equations to produce SWE layout with fewer point measurements.
7) Determine trends of SWE for slope, topography, vegetation, and aspect.
Study Area and Field Methods
The study will take place in the Rocky Mountains of Colorado. There are approximately 150 snow course sites located within the Rocky Mountains of Colorado, approximately 100 SnoTel sites located in Colorado, and weekly remote sensed SWE maps produced from NOHRSC. Combining these three methods into a GIS will provide good spatial representation of SWE measurements. I need to collect measurements made at snow course and SnoTel locations, and obtain weekly to monthly SWE maps derived from remote sensing. Snow course and SnoTel measurements can be obtained online from the NRCS. Remote sensed SWE data can be obtained online from NOHRSC; some data might have to be purchased..
Study Area Thumbnail
GIS Methods and Analysis
My study is designed to look at and identify spatial distribution of SWE. Determine factors that influence spatial distribution of SWE. Potential factors are slope, vegetation, aspect, local climatology, and elevation. My GIS model will contain a Colorado base map that contains different layers: elevation, land cover, vegetation, and monthly average metrological data. SWE measurements from snow course, SnoTel, and remote sensing will be collected and analyzed by each water year in a GIS environment. In GIS, the combination of yearly SWE data will be used to derived variables and equations for creating SWE maps based on fewer point measurements. Determination of SWE relationships and coefficients for elevation, land cover, vegetation, and metrological parameters will in turn produce an elaborate spatial distribution of SWE.
FIG. 1, GIS Model FIG 2. GIS Data Management
Anticipated Result and Output
Data collection and analysis during research will require immense amounts of data.. These include,
1) SWE data from snow course, SnoTel, and remote sensing.
2) GIS themes and coverages for Colorado SWE distribution.
3) Derived variables that represent local environments of snow course and SnoTel measurements.
4) Predictor variables for use in a simple SWE distribution model.
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Literature Cited
Goodison, B.E., H.L. Ferguson, and G.A. McKay, Measurement and data analysis, in Gray, D.M., and D.H. Male, (eds), Handbook of
Snow: Pergamon Press, Toronto, 191-233, 1981.
Serreze, M.C., M.P. Clark, R.L. Armstrong, D.A. McGinnis, and R.S. Pulwarty, Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data, Water Resources Research, 35(7): 2145-2160, 1999.