Snowmelt is one of the major sources of surface water supply and ground-water recharge in high elevation areas and can also cause flooding in snow dominated watersheds. Direct estimation of daily snowmelt requires daily snow water equivalent (SWE) measurements that are not always available, especially in places without monitoring stations. There are two alternative approaches to modeling snowmelt without using direct measurements of SWE, temperature-based and energy-based models. Due to its simplicity, computational efficiency, and less input data requirement, the temperature-based method is commonly used than the energy-based method. In the temperature-index approach snowmelt is estimated as a linear function of average air temperature, and the slope of the linear function is called the degree-day factor (DDF). Hence, the DDF is an essential parameter for utilizing the temperature-based method to estimate snowmelt. Thereby, to analyze the spatial properties of DDF, 10 years DDF from the entire state of Colorado was calculated for this research. Likewise, to study the temporal properties, DDFs for 27 years from the White Yampa water basin and the Colorado Headwaters water basin were calculated.
As a part of the spatial analysis, the calculated DDFs were correlated with spatial variables (slope, aspect, latitude and elevation) and a spatial correlation graph was created to observe the possibility of predicting DDF. Also a multivariate regression model was prepared using these spatial variables to predict the DDF using spatial variables. Further, the DDFs calculated from Colorado headwaters and the White Yampa water basins were correlated for annual temporal variation, daily variation, variation with peak snow water equivalent and variation with important temporal cycles like accumulation period and melting period of snowmelt. The result obtained from this study showed that the variability of DDF is more dependent upon temporal factors compared to the spatial factors. Also, the results showed that predicting DDF is a difficult process and requires complex methods than simple linear models or multivariate models.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc849730 |
| Date | 05 1900 |
| Creators | Pokhrel, Pranav |
| Contributors | Pan, Feifei, Dong, Pinliang, McGregor, Kent |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | xi, 166 pages : illustrations, Text |
| Coverage | United States - Colorado |
| Rights | Public, Pokhrel, Pranav, Copyright, Copyright is held by the author, unless otherwise noted. All rights reserved. |
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