Regional patterns of snow water equivalent in the Colorado River Basin using snowpack telemetry (SNOTEL) data Derry, Jeffrey Edward Snow -- Measurement Snow -- Colorado River Watershed (Colo.-Mexico) x, 92 p. Includes bibliographical references (p. 76-79) Identifying regions of homogeneity of precipitation data is often a crucial preliminary step in natural resource investigations. Previous clustering of station based snow water equivalent (SWE) data has typically grouped stations based on spatial proximity, political boundaries, or watershed boundaries, and has been restricted due to the temporal resolution of snow course data. This investigation utilized daily data from 216 snowpack telemetry (SNOTEL) stations located in and around the Colorado River Basin over a 15-year period (1991-2005) to cluster stations, i.e., identify regions of homogeneity, based on the patterns and variability of SWE. To achieve this, data were submitted to a selforganizing map (SOM), a particular application of artificial neural networks. This methodology represents a learning algorithm that is non-linear, non-parametric, unsupervised, and learns through an iterative training process. Colorado State University. Libraries 2008 Text application/pdf 2008_spring_Derry.pdf ETDF2008100001FRWS eng English Copyright of original work is retained by the author.
Regional patterns of snow water equivalent in the Colorado River Basin using snowpack telemetry (SNOTEL) data
Derry, Jeffrey Edward
Snow -- Measurement
Snow -- Colorado River Watershed (Colo.-Mexico)
x, 92 p.
Includes bibliographical references (p. 76-79)
Identifying regions of homogeneity of precipitation data is often a crucial preliminary step in natural resource investigations. Previous clustering of station based snow water equivalent (SWE) data has typically grouped stations based on spatial proximity, political boundaries, or watershed boundaries, and has been restricted due to the temporal resolution of snow course data. This investigation utilized daily data from 216 snowpack telemetry (SNOTEL) stations located in and around the Colorado River Basin over a 15-year period (1991-2005) to cluster stations, i.e., identify regions of homogeneity, based on the patterns and variability of SWE. To achieve this, data were submitted to a selforganizing map (SOM), a particular application of artificial neural networks. This methodology represents a learning algorithm that is non-linear, non-parametric, unsupervised, and learns through an iterative training process.
Colorado State University. Libraries
2008
Text
application/pdf
2008_spring_Derry.pdf
ETDF2008100001FRWS
eng
English
Copyright of original work is retained by the author.