An Examination of Trends and Patterns of Ecologically Important Streamflow Variable in Association with Climate Change Predictions
Location
ECC 303
Event Website
http://water.usu.edu/
Start Date
4-3-2012 11:20 AM
End Date
4-3-2012 11:40 AM
Description
Hydrology is one of the primary factors influencing the physical and biological characteristics of streams. We used 16 streamflow variables important to stream ecology and used K-mean clustering technique on the rotated principal components of these variables to classify the streams across the contiguous US. We also studied the spatial patterns in these 16 variables along with trends in some of them and examined if some of these trends follow trends in precipitation and temperature. Linear regression on annual values was used to observe the trends in these variables and maps were used for study of spatial patterns and trends. Linear regression and quantile regressions were used to study the trends in climate (Precipitation and Temperature). We observed daily mean flow increases in the Midwest and Northeast US. Base flow Index (BFI) was seen to be increasing in Midwest while it is decreasing along the Appalachians. Also, regions with increases in daily mean flows correspond to regions with increase in higher values of precipitation. Principal Component Analysis with varimax rotation on the 16 normalized variables reduced the dimensionality to 4 components. K means classification was used to classify the streams to 10 classes that were mapped to depict their spatial structure
An Examination of Trends and Patterns of Ecologically Important Streamflow Variable in Association with Climate Change Predictions
ECC 303
Hydrology is one of the primary factors influencing the physical and biological characteristics of streams. We used 16 streamflow variables important to stream ecology and used K-mean clustering technique on the rotated principal components of these variables to classify the streams across the contiguous US. We also studied the spatial patterns in these 16 variables along with trends in some of them and examined if some of these trends follow trends in precipitation and temperature. Linear regression on annual values was used to observe the trends in these variables and maps were used for study of spatial patterns and trends. Linear regression and quantile regressions were used to study the trends in climate (Precipitation and Temperature). We observed daily mean flow increases in the Midwest and Northeast US. Base flow Index (BFI) was seen to be increasing in Midwest while it is decreasing along the Appalachians. Also, regions with increases in daily mean flows correspond to regions with increase in higher values of precipitation. Principal Component Analysis with varimax rotation on the 16 normalized variables reduced the dimensionality to 4 components. K means classification was used to classify the streams to 10 classes that were mapped to depict their spatial structure
https://digitalcommons.usu.edu/runoff/2012/AllAbstracts/20