Association of oceanic-atmospheric oscillations and hydroclimatic variables in the Colorado River Basin

• Main Author: Kalra, Ajay
• Format: Text
• Language: English
• Published: Digital Scholarship@UNLV 2011
• Subjects: Climatic changes; Colorado River Basin; Hydrologic cycle; Hydrologic models; Hydrological forecasting; KNN; North America – Colorado River Watershed; Ocean-atmosphere interaction; Oscillations; Precipitation forecasting; Runoff – Forecasting; Streamflow – Forecasting; SVM; Civil Engineering; Climate; Fresh Water Studies; Meteorology; Water Resource Management; Pacific; North Atlantic; North Atlantic oscillation
• Online Access: https://digitalscholarship.unlv.edu/thesesdissertations/1024; https://digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=2025&context=thesesdissertations

With increasing evidence of climatic variability, there is a need to improve forecast for hydroclimatic variables i.e., precipitation and streamflow preserving their spatial and temporal variability. Climatologists have identified different oceanic-atmospheric oscillations that seem to influence the behavior of these variables and in turn can be used to extend the forecast lead time. In the absence of a good physical understanding of the linkages between oceanic-atmospheric oscillations and hydrological processes, it is difficult to construct a physical model. An attractive alternative to physically based models are the Artificial Intelligence (AI) type models, also referred to as machine learning or data-driven models. These models do not employ traditional forms of equations common in physically based models, but instead have flexible and adaptive model structures that can extract the relationship from the data. With this motivation this research focuses on increasing the precipitation and streamflow forecast lead times and enhancing the temporal resolution of precipitation within the Colorado River Basin (CRB). An AI-type data-driven model, Support Vector Machine (SVM), was developed incorporating oceanic-atmospheric oscillations to increase the precipitation and streamflow forecast lead times. The temporal resolution of precipitation was improved using the stochastic nonparametric K-Nearest Neighbor (KNN) approach. The hydrologic data used in the dissertation comprised of climate division precipitation data and naturalized streamflow data for the Colorado River Basin. The interdecadal and interannual Pacific Ocean (Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation(ENSO)) and Atlantic Ocean (Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation(NAO)) climatic variability was used in this dissertation. Initially, the coupled and individual effect of oceanic-atmospheric oscillations in relation to annual precipitation within Colorado River Basin was investigated using the ...