Hydroclimatic forecasting in the western United States using paleoclimate reconstructions and data-driven models

• Main Author: Carrier, Christopher Allen
• Format: Text
• Language: English
• Published: Digital Scholarship@UNLV 2011
• Subjects: Applied sciences; Climatic changes; Data-driven; Earth sciences; Forecast; Hydrologic cycle; Hydrologic models; Model; Precipitation; Runoff – Forecasting; Streamflow – Forecasting; West (U.S.); Atmospheric Sciences; Climate; Environmental Engineering; Hydraulic Engineering; Meteorology; Water Resource Management; Pacific; North Atlantic; North Atlantic oscillation
• Online Access: https://digitalscholarship.unlv.edu/thesesdissertations/1267; https://digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=2257&context=thesesdissertations

This thesis investigated climate variability and their associated hydrologic responses in the western United States. The western United States faces the problem of water scarcity, where the management and mitigation of available water supplies are further complicated by climate variability. Climate variability associated with the phases of oceanic-atmospheric oscillations has been shown to influence streamflow and precipitation, where predictive relationships have led to the possibility of producing long-range forecasts. Based on literature review, four oceanic-atmospheric oscillation indices were identified in having the most prominent influence over the western United States including the El Niño - Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). However, these hydroclimatic processes are not fully understood and are difficult to describe in physically-based models. A viable alternative to generating forecasts is through data-driven models, which extract relationships in a dataset of oscillation inputs and hydrologic outputs to build a structured forecasting model. One of the limitations to using oceanic-atmospheric oscillations in a data-driven model is a short instrumental record from which the model can train on. Data-driven models often perform better when they are subjected to a larger training dataset. Reconstructions have the potential to extend the period of record by several centuries, which may aid in identifying important hydroclimatological relationships and improving the quality of forecasts. With this motivation, this study focused on increasing the forecast lead time through the use of reconstructions of oceanic-atmospheric oscillations in the western United States. First, reconstructions of oscillations were investigated to increase the forecast lead time of four streamflow gages in the Upper Colorado River Basin (UCRB) by using the KStar and M5P data-driven models. Secondly, an expanded spatial ...