Engineering Commons^™

Identification Of Long Term Changes And Evaluation Of The Relationships Among Streamflow Variability And Oceanic-Atmospheric Indices, Soumya Sagarika

UNLV Theses, Dissertations, Professional Papers, and Capstones

To examine the effects of climate variability on streamflow, this thesis presents a comprehensive analysis of the streamflow variability of the continental United States and its association with oceanic-atmospheric indices. First, the presence of trends with consideration of short term and long term persistence followed by shifts over the past years in the continental U.S. streamflow were analyzed by using the non-parametric tests: Mann Kendall and Pettitt. Second, the spatio-temporal relationships between seasonal streamflow variability of continental U.S. and sea surface temperatures (SST) and 500 mbar geopotential height (Z500) of the Pacific and Atlantic were established using the singular valued …

Impacts Of Climate Changes On The Spatiotemporal Distribution Of Precipitation In The Western United States, Peng Jiang

UNLV Theses, Dissertations, Professional Papers, and Capstones

Precipitation in the Intermountain West is characterized by its great variability in both spatial and temporal distributions. Moreover, the spatiotemporal distribution of the precipitation is changing due to the climate changes. In this dissertation, three studies are conducted to investigate the multi-scale temporal variability of precipitation, the performance of current climate models on this variability, the influence of large-scale ocean oscillations on heavy precipitation, and the impact of human induced global warming on storm properties.

The first study is to examine the performance of current climate models on the simulation of the multi-scale temporal variability determined from the observed station …

Association Of Oceanic-Atmospheric Oscillations And Hydroclimatic Variables In The Colorado River Basin, Ajay Kalra

UNLV Theses, Dissertations, Professional Papers, and Capstones

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 …