HYDROLOGIC VARIABILITY WITHIN THE CLIMATE REGIONS OF CONTINENTAL UNITED STATES AND ITS TELECONNECTION WITH CLIMATE VARIABLES

Author

Balbhadra Thakur, Southern Illinois University CarbondaleFollow

Date of Award

9-1-2020

Degree Name

Doctor of Philosophy

Department

Engineering Science

First Advisor

Kalra, Ajay

Abstract

The entropy of all systems is supposed to increase with time, this is also observed in the hydroclimatic records as increased variability. The current dissertation is primarily focused on the hydrologic variability of the hydrologic records in the climate regions across Continental United States. The study evaluated the effects of serial correlation in the historical streamflow records on both gradual trend and abrupt shift in streamflow. The study also evaluated the trend before and after the shift occurrence to validate whether the observed changes in streamflow is a result of long-term variability or climate regime shift. Secondly, the current dissertation evaluated the variability within western US hydrology which is highly driven by the oscillation of Pacific Ocean such as El Niño – Southern Oscillation (ENSO). The dissertation evaluated the variability in snow water equivalent (SWE) of western US as the winter snow accumulation of the region drives the spring-summer streamflow in the region which contributes to the major portion of yearly streamflow. The SWE variability during the individual phases of ENSO were analyzed to reveal the detailed influence of ENSO on historic snow accumulations. The study is not solely limited to the hydrologic variability evaluation rather; it also delves into obtaining the time lagged spatiotemporal teleconnections between large scale climate variables and streamflow and forecast the later based on the obtained teleconnections. To accomplish the research goals the current dissertation was subdivided into three research tasks. First task dealt with the streamflow records of 419 unimpaired streamflow records which were grouped into seven climate regions based on National Climate Assessment, to evaluate the regional changes in both seasonal streamflow and yearly streamflow percentiles. Non-parametric Mann-Kendall test and Pettitt’s test were utilized to evaluate the streamflow variability as gradual trend and abrupt shift, respectively. Walker test was performed to test the global significance of the streamflow variability within each climate regions based on local trend and shift significance of each streamflow stations. The task also evaluated the presence of serial correlation in the streamflow records and its effects on both trend and shift within the climate regions of continental United States for the first time. Maximum variability in terms of both trend and shift were observed for summer as compared to other seasons. Similarly, greater number of stations showed streamflow variability for 5th and 50th percentile streamflow as compared to 95th and 100th percentile streamflow. It was also observed that serial correlation affected both trend and step while, accounting for the lag-1 autocorrelation improved shift results. The results indicated that the streamflow variability has more likely occurred as shift as compared to the gradual trend. The outcomes of the current result detailing historic variability may help to envision future changes in streamflow. The second task evaluated the spatiotemporal variability of western US SWE over 58 years (1961–2018) as a trend and a shift. The task tested whether the SWE is consistent during ENSO phases utilizing the Kolmogorov – Smirnov (KS) test. Trend analysis was performed on the SWE data of each ENSO phase. Shift analysis was performed in the entire time series of 58 years. Additionally, the trend in the SWE data was evaluated before and after shift years. Mann- Kendal and Pettit's tests were utilized for the detection of trend and shift, respectively. The serial correlation was considered during the trend evaluation, while Thiel-Sen approach was used for the evaluation of the trend magnitude. The serial correlation in time series which is the potential cause of overestimation and underestimation of the trend evaluation was found to be absent in the SWE data. The results suggested a negative trend and a shift during the study period. The negative trend was absent during neutral years and present during El Niño and La Niña years. The trend magnitudes were maximum during La Niña years followed by those during El Niño years and the entire length of the data. It was also observed that if the presence of negative shift in the SWE was considered, then most of the stations did not show a significant trend before and after the occurrence of a shift. The third task forecasted the streamflow at a regional scale within Sacramento San Joaquin (SSJ) River Basin with largescale climate variables. SSJ is an agricultural watershed located in the drought sensitive region of California. The forecast techniques involved a hybrid statistical framework that eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962 to 2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors in the non-linear regression models (K-nearest neighbor regression and data-driven support vector machine). The SVD results identified new spatial regions that have not been included in existing predefined indices. The nonparametric model indicated the teleconnections of SHUM500 and U500 being better streamflow predictors compared to other climate variables. The regression models were capable to apprehend most of the sustained low flows, proving the model to be effective for drought-affected regions. It was also observed that the forecasting approach showed better forecasting skills with preprocessed large-scale climate variables rather than using the predefined indices. The techniques involved in this task was simple, yet robust in providing qualitative streamflow forecasts that may assist water managers in making policy-related decisions when planning and managing watersheds.