Climatic drivers of Southeast U.S hydrology

• Main Author: Tabatabaei Sadeghi, Sahar
• Other Authors: Tootle, Glenn, Johnson, Pauline D., Elliot, Mark, Kam, Jonghun, Therrell, Matthew
• Format: Thesis
• Language: English
• Published: University of Alabama Libraries 2019
• Subjects: Environmental engineering; Civil engineering; Alabama; North Atlantic
• Online Access: http://ir.ua.edu/handle/123456789/6658

Electronic Thesis or Dissertation The growing demand for water has raised concerns regarding future resource scarcity. For water management, agriculture, fisheries needs and hydro-climatic research, there has been conflict over water in Southeastern United States (SEUS). In this dissertation, I intend to evaluate the temporal (monthly) variability of El Nino on SEUS streamflow and Atlantic Ocean Sea Surface Temperature (AO SST) variability and SEUS streamflow. The development of an El Nino and the association of increased moisture (e.g., precipitation, streamflow) in coastal Mississippi and Alabama is misleading in agricultural practices (Sadeghi et al., 2019b). While annual increases were ~20%, this increase was in the winter and late-spring during the planting season for crops. Thus, moist soil conditions could delay crop planting. During the summer growing season, moisture was decreased and, thus, given the majority of agricultural lands are rain-fed and not irrigated, this could severely impact crop production (drought). A multi-decadal teleconnection was established between north Atlantic Ocean SSTs and SEUS streamflow (Sadeghi et al., 2019a). SEUS streamflow has been in a multi-decadal decline since ~1990 and the warming of north Atlantic Ocean SSTs exhibits a strong, linear relationship with this decline. The use of “raw” SSTs confirms previous efforts establishing an Atlantic Multidecadal Oscillation “like” signal in SEUS streamflow. Future research efforts will utilize the multi-decadal teleconnection of Atlantic Ocean SSTs to forecast SEUS streamflow. Future forecasts of Atlantic Oceans SSTs from multiple Global Climate Models (GCMs) will be used as independent variables in regression models to forecast future multi-decadal variability of SEUS streamflow. These results can be compared to “traditional” methods in which physically based distributed hydrologic models are developed and downscaled future “forcings” are input to determine future flows.