Prasad Thota completed his B. Tech in Civil Engineering from Indian Institute of Technology, Hyderabad in 2019 and is currently a Doctoral Research Assistant in the Department of Civil, Environmental, and Architectural Engineering (CEAE) at the University of Colorado (CU) Boulder. He obtained his M.S. in Civil Engineering from CU Boulder in 2025. He worked on space-time hydroclimate models for precipitation and streamflow over India and developed climate tools for stakeholders in the Northern Great Plains Region (NGP) working with North Central Climate Adaptation Center hosted by CU Boulder. His current research is focused on understanding the large-scale climate drivers of warm season precipitation in the NGP region under current and future climate and developing predictive models for natural resource management.

Join us in supporting Prasad as he defends his PhD!

Friday, May 30, 2025, 1:00 PM MDT
Bldg./Room: SEEC S228
Zoom link: https://cuboulder.zoom.us/j/96750114571
Password: ngp

Examination of Large-Scale Climate Drivers and Modeling of Warm season Precipitation in the U.S. Northern Great Plains

Abstract:
The Northern Great Plains (NGP) is a critical agricultural and ecological region in North America, encompassing extensive croplands, grasslands, and wetland ecosystems that depend on seasonal water availability. The region’s warm-season precipitation (May–September) plays a pivotal role in sustaining both agricultural productivity and ecosystem health. However, this precipitation is highly variable across space and time and is influenced by complex interactions among large-scale atmospheric and oceanic processes. As climate change is expected to intensify hydroclimatic extremes and alter precipitation dynamics, understanding and projecting the drivers of warm-season precipitation in the NGP is vital. This dissertation makes three contributions to the literature – (i) new insights into the space–time variability of warm-season precipitation with quantitative connections to large-scale ocean temperatures in Pacific and Atlantic, (ii) semi-Bayesian Hierarchical modeling framework for seasonal totals and extremes using large-scale climate covariates and (iii) Assessment of warm-season precipitation from climate model projections under future climate. These contributions will help to support resource planners, agricultural stakeholders, and climate scientists by offering improved diagnostic tools and modeling approaches to assess and anticipate seasonal precipitation variability in the NGP. They can also inform adaptation strategies in other climate-sensitive regions facing increased precipitation variability due to climate change.