Scholar: Nishant Kumar
When: July 8th, 2026, at 10:00am
Where: SEC C107 and PKI 250
Zoom: https://unl.zoom.us/j/98373076222
Title: Bridging Extremes: Understanding the Snow’s Dual Role in Hydrologic Extremes
Abstract: Floods and droughts are the two most consequential hydrologic extremes, interconnected deeply in the hydrological cycle. All the analyses and outcomes of this dissertation aim to enhance the understanding, prediction, mitigation, and monitoring of hydroclimatic extremes, with a precise focus on the uncertainties involved in streamflow, rain-on-snow floods, snow and flash snow droughts, and hydrological drought monitoring using process-based models. Over the last several decades, climate variability and warming have continued to alter hydroclimatic conditions, and there is an urgent need to quantify uncertainty, identify and understand snow-related hazards, and make more information available to monitor these hydroclimatic extremes. To address these challenges, this dissertation combines probabilistic machine learning, hydroclimatic trend analysis, causal discovery, data assimilation, and process-based land–hydrologic modeling.
After the introductory chapter, this dissertation is organized into four interconnected chapters. The second chapter aims to modify an uncertainty method based on Mixture Density Networks using a customized loss function to optimize uncertainty in the streamflow prediction. The third chapter investigates rain-on-snow (ROS) flooding by developing definitions of ROS events with flood potential and actual flooding, and by identifying the hydroclimatic drivers and causal structures that govern ROS flood generation. The fourth chapter is focused on defining Flash Snow Droughts (FSDs). It also focuses on their trend, frequency, hotspots, and the role of ROS in FSDs. The final chapter aims to develop and assess the reach-USDM grid streamflow indicator framework for hydrological drought monitoring, with a specific focus on the impact of snow and leaf area index DA on the Elkhorn Basin.
All four chapters provide a detailed framework for better understanding hydrological extremes across multiple processes and time scales. This dissertation advances the understanding by combining data-driven approaches, ROS flood diagnostics, FSD processes, and process-based drought monitoring. Overall, this work advances streamflow prediction, deepens insight into snow-related flood and drought risks, and supports more effective hydrologic decision-making in a changing climate.