NSF Award
2429566
Rain-on-snow (ROS) events occur when rain falls on top of existing snow, significantly impacting river systems, economies, and communities. Examples include the 2019 floods in the Midwestern US, which caused $2.9 billion in damages. As global warming continues, these events will likely become more frequent in mountainous areas. To increase preparedness among local authorities and the population for such catastrophic events, effective flood forecasting is crucial to reduce loss of life and economic damage. However, forecasting floods from ROS is currently challenging due to the complex processes involved. This project specifically addresses these ROS flood forecasting challenges in mountainous areas like Montana. By collaborating with and visiting the Iowa Flood Center (IFC) at the University of Iowa (UI), this project aims to enhance ROS flood forecasting at the community scale (< 100 meters) through advancing hydrologic prediction skills through computer modeling. The proposed research will address some shortcomings of the existing forecasting systems, such as providing accurate and timely warnings at the community level. Current continental-scale flood forecasting systems typically use larger spatial resolutions, which can compromise local prediction accuracy. This project will also strengthen Montana State University’s research on flood forecasting and mitigation, creating opportunities for graduate students to conduct locally relevant research.<br/><br/>The goal of this project is to advance our understanding and prediction capability in ROS floods and strengthen the PI's and Montana State University’s research on flood forecasting. The Iowa Flood Center (IFC) at the University of Iowa (UI) has demonstrated its high reputation in flood forecasting for over 10 years of forecasts at the community level in Iowa. Prof. Witold F. Krajewski, who is also a member of the National Academy of Engineering (NAE), will host the PI’s visits and provide supervision and advisory on this project. This proposed topic will be investigated over multiple watersheds mainly using the hillslope link model. This study will provide insights into the propagation of errors from meteorological variables and antecedent hydrologic conditions to streamflow forecast and propose solutions to eliminate such errors. Leveraging this proposal, a sustainable collaboration in education and research between these two institutions will be fostered and continued by co-developing proposals on other ROS flood-related research topics. Outcomes of this research will directly contribute to the advancement of ROS flood forecasting, and such knowledge will be valuable to researchers in the weather forecasting community. The proposed framework of conducting experiments for different scenarios will serve as the starting point for future enhancements in providing streamflow forecasts to Montana's communities. Results from the research will also be published in scholarly journals, disseminated through collaboration with the NOAA National Weather Service Missouri Basin River Forecast Center (MBRFC), and integrated into undergraduate and graduate courses.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
