Location
Havener Center, Miner Lounge / Wiese Atrium, 9:30am-11:30am
Start Date
4-2-2026 9:30 AM
End Date
4-2-2026 11:30 AM
Presentation Date
April 2, 2026; 9:30am-11:30am
Description
Long-range river stage forecasts are increasingly used to support barge scheduling along the Missouri and Mississippi River navigation corridor. The reliability of these forecasts over extended lead times remains uncertain, particularly in regulated systems influenced by upstream dam operations. This study evaluates the lead-time-dependent performance of National Water Model long-range forecasts at eleven U.S. Geological Survey stations from Rulo to Thebes during 2019–2024. Results show that forecast skill does not decline smoothly with time but exhibits station-specific dips at lead times corresponding to hydraulic travel from major control structures. These periods coincide withstage bias and dispersion, indicating elevated operational uncertainty. Seasonal analysis further shows greater structural forecast degradation during high-flow periods. Long-range forecasts contain identifiable reliability windows that directly affect barge loading, routing, and traffic risk. Integrating forecast uncertainty and propagation dynamics into navigation planning frameworks can improve draft decisions and reduce economic losses associated with unexpected stage reductions.
Biography
Hydrologist and civil engineer specializing in water resources and hydroinformatics, currently pursuing a PhD at Missouri University of Science and Technology. Experience spans hydrological and hydraulic modeling, spatial analysis, and data-driven water management using tools such as Python, MATLAB, GIS platforms, and the HEC suite. Contributed to research on gauge validation and large-scale river systems, including the Rhine and Main, with a focus on improving data reliability through mass balance approaches and machine learning. Previous work includes watershed analysis, flood risk assessment, and hydropower design in Nepal, along with research on tidal modeling and prediction. Current interests center on large-scale hydrologic forecasting, rainfall interpolation, and integrating remote sensing with statistical and machine learning methods for improved water resource decision-making.
Meeting Name
2026 - Miners Solving for Tomorrow Research Conference
Department(s)
Civil, Architectural and Environmental Engineering
Document Type
Poster
Document Version
Final Version
File Type
event
Language(s)
English
Rights
© 2026 The Authors, All rights reserved
Included in
Evaluation of National Water Model Long-Range Streamflow Forecasts in Missouri
Havener Center, Miner Lounge / Wiese Atrium, 9:30am-11:30am
Long-range river stage forecasts are increasingly used to support barge scheduling along the Missouri and Mississippi River navigation corridor. The reliability of these forecasts over extended lead times remains uncertain, particularly in regulated systems influenced by upstream dam operations. This study evaluates the lead-time-dependent performance of National Water Model long-range forecasts at eleven U.S. Geological Survey stations from Rulo to Thebes during 2019–2024. Results show that forecast skill does not decline smoothly with time but exhibits station-specific dips at lead times corresponding to hydraulic travel from major control structures. These periods coincide withstage bias and dispersion, indicating elevated operational uncertainty. Seasonal analysis further shows greater structural forecast degradation during high-flow periods. Long-range forecasts contain identifiable reliability windows that directly affect barge loading, routing, and traffic risk. Integrating forecast uncertainty and propagation dynamics into navigation planning frameworks can improve draft decisions and reduce economic losses associated with unexpected stage reductions.
Comments
Advisor: BongChul Seo, bongchul.seo@mst.edu