Major human health and water quality issues are caused by flooding of coastal urban infrastructure, such as sewer systems, wastewater treatment plants, and nuclear power reactors. Floods in these coastal urban regions may directly impact local waterways by delivering bacterial and/or radioactive contaminants into local rivers and estuaries. In addition, the delivery of excess levels of nutrients can stimulate potentially harmful algal blooms, reduce oxygen levels, and reduce water clarity, all of which impact biogeochemical budgets and local ecosystems and communities. Yet, these facilities, i.e. sewer systems, wastewater treatment plants, and nuclear power reactors are particularly prone to flooding because of engineering constraints that require proximity to shorelines for effluent discharge and large supplies of water. As climate changes, floods of coastal urban areas are likely to become more frequent and extreme due to rising sea levels, changes in precipitation extremes, and slower storms. Climate change-driven alterations in flood characteristics will also impact how precipitation events affect biogeochemistry in coastal waterways, including estuaries where many urban centers are located. Additionally, in urban settings, warmer temperatures and changes in land use may impact the delivery of contaminated water and excess nutrients, as well as the rates of biogeochemical processes. Advances in process-based modeling and machine learning have improved our understanding of floods and their impact on biogeochemistry in coastal urban waterways. Yet, a predictive understanding of coastal urban biogeochemistry remains difficult during extreme events.
This project will combine process-based and statistical machine-learning modeling to address the Grand Challenge of modeling hydro-biogeochemistry during extreme events in coastal urban waterways. Specifically, the objective of this project is to analyze how floods of coastal infrastructure impact pollutant and nutrient fluxes to local waterways, and their impact on estuarine biogeochemical processes, on subseasonal timescales (days to a couple of months) in modern-day and future climates. The city of Baltimore will be used as a case study to build on and contribute to ongoing DOE and local efforts, including the Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory (Urban IFL). Model results from the Energy Exascale Earth System Model (E3SM) climate model, as well as the implementation and analysis of a local Baltimore hydrodynamic-biogeochemistry model, will be used to better understand how coastal urban flooding impacts local estuarine biogeochemistry in different climate scenarios. Additionally, a combination of machine learning and sensitivity tests of the process-based model will be used to explore upscaling local observations and models from the Baltimore case study to coastal-urban system to worldwide.