Maryland Sea Grant seeks to hire a Legal Fellow and a Graduate Assistant. More details.
Climate change, sea level rise, and associated storms are putting Maryland's people, property, natural resources, and public investments at risk. This project is directed at assessing the impacts of long-term sea level rise and episodic storm surges on the low-lying lands of Maryland's Eastern Shore at 2050 and 2100. Coastal inundation risks due to climate change are usually evaluated by simple linear additions of sea level rise, tidal ranges and storm surges. We hypothesize that sea level rise, changing tides and storm surges may produce unexpectedly high sea levels in Chesapeake Bay and cause extensive flooding over Maryland's Eastern Shore. The primary goals of our project are to quantify the impacts of climatic change on coastal inundations in Maryland's Eastern Shore and develop a new database of static and animated digital images to visualize the risks of coastal inundations to different scenarios of sea-level rise and storm surges in a warming climate.
To conduct inundation simulations, we plan to use and refine a recently developed unstructured-grid hydrodynamic model (FVCOM) that places high resolutions on the Maryland's Eastern Shore. Projections by global climate models will be used to construct scenarios for sea-level rise in Chesapeake Bay. A regional atmospheric model (WRF) will be used to simulate select recent storms and drive the FVCOM model. To assess the vulnerability to storm surges at 2050 and 2100, we will place the select storms under the higher sea level and ocean temperature projected by the global climate models. Using integrated GIS software QGIS and Google Earth, we will combine numerical model outputs with GIS information and LIDAR data to show the area extent, depth, duration of inundation at the street level. We will produce static and animated digital products depicting the inundation risks on the Eastern Shore and deliver these web-based graphics to both desktop computers and mobile devices.
To prepare for coastal inundation and flooding, emergency managers and coastal planners currently rely on planning tools such as FEMA's 100-year floodplain maps, NOAA's Digital Coast and Maryland's Department of Natural Resources (DNR) Coastal Atlas. Although these products provide user-friendly mapping and planning tools, they have not incorporated the latest research on accelerating sea-level rise, changing tides and storm surges in a warming climate. By statutory requirement, FEMA's mapping products depict today's flood risk and do not consider sea-level rise. NOAA's Digital Coast shows coastal inundation from 0 to 6 feet above the mean water level around the U.S. but does not consider storm surges and relative sea-level rise in a specific region. Maryland's Coastal Atlas is an online mapping and planning tool that utilizes datasets provided by DNR and its partners, and requires updated information to make accurate depictions. Therefore, there is an urgent need to develop an improved planning tool for coastal inundation on the Maryland's Eastern Shore. The model outputs and inundation graphics to be obtained from this project will be delivered to DNR for incorporation into Maryland's Coastal Atlas.
Secor, DH; Zhang, F; O'Brien, MHP; Li, M. 2019. Ocean destratification and fish evacuation caused by a Mid-Atlantic tropical storm. ICES Journal of Marine Science76(2):573 -584. doi:10.1093/icesjms/fsx241. UM-SG-RS-2019-07.
Secor, DH; Zhang, F; O'Brien, MHP; Li, M. 2019. Ocean destratification and fish evacuation caused by a Mid-Atlantic tropical storm ICES JOURNAL OF MARINE SCIENCE76(2):573 -584. doi:10.1093/icesjms/fsx241. UM-SG-RS-2019-07.
Zhang, F; Li, M; Miles, T. 2018. Generation of Near-Inertial Currents on the Mid-Atlantic Bight by Hurricane Arthur (2014). Journal of Geophysical Research: Oceans123(4):3100 -3116. doi:10.1029/2017JC013584. UM-SG-RS-2018-11.
Zhang, F; Li, M; Ross, AC; Lee, SB; Zhang, DL. 2017. Sensitivity analysis of Hurricane Arthur (2014) storm surge forecasts to WRF physics parameterizations and model configurations. Weather and Forecasting32(5):1745 -1764. doi:10.1175/WAF-D-16-0218.1. UM-SG-RS-2017-04.