UMBC High Performance Computing Facility
Please note that this page is under construction. We are documenting the
240-node cluster maya that will be available in Spring 2014. Currently,
the cluster tara is still available. Please see the 2013 Resources Pages
under the Resources tab.
Evaluation of Groundwater Flowpaths, Fluxes, and Stores in the Chesapeake Bay Watershed Using a
Fully Distributed Coupled Hydrologic Model
Alimatou Seck and Claire Welty, Department of Civil and Environmental Engineering and Center for Urban Environmental Research and Education (CUERE)
Evaluation of Groundwater Flowpaths, Fluxes, and Stores in the Chesapeake Bay Watershed Using a Fully Distributed Coupled Hydrologic Model abstract: The Chesapeake Bay is currently listed as an impaired water body and is the object of intensive restoration efforts to mitigate water quality issues, particularly those related to the transport of nutrients and sediments into the Bay. Groundwater discharge contributes significantly to the annual flows of Chesapeake Bay tributaries and is presumed to contribute to the observed lag time between the implementation of management actions and the environmental response in the Chesapeake Bay. The EPA Phase 5 Chesapeake Bay watershed model, based on the HSPF code, simulates river flow and associated transport and fate of nutrients and sediments and is used as a decision support tool. However, this model does not provide a mass-conserving representation of the groundwater fluxes in the watershed in that flow to deep groundwater is removed from model calculations. As an alternative, this study aims at the development of a fully distributed model of the Chesapeake Bay Watershed using PARFLOW, a three-dimensional variably saturated groundwater flow model integrated with an overland flow component. The code can be run on high performance computing facilities, thus allowing for applications at large scale with efficient run times. The CBW encompasses an area of 160,000 sq km and spans five physiographic provinces. The development of this distributed large-scale model offers an opportunity of incorporating remote-sensing data such as the MODIS, LDAS and GRACE data products as well the NEXRAD precipitation product. These data sets have spatial resolution relevant to the regional scale of the model and represent an improvement of the modeling performances considering the challenges usually encountered in obtaining temporally and spatially consistent data for large scale hydrologic modeling.