Baltimore-Washington Region

The loose coupling approach was successfully applied for the Baltimore-Washington Region coupling five independently developed models: Simple Integrated Land Use Orchestrator (SILO), Maryland Statewide Transport Model (MSTM), Building Emission Model (BEM), Mobile Emission Model (MEM), Chesapeake Bay Land Change Model (CBLCM). The integrated suite is now being applied at the National Center for Smart Growth at the University of Maryland in collaboration with the USGS Eastern Geographic Science Center to simulate and explore alternative scenarios of the region for 2040.

The outputs from the integrated modelling suite include several useful socio-economic indicators, covering: population and employment, transport flow, land use, building and mobile emissions, and more. It is known that the changes in transportation, land use and human behaviour in general impact also on nutrient loading and water quality in a region. Translating the effect of socio-economic alterations into nutrient loading in Chesapeake Bay for example will help us to explore the changes in flow and nutrients loads into the Bay and design more effective public policies and restoration plans. Adding environmental models to the policy decision making process will help to assess how social-economic changes and policy decisions in the Baltimore-Washington Region ultimately impact water quality in the Chesapeake Bay improving policy decision making. Aiming to support improved policy analysis and decision making, the following environmental models were also explored for further enhancement of the integrated suite: Integrated Transport and Health Impact Modelling Tool (ITHIM), Hydrological Simulation Program – Fortran (HSPF) and Chesapeake Bay ROMS Community Model (ChesROMS).

Greater Dublin Region

In the meantime, for the Greater Dublin Region, the approach was applied to couple land use change model MOLAND with the Source Load Apportionment Model SLAM to estimate annual nutrient losses in case of different regional development scenarios.

The results of this two case studies appear very promising. The independently developed models smoothly link in a form which does not require the user to track the models while running applications of these models. Instead, the user can more easily focus on the analysis and results, avoiding the need for a detailed understanding of model structure and data file systems. The approach can be easily applied to other models for other regions.

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