Model integration and the role of data

Model integration is becoming increasingly important as our impacts on the environment become more severe and the systems we analyze become more complex. There are numerous attempts to make different models work in concert. However model integration usually treats models as software components only, ignoring the evolving nature of models and their constant modification and re-calibration to better represent reality. As a result, the changes that used to impact only contained models of subsystems, now propagate throughout the integrated system, across multiple model components. This makes it harder to keep the overall complexity under control and, in a way, defeats the purpose of modularity, where efficiency is supposed to be gained from independent development of modules. We argue that data that are available for module calibration can serve as an intermediate linkage tool, sitting between modules and providing a module-independent baseline, which is then adjusted when scenarios are to be run. In this case, it is not the model output that is directed into the next model. Rather, model output is presented as a variation around the baseline trajectory, and it is this variation that is then fed into the next module down the chain. The Chesapeake Bay Program suite of models is used to illustrate these problems and the possible remedy. Published by Elsevier Ltd.