Integrated ecological economic modeling of the Patuxent River watershed, Maryland

Understanding the way regional landscapes operate. evolve, and change is a key area of research for ecosystem science. It is also essential to support the "place-based" management approach being advocated by the U.S. Environmental Protection Agency and other management agencies. We developed a spatially explicit, process-based model of the 2352 km(2) Patuxent River watershed in Maryland to integrate data and knowledge over several spatial, temporal, and complexity scales. and to serve as an aid to regional management. In particular. the model addresses the effects of both the magnitude and spatial patterns of human settlements and agricultural practices on hydrology, plant productivity. and nutrient cycling in the landscape. The spatial resolution is variable, with a maximum of 200 X 200 m to allow adequate depiction of the pattern of ecosystems and human settlement on the landscape. The temporal resolution is different for various components of the model. ranging from hourly time steps in the hydrologic sector to yearly time steps in the economic land-use transition module. We used a modular. multiscale approach to calibrate and test the model. Model results show good agreement with data for several components of the model at several scales. A range of scenarios with the calibrated model shows the implications of past and alternative future land-use patterns and policies. We analyzed 18 scenarios including: (1) historical land-use in 1650, 1850, 1950, 1972, 1990, and 1997; (2) a "buildout" scenario based on fully developing all the land currently zoned for development: (3) four future development patterns based on an empirical economic land-use conversion model; (4) agricultural "best management practices" that lower fertilizer applications (5) four "replacement" scenarios of land-use change to analyze the relative contributions of agriculture and urban land uses; and (6) two "clustering" scenarios with significantly more and less clustered residential development than the current pattern. Results indicate the complex nature of the landscape response and the need for spatially explicit modeling.