Bioearth: A Regional-Scale Earth System Model to Inform Land and Water Management Decisions.

One of the grand challenges of the 21st Century is to understand biogeochemical cycles in the biosphere, and in particular, to understand how to manage nitrogen (N) in the environment to maximize agricultural productivity while minimizing negative environmental effects. Developing a clear understanding of climate and human-induced changes in environmental N cycling in tightly coupled atmospheric, terrestrial, and aquatic systems, including how these changes feed back into the climate system, is critical to addressing this challenge. Our project’s overarching goal is to improve understanding of the interactions among C, N, and H2O at the regional scale in the context of global change to inform decision makers’ strategies regarding natural and agricultural resource management. Our approach is to create a regional modeling framework by integrating a network of state-of-the-art process-based models that are currently in existence and that are undergoing continuous development. Our rationale is that by choosing among the most sophisticated models for each earth system component, and either linking these models (or fully integrating them where possible) into a biosphere-relevant earth system model (BioEarth), the integrated modeling framework can be continually improved as each contributory component develops. The framework includes atmospheric models (for meteorology and atmospheric chemistry), land surface models (for hydrology, cropping systems, and biogeochemical cycling), aquatic models (for reservoir operations and nutrient export in rivers), and economic models. The end product will be a state-of-science regional earth system modeling framework that explicitly addresses N and C flows in the context of inter-annual and decadal climate variability. Relevance and utility to decision-makers will be enhanced through integrated stakeholder input throughout model development. Communications research will be used to assess and improve the relevance of the new model for land management decision making.