BioEarth: Envisioning and developing a new regional earth system model to inform natural and agricultural resource management

Abstract

As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers' needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and "usability" of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatialscale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.

Improving the representation of hydrologic processes in Earth System Models

“…Currently, many existing and new ESMs are being developed to resolve coupled human and natural systems, including representation of resource management activities [Pokhrel et al, 2012;Adam et al, 2014;Kraucunas et al, 2014]. CLM has been developed to represent maize, soybean, and spring wheat including management practices such as fertilizer application, residue management, and harvest [Drewniak et al, 2013]; irrigation from surface water [Sacks et al, 2009;Leng et al, 2013] and from groundwater [Leng et al, 2014].…”

The Variable Infiltration Capacity Model, Version 5 (VIC-5): Infrastructure improvements for new applications and reproducibility

“…Improved transparency in the forcing generation process: While VIC's core function is as a hydrologic model, specific modeling decisions were made when MT-CLIM was chosen as VIC's meteorological preprocessor and those decisions were abstracted deep within the model source code. As a result, the implications of forcing VIC with only minimum 5 and maximum temperature and precipitation were abstracted away from and mostly invisible to the user. Removing the preprocessor from VIC enhances the transparency of the model by forcing users to make specific choices about the source of the model forcings.…”

“…In RASM, VIC-5 is directly coupled to CESM's flux coupler (CPL7; Craig et al, 2012) via the Model Coupling Toolkit interface (MCT; Larson et al, 2005). As implemented within RASM, this interface allows VIC to be coupled to the Weather Research and Forecasting (WRF) atmospheric model (Skamarock,5 2008) and the RVIC streamflow routing model (Hamman et al, 2017) components, while maintaining the legacy and standalone model infrastructure, which have a large existing user base. The successful coupling of VIC-5 within RASM is a key demonstration of the benefits of the source code refactor.…”

“…Previous versions of VIC have added extended functionality to the model such as streamflow routing (Lohmann et al, 1996;Hamman et al, 2017), reservoirs, irrigation (Haddeland et al, 2006), glaciers and ice sheets, and dynamic vegetation and crops (Adam et al, 2015). These extensions were added either as stand-alone post-processing options or via highly customized interfaces.…”

Comparison of Greenhouse Gas Offset Quantification Protocols for Nitrogen Management in Dryland Wheat Cropping Systems of the Pacific Northwest

“…The accuracy of emission reductions for the iPNW could also be improved through development of regional emission factors (Tier II or Tier III) and might be achieved through field measurements or employing existing biophysical models, such as CropSyst (Stockle et al, 2012), and assessment frameworks, such as BioEarth (Adam et al, 2014). However, lower input models (e.g., COMET-Farm) rather than high input process-based models (e.g., CropSyst, DNDC) would likely reduce transaction costs associated with project development and verification (Li, 2000;Stockle et al, 2012).…”