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

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 spatial-scale 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

Biogeochemical responses to atmospheric nitrogen deposition in subalpine ecosystems of the Cascades

Increases in atmospheric nitrogen (N) emissions from agricultural and combustion sources with subsequent deposition can impact carbon (C) and N cycling in sensitive subalpine ecosystems. Chronic N deposition can decrease vegetation biodiversity, alter biogeochemical cycling, and impair montane watersheds. Study of ecosystem C and N sources and accumulation during primary succession indicated that deposition may be an important N source for unvegetated areas on the Pumice Plain of Mount St. Helens. This finding spurred further research into N deposition impacts on subalpine ecosystems in the Cascade Range.Snowpack storage and processing of N deposition has important implications for informing N emission sources and ecosystem N cycling. We measured rates, N forms, and sources of N deposition in subalpine snowpack at Mount Rainier National Park. Measured ambient deposition rates could exceed critical loads for lichen biodiversity and N enrichment of montane watersheds. Ammonium deposition was the dominant form measured in snowpack. However, snowpack microbial nitrification may be converting this ammonium into nitrate, facilitating leaching of N deposition to watersheds.We examined the influence of snow regime on subalpine ecosystem C and N cycling at Mount Rainier under ambient conditions and in climate change scenarios. Timing of snow release influenced ecosystem C and N storage and loss. Climate change may reduce snow accumulation by up to 80% at Mount Rainier by 2050. Snowpack loss may enhance ecosystem C and N accumulation during the growing season and increase winter N leaching.We examined the N deposition fate in the subalpine ecosystem as well as N deposition critical loads under ambient conditions and in climate change scenarios. Soil was the major deposition fate while leaching was the dominant N loss. Increasing N deposition by 75% above ambient rates exceeded N loss critical loads, causing detrimental impacts in subalpine meadows and watersheds. Chronic elevated N deposition was found to increase vegetation biomass and N storage. Climate change increased inorganic N leaching to watersheds and enhanced vegetation N uptake independently of the N deposition rate. Thus, critical loads should be considered dynamic thresholds that shift with ecological conditions even with stable or decreasing N deposition rates

Biogeochemical Responses to Atmospheric Nitrogen Deposition in Subalpine Ecosystems of the Cascades

Increases in atmospheric nitrogen (N) emissions from agricultural and combustion sources with subsequent deposition can impact carbon (C) and N cycling in sensitive subalpine ecosystems. Chronic N deposition can decrease vegetation biodiversity, alter biogeochemical cycling, and impair montane watersheds. Study of ecosystem C and N sources and accumulation during primary succession indicated that deposition may be an important N source for unvegetated areas on the Pumice Plain of Mount St. Helens. This finding spurred further research into N deposition impacts on subalpine ecosystems in the Cascade Range. Snowpack storage and processing of N deposition has important implications for informing N emission sources and ecosystem N cycling. We measured rates, N forms, and sources of N deposition in subalpine snowpack at Mount Rainier National Park. Measured ambient deposition rates could exceed critical loads for lichen biodiversity and N enrichment of montane watersheds. Ammonium deposition was the dominant form measured in snowpack. However, snowpack microbial nitrification may be converting this ammonium into nitrate, facilitating leaching of N deposition to watersheds. We examined the influence of snow regime on subalpine ecosystem C and N cycling at Mount Rainier under ambient conditions and in climate change scenarios. Timing of snow release influenced ecosystem C and N storage and loss. Climate change may reduce snow accumulation by up to 80% at Mount Rainier by 2050. Snowpack loss may enhance ecosystem C and N accumulation during the growing season and increase winter N leaching. We examined the N deposition fate in the subalpine ecosystem as well as N deposition critical loads under ambient conditions and in climate change scenarios. Soil was the major deposition fate while leaching was the dominant N loss. Increasing N deposition by 75% above ambient rates exceeded N loss critical loads, causing detrimental impacts in subalpine meadows and watersheds. Chronic elevated N deposition was found to increase vegetation biomass and N storage. Climate change increased inorganic N leaching to watersheds and enhanced vegetation N uptake independently of the N deposition rate. Thus, critical loads should be considered dynamic thresholds that shift with ecological conditions even with stable or decreasing N deposition rates

Framework for Data Informed Science Policy

<p>Large-scale environmental changes pose challenges that straddle environmental, economic, and social boundaries. As we design and implement climate adaptation strategies at the Federal, state, local, and tribal levels, accessible and usable data are essential for implementing actions that are informed by the best available information. Data-intensive science has been heralded as an enabler for scientific breakthroughs powered by advanced computing capabilities and interoperable data systems. Those same capabilities can be applied to data and information systems that facilitate the transformation of data into highly processed products.</p> <p>At the interface of scientifically informed public policy and data intensive science lies the potential for producers of credible, integrated, multi-scalar environmental data like the National Ecological Observatory Network (NEON) and its partners to capitalize on data and informatics interoperability initiatives that enable the integration of environmental data from across credible data sources. NSF’s large-scale environmental observatories such as NEON and the Ocean Observatories Initiative (OOI) are designed to provide high-quality, long-term environmental data for research. These data are also meant to be repurposed for operational needs that like risk management, vulnerability assessments, resource management, and others. The proposed USDA Agriculture Research Service (ARS) Long Term Agro-ecosystem Research (LTAR) network is another example of such an environmental observatory that will produce credible data for environmental / agricultural forecasting and informing policy.</p> <p>To facilitate data fusion across observatories, there is a growing call for observation systems to more closely coordinate and standardize how variables are measured. Together with observation standards, cyberinfrastructure standards enable the proliferation of an ecosystem of applications that utilize diverse, high-quality, credible data. Interoperability facilitates the integration of data from multiple credible sources of data, and enables the repurposing of data for use at different geographical scales. Metadata that captures the transformation of data into value-added products (“provenance”) lends reproducability and transparency to the entire process. This way, the datasets and model code used to create any product can be examined by other parties.</p

Connecting LTAR-NEON Data to Science Policy

<p>Large-scale environmental changes pose challenges that straddle environmental, economic, and social boundaries. As we design and implement climate adaptation strategies at the Federal, state, local, and tribal levels, accessible and usable data are essential for implementing actions that are informed by the best available information. Data-intensive science has been heralded as an enabler for scientific breakthroughs powered by advanced computing capabilities and interoperable data systems. Those same capabilities can be applied to data and information systems that facilitate the transformation of data into highly processed products for use in research, policy, and decision-making.</p> <p>At the interface of scientifically informed public policy and data intensive science lies the potential for producers of credible, integrated, multi-scalar environmental data like the National Ecological Observatory Network (NEON) and its partners to capitalize on data and informatics interoperability initiatives that enable the integration of environmental data from across credible data sources. NEON is designed to provide high-quality, long-term environmental data for research. These data are also meant to be repurposed for operational needs, such as risk management, vulnerability assessments, resource management, and others. The recently established USDA Agriculture Research Service (ARS) Long Term Agro-ecosystem Research (LTAR) network is another example of such an environmental observatory that aims to produce credible data for environmental / agricultural forecasting and informing policy.</p> <p>To facilitate data fusion across observatories like NEON and LTAR, there is a growing call for observation systems to more closely coordinate and standardize how variables are measured. Together with observation standards, cyberinfrastructure standards enable the proliferation of an ecosystem of applications that utilize diverse, high-quality, credible data. Interoperability facilitates the integration of data from multiple credible sources of data, and enables the repurposing of data for use at different geographical scales. Metadata that captures the transformation of data into value-added products (“provenance”) lends reproducibility and transparency to the entire process. This way, the datasets and model code used to create any product can be examined by other parties.</p> <p>This poster outlines a pathway for transforming environmental data into value-added products by various stakeholders to better inform sustainable agriculture using data from environmental observatories including NEON and LTAR.</p

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

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 spatial-scale 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