Search

Process-level modeling at the farm scale provides a tool for evaluating strategies for both mitigating greenhouse gas emissions and adapting to climate change. The Integrated Farm System Model (IFSM) simulates representative crop, beef, or dairy farms over many years of weather to predict performance, economics, and environmental impacts including various emissions and a farm-gate life cycle assessment of carbon, energy, water, and reactive nitrogen footprints of the feed, meat, or milk produced. To illustrate use of the model, a representative dairy farm in central New York was simulated over 25 years of recent historical weather to determine the environmental benefits and economic costs of alternative manure handling strategies. Use of an enclosed manure storage with a flare to burn the methane produced decreased the farm-gate carbon footprint of the milk produced by 20% at an increased annual cost of $42 cow-1. Using an anaerobic digester to produce gas and electricity used on the farm reduced the carbon footprint by 19% and reduced profitability by $56 cow-1. The addition of subsurface injection of manure along with a reduction in N fertilizer use greatly reduced ammonia emission from the farm and increased annual profit by $9 cow-1. Climate change is projected to affect many aspects of dairy production, including growing season length, crop growth processes, harvest timing and losses, cattle performance, nutrient emissions and losses, and ultimately farm profitability. Climate projections for high and low emission scenarios were downscaled from nine general circulation models. IFSM was then used to simulate the same New York dairy farm over 25-year periods using recent, mid-century, and late century climate projected by each of the climate models. Simulations were done without and with adaptation through modified crop varieties and planting and harvest dates. Forage production normally increased with projected climate change and corn grain yields decreased, and together feed production was maintained. Warmer temperatures increased volatile loss of ammonia N, and changes in precipitation patterns increased nutrient runoff losses in surface water. The reactive N footprint of the milk produced was increased by 2% to 11% with the change in climate, but other environmental footprints were relatively unaffected. With appropriate adaptation to climate change, annual farm profitability increased by about $100 cow-1. However, for the high emission, late century projection, profit decreased by $10 cow-1 and the risk or annual variance in profit increased by 34%, reflecting greater annual variation in crop and animal productivity. Whole-farm and climate models provide useful tools for studying the benefits and costs of greenhouse gas mitigation and the adaptation of farms to changing climate.

The article of record as published may be found at https://doi.org/10.7930/

This research estimates the impact that eight alternative climate change scenarios are likely to have on agricultural returns and the useful life of the Ogallala aquifer in the Southern High Plains (SHP) over a 90-year planning horizon, relative to the situation where climate conditions are maintained at the historical average condition for 1960 to 2009. The empirical analysis is accomplished with the aid of an integrated water policy model that couples a dynamic economic optimization model to a detailed aquifer model of the Southern Ogallala Aquifer. The integrated model controls for the effects of spatial heterogeneity in land use practices and aquifer characteristics. For each climate scenario, changes in annual economic returns, irrigated acres, water use, and aquifer storage levels are measured relative to respective estimates derived from the historic no change climate scenario. The annual 90-year time path of economic returns, water use, and cropping patterns under the eight climate change scenarios significantly varies from the baseline forecast. Moreover, relative to a baseline condition that estimates significant annual decreases in economic returns due to continued groundwater mining, the climate change scenarios generally suggest climate change will mitigate the cost of increasing groundwater scarcity due to a complimentary effect between crop yields and the various climate change scenarios.

Possible future climate changes in California are investigated from a varied set of climate change model simulations. These simulations, conducted by three state-of-the-art global climate models, provide trajectories from three greenhouse gas (GHG) emission scenarios. These scenarios and the resulting climate simulations are not “predictions,” but rather are a limited sample from among the many plausible pathways that may affect California’s climate. Future GHG concentrations are uncertain because they depend on future social, political, and technological pathways, and thus the IPCC has produced four “families” of emission scenarios. To explore some of these uncertainties, emissions scenarios A2 (a medium-high emissions) and B1 (low emissions) were selected from the current IPCC Fourth climate assessment, which provides several recent model simulations driven by A2 and B1 emissions. The global climate model simulations addressed here were from PCM1, the Parallel Climate Model from the National Center for Atmospheric Research (NCAR) and U.S. Department of Energy (DOE) group, and CM2.1 from the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluids Dynamics Laboratory (GFDL). As part of the scenarios assessment, a statistical technique using properties of historical weather data was employed to correct model biases and “downscale” the global-model simulation of future climates to a finer level of detail, onto a grid of approximately 7 miles (12 kilometers), which is more suitable for impact studies at the scales needed by California decision makers. In current climate-change simulations, temperatures over California warm significantly during the twenty-first century, with temperature increases from approximately +3ºF (1.5ºC) in the lower emissions scenario (B1) within the less responsive model (PCM1) to +8ºF (4.5ºC) in the higher emissions scenario (A2) within the more responsive model (CM2.1). Three of the simulations (all except the low-emission scenario run of the low-response model) exhibit more warming in summer than in winter. In all of the simulations, most precipitation continues to occur in winter, with virtually all derived from North Pacific winter storms. Relatively little change in overall precipitation is projected. Climate warming has a profound influence in diminishing snow accumulations, because there is more rain and less snow, and earlier snowmelt. These snow losses increase as the warming increases, so that they are most severe under climate changes projected by the more sensitive model with the higher GHG emissions.

This white paper focuses on how climate change-induced effects on weather will translate into changes in wildland fire severity and outcomes, particularly on the effectiveness of initial attack at limiting the area burned in contained fires and the number of fires that escape initial attack. Prior research has indicated that there is a potential for significant increases in the number of fires escaping initial attack, particularly in areas in which the fuel matrix is dominated by grass and brush. These results were driven primarily by predicted increases in wind speeds. Those findings, however, were derived using less sophisticated models of initial attack than currently available. The results of this study, using more sophisticated models and climate projections, indicate that subtle shifts in fire behavior of the sort that might be induced by the climate changes anticipated for the next century are of sufficient magnitude to generate an appreciable increase in the number of fires that escape initial attack, at least for areas where brush fuels dominate. Such escapes of considerable importance in wildland fire protection planning, given the high cost to society of a catastrophic escape like those experienced in recent decades in the BerkeleyOakland, Santa Barbara, San Diego, or Los Angeles areas. However, at least for the limited region in the Sierra Nevada considered in this study, it would appear that relatively modest augmentations to existing firefighting resources would be sufficient to compensate for changeinduced changes in wildland fire outcomes. Generalizing our findings with respect to wildland fire intensity and outcomes for the AmadorEl Dorado Unit to other private lands in the state will require both further analysis using the data for that CDF unit, and replication of this analysis using data for several other units and for the state’s federal forest lands. No attempt is made in this paper to extrapolate the results beyond the boundaries of the study area, in part because of necessary work that was identified with respect to further validation of this study’s modeling approach.

This study attempts to quantify the impacts of coastal flooding on land area, population and urban infrastructure that would result from an extreme tropical storm occurring over a range of for future sea level rise scenarios. This case study follows a storm named Xangsane, that occurred in Thua Thien Hue, a central province in Vietnam, in 2006. It compares the impacts caused by different scenarios of storm surges and sea levels simulated on two different datasets, a national one and a global one. A 2-dimentional hydrodynamic model is used to simulate the storm surge and coastal flooding. The numerical simulation is coupled with ArcGIS tools to analyze the level and extent of inundation areas as well as their influences on population and urban infrastructure (transportation system, schools, hospitals, and historical heritage sites). Projection indicates that the sea level rise (SLR) of 1 m and 2 m can cause huge impacts for transportation infrastructure and buildings. The impact increase on transportation infrastructure is considerably significant, from 200% to 400% increase on all types of road across different storm surge scenarios. The impact on railroads is the least, about 32 km to 50 km in SLR of 1 m and 2 m, but its impact percentage is the highest among all other categories. The maximum railroad impact increases nearly 2000% for SLR of 1 m and 3500% for SLR of 2 m with or without storm surge. For land area, building (school and hospital), and special infrastructure, the impacts increase up to 450% in SLR of 1 m and 2 m simulated on both datasets (the national and the global). The risk of flooding is closely related to the SLR more than storm surges. The impact of projected sea levels increases the need for flood control measures in Thua Thien Hue province.