March 29, 2011 | MSU Union, Parlor C | Michigan State University
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Department of Economics and Center for Agriculture and Rural Development, Iowa State University
The complex interrelationship between the production of food, fuel, and environmental goods and services is increasingly being recognized by researchers and the general public. With this backdrop, integrated modeling is being increasingly recognized as a valuable policy tool that can allow decision makers to anticipate the direct costs and benefits of potential policies prior to implementation. Perhaps even more importantly, careful models can help identify unexpected consequences thereby allowing policy makers to simultaneously adopt measures to avoid or mitigate these effects.
However, before models can accurately inform the policy process, they must adequately represent the scale appropriate to the questions being asked, they must include an adequate representation of the key consequences and alternatives that policy will affect, and the findings of the modeling exercise must be presented in understandable and reasonably transparent ways. In this presentation, I discuss a number of the challenges involved in the development, integration, and use of integrated economic and biophysical models. Examples are drawn from watershed based modeling exercises as well as biophysical models based on field scale predictions and are relevant to biofuels and energy, water quality, and carbon sequestration.
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Department of Agricultural, Food and Resource Economics, MSU
Agriculture is a managed ecosystem that generates both marketable products and non-marketed ecosystem services. We integrate biophysical and economic models to simulate how commercially managed agricultural landscapes will evolve under alternative policy scenarios. The Environmental Policy Integrated Climate (EPIC) model simulates the transformation of managed and natural inputs into crop yields and environmental outcomes over time. In current work with the Great Lakes Bioenergy Research Center (GLBRC), EPIC modelers at the USDOE Oak Ridge and Pacified Northwest National Laboratories are modeling 24-year simulations of 74 different cropping systems under Southwest Michigan and Central Wisconsin conditions to capture longterm performance under varying crop rotation, fertilization, tillage and residue removal scenarios. Results from these simulations are used as parameters for a profit maximization model of representative farm behavior that predicts farm landscape crop composition under alternative policy scenarios. Work to date in Michigan has simulated landscape response to rising prices of cellulosic feedstocks for biofuel production. The bioeconomic model is designed to permit future policy scenarios that include biofuel production mandates as well as environmental taxes and subsidies linked to greenhouse gas emissions, phosphorus and nitrate runoff, and soil erosion.
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Laura Schmitt Olabisi
Department of Community, Agriculture, Recreation and Resource Studies, ESPP, MSU
Bio-energy production, together with shifting population, energy demand and climate uncertainties, present a great challenge for state water authorities seeking to sustain future water supply. However, there are currently few dynamic models of water use and supply at a spatial scale that is useful to inform state policy. We built a model to forecast Minnesota's temporal and spatial water schemes by 2030 in response to population, energy, and climate scenarios, by integrating a system dynamics model with geographic information system (GIS) data. The results indicate that population growth and intensive power generation are two primary factors driving increasing water demand. Water balance is particularly vulnerable to spatial location of high water-demand users, such as bioenergy and electrical production plants. Temporal water availability is not evenly distributed throughout the year, with late summer and winter generally producing water surpluses, and mid-summer being a period of drawdown. We believe these results, along with further modeling efforts, can inform water conservation policies.
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Department of Geography, ESPP, MSU
Agent-based models (ABM) have been recognized as computational laboratories furnishing scientists with a plausible exploratory apparatus for learning about coupled human natural systems (CHANS) dynamics through an explicit representation of human behavior. At the same time, uncertainty exists about the nature and structure of decision protocols (DP) that should be embedded into ABM to realistically reflect the complexity of the problems addressed. In this paper I present three categories of DP with an increasing level of complexity: (a) one-way impact of agents on their environment, (b) a feedback loop between agents and their environment, and (c) a web of interactions and feedbacks relating agents to other agents and to their environment. I use a simple case study of residential development to demonstrate these three scenarios. The results of these experiments suggest that, with all other parameters fixed, significant differences may be observed in the emergent pattern. As a consequence, researchers should make judicious decisions on the form and complexity of human agency in modeling CHANS.
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Department of Geological Science, MSU
(Hyndman, Anthony Kendall, and Sherry Martin)
Changes in land use and climate will have significant effects on regional and global water resources over the coming decades. Predicting those impacts requires methods to characterize spatial and temporal variability in subsurface moisture distributions and models capable of simulating the primary processes that drive the hydrologic cycle. The Integrated Landscape Hydrology Model (ILHM) is a fully-distributed, physical-process, water- and energy-balance model that couples surface and ground water processes using readily available GIS, remote sensing, and gauged inputs. ILHM is capable of fine-resolution (~100 m), regional-scale (>10,000 km2) simulations of nearly the entire terrestrial hydrologic cycle at hourly time steps over decadal timescales.
Application of ILHM to a regional watershed in central Lower Michigan illustrates large spatial and temporal variations in groundwater recharge, evapotranspiration, and stream discharge. These simulations reveal the complex interactions between land use and climate that influence regional hydrologic fluxes, and provide insight into the hydrologic response of projected changes in climate, land uses, and biofuels. As an example, projected climate from downscaled GCM forecasts show significant changes in soil moisture, evaporation, transpiration, groundwater recharge, and stream flows. These simulations provide reasonable predictions at a variety of scales without calibration, and can thus provide significant insight into the expected hydrologic dynamics for ungaged basins. In addition, these simulations provide a mechanism to quantify the impact of past land use changes on water quality, a concept we call land use legacy.
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(Hayes and Sherry Martin)
Department of Fisheries and Wildlife, MSU
Past efforts to relate land cover to lake water chemistry have often taken a correlational approach, looking at the relationship between current land cover and one or two lake water chemistry parameters. Here we describe the results from analyses looking at the impact of past land cover (i.e., legacy effects), and their importance in understanding current lake water chemistry for a variety of parameters. We show that different chemicals have different levels of predictability, and that this predictability is related to legacy impacts and the pathways by which materials enter lakes. The key insight gained from this work is that understanding current lake water chemistry requires a dynamic representation of the processes that contribute to the flow of materials through the ecosystem.
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Department of Civil and Environmental Engineering, MSU
Research interests in Dr. Mantha's group involve the application of large-scale, physically-based computational models to address water quality and quantity issues in watersheds, lakes and costal/near-shore regions. Recent research addressed questions involving the fate and transport of chemical and biological agents in different hydrologic units in the Great Lakes region. A common unifying thread in all our activities is the development and application of coupled (e.g., biophysical) models and integration of field observations with modeling.
F. Zhang, J. Qi (Center for Global Change and Earth Observations)
As one of the largest land cover types, grassland can potentially play an important role in the ecosystem services of natural resources in China. Nitrous oxide (N2O) is a major greenhouse gas emitted from grasslands. Current N2O inventory at regional or national level in China relies on the emission factor method, and is based on limited measurements. To improve inventory accuracy and capture the spatial variability of the N2O emissions under the diverse climate, soil and management conditions across China, we adopted an approach that uses a process-based biogeochemical model, DeNitrification-DeComposition (DNDC) in this study, to map the N2O emissions from China's grasslands. The DNDC was linked to a GIS database of spatially distributed information of climate, soil, vegetation and management at county-level for all grasslands in China. Daily weather data from 2000–2007 based on the national network of 670 meteorological stations were utilized in the model simulations. The results were validated against observations from several grasslands in China and from other coun15 tries. The modeled results showed a clear geographic pattern of N2O emissions from China's grasslands. A high-emission strip was found that stretched from northeast to central China, along the eastern boundary of the temperate grassland region adjacent to the major agricultural regions. The grasslands in the western mountain regions, however, emitted much less N2O. The regional average of N2O emission rates was 0.23, 0.11 and 0.39 kgNha-1 y-1 for the temperate, mountain and tropical/subtropical grasslands, respectively. The national N2O emission was 76.5 GgN from the 337 million ha of grasslands in China. The modeled results were in good agreement with observations (R2 = 0.64 for 11 datasets), suggesting that the process-based model can be used to capture the spatial dynamics of N2O emissions as an effective alternative to statistical method currently used in China.
Chuan Qin, Gopalsamy Alagarswamy, Jiaguo Qi (Center for Global Change and Earth Observations)
The tropical savannas in East Africa are playing an important role in the global carbon cycle as its high production could serve as a modest carbon sink. It is important to estimate the carbon stored in the savanna ecosystem. CENTURY, a biogeochemical model has been successfully applied to simulate dynamics of carbon in several ecological systems. But the model has only been validated using biomass measurements on a point basis at a number of field sites. While the ground truth data is missing in our study area, the remotely sensed MODIS NPP (Net Primary Production) data was used for validation. The results showed that the values of MODIS NPP and CENTURY simulations were positively well correlated, but the magnitudes of MODIS NPP were much higher than the CENTURY. The possible reason is that remotely sensed NPP contains everything above ground, including not only grassland but also other land cover types, like forest, while CENTURY simulates grassland carbon. So the grasslands NPP were derived based on MOIDS NPP. The new comparison indicated that the magnitude of MODIS NPP has reduced a lot and been much closer to the CENTURY results. The conclusion was made that MODIS NPP can be used to validate CENTURY simulations but with some cautions.