A regional scale modeling framework combining biogeochemical model with life cycle and economic analysis for integrated assessment of cropping systems.

The goal of this study was to integrate a crop model, DNDC (DeNitrification-DeComposition), with life cycle assessment (LCA) and economic analysis models using a GIS-based integrated platform, ENVISION. The integrated model enables LCA practitioners to conduct integrated economic analysis and LCA on a regional scale while capturing the variability of soil emissions due to variation in regional factors during production of crops and biofuel feedstocks. In order to evaluate the integrated model, the corn-soybean cropping system in Eagle Creek Watershed, Indiana was studied and the integrated model was used to first model the soil emissions and then conduct the LCA as well as economic analysis. The results showed that the variation in soil emissions due to variation in weather is high causing some locations to be carbon sink in some years and source of CO2 in other years. In order to test the model under different scenarios, two tillage scenarios were defined: 1) conventional tillage (CT) and 2) no tillage (NT) and analyzed with the model. The overall GHG emissions for the corn-soybean cropping system was simulated and results showed that the NT scenario resulted in lower soil GHG emissions compared to CT scenario. Moreover, global warming potential (GWP) of corn ethanol from well to pump varied between 57 and 92gCO2-eq./MJ while GWP under the NT system was lower than that of the CT system. The cost break-even point was calculated as $3612.5/ha in a two year corn-soybean cropping system and the results showed that under low and medium prices for corn and soybean most of the farms did not meet the break-even point.

A new model to simulate climate-change impacts on forest succession for local land management.

We developed a new climate-sensitive vegetation state-and-transition simulation model (CV-STSM) to simulate future vegetation at a fine spatial grain commensurate with the scales of human land-use decisions, and under the joint influences of changing climate, site productivity, and disturbance. CV-STSM integrates outputs from four different modeling systems. Successional changes in tree species composition and stand structure were represented as transition probabilities and organized into a state-and-transition simulation model. States were characterized based on assessments of both current vegetation and of projected future vegetation from a dynamic global vegetation model (DGVM). State definitions included sufficient detail to support the integration of CV-STSM with an agent-based model of land-use decisions and a mechanistic model of fire behavior and spread. Transition probabilities were parameterized using output from a stand biometric model run across a wide range of site productivities. Biogeographic and biogeochemical projections from the DGVM were used to adjust the transition probabilities to account for the impacts of climate change on site productivity and potential vegetation type. We conducted experimental simulations in the Willamette Valley, Oregon, USA. Our simulation landscape incorporated detailed new assessments of critically imperiled Oregon white oak (Quercus garryana) savanna and prairie habitats among the suite of existing and future vegetation types. The experimental design fully crossed four future climate scenarios with three disturbance scenarios. CV-STSM showed strong interactions between climate and disturbance scenarios. All disturbance scenarios increased the abundance of oak savanna habitat, but an interaction between the most intense disturbance and climate-change scenarios also increased the abundance of subtropical tree species. Even so, subtropical tree species were far less abundant at the end of simulations in CV-STSM than in the dynamic global vegetation model simulations. Our results indicate that dynamic global vegetation models may overestimate future rates of vegetation change, especially in the absence of stand-replacing disturbances. Modeling tools such as CV-STSM that simulate rates and direction of vegetation change affected by interactions and feedbacks between climate and land-use change can help policy makers, land managers, and society as a whole develop effective plans to adapt to rapidly changing climate.

Toward efficient riparian restoration: integrating economic, physical, and biological models.

This paper integrates economic, biological, and physical models to explore the efficient combination and spatial allocation of conservation efforts to protect water quality and increase salmonid populations in the Grande Ronde basin, Oregon. We focus on the effects of shade on water temperatures and the subsequent impacts on endangered juvenile salmonid populations. The integrated modeling system consists of a physical model that links riparian conditions and hydrological characteristics to water temperature; a biological model that links water temperature and riparian conditions to salmonid abundance, and an economic model that incorporates both physical and biological models to estimate minimum cost allocations of conservation efforts. Our findings indicate that conservation alternatives such as passive and active riparian restoration, the width of riparian restoration zones, and the types of vegetation used in restoration activities should be selected based on the spatial distribution of riparian characteristics in the basin. The relative effectiveness of passive and active restoration plays an important role in determining the efficient allocations of conservation efforts. The time frame considered in the restoration efforts and the magnitude of desired temperature reductions also affect the efficient combinations of restoration activities. If the objective of conservation efforts is to maximize fish populations, then fishery benefits should be directly targeted. Targeting other criterion such as water temperatures would result in different allocations of conservation efforts, and therefore are not generally efficient.

Modeling antimicrobial contaminant removal in slow sand filtration.

Slow sand filters are used in rural regions where source water may be subjected to antimicrobial contaminant loads from waste discharges and diffuse pollution. A numerical model (LETA) was derived to calculate aqueous antimicrobial concentrations through time and depth of a slow sand filter and estimate accumulating contaminant mass in the schmutzdecke. Input parameters include water quality variables easily quantified by water system personnel and published adsorption, partitioning, and degradation coefficients. Simulation results for the tetracycline, quinolone, and macrolide classes of antimicrobials suggested greater than 3-log removal from 1 microg/L influent concentrations within the top 40 cm of the sand column, with schmutzdecke antimicrobial concentrations comparable to other land-applied waste biosolids. A 60-day challenge experiment injecting 1 microg/L tylosin to a pilot slow sand filter showed an average 0.1mg/kg of the antimicrobial remaining in the schmutzdecke layer normally removed during filter maintenance, and this value was the same order of magnitude as the sorbed concentration predicted by the LETA model.