Abstract:Implementation of sensitivity analysis (SA) procedures is helpful in calibration of models and also for their transposition to different watersheds. The reported studies on SA of Soil and Water Assessment Tool (SWAT) model were mostly focused on identifying parameters for pruning or modifying during the calibration process. This paper presents a sensitivity and identifiability analysis of model parameters that influence stream flow generation in SWAT. The analysis was focused on evaluating the sensitivity of the parameters in different climatic settings, temporal scales and flow regimes. The global sensitivity analysis (GSA) technique based on classical decomposition of variance, Sobol', was employed in this study. The results of the study indicate that modeled stream flow show varying sensitivity to parameters in different climatic settings. The results also suggest that the identifiability of a parameter for a given watershed is a major concern in calibrating the model for the specific watershed, as it might lead to equifinality of parameters. The SWAT model parameters show varying sensitivity in different years of simulation suggesting the requirement for dynamic updation of parameters during the simulation. The sensitivity of parameters during various flow regimes (low, medium and high flow) is also found to be uneven, which suggests the significance of a multi-criteria approach for the calibration of models.

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The Soil and Water Assessment Tool (SWAT) is increasingly used to quantify hydrologic and water quality impacts of bioenergy production, but crop-growth parameters for candidate perennial rhizomatous grasses (PRG) Miscanthus 9 giganteus and upland ecotypes of Panicum virgatum (switchgrass) are limited by the availability of field data. Crop-growth parameter ranges and suggested values were developed in this study using agronomic and weather data collected at the Purdue University Water Quality Field Station in northwestern Indiana. During the process of parameterization, the comparison of measured data with conceptual representation of PRG growth in the model led to three changes in the SWAT 2009 code: the harvest algorithm was modified to maintain belowground biomass over winter, plant respiration was extended via modified-DLAI to better reflect maturity and leaf senescence, and nutrient uptake algorithms were revised to respond to temperature, water, and nutrient stress. Parameter values and changes to the model resulted in simulated biomass yield and leaf area index consistent with reported values for the region. Code changes in the SWAT model improved nutrient storage during dormancy period and nitrogen and phosphorus uptake by both switchgrass and Miscanthus.Abbreviations ACRE = agronomy center for research and education BIO_E = radiation use efficiency 9 10 BLAI = maximum leaf area index CMN = rate of humus mineralization CYLD = nutrient fraction at harvest DLAI = point of the growing season when senescence begins HEFF = harvest efficiency HI = harvest index HU = heat unit LAI = leaf area index OAT = one-at-a-time method PAR = photosynthetically active radiation PLTFR = plant nutrient fraction PLTNFR = plant nitrogen fraction PLTPFR = plant phosphorus fraction PRG = perennial rhizomatous grasses RUE = radiation use efficiency SWAT = soil and water assessment tool T_BASE = base temperature WQFS = water quality field station.

Cellulosic bioenergy feedstock such as perennial grasses and crop residues are expected to play a significant role in meeting US biofuel production targets. We used an improved version of the Soil and Water Assessment Tool (SWAT) to forecast impacts on watershed hydrology and water quality by implementing an array of plausible land-use changes associated with commercial bioenergy crop production for two watersheds in the Midwest USA. Watershed-scale impacts were estimated for 13 bioenergy crop production scenarios, including: production of Miscanthus 9 giganteus and upland Shawnee switchgrass on highly erodible landscape positions, agricultural marginal land areas and pastures, removal of corn stover and combinations of these options. Water quality, measured as erosion and sediment loading, was forecasted to improve compared to baseline when perennial grasses were used for bioenergy production, but not with stover removal scenarios. Erosion reduction with perennial energy crop production scenarios ranged between 0.2% and 59%. Stream flow at the watershed outlet was reduced between 0 and 8% across these bioenergy crop production scenarios compared to baseline across the study watersheds. Results indicate that bioenergy production scenarios that incorporate perennial grasses reduced the nonpoint source pollutant load at the watershed outlet compared to the baseline conditions (0-20% for nitrate-nitrogen and 3-56% for mineral phosphorus); however, the reduction rates were specific to site characteristics and management practices.

Abstract:Ethanol from corn stover is expected to play an important role in achieving the Energy Independence and Security Act 2007 target of 136.25 billion liters (36 billion gallons) of biofuel by 2022. The 2010 USDA biofuel strategic report estimates that 16.3 billion liters (4.3 billion gallons) of biofuel from crop residues such as corn stover and straw is possible. Corn stover is expected to provide the majority of the estimated biofuel from crop residues, especially from the Midwestern US Corn Belt. A major concern related to removing corn stover is potential negative hydrologic and water quality impacts. The overall goal of this study was to estimate the watershed scale environmental impacts of corn stover removal in an agricultural watershed in the Midwest US. Soil and Water Assessment Tool was used to simulate the impacts associated with three corn stover removal rates (38%, 52% and 70%). The stream flow, nitrate and mineral phosphorus loading were reduced, and sediment and organic nitrogen loading were increased at the watershed outlet with all three stover removal scenarios. The stream flow was reduced by 1.4%, 2.0% and 2.7% from the baseline scenario (no stover removed) at 38%, 52% and 70% stover removal rates, respectively. The sediment loading increased by 19.7%, 22.5% and 29.0%, organic nitrogen increased by 0.8%, 2.0% and 5.5%, mineral phosphorus decreased by 11.7%, 15.5% and 21.0%, and nitrate decreased by 2.0%, 3.2% and 5.3% from the baseline scenario at the watershed outlet with 38%, 52% and 70% stover removal rates, respectively. The model results also indicate that the watershed response to stover removal is sensitive to watershed characteristics and management inputs, such as, slope and amount of fertilizer applied.