SWAT Check: A Screening Tool to Assist Users in the Identification of Potential Model Application Problems.

The Soil and Water Assessment Tool (SWAT) is a basin-scale hydrologic model developed by the United States Department of Agriculture Agricultural Research Service. SWAT's broad applicability, user-friendly model interfaces, and automatic calibration software have led to a rapid increase in the number of new users. These advancements also allow less experienced users to conduct SWAT modeling applications. In particular, the use of automated calibration software may produce simulated values that appear appropriate because they adequately mimic measured data used in calibration and validation. Autocalibrated model applications (and often those of unexperienced modelers) may contain input data errors and inappropriate parameter adjustments not readily identified by users or the autocalibration software. The objective of this research was to develop a program to assist users in the identification of potential model application problems. The resulting "SWAT Check" is a stand-alone Microsoft Windows program that (i) reads selected SWAT output and alerts users of values outside the typical range; (ii) creates process-based figures for visualization of the appropriateness of output values, including important outputs that are commonly ignored; and (iii) detects and alerts users of common model application errors. By alerting users to potential model application problems, this software should assist the SWAT community in developing more reliable modeling applications.

Modifying the Soil and Water Assessment Tool to simulate cropland carbon flux: model development and initial evaluation.

Climate change is one of the most compelling modern issues and has important implications for almost every aspect of natural and human systems. The Soil and Water Assessment Tool (SWAT) model has been applied worldwide to support sustainable land and water management in a changing climate. However, the inadequacies of the existing carbon algorithm in SWAT limit its application in assessing impacts of human activities on CO2 emission, one important source of greenhouse gasses (GHGs) that traps heat in the earth system and results in global warming. In this research, we incorporate a revised version of the CENTURY carbon model into SWAT to describe dynamics of soil organic matter (SOM)-residue and simulate land-atmosphere carbon exchange. We test this new SWAT-C model with daily eddy covariance (EC) observations of net ecosystem exchange (NEE) and evapotranspiration (ET) and annual crop yield at six sites across the U.S. Midwest. Results show that SWAT-C simulates well multi-year average NEE and ET across the spatially distributed sites and capture the majority of temporal variation of these two variables at a daily time scale at each site. Our analyses also reveal that performance of SWAT-C is influenced by multiple factors, such as crop management practices (irrigated vs. rainfed), completeness and accuracy of input data, crop species, and initialization of state variables. Overall, the new SWAT-C demonstrates favorable performance for simulating land-atmosphere carbon exchange across agricultural sites with different soils, climate, and management practices. SWAT-C is expected to serve as a useful tool for including carbon flux into consideration in sustainable watershed management under a changing climate. We also note that extensive assessment of SWAT-C with field observations is required for further improving the model and understanding potential uncertainties of applying it across large regions with complex landscapes.

Potential economic benefits of adapting agricultural production systems to future climate change.

Potential economic impacts of future climate change on crop enterprise net returns and annual net farm income (NFI) are evaluated for small and large representative farms in Flathead Valley in Northwest Montana. Crop enterprise net returns and NFI in an historical climate period (1960-2005) and future climate period (2006-2050) are compared when agricultural production systems (APSs) are adapted to future climate change. Climate conditions in the future climate period are based on the A1B, B1, and A2 CO(2) emission scenarios from the Intergovernmental Panel on Climate Change Fourth Assessment Report. Steps in the evaluation include: (1) specifying crop enterprises and APSs (i.e., combinations of crop enterprises) in consultation with locals producers; (2) simulating crop yields for two soils, crop prices, crop enterprises costs, and NFIs for APSs; (3) determining the dominant APS in the historical and future climate periods in terms of NFI; and (4) determining whether NFI for the dominant APS in the historical climate period is superior to NFI for the dominant APS in the future climate period. Crop yields are simulated using the Environmental/Policy Integrated Climate (EPIC) model and dominance comparisons for NFI are based on the stochastic efficiency with respect to a function (SERF) criterion. Probability distributions that best fit the EPIC-simulated crop yields are used to simulate 100 values for crop yields for the two soils in the historical and future climate periods. Best-fitting probability distributions for historical inflation-adjusted crop prices and specified triangular probability distributions for crop enterprise costs are used to simulate 100 values for crop prices and crop enterprise costs. Averaged over all crop enterprises, farm sizes, and soil types, simulated net return per ha averaged over all crop enterprises decreased 24% and simulated mean NFI for APSs decreased 57% between the historical and future climate periods. Although adapting APSs to future climate change is advantageous (i.e., NFI with adaptation is superior to NFI without adaptation based on SERF), in six of the nine cases in which adaptation is advantageous, NFI with adaptation in the future climate period is inferior to NFI in the historical climate period. Therefore, adaptation of APSs to future climate change in Flathead Valley is insufficient to offset the adverse impacts on NFI of such change.

Corn-based ethanol production and environmental quality: a case of Iowa and the conservation reserve program.

Growing demand for corn due to the expansion of ethanol has increased concerns that environmentally sensitive lands retired from agricultural production and enrolled into the Conservation Reserve Program (CRP) will be cropped again. Iowa produces more ethanol than any other state in the United States, and it also produces the most corn. Thus, an examination of the impacts of higher crop prices on CRP land in Iowa can give insight into what we might expect nationally in the years ahead if crop prices remain high. We construct CRP land supply curves for various corn prices and then estimate the environmental impacts of cropping CRP land through the Environmental Policy Integrated Climate (EPIC) model. EPIC provides edge-of-field estimates of soil erosion, nutrient loss, and carbon sequestration. We find that incremental impacts increase dramatically as higher corn prices bring into production more and more environmentally fragile land. Maintaining current levels of environmental quality will require substantially higher spending levels. Even allowing for the cost savings that would accrue as CRP land leaves the program, a change in targeting strategies will likely be required to ensure that the most sensitive land does not leave the program.

Predicting soil erosion for alternative land uses.

The APEX (Agricultural Policy-Environmental eXtender) model developed in the United States was calibrated for northwestern China's conditions. The model was then used to investigate soil erosion effects associated with alternative land uses at the ZFG (Zi-Fang-Gully) watershed in northwestern China. The results indicated that the APEX model could be calibrated reasonably well (+/-15% errors) to fit those areas with >50% slope within the watershed. Factors being considered during calibration include runoff, RUSLE (Revised Universal Soil Loss Equation) slope length and steepness factor, channel capacity flow rate, floodplain saturated hydraulic conductivity, and RUSLE C factor coefficient. No changes were made in the APEX computer code. Predictions suggest that reforestation is the best practice among the eight alternative land uses (the status quo, all grass, all grain, all grazing, all forest, half tree and half grass, 70% tree and 30% grain, and construction of a reservoir) for control of water runoff and soil erosion. Construction of a reservoir is the most effective strategy for controlling sediment yield although it does nothing to control upland erosion. For every 1 Mg of crop yield, 11 Mg of soil were lost during the 30-yr simulation period, suggesting that expanding land use for food production should not be encouraged on the ZFG watershed. Grass species are less effective than trees in controlling runoff and erosion on steep slopes because trees generally have deeper and more stable root systems.

Models for hydrologic design of evapotranspiration landfill covers.

The technology used in landfill covers is changing, and an alternative cover called the evapotranspiration (ET) landfill cover is coming into use. Important design requirements are prescribed by Federal rules and regulations for conventional landfill covers but not for ET landfill covers. There is no accepted hydrologic model for ET landfill cover design. This paper describes ET cover requirements and design issues, and assesses the accuracy of the EPIC and HELP hydrologic models when used for hydrologic design of ET covers. We tested the models against high-quality field measurements available from lysimeters maintained by the Agricultural Research Service of the U.S. Department of Agriculture at Coshocton, Ohio, and Bushland, Texas. The HELP model produced substantial errors in estimating hydrologic variables. The EPIC model estimated ET and deep percolation with errors less than 7% and 5%, respectively, and accurately matched extreme events with an error of less than 2% of precipitation. The EPIC model is suitable for use in hydrologic design of ET landfill covers.

An approach for estimating soil carbon using the National Nutrient Loss Database.

Agricultural lands have the potential to contribute to greenhouse gas mitigation by sequestering organic carbon within the soil. Credible and consistent estimates will be necessary to design programs and policies to encourage management practices that increase carbon sequestration. Because a nationwide survey of soil carbon by the wide range of natural resources and management conditions of the United States is prohibitively expensive, a simulation modeling approach must be used. The National Nutrient Loss Database (NNLD) is a modeling and database system designed and built jointly by the USDA- Natural Resources Conservation Service (NRCS) and Texas A&M University to provide science-based inferences on environmental impacts from changes in agricultural management practices and programs at the regional and national level. Currently, the NNLD simulates 16 crops and covers approximately 1.35 x 10(8) ha. For estimating soil carbon sequestration, the database will be populated with approximately 1.5 x 10(6) field-level model runs using the EPIC (Environmental Policy Impact Calculator) model, which includes newly incorporated carbon equations consistent with those in the Century model. Each run will represent a unique situation defined by state, crop, climate, soil, irrigation type, conservation practice, tillage system, and nutrient management treatment (nutrient rate, application frequency, application timing, and manure category). Results are to be assigned to specific National Resource Inventory points (NRI) to simulate regional and national baselines. In this article we present the modeling approach and discuss the strengths and limitations.

Simulated effects of crop rotations and residue management on wind erosion in Wuchuan, west-central Inner Mongolia, China.

For decades, wind erosion has triggered dust and sand storms, buffeting Beijing and areas of northwestern China to the point of being hazardous to human health while rapidly eroding crop and livestock productivity. The EPIC (Environmental Policy Integrated Climate) field-scale simulation model was used to assess long-term effects of improved crop rotations and crop residue management practices on wind erosion in Wuchuan County in Inner Mongolia. Simulation results indicate that preserving crop stalks until land is prepared by zone tillage for the next year's crop in lieu of using them as a source of heating fuel or livestock fodder significantly reduces wind erosion by 60%. At the same time, grain and potato (Solanum tuberosum L.) yields were maintained or improved. Significant reductions in erosion, 35 to 46%, also resulted from delaying stalk removal until late January through late April. Yearly wind erosion was concentrated in April and May, the windiest months. Additionally, the use of alternative crop rotations resulted in differences in wind erosion, largely due to a difference in residue stature and quality and differences in biomass produced. As a result, altering current crop rotation systems by expanding corn (Zea mays L.), wheat (Triticum aestivum L.), and millet [Sorghum bicolor (L.) Moench] and reducing potato and pea (Pisum sativum L.) production significantly reduced simulated wind erosion, thus diminishing the severity of dust and sand storms in northwestern China. Saving and protecting topsoil over time will sustain land productivity and have long-term implications for improving conditions of rural poverty in the region.