Larry E. Wagner

Application of the Wind Erosion Prediction System in the AIRPACT Regional Air Quality Modeling Framework

Abstract. Wind erosion of soil is a major concern of the agricultural community, as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, dust emissions due to wind erosion degrade air quality, reduce visibility, and cause perturbations to regional radiation budgets. PM 10 emitted from the soil surface can travel hundreds of kilometers downwind before being deposited back to the surface. Thus, it is necessary to address agricultural air pollutant sources within a regional air quality modeling system in order to forecast regional dust storms and to understand the impact of agricultural activities and land-management practices on air quality in a changing climate. The Wind Erosion Prediction System (WEPS) is a new tool in regional air quality modeling for simulating erosion from agricultural fields. WEPS represents a significant improvement, in comparison to existing empirical windblown dust modeling algorithms used for air quality simulations, by using a more process-based modeling approach. This is in contrast with the empirical approaches used in previous models, which could only be used reliably when soil, surface, and ambient conditions are similar to those from which the parameterizations were derived. WEPS was originally intended for soil conservation applications and designed to simulate conditions of a single field over multiple years. In this work, we used the EROSION submodel from WEPS as a PM 10 emission module for regional modeling by extending it to cover a large region divided into Euclidean grid cells. The new PM 10 emission module was then employed within a regional weather and chemical transport modeling framework commonly used for comprehensive simulations of a wide range of pollutants to evaluate overall air quality conditions. This framework employs the Weather Research and Forecasting (WRF) weather model along with the Community Multi-scale Air Quality (CMAQ) model to treat ozone, particulate matter, and other air pollutants. To demonstrate the capabilities of the WRF/EROSION/CMAQ dust modeling framework, we present here results from simulations of dust storms that occurred in central and eastern Washington during 4 October 2009 and 26 August 2010. Comparison of model results with observations indicates that the modeling framework performs well in predicting the onset and timing of the dust storms and the spatial extent of their dust plumes. The regional dust modeling framework is able to predict elevated PM 10 concentrations hundreds of kilometers downwind of erosion source regions associated with the windblown dust, although the magnitude of the PM 10 concentrations are extremely sensitive to the assumption of surface soil moisture and model wind speeds. Future work will include incorporating the full WEPS model into the regional modeling framework and targeting field measurements to evaluate the modeling framework more extensively.

Building Chinese wind data for Wind Erosion Prediction System using surrogate US data

Wind erosion is a global problem, especially in arid and semiarid regions of the world, which leads to land degradation and atmosphere pollution. The process-based Wind Erosion Prediction System (WEPS), developed by the USDA, is capable of simulating the windblown soil loss with changing weather and field conditions and different manmade management scenarios (Hagen 1991; Hagen 2004; Tatarko et al. forthcoming). Erosion in WEPS is driven by stochastically generated hourly wind data by the WINDGEN program, which is more appropriate than using measured data directly, and thus hourly wind data for the entire day are needed to build the statistical database (Donk et al. 2005). The current version of WEPS contains wind data for 2,718 stations within the United States. When running WEPS, wind data from the nearest station, from a station assigned to a polygon region or interpolated from nearby stations, can be used (Wagner forthcoming). Another database, named CLIGEN, which contains other climate information, including daily temperature, precipitation, solar radiation, etc., is also needed for the WEPS simulation. There is a great potential to extend WEPS to other countries and regions, such as China, which has a similar area, latitude location, and climate diversity to the United States.

Spatial Application of WEPS for Estimating Wind Erosion in the Pacific Northwest

Abstract. The Wind Erosion Prediction System (WEPS) is used to simulate soil erosion by wind on cropland and was originally designed to run simulations on a field scale. This study extended WEPS to run on multiple fields (grid cells) independently to cover a large region and conducted an initial investigation to assess how well WEPS performed in that environment by comparing simulations for two historical dust events with field observations and satellite images in the Columbia Plateau region of Washington. We modified the WEPS source code to allow it not only to run on multiple grid cells but also to save the state of the simulation so that it can be re-initiated from that state in future runs, allowing the model to be started and then stepped through time incrementally under various future climate or forecast weather scenarios. We initially ran WEPS on the entire state of Washington, with the entire Pacific Northwest region as our ultimate target area, to provide PM 10 and eventually PM 2.5 emissions from wind erosion events as input to the chemical transport model CMAQ, which is used by the AIRPACT regional air quality modeling system for the Pacific Northwest. Three principal inputs to WEPS are meteorological data, soil data, and crop management practices. These data, at a 1 km A— 1 km grid cell resolution, are the basic input data for running the spatially distributed model. The climatic data from a three-year period were stochastically generated based on statistical representations of past meteorological measurements from stations in the region and were used for initializing WEPS, and then a three-day set of meteorological data corresponding with historical dust storm events were selected for simulation by WEPS of wind erosion of cropland in the state of Washington. The crop management data were selected based on the land use and USDA Natural Resources Conservation Service (NRCS) crop management zones, and the soil data were derived from the NRCS SSURGO database. We aggregated the outputs from 1 km A— 1 km grid cells into 12 km A— 12 km grid cells for easier visualization and then mapped the total surface soil erosion, suspension, and PM 10 emissions for each 12 km A— 12 km grid cell. This study shows that WEPS can be successfully extended to run from one field grid cell to multiple field grid cells, and the model can identify regions with high potential for soil erosion by wind. It also demonstrates that WEPS can be used for real-time monitoring of soil erosion and air quality in a large region if actual and forecast weather inputs are available.

EVALUATION OF BULK DENSITY AND VEGETATION AS AFFECTED BY MILITARY VEHICLE TRAFFIC AT FORT RILEY, KANSAS

Abstract. Studies were conducted using military vehicles to determine the influence of repeated traffic on soil compaction and vegetative losses. The resultant data will eventually be incorporated into models such as the Wind Erosion Prediction System (WEPS). A replicated field experiment was conducted in the fall of 2010 on two soils that dominate the military training grounds at Fort Riley, Kansas. Treatments consisted of two vehicle types and three levels of vehicle passes. We used an Abrams M1A1 tank and a High-Mobility Multipurpose Wheeled Vehicle (i.e., Humvee), representing tracked and wheeled military vehicles, respectively. Bulk density, aboveground standing biomass, and plant cover were measured before and after vehicular traffic in the fall of 2010 as well as in the spring and summer of 2011. Samples were taken from curved, straight, and cross-over sections of the vehicle tracks. A mixed-model analysis of variance of these data indicated that the overall mean bulk density under the M1A1 was significantly greater than under the Humvee (p ≤ 0.05). In general, as the number of passes increased, the bulk density under the M1A1 increased significantly (p ≤ 0.05), but the increases under the Humvee were not significant (p ≤ 0.05). Bulk densities were significantly greater in the curved part of the tracks than the straight part of the tracks. Reduction in standing biomass and vegetation cover was more severe on average under the M1A1 than under the Humvee (although not significant at p ≤ 0.05). For both vehicles, biomass and cover were affected more at the curved sections of the track than the straight sections (significant at p ≤ 0.05). Comparison of spring and fall bulk density data showed significant differences at the 0-5 cm and 5-10 cm depths, indicating that the winter freeze and thaw cycles loosened the top soil layers. Subsequent growth showed severe reduction in grass biomass growth in the curved sections of the tracked vehicle paths. Growth in forb species was not significantly affected.

Integrating WEPP into the WEPS Infrastructure

WEPP Hydrology in WEPS WEPP hydrology routines were previously integrated into WEPS to reduce the computational requirements for WEPS simulations. Testing has shown that the runoff, infiltration, evaporation, and winter hydrology between this code and the original WEPP code are significantly different. Using a fallow soil test scenario, the factors causing these differences have been isolated to include random roughness, hydraulic conductivity, soil matric potential, reference evapotranspiration, soil layering, snowfall and snow melt processes. It is expected that test scenarios with growing crops will lead to an additional set of difference factors.

Incorporating the Wind Erosion Prediction System (WEPS) for Dust into a Regional Air Quality Modeling System

Wind erosion of soil is a major concern of the agricultural community as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, suspension of eroded soil particles results in dust emissions into the atmosphere, contributing to poor air quality, reduced visibility, and perturbations to regional radiation budgets. An important aspect of understanding the impact of agricultural activities is the ability to model windblown dust emissions within the framework of a regional air-quality system that considers atmospheric constituents from a variety of sources. The Wind Erosion Prediction System (WEPS) is a new tool for treating erosion from agricultural fields. As a process-based model, WEPS represents a significant improvement in comparison to existing empirical windblown dust modeling algorithms. WEPS includes several submodels to account for the effects of crop growth, crop management practices, soil conditions and surface cover. WEPS was originally intended for soil conservation applications and designed to simulate conditions of a single field over multiple years. In this work, WEPS has been modified so that it can be incorporated into a gridded regional air quality forecasting system. The modified WEPS model is incorporated into the WRF/CMAQ modeling framework to study the impact of windblown dust on air quality in the state of Washington (Figure 1). Preliminary results indicate that the modeling framework performs well in predicting the onset of dust storm events although the exact timing of events is off by as much as several hours and the framework appear to underestimate atmospheric PM10 concentrations. Future work will include more quantitative and comprehensive evaluation to improve the modeling framework.

Evaluation of Bulk Density and Vegetation as Affected by Military Vehicle Traffic

There is a need for greater understanding of the relationship of dust emission levels to disturbances of soil and vegetation indices that occur during military vehicle activities in Department of Defense training areas. A replicated field experiment was conducted in the fall of 2010 on two soils that dominate the military training grounds at Fort Riley, Kansas. Treatments consisted of two vehicle types, and three levels of vehicle passes. An Abrams M1A1 tank, representing tracked vehicles, and a Humvee representing wheeled military vehicles were used. Bulk density, above ground standing biomass, and plant cover were among the parameters measured before and after vehicular traffic. Samples were taken from curved, straight, and cross-over sections of the vehicle tracks. A mixed-model analysis of variance of the data indicates that the overall mean bulk density under the M1A1 were significantly higher than under the Humvee (p=0.05). In general, as the number of passes increased the bulk density under the M1A1 increased significantly (p=0.05), but the increases under the Humvee were not significant (p=0.05). Bulk densities were significantly larger in the curved part of the tracks than the straight part of the track. Large differences in biomass and vegetation cover between different treatments were observed. Comparison of spring and fall bulk density data showed significant difference at the 0-5 cm depth; indicating that the winter freeze and thaw cycles loosened the top soil layer.

CLIGEN: Addressing Deficiencies in the Generator and its Databases

CLIGEN is a stochastic generator that estimates daily temperatures, precipitation and other weather related phenomena. It is an intermediate model used by the Water Erosion Prediction Program (WEPP), the Wind Erosion Prediction System (WEPS), and other models that require daily weather data. Sub-models of these programs use CLIGEN’s output to calculate plant growth and decomposition, soil surface changes and movement of water through soil layers. Over the years, changes to CLIGEN have been made without being adequately documented. This paper documents those changes and discusses needed changes to CLIGEN. Issues in the existing program include improving the quality of the input meteorological datasets and documenting their source, improving the correlation, both cross and serial, between generated data, making the data more accurately reflect seasonal variations, making the generated distributions correspond better to the observed distributions, changing how the random number variate sets are created and tested, moving to a histogram based precipitation model, changing how interpolations are performed to better reflect seasonality and converting from FORTRAN to Java to improve code readability and maintainability. In addition, new features are being added to produce break-point precipitation for downstream models in addition to the current intensity, duration and time to peak data and to parametrically change inputs to facilitate climate change studies. These issues are discussed and the solutions being implemented are described.