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United States Department of Agriculture

Agricultural Research Service


item Fox, Jr, Fred

Submitted to: Meeting Proceedings
Publication Type: Other
Publication Acceptance Date: April 8, 2004
Publication Date: April 8, 2004
Citation: Fox, F.A. 2004. Modeling the conditions that affect wind erosion prediction. International Workshop on Applications, Enhancement and Collaborations of Agricultural Research Service (ARS) Root Zone Water Quality Model (RZWQM) and Great Plains Framework For Agricultural Resource Management (GPFARM) Models, April 20-22, 2004.

Interpretive Summary: Abstract only.

Technical Abstract: Wind erosion modeling is the art of combining knowledge of climatic patterns, soils, crops and the effects of management practices into an estimate of potential soil movement by wind. Mechanistically, wind erosion modeling focuses on two major areas: the translation of free stream or tower measured wind speed to a friction velocity, or shear stress, at the soil surface and the susceptibility of the soil surface to being eroded. Years of wind erosion research have resulted in a good understanding of the factors that affect the process of soil movement. Given the characteristics of the soil surface, and the wind impacting on it, the rate of soil movement can be estimated. The more difficult prediction is the condition of the surface of a wide array of soil types over time in response to weather and management / crop rotations. Factors Affecting Friction Velocity The physical factors affecting the translation of a know wind velocity to the surface friction velocity are, for this discussion, classified into the categories of topography, pattern roughness and random roughness. Topographic effects are large-scale surface relief, which result in large changes to the wind velocity flow patterns. Examples are hills, knolls, draws, swales, valleys, ditches, streams, windbreaks, fences and walls. Pattern roughness refers to smaller scale surface roughness elements on the land surface that have a regular geometric pattern of size, shape and placement. Examples are ridges, dikes, and crop rows. Lastly, random roughness usually refers to smaller scale surface roughness elements on the land surface that are of random size, shape and placement. Examples are soil aggregates, soil particles; broadcast seeded crops and desert bushes. Terms that have been adopted to describe these factors vary depending on the scale. Large-scale topographic elements are described geospatially with a digital elevation map and / or spatial extent, shape, height and porosity. Pattern roughness elements are described using spacing, direction, shape, height and porosity. Lastly, random roughness elements are described by distributions of size, height, frequency and porosity. One description developed to specifically deal with the effect of plant material on the wind friction velocity is the silhouette area index. It is calculated by summing the vertical perimeter of a plant divided by the ground surface area per plant. Another concept is porosity per unit depth of plant material in the direction of the wind. Both natural and management processes affect the time progression of soil and plant erodibility factors. Rainfall depth and intensity affect the decay of soil ridge height and soil roughness. Rainfall depth and frequency along with temperature and wind affect the rate at which plant material decays and the rate at which standing stems fall down. Climate cycles, soil and management all affect the growth of plant material in both crops and natural vegetation. Machinery operations also impact soil ridges, soil roughness, and the state of plant material on the surface through cutting, breaking, burial and resurfacing, changing the quantity, placement and rate of decay. Surface Erodibility Knowing how much wind force has reached the surface is not enough. Erosion will only occur if a supply of erodible material is available on the surface. Factors affecting the erodibility of the material are its size, shape, position on the surface and resistance to eroding material impact. The particle size distribution indicates what primary material is present. From the view of the wind, the soil aggregate size distribution is more descriptive. Size distributions indicate the supply of erodible material, and the sheltering effect of non-erodible material. Aggregate stability indicates how much additional erodible material will become available once erosion begins. A surface crust is described by the fraction of surface area that it covers, thickness, stability and whether there are loose, mobile aggregates on the surface. Additionally, roughness and plant material provide a sheltering and trapping effect. Finally, when the soil is wet, particles and aggregates adhere resulting in a non-erodible surface. Wind Erosion Modeling Needs A variety of sub models are incorporated into process based wind erosion modeling. Simulating the relationship between wind erosivity and soil surface erodibility imposes some unique requirements. Climate sub models need to include the prediction of the sub hourly wind velocities correlated with solar radiation, temperature, humidity and rainfall over multiple year periods in order to capture the coincidence of wind erosivity and soil erodibility. Crop growth and decomposition sub models need to give good estimates of residue biomass, early season and end of season geometry (stems, leaves, ground cover, height) under both humid region and dry land conditions. Crop residue material also needs to be characterized for its durability and ability to remain standing. Water cycle modeling needs include accurate prediction of surface dryness, snow cover, soil temperature and water content for a full range of soils under a range of climatic, residue and tillage regimes. Temperature and water content affect the aggregate size distribution, aggregate stability and in tightly linked feed back loops, crop growth, residue decomposition and the effect of machine operations on soil and residue. Models of machinery operations need to include the effect of tillage on aggregate size distribution and stability, and the ability to schedule operation based on soil, crop and residue status. Finally, the modeling framework for wind erosion is best served by capturing spatial variability on the sub-management unit level. Methods of identifying susceptible areas based on spatial field descriptions will allow wind erosion prediction modeling to focus management effort more precisely.

Last Modified: 6/29/2016
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