Wind Erosion Assessment in Austria Using Wind Erosion Equation and GIS. Andreas Klik

Transcription

1 Wind Erosion Assessment in Austria Using Wind Erosion Equation and GIS Andreas Klik BOKU University of Natural Resources and Applied Life Sciences Vienna Muthgasse 18. A-1190 Vienna; Abstract Soil erosion by wind is a serious problem in many parts of Europe and leads to degradation of the resource soil. In order to achieve sustainable land use, tools to identify problem areas and to evaluate environmental sound management practices are needed and necessary. Until now no wind erosion measurements have been carried out in Austria. For the fist time assessment of soil erosion by wind has been done for an appr. 20 km² large area in eastern Austrias cropland. Based on the limited available data and their temporal and spatial distribution the Wind Erosion Equation WEQ was chosen as simulation model. The WEQ was incorporated into a vector oriented GIS (ArcView) which was also used for data management, manipulation and analysis. Calculated erosion rates from individual fields ranged from 0 to 5.4 t ha -1 yr -1 with an average annual soil loss of 2.4 t ha -1. Although no measured data are available to validate the simulation model, results seem to be reasonable. The described approach is able to assess soil erosion risk by wind on a regional scale and to evaluate soil protection strategies. In combination with R/USLE this tool will be able to identify potential wind and water erosion risk areas. Key words: Wind erosion, WEQ, GIS Introduction Soil erosion is a major threat to the resource soil. Besides impacts on terrestrial and aquatic ecosystems it creates also socio-economic problems. More than half of the land in Europe has suffered various degrees of soil erosion by water and about a fifth has been eroded by wind (van Lynden, 1995). About 157 million hectares which accounts for 16% of Europe is seriously affected by soil erosion. 115 million hectares of European soils are suffering from water erosion and 42 million hectares from wind erosion. Compared with water erosion, wind erosion shows a more distinct regional distribution in Europe. This suggests that physical factors, in particular climate, are relatively more critical than human influences. The fairly moist conditions prevailing in Western Europe and the nature of storm events, often associated with rain from oceanic depressions, diminish the chances of wind erosion. For Austria assessments of agricultural land area affected by water erosion exist but corresponding data about wind erosion risk are lacking. Materials and Methods For the assessment of soil erosion by wind an intensively agricultural used area in eastern part of Austria was selected. A 289 km² large area in the so-called Marchfeld containing several communities was chosen. A detailed wind erosion estimation has then been carried out for a smaller area (20 km²). The selection of the chosen approach was based on the availability of model input data. Physically based simulation models like Wind Erosion Prediction System (WEPS; Hagen, 1991) need mostly temporal and spatial distributed data about climate, soil properties, soil management aso. with acceptable resolution and accuracy. An application of this model in a small Austrian area showed most of these difficulties (Madl, 1

2 1999). If theses data are not available, parameter estimation can be done using pedotransfer functions. If lack of data is significant, these correlations can increase uncertainty of simulation results and the use of complex physically based models is not justified. Therefore, the empirical Wind Erosion equation WEQ (Woodruff and Siddoway, 1965) was chosen as simulation tool and it was incorporated into a vector GIS (ArcView). A special approach was applied to consider time variable parameters. The Wind Erosion Equation WEQ was the first empirical model to assess soil erosion by wind. It calculates potential average annual erosion rates E = f (I, K, C, L, V) (1) where E is the potential annual soil loss (in t ha -1 yr -1 ), f is an indication that the equation includes functional relationships that are not straight-line mathematical calculations I is the soil erodibility, expressed as potential annual soil loss in (t ha -1 yr -1 ) from a wide, unsheltered isolated field with bare, smooth, level, loose and non-crusted surface where the climatic factor C is 0 such as Garden City, Kansas, K is the surface roughness factor which is a measure of the effect of ridges made by tillage and planting implements, or other means of creating systematically spaced ridges. Ridges absorb and deflect wind energy and trap moving soil particles. C is an index of climatic erosivity, specifically wind-speed and surface soil moisture. The factor for any given location is based on long-term climatic data and is expressed as a percentage of the C factor for Garden City, Kansas, which has been assigned a value of 0 (Lyles, 1983), L is the unsheltered, weighted travel distance (in m) along the prevailing wind direction, V is the equivalent vegetation cover expressed by relating the kind, amount, and orientation of vegetative material to its equivalent in kg ha -1 of small grain residue in reference condition (SGe). As most of the wind erosion influencing variables are changing throughout a year two approaches were developed. When applying the WEQ Critical Period Procedure the conditions during the critical wind erosion period are used to derive the estimate of annual wind erosion (NRCS, 1999). The critical wind erosion period is described as the period of the year when the greatest amount of wind erosion can be expected to occur from a field under an identified management system. It is the period when vegetative cover, soil surface conditions, and expected erosive winds result in the greatest potential for wind erosion. The WEQ Management Period Procedure was published by Bondy et al. (1980). It solves the equation for situations where site conditions have significant variation during the year or planning period. Factor values are selected which describe management periods when cover and management effects are approximately uniform. The cropping system is divided into as many management periods as necessary to describe the year or planning period accurately. Each management period is represented by a set of factor values representing conditions specific to that period of time. For each period the factors I, K, L and V are estimated and the average soil loss is calculated using the annual C factor. The average annual soil loss is then obtained by multiplying the soil loss with the erosive wind energy distribution within each period and summing up the results for a whole year. For the existing study the WEQ Management Period Procedure was applied. The variables biomass and surface roughness were considered to be time variable and corresponding V and R factors were divided into 15-d intervals. The assessment was done using measured and simulated data. Biomass production throughout growing season and temporal change of soil surface roughness was calculated with EPIC model (Williams et al., 1990). For each unit area (single field) representative WEQ factors were estimated and average soil loss was calculated. 2

3 Soil erodibility factor I The soil erodibilty factor I is related to the percentage of non-erodible surface soil aggregates larger than 0.84 mm in diameter which can be determined by dry sieving method (Chepil, 1942). Based on the soil texture 8 Wind Erodibility Groups are distinguished. A distinction is made in calcium carbonate content because soils high in CaCO 3 (> 5%) are more erodible. The Wind Erodibility Groups have been applied to the Austrian Texture Triangle (Fig. 1). The soil erodibility index was then calculated as weighted average of the area of each texture class (Table 1). non calcareous soils calcareous soils 0 WEG 7 0 WEG Silt ( 0,002-0,06 mm) % WEG 1 WEG 2 S U su ul lu WEG 3 WEG 4 WEG 4L us WEG 6 ls sl L ts WEG 5 st WEG 6 WEG 3 WEG 4 lt WEG 4L Clay ( < 0,002 mm ) % T Silt ( 0,002-0,06 mm) % WEG 1 WEG 2 su us S U ls ts lu sl ul WEG 3 WEG 4 WEG 4L L WEG 5 st lt Clay ( < 0,002 mm ) % T Figure 1 Application of Wind Eodibility Groups to the Austrian Soil Texture Triangle Table 1. Soil erodibility index (t ha -1 yr -1 ) for soil texture classes Symbol Texture noncalcareous S sand 544 us silty sand ls loamy sand ts clayey sand 245 su sandy silt U silt 130 lu loamy silt 166 sl silt loam 163 L loam 149 ul silty clay 67 st sandy clay 138 lt loamy clay 198 T clay calcareous In the 289 km² large region 12% of the soils have a low erodibilty index while 85% range between 150 and 250 t ha -1 yr -1. About 2% of the area (5.45 km²) is covered with highly erodible soils, mainly consisting of sand and fine sand with I-factors of 544 (Fig. 2a). Based on the determined I-factors the potential soil erosion can be calculated. It is obtained by multiplying the I-factor with the regional C-factor. This value 3

4 corresponds the soil loss of unsheltered bare soil with seedbed condition taking into account the regional climatic conditions (Fig. 2b). Figure 2 Distribution of I-Factors (in t ha -1 yr -1, left) and potential soil loss by wind in the 289 km² large region (in t ha -1 yr -1 ; right) Soil erodibilty factor is also affected by topographic features like knolls. Knolls are topographic features characterized by short, abrupt windward slopes. Wind erosion potential is greater on knoll slopes than on level or gently rolling terrain because wind flow-lines are compressed and wind velocity increases near the crest of the knolls. Erosion that begins on knolls often affects field areas downwind. Adjustments of the soil erodibility index I are used where windward-facing slopes are less than 160 m long and the increase in slope gradient from the adjacent landscape is 3 percent or greater. Both slope length and slope gradient change are determined along the direction of the prevailing erosive wind. Surface roughness factor K The roughness factor K describes the effect of soil surface roughness on soil erosion. It is distinguished between random roughness (Allmaras et al., 1966) and oriented rouhness made by tillage and planting implements, or other means of creating systematically spaced ridges. K-factor for oriented roughness was determined using equations by Williams (1986) where ridge height and ridge distance in prevailing wind direction are considered. K-factor for random roughness I-factors < random roughness (mm) Figure 3 Relationship between random roughness and WEQ K-subfactor for random roughness for different I-factor groups 4

5 The random roughness values used in the WEQ are the same values used in the Revised Universal Soil Loss Equation RUSLE (Renard et al., 1997). The effect of random roughness on K is only used with the Management Period Procedure. The random roughness factor accounts for roughness effects on soil erodibility. It considers that surface roughness of soils with high erodibility decreases faster than of less erodible soils (NRCS, 1999). For the applied model random roughness K-factor was adopted (Fig. 3). Climatic factor C The C factor is an index of climatic erosivity, specifically wind-speed and surface soil moisture and is expressed as a percentage of the C factor for Garden City, Kansas, which has been assigned a value of 0 (Lyles, 1983). The climatic factor equation is expressed as: C = 386. u 3 /(PE) 2 (2) where C is the annual climatic factor, u is the average annual wind velocity, PE is the precipitation-effectiveness index of Thornthwaite, and 386 is a constant used to adjust local values to the common base (Garden City, Kansas). The Thornthwaite index is calculated by: PE = >Pi / (1.8 Ti + 22)@ /9 (4) where Pi is monthly precipitation in mm and Ti average monthly air temperature in C. For each of the five meteorological stations in the Marchfeld C-factors were calculated (Table 3). To each field a C-factor was assigned using the method of Thiessen polygons. Wind speed and wind direction measurements are sparse in this area. The longest records existed for a period from 1976 to 1990 for the station Obersiebenbrunn. These data showed an average wind velocity of 3.1 m s -1. Combined with PE values between 38.5 and 45.8 for the six climatic stations in the investigation area C-factors between 5.64 and 7.75 are obtained (Table 2). The prevailing wind erosion direction is the direction from which the greatest amount of erosive wind occurs during the critical wind erosion period or time period being evaluated. In this area prevailing wind directions are NW and SE (Fig. 4). Considering also the wind velocity prevailing wind direction is W to NW. Table 2 C-Factors for climatic stations of the investigation area Station Thorthwaite index PE Wind velocity u (m s -1 ) Groß-Enzerdorf Deutsch Wagram Gänserndorf Marchegg Fuchsenbigl Obersiebenbrunn C (%)

6 < 5 m/s 5 - m/s > m/s gesamt Figure 4 Frequency of wind directions for various wind speed classes (Station Obersiebenbrunn; Dobesch and Neuwirt, 1982) Beside wind speed the erosive wind energy (EWE) has the main impact on the erosion process. When hourly wind speed data are available the hourly EWE can be assessed by following equation: EWE hr = U. U² (U Ut) (5) where EWE hr is the hourly erosive wind energy in g s -1, U is the air density (g m -3 ), U and Ut are the average hourly wind speed and average hourly threshold wind speed (m s -1 ), respectively. For the threshold wind speed a value of 8 m s -1 is often indicated. In the Marchfeld area observations showed that yield decreases started already with at an average wind velocity of 3.7 m s -1 with maximum hourly wind velocities of 7 9 m s -1. The most erosive winds with speeds > 8 m s -1 blow from west to north-west (Fig. 5). A dependency of wind direction throughout the year does not exist. Most of the erosive winds occur in November, January and February (Fig. 6). During this period the soil is often very moist, frozen and/or covered with snow and protected from erosion. Therefore, only a small part of the yearly erosive wind energy will be responsible for the soil loss EWE (> 8 m s -1 ) [%] N NNE NE ENE E ESE SE SSE S SSW Wind direction SW WSW W WNW NW NNW Figure 5 Contribution of wind directions to the erosive wind energy (threshold wind velocity = 8 m s -1 ) 6

7 30 monthly EWE amount (%) J F M A M J J A S O N D Figure 6 Temporal distribution of erosive wind energy throughout the year (threshold wind velocity = 8 m s -1 ) Unsheltered distance L The L factor represents the unsheltered distance along the prevailing wind erosion direction for the field or area to be evaluated. It is the total length of the field reduced by the length sheltered by protection measures. The determination of the unsheltered distance is done stepwise: - Determination of an isolated field - Determination of wind breaks and their properties - Determination of prevailing wind direction - Calculation of sheltered field length by wind breaks - Calculation of unsheltered distance. L begins at a point upwind where no saltation or surface creep occurs (stable) and ends at the downwind edge of the area being evaluated. The investigation area had to be divided into a number of fields which could be considered as isolated from each other. This means that no soil particles are crossing these field boundaries. Field boundaries consist of wind breaks, roads and field paths, creeks etc. For the determination of isolated fields topographical maps were combined with information from satellite images and aerial images. Topographic maps were available for the whole investigation area while aerial images only for a small parts. These images were necessary for the knowledge of the field geometry, tillage direction, existence of wind breaks and roads. Satellite images had a resolution of about m per pixel while aerial photos had one of 1 m per pixel (Fig. 7). Month a) b) Figure 7 Comparison of the information of the topographic map (a) and aerial image (b) with included tillage direction 7

8 Due to the small field sizes and the various shapes and orientation as wells as the irregular layout of the wind barriers the determination was difficult. Following procedure was applied: - Determination of area sheltered by wind breaks: as sheltered area usually a length of 15 times the height of the wind break is assumed in prevailing wind direction (Tibke, 1988). For Austrian conditions an assessment was made depending on the porosity of a wind break based on photos or electronic image analysis (Table 3). - Superposition of sheltered areas with fields - Division of each field into 30-m wide strips parallel to prevailing wind direction - Superposition of strips with unsheltered field area - Calculation of unsheltered distance as weighted average length of all 30-m strips of a single field (Fig. 8) Table 3 Sheltered field length Porosity low medium high very high Sheltered length in x H Figure 8 Determination of the unsheltered distance L Vegetation cover factor V The effect of vegetative cover in the Wind Erosion Equation is expressed by relating the kind, amount, and orientation of vegetative material to its equivalent in kg per hectare of small grain residue in reference condition (SGe). This condition is defined as l0 inch long stalks of small grain, parallel to the wind, lying flat in rows spaced inches apart, perpendicular to the wind. Position and anchoring of residue is important. In general, the finer and more upright the residue, the more effective it is for reducing wind erosion. Williams et al. (1984) proposed an equation for the V-factor based on the small grain equivalent SGe: V = (SGe) (5) 8

9 The SGe can be assessed by: SGe = g 1. B AG + g 2. SR + g 3. FR (6) where g 1, g 2, g 3 are crop coefficients, B AG is the above ground living biomass (kg ha -1 ), SR is the standing residues (kg ha -1 ) and FR is the flat residues (kg ha -1 ). The variables g 1 - g 3 were derived from Williams et al. (1984) and are shown in Table 4 for the main crops in the Marchfeld. Table 4 Coefficients g 1, g 2 and g 3 for calculation of small grain equivalent SGe for main crops in the Marchfeld Crop g1 g2 g3 Summer barley Winter wheat sugar beet soybean corn sorghum summer wheat sunflower potatoe The protective impact of the vegetation depends also on the angle between prevailing wind direction and the tillage direction or direction of planting. In the WEQ a correction factor considers this fact. For the Management Period Procedure the knowledge of temporal distribution of living biomass production, residue amounts and impact of tillage on soil surface roughness is necessary. This was simulated by EPIC and the results were used to establish a data base for WEQ. Results and recommendations Wind erosion assessment was carried out for cropland located in two communities with an overall area of 20 km². A commonly used crop rotation of summer barley, winter wheat and sugar beet was chosen. Spatial distribution of wind breaks was obtained from field survey. As no detailed information about the height of wind breaks was available a standard wind break with 8 m height and a sheltered distance of times the height was assumed. The chosen approach for soil erosion assessment by wind with WEQ linked to an GIS (ArcView) enables the designation of potential risk areas. Calculated average erosion rates from single fields ranged from 0 to 5.4 t ha -1 yr -1 and indicate low to medium erosion risk in this area (Fig. 9). Although no observed data on soil loss by wind are available for this area, the results seem to be reasonable. The model is also useful to compare different scenarios. In Fig. wind erosion at present state and with additional wind breaks in the area are compared. It can be seen that also additional wind breaks are not able to reduce greatly the soil loss. Because of the given field layout with the high percentage of long and narrow fields oriented approximately in prevailing wind direction additional barriers are not sufficient to reduce soil erosion significantly. 9

10 Figure 9 Spatial distribution of soil erosion by wind Figure Calculated soil erosion by wind at present state (left) and with additional wind breaks (right) For the whole Marchfeld area with an area of about 00 km² all necessary input data are already derived and available. A single field version of the WEQ (in German) was developed and is available as online version (Fig. 11) under: It contains a data base for 13 soil textures, climatic data of 5 stations in the wind erosion risk area east of Vienna, 20 vegetative covers and 4 types of wind breaks. Long-term average soil loss by wind can be estimated. Although the results can be seen only as relative values, the efficiency of different soil conservation measures can be evaluated with this approach. The data base has now to be enlarged for other wind erosion risk areas in Austria. In addition, when linking this program with USLE/RUSLE potential water and wind erosion risk areas can be identified on a

11 regional scale. In a second step for more detailed and smaller scale analyses the application of physically based erosion models seems to be appropriate. Figure 11 Wind erosion calculator Bibliography Allmaras, R.R., R.E. Burwell, W.E. Larson, and R.F. Holt (1966). Total porosity and random roughness of interrow zone as influenced by tillage. USDA Conserv. Res. 7. US. Gov. Pritn. Office, Washington D.C. Bondy, Earl, Leon Lyles, and W.A. Hayes (1980). Computing soil erosion by periods using wind energy distribution. Journal of Soil and Water Conservation 35(4): Chepil, W.S. (1942). Measurement of wind erosiveness of soils by the dry sieving procedure. Scientific Agriculture 25: Dobesch, H., and F. Neuwirt (1982). Wind in Niederösterreich, insbesondere im Wiener Becken und Donautal. Arbeiten aus der Zentralanstalt für Meteorologie und Geodynamik. Heft

12 Hagen, L. (1991). A wind erosion prediciton system to meet users needs. Journal of Soil and Water Conservation 46(2): Lyles, L. (1983). Erosive wind energy distributions and climatic factors for the West. Journal of Soil and Water Conservation 38(2):6-9. Madl, W. (1999). Estimating soil loss by wind for the Marchfeld region using the Wind Erosion Prediction System (WEPS). Master thesis, Institute of Hydraulics and Rural Water Management, BOKU Vienna. National Resources Conservation Service (NRCS) (1999). National Agronomy Manual, Draft version. Renard, K.R., G.R. Foster, G.A. Weesies, D.K. McCool, and D.C. Yoder (1997). Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RÙSLE). U.S. Department of Agriculture, Agriculture Handbook No Tibke G. (1988). Basic principles of wind erosion control. Agric. Ecosystem Environ. 22/23: Van Lynden, (1995). The European soil resource: current status of soil degradation in Europe: causes, impacts and need for action. ISRIC, Wageningen. Council of Europe, Press. Nature and Environment, no. 71, Strasbourg, France. Woodruff, N.P., and F.H.Siddoway (1965). A wind erosion equation. Soil Sci. Soc. Am. Proc. 29: Williams, J.R. (1986). Effect of erosion productivity EPIC water erosion model. Proc. Fourth Fed. Interagency Sediment Conference, Vol. 6: 6/1-6/8. Williams, J.R., P.T. Dyke, W.W. Fuchs, V.W. Benson, O.W. Rice, and E.D. Taylor (1990). EPIC Erosion/Productivity Impact Calculator: 2. User Manual. U.S. Department of Agriculture, Technical Bulletin No pp. 12