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The WEELS model: methods, results and limitations
Catena 52 (2003) 289 – 308 www.elsevier.com/locate/catena
The WEELS model: methods, results and limitations Juergen Bo¨hner a,*, W. Scha¨fer b, O. Conrad a, J. Gross b, A. Ringeler a a
Department of Geography, University of Go¨ttingen, Goldschmidt Str. 5, 37077 Go¨ttingen, Germany b Geological Survey of Lower Saxony, Institute of Soil Technology, Friedrich-Missler-Str. 46-50, 28211 Bremen, Germany
Abstract Within the European Union (EU)-funded Project ‘Wind Erosion on European Light Soils’ (WEELS), a model was designed and implemented with the aim of predicting the long-term spatial distribution of wind erosion risks in terms of erosion hours and wind-induced soil loss. In order to ensure wide applicability, the model structure consists of a modular combination of different approaches and algorithms, running on available or easily collected topographic and climatological data input. Whereas the ‘WIND’, ‘WIND EROSIVITY’ and ‘SOIL MOISTURE’ modules combine factors that contribute to the temporal variations of climatic erosivity, the ‘SOIL ERODIBILITY’, ‘SURFACE ROUGHNESS’ and ‘LAND USE’ modules predict the temporal soil and vegetation cover variables that control soil erodibility. Preliminary simulations over a 29-year period for the Barnham site (UK) (1970 – 1998) and a 13-year period for the Gro¨nheim site (Germany) (1981 – 1993) generally resulted in a higher erosion risk for the English test site, where the total mean soil loss was estimated at 1.56 t ha 1 year 1 and mean maximum soil loss at about 15.5 t ha 1 year 1. The highest rates exceeded 3 t ha 1 in March, September and November. On the northern German test site, the total mean soil loss was 0.43 t ha 1 year 1. The highest erosion rates were predicted in April when they can exceed 2.5 t ha 1. The total mean maximum soil loss at this site of about 10.0 t ha 1 year 1 corresponds to a loss of about 0.65 mm. Predictions based on a land use scenario for the German site revealed that the erosion risk could be reduced significantly by changing land use strategies. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Modelling; Wind; Wind erosion; Aeolian transport; Aeolian accumulation
1. Introduction Early studies on the identification and quantification of the factors influencing the location and rates of soil erosion by wind led, among others, to the development of the
* Corresponding author. Tel.: +49-551-8006. E-mail address: [email protected] (J. Bo¨hner). 0341-8162/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0341-8162(03)00019-5
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empirically based Wind Erosion Equation (WEQ) in the 1960s (Woodruff and Siddoway, 1965). The WEQ was developed to predict at the field scale the potential average annual soil loss by determining the influence of several primary variables that characterize the erodibility of the soil, the erosivity of the wind and the actual land use situation (Woodruff and Siddoway, 1965). In parallel with early estimations of water erosion rates, e.g., the Universal Soil Loss Equation (USLE), this empirical approach was found to have limited transferability. Since the 1970s, research on wind erosion has been directed towards the development of physically based simulation of the complex interactions between erosion and its controlling variables. Advances in wind erosion research have led to the design of more process-based wind erosion models, like, e.g., the Revised Wind Erosion Equation (RWEQ, Fryrear et al., 1998) and the Wind Erosion Prediction System (WEPS, Hagen, 1991). Although remarkable advances have been made in simulating wind-induced erosion dispersion and deposition processes, the application of the current generation of physically based models is still limited, as most of them require detailed input data, which are not always available, and are not easily adapted to conditions or climates different from those for which they have been calibrated. Further limitations in the spatial or temporal resolution as well as narrow model domains limit the application of complex approaches for erosion risk assessments in ecological or political studies. Against this background, the European Union (EU)-funded research project ‘Wind Erosion on European Light Soils’ (WEELS) aimed at the assessment of the spatial
Fig. 1. Locations of the WEELS test sites.
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distribution of wind erosion risks in the North European Quaternary Plains and the prediction of long-term wind-induced soil losses under different climate and land use scenarios combined with an economic analysis of the damage and costs caused by these processes. The project comprised field measurements at three representative test sites, located in Germany, England and Sweden (Fig. 1) as well as wind tunnel experiments with selected soils for process parameterisation. Four different countries were involved in the project (Germany, Netherlands, Sweden and the United Kingdom) forming a multidisciplinary consortium consisting of universities and government bodies. Within the WEELS project, one central task was to develop a spatially distributed wind erosion model that can run at various time scales, from hours to decades, can take into account different crop rotation periods and is able to simulate different management and climatic scenarios. To ensure wide applicability of the model within the EU and Eastern Europe, work focused on the development and application of a process-based winderosion model that uses readily available or easily collected data for erosion risk assessments in model domains covering up to 5 5 km. The ability to incorporate the data output into a GIS simplifies assessments of economic implications, both in the past and in the future. The objective of this paper is to provide an overview of the WEELS model, to describe the different modules and to show some results of wind erosion risk assessment in two study areas.
2. Materials and methods In view of the available database and the validation of the model results, the modelling of long-term erosion rates as well as the simulation of single erosion events have been limited to the test sites in England and Germany (Fig. 1). The UK test site is situated around Barnham near Bury St. Edmunds in Suffolk (East Anglia). The sandy soils of the study area are mainly derived from glacio-fluvial deposits of the penultimate glaciation overlying chalk. Main crop is sugar beet often undersown with spring barley. The wind climate, derived from observations at the Honington Airport, is characterized by prevailing SSW to W winds (39.7%) throughout the year and an annual mean wind speed of 4.6 m s 1. The seasonal variations with monthly means of more than 5.0 m s 1 from December to April and a maximum in January (5.7 m s 1) denote the frequent cyclonic activity in winter and spring, while the mean velocity remains below 4.0 m s 1 during the summer months. The spatial distribution of mean wind speeds at the test site is consistent with the general land use situation. At the leeward neighbouring scopes of wooded areas, the annual mean wind speed decreases below 3.9 m s 1, while the lessdisturbed flow conditions in the central northern part are characterized by increasing wind speed of more than 4.7 m s 1. The German test site is located in the Ems-Hunte geest area in the central western part of Lower Saxony, 12 km northwest of Cloppenburg near Gro¨nheim. Podzols on Pleistocene and Holocene fluviatile sands, often overlain by eolian sands, Stagno-Dystric Cambisols and Gleyic Podzoluvisols are the dominant soil types covering the gently undulating, rarely hilly landscape of the Saalian ground moraine. It is an area in which
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there is intensive maize cultivation, interspersed with blocks of coniferous woodland, where wind erosion has been causing concern. The analyses of wind data from Ahlhorn revealed a prevalence of westerly winds comparable to the UK site, with a frequency of 44.2% in the 210j– 270j direction sectors. However, the annual mean wind speed of 4.1 m s 1 and monthly means of less than 4.0 m s 1 from April to September with a minimum in June (3.5 m s 1) and maximum of 5.2 m s 1 in January at the Alhorn station indicate lower wind speed and erosion risk for the western part of Lower Saxony than East Anglia. Correspondingly, in the spatial distribution of mean annual wind speeds, the maximum mean annual wind velocity remains at 4.2 m s 1 at the German test site.
3. Model description The structure of the WEELS model consists of a modular combination of different approaches and algorithms running on available or easily sampled topographic and climatic data input. Most of the calculations and modelling were performed on the Go¨ttingen System for Automated Geoecological Analysis (SAGA), a scientific GIS for terrain analysis, remote sensing and modelling of climatological and geomorphological processes, currently being developed by the research group Geosystemanalysis of the Department of Geography, University of Go¨ttingen (http://www.geogr.uni-goettingen.de/ pg/saga/aggeosys/). The first group of modules, the ‘WIND’, ‘WIND EROSIVITY’ and ‘SOIL MOISTURE’ modules, concerns those dynamic variables that contribute to the temporal variations of climatic erosivity. In this first stage of the model, the climate-induced potential erosion intensity in terms of erosion hours and potential mass transport of standard dune sand is calculated by means of the Bagnold (1966) sediment transport formula that describes the total maximum sediment flux rate ( Q) of standard dune sand as a function of the friction velocity (u*):
QE ¼ AVu
ð3kÞ
*
1
u*t u*
1 !m
where: AVcA¼ C
n d qa ; D g
1 3 VnV 2 4
ð1Þ
where QE is sediment transport rate (kg m 1 s 1), u* is shear velocity (m s 1), u*t is threshold shear velocity (m s 1), C is an empirically derived weight factor with 1.5 V C V 2.8 (for dune sand) (s2 m 3), d is mean grain size (m), D is standard grain size 0.2510 3 (m), qa is relative density of air: 1.29 (kg m 3), g is constant of gravity 9.81 (m s 2), and k and m are empirical parameters that have to be calibrated. In contrast to other models, this approach uses the potential mass transport of standard dune sand to characterize the climate-induced potential erosion intensity. The second group of modules, the ‘SOIL ERODIBILITY’, ‘SOIL ROUGHNESS’ and the ‘LAND USE’ modules, predicts the temporal soil and vegetation cover variables that control soil erodibility. This second stage adapts the potential erosion intensity to the
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actual soil and land use conditions. It calculates the actual soil erosion in terms of actual erosion hours and total sediment transport. At the current state of development, the model domain is restricted to areas of about 5 5 km with a horizontal spatial resolution of 25 25 m (grid size). Fig. 2 illustrates the generalized model structure. The modules are described in detail below. 3.1. WIND module The simulation of wind speeds and directions for 10 m aboveground was mainly performed via WAsP, the Wind Atlas Analysis and Application Program (Mortensen et al., 1993). Primarily developed for the calculation of wind energy resources at a given site, the
Fig. 2. Structure of the WEELS model.
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estimation of wind parameters via WAsP offers the advantage of an easy processing of extensive and already available climatic data sets from an existing network of meteorological stations as well as different tools for the processing of necessary topographic data inputs. The WAsP procedure can be separated into two steps. The first ‘‘analysis’’ step estimates the ‘‘regional wind climate’’. As observations at a given site are influenced by the relief, the land use situation (roughness length as related to land use) and neighbouring obstacles, the ‘‘raw data’’ of a meteorological station is corrected with respect to the sitespecific topographic situation and reduced to standard conditions. The generated regional wind climates are expressed in terms of frequency and wind speed distributions, approximated by Weibull-A and Weibull-k parameters (Mortensen et al., 1993), separated for 12 wind direction sectors, five standard heights above ground level and four different standard roughness lengths. The determination of the regional wind climate is based on the observations of the meteorological stations located at Ahlhorn (1981 –1993) and Meppen (1976 – 1990) for the Gro¨nheim study site and the station located at Honington (1970 – 2000) for the Barnham study site. Since instruments have not been changed during the period of observation, the data sets were considered homogeneous. The second ‘‘application’’ step performs the reverse process of step one. Based on the results of the regional analyses of wind climatologies, the WAsP model simulates the modification of the wind speed and frequency distribution at the test site with respect to the local topographic situation. The models implemented in WAsP are based on the physical principles of stream modifications in the boundary layer, induced by surface friction. Assuming a steady neutral stratification and an approximately constant eddy viscosity in the friction layer, the model theory is founded on the close relationship between geostrophic and surface winds in accordance with the roughness-induced variations of the logarithmic wind profile (roughness model) and the modification of the undisturbed nearground flow pattern induced by the relief (orography model). A description of the model structure is given in Troen and Petersen (1989) and Mortensen et al. (1993). Digital terrain models (DTM) with a spatial resolution of 50 50 m were processed and converted into contour lines (vector data) with a 2-m contour interval. The assignment of roughness lengths is based on ATKIS and CORINE land use data that were reduced to four land use and roughness classes. To consider the distance-dependent influences of the land use pattern and the orography at the scale of the whole test sites, the topographical database covered an area of 20 20 km. The resulting outputs for both test sites consisted of grid maps of Weibull-A and Weibull-k parameters, calculated for 10 m aboveground, covering 5 5 km in a spatial resolution of 25 25 m. With respect to the wind erosivity estimations, separate calculations were performed for 12 wind direction sectors. 3.2. WIND EROSIVITY module As wind energy increases with the third power of its velocity, estimating temporal speed variations is an essential aspect in modelling wind-induced erosion processes. To consider the temporal variability of wind velocities for estimates of wind erosivity, the ratio of observed (at the meteorological station) and gridded Weibull parameters computed directional dependent serves as a constant transfer function for the calculation of hourly means of wind speeds and derived friction velocities (u*). The wind friction velocity is
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determined from the wind velocity and the roughness length, assuming a vertical logarithmic wind velocity profile (Priesley, 1959): for zHz0, u* z ln ð2Þ uz ¼ k z0 where uz is the wind velocity in height z, k is the Von Karman’s constant ( f 0.4), and z0 is the surface roughness length. The influence of wind barriers and hedges was taken into account by applying a reduction factor to the friction velocities in a protected zone. Based on mapped positions, heights and optical porosities of hedges and wind barriers at the test sites (Thiermann, 2001; Vigiak, 2000), the distance to neighbouring obstacle belts along the wind direction and the resulting windbreak shelter was computed. According to the field results of Du¨wel (1995), the reduction function of the RWEQ (Fryrear et al., 1998) was found to be the most suitable approach. To take into account the convection- and turbulence-induced high-frequency temporal variations of the surface wind velocities (which are not captured by hourly means), a Gaussian frequency distribution was approximated, assuming a constant increase of the standard deviation (r) with increasing friction velocities (u*) by r = 0.25u*. This approach, proposed by Beinhauer and Kruse (1994), was confirmed by statistical analysis of hourly means and gust speed values at Honington Airport. Integrating the results of the soil moisture roughness modules (see below), wind erosivity is first expressed in terms of number of erosion hours, defined as the duration of friction velocities above threshold velocities under dry topsoil conditions and calculated with an hourly resolution for each grid cell. The second measure of the impact of wind forces on soil, the wind-induced potential mass transport, is based on the Bagnold (1966) sediment transport formula that describes the total maximum sediment flux rate Q as a function of the friction velocity. Even though reductions of the potential sediment flux are made to take into account soil erodibility (see below), the use of a potential mass transport (of a well-sorted unaggregated soil) leads to an overestimation as the reduction of erodible material during the erosion process is not taken into account. In addition, this sand transport rate is expected to be higher than the transport rate of the natural aggregated soils that occurs within the test sites. Nevertheless, this process-based approach allows for a wider applicability for erosion risk assessments. 3.3. SOIL MOISTURE module As the water content of the topsoil layer significantly influences the susceptibility of a soil to wind erosion, the prediction of the daily changes in topsoil moisture (above or below the ‘critical soil moisture’) was a main requirement for the modelling of erosion hours. For sandy soils, this critical water content is assumed to be 2 –4 wt.%. For this reason, a simplified topsoil moisture model was used for the prediction of the water content in the uppermost soil layer (ca. 2 cm) of sandy soils (Wendling et al., 1991). This model provides a continuous, daily, soil water balance at the soil surface using the equation: Wiþ1 ¼ Wi þ Pi ETai SWi þ CR
ð3Þ
where Wi is the daily water content in the topsoil layer for a defined thickness, Pi is the daily precipitation (mm), ETai is the daily actual evapotranspiration according to Haude (1954)
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(mm), SWi is seepage water (mm) and CR is the capillary rise (mm). Additional input data are the thickness of the soil layer, the water content at field capacity (FC) and at the permanent wilting point (PWP) of this layer as well as the capillary rise (mm) and the water storage at the start of calculation. In this simplified approach, drainage from the topsoil to lower layers at water contents below field capacity is not taken into consideration since it is assumed to be of little significance for the changes in the topsoil moisture of sandy soils. Climate input parameters, derived from the meteorological stations at Ahlhorn and Honington Airport, are precipitation as well as mean values (14:00) of air temperature and relative humidity, global radiation and mean wind speed used for calculating the actual evapotranspiration (according to Haude, 1954) and precipitation. The amount of the daily precipitation is added to the water content of the uppermost 2 cm of the soil. Once the water content in the simulation layer reaches field capacity, any excess water is added to the succeeding soil layer as seepage water. The actual evapotranspiration then controls the drying of the soil surface. During the phase of drying, additional water is delivered by unsaturated water flow from the sublayer to the top layer by capillary rise. The amount of the capillary rise depends on the bulk density of the topsoil layer and thus on the state of cultivation. In the model, constant values are used, e.g., 0.5 mm day 1 for a field topsoil after tillage and 1 mm day 1 for a more compacted topsoil. Since the water storage capacity (ca. 5 mm/2 cm soil layer) and the amount of the capillary rise (ca. 0– 1 mm day 1) are very small for sandy soils, the temporal variability is predominantly controlled by climatic parameters (precipitation, evaporation). The described method was verified against data obtained at the intensive test field in Gro¨nheim. Permanent monitoring of the soil surface moisture was obtained by an infrared moisture sensor (Scha¨fer, 1989), which continuously monitors topsoil moisture content. Data were recorded as 10-min averages. Before being installed at the test field, the infrared sensor was calibrated against a topsoil sample obtained at the test field. In the model framework, the soil moisture module calculates the days with dry soil surface (0– 2 cm) during the periods without vegetational soil cover. Validation of the model results revealed a significant correspondence between the measured and the modelled topsoil water contents. For a 30-day test period, more than 80% of the ‘dry’ days were predicted by the model (Fig. 3). Based on comparisons between calculated and measured values obtained in the field, a water content in the topsoil layer of 2 mm defines the threshold value for ‘wet’ and ‘dry surface’ conditions, assuming that wind erosion is just possible for the condition ‘dry surface’. 3.4. SOIL ERODIBILITY module One essential component of the WEELS model is the determination and characterization of soil erodibility. Soil erodibility is a measure of the soil’s resistance to the erosive power of the wind. A dimensionless soil erodibility factor ‘K’ is introduced which expresses the intrinsic susceptibility of a dry, freshly cultivated sandy soil to erosion when not affected by surface cover, roughness, crusting or soil moisture. Soil properties that influence K values are soil texture, aggregation and aggregate stability. Particle size distribution, in particular, %fine sand, silt and clay, and the organic matter content are thus
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Fig. 3. Comparison between measured (infrared sensor) and calculated (soil moisture module) values of the daily water content in the topsoil layer of the Gro¨nheim intensive test field for the period 1st – 31st of May 1999. A water content of 2 mm defines the threshold value for ‘wet’ and ‘dry surface’ conditions.
the main determinants. On the basis of a regression equation that was empirically derived from wind tunnel experiments, K factor values are predicted as a function of the mean weighted diameter (MWD) of the texture and the proportion of aggregates >0.63 mm diameter (Neemann, 1991):
ð4Þ The larger the K factor value, the higher the susceptibility of that particular soil. In the absence of measured data, the proportion of unerodible aggregates (diameter >0.63 mm) is calculated on the basis of the organic matter content and the proportion of the silt and clay and fine sand fractions. K is assumed to be constant throughout the year. In the WEELS model procedure, the K factor is used to derive transport rates of mixed-grained and aggregated soils from the modelled values of sand transport because the amount of mass loss, calculated by the Bagnold equation, represents the potential erosion mass loss of a well-sorted unaggregated sand. However, this sand transport rate is expected to be higher
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than the transport of the natural aggregated soils. Based on wind tunnel experiments carried out with both a standard Bagnold sand and 10 different highly erodible sandy soils, a relative erodibility ratio (R) was empirically determined. In order to facilitate the direct derivation of this ratio and the associated reduction of maximum sediment flux rate, the relationship between the relative erodibility ratio (R) of the test soils and their K factors was determined empirically. There is a relatively strong (R2 = 0.88) correlation between the two variables which is best described by the following equation (Gross, 2002): R ¼ 0:6551ðK factorÞ0:5901
ð5Þ
where R is the relative erodibilty ratio (erodibility of mixed grained soils in relation to ‘Bagnold-type’ dune sand) and K factor is the erodibility factor calculated according to Neemann (1991). For the Barnham site, the K factors could partly be derived from nonclassified measured values. These K factors showed only weak statistical dependency of the terrain attribute ‘‘height above channel network’’, a terrain parameter, derived from a digital terrain model (DTM) by means of automated terrain analysis (Bo¨hner et al., 2002). Therefore, it was not useful to assume such a relationship for an advanced interpolation with the geostatistical Universal Kriging method. For the Gro¨nheim site, K factors were only available as classified values. After a translation into metric values it was finally possible to perform a spatial interpolation using the Minimum Curvature method. 3.5. SURFACE ROUGHNESS module Knowledge of the soil and surface roughness is essential to predict wind erosion accurately. The surface roughness of uncovered soils is controlled by both the intrinsic soil properties that determine the state of aggregation and by the management practices that produce roughness elements of different size. The effect of soil surface roughness on the soil transport by wind comprises several aspects (Du¨wel et al., 1994): modification of the logarithmic mean wind profile in the boundary layer which affects the aerodynamic roughness length (z0) and friction velocity (u*), protection of loose material from drag forces generated by wind shear in the lee of roughness elements and trapping of windtransported particles by roughness elements. An increase in z0 normally results in a decrease in soil erosion. The WEELS erosion model requires representative roughness lengths for all types of cultivation that are typical for the study sites. The data shown in Table 1 were obtained from the experiments performed by Du¨wel (1995) and complemented by the data obtained from the wind tunnel experiments carried out as part of the WEELS project. Based on these data, each cultivation practice can be related to a typical range of z0 values according to the equation: z0 ¼ 0:15zmax
ð6Þ
where z0 is the roughness length (mm) and zmax is the maximum height (mm) of the roughness elements. With this it is possible to model the influence of the changing types of roughness during the different cultivation conditions of the soil before being covered by
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Table 1 Soil surface roughness under different open land and management practices according to Du¨wel (1995) Type of cultivation
Surface (roughness elements)
Roughness type
Rolled Harrowed (fine) Harrowed
smooth smooth, small aggregates smooth, large aggregates large aggregates small cross-grooves
particle roughness aggregate roughness
Ploughed Drilled (seed rows) Potato ridges large cross-grooves Potato ridges rows in wind direction Stubble field stubbles
Height of Roughness Friction Threshold roughness length velocity friction elements (z10) (mm) u* (u10 m = velocity (u*t2) (mm) 10 m s 1) (m s 1) (m s 1) 0.1 5
aggregate roughness aggregate roughness cultivation roughness
20 – 30
cultivation roughness cultivation roughness
100 20 – 100
crop residue roughness
0.01 – 0.02 0.29 – 0.30 0.3 0.38
0.20 0.58
2.9 – 3.1
0.49 – 0.50
0.70 – 0.80
4.0 – 6.5
0.51 – 0.55
0.96
9.5 – 14.0 0.57 – 0.61 f 2.0 – 3.0
0.93 0.76
f 50
–
–
vegetation. In addition, the annual status of vegetation cover between sowing and harvest was calculated by means of statistical phenology functions obtained from phenological data of characteristic soil cover stages. These simplified phenology models predict the development of crop cover and the associated alteration of the surface roughness for eight prevalent crop types separated for the Barnham and Gro¨nheim test sites. According to the equation of Lettau (1969), which provides reasonable roughness length estimates for evenly spatially distributed roughness elements, the roughness length for each crop type was approximated by a function that takes into consideration the relative vegetation cover (0 = no vegetation, 1 = fully developed crops) and the roughness length of developed crops (z0), assuming that the reduction of the wind erosivity with increasing vegetation cover and associated displacement length is sufficently estimated by the third root of the relative vegetation cover. Combined with the specific tillage type and timing, the annual variation of surface and soil roughness were estimated for each day and grid cell. Based on wind tunnel observations, the surface roughness finally allows an estimate of the threshold friction velocity as described by the following equation: u*t 0:089lnðz0 Þ þ 0:6528
ð7Þ
where u*t is the threshold friction velocity (ms 1) and z0 is the roughness length (mm) (Du¨wel, 1995). Eq. (7) serves as a filter for the calculation of erosion hours and sediment flux rates by restricting the erosion process to the time when friction velocities are above the critical threshold.
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3.6. LAND USE module Land use data were collected where possible from existing maps and farmers’ records and entered into the model as gridded data input to run the roughness and soil moisture modules. Land use data were also modelled in cases where adequate data were unavailable. The land use module is used for generating land use scenarios, using average community agricultural statistics and knowledge of crop rotations where detailed knowledge of land use is lacking. Land use data were subsequently used in combination with phenological data (from published sources) to guess actual surface roughness values as well as to run the soil moisture module. 3.6.1. Erosion calculation At its current state of development, the WEELS model outputs are hourly assessments of mean wind speeds (10 m aboveground) and friction velocities as well as daily assessments of crop cover, associated surface (or tillage) roughness and topsoil moisture as the main determinants for erosion processes. The actual erosion risk is given hourly in terms of the duration of erosive conditions and the corresponding maximum sediment transport rate, calculated with and without consideration of topsoil moisture. Following the EROKLI approach (Beinhauer and Kruse, 1994) which estimates the potential mass transport of standard dune sand as a function of the friction velocity u* by means of the sediment transport formula after Bagnold (1966), given in Eq. (1), the duration of erosive conditions and the potential sediment flux rate is calculated with an hourly resolution. To have an additional scale for the evaluation of wind-forced impacts on soil degradation, a simplified daily erosion/accumulation balance derived from the transport ratio values of neighbouring grid cells, taking into consideration the actual wind direction, was added. To illustrate the ability of the model to estimate long-term means as well as impacts of events, preliminary modelling results for wind erosion events and long-term estimates are discussed below using the test sites in the UK and Germany as examples.
4. Model applications Due to the characteristics of wind climates, the model simulations of 29 years for the Barnham site (1970 –1998) (Fig. 4) and 13 years (1981 – 1993) for the Gro¨nheim site (Fig. 5), in general, revealed a higher erosion risk for the English test site, where the total mean annual soil loss, averaged over the whole arable area was estimated at 1.56 t ha 1. The regional maximum net soil loss at exposed sites reached about 15.5 t ha 1. The highest mean monthly value was more than 0.28 t ha 1 in March (Fig. 6B), but even in January, September and November, the net soil loss exceeds 3.0 t ha 1 at exposed sites. The total mean soil loss was less, at 0.43 t ha 1 year 1, on the northern German test site. As denoted in Fig. 6, erosion was highest in April, with rates in excess of 0.08 t ha 1 and local maximum values that exceed 2.5 t ha 1. The total mean maximum soil loss at this site of about 10.0 t ha 1 year 1 corresponds to a loss of about 0.65 mm.
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Fig. 4. Modelled spatial distribution of the mean annual erosion/accumulation balance at the Barnham test site (1970 – 1998). Due to changing wind directions and annually varying vegetation cover, the orientation of the soil erosion rate gradient may differ among the individual fields (dark grey indicates forests, light grey indicates settlements and the area of the Honington airport). The model domain covers 5 5 km.
The spatial distribution of the erosion/accumulation balance is given for a single event in Figs. 7 and 8 for the Barnham and Gro¨nheim test site, respectively. During the event on the 13th and 14th of March 1994, which was recorded on video by the owner of the land on parts of the Barnham site, winds reached 16 m s 1 on the 13th of March (14:00– 15:00 h GMT). The model showed that there was little erosion on fields covered by winter cereals, cover crops and oil seed rape, but showed a total maximum duration within the 2 days of more than eight erosion hours on fields that were not protected by vegetation (in that year, these were sown to sugar beet, spring cereals and maize). The maximum total soil loss on these fields was calculated at more than 7 t ha 1 in some places, particularly those not protected by hedges or other large obstacles from the westerly winds. The easterly winds of the 4th and 5th of April at the Gro¨nheim site in 1989, which had maximum means of 14 m s 1 (4th
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Fig. 5. Modelled spatial distribution of the mean annual erosion/accumulation balance at the Gro¨nheim test site (1981 – 1993). Due to changing wind directions and annually varying vegetation cover, the orientation of the soil erosion rate gradient may differ among the individual fields (dark grey indicates forests, light grey indicates settlements). The model domain covers 5 5 km.
April, 14:00 – 15:00 h GMT) caused less on-site damage. The maximum calculated soil loss was 5 t ha 1 and the corresponding maximum duration of erosive conditions was not more than 4 h. As the topsoil was simulated as being dry for both of these events and most of the fields were barely covered by vegetation, the pattern of erosion damages underlines the role of different wind directions. The westerly wind at the Barnham site produced net accumulation on the eastern parts of the affected fields especially in the hedges. As a result of easterly winds at the Gro¨nheim test site, the corresponding pattern showed net accumulation at the westernmost parts of the fields. Indeed, the damage dimension of accumulation processes is well documented for the Barnham site and for the Swedish WEELS site where depositions at field boundaries reached heights up to 1 m. At least one of these is known to have grown in the last 20 years.
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Fig. 6. Comparison of the modelled mean monthly erosion risks in terms of erosion hours (A) and net soil loss (B) for the Barnham (black) and Gro¨nheim (white) test sites.
4.1. Validation of the model results Although the WEELS model did not primarily aim at exact soil loss predictions, e.g., in t ha 1, future research needs to emphasize the validation of the modelling results against independently sampled data. Until now, the modelling approach was mainly calibrated on the basis of wind tunnel experiments. For validation purposes, the results were tested against two sets of data. The first test took place against local knowledge of significant erosion events (e.g., video records, see above). These comparisons revealed good agreement between observed and modelled patterns as well as hot spots of erosion activities within the test site. While not allowing validation of the modelled soil erosion and accumulation rates, these data prove that the spatial variability of erosion risks seems to be reasonably estimated. In a second test, the long-term simulation for the Barnham site was tested against soil erosion rates estimated over a 30-year period using the 137Cs method (Chappell and Warren, 2002). These estimates also compare closely with the modelled results in terms of the distribution of the general patterns of erosion and accumulation. However, the total net soil flux rates that were derived from the modelled erosion rates exceed the measurements of 137Cs by a factor 2.6. But since the 137 Cs method also implies a high degree of uncertainty, more field data collection may
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Fig. 7. Spatial distribution of the erosion/accumulation balance for the event on the 13th and 14th of March 1994 at the Barnham test site. Positive values (green colours) indicate accumulation, negative values (yellow and red colours) indicate erosion (deep green indicates forests, gray indicates settlements and the area of the Honington airport). Average wind direction of the storm was west (240j – 300j direction angles). The model domain covers 5 5 km.
be required before a scientifically based validation of the modelling results can be realized. Corresponding with the primary project objective, the model appears to give satisfactory estimates of the spatial distribution of the wind-induced erosion dispositions and the spatial distribution of erosion and accumulation patterns. 4.2. Scenario modelling In addition to the prediction of long-term erosion rates and the simulation of single erosion events, one primary objective of the WEELS project was the assessment of
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Fig. 8. Spatial distribution of the erosion/accumulation balance for the event on the 4th and 5th of April 1989 at the Gro¨nheim test site. Positive values (green colours) indicate accumulation, negative values (yellow and red colours) indicate erosion (deep green indicates forests, gray indicates settlements). Average wind direction of the storm was east (70j – 100j direction angles). The model domain covers 5 5 km.
possible influences of changing management strategies on the order of magnitude of the damage and costs caused by wind erosion. In order to show the applicability of the modelling approach for the detection of effects of changing crop rotations on the erosion risks, one exemplary management scenario run is presented for the Gro¨nheim test site. For this change detection, the long-term estimation of the period 1981– 1993 described above was compared with a scenario run on the same climatic input data (period 1981 –1993) but with land use data, supported by the land use module, based on crop statistics of the period 1971 –1983. With this it was possible to directly assess the effects of the changing market conditions that led to intensification of stockbreeding and thus caused a considerable land use change in this region since the early 1980s. Due to the growing need for highly
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nutritious animal food, the area of intensive maize cultivation has increased significantly, replacing other more expensive forage plants. Another important aspect was the high tolerance of maize plants to the application of liquid manure, a by-product of the intensive stockbreeding. As a consequence of these land use changes, the areas of bare or barely covered soils in spring increased significantly since the early 1980s. Consequently, with the exception of August and September (see Fig. 9), the land use scenario—representing the land use structure of the 1970s—reveals a significant reduction of erosion risks mainly induced by the increased degree of soil cover in spring. Expressed in terms of net erosion rates, the 50% reduction of the annual total mean soil loss (0.21 t ha 1 year 1) for the scenario reveals the possibility of an effective erosion risk minimisation by adjusting land use strategies. 4.3. Model limitations At the present state of model design, the first restriction of the WEELS model is caused by the soil moisture module that limits the applicability to sandy soils because of the
Fig. 9. Comparison between mean monthly estimates of the erosion risk modelled for the actual land use situation, based on crop statistics for the period 1981 – 1993 (black) and the land use change scenario, representing the land use structure of the period 1971 – 1983 (smaller proportion of erosion-sustaining crops). The results are presented as erosion hours (A) and as net soil losses (B).
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simplified parameterisations of the soil water budget. To enable more extensive applicability on different soil types, further modifications have to focus on reliable soil moisture estimations, taking into account the soil-dependent pedotransfer functions (Bo¨hner et al., 2002). Further limitations of the WEELS model concern the estimation of the net soil losses due to dust emissions during the erosion process. The current version of the WEELS model is limited to the estimation of horizontal sediment transport rates. Transport rates of mixed-grained soils are derived from calculated sand transport by means of an empirically determined relative erodibility ratio. Thus, the simulated spatial distribution of erosion dynamics is confined to the loss or deposition of particles that are predominantly transported in saltation. At present, the quantification of the suspension component of the sediment transport cannot be achieved by the model, and therefore, is not taken into consideration for the calculation of the net soil losses. In view of a more exact prediction of net soil losses and the assessment of potential off-site effects, the estimation of dust emissions, which accounts for the large part of the net soil loss, will be considered in future updated versions of the model. However, as the quantification of the suspension component in modelling approaches is mostly based on parameterisations instead of process-based simulations, these limits are a general deficit in wind erosion modelling. In addition to these deficits, the lack of knowledge on tillage-induced dust emission, which is currently recognized as one of the main sources of tropospheric dust, underlines the necessity of further investigations on all aspects of tillage and wind-induced dust dynamics. Acknowledgements All meteo data were kindly provided by the German Meteorological Service (DWD) and the English Meteorological Service.
References Bagnold, R.A., 1966. An approach to the sediment transport from general physics. Prof. Paper - Geol. Surv. 422-I. 37 pp. Beinhauer, R., Kruse, B., 1994. Soil erosivity by wind in moderate climates. Ecol. Model. 75/76, 279 – 287. Bo¨hner, J., Ko¨the, R., Conrad, O., Gross, J., Ringeler, A., Selige, T., 2002. Soil regionalisation by means of terrain analysis and process parameterisation. Proceedings of the Symposium Soil Classification 2001. Reviewed and accepted. Chappell, A., Warren, A., 2002. Spatial scales of 137Cs-derived soil flux by wind in 25 km2 arable area of Eastern England. Catena 52, 209 – 234 (this issue). Du¨wel, O., 1995. Die Bedeutung der Bodenrauhigkeit fu¨r die Bodenerosion durch Wind—Ein Beitrag zur Quantifizierung der Bodenverluste. PhD thesis, University of Go¨ttingen, Germany. 135 pp. Du¨wel, O., Scha¨fer, W., Kuntze, H., 1994. The effect of soil surface roughness on soil transport by wind. In: Buerkert, B., Allison, B.E., von Oppen, M. (Eds.), Proceedings of the International Symposium, Wind Erosion in West Africa: The Problem and Its Control. University of Hohenheim, Hohenheim, pp. 357 – 363. Fryrear, D.W., Saleh, A., Bilbro, J.D., 1998. A single event wind erosion model. Trans. Am. Soc. Agric. Eng. 41, 1369 – 1374. Gross, J., 2002. Quantifizierung winderosionsbedingter Staubaustra¨ge in Agrarlandschaften Niedersachsens. Geosynthesis 12 (142 pp.).
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Hagen, L.J., 1991. A wind erosion prediction system to meet user needs. J. Soil Water Conserv. 46, 106 – 112. Haude, W., 1954. Zur praktischen Bestimmung der aktuellen und potentiellen Evapotranspiration. Mitt. d. DWD, 8, Bad Kissingen. Lettau, H., 1969. Note on aerodynamic roughness—parameter estimation on the basis of roughness – element distribution. J. Appl. Meteorol. 8, 828 – 832. Mortensen, N.G., Landsberg, L., Troen, I., Petersen, E.L., 1993. Wind Atlas Analysis and Application Programme (WAsP) Riso National Laboratory, Roskilde, Da¨nemark. Neemann, W., 1991. Bestimmung des Bodenerodierbarkeitsfaktors fu¨r winderosionsgefa¨hrdete Bo¨den Norddeutschlands. Geol. Jahrb., Reihe F 25, 131S. Priesley, C.H.B., 1959. Turbulent Transfer in the Lower Atmosphere Univ. of Chicago Press, Chicago III, 130 pp. Scha¨fer, W., 1989. Zur Bestimmung der Oberfla¨chenfeuchte des Bodens mit einem Infrarotreflexionsphotometer. Mitt. Dtsch. Bodenkdl. Ges. 59/I, 233 – 238. Thiermann, A., 2001. Entwicklung einer GIS-gestu¨tzten Methode zur Ermittlung winderosions-gefa¨hrdeter Gebiete in Niedersachsen. Diplomarbeit Universita¨t Bremen, unvero¨ffentlicht. Troen, I., Petersen, E.L., 1989. Europa¨ischer Windatlas Meteorology and Wind Department, Riso National Laboratory, Roskilde, Da¨nemark. Vigiak, O., 2000. Spatial modelling of wind speed reductions by windbreaks. Unpubl. WEELS Report. Wageningen University, Wageningen. 27 pp. Wendling, U., Schellin, H.-G., Thoma¨, M., 1991. Bereitstellung von ta¨glichen Informationen zum Wasserhaushalt fu¨r die Zwecke der agrarmeteorologischen Beratung. Z. Meteorol. 41, 468 – 475. Woodruff, N.P., Siddoway, F.H., 1965. A wind erosion equation. Proc. - Soil Sci. Soc. Am. 29, 602 – 608.
The WEELS model: methods, results and limitations Juergen Bo¨hner a,*, W. Scha¨fer b, O. Conrad a, J. Gross b, A. Ringeler a a
Department of Geography, University of Go¨ttingen, Goldschmidt Str. 5, 37077 Go¨ttingen, Germany b Geological Survey of Lower Saxony, Institute of Soil Technology, Friedrich-Missler-Str. 46-50, 28211 Bremen, Germany
Abstract Within the European Union (EU)-funded Project ‘Wind Erosion on European Light Soils’ (WEELS), a model was designed and implemented with the aim of predicting the long-term spatial distribution of wind erosion risks in terms of erosion hours and wind-induced soil loss. In order to ensure wide applicability, the model structure consists of a modular combination of different approaches and algorithms, running on available or easily collected topographic and climatological data input. Whereas the ‘WIND’, ‘WIND EROSIVITY’ and ‘SOIL MOISTURE’ modules combine factors that contribute to the temporal variations of climatic erosivity, the ‘SOIL ERODIBILITY’, ‘SURFACE ROUGHNESS’ and ‘LAND USE’ modules predict the temporal soil and vegetation cover variables that control soil erodibility. Preliminary simulations over a 29-year period for the Barnham site (UK) (1970 – 1998) and a 13-year period for the Gro¨nheim site (Germany) (1981 – 1993) generally resulted in a higher erosion risk for the English test site, where the total mean soil loss was estimated at 1.56 t ha 1 year 1 and mean maximum soil loss at about 15.5 t ha 1 year 1. The highest rates exceeded 3 t ha 1 in March, September and November. On the northern German test site, the total mean soil loss was 0.43 t ha 1 year 1. The highest erosion rates were predicted in April when they can exceed 2.5 t ha 1. The total mean maximum soil loss at this site of about 10.0 t ha 1 year 1 corresponds to a loss of about 0.65 mm. Predictions based on a land use scenario for the German site revealed that the erosion risk could be reduced significantly by changing land use strategies. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Modelling; Wind; Wind erosion; Aeolian transport; Aeolian accumulation
1. Introduction Early studies on the identification and quantification of the factors influencing the location and rates of soil erosion by wind led, among others, to the development of the
* Corresponding author. Tel.: +49-551-8006. E-mail address: [email protected] (J. Bo¨hner). 0341-8162/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0341-8162(03)00019-5
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empirically based Wind Erosion Equation (WEQ) in the 1960s (Woodruff and Siddoway, 1965). The WEQ was developed to predict at the field scale the potential average annual soil loss by determining the influence of several primary variables that characterize the erodibility of the soil, the erosivity of the wind and the actual land use situation (Woodruff and Siddoway, 1965). In parallel with early estimations of water erosion rates, e.g., the Universal Soil Loss Equation (USLE), this empirical approach was found to have limited transferability. Since the 1970s, research on wind erosion has been directed towards the development of physically based simulation of the complex interactions between erosion and its controlling variables. Advances in wind erosion research have led to the design of more process-based wind erosion models, like, e.g., the Revised Wind Erosion Equation (RWEQ, Fryrear et al., 1998) and the Wind Erosion Prediction System (WEPS, Hagen, 1991). Although remarkable advances have been made in simulating wind-induced erosion dispersion and deposition processes, the application of the current generation of physically based models is still limited, as most of them require detailed input data, which are not always available, and are not easily adapted to conditions or climates different from those for which they have been calibrated. Further limitations in the spatial or temporal resolution as well as narrow model domains limit the application of complex approaches for erosion risk assessments in ecological or political studies. Against this background, the European Union (EU)-funded research project ‘Wind Erosion on European Light Soils’ (WEELS) aimed at the assessment of the spatial
Fig. 1. Locations of the WEELS test sites.
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distribution of wind erosion risks in the North European Quaternary Plains and the prediction of long-term wind-induced soil losses under different climate and land use scenarios combined with an economic analysis of the damage and costs caused by these processes. The project comprised field measurements at three representative test sites, located in Germany, England and Sweden (Fig. 1) as well as wind tunnel experiments with selected soils for process parameterisation. Four different countries were involved in the project (Germany, Netherlands, Sweden and the United Kingdom) forming a multidisciplinary consortium consisting of universities and government bodies. Within the WEELS project, one central task was to develop a spatially distributed wind erosion model that can run at various time scales, from hours to decades, can take into account different crop rotation periods and is able to simulate different management and climatic scenarios. To ensure wide applicability of the model within the EU and Eastern Europe, work focused on the development and application of a process-based winderosion model that uses readily available or easily collected data for erosion risk assessments in model domains covering up to 5 5 km. The ability to incorporate the data output into a GIS simplifies assessments of economic implications, both in the past and in the future. The objective of this paper is to provide an overview of the WEELS model, to describe the different modules and to show some results of wind erosion risk assessment in two study areas.
2. Materials and methods In view of the available database and the validation of the model results, the modelling of long-term erosion rates as well as the simulation of single erosion events have been limited to the test sites in England and Germany (Fig. 1). The UK test site is situated around Barnham near Bury St. Edmunds in Suffolk (East Anglia). The sandy soils of the study area are mainly derived from glacio-fluvial deposits of the penultimate glaciation overlying chalk. Main crop is sugar beet often undersown with spring barley. The wind climate, derived from observations at the Honington Airport, is characterized by prevailing SSW to W winds (39.7%) throughout the year and an annual mean wind speed of 4.6 m s 1. The seasonal variations with monthly means of more than 5.0 m s 1 from December to April and a maximum in January (5.7 m s 1) denote the frequent cyclonic activity in winter and spring, while the mean velocity remains below 4.0 m s 1 during the summer months. The spatial distribution of mean wind speeds at the test site is consistent with the general land use situation. At the leeward neighbouring scopes of wooded areas, the annual mean wind speed decreases below 3.9 m s 1, while the lessdisturbed flow conditions in the central northern part are characterized by increasing wind speed of more than 4.7 m s 1. The German test site is located in the Ems-Hunte geest area in the central western part of Lower Saxony, 12 km northwest of Cloppenburg near Gro¨nheim. Podzols on Pleistocene and Holocene fluviatile sands, often overlain by eolian sands, Stagno-Dystric Cambisols and Gleyic Podzoluvisols are the dominant soil types covering the gently undulating, rarely hilly landscape of the Saalian ground moraine. It is an area in which
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there is intensive maize cultivation, interspersed with blocks of coniferous woodland, where wind erosion has been causing concern. The analyses of wind data from Ahlhorn revealed a prevalence of westerly winds comparable to the UK site, with a frequency of 44.2% in the 210j– 270j direction sectors. However, the annual mean wind speed of 4.1 m s 1 and monthly means of less than 4.0 m s 1 from April to September with a minimum in June (3.5 m s 1) and maximum of 5.2 m s 1 in January at the Alhorn station indicate lower wind speed and erosion risk for the western part of Lower Saxony than East Anglia. Correspondingly, in the spatial distribution of mean annual wind speeds, the maximum mean annual wind velocity remains at 4.2 m s 1 at the German test site.
3. Model description The structure of the WEELS model consists of a modular combination of different approaches and algorithms running on available or easily sampled topographic and climatic data input. Most of the calculations and modelling were performed on the Go¨ttingen System for Automated Geoecological Analysis (SAGA), a scientific GIS for terrain analysis, remote sensing and modelling of climatological and geomorphological processes, currently being developed by the research group Geosystemanalysis of the Department of Geography, University of Go¨ttingen (http://www.geogr.uni-goettingen.de/ pg/saga/aggeosys/). The first group of modules, the ‘WIND’, ‘WIND EROSIVITY’ and ‘SOIL MOISTURE’ modules, concerns those dynamic variables that contribute to the temporal variations of climatic erosivity. In this first stage of the model, the climate-induced potential erosion intensity in terms of erosion hours and potential mass transport of standard dune sand is calculated by means of the Bagnold (1966) sediment transport formula that describes the total maximum sediment flux rate ( Q) of standard dune sand as a function of the friction velocity (u*):
QE ¼ AVu
ð3kÞ
*
1
u*t u*
1 !m
where: AVcA¼ C
n d qa ; D g
1 3 VnV 2 4
ð1Þ
where QE is sediment transport rate (kg m 1 s 1), u* is shear velocity (m s 1), u*t is threshold shear velocity (m s 1), C is an empirically derived weight factor with 1.5 V C V 2.8 (for dune sand) (s2 m 3), d is mean grain size (m), D is standard grain size 0.2510 3 (m), qa is relative density of air: 1.29 (kg m 3), g is constant of gravity 9.81 (m s 2), and k and m are empirical parameters that have to be calibrated. In contrast to other models, this approach uses the potential mass transport of standard dune sand to characterize the climate-induced potential erosion intensity. The second group of modules, the ‘SOIL ERODIBILITY’, ‘SOIL ROUGHNESS’ and the ‘LAND USE’ modules, predicts the temporal soil and vegetation cover variables that control soil erodibility. This second stage adapts the potential erosion intensity to the
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actual soil and land use conditions. It calculates the actual soil erosion in terms of actual erosion hours and total sediment transport. At the current state of development, the model domain is restricted to areas of about 5 5 km with a horizontal spatial resolution of 25 25 m (grid size). Fig. 2 illustrates the generalized model structure. The modules are described in detail below. 3.1. WIND module The simulation of wind speeds and directions for 10 m aboveground was mainly performed via WAsP, the Wind Atlas Analysis and Application Program (Mortensen et al., 1993). Primarily developed for the calculation of wind energy resources at a given site, the
Fig. 2. Structure of the WEELS model.
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estimation of wind parameters via WAsP offers the advantage of an easy processing of extensive and already available climatic data sets from an existing network of meteorological stations as well as different tools for the processing of necessary topographic data inputs. The WAsP procedure can be separated into two steps. The first ‘‘analysis’’ step estimates the ‘‘regional wind climate’’. As observations at a given site are influenced by the relief, the land use situation (roughness length as related to land use) and neighbouring obstacles, the ‘‘raw data’’ of a meteorological station is corrected with respect to the sitespecific topographic situation and reduced to standard conditions. The generated regional wind climates are expressed in terms of frequency and wind speed distributions, approximated by Weibull-A and Weibull-k parameters (Mortensen et al., 1993), separated for 12 wind direction sectors, five standard heights above ground level and four different standard roughness lengths. The determination of the regional wind climate is based on the observations of the meteorological stations located at Ahlhorn (1981 –1993) and Meppen (1976 – 1990) for the Gro¨nheim study site and the station located at Honington (1970 – 2000) for the Barnham study site. Since instruments have not been changed during the period of observation, the data sets were considered homogeneous. The second ‘‘application’’ step performs the reverse process of step one. Based on the results of the regional analyses of wind climatologies, the WAsP model simulates the modification of the wind speed and frequency distribution at the test site with respect to the local topographic situation. The models implemented in WAsP are based on the physical principles of stream modifications in the boundary layer, induced by surface friction. Assuming a steady neutral stratification and an approximately constant eddy viscosity in the friction layer, the model theory is founded on the close relationship between geostrophic and surface winds in accordance with the roughness-induced variations of the logarithmic wind profile (roughness model) and the modification of the undisturbed nearground flow pattern induced by the relief (orography model). A description of the model structure is given in Troen and Petersen (1989) and Mortensen et al. (1993). Digital terrain models (DTM) with a spatial resolution of 50 50 m were processed and converted into contour lines (vector data) with a 2-m contour interval. The assignment of roughness lengths is based on ATKIS and CORINE land use data that were reduced to four land use and roughness classes. To consider the distance-dependent influences of the land use pattern and the orography at the scale of the whole test sites, the topographical database covered an area of 20 20 km. The resulting outputs for both test sites consisted of grid maps of Weibull-A and Weibull-k parameters, calculated for 10 m aboveground, covering 5 5 km in a spatial resolution of 25 25 m. With respect to the wind erosivity estimations, separate calculations were performed for 12 wind direction sectors. 3.2. WIND EROSIVITY module As wind energy increases with the third power of its velocity, estimating temporal speed variations is an essential aspect in modelling wind-induced erosion processes. To consider the temporal variability of wind velocities for estimates of wind erosivity, the ratio of observed (at the meteorological station) and gridded Weibull parameters computed directional dependent serves as a constant transfer function for the calculation of hourly means of wind speeds and derived friction velocities (u*). The wind friction velocity is
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determined from the wind velocity and the roughness length, assuming a vertical logarithmic wind velocity profile (Priesley, 1959): for zHz0, u* z ln ð2Þ uz ¼ k z0 where uz is the wind velocity in height z, k is the Von Karman’s constant ( f 0.4), and z0 is the surface roughness length. The influence of wind barriers and hedges was taken into account by applying a reduction factor to the friction velocities in a protected zone. Based on mapped positions, heights and optical porosities of hedges and wind barriers at the test sites (Thiermann, 2001; Vigiak, 2000), the distance to neighbouring obstacle belts along the wind direction and the resulting windbreak shelter was computed. According to the field results of Du¨wel (1995), the reduction function of the RWEQ (Fryrear et al., 1998) was found to be the most suitable approach. To take into account the convection- and turbulence-induced high-frequency temporal variations of the surface wind velocities (which are not captured by hourly means), a Gaussian frequency distribution was approximated, assuming a constant increase of the standard deviation (r) with increasing friction velocities (u*) by r = 0.25u*. This approach, proposed by Beinhauer and Kruse (1994), was confirmed by statistical analysis of hourly means and gust speed values at Honington Airport. Integrating the results of the soil moisture roughness modules (see below), wind erosivity is first expressed in terms of number of erosion hours, defined as the duration of friction velocities above threshold velocities under dry topsoil conditions and calculated with an hourly resolution for each grid cell. The second measure of the impact of wind forces on soil, the wind-induced potential mass transport, is based on the Bagnold (1966) sediment transport formula that describes the total maximum sediment flux rate Q as a function of the friction velocity. Even though reductions of the potential sediment flux are made to take into account soil erodibility (see below), the use of a potential mass transport (of a well-sorted unaggregated soil) leads to an overestimation as the reduction of erodible material during the erosion process is not taken into account. In addition, this sand transport rate is expected to be higher than the transport rate of the natural aggregated soils that occurs within the test sites. Nevertheless, this process-based approach allows for a wider applicability for erosion risk assessments. 3.3. SOIL MOISTURE module As the water content of the topsoil layer significantly influences the susceptibility of a soil to wind erosion, the prediction of the daily changes in topsoil moisture (above or below the ‘critical soil moisture’) was a main requirement for the modelling of erosion hours. For sandy soils, this critical water content is assumed to be 2 –4 wt.%. For this reason, a simplified topsoil moisture model was used for the prediction of the water content in the uppermost soil layer (ca. 2 cm) of sandy soils (Wendling et al., 1991). This model provides a continuous, daily, soil water balance at the soil surface using the equation: Wiþ1 ¼ Wi þ Pi ETai SWi þ CR
ð3Þ
where Wi is the daily water content in the topsoil layer for a defined thickness, Pi is the daily precipitation (mm), ETai is the daily actual evapotranspiration according to Haude (1954)
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(mm), SWi is seepage water (mm) and CR is the capillary rise (mm). Additional input data are the thickness of the soil layer, the water content at field capacity (FC) and at the permanent wilting point (PWP) of this layer as well as the capillary rise (mm) and the water storage at the start of calculation. In this simplified approach, drainage from the topsoil to lower layers at water contents below field capacity is not taken into consideration since it is assumed to be of little significance for the changes in the topsoil moisture of sandy soils. Climate input parameters, derived from the meteorological stations at Ahlhorn and Honington Airport, are precipitation as well as mean values (14:00) of air temperature and relative humidity, global radiation and mean wind speed used for calculating the actual evapotranspiration (according to Haude, 1954) and precipitation. The amount of the daily precipitation is added to the water content of the uppermost 2 cm of the soil. Once the water content in the simulation layer reaches field capacity, any excess water is added to the succeeding soil layer as seepage water. The actual evapotranspiration then controls the drying of the soil surface. During the phase of drying, additional water is delivered by unsaturated water flow from the sublayer to the top layer by capillary rise. The amount of the capillary rise depends on the bulk density of the topsoil layer and thus on the state of cultivation. In the model, constant values are used, e.g., 0.5 mm day 1 for a field topsoil after tillage and 1 mm day 1 for a more compacted topsoil. Since the water storage capacity (ca. 5 mm/2 cm soil layer) and the amount of the capillary rise (ca. 0– 1 mm day 1) are very small for sandy soils, the temporal variability is predominantly controlled by climatic parameters (precipitation, evaporation). The described method was verified against data obtained at the intensive test field in Gro¨nheim. Permanent monitoring of the soil surface moisture was obtained by an infrared moisture sensor (Scha¨fer, 1989), which continuously monitors topsoil moisture content. Data were recorded as 10-min averages. Before being installed at the test field, the infrared sensor was calibrated against a topsoil sample obtained at the test field. In the model framework, the soil moisture module calculates the days with dry soil surface (0– 2 cm) during the periods without vegetational soil cover. Validation of the model results revealed a significant correspondence between the measured and the modelled topsoil water contents. For a 30-day test period, more than 80% of the ‘dry’ days were predicted by the model (Fig. 3). Based on comparisons between calculated and measured values obtained in the field, a water content in the topsoil layer of 2 mm defines the threshold value for ‘wet’ and ‘dry surface’ conditions, assuming that wind erosion is just possible for the condition ‘dry surface’. 3.4. SOIL ERODIBILITY module One essential component of the WEELS model is the determination and characterization of soil erodibility. Soil erodibility is a measure of the soil’s resistance to the erosive power of the wind. A dimensionless soil erodibility factor ‘K’ is introduced which expresses the intrinsic susceptibility of a dry, freshly cultivated sandy soil to erosion when not affected by surface cover, roughness, crusting or soil moisture. Soil properties that influence K values are soil texture, aggregation and aggregate stability. Particle size distribution, in particular, %fine sand, silt and clay, and the organic matter content are thus
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Fig. 3. Comparison between measured (infrared sensor) and calculated (soil moisture module) values of the daily water content in the topsoil layer of the Gro¨nheim intensive test field for the period 1st – 31st of May 1999. A water content of 2 mm defines the threshold value for ‘wet’ and ‘dry surface’ conditions.
the main determinants. On the basis of a regression equation that was empirically derived from wind tunnel experiments, K factor values are predicted as a function of the mean weighted diameter (MWD) of the texture and the proportion of aggregates >0.63 mm diameter (Neemann, 1991):
ð4Þ The larger the K factor value, the higher the susceptibility of that particular soil. In the absence of measured data, the proportion of unerodible aggregates (diameter >0.63 mm) is calculated on the basis of the organic matter content and the proportion of the silt and clay and fine sand fractions. K is assumed to be constant throughout the year. In the WEELS model procedure, the K factor is used to derive transport rates of mixed-grained and aggregated soils from the modelled values of sand transport because the amount of mass loss, calculated by the Bagnold equation, represents the potential erosion mass loss of a well-sorted unaggregated sand. However, this sand transport rate is expected to be higher
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than the transport of the natural aggregated soils. Based on wind tunnel experiments carried out with both a standard Bagnold sand and 10 different highly erodible sandy soils, a relative erodibility ratio (R) was empirically determined. In order to facilitate the direct derivation of this ratio and the associated reduction of maximum sediment flux rate, the relationship between the relative erodibility ratio (R) of the test soils and their K factors was determined empirically. There is a relatively strong (R2 = 0.88) correlation between the two variables which is best described by the following equation (Gross, 2002): R ¼ 0:6551ðK factorÞ0:5901
ð5Þ
where R is the relative erodibilty ratio (erodibility of mixed grained soils in relation to ‘Bagnold-type’ dune sand) and K factor is the erodibility factor calculated according to Neemann (1991). For the Barnham site, the K factors could partly be derived from nonclassified measured values. These K factors showed only weak statistical dependency of the terrain attribute ‘‘height above channel network’’, a terrain parameter, derived from a digital terrain model (DTM) by means of automated terrain analysis (Bo¨hner et al., 2002). Therefore, it was not useful to assume such a relationship for an advanced interpolation with the geostatistical Universal Kriging method. For the Gro¨nheim site, K factors were only available as classified values. After a translation into metric values it was finally possible to perform a spatial interpolation using the Minimum Curvature method. 3.5. SURFACE ROUGHNESS module Knowledge of the soil and surface roughness is essential to predict wind erosion accurately. The surface roughness of uncovered soils is controlled by both the intrinsic soil properties that determine the state of aggregation and by the management practices that produce roughness elements of different size. The effect of soil surface roughness on the soil transport by wind comprises several aspects (Du¨wel et al., 1994): modification of the logarithmic mean wind profile in the boundary layer which affects the aerodynamic roughness length (z0) and friction velocity (u*), protection of loose material from drag forces generated by wind shear in the lee of roughness elements and trapping of windtransported particles by roughness elements. An increase in z0 normally results in a decrease in soil erosion. The WEELS erosion model requires representative roughness lengths for all types of cultivation that are typical for the study sites. The data shown in Table 1 were obtained from the experiments performed by Du¨wel (1995) and complemented by the data obtained from the wind tunnel experiments carried out as part of the WEELS project. Based on these data, each cultivation practice can be related to a typical range of z0 values according to the equation: z0 ¼ 0:15zmax
ð6Þ
where z0 is the roughness length (mm) and zmax is the maximum height (mm) of the roughness elements. With this it is possible to model the influence of the changing types of roughness during the different cultivation conditions of the soil before being covered by
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Table 1 Soil surface roughness under different open land and management practices according to Du¨wel (1995) Type of cultivation
Surface (roughness elements)
Roughness type
Rolled Harrowed (fine) Harrowed
smooth smooth, small aggregates smooth, large aggregates large aggregates small cross-grooves
particle roughness aggregate roughness
Ploughed Drilled (seed rows) Potato ridges large cross-grooves Potato ridges rows in wind direction Stubble field stubbles
Height of Roughness Friction Threshold roughness length velocity friction elements (z10) (mm) u* (u10 m = velocity (u*t2) (mm) 10 m s 1) (m s 1) (m s 1) 0.1 5
aggregate roughness aggregate roughness cultivation roughness
20 – 30
cultivation roughness cultivation roughness
100 20 – 100
crop residue roughness
0.01 – 0.02 0.29 – 0.30 0.3 0.38
0.20 0.58
2.9 – 3.1
0.49 – 0.50
0.70 – 0.80
4.0 – 6.5
0.51 – 0.55
0.96
9.5 – 14.0 0.57 – 0.61 f 2.0 – 3.0
0.93 0.76
f 50
–
–
vegetation. In addition, the annual status of vegetation cover between sowing and harvest was calculated by means of statistical phenology functions obtained from phenological data of characteristic soil cover stages. These simplified phenology models predict the development of crop cover and the associated alteration of the surface roughness for eight prevalent crop types separated for the Barnham and Gro¨nheim test sites. According to the equation of Lettau (1969), which provides reasonable roughness length estimates for evenly spatially distributed roughness elements, the roughness length for each crop type was approximated by a function that takes into consideration the relative vegetation cover (0 = no vegetation, 1 = fully developed crops) and the roughness length of developed crops (z0), assuming that the reduction of the wind erosivity with increasing vegetation cover and associated displacement length is sufficently estimated by the third root of the relative vegetation cover. Combined with the specific tillage type and timing, the annual variation of surface and soil roughness were estimated for each day and grid cell. Based on wind tunnel observations, the surface roughness finally allows an estimate of the threshold friction velocity as described by the following equation: u*t 0:089lnðz0 Þ þ 0:6528
ð7Þ
where u*t is the threshold friction velocity (ms 1) and z0 is the roughness length (mm) (Du¨wel, 1995). Eq. (7) serves as a filter for the calculation of erosion hours and sediment flux rates by restricting the erosion process to the time when friction velocities are above the critical threshold.
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3.6. LAND USE module Land use data were collected where possible from existing maps and farmers’ records and entered into the model as gridded data input to run the roughness and soil moisture modules. Land use data were also modelled in cases where adequate data were unavailable. The land use module is used for generating land use scenarios, using average community agricultural statistics and knowledge of crop rotations where detailed knowledge of land use is lacking. Land use data were subsequently used in combination with phenological data (from published sources) to guess actual surface roughness values as well as to run the soil moisture module. 3.6.1. Erosion calculation At its current state of development, the WEELS model outputs are hourly assessments of mean wind speeds (10 m aboveground) and friction velocities as well as daily assessments of crop cover, associated surface (or tillage) roughness and topsoil moisture as the main determinants for erosion processes. The actual erosion risk is given hourly in terms of the duration of erosive conditions and the corresponding maximum sediment transport rate, calculated with and without consideration of topsoil moisture. Following the EROKLI approach (Beinhauer and Kruse, 1994) which estimates the potential mass transport of standard dune sand as a function of the friction velocity u* by means of the sediment transport formula after Bagnold (1966), given in Eq. (1), the duration of erosive conditions and the potential sediment flux rate is calculated with an hourly resolution. To have an additional scale for the evaluation of wind-forced impacts on soil degradation, a simplified daily erosion/accumulation balance derived from the transport ratio values of neighbouring grid cells, taking into consideration the actual wind direction, was added. To illustrate the ability of the model to estimate long-term means as well as impacts of events, preliminary modelling results for wind erosion events and long-term estimates are discussed below using the test sites in the UK and Germany as examples.
4. Model applications Due to the characteristics of wind climates, the model simulations of 29 years for the Barnham site (1970 –1998) (Fig. 4) and 13 years (1981 – 1993) for the Gro¨nheim site (Fig. 5), in general, revealed a higher erosion risk for the English test site, where the total mean annual soil loss, averaged over the whole arable area was estimated at 1.56 t ha 1. The regional maximum net soil loss at exposed sites reached about 15.5 t ha 1. The highest mean monthly value was more than 0.28 t ha 1 in March (Fig. 6B), but even in January, September and November, the net soil loss exceeds 3.0 t ha 1 at exposed sites. The total mean soil loss was less, at 0.43 t ha 1 year 1, on the northern German test site. As denoted in Fig. 6, erosion was highest in April, with rates in excess of 0.08 t ha 1 and local maximum values that exceed 2.5 t ha 1. The total mean maximum soil loss at this site of about 10.0 t ha 1 year 1 corresponds to a loss of about 0.65 mm.
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Fig. 4. Modelled spatial distribution of the mean annual erosion/accumulation balance at the Barnham test site (1970 – 1998). Due to changing wind directions and annually varying vegetation cover, the orientation of the soil erosion rate gradient may differ among the individual fields (dark grey indicates forests, light grey indicates settlements and the area of the Honington airport). The model domain covers 5 5 km.
The spatial distribution of the erosion/accumulation balance is given for a single event in Figs. 7 and 8 for the Barnham and Gro¨nheim test site, respectively. During the event on the 13th and 14th of March 1994, which was recorded on video by the owner of the land on parts of the Barnham site, winds reached 16 m s 1 on the 13th of March (14:00– 15:00 h GMT). The model showed that there was little erosion on fields covered by winter cereals, cover crops and oil seed rape, but showed a total maximum duration within the 2 days of more than eight erosion hours on fields that were not protected by vegetation (in that year, these were sown to sugar beet, spring cereals and maize). The maximum total soil loss on these fields was calculated at more than 7 t ha 1 in some places, particularly those not protected by hedges or other large obstacles from the westerly winds. The easterly winds of the 4th and 5th of April at the Gro¨nheim site in 1989, which had maximum means of 14 m s 1 (4th
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Fig. 5. Modelled spatial distribution of the mean annual erosion/accumulation balance at the Gro¨nheim test site (1981 – 1993). Due to changing wind directions and annually varying vegetation cover, the orientation of the soil erosion rate gradient may differ among the individual fields (dark grey indicates forests, light grey indicates settlements). The model domain covers 5 5 km.
April, 14:00 – 15:00 h GMT) caused less on-site damage. The maximum calculated soil loss was 5 t ha 1 and the corresponding maximum duration of erosive conditions was not more than 4 h. As the topsoil was simulated as being dry for both of these events and most of the fields were barely covered by vegetation, the pattern of erosion damages underlines the role of different wind directions. The westerly wind at the Barnham site produced net accumulation on the eastern parts of the affected fields especially in the hedges. As a result of easterly winds at the Gro¨nheim test site, the corresponding pattern showed net accumulation at the westernmost parts of the fields. Indeed, the damage dimension of accumulation processes is well documented for the Barnham site and for the Swedish WEELS site where depositions at field boundaries reached heights up to 1 m. At least one of these is known to have grown in the last 20 years.
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Fig. 6. Comparison of the modelled mean monthly erosion risks in terms of erosion hours (A) and net soil loss (B) for the Barnham (black) and Gro¨nheim (white) test sites.
4.1. Validation of the model results Although the WEELS model did not primarily aim at exact soil loss predictions, e.g., in t ha 1, future research needs to emphasize the validation of the modelling results against independently sampled data. Until now, the modelling approach was mainly calibrated on the basis of wind tunnel experiments. For validation purposes, the results were tested against two sets of data. The first test took place against local knowledge of significant erosion events (e.g., video records, see above). These comparisons revealed good agreement between observed and modelled patterns as well as hot spots of erosion activities within the test site. While not allowing validation of the modelled soil erosion and accumulation rates, these data prove that the spatial variability of erosion risks seems to be reasonably estimated. In a second test, the long-term simulation for the Barnham site was tested against soil erosion rates estimated over a 30-year period using the 137Cs method (Chappell and Warren, 2002). These estimates also compare closely with the modelled results in terms of the distribution of the general patterns of erosion and accumulation. However, the total net soil flux rates that were derived from the modelled erosion rates exceed the measurements of 137Cs by a factor 2.6. But since the 137 Cs method also implies a high degree of uncertainty, more field data collection may
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Fig. 7. Spatial distribution of the erosion/accumulation balance for the event on the 13th and 14th of March 1994 at the Barnham test site. Positive values (green colours) indicate accumulation, negative values (yellow and red colours) indicate erosion (deep green indicates forests, gray indicates settlements and the area of the Honington airport). Average wind direction of the storm was west (240j – 300j direction angles). The model domain covers 5 5 km.
be required before a scientifically based validation of the modelling results can be realized. Corresponding with the primary project objective, the model appears to give satisfactory estimates of the spatial distribution of the wind-induced erosion dispositions and the spatial distribution of erosion and accumulation patterns. 4.2. Scenario modelling In addition to the prediction of long-term erosion rates and the simulation of single erosion events, one primary objective of the WEELS project was the assessment of
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Fig. 8. Spatial distribution of the erosion/accumulation balance for the event on the 4th and 5th of April 1989 at the Gro¨nheim test site. Positive values (green colours) indicate accumulation, negative values (yellow and red colours) indicate erosion (deep green indicates forests, gray indicates settlements). Average wind direction of the storm was east (70j – 100j direction angles). The model domain covers 5 5 km.
possible influences of changing management strategies on the order of magnitude of the damage and costs caused by wind erosion. In order to show the applicability of the modelling approach for the detection of effects of changing crop rotations on the erosion risks, one exemplary management scenario run is presented for the Gro¨nheim test site. For this change detection, the long-term estimation of the period 1981– 1993 described above was compared with a scenario run on the same climatic input data (period 1981 –1993) but with land use data, supported by the land use module, based on crop statistics of the period 1971 –1983. With this it was possible to directly assess the effects of the changing market conditions that led to intensification of stockbreeding and thus caused a considerable land use change in this region since the early 1980s. Due to the growing need for highly
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nutritious animal food, the area of intensive maize cultivation has increased significantly, replacing other more expensive forage plants. Another important aspect was the high tolerance of maize plants to the application of liquid manure, a by-product of the intensive stockbreeding. As a consequence of these land use changes, the areas of bare or barely covered soils in spring increased significantly since the early 1980s. Consequently, with the exception of August and September (see Fig. 9), the land use scenario—representing the land use structure of the 1970s—reveals a significant reduction of erosion risks mainly induced by the increased degree of soil cover in spring. Expressed in terms of net erosion rates, the 50% reduction of the annual total mean soil loss (0.21 t ha 1 year 1) for the scenario reveals the possibility of an effective erosion risk minimisation by adjusting land use strategies. 4.3. Model limitations At the present state of model design, the first restriction of the WEELS model is caused by the soil moisture module that limits the applicability to sandy soils because of the
Fig. 9. Comparison between mean monthly estimates of the erosion risk modelled for the actual land use situation, based on crop statistics for the period 1981 – 1993 (black) and the land use change scenario, representing the land use structure of the period 1971 – 1983 (smaller proportion of erosion-sustaining crops). The results are presented as erosion hours (A) and as net soil losses (B).
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simplified parameterisations of the soil water budget. To enable more extensive applicability on different soil types, further modifications have to focus on reliable soil moisture estimations, taking into account the soil-dependent pedotransfer functions (Bo¨hner et al., 2002). Further limitations of the WEELS model concern the estimation of the net soil losses due to dust emissions during the erosion process. The current version of the WEELS model is limited to the estimation of horizontal sediment transport rates. Transport rates of mixed-grained soils are derived from calculated sand transport by means of an empirically determined relative erodibility ratio. Thus, the simulated spatial distribution of erosion dynamics is confined to the loss or deposition of particles that are predominantly transported in saltation. At present, the quantification of the suspension component of the sediment transport cannot be achieved by the model, and therefore, is not taken into consideration for the calculation of the net soil losses. In view of a more exact prediction of net soil losses and the assessment of potential off-site effects, the estimation of dust emissions, which accounts for the large part of the net soil loss, will be considered in future updated versions of the model. However, as the quantification of the suspension component in modelling approaches is mostly based on parameterisations instead of process-based simulations, these limits are a general deficit in wind erosion modelling. In addition to these deficits, the lack of knowledge on tillage-induced dust emission, which is currently recognized as one of the main sources of tropospheric dust, underlines the necessity of further investigations on all aspects of tillage and wind-induced dust dynamics. Acknowledgements All meteo data were kindly provided by the German Meteorological Service (DWD) and the English Meteorological Service.
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