WEELS: Wind Erosion on
European Light Soils
EU Framework 5 Research Programme
Partners:
University College London (co-ordination):
Andrew Warren, Dave Gasca-Tucker and others - subcontract to
Salford University: Adrian Chappell
Soil Survey of Lower Saxony: Walther Schäfer, Jens Groß,
Annette Thiermann, Jan Sbresny - subcontract to
Göttingen University (research group geosystem-analysis):
Jürgen Böhner, Olaf Conrad, Andre Ringeler, Anke Wehmeyer and others
Wageningen University: Jan de Graaf, Wim Spaan, Dirk Goossens,
Michel Riksen, Olga Vigiak and Floor Brouwer
Lund University: Lars Bärring, Marie Ekström and others
Three Field Sites (“Supersites”) :
All on glacial outwash sands, with similar mean
annual rainfall; more snow and frost in the east
Main Elements::
• The WEELS model, running with data on wind,
temperature, rainfall, soil erodibility and land use
• Validation:
(a) against a few “event records” in Grönheim and
Barnham
(b) against estimates of erosion based on the use of
137Cs, for Barnham only
• Development of a risk-assessment system, for use
where there are fewer data, for Grönheim
• Sand and dust monitoring
• Climate change scenarios
• Economic and policy analysis
The WEELS Model::
Jürgen Böhner, Walther Schäfer, Olaf Conrad, Jens Groß and Andre Ringeler
Choices:
Wind-Erosion Equation (WEQ)
Revised Wind Erosion Equation (RWEQ)
Wind Erosion Prediction System (WEPS)
The WEELS Model - developed from EROKLI
(Beinhauer and Kruse, 1994)
Components of the WEELS Model (1)
WIND: WAsP (Wind Atlas Analysis and Application
Program) used to convert hourly wind observations at
a meteorological station to values across the supersite
according to variation in topography and roughness.
WIND EROSIVITY: Several elements, mainly shear
velocity U* and mass transport
SOIL MOISTURE: The water content of the top 2 cm
of soil layer, calculated with a simple model using
standard meteorological data
Components of the WEELS Model (2)
SOIL ERODIBILITY: Essentially, the dimensionless soil
erodibility factor‚ ‘K’, depending on aggregate structure
and derived from wind tunnel studies, and regressions
against soil factors, such as texture and organic matter
content.
SURFACE ROUGHNESS:
soil roughness: aggregate size and tillage (from
empirical data, with big assumptions)
vegetation roughness: crop type and crop phenology
Components of the WEELS Model (3)
Michel Riksen, David Gasca-Tucker, Olaf Conrad and others
LAND USE:
Forage crops: Alfalfa, lucerne
Oil seed rape
Potatoes, parsnips
Set A Side
Spring cereals, Linseed
Sugar beet, carrots, onions
Winter barley, rye, triticale,
Winter wheat
Maize, sunflower
Sugar beet with cover crop
Coverage for 1985
(no data brown)
Data + simulation for 1985
Windbreak Modelling
Olga Vigiak and Annette Thiermann
Wind Speed Reduction by Windbreaks
1.0
0.9
Reduction [Ux/U0]
0.8
Optical Porosity 80%
0.7
0.6
Optical Porosity 20%
0.5
0.4
0.3
0.2
0.1
0.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Distance in Barrier Heights [h]
Reduction of Friction Velocities
Output:
• Hourly assessment of mean wind speed (10 m
above ground) and friction velocity
• Daily assessments of crop cover, tillage roughness
and top soil moisture
• Hourly duration of erosive conditions
• Maximum sediment transport rate, calculated with
and without top-soil moisture
• A simplified daily erosion/accumulation balance.
Events:
• Events recorded during field monitoring: about two
at the monitoring site
• Events recorded by farmers: mostly rather
inaccurate, but one very well recorded event on
video: see later
137Caesium
Analysis
Adrian Chappell
• Direct measurement is difficult mainly because it is
very episodic (as we found)
•
137Cs
is an artificial isotope created in nuclear
reactions, as in bombs and nuclear power stations
(cf Chernobyl)
• Output to the atmosphere reached a peak in the
mid 1960s, so that one is measuring net erosion
over about 35 years
• It is now widely used to measure erosion. It is
simple, but time-consuming to measure
137Caesium
Sampling
137Caesium
Theory
137Caesium
Profiles, Barnham
137
0
500
1000
Cs 1500
(Bq m-22000
)
2500
0
10
Depth (cm)
20
30
40
50
Pasture
60
Field
Boundary
Field
70
Forest
80
3000
Sampling Pattern
Semi-variogram
500000
400000
Semi-variance of
137
-2
Cs (Bq m )
600000
300000
200000
caesium-137
Model
100000
0
0
100
200
300
400
500
Lag (m)
600
700
800
900
Caesium Mass-Balance Model
• An existing model (Owens 1994) was modified to
include the major factors controlling wind erosion:
Land cover and phenology (including plough events)
Rainfall to estimate daily 137Cs fallout
Wind speed and a fuzzy threshold (5-7 m s-1) for
erosion
• Erosion and deposition models are for each field
and each day
Sediment Transport Sampling
Dirk Goossens and Jens Groß
• Testing sediment samplers (the now widely used
MWAC sampler found to be best by many criteria
• Very detailed recording of one of the few events on
18 May 1999
Example (a)
mean wind direction
sand transport (g/cm)
> 200
50-200
40-50
35-40
0
50
metres
100
30-35
25-30
20-25
15-20
10-15
5-10
<5
0
25.01.00 - 08.02.00
12.01.00 - 25.01.00
29.12.99 - 12.01.00
16.12.99 - 29.12.99
02.12.99 - 16.12.99
16.11.99 - 02.12.99
04.11.99 - 16.11.99
21.10.99 - 04.11.99
05.10.99 - 21.10.99
21.09.99 - 05.10.99
07.09.99 - 21.09.99
24.08.99 - 07.09.99
12.08.99 - 24.08.99
27.07.99 - 12.08.99
14.07.99 - 27.07.99
01.07.99 - 14.07.99
16.06.99 - 01.07.99
02.06.99 - 16.06.99
19.05.99 - 02.06.99
04.05.99 - 19.05.99
21.04.99 - 04.05.99
08.04.99 - 21.04.99
13.03.99 - 08.04.99
02.03.99 - 13.03.99
16.02.99 - 02.03.99
02.02.99 - 16.02.99
19.01.99 - 02.02.99
05.01.99 - 19.01.99
22.12.98 - 05.01.99
03.12.98 - 22.12.98
16.11.98 - 03.12.98
04.11.98 - 16.11.98
-2
-1
dust accumulation (g m day )
Example (b)
0.5
total dust
0.4
0.3
0.2
0.1
Wind Erosion and Climate Change
Lars Bärring, Marie Ekström and others
Economics
Michel Riksen, Jan de Graaf, and Floor Brouwer
For Example: Benefits in €/ha
Productio On-site
n costs1) costs due to
wind
erosion2)
Without case: sugar
beet
With case: sugar beet
with cover crop
With case: sugar beet
with plough and
press
With case: sugar beet
with Vinamul layer
Net benefits
of GAP in
case off-site
costs=0
Net benefits of
GAP for offsite costs=10
times on-site
costs
Net benefits of
GAP for offsite costs=20
times on-site
costs
586
175
666
50
45
1170
2420
586
98
77
770
1540
800
50
-89
1036
2286
Some Results: Risk Assessment, Grönheim
Some Results: Event Modelling, Barnham
L
H
H
Circulation Pattern over Europe
13.03.1994
Some Results: Event Modelling - Barnham
Erosion/Accumulation Balance
(12.03. - 15.03.1994)
Wind Speed [10 m a.G.] Honington
18.0
16.0
Wind Speed [m/sec]
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
0
4
8
12 16 20
12.03
0
4
8 12 16 20
13.03
0
4
8
12 16 20
14.03
0
4
8
12 16 20
15.03
Some Results: Longterm Estimation (1970-98)
Duration - Barnham
0.14
Erosion Hours
0.12
0.10
0.08
0.06
0.04
0.02
0.00
1
2
3
4
5
6
7
8
9
10
11
12
9
10
11
12
11
12
Transport - Barnham
5.0
Transport Rate [Kg]
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1
2
3
4
5
6
7
8
Erosion/Accumulation Balance - Barnham
0.00
1
Balance [Kg/month]
-0.01
-0.01
-0.02
-0.02
-0.03
-0.03
Erosion/Accumulation Balance: -1.5 to 1.8 Kg/m²
2
3
4
5
6
7
8
9
10
Some Results: Cs-derived Estimates
Cs-derived estimates: soil flux (Adrian Chappell)
Net loss:
0.6 t ha-1 yr-1
279000
Huntswell
Plantation and Works
Soil flux
(g/cm2/yr)
Area of erosion

deposition
278000
0.35
Northings (m)
0.25
277000
0.15
0.05
276000
-0.05
-0.15
RAF Honington
275000
The King's
Forest
-0.25
-0.35
Ampton Hall
274000
584000
585000
586000
587000
588000
589000
Eastings (m)
Top of scale 0.45 gain; bottom of scale 0.35 erosion (g cm2yr -1)
Rate of erosion

deposition
Model vs Measurements
• Crude comparison of the distribution of “measured”
as against “modelled” erosion shows similar
patterns, with erosion concentrated in the northeast of the site, but
• Model estimates:
137Cs
Method:
- 1.56 t ha-1 yr-1
vs
- 0.60 t ha-1 yr-1
Most models overpredict, but
• The disparity is even greater if we acknowledge
removal on root crops (2.4 t ha-1 per crop).