weels: wind erosion on european light soils
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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
Wageningen University: Jan de Graaf, Wim Spaan, Dirk Goossens, Michel Riksen, Olga Vigiak and Floor Brouwer
Soil Survey of Lower Saxony: Walther Schäfer, Jens Groß, Annette Thiermann, Jan Sbresny - subcontract to
Lund University: Lars Bärring, Marie Ekström and others
Salford University: Adrian Chappell
Göttingen University (research group geosystem-analysis): Jürgen Böhner, Olaf Conrad, Andre Ringeler, Anke Wehmeyer and others
All on glacial outwash sands, with similar mean annual rainfall; more snow and frost in the east
Three Field Sites (“Supersites”) :
• 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
• Economic and policy analysis
• Sand and dust monitoring
• Climate change scenarios
Main Elements::
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)
The WEELS Model::
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 (1)
SURFACE ROUGHNESS: soil roughness: aggregate size and tillage (from empirical data, with big assumptions)vegetation roughness: crop type and crop phenology
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.
Components of the WEELS Model (2)
Foragecrops: Alfalfa, lucerne
Oil seedrape
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
Data + simulation for 1985Coverage for 1985 (no data brown)
LAND USE:
Michel Riksen, David Gasca-Tucker, Olaf Conrad and others
Components of the WEELS Model (3)
Olga Vigiak and Annette Thiermann
Windbreak Modelling
Wind Speed Reduction by Windbreaks
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
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]
Re
du
cti
on
[U
x/U
0]
Optical Porosity 20%
Optical Porosity 80%
Reduction of Friction Velocities
• 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.
Output:
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
• 137Cs is an artificial isotope created in nuclearreactions, as in bombs and nuclear power stations (cf Chernobyl)
• Output to the atmosphere reached a peak in themid 1960s, so that one is measuring net erosion over about 35 years
• Direct measurement is difficult mainly because it is very episodic (as we found)
• It is now widely used to measure erosion. It is simple, but time-consuming to measure
Adrian Chappell
137Caesium Analysis
137Caesium Sampling
137Caesium Theory
0
10
20
30
40
50
60
70
80
0 500 1000 1500 2000 2500 3000
137Cs (Bq m-2)
De
pth
(cm
)
Pasture
FieldBoundary
Field
Forest
137Caesium Profiles, Barnham
Sampling Pattern
0
100000
200000
300000
400000
500000
600000
0 100 200 300 400 500 600 700 800 900
Lag (m)
Se
mi-
vari
an
ce o
f 13
7 Cs
(Bq
m-2
)
caesium-137
Model
Semi-variogram
• An existing model (Owens 1994) was modified to include the major factors controlling wind erosion:
• Erosion and deposition models are for each field and each day
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
Caesium Mass-Balance Model
• 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
Dirk Goossens and Jens Groß
Sediment Transport Sampling
m ean w ind d irection
0 50 100
m etres
5-10< 5
sand transport (g /cm )
35-40
25-30
20-25
15-20
10-15
30-35
> 200
50-200
40-50
Example (a)
0
0.1
0.2
0.3
0.4
0.50
4.1
1.9
8 -
16
.11
.98
16
.11
.98
- 0
3.1
2.9
8
03
.12
.98
- 2
2.1
2.9
8
22
.12
.98
- 0
5.0
1.9
9
05
.01
.99
- 1
9.0
1.9
9
19
.01
.99
- 0
2.0
2.9
9
02
.02
.99
- 1
6.0
2.9
9
16
.02
.99
- 0
2.0
3.9
9
02
.03
.99
- 1
3.0
3.9
9
13
.03
.99
- 0
8.0
4.9
9
08
.04
.99
- 2
1.0
4.9
9
21
.04
.99
- 0
4.0
5.9
9
04
.05
.99
- 1
9.0
5.9
9
19
.05
.99
- 0
2.0
6.9
9
02
.06
.99
- 1
6.0
6.9
9
16
.06
.99
- 0
1.0
7.9
9
01
.07
.99
- 1
4.0
7.9
9
14
.07
.99
- 2
7.0
7.9
9
27
.07
.99
- 1
2.0
8.9
9
12
.08
.99
- 2
4.0
8.9
9
24
.08
.99
- 0
7.0
9.9
9
07
.09
.99
- 2
1.0
9.9
9
21
.09
.99
- 0
5.1
0.9
9
05
.10
.99
- 2
1.1
0.9
9
21
.10
.99
- 0
4.1
1.9
9
04
.11
.99
- 1
6.1
1.9
9
16
.11
.99
- 0
2.1
2.9
9
02
.12
.99
- 1
6.1
2.9
9
16
.12
.99
- 2
9.1
2.9
9
29
.12
.99
- 1
2.0
1.0
0
12
.01
.00
- 2
5.0
1.0
0
25
.01
.00
- 0
8.0
2.0
0
du
st a
ccu
mu
latio
n (
g m
-2 d
ay-1
)
total dust
Example (b)
Lars Bärring, Marie Ekström and others
Wind Erosion and Climate Change
Production costs1)
On-sitecosts due towinderosion2)
Net benefitsof GAP incase off-sitecosts=0
Net benefits ofGAP for off-site costs=10times on-sitecosts
Net benefits ofGAP for off-site costs=20times on-sitecosts
Without case: sugarbeet
586 175
With case: sugar beetwith cover crop
666 50 45 1170 2420
With case: sugar beetwith plough andpress
586 98 77 770 1540
With case: sugar beetwith Vinamul layer
800 50 -89 1036 2286
For Example: Benefits in €/ha
Michel Riksen, Jan de Graaf, and Floor Brouwer
Economics
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
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20
12.03 13.03 14.03 15.03
Win
d Sp
eed
[m/s
ec]
Wind Speed [10 m a.G.] Honington
Erosion/Accumulation Balance (12.03. - 15.03.1994)
Some Results: Longterm Estimation (1970-98)
Erosion/Accumulation Balance: -1.5 to 1.8 Kg/m²
Duration - Barnham
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
1 2 3 4 5 6 7 8 9 10 11 12
Eros
ion
Hou
rs
Transport - Barnham
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
1 2 3 4 5 6 7 8 9 10 11 12
Tra
nsp
ort
Rat
e [K
g]
Erosion/Accumulation Balance - Barnham
-0.03
-0.03
-0.02
-0.02
-0.01
-0.01
0.00
1 2 3 4 5 6 7 8 9 10 11 12
Bal
ance
[K
g/m
on
th]
Net loss: 0.6 t ha-1 yr-1
Area of erosion deposition
Rate of erosion deposition
Cs-derived estimates: soil flux (Adrian Chappell)
584000 585000 586000 587000 588000 589000
Eastings (m )
274000
275000
276000
277000
278000
279000
Nor
thin
gs (
m)
-0 .35
-0 .25
-0 .15
-0 .05
0.05
0.15
0.25
0.35
H untsw ellP lan ta tion and W orks
The K ing 'sForest
A m pton H a ll
R A F H on ing ton
S oil flux(g /cm 2/yr)
Top of scale 0.45 gain; bottom of scale 0.35 erosion (g cm2yr -1)
Some Results: Cs-derived Estimates
Model vs Measurements
• Crude comparison of the distribution of “measured”as against “modelled” erosion shows similar patterns, with erosion concentrated in the north-east of the site, but
• Model estimates: - 1.56 t ha-1 yr-1 vs
137Cs Method: - 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).
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