Transcript Goettingen-WEELS - UCL Department of Geography

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
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
0
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).