weels: wind erosion on european light soils

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).