JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 102, NO. D25, PAGES 30,091-30,105,DECEMBER 27, 1997
Wind erosion prediction
over the Australian
continent
Yaping Shao
Centrefor AdvancedNumericalComputationin Engineeringand Science
Universityof New SouthWales, Sydney,Australia
Lance M. Leslie
Schoolof Mathematics,
University
of New SouthWales,Sydney,Australia
Abstract. Wind erosion is a complicated processinfluenced by weather patterns,
soil conditions, and vegetation cover. In this work we present an integrated wind
erosionassessmentand prediction systemwhich couplesa wind erosionschemewith
an atmosphericprediction model and a GeographicInformation System database.
The system is applied to the February 1996 dust storms over the Australian
continent, and the predictions are in good agreement with meteorologicalrecords
and satellite images. It is found that over the I week period from February 6 to 12,
1996, the total dust emissionfrom the Australian continent was around 6 million
tons. As demonstrated in this study, the system can be used to identify areas and
periods under wind erosion threat and to identify the responsibleenvironmental
factors. For atmospheric studies the integrated system provides a possibility of
quantifying the sourcesof dust particles which in turn play an important role in
atmosphericradiative processes.
1. Introduction
dices[Burgesset al., 1989;McTainshet al., 1990;McTainsh et al., 1997]. Empirical relationshipsderived
Wind erosion is a serious problem in Australia and
many other arid or semiarid regionsin the world. During an erosion event, small soil particles rich in nutrient and organic matter become suspendedand are dispersedaway from the surfaceby atmosphericturbulence
and then transported over distances up to thousands of
kilometers, leading to soil degradation. From the perspectiveof land care it is important to quantify the risks
of wind erosion on scalesfrom paddock to region and
from experimental studies have been used for wind erosion assessmentin the United States by Gillette and
Hanson [1989] and in Africa by Nickling and Gillies
[1993].On the other hand, in atmosphericstudies[e.g.,
Westphalet al. 1988; Joussaume,1990],in which the
sourceregion and the intensity of dust storms have to
be specified,the treatment of wind erosion is mostly
relativelysimple. Marticorenaand Bergametti[1995]
and Marticorenaet al. [1997]havedevelopeda physical
to identify the responsibleenvironmentalmechanisms. dust emissionschemeaccountingfor the influenceof the
From the perspective of atmospheric studies, particles surfacefeatures on dust emission,and have applied the
suspendedin the atmospheremay significantlyinfluence modelto improvethe generalcirculationmodel (GCM)
the radiative processesas they absorb and scatter vari- simulationsof the desert dust cycle.
ous radiative components. For this reason, the estimaWind erosion is a complex interacting set of physition of areas and intensity of dust emissionis important cal processesgoverned by many factors which can be
for atmosphericmodeling and is under active research broadly grouped into three categories:weather and cli[e.g.,Tegenand Fung,1994,1995].
mate (especiallyhigh windsand low precipitation);soil
The climatological aspects of wind erosion such as state (mineralcomposition,particlesizecharacteristics,
dust storm frequency can be examined from meteoro- crustingand aggregation,and soil moisture); surface
logical data. Figure I shows the annual dust storm roughness(nonerodiblesoil aggregates,microtopografrequencies
for the period1957-1984[McTainshandPi- phy, and vegetationcover). Land managementpractice
blado,1987], which can be as high as 5 to 10 times also plays an important role. As a consequence,wind
per year in some areas. Wind erosionpotential in Aus- erosioneventsare spatially variable and highly intermittralia has been described in terms of wind erosion intent and often are associatedwith mesoscalefrontal system under dry climatological conditions. Therefore, to
predict wind erosion, it is necessarynot only to predict
Copyright 1997 by the American Geophysical Union.
atmosphericconditionsbut also to model and/or describethe conditionsof the surface,includingsoil state,
Paper number 97JD02298.
0148-0227/ 97/ 97JD- 02298$09.00
surface vegetation cover, and soil moisture.
30,091
30,092
SHAO AND LESLIE: WIND EROSION PREDICTION OVER AUSTRALIA
ANNUAL
FREQUENCY
OF EVENTS (1957-84)
•
<1/2
/
/
A Central
Australia
B Central
Queensland
C Mallee
region
400 mm median annual
D Nullarbor
region
E
rainfall(50 percentlie)
coastal
Western
Australia
Figure 1. Mcanannualduststormfrequencies
overthe Australiancontinentfor the period19571984[afterMcTainshandPitbloda,1987,CopyrightJohnWiley& SonsLimited.Reproduction
with permission].
Our approachto the problem is to developan inte- 2. The
grated systemwhich couplesan essentiallyphysically,
Wind
Erosion
rather than empirically, based wind erosion scheme 2.1. Brief Description
Scheme
of the Scheme
[Shaoet al., 1996]with a high resolutionatmospheric The centerpieceof the integrated system is a wind
model [Leslieand Purser, 1991]and a detailedGeo- erosionschemefor the prediction of the streamwisesand
graphicInformationSystem(GIS) database.It appears flux Q and the (vertical) dust entrainmentflux F. By
that only suchan integrated systemcan providequantitative predictionof wind erosionon continentalscales.
Much of eastern Australia has experienceda drought
in the 1990s. In 1994, wind erosionwas active in the
southern and eastern parts of Australia. In summer
1996,largeareasin Australia receivedrainfall belowor
muchbelowaverageand werethereforeunderhigh wind
erosionrisks. In this paper we describethe integrated
systemand its applicationto the February 1996 wind
erosion event over the Australian
continent.
Model re-
sultsof the erosionevents,includingwind erosionorigin
and intensity, are comparedwith availabledata. We
alsoexaminethe responsibleenvironmentalfactors,illustratethe usefulness
of the systemand, finally, outline
someplanned improvementsto the system.
definition, Q is the vertically integrated sand drift intensity in the direction of wind'
Q-
q(z)dz
(1)
wherez is height. Q hasthe dimension
of g m-1 s-1,
whileF hasthe dimension
of g m-2 s-1.
The
details
of the wind
erosion scheme have been
described
by Shaoet al. [1996]. The predictionof Q
and F is achievedby modelingboth the capacityof the
wind in entraining and transporting aeolian particles,
through modelingof the surfacefriction velocity, u,,
and the resistanceof the surfaceagainst wind erosion,
through modelingof the thresholdfriction velocity,u,t,
SHAO AND LESLIE: WIND
EROSION PREDICTION
that definesthe minimum friction velocity necessaryfor
wind
erosion
to occur.
The wind erosion scheme describes three physical
componentsof the erosionprocess:saltation, saltation
bombardment for dust entrainment, and sheltering effect of surfaceroughnesselements.Saltation is the key
processresponsiblefor sand drift in the direction of
OVER AUSTRALIA
30,093
depend on soil types. In the present model the releaseof
clay particles from saltation particles is not considered.
The dust flux can be estimated as a double integration
dl
F(dd, d•)p(dd)p(d•)JddJd•
(6)
The capacity of the surface to resist wind erosion is
wind [$haoand Raupach,1992]and for dust entrainconsidered
through u,t, a key variable parameterized in
ment [$haoet al., 1993a]. Owen[1964]suggested
that
for soils with uniform particle size, d, the streamwise
sand flux, Q, is given by
-
{o
.[1 -
the model. The most important environmental factors
influencingu,t are frontal area index of surfacerough-
nesselements[Raupach,1992],A, soilmoisture,w, soil
particle size, ds, and surface crusting. For a given ds,
>
_
<
u,t is parameterized as
x, w,
where c• is a constant of order unity, p is air density,
and g is gravitationalacceleration.Owen'smodelis derived under several assumptions, one of which is that
-
0, 0,
where c is a measure of surface crusting. The function R(%) that describesthe shelteringeffectof surface
the soil particle size is uniform. Nevertheless,Owen's roughnesselements on surface is determined based on
model has also been used widely to fit observed soil the studiesof Raupach[1992]and Raupachet al. [1993].
transport rates by, for instance, •illette and Stockton H(w) is estimatedfrom wind tunnel experimentalwork
[1989],giventhat •,t is adequatelyestimated.In the [Shaoet al., 1996],while $(c) is purely empiricalin the
winderosionscheme
the saltationtheoryof Owen[1964] current version of the model.
has been extended to soils with multi-particle sizes, by
assuming
thatthedependence
ofthesaltation
flux0 on
•, and •,t is not significantlyaltered by the presenceof
other particle sizes. Despite somelimitations of this assumption,it remainsa simpleand sensibleapproachfor
predicting the streamwisesand flux of soils with multiparticle sizes. With this assumption the streamwise
flux can be estimated by
It is important to emphasize that wind erosion is determined both by the capacity of wind to lift and trans-
port particles(wind shear)and the capacityof the sur-
face to suppresserosion. Whether wind erosionactually
occursdependson the balance of the two very different
factors, with the consequenceof strong spatial variation
in erosion patterns and temporal fluctuation in erosion
occurrence. Even during the processof wind erosion,
the particle size distribution of the top soil changesas
smaller particles are transported away from the source,
and the surface conditions that determine u,t change
as the emergenceof surface roughnesselements, such
where d• and d2 define the saltation particle size range
as large surface aggregates. Thus embedded in u,t is
(d• < d• < d2) and p(d•) is the probability density
an extremely complicated process which evolves with
function of particle size distribution.
time. Our challenge lies in the quantification of the
Saltation bombardment is consideredas the primary
changesin u, and u,t both in space and time.
processfor dust emissionas identified in previous wind
Q- f•i2
0p(d•)Sd•(3)
tunnelexperimentalwork [Shaoet al., 1993a].The rate
at which clay coats breaks down is proportional to the
streamwise sand flux. The wind tunnel experiment of
Shaoet al. [1993a]indicatesthat
P(dd,d•) - aO(d•)/u2,
t(dd)
(4)
where a is an empirical parameter with the dimension
2.2. Scheme Verification
for Single Point
The wind erosion schemehas been tested for single
point against several wind tunnel and field data sets
available[Shaoet al., 1996]. Only sanddrift data was
available for comparison. Figure 2 showsthe predicted
and observed
streamwise
sand fluxes for Mendook
ex-
of m s-2. Accordingto a limitedwind tunneldata set periment[Leysand McTainsh,1996]. In that experiment, five masts were set up around a paddock, each
of $haoet al. [1993a],a is approximately
equippedwith six Fryreartraps [Fryrear,1986;$hao et
a- [0.61n(d•)+1.6]exp(-140d•)
(5) al., 1993b],mountedat heights0.02, 0.25, 0.5, 1, 1.5,
and 2 m. The aeolian particles accumulating in the
with ds and da being sand and dust particle sizes in samplerswere collectedat weeklyintervalsfor weighing
millimeters, respectively. The other major mechanism and analysis. Averaged streamwise sand fluxes were
for dust emissionis the release of clay particles coated derived from the total mass of the sediments collected
on sand grains during the saltation. It is most probable by each mast. Meteorological data were gathered by
that equation(4) remainsvalid this process,but c•may a weather station near one of the masts, which con-
30,094
SHAOAND LESLIE:WIND EROSIONPREDICTIONOVERAUSTRALIA
strong wind eventsand the relatively loosesurfaceaf-
10ø
OModel
10-1
I
ß
.,-, 10-2
•,, O • ß....
nSite
A
ter cultivation. For the last 8 weeks, both observations
and measurementsshowedweak erosionactivity, consistent with the observed relatively frequent rainfall and
low wind speeds.For the other 6 weeks(week7 to week
12), however,the predictedwind erosionis muchhigher
•---*
Site
B•
ß Site C
•=--
'•'
:":
!'}tf/• •Site
than the observations,particularly for week 11.
The differences indicate
the role of factors other than
those already consideredin the model. The principal
• 10-3
omission from the model at the moment
10-4 _
•
is the evolution of the threshold friction velocity caused
by that of surfacecrust, surfaceroughness,and particle sizedistribution may have causedthis disagreement.
,
10-s
0
4
8
12
16
in this context
20
Figure 3 showsthe measuredmeteorologicaldata and
the correspondingpredictions of sand drift and dust
emissionat Mendookfor week 11 (starting December
25, 1990). On December27, 1990 (day 4 in Figure
3), the weatherconditionswere ideal for wind erosion,
Figure 2. Comparisonof predictedand observedweekly with a strongprefrontalnortherly wind (exceeding12
averagestreamwisesandflux Q at Mendook.Observa- m s-1 at height4m) accompanied
by highair temperaWeek
tions are from five different masts. Modeled streamwise
sand flux for week 2-5, 17 is zero.
0.006
0.004
0.002
tinuouslyrecorded12-minaverages
of windspeed(at
o.ooo
heights0.5, 1, 2, and 4 m), wind direction,temperature, relativehumidity,atmospheric
pressure,
solarradiation,and soil moisturecontent. For someperiods,
vegetationand residualcoverwasestimatedby digitizationof photographic
images.In Figure2, 20 weeksof
data obtained between October 22, 1990, and April 8,
1991, were usedfor comparison.
In assessing
the comparison,it is necessaryto rec-
•
•
0.2
d 0.0
0.03
?E 0.02
v
E
0.01
ß o.oo
ognizethat the erosionpatternat the Mendooksiteis
complicatedby severalfactors. First, there are several soiltypeson the site, consistent
with the localgeomorphological
patternof sandydunesinterspersed
by
0.8
ßr• 0.6
• 0.4
12
ß7e 8
o
swales with heavier-textured soils. This implies that
360
wind erosion is different at different masts, even un-
24o
12o
der uniform meteorologicalconditionsacrossthe paddock. Second,the masts with Fryrear traps and the
weatherstationswere distributedon a low hill (a typical relic dune in the area), so topographiceffectsmay
causespatial variations of wind speed, surfacefriction velocity, and other micrometeorological
parameters. Third, there was a spatial variationin the surface
cover,an important variable in determiningthe saltation flux. These complexities led to variations in mea-
o
lOO
•
•rr
8o
60
40
20
i
i [
i
[
40
o•'30
• 20
10
24
suredweekly-average
valuesof streamwisesanddrift at
26
28
30
Time, Dec. 1990
the five masts.
Duringthe 20 weeks,a reasonableagreementis found Figure 3. Observedmeteorologicaltime seriesfor air
between the model and the observations for the first 6
weeks and the last 8 weeks. Since the soil moisture
measurementswere not available for weeks 2, 3, and 4,
the soilmoisture,w, wasassumed
to be 0.02 m3m-3.
temperature, T, relative humidity, Rh, wind direction,
DD, wind speedat 4m height, U, volumetric soil water
content, w, and calculated friction velocity, u., threshold friction velocity u.t and sand drift intensity Q. The
model predictedstrongwind erosionat Mendook during
It is important to point out that the observationperiod was shortly after cultivation. The large erosion week 11 (starting December24 1990), in disagreement
rate for the first 4 weeks can be attributed
to several
with
the observation.
SHAO AND LESLIE' WIND EROSION PREDICTION OVER AUSTRALIA
30,095
tures, low relative humidity, and low soil moisture. AcIn this application, HIRES is run continuouslyover
cordingly,the model predicts a strong erosionevent on the Australian region at 20 km horizontal resolution
that day. However,the observationsshowedno sign of and 31 levels in the vertical plane. In order to resolve
strongerosion. In the Mendook casethe paddockwas the boundary layer, there are 10 levels from 850 hPa
cultivated before the start of the experiment at week 1 to the surface, with the lowest level at screen level 1.2
but remained uncultivated
until week 15 with a blade
m. The time coveredby the simulationsis the 60-day
plough and week 19 with harrows. The observations period from January I to February 29, 1996. HIRES
showthat in the earlyweeks(shortlyafter cultivation), derived its initial conditions and boundary conditions
strong erosion events did occur. This erosion proba- for this period from the Australian Bureau of Meteorolbly stabilized the soil surfaceby exposingthe large soil ogy's global general circulation model archives.
aggregatesand leaving a population of coarserparticlesat the surface,through selectiveremovalof the fine 4. Structure of the Integrated System
particles. Figures 2 and 3 imply that the current wind
and Geographic Information Database
erosionmodel has the tendency to overpredict wind erosion.
The structure of the integrated wind erosion modeling system is illustrated in Figure 4. Wind erosion
events over large spatial scales can be predicted, us3. Atmospheric Model
ing GIS data to infer parameterswhich vary primarily
The meteorologicalinput variablesare obtained from in space and using atmospheric forcing data obtained
a high-resolutionlimited area numerical weather predic- from HIRES, which vary both in spaceand time.
tion model developedat the University of New South
Wales. It was originally developedand documentedby 4.1. Friction Velocity
LeslieandPurser[1991].It is computationally
economical in terms of both storagerequirementsand algorithm
efficiency. It is a two-time-level schemecomprising a
semi-Lagrangianadvectionstep followedby a number
(usually four or five) of adjustment steps. The ad-
The friction velocity u. is estimated from the atmosphericmodel predictionsof surfacewind speedU at a
specifiedreferenceheight zv. To this end, the MoninObukhov similarity theory is used
justment steps use the forward-backward scheme. The
temporal differencingis formally secondorder, and the
interpolations in the semi-Lagrangianstep is third order, using bicubic splines. The model is referred to as
•,, = •V/•{[(•-
Z))/•0]- •)
(8)
where n = 0.4 is the von Karman constant,D is the
zero-displacementheight, z0 the aerodynamicroughHIRES and its features are summarized in Table 1.
ness length of the surface, and q• is a filnction which
The model has been tested extensively over the past takesinto accountthe effectof thermalstability on wind
in bothresearchandoperationalmodes[e.g.,Leslieand profile [Businger,1973].
Skinner,1994]. Standardstatisticalevaluation,aver- 4.2. Land Surface Information
A detailed GIS database is used to estimate the erodimajor agricultural region of Australia, has shownthat
the model performance is very good: for near-surface bility of the land surface, reflected in u,t. The land
air temperature predictions, the rms error is 2.1 K with surface information required for the model is as suma mean absolute error of 1.7 K; for near-surface wind marized in Table 2. Wind erosion is sensitive to surface
speedthe rms error is approximately3 m s-1.
vegetation cover and soil moisture content. During dry
Table 1. Summary of the Main Features of the AtmosphericModel HIRES.
Model
Horizontal
Number
Feature
resolution
of vertical
Numerical
levels
scheme
HIRES
variable, typically 10-50 km
variable, usually 16 to 31
split semi-Lagrangian(high order)
Analysis scheme
4-D 6-hourly cycled statistical interpolation
Initialization
dynamic
Orography
Boundary layer scheme
2 arcmin x 2 arcmin
Radiation
Fels-Schwarzkopf
Fritsch-Chappell
5-day average
from global model
Convective
scheme
scheme
Sea-Surfacetemperature
Lateral boundary conditions
Mellor-Yamada(2.25)
30,096
SHAO AND LESLIE: WIND
EROSION PREDICTION
OVER AUSTRALIA
Figure 4. A diagram for the integrated wind erosionassessment
and prediction system.
spells, areas with annual vegetation may become bare [1996]usingfield observations.
For the Murray-Darling
and exposed to wind erosion. For wind erosion mod- Basin, for instance,the followingrelationshipbetween
elling, informationabout vegetationheightand leaf area LAI and NDVI, determinedby McVicar et al. [1996]
index (LAI) is required.
The estimation of LAI for the simulationperiod draws
on the remotely sensedNormalized Difference Vegetation Index (NDVI) data. NDVI data are derivedfrom
advanced very high radiometric resolution (AVHRR)
satellite recordsof reflectiveradiation in the red region
(0.55-0.68t•m) and the near infrared region(0.72-1.1
/zm) of the eh•ctromagnetic
spectrum. A compositeof
satellite images over a 2-week period in February 1996
was used in this study. For major vegetation types, empirical relationshipsbetween NDVI and LAI have been
previouslyestablishedby, for instance, Mc Vicar et al.
was used:
1 + NDVI
LAI- -4.65+ 4.241
_ NDVI
This type of empiricalrelationshiphas beenusedto estimate
the LAI for the whole continent.
the distribution
of LAI
for the Australian
Plate I shows
continent in
February 1996. Aerodynamicparameters,suchas surfaceroughness
lengthand zero-displacement
height,can
be estimatedfrom leaf area indexand vegetationheight,
accordingto Raupach[1994].
Table 2. Summary of Land Surface Information Required by the Integrated Wind Erosion
Prediction System
Parameter
Treatment
Name
Aerodynamic roughnesslength
constant
for bare soil
derivedfrom vegetationheight and LA! for vegetatedsurface
Zero-displacementheight
zero for bare soil
derivedfrom vegetationheight and LAI for vegetatedsurface
Leaf area index (LAI)
Vegetation height
Soil particle size distribution
Soil moisture
derived
(9)
from satellite
NDVI
data
adapted from the atlas of Australian resources
particle size analysisfrom soil samples
integrated soil moisture model
SHAO AND LESLIE: WIND EROSION PREDICTION OVER AUSTRALIA
Soil texture is a primary parameterfor the wind erosion model. It is characterizedby the particle size distribution density function. Accordingto the Atlas of
AustralianResources,
vol. 1 [1980],Australiansoils
can be classifiedinto 28 soil classes,with 21% being
shallowpermeablesandysoil (Cfl), 17% deep massive
earths (Bb4), 11.2% crackingclay soilswith low permeabilitywhenwet (Cbl), and 11% shallowloam soil
(Cf3). Other relativelyimportant soilsare Cal (sandy
soil,8.4%), Cd2 and Ccl (duplexsoils,6.7%, 5.5%, respectively)and Bc2 (calcareousearth, 5.4%); the rest
of the soil types occupy 13.8%. For major soil types
in Australia, particle size analysis has been conducted
by McTainsh and colleaguesusing a Coulter Multisizer,
an electronicsizing instrument that can perform highly
detailedanalysis(up to 256 sizeclasses)on very small
samples. This work usesa preliminary version of particle size database which is currently being improved.
The particle sizedistributionsare consideredunchanged
during the erosionevent in the present study.
Surface crusting may change slowly with time but
is altered very significantlyby agricultural activities.
Currently, there is virtually no information available
for this surface properties. In this work, surface crusting is subjectivelyestimated from a general description
of soil types givenin the Atlas of Australian Resources,
vol. i [1980],with the valuesof $(c) beingset to I for
sandy soilsto 0.3 for soilswith heavy clay. The random
changes(suchas through ploughing)on the compact-
nessof the soilsurfaceis ignored.For agriculturalareas
under frequent cultivation this may result in an underestimation
4.3.
Soil
of sand drift and dust emission.
Moisture
Soil moisture has a significantimpact on wind erosion. An important difference between the influence
on wind erosioncausedby soil moistureand vegetation
cover is the timescale of fluctuations, whereas surface
vegetation cover varies slowly on a timescale of several
weeks,the fluctuationof soilmoisturein the (very) top
soillayerhasa timescaleof a few hours. Underlowvegetation coverduring drought, strongwind erosionevents
•ro mn.•t.
ly •.•.•nci•t.oclwlt.h dry cnlclfrnnt..•,accompanied
by hot, dry, and gustywindswhichproducesextremely
dry top soil surfaces. Soil moisture simulation is in itself a difficult and complex subject, as its variation de-
pendson atmospheric,
ecological
and soilhydrblogical
parameters. We are actively working on soil moisture
prediction on continental scales,as reported by $hao
et al. [1996].Soilmoisturedata requiredfor the wind
erosionmodelstakes advantageof that work.
5.
Results
5.1. Wind Erosion Events of February 8-11, 1996
In early 1990s, the central and eastern parts of Australia experiencedoneof the worstdroughtsfor decades.
Figure 5. Surfacepressurefield and the coldfront overAustraliafor the periodof the February
8-11, 1996, dust storm event.
30,097
30,098
SHAO AND LESLIE: WIND EROSION PREDICTION
OVER AUSTRALIA
Consequently, protective annual vegetation cover was one in Australia. During the secondweek of February
significantly demolishedfor some areas. Under such a deep low-pressuresystem crossedthe Southern Ocean
a climatic condition, wind erosion risk in Australia is to the south of Australia. The evolution of the surface
high. In summer (southernhemisphere),cold fronts pressurefield for this event is illustrated in Figure 5.
followingon from gusty warm airflow but little rainfall An associatedcold front producedvery strongto gale
often result in very dry top soil layer and wind erosion. force north to northwesterly winds ahead of the front.
During summer 1996, frequent wind erosionactivities This was followedby a vigorouscool to cold south to
have been observedin Australia. Taking New South southeasterlyairstream that persistedfor severaldays
'Wales,Australia (NSW) as an example,there werenine afterthe passage
of the coldfront. Strongwindandgale
reported dust events in January 1996, with wind erosion warningswere issuedby the Australian Bureau of Meactivitiescoveringlarge areasof NSW betweenJanuary teorologyon each of the days betweenFebruary 8 and
24 and 25. In February 1996, 10 wind erosion events 11. The numerical predictions of surface wind speed
were reported in NSW, with the February 8-11 events and near-surfaceair temperature field, using HIRES,
are shownin Figure 6. The location of the frontal sysbeing the most severe.
The particular weather pattern that produced the tem can be easily identifiedfrom the narrow regions
dust storms of February 8-11, 1996, is the most common with sharp temperature gradient. Accordingto the
OOOOUTFebruary B, 1996
F'"-•-'•' -'-',-'q_zz...;•
',•..'-•-•.•--•
OOOOUTFebruary 9, 1996
,.,•
r •.'•. • ••...•
- ,•,•• .• •,
ß
302
'"1.7'
- ,••* •-•- -- ' '" '
1-'_'••½0• .... •'/1W' -'
I-•• 2• •
2•,
k •"-
.
.
15
OOOOUTFebruary
1õ
10, 1996
OOOOUTFebruary 11, 1996
r-."
I
.....
K- _'•••x
, , //•••
268•
1.•
I i '
I ', ',.••
ß
t t•
1•o•
t•E
x' /
t
%/
/ /•.,•
1•o•
ta•E
'
t/o•
t•
,
15
•.,. ••
1•o•
,
15
Figure 6. Numericalpredictionsof surfacewindsassociated
with the coldfront (whichcan be
identifiedfrom the temperaturegradient)for the February8-11, 1996,wind erosionevent.
SHAO AND LESLIE: WIND EROSION PREDICTION OVER AUSTRALIA
30,099
prediction, by 0000UT February 9,1996, strong winds
in the SimpsonDesert(centralAustralia)and scattered
reached
up to 20 m s-1 (galeforce)in centralAustralia
weak erosion activities in the western and southern ad-
(Figure6b). In the next 2 days,the low-pressure
sys- jacent areas. The area affectedby erosionwasin good
tem moved farther east and by 0000UT February 10,
coincidence with that
the cold front has moved offshore of the Australian
front. By February 9, 1996, the intensity and extensive-
east
coast. By 0000UT February 11, the Australian continent was dominated by the high-pressuresystem with
wind speedeasing.
During this 4-day period, dust stormswere reported
overlargepartsof the Australiancontinentasa resultof
the dry conditionsand the sustainedstrongwinds that
coveredmost of Australia. The areasexperiencingdust
under the influence of the cold
ness of wind erosion activities have increased over the
Australiancontinent,especiallyin the SimpsonDesert
and surroundedareas, as near-surfacewinds in these re-
gionsincreased(Figure 6b). In the 4-day period, wind
erosionwasstrongeston February9, 1996. By February
10, while erosion remained severe in central Australia
and extendedfarther toward north-east, it was reduced
stormsranged from the west and southwestcoastsand in the westernparts of Australia. As the frontal system
adjacent regions,much of the interior of the continent, moved further east and wind speed decreasedover the
and even part of the southeast. The dust storms were continent,erosionactivities weresignificantlyweakeron
February 11, 1996, and was virtually reduced to zero
most pronouncedon February 9 and 10.
late in the day.
Unfortunately, there is a lack of measurementsfor
5.2. Prediction
of Wind
Erosion
quantitative verification. However, the predictions can
The integrated wind erosionassessmentand predic- be verifiedto someextent by satelliteimages.Figure 7
tion system was applied to the above described event. showsthe GMS visible light picture for 0000UT FebruThe predicted daily averagesof dust emissionrate are ary 9, 1996, exactly the same time as for Plate 2b. Dust
shownin Plate 2 for four differentdays. The pattern of cloudsare visiblein the regionaround 140øEand 28øS,
streamwise
sanddrift is similar(not shown).For Febru- whichare identifiedby comparingvisiblelight and near
ary 8, 1996, the system predicted strong wind erosion infrared satellite images. The location and extend of
Figure 7. Visible satellite image for 0600UT February 9, 1996. Dust cloudscan be seen in
central Australia
as indicated
with an arrow.
30,100
SHAO AND LESLIE: WIND EROSION PREDICTION OVER AUSTRALIA
(a) DustLocations
Feb.8, 1996
(b) Feb. 9, 1996
(c) Feb.10, 96
(d) Feb.11, 96
Figure 8. Stationlocationswhereraiseddustwasobserved
for fourdifferentdays.The locations
are determined by examining the surfaceweather maps issuedby the Australian Bureau of
Meteorology.
dust cloudsare in good coincidencewith the areasof
wind erosionpredictedby the model.
In inland Australia
where wind erosion was most in-
The output of the integrated systemallowsan examination of the major factor influencingthe occurrenceof
wind erosion.
Plate
3 shows the available
soil moisture
(definedas the differencebetweensoilmoistureand air
dry soil moisture)in the top 5 cm soil layer over the
tensiveduring the February8-11 events,there are few
meteorological
stations.Also,the meteorological
record
for dust storms is mainly a descriptionof raised dust,
whereasthe presentmodelis mainly concernedwith the
Australian continent for time step 0000UT of the same
4 days as Plate 2. The methods used for soil moisture
emission rate of dust from the surface.
predictionare described
by $haoet al. [1996].The pre-
Therefore an
exact match of the predicted wind erosionpattern and
the dust storm pattern from meteorologicalrecordsis
not expected.Figure 8 showsthe locationswheredust
stormswere observedfor February 8, 9, 10 and 11, 1996.
For all 4 days the predicted wind erosionpattern is in
dictions show that the soil moisture pattern underwent
a dramatic changein the period of interest. Around
0000UT February 8, top layer soil moisturein the southern parts and the east coast of Australia is relatively
high (above10%), while top layer soil moisturein the
goodagreement
with the observations.
The reasonable rest
ofthecontinent
isclose
toairdryvalues
(w- Wdry
agreementbetweenthe predictedwind erosionpattern closeto 0). Rainfallsassociatedwith the coldfront reand the satellite imagesand meteorologicalrecordsis
encouraging.
sulted in a shift in soil moisture distribution, and by
0000UT February 10, 1996, large parts of the eastern
SHAO AND LESLIE: WIND EROSION PREDICTION
OVER AUSTRALIA
$0,101
.,
0
0.1 0.5 '1
1':5 2
3
4
5
6
Plate 1. Leaf area index derived from a 2-week composite of NDVI used for the prediction of
February 8-11, 1996, dust storm event. Frontal area index used in the wind erosion model is
assumed to be a half of the leaf area index.
(o) DustFlux(g/m2/s),10^4.,
8 Feb96
(b) 9 Feb96
30
20
10
5
2
1
0.5
0.1
0
(c) 10 Feb96
(d) 11 Feb96
Plate 2. Predicted daily averagesof dust emissionrate, in g m-
February9, (c) February10, and (d) February11, 1996.
2
$
--1, for (a) February8, (b)
30,102
SHAO AND LESLIE: WIND EROSION PREDICTION
regionwere quite wet. This is not surprising,as the top
soil layer can be dried very quickly in summer when
evaporationis large. This variation of soil moisturein
the very top soil has a strong influenceon the threshold
friction velocity for wind erosion, u,t.
The other important parameter influencingu,t is the
surfacevegetationcover as shownin Plate 1. As can
be seenthere, vegetation coverin central Australia and
large areas of the Great Sandy Desert was very low,
as a consequenceof the low annual rainfall at around
OVER AUSTRALIA
sity depends on the prediction of soil moisture and the
specificationof surfacevegetation coverage. It is well
known that these two parameters are extremely difficult to predict with great accuracy.It is surprisingthat
the predictederosionpattern for the February 8-11 dust
storm event agreesso well with the regionsof high dust
storm frequenciesshownin Figure 1.
Plate
6 shows the total
dust emission rate over the
period of February 6-12, 1996. The prediction shows
that a considerableproportion of the Australian conti100 mm yr-1. Superimposing
the effectsof soilmois- nent experiencedsomedegreeof wind erosion.While for
ture, soil texture, and surfacevegetationcoveron u,t, someareasthe total dust emissionrate wassmall (less
the distribution pattern of u,t can be determined, as than1 g m-2), that for areasaround137.5øE,
27øSwas
shown in Plate 4. Areas of low soil moisture content and
ashighas 1000g m-2. It is foundthat overthe 1-week
low vegetation coverhave small u,t valueswhich correspondsto high wind erosionrisk. As shown in Plate
4a, on February 8, in large parts of western and central Australia, wind erosion risk is high, while erosion
was virtually impossiblefor the southern parts. The
distribution of regionswith high erosionrisks changes
with the changein soil moisture distribution. Of course,
whether wind erosion actually occurs depends on the
fact whether u, exceeds u,t, and the wind erosion intensity is proportional to u, -u,t. The distribution of
period from February 6 to 12, 1996, the total dust emis-
u, for the four times is as shown in Plate 5. The model
sion from the Australian
continent was around 6 million
tons.
6. Conclusions
In this paper we describedan integrated wind erosion
assessmentand prediction system which comprisesa
wind erosionscheme,an atmosphericpredictionmodel,
and a GIS database. We predicted wind erosion over
the Australian continent in a quantitative sense. It
predicted that the differencebetween u, and u,t is the is not claimed that the predictionsare quantitatively
strongestaround 0000UT February 10, hencethe strong accurate, and a reliable evaluation of the model perwind erosion.
formance is not yet possible. Nevertheless,this study
Comparingpredicted and observedwind erosionpat- representsa major step toward quantitative prediction
terns, the main qualitative difference appears to be of wind erosion and a clear alternative to studies of dethat according to observations, erosion was probably scriptive nature. In the integrated system, both wind
most widely spreadon February 8 and 9, and decreased erosiveness
(atmosphericconditions)and soil erodibilmore rapidly on February 10 and 11 than the model ity (surfaceconditions)are taken into account. Given
predictions. This discrepancyreflects the difficult na- the difficultiesinvolved in wind erosionprediction, the
ture of the problem, and to some extent also reflects achievementsof this study are obvious:the systemcorthe limitations of the integrated system,in atmospheric rectly predicted the wind erosioneventsbetween Februmodel, the wind erosionschemeand the reliability of the ary 8 and 11, 1996 in central Australia, with predicted
database. Four possible problems are worth of men- erosionpattern and timing in good agreementwith obtioning. First, the atmosphericforcing data used in servations. The system also provided insight into the
this study were the forecast over the period of the week environmental factors responsiblefor wind erosion.
The practical importance of the model is twofold.
starting from February 6 when data assimilationtechniques were applied. In comparison with observations, First, the system can be used to identify areas and pethe predicted weather patterns seem to move slightly riods of wind erosionthreat. Short-term prediction of
slower,althoughthe forecastremainsan excellentonein wind erosion threat can be taken into consideration of
most aspects. By February 9, 1996, the forecastappears agriculturalactivities. By carryingout sensitivitytests,
to be about 6 to 12 hours behind. Second,it is com- the systemcan be usedto identify environmentalor humonly recognizedthat wind erosionevents may cause man factors responsiblefor wind erosion. This kind of
modificationsin surfaceconditionson a microscale,such information is useful to strategic land care programs.
as the changein particle size distribution in the very top Second,the systemcan be usedto providea description
soil layer (top few millimeters),whichmay limit wind of the sourcesof dust particles which in turn influence
erosion. The current version of the wind erosion scheme
atmosphericradiative processes.It was found, for indoes not describethese changes. Third, the system is stance, that over the period February 6-12, 1996, the
currently run over a 50 x 50 km resolution,the contribu- total dust emission from the Australian continent was
tion of wind erosion by processeson a smaller scale was approximately6 million tons, and the origin of the dust
neglected. Subgrid eventsmay have contributed to the was mainly in central Australia (around the Simpson
disagreementbetweenthe predictionsand observations. Desert) for the particularevents.
In a future study, we intend to increasethe resolution of
In this study a considerableeffort has been made to
the surfacedata to 5x5 km. Finally, wind erosioninten- produce quantitative predictions of wind erosion inten-
SHAO AND LESLIE: WIND EROSION PREDICTION
W-W_dry
(•), OOZ8 Feb96
OVER AUSTRALIA
(b) OOZ9 Feb96
27
24
21
18
15
12
9
:6
3
1
o
(c) OOZ10 Feb96
(d) OOZ11 Feb96
Plate 3. Predictedavailablesoilmoisturein the top 5 cm layerfor times 0000UT (a) February
8, (b) February9, (c) February10, and (d) February11, 1996. Availablesoil moisture,defined
as the differencebetween real and air dry soil moisture content, influencesthe threshold friction
velocity of wind erosion.
(e) u_,t (m/s), OOZ8 Feb96
(b) OOZ9 Feb96
0.8
0.6
0.4
0
(c) OOZ10 Feb96
(d) OOZ11 Feb96
Plate 4. As Plate 3 but for predicted threshold friction velocity u,t.
30,103
30,104
SHAO AND LESLIE: WIND EROSION PREDICTION OVER AUSTRALIA
(b) OOZ9 Feb96
(=) u._,(m/s), OOZ8 Feb96
1
0.8
0.7
0.6
0.5
0.4
o.5
0.2
o.1
o
(d) OOZ11 Feb96
(c) OOZ10 Feb96
Plate 5. As Plate 3 but for surfacefriction velocity u..
..
0
Plate
1996.
6.
0.1
1
5
10
50 <'100 '50'0'"!000
Dust emission
(g m-2) overthe Australiancontinent
for the periodFebruary6-12,
SHAO AND LESLIE: WIND EROSION PREDICTION OVER AUSTRALIA
30,105
sity and pattern. However,becauseof the nature of the
in south eastern Australia, Earth Surf. ProcessesLandforms, 21, 661-671, 1996.
problem and the limitations of the present system, it
cannot be claimed that the system has achieveda wind Marticorena, B., and G. Bergametti, Modelling the atmospheric dust cycle, 1, Design of a soil-derived dust emiserosionpredictionwith great accuracy.As far as wind
sion scheme,J. Geophys.Res., 100, 16415-16430,1995.
erosion assessmentand prediction is concernedon the Marticorena, B., G. Bergametti, B. Aumont, Y. Callot, C.
continental scale, the major challengeslie in the areas
N'Doum•, and M. Legrand, Modelling the atmospheric
dust cycle,2, Simulationof Saharandust sources,J. Geolisted below, and researchis being carried out to imphys. Res., 102, 4387-4404, 1997.
provethe treatment of these problems.
McVicar, T. R, J. Walker, D. L. B. Jupp, L. L. Pierce, G.
T. Byrne, and R. Dallwitz, Relating AVHRR vegetation
dex of surfaceroughnesselements,soil moisturecontent
indicesto in situ measurementsof leaf area index, Tech.
and surfacecrusting,especiallywhen valuesof these]
Memo. 96.5, CSIRO, Div. of Water Resour., Canberra,
1. The rate of wind erosion is sensitive to frontal area in-
1996.
quantities are small. This sensitivity makes it difficult
McTnin.qh,
G. H., and-J. R PifhlnRn, l')•,qt'qt-nrrnqand to.L
.........
:
_1
.......
_1_'
_.z_
.z_l
.....
.z_
Wllltl
•I
•u •xccur•xte•y
lo•emct me •x•e of ---'- j ubiuu.
2. The dynamic effect of surface roughnesselements
Australia, Earth Surf. ProcessesLandforms, 12, 415-424,
is difficult to describe: the concept of frontal area in1987.
dex is useful in describingthe aerodynamic sheltering McTainsh, G. H., A. W. Lynch and R. C. Burgess,Wind
•osion in eastern Australia, Aust. J. Soil Res., 28, 323effect of standing roughnesselementson surfacebut is
not effectivein describingthe effectof flat surfacecov-
339, 1990.
McTainsh, G. H., A. W. Lynch and K. Tews, Climatic coners. In reality, surfacesare often composedof standing
trols upon dust storm occurrencein eastern Australia, J.
roughnesselements,flat surfacecovers,tillage ridges,
Arid. Env., in press, 1997.
and various level of random roughnesselements. For Nickling, W. G., and J. A. Gillies, Dust emissionand transport rates, Mali, west Africa, Sedimentology,J O,859-868,
such complex surfacesthe conceptof frontal area index
1993.
may be too simplistic.
Owen, R. P., Saltation of uniform grains in air, J. Fluid
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Mech., 20, 225-242, 1964.
parameters,including vegetationcoverfrom the NDVI, •upach, M. R., Drag and &ag partitioning on rough s•other surfaceroughnesselements,soil texture, and soil
faces, Boundary Layer Meteorol., 60, 375-395, 1992.
moisture.
4. It is necessaryto improve the accuracy of atmospheric weather prediction system, especially near surface wind speed and precipitation.
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