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Environmental Modelling & Software 16 (2001) 233–249www.elsevier.com/locate/envsoft

Toward quantitative prediction of dust storms: an integrated winderosion modelling system and its applications

Hua Lu a,*, Yaping Shaob

a CSIRO Land and Water, Canberra Lab, GPO Box 1666, Canberra, ACT 2601, Australiab School of Mathematics, University of New South Wales, Sydney, Australia

Received 7 July 2000; received in revised form 4 September 2000; accepted 25 October 2000

Abstract

In this paper, we present an integrated wind-erosion modelling system which couples a physically based wind-erosion scheme,a high-resolution atmospheric model and a dust-transport model with a geographic information database. This system can be usedto determine the pattern and intensity of wind erosion, in particular, dust emission from the surface and dust concentration in theatmosphere. The system can also be used for the prediction of individual dust-storm events. We have implemented the system forexamining the dust storms over the Australian continent in February 1996. It is shown that over the 1 month period, the total dustemission from the continent was about 1.87 Mt. The dominant dust particles are found in the size range from 0 to 11µm. Largerparticles are only found during dust-storm periods. The major dust emission locations and dust pathways detected by the modelare comparable with satellite image and climatology of wind erosion in Australia. A discussion is given on the limitations anduncertainties of the system. 2001 Elsevier Science Ltd. All rights reserved.

Keywords:Wind erosion; Dust emission; Dust transport; GIS database; Regional scale

1. Introduction

The importance of mineral dust in the atmosphere andthe associated impacts have long been recognised(Andreae, 1995; Duce, 1995; Junge, 1979). A prerequi-site for studying the impact of aeolian dust on atmos-pheric radiation and circulation is to determineadequately the spatial distribution of dust concentrationand its temporal variations. However, despite numerousstudies on the subject (Genthon, 1992; Joussaume, 1990;Tegen and Fung, 1994; Westphal et al., 1988), quantitat-ive estimates of dust-emission rate have not been poss-ible until recently. This is because the processes govern-ing the emission and transport of dust particles are verycomplex and difficult to model. As shown in Fig. 1, dustemission is determined by an interacting set of physicalprocesses governed by many factors, namely, climate(high wind and low rainfall), soil state (composition, tex-

* Corresponding author. Tel.:+61-2-6246-5923; fax:+61-2-6246-5965.

E-mail address:[email protected] (H. Lu).

1364-8152/01/$ - see front matter 2001 Elsevier Science Ltd. All rights reserved.PII: S1364-8152 (00)00083-9

ture, aggregation and crusting), and surface roughness(non-erodible elements and vegetation). Since wind ero-sion is sensitive to a range of environmental factors, ero-sion events are often spatially variable and temporallyhighly intermittent. The transport of dust involves par-ticle–turbulence interactions in the atmospheric bound-ary layer. At large scale, it is often driven by intensivemeso-scale to synoptic-scale systems, such as squalllines and cold fronts.

This paper constitutes a mathematical and compu-tational modelling effort for the quantitative assessmentand prediction of dust-storm events, including the regionand the intensity of dust emission and the transport ofdust in the atmosphere. For this purpose, we havedeveloped an integrated wind-erosion modelling system(IWEMS), which couples a regional weather-predictionmodel, a dust-emission model and a dust-transportmodel. The system links these dynamic models with ageographic information database, which provides thenecessary input parameters for the system. We haveapplied the system to the simulation of the dust-stormevents over the Australian continent in the period of Feb-ruary 1996. In this paper, we first describe the system

234 H. Lu, Y. Shao / Environmental Modelling & Software 16 (2001) 233–249

Fig. 1. An illustration of the physical processes which influence dust emission, transport and deposition.

and then discuss the modelling results in the light of fieldobservations. We shall also discuss the uncertainties ofthe system.

2. System description

Fig. 2 shows the structure and computational pro-cedure of IWEMS which is a modularised three-dimen-sional system with various options for representing winderosion and dust transport. The high-resolution weather-prediction model, that drives IWEMS, is a primitive equ-ation model formulated in thes-coordinate system witha Lambert conformal projection. It uses a flexible hori-zontal resolution with multi-layer structure and can beself-nested. In this study, we have used horizontal resol-utions between 5 and 75 km and vertical layers of up to31 vertical layers. Details of this model can be found inLeslie and Purser (1991) and Shao and Leslie (1997).

The atmospheric model provides atmospheric forcingdata to both the wind-erosion and the dust-transportmodels. For each physical time step, relevant atmos-pheric data (e.g. wind velocity and eddy diffusivity) andland-surface data (e.g. soil moisture) are passed to thewind-erosion scheme through an interface, for the com-putation of dust-emission rate. This emission rate,together with atmospheric variables, is then passed to thedust-transport model for the calculation of instantaneous

grid-mean dust concentration. This calculation is loopedover a number of particle-size classes. The land-surfaceparameters required for the wind-erosion scheme areprepared by a GIS preprocessor. In the current versionof IWEMS, dust is considered as a passive scalar. Nochemical reactions are considered in the model.

2.1. The wind-erosion scheme

The wind-erosion scheme is an improved version ofthat developed by Shao et al. (1996) and Shao and Leslie(1997). Its structure is as shown in Fig. 3. It comprisesthree key parameterisations representing: (1) the erosionthreshold friction velocityu∗t; (2) the streamwise sal-tation flux Q; and (3) the dust-emission rateF(i) for Nsize classes of dust particles. The treatments of theseprocesses are mainly based on the studies of Raupach(1991) on drag partitioning, Owen (1964) on saltationand Lu and Shao (1999) on dust emission. The effectsof surface non-erodible elements are further taken intoaccount by multiplyingQ by two factors: the ratio oferodible-to-total surface due to vegetation coverEv, andthe surface fraction of the erodible part of the uncoveredsoil surface due to the presence of gravels, pebbles androcksEs. No sediment movement is allowed in the areasoccupied by non-erodible elements. The grid values ofEv andEs are estimated from the GIS database. The sal-tation scheme has been validated against field obser-

235H. Lu, Y. Shao / Environmental Modelling & Software 16 (2001) 233–249

Fig. 2. Components and computational procedure of IWEMS.

Fig. 3. The input and output structure of the wind-erosion scheme.

vations by Shao et al. (1996). The dust-emission schemehas been tested by Lu and Shao (1999), against the wind-tunnel data of Rice et al. (1996) and the field obser-vations of Gillette (1977).

The wind-erosion scheme receives weather data fromthe atmospheric prediction model and soil and vegetationparameters from the GIS database. The main outputs ofthe wind-erosion scheme are the threshold velocityu∗t


(ms21), horizontal sand fluxQ (g m21 s21), and verticaldust fluxF (g m22 s21). F then becomes the input to thedust-transport model.

2.1.1. Threshold friction velocityBased on sound physical reasoning, Shao and Lu

(2000) found that the threshold friction velocity for uni-form particles over bare, dry and loose soil surfaces canbe calculated using a simple expression

u∗t05!a1Sspgd+a2

rdD (1)

whered is the particle diameter,g is gravity acceleration,r is the air density andsp is the particle-to-air densityratio. The coefficients,a1=0.0123 anda2=3×10−4 kg s22,are determined by fitting the above expression to severalwind-tunnel data sets.

For a natural soil surface,u∗t not only depends onparticle size but also other factors, such as surface rough-ness elements, soil moisture and soil aggregation. Thesefactors are considered to modifyu∗t0 in the followingway

u∗t(d)5u∗t0(d)RHM

whereR, H, M are the enlargement functions describingthe influence of surface roughness elements, soil moist-ure and soil surface aggregation and crusting, respect-ively, as proposed by Shao et al. (1996).

Raupach (1991) presented an analytical drag partitionscheme as

R−1(l)5F 1(1−srl)(1+mbrl)

G1/2

with br=CR/CS, whereCR is the drag coefficient for iso-lated roughness elements andCS is that for the surface;sr is the basal-to-frontal area ratio andmr is a parameter#1 accounting for non-uniformity in the surface stress.Raupach et al. (1993) suggested thatbr.90, mr.0.5,and s.1 are typical values. However, it is difficult toobtain the parameters likebr andm for a specific covercondition. Thereafter, those typical values are used forall conditions.

The system uses a simple empirical expressionderived by Shao et al. (1996)

H(w)5e22.7w

The reason for using this simple empirical equation isdue to its robustness and the fact that no additional para-meter is required. Lu (2000) has discussed the differencebetween several available formulae andRandH. A satis-factory expression forM is not available at this stageand hence the values ofM are temporarily set to 1 forall soils.

2.1.2. Horizontal sand fluxOwen’s (1964) theoretical equation for transport-lim-

ited saltation flux is used. For uniform sand particle soil,the horizontal sediment flux is calculated as

Q5H(cru3∗/g)[1−(u∗t(d)/u∗)2] u∗$u∗t(d)

0 u∗,u∗t(d).

wherec is Owen’s coefficient which can be calculatedas 0.25+0.33wt(d)/u∗ and is of the order of 1.

The relative contribution to the total flux of each sizerange is assumed to be proportional to its weight fractionin the soil particle size distribution. The total horizontalsediment flux is then evaluated as a weighted integralof Q(d) over each size class defined by the particle sizedistributionp(d)

Q5EQ(d)p(d)dd (2)

2.1.3. Vertical dust fluxFor each particle-size class, the vertical dust flux,F,

is calculated using the dust-emission model of Lu andShao (1999) as

F5Cagfrb

2p 10.241Cbu∗!rp

p2Q (3)

where Q is the horizontal saltation flux,f is the totalvolumetric fraction of dust in the sediment,rb is the bulksoil density,rp is the particle density,u∗ is the frictionvelocity, p is the soil plastic pressure exerted by soil ona particle moving through it,Ca andCb are coefficientsof order 1. The effects of surface non-erodible elementsare only considered during the calculation ofQ. Othereffects, such as saltators impacting on non-erodibleelements, are not modelled. An integration of Eq. (3)over the sand particle-size range gives the total verticaldust flux from the soil surface for a givenu∗.

For most natural soils, the plastic pressure,p, is largerthan 105 N m22 (Lu and Shao, 1999). This allows afurther simplification of Eq. (3) to

F50.12Cagfrb

pQ (4)

which is used in this study.

2.1.4. Definition of dustThe upper limit of dust-particle size,dd, particles with

diameters smaller than which can remain in suspensiononce lifted from the surface, is defined as the solution of


wt(d)50.7u∗ (5)

as proposed by Gillette (1974), wherewt(dd) is the set-tling velocity for particles with diameterd. Other defi-nitions ofdd can be found in the literature with the ratioof wt(d1)/u∗ varying between 0.2 and 1.25 (Pye, 1987;Shao et al., 1996). Fig. 4 shows the dependence ofdd

on u∗, with wt(dd) being set to 0.2u∗, 0.7u∗ and 1.25u∗.Particles as large asd=200 µm have been detected insuspension during dust-storm events (Westphal et al.,1987). The choice ofwt(dd)=0.7u∗ givesdd=100 µm foru∗=0.8 m s21, which is reasonable for dust-storm con-ditions (Gillette, 1974).

2.2. Transport model

If the concentration of theith dust-particle group isci=c(di), then the total dust concentration is given by

ctotal=ONi51

ci. The transport scheme predicts dust concen-

tration by solving the dust concentration equation (Eq.(6)) with the boundary conditions given by Eq. (7). Inthe s-coordinate system, these equations can be writ-ten as

∂psci

∂t1

∂psuci

∂x1

∂psvci

∂y1

∂ci

∂s(pss1grwti) (6)

5ps

∂∂x

Kphr∂ci/r

∂x1ps

∂∂y

Kphr∂ci/r

∂y1

g2

ps

∂∂s

Kpzr3∂ci/r∂s

with boundary conditions

ci(pss1grwti)2g2

psKpzr3

∂ci/r∂s 5grFi at the surface (7)

Fig. 4. Upper limit particle diameterdd, under which particles canbe suspended in the air, defined as the solutions ofwt(d1)/u∗=0.2, 0.7 and 1.25.

∂ci/r∂s

50 at the top

wherewti andFi are the particle settling velocity and thesurface vertical dust flux for theith group, respectively;u, v ands are wind velocities,ps is the surface pressureandr is the air density. The horizontal particle diffusiv-ity Kph is assumed to be equal in thex andy directions.The vertical particle diffusivityKpz is a function of par-ticle size, estimated through a modification of the eddydiffusivity for neutral particles. The relationship betweenthe eddy diffusivities for neutral and heavy particles iscomplicated in reality, but can be simplified for station-ary, homogeneous and isotropic turbulence (Csanady,1963). While there are more recent models on particlediffusivity, it can be shown that the Csanady (1963)model is sufficiently accurate for our purposes and hastherefore been used in this study. Dust particles areremoved only by dry deposition at the surface. Althoughknowing that wet removal is an important process, alarge uncertainty exists in available wet removalschemes and the resolution of particle size distributiondoes not allow us to achieve a proper estimate of thescavenging rate. The value of modelling wet removal islimited and we excluded it from this study. Dry depo-sition is represented in the model as a boundary con-dition in the finite difference form of the vertical turbu-lent transport term. The dry removal flux by gravitationalsettling and turbulent mixing is modelled as

Fd5C(r)(wt1vd)

and is included in Eq. (7). The dry-deposition velocity,vd, is parameterised following Genthon (1992). The set-tling velocity, wt, can be estimated using

wt(d)5S 4rpgd3rCD(Ret)

D1/2

(8)

whereRet=wtd/n is the particle Reynolds number at the

settling velocity, andCD(Ret)=24Ret

(1+0.15Re0.687t ) is the

drag coefficient for a sphere (Durst et al., 1984).Six dust particle size groups are considered in this

study. These are,d#2 µm (clay), 2,d#11 µm (finesilt), 11,d#22 µm (medium silt), 22,d#52 µm (largesilt), 52,d#90 µm (fine sand), and 90,d#125 µm(medium sand). The selection of the groups is due to theconsideration of observation on atmospheric dust sizedistribution and the availability of the soil particle sizedistribution (PSD). It is observed that particles at around1 and 10µm are found as dominant classes in the atmos-phere (Joussaume, 1990; Westphal et al., 1988). Thelower bound of the sand particles is defined by particlesof size 20µm (Gillette, 1977; Gillette and Walker, 1977)and 60µm particles are used under severe wind-erosionactivities (Shao et al., 1996). However, the soil PSDs


data, which have 38 classes from 2 to 1159µm do notuse 1, 10, 20, and 60µm as class bounds. Therefore, thenearest values (2, 11, 22, and 52µm) are used. Twolarger upper bounds (90 and 125µm) are used to accountfor extreme events. During the simulation, the real upperbound is dynamically determined by the model itselfusing Eq. (5).

The effective settling velocity for each dust section iscalculated as

wt5

Ewt(d)f(d)dd

Ep(d)dd

where f(d)=13prr

3dN/dd with d being the particle diam-eter anddN/dd being the number distribution. Followingthe work of Tegen and Fung (1994) and assuming thatdN/dlogd~d−3 for d#22 µm, anddN/dd~d−3 for 22#d#125 µm, the effective settling velocity for each classcan be calculated. The values are given in Table 1. Thesevalues are in good agreement with several measurements(Westphal et al., 1987).

Eq. (6) is solved by splitting the advection and dif-fusion terms. A multidimensional wave-propagationslope-limiter scheme (LeVeque, 1996) is used for hori-zontal advection. This scheme is second order accurateboth in time and space. It eliminates oscillations andundershoots and maintains positivity, which is importantfor dust concentration. In the vertical direction, Bott’s(1989) advection scheme is used. The scheme is positivedefinite but not monotonic. It is mass conservative andcauses very small numerical diffusion. Second order,area preserving polynomials are used inside the domain.These polynomials were derived assuming variable gridspacing. The order of the polynomials is reduced to onenear the domain boundary. The vertical diffusion issolved by using a fully implicit scheme, with the associa-ted algebraic equation system being solved by using theThomas algorithm. Emission and dry deposition arehandled together with the vertical diffusion.

2.3. Data handling

2.3.1. Input parameters: access to the GIS databaseThe land-surface parameters required for the model

are: soil type, vegetation type, vegetation height, leaf-area index (LAI) and land-use index. These parametersare obtained from theAtlas of Australian Resources,

Table 1Effective settling velocity for each particle-size group

Section (µm) d#2 2,d#11 11,d#22 22,d#52 52,d#90 90,d#125

wt (m s21) 1.8×1024 2.86×1023 1.876×1022 9.896×1022 0.4259 0.8

(AAR) (Volumes 1, 3 and 6), except for LAI. Leaf-areaindices used for the simulation period are derived fromremotely sensed NDVI (Normalised Difference Veg-etation Index) data, using empirical relationships(McVicar et al., 1996). The GIS database is of 0.05°spatial resolution.

2.3.1.1. Soil parameters In the Atlas of AustralianResources — Soils, soil types are aggregated into 29mapping units. Seven of these units are well structuredand stabilised soils and three are rocks, peats and salinelakes. These 10 units are non-erodible. The remaining20 units are reclassified according to the availability ofparticle-size distributions and chemical similarities.Eight particle-size distributions (PSDs) of typical Aus-tralian soils provided by Dr McTainsh are assigned to the20 erodible mapping units. In these PSDs, the smallestresolvable particle size is 2µm. No attempt has beenmade to model the emission of smaller particles. Thepossible variation of particle sizes with wind velocityhas also been neglected.

In addition to PSDs, several soil parameters determinethe intrinsic ability of a soil to resist wind erosion. Theseinclude soil bulk densityrb, the plastic pressurep (alsoknown as penetrometer resistance),Ca and Es. Theseparameters are assigned on the basis of the likelihood ofcrust formation, the chemical composition, structure andpermeability of the soils. The assigned values are aslisted in Table 2.

2.3.1.2. Vegetation and roughnessThree types ofvegetation data are used, namely, LAI, vegetation type(Vt) and vegetation height (Hc). Frontal-area index,which is required for the computation of threshold fric-tion velocity, is estimated from LAI (Shao et al., 1994).The value ofEv is calculated from

Ev512exp(2LAI/2)

assuming a random distribution of foliage above the soiland uniform leaf-angle distribution (Choudhury, 1989).

2.3.2. Preprocessor: GIS converterThe horizontal grid spacing used in the atmospheric

model is often too coarse to resolve effects of the subgridscale land-surface heterogeneity on dust emission. Theexplicit subgrid scale treatment of heterogeneity usingthe nesting method based on the resolution of the GISdata requires running the wind-erosion model for morethan 900×700 nodes over the Australian continent, which


Table 2Soil type classification and the values of assigned parameters

Mapping unit Sample site (soil type) p (105 N/m2) rb (kg/m3) Ca Es

Bb1 Charleville (massive sesquioxide) 0.5 1000 5 1Bb2 Charleville (massive sesquioxide) 1 1000 5 1Bb3 Charleville (massive sesquioxide) 0.5 800 2 1Bb4 Charleville (massive sesquioxide) 100 900 2 1Bb5 Charleville (massive sesquioxide) 100 1000 1 0.4Bc1 Buronga (calcareous earth) 25 1000 5 1Bc2 Buronga (calcareous earth) 20 1000 5 1Bd1 Betoota (saline soil) 20 1000 5 1Bd2 Betoota (saline soil) 300 1200 1 1Bd3 Betoota (saline soil) 300 1000 5 1Ca1 Diamantina Lakes Dune (sandy) 25 1000 5 1Cb1 Narrabri (cracking clay) 200 1000 5 1Cb2 Narrabri (cracking clay) 200 1000 5 1Cc1 Lark Quarry (loam sandy) 100 1200 2 1Cd1 Tambo (hard-setting soil) 25 1000 5 1Cd2 Tambo (hard-setting soil) 25 1000 5 1Cf1 Diamantina Lakes Dune (sandy) 25 1000 5 1Cf2 Diamantina Lakes Dune (sandy) 25 1000 5 0.8Cf3 Winton (shallow loam soil) 50 1000 2 0.8Cf4 Winton (shallow loam soil) 100 1000 1 0.8A1, A2, Ba1, Ba2, Ba3, Ce1, Ce2, Cf5, O1 Non-wind-erodible

is numerically too expensive to implement in theweather-prediction model. A preprocessor is thereforedesigned to extract land-surface parameters from the GISdatabase (with 0.05° resolution) into the Lambert con-formal projection-based grid of the atmospheric model.The preprocessor minimises the information loss of theoriginal land-surface data and allows computationalefficiency of the modelling system.

Two important variables display subgrid scale vari-ations, namely, friction velocityu∗, which controls themagnitude of surface wind stress, and threshold frictionvelocity u∗t, which controls the resistance of the surfaceto wind erosion. The local values ofu∗ andu∗t are prob-ably distributed about their mean values according tocertain probability density functions. Thus, for a givengrid, u∗ may exceedu∗t locally and temporarily evenwhenu∗¿u∗t. The effect of this phenomenon has beeninvestigated by Westphal et al. (1988). They found thatthe calculated dust emission can double if the Rayleighdistribution is assumed foru∗. Assumingu∗t to be uni-formly distributed between 0.25 and 1.5 m s21, the dustflux can be 10 times larger for the sameu∗. Hence, thesubgrid scale distribution ofu∗t is of central importanceto the calculation of wind erosion.

Although the enlargement functionR is time depen-dent, it is reasonable to treatu∗td as stationary if amonthly or short time prediction is of interest and theeffect of soil moisture onu∗td is considered during anactual model run. For each GIS grid,R andEv are calcu-lated using the mosaic approach. In this approach, eachatmospheric model grid is divided into several fractionsaccording to the soil type. The fractions with the same

soil index are added together regardless of their locationwithin the grid. Typically, each atmospheric model gridcontains 1–7 soil types. For each fraction, the parametersneeded for the calculation ofR are averaged.

For a given atmospheric grid, a set of land-surfaceparameters is generated as

k

Is1 Is2 % Isk

R1 R2 % Rk

Ev1 Ev2 % Evk

where Is is soil index andk is the number of fractions.These parameters are input to the wind-erosion modeland an arrayu∗t(i,j) is calculated, withi=1,...,k andj=1,...,np, wherenp is the number of particle-size groups.The dust flux is calculated for each surface fraction andeach particle-size group. The total dust flux for the givenatmospheric model grid is given by

F5Ok

i51

Onp

j51

Fij

whereFij is the dust flux for theith surface fraction andthe jth particle-size group. No subgrid distribution isconsidered for the friction velocityu∗.

3. Modelling results

IWEMS has been applied to the simulation of the Feb-ruary 1996 dust-storm events over the Australian conti-


nent (Fig. 5). A 50 km grid spacing is used (for theatmospheric model) and the output is written in 6-hourlyintervals. The integration time step is set to be 120 s.Throughout the simulation, the Australian Eastern Stan-dard Time (EST=UTC+11 h) is used. The simulationbegan at 23:00 31 January 1996 and ended at 23:00 28February 1996. The initial dust concentration was set tozero. The initial soil moisture was obtained by runningthe atmospheric model coupled with the land-surfacescheme (without IWEMS) for the entire month of Janu-ary 1996, starting from a typical summer-time soil-moisture pattern.

3.1. Prediction of dust emission

Fig. 6 shows the simulated patterns of daily averageddust emission. The days free of dust emission wereexcluded in the graph. Wind erosion occurred at variouslocations over the Australian continent for the remaining21 days, including three major dust-storm events. Theareas of intensive wind erosion were found mainly inthe southern part of the Simpson Desert to the north ofLake Eyre and areas of medium dust uplifting wereidentified in the Great Victoria Desert, Gibson Desertand the west coast of Western Australia. Relativelyweak, intermittent wind erosion activities were predictedin areas around Broken Hill and in Western Australia.

To facilitate descriptions, we denote the 6-hourly

Fig. 5. Map of Australia showing state names, locations of major deserts, lakes and towns referred to in this paper.

averages of near surface dust concentration, streamwisesediment flux and dust emission rate asC, Q andF.According to the model simulation, the first dust stormoccurred on 1–2 February. An extensive area of the Aus-tralian continent experienced dust uplifting during thisperiod, with regions in Western Australia being worstaffected. However, dust emission for the particular eventwas not very high and the wind erosion was mainlyintermittent. The dust emission in the Simpson Desertwas weak with the daily averaged emission rate wellbelow 10 µg m22 s21. During the time, the highestC, Q andF were estimated to be around 7255µg m23,200 mg m21s21 and 0.4 mg m22s21, respectively, at(125.9E°, 25.6S°) over the time period between 5:00 and11:00 on 2 February. The most severe dust storm for thesimulation period occurred between 8 and 10 Februaryin Central Australia. The dust storm was associated withthe strong winds generated by a deep low-pressure sys-tem which was moving from the Southern Ocean towardthe northeast. The highestC, Q andF were estimated tobe around 9528µg m23, 1890 mg m21s21 and 0.7 mgm22s21, respectively, at (136.8E°, 27.9S°) during thetime period from 5:00 on 9 February to 11:00 on 10February. The instantaneous near surface dust concen-tration was as high as 27,060µg m23. The third, some-what weaker, dust storm occurred between 14 and 16February, in the same region. The highestC, Q andFwere estimated to be around 3236µg m23, 573 mg


Fig. 6. Predicted daily averaged dust emission patterns.F is in µg m22 s21. The head of “010296” reads as 1 February 1996 and so on.


Fig. 7. Time evolution of the domain averages of total suspendeddust for each particle-size group.

m21s21 and 0.2 mg m22s21, respectively, at (136.8E,27.9S°) at 11:00 on 14 February. The highest instan-taneous near surface dust concentration was over 5000µg m23.

3.2. Budget of dust concentration

The evolution of the 6-hourly total amount of (a) sus-pended dust for each particle-size group and (b) the emit-ted dust from each group are shown in Figs. 7 and 8.

A comparison of Figs. 7 and 8 reveals that althougha large amount of particles withd.52 µm were emitted,only a small amount of those particles were suspendedin the air due to their large settling velocities. Fig. 8shows the similar shapes of dust-emission rates for all

Fig. 8. Time evolution of the domain averages of total dust emissionfor each particle-size group.

particle-size groups. This suggests that there is a strongcorrelation between the particle-size distribution of theemitted dust and that of the parent soil. In Fig. 7, nosuch similarity can be found. The smoother curves ford,11 µm suggest that the fine particles can remain insuspension longer than larger particles. Larger particlesare found mostly during the wind erosion events.

3.3. Time evolution of variables at given grid points

More detailed information can be gained by analyzingvarious model variables for a single grid point. Fig. 9shows the evolution of dust concentrationC (in log10 µgm23), Q and simulated weather parameters for the gridpoint located at (126.8E°, 27.9S°).

The diurnal variations of wind speedU, temperatureT andu∗ can be clearly seen. During the simulation per-iod, two severe dust-storm events occurred at thislocation, associated with the strong near surface wind.

Fig. 9. Predicted time evolution of weather parameters and the small-est threshold friction velocityu∗t, streamwise sediment fluxQ, andinstantaneous near surface layer dust concentration for the grid point(136.8E°, 27.9S°).


The dust emission during the period between 9 and 10February is produced by a south to southwesterly coldair stream driven by a frontal system. Temperature wasincreasing from 5 February and peaked on 7 and 8 Feb-ruary. The cold front generated a near surface wind aslarge asU=10 m s21 corresponding to au∗ of 0.45 ms21 at 5:00 on 9 February. Air temperature during 5–8February was very high and there was no rainfall, lead-ing to low values of soil moisture. The smallest predictedthreshold friction velocity isu∗t=0.33 m s21. It wasfound that wind erosion started in the early morning of9 February, peaked at 11:00 of the same day and easedat 17:00 of the next day. For the second dust-stormevent, which occurred during the time period between14 and 16 February,U reached 9.2 m s21 andu∗ reached

Fig. 10. Predicted monthly mean dust concentration in the first model layer for the six different size sections.

0.4 m s21. The predicted air temperature was below25°C. The light rainfall associated with the cold front inthe previous days did not increase soil moisture substan-tially. The threshold friction velocityu∗t was predictedto be around 0.35 m s21. The maximum streamwise sedi-ment flux is about one-third of that in the previous dust-storm event. The wind erosion lasted for 2 days, startingin the early morning, peaking at noon time, then weaken-ing at midnight. AlthoughU at this grid was not thelargest (the largestU in the computational domain wasover 20 m s21), severe wind erosion occurred at this gridpoint because of its low vegetation cover and low soilmoisture, which provide ideal surface conditions forwind erosion. Again, this shows that wind erosion iscontrolled by a combination of surface properties andwind stress.


Fig. 11. Predicted vertical profile of dust concentration for six continuous days at 11:00 at 136.8E°.

3.4. Long-range transport

Fig. 10 shows the modelled near surface (s=0.999)monthly mean concentration of the individual particle-size groups. Particles withd,11 µm were found to tra-vel far beyond the surrounding oceans, while particleswith d.52 µm were found to be deposited in areas sur-rounding the source region. The relative amount ofsmaller particles to larger particles increases withincreasing distance from the sources, as the larger par-ticles are removed more quickly due to their larger set-tling velocities. The horizontal distribution pattern of thesmall particles is more diffusive than of the larger par-ticles. In reality, part of the fine particles will be washedout by rain and the actual travelling distance of those

fine particles might be smaller than simulated, as wetdeposition is not included in the model.

Fig. 11 shows the modelled vertical distribution of theinstantaneous dust concentration at 11:00 for the 6 daysfrom 9 to 14 February. For times when dust stormsoccurred, dust plumes reached higher levels on hot days(9th) than on cool windy days (14th). This confirms thatconvection plays an important role in the transport ofparticles into higher levels of the troposphere. For thesame location and same altitude, dust concentration maychange dramatically with time. For times with dust emis-sion, dust concentration was higher than 1000µg m23.For times without dust emission, just 1 day after thestorm, dust concentration was smaller than 1µg m23,because a large proportion of the large dust particles


lifted during the storm deposited back to the surface.They were removed within several hours from theatmosphere due to their large settling velocities. Thiseffect can be seen more clearly in Fig. 12 which showsthe instantaneous profile of dust concentration for theindividual particle-size groups at grid point (136.8E°,27.9S°) for two different times. The larger particles canonly be found for the wind-erosion day and only reachthe height withs=0.95. The change in the magnitude ofdust concentration for particles withd,11 µm is in theorder of 1–2, while for particles withd.11 µm, thechange can be as large as several orders of magnitude.Therefore, particles suspended in the atmosphere for along time are mainly those withd,11 µm.

Fig. 12. Predicted vertical profile of concentration of six particle-size groups for a wind-erosion day (11:00 9 February 1996) and non-erosionday (11:00 21 February 1996) at the grid point (136.8E°, 27.9S°).

If the residence time for each particle-size group isdefined as

tr5te2td

where te is the time of particle emission andtd is thetime when all particles from this group are deposited,then tr is estimated to be 6–10 h for particles with90,d#125 µm, 12–18 h for 52,d#90 µm, 1–2 daysfor 22,d#52 µm, 10 days for 11,d#22 µm and morethan 1 month ford#11 µm, if no wet removal is con-sidered. These findings agree well with the variousmeasurements mentioned by Westphal et al. (1987) andthe model results of Tegen and Fung (1994).

As shown previously, the size distribution of airborne


dust particles can change dramatically both in time andspace. Near the surface, the dominant mode is 52,d#90µm during wind-erosion and 2,d#11µm during per-iods free of wind-erosion. The dominant mode shiftsgradually to the particle-size group of 2,d#11 µm withincreasing height during wind-erosion episodes, whichis also the overall long-term dominant mode as can beseen in Fig. 7. These rapid and dramatic changes of sizedistribution of suspended particles in space and timeimply that the assumption of static particle-size distri-bution in the atmosphere may lead to poor assessmentof the effect of mineral aerosols on atmospheric radi-ation balance.

4. Comparison with observations

Very few measurements are available for a quantitat-ive verification of the model. Nevertheless, the predicteddust concentration can be compared with measurementsfrom two sites. Fig. 13 shows that the model has pro-duced reasonable estimates of dust concentration forBirdsville for 9 and 16 February, although the predictedvalues were smaller than the measured ones by a factorof two. However, the comparison for the Mildura site israther unsatisfactory. The predicted dust concentrationwas 500 times smaller than the measured values thoughthe model has predicted correctly the timing of high dustconcentrations, except for 1 February. Several factorsmay have contributed to such a quantitative disagree-ment. The first one is the coarse resolution of the model.Although the subgrid variation ofu∗t arising from the

Fig. 13. Measured and modelled dust concentrations for Birdsvilleand Mildura.

heterogeneity of soil type and vegetation cover has beenmodelled, that arising from subgrid variations of frictionvelocity u∗, soil moisture and other quantities has notbeen taken into consideration. At the Mildura site,human-induced erosion, which happens at a much finerscale, is the dominant cause compared with a desert dustsource area, such as Birdsville. The land surface con-ditions at Birdsville are more uniform compared with theMildura site. In Australia, wind-erosion prone agricul-tural areas are relative small. They spread in the southpart of the country with paddock sizes smaller than 1km2. The 5 km GIS database is not capable of detectinghuman-induced wind erosion activities even though thesubgrid variation of other variables are modelled. Thesecond factor is that the representation of surface crusts,roughness elements and soil moisture may still be toocrude. The third factor is the parameters, especially soilparameters, such as horizontal plastic pressurep, particlesize distribution, and bulk density are insufficientlyaccurate for the entire continent.

The predictions can also be compared with satelliteimages. Fig. 14 shows the comparison between the simu-lated dust emission rate and the visible light picture fromthe Geostationary Meteorological Satellite (GMS) for00Z 9 February (11:00 EST). From the satellite image,dust clouds are visible in the region around 137°E and28°S, which are identified by comparing visible light andnear infrared satellite images. Fig. 15 shows the simu-lated results of several variables for the same time atcontinent scale. The simulated dust clouds, in general,show good qualitative agreement when compared withthe visible features from the satellite picture andmeteorological records during the episode. The shape,location and extent of the dust cloud coincide well withthe wind-erosion areas predicted by the model. The mostintensive wind-erosion areas are well identified.

5. Comparison with other model results

Shao and Leslie (1997) have modelled the dust stormsduring 8–11 February. In their study,u∗t was calculatedfor each atmospheric model grid using the dominant soiltype and the average value of LAI with no subgrid treat-ment. Their prediction of wind erosion was done off-lineusing a predicted wind field at half hourly intervals.While there is a general agreement between Shao andLeslie (1997) and this work, the former predicted astronger wind-erosion event both in affected areas anddust-emission rates. The largest vertical dust fluxF pre-dicted by Shao and Leslie (1997) is over 3 mg m22 s21,about five times that of the present work. Overall, thetotal dust emission calculated by Shao and Leslie wasabout 6 Mt for four intensive dust-storm days. For thesame period, we found the total dust emission was about1.113 Mt, again about five times smaller than their pre-


Fig. 14. Predicted dust emission locations at the Central Australia and visible satellite image at the time 00Z 9 February 1996.

diction. There are two possible reasons for the differ-ences between the two studies. The first one is that thedust-emission schemes used in the two studies differ.The dust-emission scheme used by Shao and Leslie(1997) is based on the study of Shao et al. (1993), whilethe one used here is based on the study of Lu and Shao(1999). Both models have quantitative uncertainties asdiscussed in Lu and Shao (1999). The scheme of Shaoet al. (1993) was derived from wind tunnel experimentsusing a loosely packing dust bed as the saltating target.Compared with the field data of Gillette (1977), thescheme of Shao et al. (1993) appears to overestimatedust emission for large values ofu∗ (Lu, 2000). Thesecond possible reason lies in the fact that subgrid vari-ations of u∗t were not considered by Shao and Leslie(1997). The values for LAI and vegetation cover usedin their study may have resulted in a smalleru∗t and

hence larger streamwise sediment flux and dust-emis-sion rate.

The total amount of dust emitted from the continentis estimated to be 1.87 Mt in the simulated month. Com-paring with the estimated global dust uplifting rates(ranging from 200 to 5000 Mt yr21) (Pye, 1987; Tegenand Fung, 1994), it suggests that Australia is not a strongsource of aeolian dust though it occupies over 10% ofglobal desert area. This is consistent with other obser-vations. Maximum dust storm frequencies in Australiaare 15 day yr21, compared with 30–60 days in Asia (Pye,1987). Satellite data shows that the optical thicknessover the oceans surrounding Australia is often less than0.1 compared with 0.6–2 over the oceans near the Sah-ara. One of the causes of these differences in emissionrate is that the level of human disturbance in the Saharanand Asian dust-source regions is much higher compared


Fig. 15. Predicted wind erosion intensity, near surface dust concentration, vertically integrated dust concentration, surface wind, friction velocity,and threshold friction velocity for the time 00Z 9 February 1996 dust-storm events.

with that in Australia. It was found that about 60–90%contemporary atmospheric dust is generated from dis-turbed regions (Tegen and Fung, 1995).

The predicted wind-erosion areas coincide with theareas of largest dust-storm frequencies derived byMcTainsh and Pitblado (1987) based upon meteorologi-cal records at 149 stations. This study also gives a goodindication of the dust paths and deposition regions. Fig.15 shows that dust particles emitted from Central Aus-tralia were transported toward the Indian Ocean in thewest, passing offshore to the south-east. The dust plumewas confined to a narrow region of the frontal area. For

the whole simulation month, it is found that the largestfraction of dust particles was transported toward thenorthwest. All these features are consistent with theclimatology of wind erosion in Australia (Sprigg, 1982;McTainsh and Leys, 1993).

6. Discussion and conclusion

An integrated wind-erosion prediction system hasbeen briefly described with emphasis on the physicallybased wind-erosion scheme and its coupling with a GIS


database. Comparing with some existing dust-transportmodels, this system approach has the ability to detectthe dust sources and to calculate dust-emission rate usingboth meteorological and surface information. The simul-ations of the February 1996 dust-storm events over theAustralian continent have been carried out to demon-strate the suitability of the approach. The simulated evol-ution of dust storms has been found to be in qualitativeagreement with the observations. Quantitative agreementhas also been partially achieved, although still less thansatisfactory. Some subjectivity has been inevitablyinvolved in the derivation of soil parameters. Futurework will be directed at improving the quality of theGIS database, enhancing the reliability of the model,studying the impact of dust concentration on the atmos-pheric radiation budget, and eventually creating a satis-factory modelling system for quantitative dust-stormprediction.

Acknowledgements

This work is supported by Australia Research Council.The authors are grateful to M.R. Raupach and P.A.Coppin at CSIRO Land and Water for insightful com-ments which greatly improved the original manuscript.

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