ecosystem feedbacks arising from wind transport in ...impact of aeolian processes on drylands •...

Ecosystem feedbacks arising from wind transport in drylands: Results

from field experiments and modeling

Gregory S. Okin, U. California Los Angeles

[email protected]

David Rachal, Debra Peters, Finn PillsburyNew Mexico State University

Vegetation Community Change in DesertsShrub Encroachment

Note: These transformations have been ABRUPT and IRREVERSIBLE

The Teeter Totter/Islands of Fertility Model

Grasslands Shrublands

FEEDBACKS

Transport in Shrub EncroachmentThe Role of Connected Pathways

YesD

urin

g dr

ough

t

Fire fuel pathways

Vegetation cover

Vegetation cover

Climatic aridification

Mesic climate

Transport Pathways

Heterogeneouslydistributed

Low averagebiomassWoody

growth

Decreasedfire frequency

Increasedfire frequency

Woody mortality

Introduction ofexotic grasses Is cover dominated

by annuals orshort-lived perennials?

Homogenouslydistributed

High averagebiomass

Drought sensitivity

High P/PElow variability

Low intensityprecipitation

Low windspeeds

Low P/PEHigh variability

High intensityprecipitation

High windspeeds

Decreased transportDecreased runoff

Increased infiltration

Increased transportIncreased runoff

Decreased infiltration

Overgrazing, agriculture,other land use(fuelwood, ORV’s, etc.)

Shorter Longer

Longer Shorter

LOCOP: Length of Connected Pathways

Conceptual Cusp Catastrophe Models

Aeolian Processes: Basic Physics

•  Saltation Flux –  Efficient Saltators: 70 – 100 μm –  Majority of momentum and mass

horizontal flux –  Abrasion –  Pedestaling –  Burial (coppice dunes)

•  Suspension Flux –  Dust emission –  Winnowing of soil –  Removal of fines –  Downwind deposition of fines

Verti

cal f

lux

Impacts of Saltation FluxAbrasion

Impacts of Saltation FluxPedestaling

Impacts of Saltation FluxBurial (coppice duning)

Effects of SuspensionDust Emission

•  Emitted dust is generally < 50 μm •  Particles < 50 μm are generally responsible for

–  Most of the surface area of a soil –  Most of the cation exchange capacity of a soil –  Most of the water holding capacity of a soil –  Most of the soil organic matter of a soil –  Most of the available soil nutrients of a soil

•  Losing these particles means degrading the ability of the soil to sustain plants, particularly during the germination and establishment phase

Testing the impact of dust emission on ecosystems

25m 25m 50

m

50m

Prevailing wind direction

10m meteorological tower

upwind

downwind

T4

100%

T3

75%

T2

50%

T1

25%

C

0%

Vegetation Removal

Aeolian sediment collectors (BSNEs)

25m

50m


50m

Upwind vegetation removed

Downwind no vegetation removed

25m

50m


50m



line intercept transects

25m

50m


50m



10m 10m

5m

10m

10m

5m

10m

10m 5m

10m

10m

5m

5x10m soil plots

25m

50m


50m



10m

20m 10x20m veg plots

Changing Upwind BiogeochemistryMagnitude

Changing Upwind BiogeochemistryDistribution

0

0.5

1

1.5

2

2.5

Mar. 04 Jul. 04 Jul. 05

ratio

of C

.V. (

T4/C

)

TOCTN

Changing Upwind BiogeochemistryVariability - Geostatistics

γ(

h ) =

1

2nVt −Vt+

h ( )2

t

∑

Changing Upwind BiogeochemistryVariability - Geostatistics

0.0

0.5

1.0

1.5

2.0

2.5

T4 Mar. 04 T4 Jul. 04 C Mar. 04 C Jul. 04

0.00

0.05

0.10

0.15

0.20

0.25

T4 Mar. 04 T4 Jul. 04 C Mar. 04 C Jul. 04 0.00

0.02

0.04

0.06

0.08

0.10

0.12


TOC TN

Ran

ge (m

) V

aria

nce

(CO+

C)

0.0

0.5

1.0

1.5

2.0

2.5


Treatment Control Treatment Control

Treatment Control Treatment Control

Changing Upwind BiogeochemistrySoil Texture

Grain size category (D, µm)D>500 [250,500] [125,250] [50,125] D< 50

Mas

s pe

rcen

t

0

10

20

30

40

50

2004 2006

Changing Upwind Seed Bank

0

5

10

15

20

25

30

C T1 T2 T3 T4

Number of sprouts

Changing Downwind Biogeochemistry

0

1

2

3

4

5

6

7

2004 2006

CV

Dow

nwin

d/C

V C

ontr

ol

Navail

Na+

0

0.5

1

1.5

2

2.5

2004 2006

Con

c. D

ownw

ind/

Con

c. C

ontr

ol

Navail

Na+

Changing Downwind Communities

Feedbacks

Impact of aeolian processes on drylands

•  Physical impact on plants – Abrasion, Pedestaling, Burial

•  Biogeochemical impacts on soils –  Soil texture and nutrient changes – Change in spatial distribution of nutrients

•  Community composition changes – Changes to seedbank – Grass reduction and shrub increase

•  Conceptual and mathematical models to integrate these effects

The ConMod Experiment

Wind Erodible SiteBasin Floor

Net

Sed

imen

t Flu

x (Δ

q z)

(g m

-2 d

-1)

-45

-30

-15

0

15

30

45N

et S

edim

ent F

lux

(Δq z)

(g m

-2 d

-1)

-10

-5

0

5

10

Net

Sed

imen

t Flu

x (Δ

q z)

(g m

-2 d

-1)

-20

-10

0

10

20N

et S

edim

ent F

lux

(Δq z

)(g

m-2

d-1

)

-10

-5

0

5

10

Non-WindErodible Site

Bajada

ConMods

•  ConMods are effective at reducing the connectivity of degrading plots and changing them from deflationary to depositional

•  Fallout Radionuclide work shows concentration of short-lived isotopes in ConMods and scouring from between them (with seasonal washing back into interspaces)

•  A potential mechanism for rehabilitation?

A Simplistic Model for the Emergence of Bistability

•  A bistable system is strongly suggested by – Presence of strong feedbacks – Abrupt and Irreversible transition

dG

dt= αG 1−

G

CG

⎛

⎝ ⎜

⎞

⎠ ⎟ − kGG

CG = f G( )

dS

dt= βS 1−

G + S( )CS

⎛

⎝ ⎜

⎞

⎠ ⎟ − kSG

G = Grass biomass S = Shrub biomass Ci=Carrying Capacity α, β, kG, kS = constants

The less grass there is, the lower the carrying capacity due to physical and biogeochemical factors

Grass growthdecreased

Grass growth Increased

threshold

ABRUPT

IRREVERSIBLE

A Bistable System

G: Grass Biomass α:Grass growth constant kG: Grass biomass decay rate

Grass and shrub states

Only shrub state

Threshold

More mesic climate, less grazing More arid climate, more grazing

The (very) Basics of Wind Erosion

•  Wind erosion is threshold-controlled•  Nonlinear above the threshold•  Dust produced by sand blasting

Q∝u*−u*t( )a>1

for u*> u*t

0 for u*≤ u*t

⎧ ⎨ ⎪

⎩ ⎪ F = kQ

Total Horizontal Mass Flux(Mostly saltation)

Vertical Flux(Dust Emission)

36

Non-Erodible Elements

Viking Lander

Atriplex polycarpa

Spatially-Explicit Wind Erosion Model (SWEMO) (with vegetation)

QTot =ρg

u* u*2 − u*tv

2( )ΔTu*

∑ Shao & Raupach, 1993

F = QTotKGillette et al. 1997

U(z) =u*

kln

z − d

zo

⎛

⎝ ⎜

⎞

⎠ ⎟

zo =0.479λ − 0.001( )hc for λ ≤ 0.11

0.005hc for λ > 0.11

⎧ ⎨ ⎩

Marticorena et al. 1997

u*tv = u*t 1−mσλ( ) 1+ mβλ( )Raupach et al. 1993

u*tv = u*t (1−mC) 1+ mβCAp

AB

⎛

⎝ ⎜

⎞

⎠ ⎟

⎛

⎝ ⎜

⎞

⎠ ⎟

12

Okin and Gillette, 2004

Lateral Cover

•  An index of how much profile area the wind encounters as it passes over a surface

•  λ=N Ap (for a cylinder Ap= d h )

TextureSandy Loam

Loamy Sand

Clay LoamGravelly

Sand

u*t (cm/s) 29 34 68 28

Log (Fa/Qtot (cm-1

)) -3.7 -4.5 -5.7 -5.7

A 1.0 1.0 1.0 1.0

Vegetation Type

Grass Mesquite Creosote Tarbush SnakeweedOther Shrubs

Bare

Fractional Cover

0.25 0.21 0.17 0.12 0.29 0.17 0.001

Basal Area

(cm2)

900 5100 10800 13200 100 8500 0.001

Profile Area

(cm2)

1000 18500 9200 13800 100 8000 0.001

β 100 100 100 100 100 100 1

zo (cm) 3.8 4.1 8.2 4.6 1.8 5.6 0.04

Soil

Veg

etat

ion

Win

d

Okin and Gillette, 2004Okin and Gillette, 2004

Model Parameters

U*t (cm/s) U*tv (cm/s)


Vegetation Type Grassland Mesquite Creosote Tarbush Playa Bare

Average -1.1 0.0 -1.0 -1.0 -0.8 1.5

Minimum -3.0 -0.6 -3.0 -2.3 -1.9 0.7

Maximum -0.3 0.9 -0.2 -0.4 -0.3 2.3

Log (QTot (g/cm/day))

Summary of measured values of QTot for different vegetation types in the Jornada Basin for the period July 24, 1998 to April 19, 2001. (Gillette, unpublished data)


•  Spatial–  Soil and vegetation maps typically do not represent real

environmental variability–  Variability in soil and vegetation parameters at scale smaller

than grid cell–  Land use will be an important determinant of variability–  Spatial organization in vegetation may exist

•  Streets - elongated vegetation-free areas

•  Temporal–  Vegetation cover changes throughout the year

•  Green and NPV BOTH have sheltering effect

–  Land cover change & land use has a temporal signature

Sub-Grid Cell Heterogeneity

Modeling Spatial Variability

•  Start with 2-D model

•  Assume that u*t, C, β, Ap/AB are variable at a scale smaller than the grid cell (30 m)–  (Modified) normal distribution characterized by

mean and coefficient of variation

•  Parameterized bootstrap (Monte Carlo) estimation of the distribution of u*tv

•  Calculate horiz. and vert. mass flux

QTot =ρg

u* u*2 − u*tv

2( )ΔTu*

∑ u*tv = u*t (1−C) 1+ βCAp

AB

⎛

⎝ ⎜

⎞

⎠ ⎟

⎛

⎝ ⎜

⎞

⎠ ⎟

12

QTot = Q(u*,u*tv )p(u*,u*tv )∑

Spatial Variability & Threshold

Okin, 2005

N=500,000

Spatial Variability and Wind Erosion

Okin, 2005

Variability is as important as mean values

Okin, 2005

Field Measurements

λ ~ 0.05

λ = 0

λ > 0.3

Conclusions so far….

•  Wind erosion is not scale-independent•  Variability at a scale smaller than the scale

of modeling is a key component of the observed fluxes

•  Variability explains plumes– Can understand why they might happen, but

not where

…But, •  The Raupach et al. (1993) model predicts NO flux at

relatively high lateral covers

•  Even if we use the stochastic version

Some Results from the Field •  Significant flux even at high lateral cover•  No threshold behavior

-5

-4

-3

-2

-1

0

1

0 0.05 0.1 0.15 0.2 0.25Lateral Cover

Log

Flux

(g m

-2 s

-1)

3/27/961/26/9612/18/9511/30/951/2/961/22/96

Gillette and Pitchford, 2004

λgrama~0.3

Lancaster and Baas, 1998

u*tv ~ 6 -8 times u*ts

10

100

1000

0 200 400 600 800Average Gap Size (cm)

Horiz

onta

l Flu

x (g

m-1 d

-1)

Spring 05Summer 05

Okin et al, Journal of Arid Environments, 2006

Unpublished Data

λ~0.45

•  Unvegetated Gap Size Seems to Matter

A closer look at the Raupach et al. (1993) model…

u*tv = u*ts 1−mσλ( ) 1+ mβλ( )

•  u*tv is the threshold shear velocity in the presence of vegetation

•  u*ts is the threshold shear velocity in the absence of vegetation

•  σ is related to the shape of the plant (AB/AP)•  β is the drag coefficient ratio

Decreased due to less affective areaIncreased due to drag on vegetation

The m Parameter

•  Fits to experimental data gave m values from 0.13 to 0.58 (and not near 1 as Raupach suggested)

′ ′ τ s(λ) = ′ τ s(mλ)

Maximum surface shear stress Average surface shear stress

The Telephone Pole Problem

Lateral Cover = (Total Profile Area)/(Total Ground Area)

A

Raupach et al. (1993) model

•  In the wake zone (A), the shear velocity is zero

Shear velocity is not everywhere zero in the wake zone

Bowker et al, Env. Fluid Mech, v6, 359-384, 2006 Bradley and Mulhearn, 1983

Problems with the existing model •  In the field, flux is observed even at relatively high

lateral cover•  In the model, saltation begins at every point in the

landscape at the same wind speed. In reality there are hotspots (Gillette, 1999)

•  Scale unclear (Telephone Pole Problem)

•  Wakes aren t on/off•  m is a fudge factor:

–  Literature Values:•  m ~ 0.5, 1.0 (Raupach et al. 1993)•  m = 0.16 (Wyatt and Nickling, 1997)•  m = 0.53 - 0.58 (Crawley and Nicking, 2003)

Oblique Aerial PhotoWest TexasGillette (1999)

Mental Picture

PLA

NT

Shear Velocitylow high

Saltation Fluxlow high

•  The landscape is made of areas that are more or less protected•  Wakes are essentially 2-dimensions•  The more protected areas are activated at higher u* and exhibit less flux than the less protected areas•  For plant-erosion feedbacks, we need to have a grip on very local transport•  At this point, the model doesn t presuppose any plant distribution

Wind

QTot = Pdqx∫ dx

All we need to know….

•  The probability of any point being a certain distance from the closest upwind nonerodible element, Pd

•  –  Pg = gap size probability distribution –  Pd = probability of being x from upwind shrub

•  Can assume statistical distribution for Pg or Pd

•  Can measure Pg by line-intercept transects, Pacing method

•  Can measure Pd using Spatial Connectivity (McGlynn and Okin, RSE, 2006)

Pg (x)∝ xPd (x)

Gap size vs. lateral cover

•  AP = Profile Area•  AB = Basal Area•  W = Average Plant Width along a transect = π/4*diameter for circular plants•  L = Average Gap Width

λ =APW

AB L + W ( )0

0.1

0.2

0.3

0.4

0 10 20 30 40 50Average Gap Size/Average Plant Height

Late

ral C

over

 In the new model, gap size (normalized by canopy height) is the controlling variable, not lateral cover

Mathematical Description of Model

QTot = Pdqx∫ dx Raupach and Lu (2004)

Shao & Raupach, 1993

x = distance from upwind plant

qx =ρg

u*x u*x2 − u*ts

2( )

Bradley and Mulhearn, 1983

Raupach et al (1993) modelu*x = u*

u*s

u*

10

100

1000

0 5 10 15 20Average Gap Size/Canopy Height

Horiz

onta

l Flu

x (g

m-1 d

-1)

Spring 05Summer 05

Solid: New Model Dashed: Old ModelHeavy: Real Wind Record Light: Constant u*

-5

-4

-3

-2

-1

0

1

0 0.05 0.1 0.15 0.2 0.25Lateral Cover

Log

Flux

(g m

-2 s

-1)

3/27/961/26/9612/18/9511/30/951/2/961/22/96

Lancaster and Baas, 1998

log

(hor

izon

tal fl

ux +

1 (

g cm

-1 d

-1))

λ~0.45

Shear Stress Ratio

SSR =′ τ s (vegetated)

′ τ s (unvegetated)

How can we reconcile the new model with the old model?

Qnew > Qold

Qnew < Qold

Symbols: Constant u*, Constant Gap SizeLight:Constant u*, Gamma Distribution for Gap SizeBold: Variable u, Gamma Distribution for Gap Size

Advantages of this model

•  Explains observations of saltation even at high lateral cover•  Explains hot spots , and where they occur (in streets )

•  Explains variability in m•  Allows calculation of flux at any place

–  This is important for understanding plant-wind erosion feedbacks in desertification

•  Its easier to measure gap size distribution than λ

ecosystem feedbacks arising from wind transport in ...impact of aeolian processes on drylands •...

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