The Impact of surface heterogeneity on dust emission

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The Impact of surface heterogeneity on dust
emission
Multiscale Controls on Aeolian Processes

• Gregory S. Okin
• Department of Environmental Sciences
• University of Virginia

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• Regional to Global Scale (gt 100 km)

February 25, 2000 - SeaWiFS
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• Landscape Scale (100 m -10 km)

Oblique Aerial PhotoCentral TexasGillette (1999)
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The Problem How does landscape-scale
heterogeneity manifest as continental scale
pattern?

• Variability in parameter fields
• A Role for Remote Sensing?
• Soil Texture
• Green and Senescent Vegetation Cover
• Vegetation Distribution
• Sub-grid cell variability
• Raupach and Lu (2002)

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Spatially-Explicit Wind Erosion Model (SWEMO)
Shao Raupach, 1993
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Soil
Vegetation
Wind
Okin and Gillette, 2004
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Model Parameters
Ut (cm/s)
Utv (cm/s)
Okin and Gillette, 2004
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SWEMO Variability in Parameter Fields
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(Gillette, unpublished data)
Okin and Gillette, 2004
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Sub-Grid Cell Heterogeneity

• Spatial Isotropic
• 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 Anisotropic
• Spatial organization in vegetation may exist
• Streets - elongated vegetation-free areas
• Model (Gillette and Chen, 2001)
• Temporal
• Vegetation cover changes throughout the year
• Green and NPV BOTH have sheltering effect
• Land cover change land use has a temporal
  signature

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Modeling Isotropic Spatial Variability

• Start with 2-D model
• Assume that ut, C, b, 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 utv
• Calculate horiz. and vert. mass flux

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Spatial Variability Threshold
N500,000
Okin, in prep,
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Spatial Variability and Wind Erosion
Okin, in prep,
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Variability is as important as mean values
Okin, in prep,
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Conclusions to this point

• For Vegetated Surfaces
• Variability in input fields (grid-cell
  heterogeneity) is important,
• Butflux will still be underestimated without
  sub-grid cell variability
• The mean is meaningless, or alternatively,
• Using the Raupach et al (1993) model, erosion
  happens even when
• Threshold shear velocity is best thought of
  probabilistically

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Anisotropic Spatial Variability

• Mesquite dunelands produce 4 to 8 times more dust
  than nearby shrublands with same biomass
• Are there streets?
• Directions along which preferential elongation of
  soil patches exists
• Yes Okin and Gillette (2001)
• Modification to model

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Anisotropic Vegetation Cover
M-Nort
M-Rabb
M-Well
200 m
Sill
Range
Nugget
Okin and Gillette, 2001
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Unidirectional Variograms

• Unidirectional variograms exhibit
• Sharp rise to global maximum (at Range)
• Intershrub Distance2Range
• Hole Effect

Range
Range
Okin and Gillette, 2001
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Semivariogram Results
M-Nort Well-Developed Streets
M-Well Little Street Development
Dashed- Field Mapping Solid- Semivariogram
Results
Polar plots show intershrub distance as a
function of direction
Okin and Gillette, 2001
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Spatial Connectivity

• Connectivity gives the probability that n
  contiguous pixels will be occupied by the same
  class (I.e. the probability of having a certain
  fetch)
• Fit Parameter (Range) can be interpreted as the
  distance at which the probability of remaining in
  the same cover is 1/e.

Fractional Cover
(Fractional Cover)/e
e-folding distance for connectivity Range
Okin et al, in prep,
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Spatial Connectivity Results
MesquiteDuneland
Mixed Grass/Mesquite
Mixed Grass/Creosote
Grassland
Okin et al, in prep,
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Connectivity Results
Okin et al,, in prep,
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Theoretical Impact of Streets
Simplify
For the cases where Range2 1.0 m
Range1 2.5 m
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Anisotropic Variation

• Vegetation can display isotropic variability
• This means that in some areas and some
  directions, the fetch length can vary
• Geostatistical methods provide a means to
  quantify this effect in a manner consistent with
  Raupach and Lu (2002)

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Implications

• Land use in arid and semiarid regions modulates
  land cover
• Irrigated agriculture in the Mojave Desert
  (Manix)
• Grassland-to-shrubland conversion in the
  Chihuahuan Desert (Jornada)
• Land cover modulates wind erosion and dust flux
• Soil Type
• Vegetation Type
• Cover Amount
• Variability is extremely important
• Wind erosion and dust flux shape the land surface
• Soil texture
• Vegetation distribution

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2-D Modeling Implications
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Temporal Changes in Vegetation Cover

• MODIS Nadir-adjusted Reflectace
• Feb, 2000 - August 2003
• Simple, unconstrained linear spectral mixture
  analysis
• Colors represent relative changes in surface
  components
• Red Pseudosoil
• Green Green Vegetation
• Blue Non-photosynthetic Vegetation

Okin and Ravi, in prep
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Vegetation Cover Temporal Variability

• Tegen et al. (2002) recognizes the importance of
  NPV
• VegetationCoverf(FPARmax)
• (Relative) Spectral Mixture Analysis

Lubbock, TX
Monument Valley, AZ
Okin, in prep
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Temporal Cover Variability
Underestimation of vegetation cover in Spring
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Conclusions(Discussion Points)

• Both grid-cell and sub-grid cell variation are
  equally important in determining flux rates
• Spatial anisotropy (in mesquite
  dunelands-savannas, generally?) can have a major
  impact
• More care is needed in parameterization of
  nonphotosynthetic vegetation in dust-producing
  regions

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Spectral Mixture Analysis of an AVIRIS scene
Okin et al, 2001
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Reflectance vs. Particle Size
Okin and Painter, 2004
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Effective Grain Size Mapping SWIR 2
Grain size is related to threshold shear velocity.
Okin and Painter, 2003
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First-Order Approximation Soil erodibility
f(Albedo)
MESMA of AVIRIS data
Regional Map of Areas Susceptible to Wind Erosion
Raw Landsat TM/ETM data
Painter and Okin, in prep