# Presentation: 688183

## The Impact of surface heterogeneity on dust emission

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

- Multiscale Controls on Aeolian Processes

## Regional to Global Scale (> 100 km)

- February 25, 2000 - SeaWiFS

## Landscape Scale (100 m -10 km)

- Oblique Aerial PhotoCentral TexasGillette (1999)

## 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):

## Spatially-Explicit Wind Erosion Model (SWEMO)

- Shao & Raupach, 1993

## Soil

- Vegetation

- Wind

- Okin and Gillette, 2004

## Model Parameters

- U*t (cm/s)

- U*tv (cm/s)

- Okin and Gillette, 2004

## SWEMO: Variability in Parameter Fields

- Summary of measured values of *Q**Tot* 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 (Gillette, unpublished data)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

## 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

- 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

- Sub-Grid Cell Heterogeneity

## Modeling Isotropic Spatial Variability

- Start with 2-D model
- Assume that *u***t**, C, ****, A**p**/A**B* 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

## Spatial Variability & Threshold

- Okin,  in prep,

- N=500,000

## Spatial Variability and Wind Erosion

- Okin,  in prep,

## Variability is as important as mean values

- Okin,  in prep,

## Conclusions... to this point

- For Vegetated Surfaces:
- Variability in input fields (grid-cell heterogeneity) is important,
- But...flux 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

## 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:

## Anisotropic Vegetation Cover

- 200 m

**M-Nort**

**M-Rabb**

**M-Well**

- Nugget

**Range**

**Sill**

- Okin and Gillette, 2001

**Notes:**
I used semivariograms to probe the existence of streets.  

This was done at three mesquite duneland sites in Jornada- M-Nort, M-Rabb, and M-Well-- where there was detailed field mapping of mesquites done by my co-author on this paper.  

The images that were used were from aerial photos taken in 1996 that had 1-m resolution.

I these images the dark spots are individual Mesquite shrubs and the light areas are bare soil.

## Unidirectional Variograms

- Unidirectional variograms exhibit:
- Sharp rise to global maximum (at Range)
- Intershrub Distance=2*Range
- Hole Effect

**Range**

- Okin and Gillette, 2001

- Range

**Notes:**
Looking at a variogram for one of the sites in one direction we see several things.  

First, a sharp rise to the global maximum.  In my analysis the range is given by the location of the maximum and it represents the average distance from the center of a shrub to the center of the adjacent soil patch.

The shrub-shrub distance, therefore, is given y two times the range.

Also notice that this variogram displays a week hole effect with the sharp rise to the global maximum and the sinusoidal sill.

In order to probe the existence of streets, what I did was to calculate the variogram at a series of compass angles for each site.

## Semivariogram Results

- M-Nort
- Well-Developed Streets

- M-Well
- Little Street Development

- Polar plots show intershrub distance as a function of direction

- Dashed- Field Mapping       Solid- Semivariogram Results

- Okin and Gillette, 2001

**Notes:**
The results of this analysis are shown here.... The solid line gives the intershrub distance at all compass angles using the semivariogram results.  The dashed line is the expected width of the average soil patch also as a function of compass angle.

At M-Nort, where streets appear to be best developed, both the field mapping and semivariogram results show a strong SW-NE elongation.

At M-Rabb, where streets are not as well developed, there are two orientations of elongation:  SW-NE and SE-NW.  The semivariogram results do show some elongation but apparently smeared between these two elongations.

The M-Well site dies not show street development for either the field mapping or semivariogram analysis.

## 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,

## Spatial Connectivity Results

- Grassland

- MesquiteDuneland

- Mixed Grass/Mesquite

- Mixed Grass/Creosote

- Okin et al,  in prep,

## Connectivity Results

- Okin et al,,  in prep,

## Theoretical Impact of Streets

- For the cases where:
-      Range2 = 1.0 m
-      Range1 = 2.5 m

- Simplify:

## 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)

**Notes:**
From these results, I conclude that streets do indeed exist and, where developed are oriented roughly in the direction of the prevailing winds.  

This suggests that wind has played a role in in the development of street and in particular, has played a role in the distribution of the vegetation as the area underwent the transition from a grassland to a shrubland over the past century.

This result makes sense. In a grassland that is starting to be invaded by shrubs, a bare area is not a likely place for establishment of shrubs.  Instead, plants will fare better in places where they are shielded by wind erosion--behind other plants.  As this process goes on, streets will develop and they will be the areas of increased wind erosion, and therefore elevated dust emission.

The streets, as sites of increased dust emission, are also going to be areas of increased nutrient loss by suspension, as we have seen. 

## 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

**Notes:**
In the course of this talk, I have discussed several aspects of the role that wind plays in degrading desert landscapes.  

First, we see that disturbance leads to increased wind erosion, both by the removal of vegetation and the disturbance of soil-protecting crusts.

Wind yields its influence through both its physical effects, through saltation, as well as nutritional effects through suspensions

Finally, in the discussion of streets we saw that wind itself influences vegetation distribution and therefore can exacerbate nutrient loss from the landscape.  In addition, since it plays a role in vegetation distribution, we can begin to say that wind also plays a role in the soil nutrient distribution in desert ecosystems.

What I have done integrated these considerations as well as others in to a new process model of wind-driven degradation in desert lands.  

## 2-D Modeling Implications

## 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

## Vegetation Cover Temporal Variability

- Tegen et al. (2002) recognizes the importance of NPV
- VegetationCover=*f*(FPARmax)
- (Relative) Spectral Mixture Analysis

- Lubbock, TX

- Monument
- Valley, AZ

- Okin, in prep

## Temporal Cover Variability

- Underestimation of vegetation cover in Spring

## Slide 29

## 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

## Spectral Mixture Analysis of an AVIRIS scene

- Okin et al, 2001

## Reflectance vs. Particle Size

- Okin and Painter, 2004

## Effective Grain Size Mapping SWIR 2

- Okin and Painter, 2003

- Grain size is related
- to threshold shear
- velocity.

## First-Order Approximation: Soil erodibility = f(Albedo) 

- First-Order Approximation: Soil erodibility = f(Albedo)

- MESMA of AVIRIS data

- Raw Landsat TM/ETM+ data

- Regional Map of Areas Susceptible to Wind Erosion

- Painter and Okin, in prep