Dynamics of Post-Wildfire Aeolian Transport in Cold Desert Shrub Steppe
by
Joel Brown Sankey
A dissertation
submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy in the Department of Geosciences
Idaho State University
December 2009
ii
In presenting this dissertation in partial fulfillment of the requirements for an
advanced degree at Idaho State University, I agree that the Library shall make it freely
available for inspection. I further state that permission for extensive copying of my
dissertation for scholarly purposes may be granted by the Dean of Graduate Studies,
Dean of my academic division, or by the University Librarian. It is understood that any
copying or publication of this dissertation for financial gain shall not be allowed without
my written permission.
Signature ___________________________________
Date _______________________________________
i
To the Graduate Faculty:
The members of the committee appointed to examine the dissertation of Joel
Brown Sankey find it satisfactory and recommend that it be accepted.
_________________________________
Dr. Nancy F. Glenn
Major Advisor
_________________________________
Dr. Matthew J. Germino
Committee Member
_________________________________
Dr. Ann Inez N. Gironella
Committee Member
_________________________________
Dr. Glenn D. Thackray
Committee Member
_________________________________
Dr. Galen K. Louis
Graduate Faculty Representative
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Acknowledgments
Nancy Glenn and Matt Germino have been terrific mentors throughout this
process. I appreciate their thoughtful advice and reviews, as well as their good ideas for
my work. Nancy and Matt, I have enjoyed working with and getting to know both of you
and I look forward to continuing our relationships in the future. I would like to thank
Ann Gironella and Glenn Thackray for serving on my committee and helping with and
reviewing my work. I would also like to thank Galen Louis who served as my graduate
faculty advisor. My research and study were supported by: an Inland Northwest
Research Alliance Ph.D. graduate fellowship, the US Army and Research Laboratory and
US Army Research Office under grant #W911NF-07-1-048, a National Center for
Airborne Laser Mapping seed proposal data grant supported by NSF, and through
logistical support from S.M. Stoller Corporation. I would like to thank my parents Eric
and Pam, my sister Jessica, and her family, who were each there for me throughout the
entire Ph.D. program - as well as my boys Levi and Luka who showed up in the middle
of it! Finally, I would like to thank my wife Teki who is so many things, but most
importantly my best friend.
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Table of Contents
Photocopy Use and Authorization .......................................................................................i
Title Page.............................................................................................................................ii
Committee Approval Page..................................................................................................iii
Acknowledgements.............................................................................................................iv
Table of Contents ................................................................................................................v
List of Figures ...................................................................................................................vii
List of Tables ......................................................................................................................x
Dissertation Abstract ………………………….................................................................xi
Chapter 1: Introduction to Dissertation……………….......................................................1
1.1. Outline of dissertation
Chapter 2: Aeolian Sediment Transport Following Wildfire in Sagebrush Steppe……..11
Abstract
2.1. Introduction
2.2. Materials and methods
Study sites
Site characterization
Micrometeorological data collection
Aeolian sediment collectors
Data analysis
2.3. Results
Fall/Winter
Spring/Summer
2.4. Discussion and conclusions
Fire effects on aeolian sediment transport
Temporal and spatial characteristics of post-fire transport
Mechanisms by which fire increases wind erosion
Summary and management implications
Chapter 3: Relationships of Post-Fire Aeolian Transport to Soil and Atmospheric
Conditions…………………………………………………………………...36
Abstract
3.1. Introduction
3.2. Physical setting
Climate and fire
3.3. Materials and methods
Micrometeorological data
Aeolian sediment
Data analysis
Micrometeorological data
Aeolian sediment data
3.4. Results
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Atmospheric and soil moisture
Aeolian sediment
Threshold wind speeds
Threshold wind speed/moisture relationships
3.5. Discussion
3.6. Conclusions
Chapter 4: Relationships of Aeolian Surface Change with LiDAR-Derived
Landscape Surface Roughness following Wildfire…………………………..67
Abstract
4.1. Introduction
LiDAR surface roughness
Objectives
4.2. Study area
4.3. Methods
Surface change measurements
LiDAR data and processing
Scales of analysis
Analysis of surface change and LiDAR surface roughness
4.4. Results
Surface change
Spatial patterns of surface change
LiDAR surface roughness
LiDAR surface roughness and surface change
4.5. Discussion
Surface change
Landscape scale
Finer scales
LiDAR surface roughness and surface change
4.6. Conclusion
References Cited……………..………………………......................................................97
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List of Figures
Figure 2.1. Mean wind speed measured below (0.4 m above ground) the unburned
vegetation canopy, and at the top (1.1 m above ground) of the unburned
vegetation canopy, at unburned and burned sites.
Figure 2.2. Saltation activity detected at unburned (upper panel) and burned (lower panel)
sites. Saltation activity is unitless and is calculated as the number of seconds
in a 300 second period during which particle counts were recorded.
Figure 2.3. Examples of maximum wind speed and aeolian critical threshold determined
for unburned and burned sites on November 11th and 23rd, 2006. Maximum
wind speed and threshold wind speed data are determined for 5 minute
periods.
Figure 2.4. Mean horizontal sediment mass flux at unburned (upper panel) and burned
(lower panel) sites with standard error bars. The unburned and burned sites
were sampled at the same intervals and missing values for the unburned site
represent periods when no measurable amount of sediment accumulated in the
collector.
Figure 2.5. Photo taken on 06/10/07 showing herbaceous vegetation surrounding a
blowout that developed in fall, post-fire (note sediment collector in
background). This blowout persisted throughout the spring/summer study
period and the nearby collector exhibited consistently higher sediment fluxes
than many of the other burned site collectors.
Figure 3.1. Satellite image of the Eastern Snake River Plain (ESRP), Idaho, USA (a);
location of Idaho relative to USA (b); and location of study sites within the
ESRP (c). The satellite image (a) illustrates the influence of aeolian processes
on the surface of the ESRP. Note the light-toned streaks of eroded bare soil in
the upper half of the image that trend parallel to prevailing winds (SW-NE),
cutting through both crop and rangelands. The light-toned crescent features in
the upper right corner are sand dunes. The medium-toned rounded feature at
the bottom center of the image is a basalt lava flow on which very little soil
has developed.
Figure 3.2. Micrometeorological parameters during saltation events at the heavy and light
burn sites. Parameters are determined for 5 minute periods, with the
exception of soil volumetric water content which is determined for 6 hour
periods. Saltation activity is the fraction of each 5 minute period during
which particle counts were recorded by the Sensit® (# seconds with particle
counts / 300 seconds).
vii
Figure 3.3. Horizontal sediment mass discharge at the heavy burn, light burn, and
unburned sites. The plotted discharge values are rates for each preceding
sampling interval (i.e. since the previous plotted value).
Figure 3.4. Median, minimum, and maximum threshold wind speeds for days with an
erosion event that lasted at least 15 minutes.
Figure 3.5. Examples of within day relationships for threshold wind speeds with time and
atmospheric relative humidity. Threshold and relative humidity are
determined for 5 minute periods.
Figure 3.6. Relationships of median daily threshold wind speed and soil volumetric water
content, for days with an erosion event that lasted at least 15 minutes.
Figure 3.7. Photo taken at the study area, showing locations where deflation has increased
the presence of more cohesive, calcareous soil at the surface (lighter colored
surfaces with polygonal cracks).
Figure 4.1. (a) Location of study relative to Idaho, with inset showing location of Idaho
relative to USA. (b) Aerial photograph of the eastern Snake River Plain, Idaho
with outline of Twin Buttes and Moonshiner wildfire boundaries, study site
locations (S = severely burned, M = moderately burned, and U = unburned),
and bare-earth digital elevation model for LiDAR data collection area. (c)
Schematic of erosion bridge locations within a 100 m radius hypothetical
study site. At each site, erosion bridge transects 3 and 1 were oriented along
an axis from SW-NE, and transects 2 and 4 were oriented from SE-NW.
Interval values denote distance of erosion bridge inner post from the center of
the study site.
Figure 4.2. Wind direction for wind speeds greater than 5 and 8 m s-1 from fall 2007 – fall
2008. Chart demonstrates that winds predominantly trended SW-NE. Wind
data were acquired from the Idaho National Laboratory (collected at 10 m
height). 5 and 8 m s-1 correspond to approximate minimum and middle
values, respectively, of threshold wind speed for aeolian transport measured
on burned surfaces at the study location previously by Sankey et al. (2009b).
Figure 4.3. Mean (with standard error bars) surface change and LiDAR-derived surface
roughness measurements for severely burned, moderately burned, and
unburned areas, and for individual study sites within these areas. Surface
roughness is calculated as the standard deviation of all LiDAR point heights
(ground + vegetation) per raster cell (2 m resolution) after the point heights
have been detrended for topographic slope.
Figure 4.4. Mean (with standard error bars) surface change and LiDAR-derived surface
roughness vs. vegetation basal cover (fall, 2007) for severely burned,
moderately burned, and unburned study sites.
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Figure 4.5. Autocorrelation structure of surface change measurements at the amongplayette/coppice scale, as depicted by directional semivariograms constructed
for erosion bridge data aggregated by severely, burned, moderately burned,
and unburned areas with 5 m lag and 40 m maximum separation distance.
Semivariograms depict the spatial dependence (semivariance – y-axis) of
samples as a function of separation distance (distance – x-axis). Smaller
semivariance indicates greater relative spatial dependence (autocorrelation).
Figure 4.6. Autocorrelation structure of surface change measurements at the withinplayette/coppice scale, as depicted by directional semivariograms constructed
for erosion bridge data aggregated by severely burned, moderately burned,
and unburned areas with 0.1 m lag and 1.0 m maximum separation distance.
Semivariograms depict the spatial dependence (semivariance – y-axis) of
samples as a function of separation distance (distance – x-axis). Smaller
semivariance indicates greater relative spatial dependence (autocorrelation).
Figure 4.7. Relationship of mean surface change aggregated by site with the inverse of
LiDAR-derived surface roughness amongst severely burned, moderately
burned, and unburned sites. Surface roughness is calculated as the standard
deviation of all LiDAR point heights (ground + vegetation) per raster cell (2
m resolution) after the point heights have been detrended for topographic
slope.
Figure 4.8. Mean surface change aggregated by erosion bridge vs. the inverse of surface
roughness for corresponding nearest neighbor LiDAR pixels amongst severely
burned, moderately burned, and unburned areas. Surface roughness is
calculated as the standard deviation of all LiDAR point heights (ground +
vegetation) per raster cell (2 m resolution) after the point heights have been
detrended for topographic slope. τ values represent the quantile of the surface
change distribution analyzed conditional on the inverse of surface roughness.
β values are the quantile regression slope coefficients.
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List of Tables
Table 2.1. Summary of sediment transport from burned and unburned areas, as assessed
with passive sediment collectors following summer/fall fires in ungrazed areas
of Western North America. Sediment collection rates (g d-1) were estimated
from data presented with other units in the previous studies.
Table 2.2. Mean (standard error) ground cover of sampling sites.
Table 2.3. Saltation detection results.
Table 2.4. Pearson correlation coefficients (p-values) measured at Heavy and Light burn
sites.
Table 4.1. Vegetation basal cover aggregated for burned and unburned areas.
Table 4.2. Install date and vegetation aggregated for severe (S) moderate (M) or
unburned (U) sites.
Table 4.3. Site mean surface change (y) vs. site mean inverse LiDAR surface roughness
(x).
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Dynamics of Post-Wildfire Aeolian Transport in Cold Desert Shrub Steppe
Dissertation Abstract--Idaho State University (2009)
Aeolian sediment transport is an important contemporary biogeomorphic agent in
semiarid shrublands, particularly when wildfire temporarily reduces the protective cover
of vegetation. This research examined spatial and temporal differences in aeolian
transport for burned and unburned semiarid, cold desert shrub steppe of the eastern Snake
River Plain, Idaho. Relationships of soil erodibility to soil and atmospheric conditions, as
well as relationships between aeolian surface change and landscape surface roughness
were additionally examined in detail following wildfire. Research was conducted at
locations burned in late-summer by the Crystal fire (2006) and Twin Buttes and
Moonshiner fires (2007) and adjacent unburned locations. Methods included field
monitoring of: soil and atmospheric micrometeorological conditions, saltation and
associated threshold wind speeds, vegetation cover, changes in relative surface
elevation(s), and sediment flux. Remote sensing characterization of landscape surface
roughness was performed with a LiDAR dataset. Results indicated that the increase in
aeolian transport following burning at all sites was substantial and of a greater magnitude
to that reported for many other burned environments of the world, though the increase in
transport appeared to persist over a shorter time period in cold desert shrub steppe due to
the seasonality of climate and regrowth of herbaceous vegetation in subsequent spring.
Soil and atmospheric moisture were determined to be important influences on transport
potential during the fall months of high erosion that immediately followed late-summer
fire. Erodibility generally decreased with increased moisture during the fall, though more
xi
complex relationships were observed - often at finer temporal scales. Surface change
varied strongly as a function of landscape surface roughness, with the greatest deflation
observed on the relatively smooth, burned surfaces, and greatest inflation observed on the
rough, vegetated, unburned surfaces. Furthermore, effects of surface roughness were
greater on erosion compared to deposition processes. Spatial analysis suggested that
aeolian processes increased the heterogeneity of microtopography in burned surfaces,
whereas the downwind surfaces that were unburned experienced a more homogeneous
pattern of surface change. Findings of this research have direct application to regional
rangeland management, as resource managers determine the best practices for, and
implications of, remediation of burned shrub steppe rangelands.
xii
Chapter 1: Introduction to Dissertation
Aeolian transport is a driver of sediment, nutrient, and pollutant cycling in the
world, with major impacts on ecosystem functioning and human activities. Ecosystem
functioning and human actions have major impacts on the magnitude and timing of
aeolian processes, conversely (Neff et al., 2008). While aeolian sediment generally
originates in desert environments, the potential for long-range transport of fine sediment
with adsorbed nutrients and pollutants can have global implications in non-desert
environments such as mountains, rainforests, and oceans. Desert dust originating in
Central Asia can decrease air quality throughout western North America, for example
(Husar et al, 2001; VanCuren and Cahill, 2002). Airborne sediment originating in
northern Africa provides a necessary source of elemental phosphorous to the Amazon in
South America (Okin et al., 2004).
One of the most important influences of dust in the atmosphere at global and
hemispheric scales is on hydroclimate. Atmospheric dust can specifically alter both
radiation balance as well as cloud nucleation (McConnell et al., 2007; Rosenfeld et al.,
2007). Atmospheric dust generated by storms in western Africa, for example, is
hypothesized to cool the surface of the Atlantic Ocean and consequently quell hurricanes
in the Caribbean and eastern coast of North America (Lau and Kim, 2007). Dust in the
atmosphere covaries with hydroclimate at global and hemispheric scales over geologic
timescales, with records from alpine and Antarctic ice cores providing evidence that dust
transport has historically occurred both as a function and driver of variability in
hydroclimate (Kang et al., 2003; McConnell et al., 2007). Furthermore, human actions
can act in cohort with hydroclimate to influence cycling of dust at global scales.
1
Increased dust deposition during the 20th century in Antarctica has been linked to humancaused deforestation in South America in addition to desertification and climate warming
(McConnell et al., 2007).
At regional scales, aeolian transport is an important biogeomorphic agent which
has implications for feedbacks between, and the connectivity of: climate, water,
ecosystem function, biogeomorphic disturbance such as wildfire, and human health (Neff
et al., 2008). Recent research has demonstrated that dust derived from deserts in the
southwestern USA is advancing the Rocky Mountain snowmelt cycle (Painter et al.,
2007). Earlier snowmelt in the mountains advances phenological changes of earlier
spring due to alterations in the timing at which water is available to plants (Steltzer et al.,
2009). Earlier snowmelt in the mountains (resultant from climate warming in addition to
dust deposition) alters runoff and streamflow (Mote, 2003; Stewart et al., 2005; Painter et
al., 2007) changing the supply of mountain water to the downstream lowlands of deserts.
In the deserts, changes in the supply of water and climate warming are contributing to
phenological changes of earlier spring, greater evaporative loss, and extended summer
drought (Hughes and Diaz, 2008). Such changes in climate, hydrology and phenology
are, in turn, increasing wildfire (Westerling et al., 2006).
Wildfire is known to exacerbate aeolian processes in forests and deserts (Whicker
et al., 2006a; Sankey et al., 2009a). Resultant increases in airborne sediment can
subsequently provide feedback to climate and hydrology in mountain and desert
ecosystems (Hughes and Diaz, 2008; Steltzer et al., 2009). Airborne particulate matter
generated in lowlands can reduce precipitation in mountains that are downwind
(Rosenfeld et al., 2007). Desert dust can also drive aridification of climate in desert
2
lowlands themselves (Han et al., 2008). Airborne particulate matter can negatively
impact human health by significantly reducing air quality in populated communities
(Delfino et al., 2009; Wegesser et al., 2009).
The regional aeolian sediment cycle described above is relevant for desert and
downwind mountain ranges not just in the western USA, but throughout the world
(Painter et al., 2007). Climate and changes in climate are an expectedly central variable
in this cycle. Specifically in the western USA, one of the most prominent effects of
climate change is an increase in wildfire (Westerling et al., 2006). Wildfire increases
aeolian transport of soil in desert landscapes largely due to the reduction of the protective
cover of vegetation (Ash and Wasson, 1983; Wasson and Nanninga, 1986; Zobeck et al.,
1989; Wiggs et al., 1994, 1995, 1996; Whicker et al., 2002, 2006a,b; Vermeire et al.,
2005; Ravi et al., 2007, 2009; Breshears et al., 2009; Sankey et al., 2009a, 2009b).
Previous research suggests that a very small presence of vegetation is required to
substantially limit aeolian transport in warm desert environments (Thomas et al., 2005).
A small amount of moisture at the soil surface in addition to, or in lieu of, vegetation can
also limit transport in warm deserts (Ravi and D’Odorico, 2005). The presence of
vegetation generally reduces the erosivity of the soil surface (Breshears et al., 2009),
while the presence of moisture generally limits the erodibility of the soil surface (Ravi et
al., 2006a,b). Erosivity refers to the wind’s ability to entrain soil particles, whereas
erodibility relates to the soil’s susceptibility to entrainment (Bagnold, 1941; Okin et al.,
2006).
Field-based research on erosivity and erodibility in non-agricultural
biogeomorphic systems (i.e. other than croplands) has largely focused on warm desert
3
landscapes that are unvegetated. However, desert landscapes that are vegetated can
exhibit large fluxes of wind-transported sediment, particularly following disturbance
(Breshears et al., 2003, 2009; Thomas et al., 2005). Vegetation limits erosivity in warm
deserts by providing cover to the soil surface, extracting momentum from the wind, and
trapping soil particles (Wolfe and Nickling, 1993). The protective cover provided by
vegetation in undisturbed shrublands has been hypothesized to be less effective compared
to other undisturbed landscapes (e.g. grasslands, forests) because wind flow can
penetrate, and even increase in erosive potential, below the height of the mean shrub
vegetation canopy (Breshears et al., 2009). Wind flow in undisturbed warm deserts with
shrub vegetation is generally considered to be near the boundary of the conceptual wake
interference and skimming flow regimes (Lee and Soliman, 1977; Lee, 1991a; Wolfe and
Nickling 1993; Breshears et al., 2009). In skimming flow, the wind’s erosive force is
attenuated at or just within the vegetation canopy over the entire landscape. Wake
interference flow, in contrast, occurs on landscapes where vegetation is spaced widely
enough that eddies develop around individual surface roughness elements and overlap,
negating aeolian transport within vegetation and associated eddies. Disturbance of
vegetation, such as by wildfire, decreases the density and stature of herbaceous and
woody vegetation, which increases the propensity for wake interference flow or isolated
roughness flow (Wolfe and Nickling 1993; Breshears et al., 2009). Isolated roughness
flow is a third conceptual flow regime in which eddies do not overlap, thereby increasing
entrainment and transport of sediment (Lee and Soliman, 1977; Wolfe and Nickling
1993).
4
Moisture at the soil surface physically limits erodibility in warm deserts through
the adsorption of water to soil particles. This can increase the weight of particles as well
as increase interparticle cohesion, both of which reduce the transport potential of the soil
(Cornelis, 2006). While erodibility generally decreases with increased moisture, it is
possible in dry conditions for water to disperse individual soil particles, decreasing
interparticle cohesion and making the soil surface more erodible (Ravi et al., 2004).
Furthermore, erodibility can increase with increased moisture when air temperatures are
cold (McKenna Neuman and Sanderson, 2008).
Detailed analyses of relationships of moisture with erodibility and vegetation with
erosivity are often carried out in wind tunnel experiments in laboratories and much less
so in natural field settings. Consequently, there is no clear understanding of the relevance
of many fine scale processes which have been well documented, to patterns and processes
observed at coarser scales (Okin et al., 2006). Conceptually (e.g., in the cycle of regional
aeolian transport presented earlier for the western USA), transport potential is assumed to
be much less during wetter climate at a landscape scale despite the complexities related to
interparticle bonding that are known to exist at very fine scales. Similarly, fine scale
processes of flow, turbulence, and transport within and adjacent to individual plants are
generally oversimplified by conceptual flow regimes at coarser scales. Nonetheless,
many fine scale processes (the entrainment of dust particles by the saltation of individual
sand grains, for example) are known intuitively to coalesce into regional and global scale
processes (e.g. dust deposition) which have identifiable impacts on environment and
humans.
5
This dissertation examines the potential impact of changing wildfire regime on
erosivity and erodibility in a field setting, and predominantly at a landscape scale, in cold
desert shrub steppe. Cold desert shrub steppe differs from most environments where
effects of fire on wind erosion have been previously examined in that it is a higher
latitude landscape, with potential for relatively high vegetative cover and a short season
for erosion, particularly in comparison to warm deserts. In cold desert shrub steppe, the
degree to which wildfire increases aeolian transport is not well understood due to the
strong seasonality in annual climate (largely resultant of latitudinal position). These
landscapes are defined as having cold (air temperatures below freezing) winters that are
often snowy, hot dry summers, wetter spring and fall seasons, with a growing season that
occurs in spring (Smith et al., 1997).
When wildfires burn in the summer in cold desert shrub steppe, subsequent
aeolian transport might be relatively insignificant if post-fire green-up of herbaceous
vegetation occurs rapidly (e.g., prior to the winter dormant season). Conversely, surfaces
might be prone to erosion over a much longer time period if herbaceous vegetation is
slow to recover following wildfire. In such instances, green-up might not occur until
spring of the subsequent year’s growing season. Variability in climate during the
season(s) following fire and prior to the regrowth of vegetation might have important
effects on the magnitude and timing of aeolian transport.
Wildfires generally produce heterogeneous surfaces of burned vegetation and soil,
so the presence of vegetation and microtopography that remain following fire are further
expected to influence aeolian processes. Very little entrainment of sediment by wind is
expected to occur in undisturbed surfaces of the cold desert shrub steppe, for example,
6
because the flow of wind likely occurs over the surface in a conceptual regime of
skimming flow in which little to none of the erosive force penetrates the relatively
continuous canopy of shrub vegetation. Conversely, when vegetation has been reduced
by fire, wind flow might occur within the conceptual isolated roughness regime,
indicating that the landscape would consist of individual surfaces in which erosion either
occurs or does not occur, conditional on the absence or presence, respectively, of
vegetation and microtopography. Variability in the relative elevation (roughness) of
vegetation and microtopography might then be expected to explain further variability in
wind erosion in these disturbed surfaces. Landscapes of the cold desert shrub steppe are
comprised of alternating microtopographic surfaces of coppices (raised mounds located
beneath shrub vegetation) and playettes (interspaces between shrubs/coppices). Coppices
and playettes can have spatial dimensions of ~ 1-5m in length and width (Hilty et al.,
2003) and spatial variability in erosion potential might therefore be expected to occur at
this scale, for disturbed surfaces.
The overall goal of this dissertation is to determine the potential for aeolian
transport following wildfire in cold desert shrub steppe rangelands of southeastern Idaho.
The objectives for my research reflect a progressive development of research questions
stemming from observations during my fieldwork and analysis of results. I initiated
fieldwork at the Crystal Fire in fall, 2006 with the objective to:

Determine differences in aeolian transport potential, and the longevity of
these differences, for burned and unburned semiarid cold desert shrub
steppe.
7
At the Crystal Fire, increases in wind erosion following fire were greater in comparison
to many other previously studied environments of the world, but the increases in erosion
were less prolonged for cold desert shrub steppe and occurred predominantly during the
fall months prior to winter and subsequent spring. Over the course of one year of
monitoring, I observed, in addition to my stated objective, variability in aeolian transport
resultant of: 1) soil and atmospheric climate controls on soil erodibility, and 2) vegetation
and microtopographic controls on wind erosivity. Very little erosion continued to occur
one year following fire at the Crystal site, so I explored climate-erodibility relationships
more explicitly at two new fires that burned adjacent to one another at the Idaho National
Laboratory (INL) in fall, 2007. The stated objectives of my research at the INL fires
were to:

Determine and describe relationships of erodibility to soil and atmospheric
moisture and time, prior to the re-emergence of vegetation following
wildfire.

Describe spatial patterns of aeolian surface change, and determine
relationships between aeolian surface change and landscape surface
roughness, characterized by LiDAR remote sensing, in recently burned
cold desert shrub steppe.
At the INL fires, erodibility decreased with time following burning and decreased with
increased soil and atmospheric moisture during the fall months, in general. Complexity
in the moisture/erodibility relationship was evident, however, that was analogous to
relationships previously described by theoretical, laboratory, and notably only one field
study (in warm desert of the USA) that I am aware of. Smooth surfaces which were
8
burned were characterized by erosion, and rough surfaces which were unburned were
characterized by deposition at the INL fires. Erosion, in comparison to deposition, varied
more strongly with surface roughness characterized by LiDAR.
Findings of this dissertation suggest that burned shrub steppe of the cold desert is
subject to increased levels of aeolian transport. If wildfires continue to increase with
future climate change, subsequent increases in aeolian transport might be expected in this
environment. Results suggest that increased aeolian transport might be particularly
substantial during the fall months following summer fire. The degree of vegetation and
microtopographic roughness that remain following burning, variability in climate
(specifically wetness of the soil and atmosphere) during the months following fire, and
the degree to which vegetation recovers during the growing season of the subsequent
spring, collectively play an important role in determining the magnitude and timing of
increased aeolian transport.
An increase in wildfire frequency and an increase in the length of the annual
wildfire season are two of the most prominent effects of climate change that is now
occurring in the western USA (Westerling et al., 2006). While results of this dissertation
suggest that an increase in wildfire frequency might be expected to increase the
prevalence of wind erosion of soil in cold desert shrub steppe, the expected effects of
increased length of the annual fire season on the current wind erosion regime remains an
important question for future research. I expect that variability in temporal dynamics of
erodibility (i.e. climate at the soil surface) and spatial dynamics of erosivity (i.e.
roughness of vegetation + microtopography) would differ following early spring vs. midsummer vs. late-fall fires. To guide future research, I hypothesize that surfaces that burn
9
during the seasonal drought of mid-summer are subject to greater and more sustained
increases in wind erosion than surfaces which burn in spring or fall. I further hypothesize
that this effect is amplified for “dry” vs. “wet” years and “warm” vs. “cold” years.
1.1. Outline of dissertation
This dissertation follows a three-manuscript format. It consists of this
introduction chapter, and three subsequent research chapters. Each of the research
chapters is written as a manuscript and addresses one of my stated research objectives.
Contents of the first two chapters have been published in the Journal of Arid
Environments and the Journal of Aeolian Research, respectively.
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Chapter 2: Aeolian Sediment Transport Following Wildfire in
Sagebrush Steppe
Abstract
Wind erosion of soil is an appreciable but unstudied event following fires in cold
desert. We examined aeolian transport of sediment for 1 year following fire in semi-arid
shrub steppe on loess soils in southern Idaho, USA. Sediment collectors were used to
determine horizontal mass transport of soil and saltation sensors and anemometers were
used to determine saltation activity (fraction of time having saltation) and threshold wind
speed in an area burned in August and an unburned control site. Horizontal mass
transport (per 30-day period) was negligible in the unburned area, but in the burned area
was 6.35 kg m-1 in October and decreased to 3.31 kg m-1 in November and 0.50 kg m-1 in
December. Saltation activity was high enough to determine threshold wind speeds only in
the burn site during fall, when values ranged from 10.0 to 10.6 m s-1. Sediment flux and
saltation activity in the burned site became much less pronounced following the
emergence of herbaceous vegetation in the spring. Post-fire sediment flux in the shrub
steppe we examined was of greater magnitude but shorter duration than post-fire fluxes in
warm deserts or sandier regions that experience more frequent wind erosion.
2.1. Introduction
Aeolian sediment transport is a fundamental geomorphic process that has wideranging environmental implications for human and environmental health (Whicker et al.,
2006), ecological functioning at multiple spatial and temporal scales (Okin et al., 2006),
local and global biogeochemical cycling (Okin at al., 2004; Reynolds et al., 2001;
Chadwick et al., 1999), and contaminant transport (Whicker et al., 2004). Aeolian
sediment transport is a function of the wind’s ability (impeded by vegetation and terrain)
11
to entrain soil particles, and the soil’s susceptibility to this entrainment (Okin et al., 2006;
Bagnold, 1941). Field-based research on aeolian transport in non-agricultural systems
has largely focused on dunefields and arid landscapes, however, sediment transport via
wind is substantial in semi-arid shrublands and can exceed that by water (Breshears et al.,
2003). Wind erosion can be especially significant following fire, as shown for prairie and
warm desert in the southern United States, dunes of the southwestern Kalahari desert of
Africa, and arid and semi-arid dunefields of Australia (Thomas and Leason, 2005;
Vermeire at al., 2005; Whicker et al., 2002; Wiggs et al., 1996; Wiggs et al., 1995; Wiggs
et al., 1994; Wasson and Nanninga, 1986; Ash and Wasson, 1983; Zobeck et al., 1989).
Increased wind erosion has been reported immediately following prescribed fires
in mixed prairie (Vermeire et al., 2005), at various intervals following summer wildfire in
warm desert shrublands and rangelands (Whicker et al., 2002; Zobeck et al., 1989), and
following wildfire in linear dunes of the SW Kalahari (Thomas and Leason, 2005; Wiggs
et al., 1996; Wiggs et al., 1995; Wiggs et al., 1994), in dunefields of Australia (Wasson
and Nanninga, 1986; Ash and Wasson, 1983), and in model simulations of semi-arid
dunefields (Nield and Baas, 2008) (additionally see Table 2.1). It is generally known that
the removal of vegetation cover by fire or other disturbance can increase the potential for
wind erosion. Relationships between vegetation cover and aeolian transport have been
determined for specific landscapes, both burned and unburned. For example, a vegetative
cover of 14% is commonly cited as a threshold for sand transport on the linear dunes of
SW Kalahari (Wiggs et al., 1995). A threshold of 15 % grass cover was determined for
sand transport at Owens Lake, California (Lancaster and Baas, 1998). In northern
Australia, sand transport was estimated to occur decreasingly with up to 45 % vegetative
12
cover on unvegetated dunes and sandplains (Wasson and Nanninga, 1986). When
vegetation is reduced specifically by fire, substantially increased wind erosion has been
estimated to occur for up to 5 years in the SW Kalahari and 10 years in N Australia,
based on observations and estimates of erosion, post-fire plant re-colonization, and fire
return intervals (Wiggs et al., 1994; Wasson and Nanninga, 1986). Similar time scales of
increased dune activity following vegetation removal were recently reported for model
simulations of semi-arid dunefield evolution (Nield and Baas, 2008).
Substantial pulses of erosion have recently been noted (qualitatively) following
wildfires in cold desert between 40-45°N latitude. Wind erosion has been previously
known only to occur on unvegetated surfaces in these landscapes (e.g. agricultural fields,
active sand dunes, playas, and floodplains). It is not well known whether wind erosion
persists through plant re-colonization of burned areas in mid-latitude cold-desert,
however, where plant reestablishment can be delayed in cold or dry dormancy periods
following burning seasons. Moreover, previous studies of fire effects on wind erosion
have all occurred on sandy soils. We examined the course of wind erosion as it related to
changes in plant abundance following a fire in cold desert sagebrush-steppe occurring on
fine-textured loess soils, which appeared to otherwise have not been affected by recent
fire or aeolian losses of soil.
13
Table 2.1. Summary of sediment transport from burned and unburned areas, as assessed with passive sediment collectors following
summer/fall fires in ungrazed areas of Western North America. Sediment collection rates (g d-1) were estimated from data presented
with other units in the previous studies.
Biome and location
Period
Heighta
nb
Burn (g d-1)
Unburn (g d-1)
Burn/Unburn
Zobeck et al.,1989
(Table 2)
Shinnery Oak Rangeland
Southern High Plains, TX
~43 days
Mar - May
Whicker et al., 2002
(Figure 7)
Desert Shrubland
Chihuahuan Desert, NM
~272 days
Jun - Feb
Low
High
Low
High
Low
High
Low
High
9
9
3
3
4
4
4
4
1.0147
0.0867
0.3500
0.1500
0.1146
0.0343
0.0250
0.0074
0.0014
0.0012
0.0500
0.0100
0.0047
0.0028
0.0046
0.0007
727
75
7
15
24
12
5
10
Low
6d3e
0.0250
0.0004
67
High
d e
0.0051
0.0002
28
0.31 (0.19)
0.06 (0.03)
0.01 (0.01)
0.00 (0.00)
166 (141)
28 (12)
Study
Vermeire et al., 2005
(Table 1, Figure 2) c
Sand Sagebrush Mixed
Prairie, NW OK
Current study
Shrub steppe, cold desert
Snake River Plain, SE ID
~166 days
Dec - Apr
~168 days
Dec - Apr
323 days
Sep - Aug
Mean (SE) at low (approximately 0.2 m) collection height
Mean (SE) at high (approximately 0.5 m) collection height
a“
63
Low” collection heights: 0.15m (Zobeck et al., 1989); 0.225m (Whicker et al., 2002); 0.2m (Vermeire et al., 2005 and This study).
“High” collection heights: 0.5m (Zobeck et al., 1989); 0.525m (Whicker et al., 2002); 0.4m (Vermeire et al., 2005); and 0.55m (This
study).
b
number of collectors used per burn (or unburn) site in study
c
results presented for only the ungrazed treatments in Vermeire et al., 2005 (note a two year study)
d
number of collectors used in burned sites
e
number of collectors used in unburned site
14
Cold desert sagebrush-steppe is a vast habitat type that is uniquely characterized
by warm, dry summers; and cold, snowy winters. Snowmelt and rain in spring provide
most of water available to plants, and spring "green-up" is followed by summer drought
and leaf senescence by August (Smith et al., 1997). Fire frequencies and area burned per
year have increased substantially in the recent decade in sagebrush steppe, with area
burned approaching 500 ha in some years (e.g. 2007 Murphy complex fire in Western
North America). Fire typically occurs in summer, and charred soils frequently remain
unvegetated until re-sprouting of grasses (and some forbs and occasionally shrubs) occurs
in the subsequent spring and summer (Harniss and Murray, 1973). Herb cover, especially
grasses, dominate vegetation cover for decades following fire, while species such as
sagebrush slowly recover to pre-fire levels. Seeding and planting are commonly used in
attempt to bolster cover in the first years after fire [United States Department of Interior,
Bureau of Land Management] (e.g. Hilty et al., 2003), which implicitly assumes that
natural plant recovery is not adequate to stabilize soil.
Observations of saltation activity (the fraction of time in which saltating particles
can be detected) and critical aeolian threshold (hereafter "threshold", the minimum wind
speed required for saltation) were useful for distinguishing erodibility of soil surfaces
with expectedly different susceptibility to erosion by wind (Stout, 2007). We examined
whether saltation activity and threshold, in conjunction with sediment flux measurements,
could also reveal differences between the wind erosion potential of burned and unburned
rangelands. Saltation activity, threshold, and actual rates of horizontal sediment transport
can be determined from simultaneous micrometeorological measurements of wind and
other boundary layer atmospheric characteristics, passive sediment sampling systems for
15
measurement of horizontal sediment mass flux, and observations of soil loss and
deposition at the soil surface (Vermeire et al., 2005; Stout, 2004; Whicker et al., 2002;
Zobeck et al., 1989). Our objective was to use these approaches to determine differences
in wind erosion potential, and the longevity of these differences for a burned and an
unburned (control) area in semi-arid cold desert shrub steppe. We hypothesized that: (1)
wind erosion potential would be greater at burned compared to unburned sites during fall,
and (2) differences in wind erosion potential between burned and unburned sites would
not be detectable following the spring emergence of herbaceous vegetation.
2.2. Materials and methods
Study sites
The study sites are located in the southern portion of the Eastern Snake River
Plain, near Aberdeen, Idaho (Lat. 43° 00’ N, Long. 113° 00’ W, 1460 m elevation). The
area is located in a zone of 200 mm to 280 mm of mean annual precipitation and 7 to 13
°C mean annual temperature (NCSS Web Soil Survey). This portion of the Snake River
Plain is characterized by near surface winds that trend generally from SW to NE
throughout the year (Clawson et al., 1989).
The rangeland vegetation includes Wyoming big sagebrush (Artemisia tridentata
ssp. wyomingensis Rydb.) and the less abundant shrubs “three-tip” sagebrush (Artemisia
tripartita Rydb.) and green rabbitbrush (Chrysothamnus viscidoflorus [Hook.] Nutt.),
bluebunch wheatgrass (Agropyron spicatum Pursh.), prairie junegrass (Koeleria
macrantha Ledeb.), and sandberg bluegrass (Poa secunda J. Presl) (NCSS Web Soil
Survey). Soils at the study sites are predominately Xeric Haplocalcids that have
developed from silty loess overlying basalt bedrock (NCSS Web Soil Survey). Depth to
bedrock ranges from 20 to 150 cm, and surficial soil textures are silt loam in deeper
16
loess-derived soils and a stony loam in some locations where fractured bedrock is nearer
the surface (NCSS Web Soil Survey). Soils at the sites are rated with a Wind Erodibility
Index value of 1.9 x 105 kg ha-1 year-1 soil loss, and have a Wind Erodibility Group rating
of 4, which is intermediate to values for all USA soils (NCSS Web Soil Survey).
Site characterization
Sampling was initiated in September, 2006 at a large fire on the Eastern Snake
River Plain (Crystal Fire, approximately 80,000 ha, August, 2006). Two study sites in
the burn and one site in the neighboring unburned area were selected, at the easternmost
extent of the fire. Sampling intensity was greater at one of the burn sites (hereafter
referred to as the primary burn site), compared to the other (secondary) burn site. Each
site was delineated as a 100-m radius circle, and there was at least 1,000 m between the
study sites and the edge of the burn area. Four transects radiating out from the center of
the circle in the cardinal directions were used for the characterization of vegetation
composition at each site. Percent cover of shrubs, herbaceous plants, and bare ground
was measured along each transect using the line intercept method, in which cover beneath
all lengths of transects was recorded and reported as a fraction of total cover. Height of
individual shrubs along each transect were measured. Cover and height means were
calculated from the eight transects for the two burned sites and from the four unburned
site transects at the outset of the study. Vegetation cover and height were resampled on
the unburned and primary burn site in spring 2007 following the emergence of
herbaceous vegetation.
Micrometeorological data collection
Micrometeorological and saltation monitoring equipment was installed at the
center of the unburned site and the primary burned site in the last week of September,
17
2006. Sensors included two anemometers (Model 014A, MetOne Instruments Inc.,
Grants Pass, OR, USA) mounted at heights above and below the unburned vegetation
canopy (1.1 and 0.4 m), a SENSIT® piezoelectric sensor (Sensit Company, Portland, ND,
USA) mounted at 5 cm above the ground surface that records impacts from saltating soil
particles, and temperature and relative humidity sensors (Model HMP50L, Vaisala
Group, Vantaa, Finland) mounted at 5 cm above ground. The mean (time-averaged),
standard deviation, and maximum wind velocity, the number of seconds with particle
impacts, and average relative humidity and temperature were recorded at five-minute
intervals, at a 1-second scan rate using dataloggers (model CR10, Campbell Scientific
Inc., Logan, UT, USA). Data collection was suspended in mid-January due to winter
access problems. Data were simultaneously collected at the burned and unburned sites
during approximately 105 days during this period of the study (hereafter referred to as,
fall/winter).
The micrometeorological and saltation monitoring equipment was reinstalled
following the spring emergence of herbaceous vegetation on May 16, 2007 and data were
collected until August 11, 2007. Data were collected simultaneously at the two sites for
85 days during this period of the study (hereafter referred to as, spring/summer).
Aeolian sediment collectors
Big Springs Number Eight (BSNE®) (Custom Products, Big Spring, TX, USA)
omnidirectional passive sediment collectors mounted on towers at 5, 10, 20, 55, and 100
cm height were installed at the center of all three sites. Two additional towers with
collectors at 5, 10, and 20 cm height were installed at each site approximately 20 meters
on either side of the central tower. The three towers were positioned in a straight line
oriented perpendicular to the prevailing wind direction. To summarize, wind-transported
18
sediment was captured in six collectors at each of the 5, 10, and 20 cm heights, and in
two collectors at the 55 and 100 cm heights, for the combined burned sites. At the
unburned site, sediment was captured in three collectors at 5, 10, and 20 cm heights, and
in one collector at 55 and 100 cm heights. Sediment collectors were installed on
September 28th, 2006 at all three sites. Collections were then made in the fall and winter
on October 30, November 30, and January 18. Collections were made in the following
spring and summer (2007) on May 14, June 10th, July 21st, and August 17th. Thus,
collections were made monthly from October 2007 to August 2008, except in December
and February-April.
When the collectors were emptied, the sediment was placed in airtight bags, dried
at 110 °C for 16 hours and weighed to 0.000 g. Mean sediment mass was determined for
each collector height and sampling interval, for the unburned site and for the combined
burned sites. There were nine instances on January 18th, and six on May 14th when a
collector at a specific height could not be reliably sampled due to water or ice in the
collector.
Data analysis
The root mean squared difference (RMSD) of time-averaged wind speeds
between the burned and unburned sites was determined for each anemometer height for
simultaneous five-minute intervals. This provided an average difference between the
burned and unburned sites in wind speed at heights corresponding to the top and beneath
the shrub canopy height of the unburned site, for both fall/winter and spring/summer.
The proportion of each five-minute period during which impacts were recorded
by the saltation sensor, termed saltation activity, was calculated by dividing the number
of seconds with particle impacts by 300 seconds. Following Stout (2004), saltation
19
activity is related to the minimum wind speed required to entrain saltating soil particles,
termed the critical aeolian threshold, by equation 1:
 
  t

(1)
where  is saltation activity,  t is the critical aeolian threshold wind speed of the
higher anemometer in units of m s-1,  is the time-averaged wind speed of the higher
anemometer in units of m s-1,  is the standard deviation of wind speed for the higher
anemometer in units of m s-1, and  is the normal distribution function. The critical
aeolian threshold in wind speed units of m s-1 was determined by solving the previous
equation for critical aeolian threshold (  t ) (Stout, 2004). Rain drops and splash can be
falsely recorded as saltating sediment by the Sensit, so saltation activity and critical
aeolian threshold were not reported on days in which rain was measured at the University
of Idaho Aberdeen Research and Extension Center weather station located approximately
14 km east of the sites. Critical aeolian threshold was determined for saltation activity
values between 0.02 and 0.98 following Stout (2004), as threshold is indeterminate for
saltation activities of 0 and 1.
Aeolian sediment horizontal mass flux was calculated for each sediment sampling
interval and height using equation 2:
mass  area 1  time 1
(2)
where mass is kg of sediment, area is size of the opening on the BSNE© sampler (0.0002
m2 at heights 5 and 10 cm, and 0.001 m2 at 20, 55, and 100 cm), and time is the sampling
interval in days. Sediment flux was normalized to a 30-day rate for each sampling
interval (i.e. kg m-2 30 d-1), and plotted as a function of sample height. A power model
20
was fit to this plot and integrated over 200 cm to calculate a sediment mass transport
value using equation 3 (Van Donk et al., 2003):

2m
mass  area 1  time 1
(3)
height0
with units of kg m-1. This resulted in 30-day sediment mass flux and sediment mass
transport estimates over each sampling interval for the combined burned sites, and for the
unburned site.
2.3. Results
Fall/Winter
The unburned site had greater shrub and herbaceous vegetation cover than the
burned sites, during fall/winter following wildfire (Table 2.2). The burned sites had more
exposed bare soil. Shrub mean height was 2 to 3 times greater for the unburned site
(mean height = 0.60 m  0.22 SE) relative to the residual shrubs at burned sites (0.26 m 
0.02 SE). Time-averaged wind speed at the top of (110 cm) and below (40 cm) the
unburned vegetation canopy was noticeably smaller compared to the burned site for
almost all periods of time during fall/winter (Figure 2.1). The average difference
(RMSD) between the burned and unburned sites for measured wind speeds at the 110 and
40 cm heights was 1.79 and 1.55 m s-1, respectively.
21
Figure 2.1 Mean wind speed measured below (0.4 m above ground) the unburned vegetation canopy,
and at the top (1.1 m above ground) of the unburned vegetation canopy, at unburned and burned
sites.
22
Table 2.2. Mean (standard error) ground cover of sampling sites
Ground Cover
Time
Site
shrub % herbaceous %
Fall/Winter 2006-07
Unburned (4 transects)
35 (2)
40 (3)
Burned (8 transects)
6 (1)
3 (1)
Spring/Summer 2007
Unburned (4 transects)
40 (2)
54 (2)
Burned (4 transects)
6 (1)
45 (9)
bare %
25 (4)
91 (9)
6 (1)
59 (6)
Saltation activity was more frequently detected, and greater in magnitude when
detected, at the burned compared to unburned site over the course of fall/winter (Figure
2.2). Saltation was detected during less than 2% of the time, indicating an episodic
nature of erosion events (Table 2.3). The fraction of time during which saltation was
detected at the burned site was an order of magnitude greater relative to the unburned site
(Table 2.3). There were five 5-minute periods when saltation was detected at only the
unburned site, 526 periods when saltation was detected at only the burned site, and 39
periods during which saltation activity was simultaneously determined for both sites in
fall/winter. There appeared to be substantial differences during the periods in which
saltation was simultaneously detected; with a mean saltation activity of 0.35  0.04 SE in
the burned site, and 0.05  0.00 in the unburned site for these 39 periods.
Table 2.3. Saltation detection results
Time
Fall/Winter 2006-07
Spring/Summer 2007
Site
Unburned
Burned
Unburned
Burned
Saltation Detected
# of 5-minute Periods
Fraction of Time
44
0.1
565
1.9
7
0.0
31
0.1
23
Figure 2.2 Saltation activity detected at unburned (upper panel) and burned (lower panel) sites.
Saltation activity is unitless and is calculated as the number of seconds in a 300 second period during
which particle counts were recorded.
24
Reliable estimates of threshold wind speed could not be made for the unburned
site because of the absence of sustained saltation events at this site (Figure 2.3). There
were five days with sustained saltation events when threshold wind speeds could be
determined at the burned site. Time-averaged threshold wind speeds for these days were
between 9.15 and 10.62 m s-1 [October 19 (average = 10.02 m s-1, s.d. = 0.30 m s-1, n =23
five-minute periods), October 20 (average = 10.10 m s-1, s.d. = 0.32 m s-1, n = 17 fiveminute periods), November 8 (average = 9.15 m s-1, s.d. = 0.71 m s-1, n = 21 five-minute
periods), November 13 (average = 10.62 m s-1, s.d. = 0.63 m s-1, n = 40 five-minute
periods), November 23 (average = 10.12 m s-1, s.d. = 0.54 m s-1, n = 94 five-minute
periods).
Figure 2.3 Examples of maximum wind speed and aeolian critical threshold determined for
unburned and burned sites on November 11th and 23rd, 2006. Maximum wind speed and threshold
wind speed data are determined for 5 minute periods.
25
Figure 2.4 Mean horizontal sediment mass flux at unburned (upper panel) and burned (lower panel)
sites with standard error bars. The unburned and burned sites were sampled at the same intervals
and missing values for the unburned site represent periods when no measurable amount of sediment
accumulated in the collector.
26
Horizontal sediment flux at the burn sites appeared comparable at most heights
for the first two sampling intervals of fall/winter (Figure 2.4). Horizontal flux was
substantially smaller in December and the first half of January. There was never a
detectable quantity of sediment at any heights at the unburned site during fall/winter.
The integrated power functions fit to burned site sediment flux measurements yielded
sediment mass transport values of 6.35, 3.31, and 0.50 kg m-1 for the three fall/winter
sampling intervals from the end of September through mid-January.
Spring/Summer
After the emergence of herbaceous vegetation in the spring, large differences
persisted between the amount of exposed bare soil and shrub cover at the unburned site
and primary burned site, though the sites were more comparable in terms of herbaceous
vegetation cover (Table 2.2). The average difference between wind speeds at the burned
and unburned sites was not as great for either anemometer height during spring/summer
(RMSD = 1.15 m s-1 [110 cm] and 0.92 m s-1 [40 cm]) compared to fall/winter. Saltation
activity was never greater than 0.30 during spring/summer at either the burned or
unburned sites (Figure 2.2). Saltating particles were detected during less than 0.1% of
the study period during spring/summer at both sites (Table 2.3). Differences did persist
in saltation at the burned and unburned sites, however there were just 26 5-minute
periods when saltation was detected at only the burned site, five 5-minute periods during
which saltation was detected at only the unburned site, and two 5-minute periods during
which saltation activity was simultaneously determined at the burned and unburned sites
in spring/summer. Reliable estimates of threshold wind speeds could not be determined
for spring/summer at either site, due to the scarcity and very short duration of saltation
events.
27
Detectable but small quantities of sediment did accumulate in the unburned site
collectors during spring/summer, in contrast to fall/winter and despite bare soil
comprising only 6% of ground area. Sediment flux was substantially lower at all
collector heights at the unburned site compared to the burned sites during this time period
(Figure 2.4). Mean flux values were not integrated to estimate sediment mass transport
values for the June 10 and August 17 collection dates at the unburned sites, because the
flux data did not fit the power-of-height function required for the calculation (VanDonk
et al., 2003). Unburned site mean flux values for the July 21 collection date fit a power
function with a moderate correlation (R2 = 0.7) and the estimated sediment mass transport
value was 0.07 kg m-1. Mean flux values at the burned site fit power functions with high
correlation (R2 ≥ 0.9) and the estimated sediment mass transport values were 0.46, 1.04,
0.21, and 0.36 kg m-1 for the four spring/summer sampling dates (May – August).
2.4. Discussion and conclusions
Fire effects on aeolian sediment transport
Wind erosion of soil increased following a lightning-ignited wildfire in sagebrush
steppe, then decreased throughout the fall/winter to levels only slightly greater than in an
unburned site by spring green-up. Sediment transport was 28 to 67-fold greater in burned
compared to unburned sites over the course of the year following fire (Table 2.1). This is
comparable to the range of values estimated from three previous studies of wind erosion
following rangeland fires, in regions of the USA that also had semi-arid (or seasonally
dry) continental climates (Vermeire et al, 2005; Whicker et al., 2002; Zobeck et al., 1989;
Table 2.1). The studies in Table 2.1 each had no replication of burn areas except
Vermeire et al. (2005), which constrains statistical inference of fire effects on wind
erosion. Fortuitously, pairs of burned and unburned areas examined in the current and
28
previous studies in Table 2.1 can be combined to generate five replicates, providing for a
meta-analysis of fire effects on wind erosion. Over all studies, sediment capture was 28
to 166-fold greater on burned compared to unburned areas (P = 0.043 for each
measurement height in Table 2.1; Wilcoxon Signed Rank test, SPSS 14.0 for Windows,
2005). Burning led to greater increases in sediment transport in sagebrush steppe
compared to the other habitat types in Table 2.1, with the exception of the Southern High
Plains of Texas examined by Zobeck et al. (1989, where a grassland burn was compared
to unburned Shinnery Oak rangeland). Adjusting our calculations to the shorter time
frames evaluated in the other two studies would indicate that sagebrush steppe may be
particularly prone to wind erosion following burning, under the conditions we examined
(Figure 2.4 and Table 2.1).
Prior to most studies in the USA, fire effects on aeolian transport have been
examined in the SW Kalahari and Australia (Thomas and Leason, 2005; Wiggs et al.,
1996; Wiggs et al., 1995; Wiggs et al., 1994; Wasson and Nanninga, 1986; Ash and
Wasson, 1983) and in dunefield model simulations (Nield and Baas, 2008). While these
studies did not measure transport rates with passive sediment collectors, which precluded
their inclusion in the meta-analysis presented above, findings reported here are
comparable. Specifically, Wasson and Nanninga (1986) and Wiggs et al. (1994, 1995,
1996) found 2 times to an order of magnitude difference between parameters of aeolian
transport determined for burned and unburned dune surfaces.
Findings specific to cold desert shrub steppe compare similarly to those of Ash
and Wasson (1983), where a vegetated semi-arid dunefield (Mallee dunefield, ~ 35° S
latitude) experienced very little sand transport without vegetation removal by fire. The
29
linear dunes of the SW Kalahari are also considered to be predominately inactive in the
absence of disturbance, however, in addition to fire, drought and grazing can also induce
wind erosion (Thomas and Leason, 2005). It is unlikely that drought and grazing at
stocking rates currently used on publicly-managed lands would reduce vegetation to
levels at which substantial wind erosion might occur in the cold desert shrub steppe we
studied. A long term study of vegetation at the Idaho National Laboratory in SE Idaho
sagebrush steppe, for example, showed that following drought and overgrazing from
1920 - 1950, total vegetation cover was still more than 2/3 of that observed during more
contemporary ungrazed periods of either drought or above-average moisture (Anderson
and Inouye, 2001). In general, and with exception of the semi-arid Malle dunefield
studied by Ash and Wasson (1983), the previous studies of post-fire wind erosion that we
are aware of were conducted in landscapes where substantial aeolian transport appeared
to occur in the absence of fire. This is likely because vegetation cover was inherently
low as a function of hydroclimatology and/or was reduced by disturbance(s) in addition
to fire in these environments.
Temporal and spatial characteristics of post-fire transport
The bulk of wind erosion at our sites occurred during the initial months following
fire, which were dry autumn and cool winter months. Herb recovery in the following
spring coincided with a marked reduction in saltation and sediment flux, and relatively
small levels of transport occurred again during the warmest and driest part of summer.
Comparisons of the relationship of phenology and post-fire erosion in our study area with
the wetter sand sagebrush prairie (~600 mm yr-1; Vermeire et al., 2005), warmer and drier
Chihuahuan (300 mm yr-1, with greater evaporative demand than cold desert; Whicker et
al., 2002; Smith et al., 1997) and SW Kalahari (~200 mm yr-1; Thomas and Leason,
30
2005) deserts, and even drier N Australian dunefields (<125 mm yr-1) can provide insight
on the often greater effect of fire on aeolian transport in sagebrush steppe.
Erosion was more evident in sand sagebrush prairie when bare soil exposure was
prolonged following fire by seasonal dormancy (Vermeire et al., 2005), similar to
findings of our study. Post-fire vegetation recovery is generally more vigorous in sand
sagebrush prairie, however, due to resprouting of sand sagebrush and quicker
reestablishment of herb cover (S. Fuhlendorf, pers. comm.). Big sagebrush reestablishes
slowly by seed and herb abundances may not recover to pre-fire levels until midsummer
of the year or two following fire in cold desert (Seefeldt et al., 2007). The net result is
greater duration of bare soil exposure following fire in big sagebrush steppe compared to
sand sagebrush prairie.
In the Chihuahuan and SW Kalahari deserts, unburned sites produced a relatively
high level of background sediment transport (Whicker et al., 2005; Wiggs et al., 1996;
Wiggs et al., 1995). This lead to a smaller effect of burning on sediment transport at the
Chihuahuan desert compared to cold desert shrub steppe (Table 2.1). Greater aridity and
presumably less vegetative cover could have contributed to the greater background
erosion in control sites in the Chihuahuan desert, and thus less effect of fire. It’s not
possible to say whether such difference existed between the Kalahari and cold desert
shrub steppe because of the different measures used to assess transport. The longevity of
increased erosion post-fire appears to be shorter in cold desert shrub steppe compared to
both the SW Kalahari and N Australia, where heightened rates of erosion are estimated to
occur for up to 5 and 10 years post-fire, respectively (Wiggs et al., 1994; Wasson and
Nanninga, 1986). In N Australia, post-fire wind erosion is predicted to be ~ 2 times
31
greater than that of unburned surfaces with an average fire return interval of ~ 10 years,
suggesting a lesser magnitude increase in aeolian transport immediately following
burning than in cold desert shrub steppe.
These comparisons of post-fire erosion in different regions of the world point to
hydroclimatological controls of vegetation as an important factor affecting the magnitude
and longevity of post-fire increases in erosion. Snow cover, for example, is rare in warm
deserts but will limit aeolian transport during most winters in cold desert shrub steppe.
Findings from this study suggest that spring green-up also minimizes aeolian transport,
post-fire. This leaves a brief but intense period of greatly increased wind erosion
potential during the fall months following summer burning in cold desert shrub steppe.
The degree to which fall moisture and temperatures might influence wind erosion
potential during this period will require further study.
In addition to seasonal variation in erosion, we also observed a pulsed nature of
sediment transport. The frequency of saltation events was low at burned and unburned
sites compared to more erodible landscapes such as sand dunes, sand beaches, or playas
(Stout, 2007, 2004; Wiggs et al., 2004). Saltation events were also relatively infrequent
in burned and unburned semi-arid rangelands evaluated by Vermeire et al. (2005) and
Whicker et al. (2002). Wind erosion appears particularly episodic where it does not
normally occur without first having wildfire or other disturbances (Vermeire et al., 2005;
Whicker et al., 2002).
We also detected high spatial variation in sediment capture within a site (see
errors in Figure 2.3) that was related to obvious blowouts in the burn area (Figure 2.5).
32
Figure 2.5 Photo taken on 06/10/07 showing herbaceous vegetation surrounding a blowout that
developed in fall, post-fire (note sediment collector in background). This blowout persisted
throughout the spring/summer study period and the nearby collector exhibited consistently higher
sediment fluxes than many of the other burned site collectors.
High spatial variability in sediment flux following fire was also reported for warm deserts
(Whicker et al., 2002), but blowouts were not as evident in the more temperate sand
sagebrush prairie (Vermeire et al., 2005) where pre-fire vegetative cover is likely more
continuous. The interspersion of shrubs, bunchgrass clusters, and unvegetated soils
typical of sagebrush steppe contributes to relatively greater pre-fire heterogeneity. A
similar heterogeneity might be observed in the semi-arid Malle dunefield where
vegetation included tall shrubs, short shrubs, tussock grasses, and bare soil (Ash and
Wasson, 1983). Spatial variability in wind erosion can increase the degree of
heterogeneity in landcover in warm desert shrublands, producing “hotspots” that emit
33
greater concentrations of aeolian sediments than the surrounding landscape (Okin, 2005).
Determining the drivers and consequences of this spatial variability in cold desert shrub
steppe requires further study.
Mechanisms by which fire increases wind erosion
Fire could increase erodibility by directly affecting soil physical and chemical
properties (Ravi et al., 2006), or increase erosivity via altering vegetation cover and
corresponding wind fields (Whicker et al., 2002; Wiggs et al., 1996; Wiggs et al., 1994;
Wasson and Nanninga, 1986). Erodibility can be assessed from differences in threshold
wind speed (Stout, 2007, 2004; Wiggs et al., 2004), but we did not observe enough
saltation activity to calculate thresholds for the unburned site. Experiments using
portable wind tunnels or experimental modification of vegetation would be necessary to
assess direct effects of fire on soil erodibility in shrub steppe cold deserts. Our data can
more reliably indicate that fire increased erosivity by temporarily excluding the boundary
layer protection of soil surfaces provided by the vegetation, similar to findings of Wiggs
et al. (1996, 1994) and Whicker et al. (2002).
Greater vegetation cover, shrub height, and variability in shrub height at the
unburned site would be expected to result in fundamentally different wind-height
relationships than at the burned sites (Campbell and Norman, 1998; Lancaster and Baas,
1998; Driese and Reiners, 1997; Wiggs et al., 1996; Hagen and Armbrust, 1994; Wiggs et
al., 1994). We predicted our data to reflect this through observed differences in wind
speed at the two measurement heights, because our sites were otherwise not instrumented
with enough anemometers to determine intrinsic aerodynamic properties (i.e., zero plane
displacement, aerodynamic roughness, and friction velocity). Indeed, wind speeds were
34
less at the unburned site, particularly in fall/winter, suggesting greater wind attenuation
within the unburned plant canopy relative to the burned site.
Summary and management implications
We conclude that burned rangelands are prone to wind erosion, particularly in
cold-desert shrub steppe. Comparisons of fire effects in this cold desert site with other
biomes indicated that the relative increase in wind erosion following burning might be of
shorter duration, but as great (or greater) magnitude than many of the environments
previously studied in Africa, Australia, and USA. Aeolian transport following summer
wildfire in cold desert shrub steppe has the potential to vary as a function of seasonal
hydroclimatological events that drive the degree of vegetation recovery prior to and
following the dormant season (e.g.: fall precipitation and temperature; onset of winter;
accumulation of snow; spring snowmelt). Fire increases the erosivity of the environment
by excluding vegetation and thereby increasing winds. Whether fire also increases the
erodibility of soils in cold desert shrub steppe by altering physical properties will require
further experimentation. Findings suggest that a restoration effort of post-fire seeding for
wind erosion control purposes might have little utility, in light of the lack of aeolian
transport observed following spring emergence of herbaceous vegetation, in the burned
and unseeded cold desert shrub steppe we studied. Future research is required to either
corroborate or refute this finding, as this study is based on comparison of burned and
unburned sites during a single year and fire. Future research that examines the relative
degree of soil disturbance incurred by post-fire restoration efforts compared to that
induced by aeolian processes after burning is specifically required in this environment.
35
Chapter 3: Relationships of Post-Fire Aeolian Transport to Soil and
Atmospheric Conditions
Abstract
Aeolian transport is an important contemporary geomorphic process in semiarid
cold deserts; most evident when vegetative cover is temporarily eliminated by wildfire.
The erodibility of recently burned surfaces is not stable in time, and near-surface
moisture is expected to affect erodibility prior to regrowth of vegetation. In this study we
examine effects of soil and atmospheric moisture on wind erodibility of loess soil
following late-summer (2007) wildfire at the US Department of Energy, Idaho National
Laboratory in southeastern Idaho, prior to re-emergence of vegetation. We measured
threshold wind velocities, soil volumetric water content, air temperature, relative
humidity, and vapor density at two sites with severe and moderate burn intensity,
respectively, and an unburned site. Results indicate that erodibility, as measured by
threshold wind velocities, decreased with time following fire. Little sediment transport
was detected at the unburned site and erodibility could therefore not be determined.
Multiple regression models with predictors including soil water, atmospheric moisture,
and time variables explained 83% and 69% of the variability in erodibility at the severe
and moderate burn intensity sites, respectively. Erodibility decreased as soil volumetric
water content increased to 15–20%, but was less responsive to further wetting. Erodibility
predominantly decreased as atmospheric moisture increased, however, relationships were
complex. Multiple regression coefficients indicated erodibility increased as relative
humidity increased at timescales of days–months. Positive and negative relationships
were observed between erodibility and atmospheric moisture, within individual saltation
36
events. Atmospheric and soil moisture appear important for post-fire wind erosion before
re-establishment of vegetation.
3.1. Introduction
Wildfire can increase the potential for the wind-driven redistribution of soils in
deserts and semiarid environments (Ash and Wasson, 1983; Breshears et al., 2009; Ravi
et al., 2007, 2009; Sankey et al., 2009a; Vermeire et al., 2005; Wasson and Nanninga,
1986; Whicker et al., 2002, 2006a, 2006b; Wiggs et al., 1994, 1995, 1996; Zobeck et al.,
1989). The increase in the potential for aeolian transport immediately following fire has
been linked to the removal of the protective cover of vegetation above the soil surface
(Stout and Zobeck, 1998; Vermeire et al., 2005; Whicker et al., 2002; Zobeck et al.,
1989). Temporal variability in aeolian transport following burning is generally
considered to be controlled by the revegetation of the burned surface (Stout and Zobeck,
1998).
In addition to vegetation, soil and atmospheric moisture have been shown to be
important environmental controls of soil erodibility in theoretical, wind tunnel, and a
limited number of field studies on unburned surfaces (Chen et al., 1996; Cornelis, 2006;
Davidson-Arnott et al., 2008; Fecan et al., 1999; McKenna Neuman, 2003; McKenna
Neuman and Langston, 2006; McKenna Neuman and Scott, 1998; Ravi et al., 2004,
2006b, 2007; Ravi and D’Odorico, 2005; Stout, 2001; Wiggs et al., 2004b). Erodibility
has been demonstrated to often, but not always, decrease with increasing soil moisture, in
theory and application (Chen et al., 1996; Cornelis, 2006; McKenna Neuman, 2003; Ravi
et al., 2006b). Large pulse-emissions of dust have been linked to dry atmospheric
conditions (Stout, 2001). Surface soil moisture and atmospheric moisture are strongly
related in arid conditions and likely covary as controls on erodibility (Ravi et al., 2004,
37
2006b). Furthermore, air temperature can affect wetness of air and soils, thereby
affecting erodibility. Erodibility has been demonstrated to be higher at colder air
temperatures in a wind tunnel experiment, for example, due to positive relationships
between air temperature and both atmospheric moisture and soil surface matric potential
(McKenna Neuman, 2003).
One way to measure erodibility with high temporal resolution is Stout’s (2004,
2007) method of determining the critical aeolian threshold for a soil surface. Critical
aeolian threshold measurements can be coupled with micrometeorological measurements
in order to explore relationships of erodibility to soil and atmospheric moisture. In this
study, we applied this approach on a burned loess soil surface in cold desert shrub steppe
on the Snake River Plain (SRP) in Idaho, USA (Figure 3.1). On the SRP, a number of
exceedingly large fires have occurred during recent years (e.g., Murphy Complex fire,
2007 ~ 264,000 ha; Crystal fire, 2006 ~81,000 ha; Clover fire, 2005 ~ 78,000 ha). In
recent decades, wildfires throughout the western U.S. have become more frequent, larger
in size, and can occur over a longer annual wildfire season (Westerling et al., 2006).
Aeolian transport of sediment that occurs following wildfire is a specific concern on the
SRP due to the potential for contaminant transport off-site from disturbed soil surfaces at
U.S. Department of Energy sites such as the Idaho National Laboratory (INL), located on
the eastern SRP (Whicker et al., 2004, 2006a, 2006b). Post-fire aeolian transport is also a
concern for human health and safety issues associated with highway closures, traffic
accidents, and air quality.
Sankey et al. (2009a) observed considerable temporal variability in erosion
following a late summer wildfire on the SRP. Vegetation recovery in the subsequent
38
Figure 3.1 Satellite image of the Eastern Snake River Plain (ESRP), Idaho, USA (a); location of
Idaho relative to USA (b); and location of study sites within the ESRP (c). The satellite image (a)
illustrates the influence of aeolian processes on the surface of the ESRP. Note the light-toned streaks
of eroded bare soil in the upper half of the image that trend parallel to prevailing winds (SW-NE),
cutting through both crop and rangelands. The light-toned crescent features in the upper right
corner are sand dunes. The medium-toned rounded feature at the bottom center of the image is a
basalt lava flow on which very little soil has developed.
39
spring explained much of the variability in erosion at seasonal time scales within one year
after fire. Burned soil surfaces remain bare and prone to aeolian transport through fall
and early winter until snow accumulates following late summer wildfire in the cold desert
shrub steppe of the SRP. The largest fluxes of aeolian sediment on burned surfaces in the
SRP are observed during this fall and early winter dormancy period. In the absence of
vegetation, erodibility of these soils could be strongly affected by variations in the
wetness of soil and air. The SRP provides a useful field setting to examine relationships
between surface moisture and post-fire aeolian transport because of the relative regularity
of the annual fire season, as well as climatic trends following the fire season.
Precipitation increases in frequency, air temperature decreases, and atmospheric moisture
increases during the fall months following hot, dry summers (NOAA INL Weather
Center, 2008). The large increases in aeolian transport during the fall period following
fire in the SRP are comparable in magnitude although less prolonged than post-fire wind
erosion reported for Africa, Australia, and elsewhere in the USA (Sankey et al., 2009a).
In light of these previous findings in the SRP, an examination of the relationships of
erodibility to soil and atmospheric conditions during aeolian transport in the fall,
following summer wildfire is particularly relevant for this environment.
Our objectives for this study were to: 1) describe temporal trends in erodibility,
and 2) determine and describe relationships of erodibility to soil and atmospheric
moisture, prior to the re-emergence of vegetation following wildfire. We hypothesized
that decreases in erodibility with time since fire will relate significantly to soil and
atmospheric moisture during the fall dormancy period. Pursuit of these objectives and
40
testing our hypothesis will additionally lead to a better understanding of the temporal
variability of critical aeolian threshold wind speeds for soil surfaces, in general.
3.2. Physical setting
This study was located within an ~16 km2 area (Lat. 43° 30’ N, Lon. 112° 38’ W,
1650 m elevation) near the Idaho National Laboratory (INL) on the eastern half of the
SRP (ESRP), near Atomic City, Idaho, USA (Figure 3.1). The SRP is an alluvial plain
superimposed on the migratory path of a track of volcanism that originated in Eastern
Oregon approximately 16 ma, and formed a progression of calderas trending NE to the
Yellowstone Plateau (Pierce and Morgan, 1992). Local geomorphology is characterized
by volcanic buttes and basalt lava flows.
Soils have formed on all but the youngest (~ Holocene-aged) lava flows (Lewis
and Fosberg, 1982). Soil parent materials of the ESRP are predominately of aeolian
origin. Loess covers thousands of km2 of the plain and aeolian sands cover hundreds of
km2 directly adjacent to the Snake River floodplain (Busacca et al., 2004). Loess
deposits range from meters to tens of meters thick, depending on landscape position and
age of deposition. The absence of loess on the youngest lava flows suggests little aeolian
deposition occurred during the last 12,000 years (Busacca et al., 2004). Contemporary
surface soils are Aridisols that have developed in loess deposited between 70,000 and
12,000 years ago (Busacca et al., 2004; Soil Survey Staff, 1999). Contemporary soils
have thin (centimeters thick) A horizons, underlain by thin, weakly developed argillic
horizons, which are just above thicker (decimeters) calcic horizons (NCSS Web Soil
Survey, 2008). These soils are characterized by silt loam textures throughout the soil
profile, with very few coarse fragments, except in locations with a shallow (~ less than 1
m) depth to the underlying lava flow (NCSS Web Soil Survey, 2008). Vegetated loess
41
surfaces in the ESRP are generally stable; however reduction of vegetation cover and
structure by disturbance can make these soils prone to aeolian processes (Jeppesen,
personal communication).
Climate and fire
Summer wildfires are common in the ESRP and are most often ignited by
lightning in mid-to-late-summer (July-August) thunder storms. Burned surfaces often
remain bare of vegetation throughout fall and are susceptible to aeolian transport during
the approximate four to five months (August-December) of time between summer
wildfire and early winter. Erosion potential of burned soil surfaces is limited through
winter, spring and the following summer by snow cover and growth of herbaceous
vegetation.
The portion of the ESRP that we studied receives approximately 216 mm mean
annual precipitation, based on 55 years of climate data from 1950 - 2005 (NOAA INL
Weather Center, 2008). Less than 40% of the mean annual precipitation falls from
August through December. The average monthly precipitation varies by ~ 6 mm from
the wettest average month (December) to the driest (October). The frequency of
precipitation events is greater on average in November and December compared to
August – October. Average monthly air temperatures are above 20 °C in August and
decrease throughout fall to winter temperatures below freezing (NOAA INL Weather
Center, 2008). Atmospheric moisture, conversely, increases from summer throughout
fall and into winter. Specifically, diurnal maximum and minimum wet bulb temperatures
increase during fall, and daily minimum-maximum ranges of relative humidity (rH)
increase from 10 - 80 % in summer to 35 – 90 % in winter (Clawson et al., 1989; NOAA
INL Weather Center, 2008).
42
3.3. Materials and methods
Micrometeorological data
Two wildfires ignited by lightning in July, 2007 (Twin Buttes fire) and August,
2007 (Moonshiner fire) burned approximately 5,000 hectares, combined, of juniper and
sagebrush shrubland on the INL and adjacent public land. Following containment of the
second wildfire, three study sites were instrumented with micrometeorological equipment
on August 23, 2007. The sites included a severely burned area with no vegetation
remaining (hereafter referred to as “heavy burn”), a moderately burned area with burned
sagebrush and juniper skeletons (hereafter referred to as “light burn”), and an unburned
site (Figure 3.1).
Basal vegetation cover was estimated in August, 2007 using the point-intercept
method at 11 points evenly spaced along twenty, 1.1 m long erosion bridges installed at
each site as part of a longer term study (n = 220 basal cover estimates per site). Mean
(standard error) basal vegetation cover (live and dead) by functional group was: 0(0) %
herbaceous and shrub at the heavy burn site; 0(0) % herbaceous and 1(0) % shrub at the
light burn site; and 7(3) % herbaceous and 4(1) % shrub at the unburned site. Mean (se)
live shrub canopy cover was 40(4) % at the unburned site in July, 2008, estimated as part
of a separate study using the point-intercept method within a 0.25 m2 frame (36 points per
frame, n = 24 frame estimates) (Hoover, unpublished data). Vegetation (live and dead)
mean (se) height was: 0.00(0.00) m , 0.10(0.00) m, and 0.12(0.08) m for the heavy, light,
and unburned sites, respectively, as estimated for a 40,000 m2 area centered on each site
using laser altimetry (LiDAR) remote sensing data collected in November, 2007 and
LiDAR processing tools previously evaluated for shrub steppe vegetation in the SRP
(Streutker and Glenn, 2006; http://bcal.geology.isu.edu/envitools/index.html) (Sankey,
43
unpublished data). Shrub steppe landscapes of the SRP generally exhibit an alternating
pattern of crusted, intershrub (playette) and less-cohesive, shrub coppice soil surfaces
(Hilty et al., 2003). Playette and coppice surfaces comprised approximately equal
fractions of the landscape one year after vegetation was removed by fire at the heavy
burn site (Hoover, unpublished data).
Sensors installed at each site included: one anemometer at 2 m height above
ground (Model 014A, MetOne Instruments Inc., Grants Pass, OR, USA), a SENSIT®
piezoelectric saltation sensor (Sensit Company, Portland, ND, USA) mounted at 0.05 m
above the soil surface, a temperature and relative humidity sensor (Model HMP50L,
Vaisala Group, Vantaa, Finland) mounted at 0.05 m height, and a soil water content
reflectometer with a 0.3 m length probe inserted at a 45 degree angle to the soil surface to
0.15 m depth (Model CS616, Campbell Scientific Inc., Logan, UT, USA). The SENSIT
is an omnidirectional sensor that produces an electronic signal when impacted by a
saltating particle. The signals produced by individual particles were detected at a 1second scan rate using dataloggers (model CR10, Campbell Scientific Inc., Logan, UT,
USA) and the number of seconds in which at least one particle impacted the sensor were
summed and recorded at five-minute intervals, following Stout (2004, 2007). The mean,
standard deviation, and maximum wind velocity, the average relative humidity and
temperature, in addition to the number of seconds with particle impacts, were recorded at
five-minute intervals, at a 1-second scan rate. Relative humidity and air temperature
were not measured at the unburned site. Mean volumetric soil water content (cm3
H2O/cm3 soil) was recorded at six-hour intervals, at a 1-hour scan rate. Data collection
44
was suspended on December 10, 2007, when snow began to accumulate on the ground
and thus limited the aeolian transport of sediment.
Aeolian sediment
A tower with 5 omnidirectional passive sediment collectors [Big Springs Number
Eight (BSNE®) (Custom Products, Big Spring, TX, USA)] mounted at 0.05, 0.10, 0.20,
0.55, and 1.00 m height above ground was installed at each site to provide additional
evidence of sediment transport. The aeolian sediment collectors were installed on August
23, 2007. Collections were made on nine occasions, every 1-3 weeks from August 31 to
December 10 at the burned sites, and on four occasions at the unburned site that had very
little aeolian activity. The collected sediment was placed in airtight bags, dried for 16
hours at 110 °C and weighed to a precision of 0.000 g.
Data analysis
Micrometeorological data
The proportion of each five-minute period during which impacts were recorded
by the saltation sensor, termed saltation activity, was calculated by dividing the number
of seconds with particle impacts by 300 seconds (Stout, 2004). Following Stout (2004),
the minimum wind speed required to entrain saltating soil particles, termed the critical
aeolian threshold (and hereafter referred to as “threshold” or “threshold wind speed”), is
related to saltation activity by the following equation:
 t     1 ( )
(1)
where  t is the threshold wind speed (m s-1),  is the mean wind speed in units of (m s1
),  is the standard deviation of wind speed (m s-1),  1 is the inverse normal
distribution function, and  is saltation activity. Threshold was determined at five
45
minute intervals for saltation activity values between 0.02 and 0.98 (Stout, 2004). Rain
impacts can be falsely recorded as saltating sediment by the SENSIT, so saltation activity
and threshold were not reported on days during which rain was measured at any of the
four closest weather stations in the INL Mesonet climate monitoring network. The
weather stations were located approximately: 5 km NE, 20 km SSE, 16 km SW, and 14
km WNW of the study location, respectively.
We estimated air vapor density, the mass of water vapor per unit volume of air,
from our measurements of air temperature and relative humidity using the following
equation (Campbell and Norman, 1998):
ρv = ea ∙ Mw /(R∙T)
(2)
where ρv is the air vapor density in units of g m-3, ea is the ambient vapor pressure in
kPa, Mw is molecular mass of water vapor in air (assumed 18.02 g/mol), R is the universal
gas constant (8.3143 J mol-1 K-1), and T is air temperature in Kelvin. This required that
we estimate ea, which we did by first calculating the saturation vapor pressure (es) from
air temperature (°C) using the empirical Teten’s formula:
es = 0.611 kPa exp[17.502 ∙ °C /(°C + 240.97)]
(3)
and then calculating ea from es and relative humidity:
ea = rH ∙ es
(4)
where rH is relative humidity expressed as a fraction from 0 to 1 (Campbell and Norman,
1998).
Analysis focused on among day variability in threshold wind speed and
atmospheric variables. For this, the maximum, median, and minimum threshold wind
speeds were extracted for each day with a saltation event that sustained at least 3, 5-
46
minute periods for which threshold was determined. The soil water content, air
temperature, and relative humidity measured for the corresponding time interval were
also extracted. The resulting data set consisted of a daily minimum, median, and
maximum threshold wind speed, and corresponding soil water content, air temperature,
relative humidity, and vapor density measurements. We used Pearson correlation
coefficients and multiple linear regression to examine relationships between minimum,
median, and maximum threshold wind speeds, time (Julian Date), and the
micrometeorological variables. Within day variability in threshold and corresponding
atmospheric variables was additionally examined using the data collected at 5-minute
intervals for individual days with saltation events.
Aeolian sediment data
Aeolian sediment horizontal mass flux (q) was calculated for each sediment
sampling interval and height (z) as (Van Donk et al., 2003):
q  mass  area 1  time 1
(5)
where mass is kg of sediment, area is size of the opening on the BSNE© sampler (0.0002
m2 at z = 0.05 and z = 0.10 m, and 0.001 m2 at z = 0.20, z = 0.55, and z = 1.00 m), and
time is the sampling interval in days. Sediment flux (q) with units of kg m-2 d-1 was
plotted as a function of sample height (z) in meters. Following Van Donk et al. (2003) a
power model:
q( z )  a( z  1) b
(6)
where a and b are model parameters, was fit to this plot and integrated over 2.00 m to
calculate a sediment mass transport value (Q, and here forth referred to as sediment
discharge) with units of kg m-1 d-1:
47
2m
Q   q( z )dz
z 0
(7)
This resulted in daily sediment discharge estimates over each sampling interval for each
site.
3.4. Results
Atmospheric and soil moisture
Near surface air temperatures for days with saltation events ranged from daily
maximums close to 35 °C at the beginning of the study to daily minimums below
freezing at the end of the study (Figure 3.2). Relative humidity was negatively correlated
with air temperature at both burn sites over the course of the study [Pearson correlation
coefficient (p) = -0.87(0.00) at each site]. Relative humidity increased significantly with
Julian Date for daily minimum, median, and maximum threshold during wind speed
events (r2 = 0.46, 0.56, 0.59 for min., med., and max. threshold at the heavy burn site,
respectively, and r2 = 0.50, 0.60, and 0.72 respectively at the light burn site; all p < 0.05;
see Figure 3.2 for example of seasonal trend). There was no apparent seasonal trend in
vapor density of air (all r2 ≤ 0.07, all p > 0.15 for regressions of vapor density vs. Julian
Date during minimum, median, and maximum threshold wind speed events; see Figure
3.2).
Soil water content measured at both burned sites (Figure 3.2) and the unburned
site were similar. Volumetric water content (VWC) initially fluctuated between
approximately 10 % and 14 % due to relatively small precipitation and drying events,
until a major precipitation event in the first week of October wetted the soil at all sites to
above 25% VWC. Soil water at all sites then progressively dried for the remainder of the
study period, back to about 12 % VWC, despite some minor and transient wetting events.
48
Figure 3.2 Micrometeorological parameters during saltation events at the heavy and light burn sites.
Parameters are determined for 5 minute periods, with the exception of soil volumetric water content
which is determined for 6 hour periods. Saltation activity is the fraction of each 5 minute period
during which particle counts were recorded by the Sensit® (# seconds with particle counts / 300
seconds).
49
The study area received 0.05 cm of rain from August 23rd – 31st (calculated as the mean
of 5-minute rainfall data recorded at the four INL weather stations), 2.09 cm in
September, 3.50 cm in October, 0.36 cm in November, and 0.00 cm of rain from
December 1st through December 10th. Precipitation frequency increased during the first
half of the study and then decreased: rain was detected for five-minute intervals by at
least one of the four INL weather stations six times from August 23rd – 31st, 218 times in
September, 385 times in October, 45 times in November, and zero times from December
1st-10th.
Aeolian sediment
Sediment discharge was greater at the heavy burn site compared to the light burn
site, for all collection intervals (Figure 3.3). At the unburned site, sediment discharge
never exceeded 0.03 kg m-1 d-1 and was smaller than discharges measured at the burned
sites for corresponding collection intervals. Burned area discharge values ranged from a
maximum of 100.78 kg m-1 d-1 at the beginning of the fall at the heavy burned site, to a
minimum of 0.02 kg m-1 d-1 in December at the light burn site. Sediment discharge was
greater at the beginning of fall and less at the end of the fall at both burned sites, showing
an overall decrease in horizontal sediment transport with time following burning, though
there was substantial variability in sediment discharge between successive collection
intervals.
50
Figure 3.3 Horizontal sediment mass discharge at the heavy burn, light burn, and unburned sites.
The plotted discharge values are rates for each preceding sampling interval (i.e. since the previous
plotted value).
The use of a single BSNE tower at each site precluded the analysis of inter- and
intra-site variability in horizontal sediment flux and discharge. However, we can
compare results from additional BSNE towers which were installed on October 11th for a
related, longer-term research project (n = 5 heavy burn locations, n = 3 light burn
locations, and n = 5 unburned locations) (Sankey, unpublished data). Mean (standard
error) of sediment discharge for the additional towers were: 1) at the heavy burn site;
8.67(3.50), 1.08(0.34), 23.05(5.74), 5.51(1.67) kg m-1 d-1 for collection dates of 22-Oct,
30-Oct, 20-Nov, 7-Dec, respectively, 2) at the light burn site; 0.01(0.00), 0.01(0.01),
0.16(0.17), 0.13(0.07) kg m-1 d-1 for collection dates of 22-Oct, 30-Oct, 20-Nov, 7-Dec,
51
respectively, and 3) at the unburned site; 0.00(0.00) kg m-1 d-1 for collection dates of 20Nov and 10-Dec. Additionally, a related study of post-fire aeolian transport of loess soils
on the SRP indicated a great deal of variability in horizontal sediment flux within burned
locations, with standard error estimates exceeding 4 kg m-2 over 30 day intervals
(standard error = 4.17 and 4.69 kg m-2 30 d-1, and coefficient of variation = 97 % and 105
%, for two consecutive sampling intervals, respectively) for the lowest (5 cm) BSNE
collection heights (n=6) (Sankey et al., 2009a).
Splash from rain drop impacts can increase sediment concentrations measured at
the lowest BSNE sampler heights. Because at least a small amount of precipitation
occurred during a majority of the BSNE sampling intervals, we present sediment
discharge values measured for all sampling intervals. We acknowledge that sediment
discharge estimates might be inflated for periods of particularly frequent and/or intense
precipitation, though the contribution of sediment by rainsplash was likely a small and
insignificant fraction of the often large amounts of sediment captured at the heavy burn
site.
Threshold wind speeds
Threshold wind speeds (m s-1) increased (i.e. soil erodibility decreased) with time
following fire at both heavy [median daily threshold = 3.71 + 0.02 Julian Date (r2 = 0.64,
p = 0.00)] and light burn sites [median daily threshold = 5.01 + 0.02 Julian Date (r2 =
0.21, p = 0.01)] (Table 3.1). Considerable temporal variability in threshold wind speed
was observed among and within days, at both burn sites during the study. Threshold
ranged from 4.8 m s-1 to 12.7 m s-1 at the heavy burn site and from 5.1 m s-1 to 13.9 m s-1
at the light burn site. At the unburned site, threshold could not be determined because
measured saltation activities never exceeded 0.02. The variability in threshold between
52
successive days with erosion events appeared to increase near the end of the study,
particularly at the light burned site (Figure 3.4). Within days, plots of the data suggest
that threshold changed in a variety of ways with time, including flat, increasing, or
decreasing linear trends, as well as curvilinear trends (Figure 3.5).
Threshold wind speed/moisture relationships
Daily threshold wind speeds were positively correlated with soil water content at
both sites (Table 3.1) indicating soil erodibility was lower when soils were wetter. Plots
of median daily threshold wind speed versus soil water content (Figure 3.6) demonstrate
the positive correlation between the variables. The plots suggest that a potentially
nonlinear relationship existed at both sites, although we did not statistically fit nonlinear
models to these trends. Threshold wind speeds appeared to increase with soil water
content to a point (~ 15-20 % VWC) above which increasing water content appeared to
have less of an effect on threshold.
Daily threshold wind speeds were more strongly correlated with air temperature
and relative humidity than with vapor density at the heavy burn site (Table 3.1). At the
light burn site, daily threshold wind speeds were not significantly correlated with the
atmospheric variables, in most instances (Table 3.1). When examined within days, plots
of the data suggest a majority of saltation events showed either positive relationships, or
no linear relationship, between threshold and relative humidity; negative and curvilinear
relationships were also observed, however (Figure 3.5). This was similarly the case for
within-day relationships between threshold and atmospheric vapor density.
53
Table 3.1. Pearson correlation coefficients (p-values) measured at Heavy and Light burn sites
Daily Threshold Wind Speed (m s-1)
Median
Minimum
Maximum
Heavy Burn
Light Burn
Heavy Burn
Light Burn
Heavy Burn
Light Burn
Julian Date
0.80 (<0.00)
0.46 (0.01)
0.41 (0.02)
0.24 (0.19)
0.76 (<0.00)
0.72 (<0.00)
Soil Water (% Vol.)
0.67 (<0.00)
0.69 (<0.00)
0.58 (<0.00)
0.51 (<0.00)
0.60 (<0.00)
0.50 (<0.00)
Air Temperature (C)
-0.63 (<0.00)
-0.21 (0.28)
-0.35 (0.05)
0.01 (0.97)
-0.66 (<0.00)
-0.57 (<0.00)
Air Relative Humidity (%)
0.66 (<0.00)
0.06 (0.74)
0.34 (0.06)
-0.19 (0.34)
0.75 (<0.00)
0.53 (<0.00)
Air Vapor Density (g m-3)
0.21 (0.26)
-0.08 (0.68)
0.21 (0.25)
-0.13 (0.50)
0.29 (0.10)
-0.08 (0.70)
54
Figure 3.4 Median, minimum, and maximum threshold wind speeds for days with an erosion event
that lasted at least 15 minutes.
55
Figure 3.5 Examples of within day relationships for threshold wind speeds with time and
atmospheric relative humidity. Threshold and relative humidity are determined for 5 minute
periods.
56
Figure 3.6 Relationships of median daily threshold wind speed and soil volumetric water content, for
days with an erosion event that lasted at least 15 minutes.
57
Multiple regression models with Julian Date, soil water (%), relative humidity
(%), and atmospheric vapor density (g m-3) as predictor variables explained 83% (Daily
Median Threshold = 1.18 + 0.02 Julian Day + 0.06 Soil Water - 0.01 Relative Humidity +
0.23 Vapor Density; all p = 0.00, except rH p = 0.06) and 48% (Daily Median Threshold
= -0.12 + 0.03 Julian Day + 0.18 Soil Water - 0.04 Relative Humidity + 0.27 Vapor
Density; all p < 0.05) of the temporal variability in median daily threshold wind speed at
the heavy and light burn sites, respectively. Sixty-nine percent of the variability in
threshold wind speed was explained at the light burn site by a model that additionally
included interaction terms between vapor density and Julian date, soil water, and relative
humidity (Daily Median Threshold = 22.7 + 0.08 Julian Day + 3.27 Soil Water – 0.05
Relative Humidity – 4.96 Vapor Density – 0.01 Julian Day * Vapor Density + 0.01
Relative Humidity * Vapor Density + 0.84 Soil Water * Vapor Density; all p ≤ 0.05).
The addition of interaction terms for the heavy burn site, as well as quadratic terms at
both sites for variables that appeared potentially nonlinear (Julian Date, VWC, and rH)
did not result in models that explained greater variability in threshold wind speed. The
models demonstrate that soil moisture, and atmospheric moisture and temperature
explained variability in threshold wind speed, which increased with Julian Date.
3.5. Discussion
Erodibility and sediment discharges decreased with time following burning, at
both sites. A decrease in erodibility was also observed with time following wildfire by
Stout and Zobeck (1998) that could not be attributed to reestablishment of vegetation
following burning. Vegetation is slow to regrow following burning in cold desert
shrubland, and there was no plant cover for our 110 day, post-fire sampling period, other
than leafless remains of burned sagebrush and juniper stems and stumps at the light burn
58
site. We expected erodibility, therefore, to vary as a function of soil and atmospheric
moisture. We specifically expected to observe a decrease in erodibility with time since
fire, because both the frequency of precipitation events and near surface atmospheric
moisture are greater in most years in late fall and early winter compared to late summer
and early fall. We found significant relationships between threshold wind speed and
below and above-ground moisture measures, supporting the hypothesis that decreases in
erodibility with time are significantly related to near-surface moisture, post-fire. Analysis
focused on among-day variability in erodibility during months following fire, before
winter snow and spring green-up. Substantial variability in erodibility was also observed
within days, consistent with unburned surfaces studied by Stout (2004, 2007) and Wiggs
et al. (2004a, 2004b).
The progressive increase in threshold from September through early November at
both burn sites was likely attributable to increased moisture, as precipitation frequency
and quantity increased from September through October. The increased day-to-day
variability in erodibility near the end of the study (particularly evident at the light burn
site for Julian Date ≥ 330; Figure 3.4) probably relates to the increase in intermittent
wetting/drying cycles, as precipitation unexpectedly decreased in November and early
December. Measures of moisture generally explained more variability in erodibility at
the heavy burn compared to light burn site. This might have been a result of the presence
of burned vegetation at the light burn site, within which minor fluctuations in wind
direction might have resulted in widely different wind fields and erosivity. Threshold
wind speeds were more variable at the light burn site, which might have been partially a
function of a greater range in atmospheric conditions (atmospheric temperature and vapor
59
density spanned -9 – 33 °C and 2 – 11 g m-3 vs. -8 – 30 °C and 2 -7 g m-3, for light vs.
heavy burn sites, respectively) (Figure 3.2).
Results demonstrated multi-modal and complex relationships between erodibility
and measures of atmospheric moisture. For example, relative humidity and vapor density
were both included as atmospheric moisture-based predictor variables in multiple
regression models. Relative humidity fluctuates widely diurnally as a function of
saturation vapor pressure, whereas vapor density does not. Models for both sites (with
Julian Date, soil water, relative humidity, vapor density, and no interaction terms as
predictors) have a positive coefficient for vapor density, suggesting that erodibility
decreased as atmospheric moisture increased. The coefficients for relative humidity
suggest that rH was positively related to erodibility, counter to our initial expectations.
Furthermore, within-day variability in threshold did not show a relationship with relative
humidity or vapor density which was in common amongst all saltation events, and instead
the relationship between the variables was either positive or negatively linear, or
curvilinear during each event (Figure 3.5).
Erodibility is generally expected to be negatively related to near surface
atmospheric moisture. Positive relationships have also been described, however, in wind
tunnel studies (McKenna Neuman, 2003; McKenna Neuman and Sanderson, 2008; Ravi
et al., 2004, 2006b) and a field-based study (Ravi and D’Odorico 2005). One explanation
is based on the covariance of relative humidity and temperature. Cooler air has lower
saturation vapor pressure and less water-holding capacity, so cooling in itself can lead to
drier air, decreased interparticle cohesion, and increased erodibility (McKenna Neuman,
2003; McKenna Neuman and Sanderson, 2008). In support of this explanation for a
60
positive relationship between erodibility and atmospheric moisture, we observed many of
the largest relative humidity values during the coldest days of our study (Figure 3.2).
This explanation appeared particularly relevant at the light burn site, where erodibility
tended to be higher during cold conditions (mean threshold = 8.0 m s-1, s.e. = 0.15, n =
103 for below freezing air temperatures; mean threshold = 9.2 m s-1, s.e. = 0.03, n = 959
for above freezing air temperatures). Air temperatures below freezing were generally
sustained for less than a day and probably did not result in freezing of the soil surface in
most instances, therefore freeze and thaw of the soil surface likely had a minimal
influence on erodibility during the study.
An alternative explanation for a positive relationship between erodibility and
atmospheric moisture is the hyperbolic relationship previously described for unburned
soil surfaces by Ravi et al. (2004, 2006b) and Ravi and D’Odorico (2005), whereby
erodibility decreased with relative humidity at low and high relative humidity, but
increased throughout a middle range of relative humidity values (~ 35 % - 65 % rH). In
the middle range of rH values, increases in rH lead to a weakening of Van der Waals and
electrostatic forces that adhere soil particles to one another, with the change in bonding
force explained in part by the product of the absolute value of soil water potential and the
wet contact area between particles (McKenna-Neuman and Sanderson, 2008; Ravi et al.,
2006b). Increases in rH of air should increase water potentials of surface soils towards
less negative MPa values, thereby weakening bonding. Such a hyperbolic relationship
might be evident in the data from October 10, 2007 where threshold qualitatively
appeared to demonstrate a curvilinear response to increases in rH, at both sites (Figure
3.5). This specific curvilinear response was not replicated on another date, possibly
61
because there were few saltation events that spanned as large a range of rH values as
observed on October 10. It’s noteworthy that while we excluded data collected during
rain events, we did not exclude data from subsequent day(s) when the soil surface would
have remained quite wet. This might have increased the number of observations of high
threshold wind speeds at moderate or low relative humidity values due to surface
moisture conditions that were much wetter than atmospheric. Conversely, this afforded a
relatively wide range of soil moisture conditions within which to examine variability in
threshold wind speeds.
Fire-induced, water repellent conditions on the soil surfaces we studied might
have contributed to the complexity observed in the relationship between erodibility and
atmospheric moisture. Fire-induced water repellency in burned soil surfaces can increase
erodibility by increasing the amount of moisture required for capillary forces to influence
interparticle cohesion (Ravi et al., 2006a). This might have resulted in a greater number
of observations of low threshold wind speeds at conditions of moderate to high
atmospheric moisture in our data. While we did not examine water repellency in the soils
we studied, fire-induced soil water repellency has been described for burned surfaces in a
SE Idaho rangeland similar to the one we studied (Finley, 2006).
Unexplained variability in erodibility is likely a function of the fact that our
measurements of soil and atmospheric moisture were proxies of water at the soil surface.
Measurements of soil water content over the upper 15 cm of soil indicated the general
degree of wetness for the upper horizons of the soil profile, but did not reflect the
expectedly dynamic characteristics of soil water at the surface-atmosphere interface.
Measurements of atmospheric parameters at 5 cm above ground potentially under- and
62
over-estimated daily maximum and minimum values at the soil surface, respectively,
because diurnal variability in atmospheric moisture generally increases with proximity to
the surface (Campbell and Norman, 1998).
Additional unexplained variability might be due to chemical and physical controls
on post-fire erodibility that we did not examine in the context of this study. Such
controls might include: the decrease and/or degradation of water repellent compounds in
the soil with time following burning (as discussed above, Ravi et al., 2006a, 2007);
spatial heterogeneity in drying rates, and therefore erodibility, of the soil surface
following precipitation (Wiggs et al., 2004b); temporal changes in the abundance of
erodible mass at the soil surface; and the exposure of more well-cemented argillic and
calcic subsurface soils, and development of crusted, calcareous vesicular surfaces, at our
sites as a result of deflation of the soil surface following burning (see for example, Figure
3.7). Supply-limited surfaces might have developed with time following burning as a
function of one or more of the chemical and physical controls proposed above, providing
further explanation for the decrease in erodibility observed with time throughout the
study (Gillette and Chen, 2001; Macpherson et al., 2008). We qualitatively observed
changes in the abundance of erodible mass and patterns of surface cracks on the crusted
playettes (e.g. the cracked surface depicted in Figure 3.7) at all sites throughout the study.
Furthermore, results of an ongoing study suggest that the erosion potential of the playette
and adjacent coppice surfaces differ substantially following wildfire in this environment,
as the abundance of erodible mass and infiltration rates were greater, and soil strength
(penetrometer test) smaller, for less cohesive, coppice compared to crusted, playette
surfaces one year following burning at the heavy burn site (Hoover, unpublished data).
63
Figure 3.7 Photo taken at the study area, showing locations where deflation has increased the
presence of more cohesive, calcareous soil at the surface (lighter colored surfaces with polygonal
cracks).
Direct examination of such hypothesized chemical and physical controls on post-fire
erodibility that might operate in concert with hydroclimatological controls warrants
further study.
Threshold wind speeds were generally lower and saltation activity greater at the
heavy and light burn sites than measured at a similar burned (Crystal Fire 2006)
rangeland site in the SRP (Sankey et al., 2009a). The increase in wind erosion following
the Crystal Fire was determined by Sankey et al. (2009a) to be comparable to that
described for burned rangelands in Africa, Australia, and USA (Ash and Wasson, 1983;
Vermeire et al., 2005; Wasson and Nanninga, 1986; Whicker et al., 2002; Wiggs et al.,
64
1994, 1995, 1996; Zobeck et al., 1989). The maximum sediment discharge determined
for the heavy burn site was large (100.78 kg m-1 d-1 measured over the first 8 days of the
study), however, it still appeared smaller than maximum values cited for: 1) a burned
grassland (800 kg m-1 storm-1) studied by Skidmore (unpublished data – cited by Van
Donk et al., 2003), and 2) an unvegetated surface at the Jornada Experimental Range (>
100 kg m-1 d-1) studied by Gillette and Chen (2001), though Gillette and Chen (2001) fit a
different model to the relationship of flux vs. height, and integrated over 1 m instead of 2
m height. Threshold wind speeds measured at the heavy and light burned sites (min. –
max. = 4.8 m s-1 – 13.9 m s-1) can be compared to those determined for unburned surfaces
in several previous studies that employed the same or very similar method (Stout, 2004,
2007; Wiggs et al., 2004a, 2004b). Erodibility of the loess soils at the heavy and light
burn sites was more similar to the fine-textured playa studied by Stout (2007) where
threshold ranged from 6.4 – 10.8 m s-1, than sand dunes and beach sands studied by Stout
(2004, 2007) and Wiggs et al. (2004a, 2004b), respectively, where threshold was
generally less than 6 m s-1. Wiggs et al. (2004b) measured higher threshold values of
6.36 – 8.78 m s-1 for wet beach sands. Future research that examines temporal and spatial
variability in threshold wind speeds for burned soil surfaces in the SRP and other
environments is required.
3.6. Conclusions
We monitored the wind erodibility of two burned loess soil surfaces in the Snake
River Plain, Idaho, USA, for approximately four months following a late-summer
wildfire until snow accumulation in early winter limited aeolian transport. We found that
erodibility, as measured by daily threshold wind speeds, decreased significantly with time
following burning, but was variable for both severely and moderately burnt surfaces.
65
Erodibility predominantly decreased with increases in soil water and atmospheric
moisture. Positive relationships between erodibility and atmospheric moisture were also
observed at among- and within-day timescales, however. Results of this study provide
insight into the multi-modality and complexity of relationships between
hydroclimatology and aeolian transport during the significant period of increased wind
erosion potential that often occurs prior to the re-emergence of vegetation following fire
in this and other environments. Development of techniques for directly measuring
surface soil moisture could greatly improve our understanding of why threshold varies so
much in time.
66
Chapter 4: Relationships of Aeolian Surface Change with LiDARDerived Landscape Surface Roughness following Wildfire
Abstract
The reduction of vegetation by wildfire can subject stable soil surfaces to
increased aeolian transport. Vegetation and associated microtopography function as
surface roughness elements which influence the entrainment, transport, and deposition of
sediment by wind in burned and unburned semiarid shrublands. We examined whether
surface roughness derived from LiDAR can explain variability in aeolian surface change
following wildfire in loess soils of cold desert shrub steppe in SE Idaho, USA. Erosion
bridges were installed in fall 2007, following a late summer wildfire to monitor soil
surface change at sites in burned and downwind unburned areas. Surface elevation
measurements were made when the erosion bridges were installed and again in fall 2008.
Surface change was determined from the difference in relative elevation between the two
dates. Airborne LiDAR data were acquired in fall 2007 following erosion bridge
installation. Surface roughness was calculated at 2 m raster resolution using the standard
deviation of all LiDAR elevations within the 2 m cells, after elevations were detrended to
remove the effects of topographic slope. Surface change varied as a function of surface
roughness among burned and unburned surfaces, with net erosion occurring on the
relatively smooth, burned surfaces, and net deposition on the rough, unburned surfaces.
Site mean surface change (inflation or deflation) decreased as a function of the inverse of
site mean surface roughness (r2 = 0.77, p < 0.00). Quantile regression analysis indicated
that effects of surface roughness were greater on erosion compared to deposition
processes. Analysis of surface change at finer spatial scales suggested that aeolian
processes increased the heterogeneity of the existing microtopography in burned surfaces,
67
whereas the downwind surfaces that were unburned experienced a more homogeneous
pattern of surface change. Future research to examine relationships between aeolian
transport and fine spatial resolution topographic variability, for example from groundbased LiDAR systems, is recommended.
4.1. Introduction
Aeolian transport is an important biogeomorphic agent that can mobilize
ecologically sequestered sediment, minerals, nutrients, and pollutants at local to global
spatial scales (Okin et al., 2006; Okin et al., 2009). The reduction of vegetation by
wildfire is one particularly ubiquitous process which can subject otherwise stable
surfaces to increased potential for aeolian transport. Increases in wind erosion following
wildfire have been reported for a large range of biomes including cold and warm deserts,
grasslands, shrublands, and forests (Ash and Wasson, 1983; Wasson and Nanninga, 1986;
Zobeck et al., 1989; Wiggs et al., 1994, 1995, 1996; Whicker et al., 2002, 2006a, 2006b;
Vermeire et al., 2005; Ravi et al., 2007, 2009; Breshears et al., 2009; Sankey et al.,
2009a). Particularly in semiarid shrublands, aeolian transport is recognized as a
predominant biogeomorphic process that shapes contemporary pattern and form of
vegetation and soil surfaces (Breshears et al., 2003; Okin et al., 2006). Cold desert shrub
steppe is one type of semiarid shrubland in which aeolian transport following wildfire has
been shown to be especially influential (Sankey et al., 2009a, 2009b).
Aeolian transport has two functional components: 1) the wind’s ability to entrain
soil particles (erosivity) which is impeded by surface roughness components including
micro- and macro-topography, and vegetation; and 2) the soil’s susceptibility to this
entrainment (erodibility) (Bagnold, 1941; Cornelis, 2006; Okin et al., 2006). Erosivity
and erodibility interact in complex ways involving potentially nonlinear, recursive and/or
68
self-reinforcing relationships, and the study of aeolian transport in biogeomorphic
systems therefore has many existing challenges (Baas, 2007, 2008). One challenge is the
development of a more clear understanding of spatial dynamics of aeolian processes: a)
on landscapes that span a wide range of surface roughness (e.g. bare to densely
vegetated), and b) at multiple spatial scales - ranging from entire landscapes to that of
individual plants and/or associated surface microtopography, such as raised mounds often
located beneath woody vegetation and adjacent lower elevation interspaces (Okin et al.,
2006; Ravi et al., 2007, 2009; Breshears et al., 2009). Conceptual models for warm
desert shrublands suggest that when vegetation is reduced by disturbance, wind removes
sediment from coppices and deposits it in interspaces, which serves to redistribute
biogeochemical resources and result in a more physically homogenous surface (Ravi et
al. 2007, 2009; Ravi and D’Odorico, 2009). Disturbance of vegetation by fire in cold
desert shrub steppe has been suggested to homogenize the microtopography of the soil
surface as well (Hilty et al., 2003), but the contributions of wind erosion and deposition
to this process have not been explicitly examined to our knowledge.
In semiarid shrublands, vegetation and associated microtopography function as
surface roughness elements which influence aeolian transport (Wolfe and Nickling, 1993)
by: 1) providing protective cover to the soil surface, 2) altering wind flow by extracting
momentum from the wind, and 3) trapping soil particles. The protective cover provided
by vegetation in undisturbed shrublands has been hypothesized to be less effective
compared to other undisturbed landscapes (e.g. grasslands, forests) because: a) the
relatively sparse spatial distribution of shrubland vegetation permits wind flow in
instances to penetrate and even increase in erosive potential below the height of the mean
69
vegetation canopy, and b) intershrub spaces tend to have relatively high amounts of
exposed soil and relatively low cover of herbaceous vegetation (Breshears et al., 2009).
Disturbance, such as wildfire, decreases the density and stature of herbaceous and woody
vegetation, which increases the propensity for more erosive regimes of wind flow (Wolfe
and Nickling 1993; Breshears et al., 2009). In disturbed and undisturbed shrublands,
sediment entrained by aeolian processes can be removed from transport (trapped) by
surface roughness elements (Grant and Nickling, 1998; Raupach et al., 2001; Lee et al.,
2002; Okin et al., 2006). Physical processes of sediment entrainment, transport, and
deposition are well defined for surfaces that are vegetated (or unvegetated) with a
homogenous and/or regular spatial pattern. Shrubland surfaces are often heterogeneous,
however, and effects of surface roughness on aeolian processes are therefore not well
understood across the continuum of disturbed to undisturbed surfaces in these
environments (Okin et al., 2006).
LiDAR surface roughness
Light detection and ranging (LiDAR) remote sensing technology is particularly
well-suited for inquiry of spatial variability of surface roughness and aeolian processes,
and is recognized as having great utility for the quantitative characterization of a wide
range of biogeomorphic processes and systems (Tratt et al., 2007; Bauer, 2009; Pelletier
et al., 2009). Digital elevation models (DEM) constructed from LiDAR point data have
been used to examine the morphology and migration of desert and coastal sand dunes, for
example (e.g. Pelletier et al., 2009; Ewing and Kocurek, 2009; Bourke et al., 2009).
Specific to semiarid shrublands, LiDAR remote sensing has been used successfully to
describe variability in microtopographic and shrub vegetation height and morphology
70
(Menenti and Ritchie, 1994; Ritchie, 1995; Rango et al., 2000; Mundt et al., 2006;
Streutker and Glenn, 2006).
The term surface roughness implies a measure of topographic variability. A
variety of approaches for quantitatively describing the roughness of biogeomorphic
surfaces using LiDAR data have been reported. Determination of surface roughness
using LiDAR has been most commonly performed by calculating a measure of the
variability, often the standard deviation, of point elevations within a moving window or
pixel (Ritchie, 1995; Davenport et al., 2004; Glenn et al., 2006). LiDAR returns can be
first classified as having reflected from ground or vegetation surfaces, and then roughness
of bare earth, vegetation, and/or combined bare earth-vegetation calculated accordingly
(Glenn et al., 2006; Streutker and Glenn, 2006). Prior to the roughness calculation,
LiDAR point elevations can be detrended to remove variability due to macro-topographic
slope (Davenport et al., 2004). In lieu of LiDAR point elevation, variability in
derivatives of elevation (e.g. slope, aspect) can be examined using a LiDAR-derived
DEM (McKean and Roering, 2004; Frankel and Dolan, 2007). Pelletier et al. (2009)
calculated surface roughness from a LiDAR-derived DEM as the difference between
maximum and minimum elevations within a neighborhood of pixels, for example.
Surface roughness estimates derived from LiDAR were presented for vegetated surfaces
in several landscapes by Ritchie et al. (1995), who noted their potential utility in the
prediction of aeolian processes. In semiarid shrublands, surface roughness from LiDAR
has been used to estimate aerodynamic roughness, an important parameter for physical
models of aeolian transport (Menenti and Ritchie, 1994). Recently, an inverse
relationship between surface roughness derived from a LiDAR DEM and the entrainment
71
of sand particles was employed to model the migration of coastal sand dunes (Pelletier et
al., 2009).
Objectives
Surface roughness elements (e.g. vegetation and microtopography) vary spatially
as a function of landscape evolution processes in natural environments. Resultantly,
wind flow and aeolian transport vary spatially. Spatial patterns associated with aeolian
transport and the interaction of roughness elements with aeolian processes, in general, are
not well understood in heterogeneous environments at landscape scales (i.e. spanning a
continuum of disturbed and undisturbed surfaces) (Okin et al., 2006). The objectives of
our study were therefore to: 1) describe spatial patterns of surface change, and 2)
determine relationships between LiDAR-derived surface roughness and surface change,
in recently burned cold desert shrub steppe. We hypothesized that: 1) surface change
would be significantly related to surface roughness, and 2) the relationship would
indicate that smooth, burned surfaces are characterized by erosion and adjacent, rough
unburned surfaces are characterized by deposition, at the landscape scale. Additional
analysis was conducted to determine whether patterns of surface change and the
relationship between surface roughness and surface change, observed at a landscape
scale, were consistent with observations at finer spatial scales.
4.2. Study area
This study was conducted in shrub steppe rangelands of the eastern Snake River
Plain (SRP), Idaho, USA (Lat. 43° 30’ N, Lon. 112° 38’ W, 1650 m elevation) from fall
2007 through fall 2008 (Figure 4.1). Geomorphology of the SRP is characterized by
basalt lava flows and calderas that lie along a migratory path of volcanism which
originated ~ 16 ma in eastern Oregon and currently resides in the Yellowstone Plateau
72
Figure 4.1. (a) Location of study relative to Idaho, with inset showing location of Idaho relative to
USA. (b) Aerial photograph of the eastern Snake River Plain, Idaho with outline of Twin Buttes and
Moonshiner wildfire boundaries, study site locations (S = severely burned, M = moderately burned,
and U = unburned), and bare-earth digital elevation model for LiDAR data collection area. (c)
Schematic of erosion bridge locations within a 100 m radius hypothetical study site. At each site,
erosion bridge transects 3 and 1 were oriented along an axis from SW-NE, and transects 2 and 4
were oriented from SE-NW. Interval values denote distance of erosion bridge inner post from the
center of the study site.
(Pierce and Morgan, 1992). Surface soils have predominantly developed in aeolian
sediments, which include loess deposited ~12,000 – 70,000 years ago, and (to a much
lesser extent) sand dunes as well as fluvial sands adjacent to the Snake river which have
been reworked by wind (Busacca et al., 2004). The geomorphic setting of the study area
is described in further detail in Sankey et al. (2009b).
The aeolian surfaces studied included areas burned by one of two wildfires (Twin
Buttes fire – 3,819 ha July 2007, and Moonshiner fire – 1,081 ha August 2007) and an
73
adjacent, predominantly downwind unburned area. The Twin Buttes fire produced a
severely burned landscape with almost no vegetation remaining. The area burned by the
Moonshiner fire was less severely burned, with little herbaceous vegetation but a greater
presence of burned sagebrush and juniper remaining following fire. All surfaces studied
were characterized by silt loam-textured soils originally deposited as loess and classified
as the Atomic series - coarse-silty, mixed, frigid Sodic Xeric Haplocalcids in the United
States soil classification system (NCSS Web Soil Survey). A detailed vegetation survey
conducted as part of an associated study during summer 2008 described mean and
standard error (SE) live + dead shrub canopy cover of 43 (4) % in the unburned shrub
steppe and 2 (1) % in the moderately burned area (Moonshiner Fire) (Amber Hoover,
M.S. thesis in preparation, Idaho State University). The mean (SE) heights of the tallest
shrub within 24, 0.25 m2 sampling frames in July 2008 within the unburned and
moderately burned areas were 0.31 (0.02) and 0.16 (0.01) m, respectively (Amber
Hoover, M.S. thesis in preparation, Idaho State University).
Threshold wind speeds (the minimum windspeed required to initiate saltation)
were measured during fall 2007 on severely and moderately burned surfaces, and could
not be determined at an unburned surface due to very low amounts of saltation (Sankey et
al., 2009b). Winds exceeding the approximate measured lower limit of threshold (5 m s1
) and a slightly larger, though still below middle, value of threshold (8 m s-1)
predominantly trended from the SW to NE from fall 2007 – fall 2008 according to
windspeed and orientation data (10 m height) acquired from a weather station located 5
km NE of the study location (Figure 4.2).
74
Figure 4.2. Wind direction for wind speeds greater than 5 and 8 m s -1 from fall 2007 – fall 2008.
Chart demonstrates that winds predominantly trended SW-NE. Wind data were acquired from the
Idaho National Laboratory (collected at 10 m height). 5 and 8 m s -1 correspond to approximate
minimum and middle values, respectively, of threshold wind speed for aeolian transport measured on
burned surfaces at the study location by Sankey et al. (2009b).
A major source of surface variability within burned and unburned areas was the
pattern of playette and coppice surface microtopography, which is typical of shrub steppe
rangelands of the SRP (Hilty et al., 2003). Playettes are intershrub spaces which are
generally unvegetated and have a crusted, vesicular soil surface. Coppices are small,
vegetated mounds which have a less cohesive surface soil structure. Following wildfire,
coppices in the study area were vegetated with shrub and herbaceous plants: commonly
in the unburned area, occasionally in the moderately burned area (Moonshiner), and
rarely in the severely burned area (Twin Buttes). The vegetation survey conducted in
75
summer 2008 estimated that approximately half of the landscape was covered by coppice
and half by playette, and indicated that both microtopographic units were generally
anisotropic in shape (Amber Hoover, M.S. thesis in preparation, Idaho State University).
Mean (SE) (n = 24) playette dimensions were 1.66 (0.16) m along the longest axis and
0.86 (0.12) m along the perpendicular axis (Amber Hoover, M.S. thesis in preparation,
Idaho State University).
4.3. Methods
Surface change measurements
Erosion bridges were installed beginning in August 2007 to monitor soil surface
change at 5 locations in the area burned by the Twin Buttes fire (henceforth collectively
referred to as severely burned study sites), 3 locations in the area burned by the
Moonshiner fire (henceforth moderately burned study sites), and 6 locations in a nearby
unburned area (henceforth unburned study sites) (Figure 4.1). Twenty erosion bridges
were installed at each study site, oriented such that samples were collected on axes
perpendicular and parallel to predominant wind direction (Figures 4.1 and 4.2). Erosion
bridge positions were recorded using a Trimble GeoXT GPS receiver. Position
coordinates were subsequently differentially corrected and estimated to average < 1 m
horizontal accuracy. Each erosion bridge consisted of two 0.7 m lengths of rebar driven
approximately 0.4 m into the ground, 1.2 m apart. Using a 1.2 m long carpenter’s level,
the height of each rebar post was carefully adjusted to ensure that a level plane existed
between the tops of the two posts. Eleven measurements of the height of the carpenter’s
level above the ground surface were taken at 0.1 m increments along each erosion bridge
(i.e., the 1st and 11th measurement were separated by 1.0 m) on the installation date in fall
2007. Basal cover, a measure of cover at the soil surface (i.e. either soil, or the rooted
76
portion of herbaceous or shrub vegetation) was recorded at each erosion bridge
measurement. Erosion bridge height and basal cover measurements were repeated during
October 16-19 2008. Rate of surface change (mm y-1) for each measurement location
was estimated by subtracting the 2008 height value from the 2007 height value, dividing
by the number of days between the two measurement dates, and multiplying by 365 days.
Measurements in fall 2008 at several individual locations and/or entire erosion
bridges were either not performed or subsequently removed from analysis due to: 1) rebar
posts that were no longer in level alignment, 2) posts that could not be located in
unburned areas of dense vegetation, or 3) observation of an animal hole or mound at the
measurement location. Additionally, individual measurements indicating an absolute
value of surface change greater than 50 mm were determined to be likely indicative of
measurement error and removed from analysis. The number of measurements (and
erosion bridges) in the final data set analyzed were: 1094 (97) at the severely burned area,
659 (59) at the moderately burned area, 1278 (111) at the unburned area.
The repeatability of surface change measurements was assessed on the second
measurement date for 10 bridges (i.e., N = 110 measurements) located in burned sites,
and 10 bridges located in unburned sites by calculating a standard error of the lab (SEL):
SEL 
 Y
1
 Y2 
2
2N
where (Y1, Y2) are duplicate reference analysis and N is the number of replicate pairs.
LiDAR data and processing
LiDAR data were acquired in November 2007 for an ~ 60 km2 area covering
portions of the Twin Buttes fire, Moonshiner fire, adjacent unburned area, and including
all of the surface change measurement locations (Figure 4.1). LiDAR acquisition was
77
performed by the National Center for Airborne Laser Mapping (NCALM) using a Gemini
ALTM© laser scanner mounted on a fixed-wing aircraft flying at ~ 700 m AGL. The
sensor acquired data with a pulse rate frequency of 71 KHz and a scan frequency of 40
Hz. Average down-track and cross-track point spacing were 0.75 and 0.74 m,
respectively. The data were collected in 21 flight lines that averaged 537 m width with ~
50 % overlap. Average point density of the entire data set (i.e., overlapping flight lines)
was 3.63 points m-2 and ranged from ~ 1 point m-2 in locations of no overlap to > 6 points
m-2 in locations with multiple overlapping flight lines.
Vertical accuracy of the LiDAR data were evaluated by NCALM using 2161
check points collected along the paved surface of Highway 20 located near the northern
boundary of the LiDAR collection area. Check points were collected with a vehiclemounted GPS antenna (Ashtech 700700.c Marine antenna). Comparison of check points
with nearest neighbor LiDAR points indicated the LiDAR data had relative vertical bias
of -0.050 m with a scatter of 0.051 m.
The LiDAR data consisted of up to 4 returns per LiDAR pulse. The first two
returns were included in the analysis for the sake of processing efficiency, and 3rd and 4th
returns comprised a small and likely insignificant fraction (≤ 0.1%) of the data sets.
LiDAR data were spatially subset by study site, with one 200 m by 200 m subset centered
on each study site. Surface roughness was characterized using previously developed and
described methods and LiDAR tools (http://bcal.geology.isu.edu/envitools/index.html,
Glenn et al., 2006; Mundt et al., 2006; Streutker and Glenn, 2006). In this study, surface
roughness is defined as the standard deviation of all LiDAR point elevations (i.e.
reflected from ground and vegetation) within an area (raster cell) of specified dimensions.
78
LiDAR elevations were first detrended to remove the effects of coarser-scale topographic
slope (Davenport et al., 2004). Surface roughness was determined for each subset at 1, 2,
3, and 5 m raster cell resolution. A single surface roughness value was estimated for each
raster cell.
Scales of analysis
Comparison of data amongst severely burned, moderately burned, and unburned
areas represented the predominant and coarsest scale of analysis for this study, which is
henceforth referred to as landscape scale. At the landscape scale, analyses were
performed for surface change data aggregated by erosion bridge, as well as aggregated by
individual severely burned, moderately burned, and unburned sites, and by severely
burned, moderately burned, and unburned areas. In addition to landscape scale, surface
change data were analyzed at two finer spatial scales, henceforth termed amongplayette/coppice scale and within-playette coppice scale, respectively. Analyses at
among-playette/coppice scale focused within individual burned or unburned areas (i.e.,
either severely burned, moderately burned, or unburned) and were generally performed
for surface change data aggregated by erosion bridge. At within-playette/coppice scale,
analyses focused on measurements within individual erosion bridges with separation
distances of 0.1-1.0 m, and the major source of surface variability was that of individual
coppice or playette units, or the transition zone between the two types of
microtopographic units.
LiDAR surface roughness and relationships between LiDAR surface roughness
and surface change were analyzed at the landscape and among-playette/coppice scales.
The LiDAR data collected for this project did not have a sufficiently fine spatial
resolution for application to within-playette/coppice scales.
79
Analysis of surface change and LiDAR surface roughness
LiDAR-derived surface roughness values were related to corresponding fieldbased surface change measurements at the landscape and among-playette/coppice scales
using correlation and least squares regression (SPSS 15.0 for Windows) as well as
quantile regression analysis (QUANTREG package, R). Quantile regression is a useful
analysis technique when a response variable is expected to vary at different rates as a
function of the predictor variable depending on what portion of the response variable
distribution is analyzed (Cade and Noon, 2003). Because the response variable, surface
change, encapsulated two different physical processes (i.e. erosion and deposition) we
anticipated that values representative of the higher quantiles (τ) of surface change (values
predominantly indicating deposition) might vary differently as a function of surface
roughness compared to values representative of the lower quantiles (values
predominantly indicating erosion) of surface change conditional on surface roughness.
Plots of surface change and LiDAR-derived surface roughness vs. corresponding
basal cover measurements were examined qualitatively. Directional semivariograms
were constructed along the SW-NE and SE-NW axes (perpendicular and parallel to
predominant wind direction, Figures 4.1 and 4.2) of surface change measurements
aggregated for each of the severely burned, moderately burned, and unburned areas
(Geostatistical Analyst, ArcGIS 9.3). The directional semivariograms were constructed
with 1) a 5 m lag spacing and maximum separation distance of 40 m, and 2) a 0.1 m lag
spacing and maximum separation distance of 1.0 m, as representations of the spatial
autocorrelation structure of surface change at the among- and within-playette/coppice
scales, respectively.
80
4.4. Results
Surface change
Vegetation in the burned areas in fall 2007 consisted of burned shrubs, with no
herbaceous vegetation detected at the landscape scale (Table 4.1). Some vegetation,
predominantly herbaceous, had regrown in the burned areas by fall 2008 (Table 4.1).
Mean basal shrub cover ranged from 0-2% at the severely and moderately burned sites,
respectively, in fall 2007 (Table 4.2). Unburned sites spanned a wider range of
vegetation basal cover with mean total cover (shrub + herbaceous) ranging from 9–28%
amongst sites in fall 2007 (Table 4.2).
Table 4.1. Vegetation basal cover aggregated for burned and unburned areas
Area
Severe Burn
Moderate Burn
Unburned
Mean (SE) Basal Cover % Fall 2007
Shrub
Herbaceous
0.5 (0.2)
0.0 (0.0)
1.4 (0.4)
0.0 (0.0)
7.6 (0.7)
8.6 (0.8)
Mean (SE) Basal Cover % Fall 2008
Shrub
Herbaceous
0.8 (0.3)
5.4 (0.7)
1.4 (0.4)
5.3 (0.9)
8.2 (0.7)
10.1 (0.8)
Table 4.2. Install date and vegetation aggregated for severe (S) moderate (M) or
unburned (U) sites
Site
S1
S2
S3
S4
S5
M1
M2
M3
U1
U2
U3
U4
U5
U6
Install Date
8/23/2007
8/29/2007
9/18/2007
9/19/2007
10/4/2007
8/28/2007
8/29/2007
9/19/2007
8/28/2007
8/30/2007
8/31/2007
9/6/2007
10/2/2007
10/3/2007
Mean (SE) Basal Cover % Fall 2007_______
Shrub
Herbaceous
0.0 (0.0)
0.0 (0.0)
0.4 (0.4)
0.0 (0.0)
0.0 (0.0)
0.0 (0.0)
2.3 (1.0)
0.0 (0.0)
0.0 (0.0)
0.0 (0.0)
0.4 (0.4)
0.0 (0.0)
1.4 (0.8)
0.0 (0.0)
2.3 (1.0)
0.0 (0.0)
4.1 (1.3)
7.3 (2.0)
2.2 (1.0)
6.4 (1.6)
14.5 (2.4)
10.0 (2.0)
11.8 (2.2)
16.4 (2.5)
6.4 (1.6)
6.8 (1.7)
6.8 (1.7)
4.5 (1.4)
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The severely burned area had mean (SE) rate of surface change of -2.1 (0.2) mm
y-1, indicating net deflation of the soil surface, while the moderately burned area had
mean (SE) rate of surface change of -0.1 (0.2) mm y-1(Figure 4.3). The unburned area,
which was downwind of the burned areas, had mean (SE) rate of surface change of 1.5
(0.1) mm y-1 indicating net inflation (Figure 4.3). SEL values, a measure of repeatability
for surface change measurements, were smaller for burned surfaces (SEL = 1.3 mm)
likely because the lack of vegetation afforded greater accuracy in repeat measurements
compared to the unburned surfaces (SEL = 2.3 mm). Some of the variability in surface
change observed at the among-playette/coppice scale (Figure 4.3) might have been
influenced by the installation date of study sites (Table 4.2). Large deflation was
observed for the first severely burned site to be installed (S1), for example (Figure 4.3).
Amongst the unburned sites, large inflation was observed for the 1st installed unburned
site (U1) (Figure 4.3). Plots of surface change with basal cover measured in fall 2007
demonstrate a trend from deflation to inflation with increasing vegetation cover, in
general, though the largest values of mean inflation were observed for sites with lowintermediate vegetation cover (Figure 4.4)
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Figure 4.3. Mean (w/ standard error bars) surface change and LiDAR-derived surface roughness
measurements aggregated for severely burned, moderately burned, and unburned areas, and for
individual study sites within these areas. Surface roughness is calculated as the standard deviation of
all LiDAR point heights (ground + vegetation) per raster cell (2 m resolution) after the point heights
have been detrended for topographic slope.
Spatial patterns of surface change
Experimental semivariograms demonstrated a random spatial pattern of surface
change at the among-playette/coppice scale in severely and moderately burned areas
(Figure 4.5). In unburned areas, surface change at among-playette/coppice and withinplayette/coppice scales occurred with a spatially uniform pattern, as indicated by
experimental semivariograms that appeared to have a relatively linear-flat structure
(Figures 4.5 and 4.6). Surface change in burned areas demonstrated strong spatial
autocorrelation structure at the within-playette/coppice scale (Figure 4.6). The spatial
83
patterns were directional in the severely burned area with a larger range for
measurements oriented perpendicular, in comparison to parallel, to predominant wind
directions. Models fit to experimental semivariograms at the within-playette/coppice
scale for burned surfaces had ranges < 0.90 m, suggesting that patterns of surface change
occurred within landscape units less than 0.90 m in length and/or width (Figure 4.6).
LiDAR surface roughness
At the landscape scale, the severely burned area had on average small surface
roughness values, the unburned area had large surface roughness values, and the
moderately burned area had intermediate values (Figure 4.3). Roughness increased with
increasing vegetation basal cover at the landscape scale, in general, though the largest
mean roughness values were observed for sites with low-intermediate vegetation cover
(Figure 4.4). Site S1 had small roughness values relative to the other 4 severely burned
sites and site U2 had large roughness values relative to the other unburned sites,
providing examples of variability in LiDAR roughness at the among-playette/coppice
scale (Figure 4.3). Relationships of surface roughness values amongst sites and amongst
the three surface types were consistent for rasters processed at 1, 2, 3, and 5 m cell size.
84
Figure 4.4. Mean (w/ standard error bars) surface change and LiDAR-derived surface roughness vs.
vegetation basal cover (fall 2007) for severely burned, moderately burned, and unburned study sites.
85
Figure 4.5. Autocorrelation structure of surface change measurements at the among-playette/coppice
scale, as depicted by directional semivariograms constructed for erosion bridge data aggregated by
severely burned, moderately burned, and unburned areas with 5 m lag and 40 m maximum
separation distance. Semivariograms depict the spatial dependence (semivariance – y-axis) of
samples as a function of separation distance (distance – x-axis). Smaller semivariance indicates
greater relative spatial dependence (autocorrelation).
86
Figure 4.6. Autocorrelation structure of surface change measurements at the within-playette/coppice
scale, as depicted by directional semivariograms constructed for erosion bridge data aggregated by
severely burned, moderately burned, and unburned areas with 0.1 m lag and 1.0 m maximum
separation distance. Semivariograms depict the spatial dependence (semivariance – y-axis) of
samples as a function of separation distance (distance – x-axis). Smaller semivariance indicates
greater relative spatial dependence (autocorrelation).
87
LiDAR surface roughness and surface change
Surface change was strongly and negatively related to the inverse of surface
roughness derived from LiDAR at the landscape scale (Figure 4.7). The strongest
relationship (r2) using least squares regression was observed with raster cell size of 2 m,
though results were very similar at 1, 3, and 5 m raster cell size (Table 4.3). The
relationship implies that smoother surfaces (burned areas) were characterized by
deflation, while rougher surfaces (unburned) were characterized by inflation. The
modeled relationship suggests that a transition from deflation to inflation occurred at a
surface roughness of ~0.05 m (i.e. inverse surface roughness ~ 20 m, Figure 4.7). The 1st
sites installed in the severely burned and unburned areas appeared to be potentially
influential and outlying points, respectively. However, when these points were removed
from analysis the model fit only improved very slightly (r2 = 0.78, p < 0.00, for 2 m raster
cell size).
Table 4.3.Site mean surface change (y) vs. site mean inverse LiDAR surface roughness
(x)
Raster Cell Size
1m
2m
3m
5m
Model
y = -0.3x + 4.9
y = -0.3x + 5.6
y = -0.3x + 4.8
y = -0.3x + 4.2
r2 (p)
0.76 (0.00)
0.77 (0.00)
0.75 (0.00)
0.73 (0.00)
MSE
1.03
0.99
0.88
1.18
SE (estimate)
1.02
0.99
0.94
1.09
At the landscape scale, quantile regression analysis of erosion bridge mean
surface change versus inverse surface roughness indicated a greater effect of surface
roughness on surface change for lower τ values (i.e. erosion) in comparison to higher τ
values (i.e. deposition) (Figure 4.8). The estimated slopes (β1) for τ = 0.10 and 0.25 (β1
= -0.27 and -0.21, respectively) were 2-4 times as large as the estimated slopes for τ =
0.50, 0.75, and 0.90 (β1 = -0.10, -0.08, and -0.06, respectively), for example (Figure 4.8).
88
Quantile regression slope and intercept coefficients were significant for τ = 0.1, 0.25, 0.5,
0.75, 0.90 [all p < 0.05 and SE(β1) = 0.06, 0.03, 0.03, 0.02, 0.02, respectively]. Overall,
findings indicate that the effect of surface roughness on aeolian transport was greater for
erosion versus deposition processes.
Figure 4.7. Relationship of mean surface change aggregated by site with inverse of LiDAR-derived
surface roughness amongst severely burned, moderately burned, and unburned sites. Surface
roughness is calculated as the standard deviation of all LiDAR point heights (ground + vegetation)
per raster cell (2 m resolution) after the point heights have been detrended for topographic slope.
Relationships between surface roughness and surface change at the amongplayette/coppice scale were consistent with, though not as strong, as observations at the
landscape scale. Mean surface change (aggregated by erosion bridge) was negatively
related with inverse surface roughness, and the relationship was significant within the
89
unburned and severely burned areas, but not the moderately burned area [Severe burn:
Pearson correlation coefficient (R) = -0.24, p = 0.01. Moderate burn: R = -0.21, p = 0.11.
Unburned: R = -0.22, p = 0.02]. When performed within the burned and unburned areas,
quantile regression analysis of surface change versus inverse surface roughness produced
models with intercept and/or slope coefficients that were not statistically significant (p >
0.05).
Figure 4.8. Mean surface change calculated by erosion bridge vs. the inverse of surface roughness for
corresponding nearest neighbor LiDAR pixels amongst severely burned, moderately burned, and
unburned areas. Surface roughness is calculated as the standard deviation of all LiDAR point
heights (ground + vegetation) per raster cell (2 m resolution) after the point heights have been
detrended for topographic slope. τ values represent the quantile of the surface change distribution
analyzed conditional on the inverse of surface roughness. β values are the quantile regression slope
coefficients.
90
4.5. Discussion
Surface change
Landscape scale
A simple calculation suggests that 1.0 * 108 and 1.3 * 105 kg of soil might have
been mobilized by wind from burned surfaces at the Twin Buttes and Moonshiner fires,
respectively [mean surface change * area burned * assumed bulk density of 1.25 g/cm3].
Results imply that a net loss of soil occurred from the burned areas and that some soil
particles were likely transported to the unburned area which was located downwind, over
the course of one year, post-fire. Localized erosion and deposition occurred within both
burned and the unburned areas, however (Figure 4.8). The small net deflation observed
within the Moonshiner fire and net inflation observed within the unburned area might not
have been different than no change when considered with regards to measurement error
(SEL).
The mean rate of surface deflation observed in the severely burned area (-2.1 mm
y-1) appeared smaller than that measured by Whicker et al. (2002) who estimated a mean
of -5.8 mm 162 d-1 from 6 erosion bridges in a burned semiarid shrubland in New
Mexico. A mean inflation of 1.9 mm 162 d-1 was estimated by Whicker et al. (2002)
from six erosion bridges in an unburned area, which appears larger than the range
observed at the unburned sites we studied (0.81 – 3.54 mm y-1). It is unclear to what
extent inflation at the unburned site studied by Whicker et al. (2002) was resultant of
background atmospheric deposition versus having been transported from the burned area.
The net inflation we observed in the unburned area appears to be substantial relative to
background atmospheric deposition that might be expected in the absence of local
wildfire and subsequent aeolian transport. Analyses and reviews of dust deposition rates
91
in the western USA measured with sediment traps have found ranges of: 1 – 48 g m-2 y-1
for suspension-sized particles at sites across 7 states (Reheis and Kihl, 1995), and 2 – 20
g m-2 y-1 for long-term monitoring sites in the southern Great Basin and Mojave desert,
USA (Reheis, 2006). Dividing the rates presented by Reheis and Kihl (1995) by a
particle density of 2.65 * 106 g m-3, suggests a range of deposition of 0.0004 – 0.02 mm
in units of thickness. It is uncertain how deposition rates measured with sediment traps
translate to vertical accumulation of sediment across an actual landscape surface,
however, as such measurements represent a lower limit of background dust that might
accumulate in soil (Reheis, personal communication).
Finer scales
Aeolian transport resulted in zones of erosion and deposition that were
comparable in size or smaller (average dimensions < 0.90 x 0.90 m as indicated by
semivariogram ranges, Figure 4.6) than the surface microtopography in burned areas (e.g.
mean playette dimensions = 1.66 x 0.86 m). The zones of erosion and deposition were
anisotropic in shape on the severely burned surfaces, with a long axis oriented
perpendicular to prevailing winds. In contrast, erosion and deposition occurred in
unburned areas with a more uniform spatial distribution. Based on these results, fire and
subsequent wind erosion appeared to exacerbate heterogeneity of the microtopography on
burned surfaces, while homogenizing microtopography on downwind unburned surfaces.
Our findings at finer scales appear analogous to those of Whicker et al. (2002)
who found differences in surface change resultant from wind erosion in shrub versus
intershrub patches, in burned but not unburned areas. Our findings at finer scales appear
counter to the conceptual model for aeolian transport in burned shrublands and shrubencroached grasslands presented by Ravi et al. (2007, 2009) and Ravi and D’Odorico
92
(2009). In their model, the microtopography of burned landscapes is homogenized by the
removal of sediment by wind from the raised coppice surfaces and deposition in the
lower interspaces. Our findings also appear somewhat counter to those of Hilty et al.
(2003) who found that the regular pattern of coppice and playette microtopographic units
evolved to a flatter, disturbed surface following burning in a shrub steppe landscape in
Idaho, similar to our study area. Hilty et al. (2003), however, analyzed the evolution of
surfaces over timescales of multiple years, post-fire, and the evolutionary mechanism
they proposed included a combination of burning, livestock trampling, cheatgrass
(Bromus tectorum L.) invasion, and water erosion, though not aeolian transport. The
areas we examined were not grazed during the course of the study, and cheatgrass has not
invaded the study areas yet. The degree to which post-fire aeolian transport serves to
increase either the heterogeneity or homogeneity of surface microtopography through the
redistribution of sediment is important, as it can directly influence the cycling and spatial
distribution of biologically important nutrients, as well as the evolution of plant
communities which affects surface roughness and provides feedback to aeolian processes
(Su et al., 2006; Ravi et al., 2007).
LiDAR surface roughness and surface change
Characterization of surface (ground + vegetation) roughness with LiDAR
indicated that severely burned surfaces were generally smoother than moderately burned
surfaces, which were in turn smoother than unburned surfaces. Results suggest that
LiDAR detected variability in roughness that existed largely due to the degree of
vegetation present and/or absent amongst the three types of surfaces. Surface change
varied as a function of surface roughness at the landscape scale, and the modeled
relationship supported the hypothesis that smooth, burned surfaces are characterized by
93
deflation and adjacent rough, unburned surfaces are characterized by inflation. The
transition from erosion to deposition in the modeled relationship (Figure 4.7) occurred at
surface roughness ~ 0.05 m (i.e. inverse surface roughness ~ 20 m) which approximately
corresponded with the threshold in roughness between burned and unburned surfaces.
Streutker and Glenn (2006) found burned and unburned shrub steppe to have mean
vegetation roughness of ~ 0.05 and 0.09 m, respectively, in a nearby study location. The
modeled relationship appeared in accord with the inverse relationship between surface
roughness and sand entrainment that Pelletier et al. (2009) employed for modeling coastal
dune migration.
Quantile regression analysis indicated that deposition increased gradually with
increased roughness, whereas erosion increased rapidly with decreased roughness.
Erosion is generally expected to increase with decreased vegetation cover and density,
though complexity in these relationships exists at lower densities of vegetation due to the
spatial structure of turbulence and related potential for increased erosivity adjacent to
individual roughness elements (e.g. shrubs) (Fryrear, 1985; Findlater, et al., 1990; Lee,
1991a, 1991b; Raupach et al., 1993). Deposition, in contrast, generally occurs in
shrublands when sediment is trapped by vegetation and is greatest at intermediate levels
of vegetation porosity (Grant and Nickling, 1998; Raupach et al., 2001; Lee et al., 2002;
Okin et al., 2006). While we did not relate surface change or surface roughness to
vegetation porosity, we did relate these variables to field measurements of vegetation
basal cover, a measure of vegetation abundance. Results implied that both surface
roughness and sediment deposition were greatest for low to intermediate values of
vegetation basal cover.
94
Breshears et al. (2009) suggest that in undisturbed landscapes aeolian transport is
greatest for intermediate levels of shrub cover, whereas in disturbed landscapes transport
should decrease linearly with increasing shrub cover. The findings of our study indicate
a linear relationship between the inverse of surface roughness and erosion/deposition.
While we did not model a non-linear relationship for the upper quantiles of surface
change, the plotted data emphasize that the greatest mean deposition amongst erosion
bridges corresponded with intermediate values of surface roughness (e.g. inverse surface
roughness ~ 15-30 m in Figure 4.8). Surface change at the unburned sites was likely
influenced by transport from the upwind burned areas. As such, a different model might
be expected between surface change and surface roughness were unburned control sites
(in which only deposition from background atmosphere occurred) analyzed.
Relationships between surface roughness and surface change at the amongplayette/coppice scale were consistent with, though not as strong as, at the landscape
scale. The low correlation might be resultant of several sources of measurement error,
including the average: vertical (~ 5 cm) and horizontal (< 1 m) errors in LiDAR
measurements, horizontal error (< 1 m) in erosion bridge GPS locations, and vertical
error (~ 1 – 2 mm) in erosion bridge surface change measurements. In light of the
findings of spatial patterns of aeolian transport at finer-than-landscape scales, future
research to examine relationships of aeolian transport with surface roughness from higher
resolution LiDAR (e.g. increased point density or ground-based LiDAR systems) is
needed.
4.6. Conclusion
LiDAR characterization of the roughness of burned and unburned surfaces
demonstrated that the reduction of vegetation by fire produced surfaces which were
95
relatively smooth compared to unburned surfaces in shrub steppe at a landscape scale.
Surface change due to subsequent aeolian transport varied as a function of surface
roughness at the landscape scale. Burned surfaces which were smooth were
characterized by net erosion, and the adjacent unburned surfaces which were rough were
characterized by net deposition. Erosion varied more strongly as a function of surface
roughness compared to deposition. At finer spatial scales, aeolian transport appeared to
increase the heterogeneity of the existing coppice and playette soil microtopographic
units in burned surfaces. In downwind unburned surfaces, aeolian transport resulted in a
more homogeneous pattern of surface change at fine to coarse spatial scales.
96
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