Terrain Modeling
and Soil Erosion Simulation
Final Report
Prepared by:
H. Mitasova
Dept of Marine, Earth and Atmospheric Sciences (MEAS), North
Carolina State University (NCSU)
formerly at Geographic
Modeling Systems Lab, University of Illinois at Urbana-ChampaignW.M. Brown
D.M. Johnston
Geographic
Modeling Systems Lab, University of Illinois at Urbana-Champaign
Prepared for:
U.S. Army Engineering Research and Development Center
(ERDC),
under DACA88-99-D-0002 Task Order No. 05,
Project Title: Terrain Modeling
and Soil Erosion Simulation,
Matthew Hohmann, POC/COR ERDC/USACERL,
under
contract to the University of Illinois Board of Trustees,
Douglas M. Johnston,
Principal Investigator.
Contents:
One
of the key challenges in environmental research is to model interacting physical processes with sufficient accuracy and
efficiency. Rapid development of computer technology offers new
opportunities to tackle extremely complex environmental problems.
Computational approaches belong to "young" methodologies
which were developed only over the past few decades and as such, they
have their own rules, challenges, successes and limitations. The role
of algorithms, data structures, computationally efficient methods,
advanced visualization and exploration of parallelism are crucial for
new advances in environmental research and require close
collaboration between traditional research disciplines and
computational science.
Originally,
GIS applications were focused on static spatial data processing,
analysis and computer cartography. However, development of new
mapping technologies and computer capabilities together with acute
environmental problems have pushed the GIS applications into more
sophisticated levels. Advanced geoscientific applications involve
modeling landscape processes at an unprecedented level of detail.
Nevertheless, the process-based modeling of geospatial phenomena
involves substantially more uncertainty than modeling in physics or chemistry
because of the above mentioned complexity. Practical solutions have to rely,
then, on
the best possible combination of physical models, empirical evidence,
intuition and available measured data. In physics, accuracy is
usually understood in a much stricter sense, because many fundamental
laws are known over a broad range of scales in energy, distance or
time. For example, the Schrodinger equation describes matter at the
electronic level virtually exactly, that means, within spectroscopic
accuracy of 6 to 12 digits. This is seldom the case in complex
geoscientific applications where 50% differences between measurements
and model predictions can be in many instances considered
satisfactory.
Computational
approaches to investigations of physical systems are based on
simulation and modeling. By simulation we mean a
computer representation of reality in which the simulated system is
governed by a set of known physical laws expressed in mathematical
language. In this case the model is already in place and its range of
validity and accuracy is supposed to be well-known and verified. The
task of simulation is to solve it for a particular realistic
situation. The fact that the fundamental laws are known does not mean
that simulations are straightforward or easy. The corresponding
equations are often difficult to solve and barriers in computational
feasibility and efficiency often limit accuracy, resolution or size
of the modeled system.
On the other
hand, by modeling we mean a process by which the
scientist is trying to build a simplified version of reality for a phenomenon for which the fundamental laws are either unknown,
impractical to use or simply do not exist. This typically involves
systems which are very complex, with many constituents and a variety of
interactions between them and with limited amount of available
experimental information. Typical examples are many ecological
models, and systems which involve anthropogenic activities. The modeling
process often involves trial and error and in some cases its
predictive power, accuracy and relation to reality may be a
research problem on its own.
Physical
systems are often described by a combination of deterministic
(physically based) and empirical (observation based) models. Empirical models are
based on observations and statistical analysis of observed data and
their applicability is limited to the conditions for which they were
developed. They can provide a rough picture of the phenomenon under
study, but they cannot explain how the system works. Because of their
simplicity, they are widely used for practical applications and as
components of more complex models for the sub-processes for which the
physical model is unknown or too complicated.
Current
research and development in physical systems modeling is focused on
distributed, process-based models, often dynamic in three dimensional (3D) space.
This trend has been stimulated by the availability of geospatial data
and supporting geographic information system (GIS) tools. GIS has greatly reduced the time for
preparation of inputs, however, this task can still be rather tedious
and time consuming. Artifacts from interpolation remain a perennial
problem and the support for high precision floating point, temporal
and multidimensional data is still inadequate. Therefore coupling of
GIS and models is done at various levels and incorporation of GIS
functionality within the modeling systems is now quite common. The
models of physical processes are often core modules for integrated
modeling systems, due to their impact on ecosystems and society.
Models of physical systems are also important components of decision
support systems.
A typical
example of modeling of a complex landscape process which requires combination of
empirical and physics-based methods
is soil erosion, and sediment transport and deposition modeling.
This combination is necessary because there are still gaps in understanding of the
relevant processes. Also, some of the processes are too complex or require
difficult to measure parameters to be incorporated using the known
physics principles.
A number of erosion models explicitly distinguish and
simulate various types of erosion processes, such as sheet erosion,
rills, gullies etc. However such an approach requires a large number of
empirical input parameters which cannot be obtained at sufficient accuracy for landscape scale applications without substantial cost.
Therefore in simpler models, the different impact of these various
processes is averaged and incorporated through empirical parameters,
which is the case for RUSLE3d and USPED models.
These two models and their various applications have
been covered in previous reports and publications (Mitasova et al. 1997-2000) and in the Landscape Erosion Modeling Tutorial. Here we
provide some explanations regarding the types of erosion modeled and
selection of parameters (Engel. 1999, Desmet and Govers.1996.).
Both models
replace slope length by upslope area as a measure of water flow. This
makes the models applicable to complex topography. It also means that
the model captures impact of a wider range of types of flow than the
original USLE. It includes the combined, averaged impact of sheet and
rill flow on hillslopes as well as concentrated flow erosion and
potential for gully formation that has not been covered by
traditional USLE. While the sheet and rill flow rates are comparable
to USLE for the range of slopes and hillslope length typical for USLE
applications erosion rate estimates for
concentrated flow erosion and very long hillslopes are not sufficiently verified because of lack of
empirical data. However,
preliminary comparisons show that the models correctly predict over one to two
magnitudes of increase in erosion rates due to concentrated flow and
these areas should be classified as areas of high erosion risk
(rather than excluded from the results). When making erosion risk assessment using RUSLE3d and USPED it is therefore not necessary to
add impact of gullies from field observations because they are
already incorporated.
The basic equation for RUSLE estimates
erosion rate by multiplying the following empirical factors:
E = R K LS C P
where E
is the average soil loss, R is the rainfall intensity, K is the soil,
C is the cover, P is the prevention practices and LS is the
topographic factor:
LS(r)
= (m+1) [ A(r) / a0 ]m
[ sin b(r) / b0 ]n
where A is upslope contributing
area per unit contour width, b is the slope, a0
= 22.1m, b0 = 0.09. Typical values for m=0.4-0.6
and n=1-1.3.
Exponents for water and slope terms in the sediment
transport and detachment equations reflect the interaction between
different types of flow and soil detachment and transport. We
explain their application and impact on the result in the following
sections.
For sheet
flow, detachment and sediment transport increases relatively
slowly with the amount of water. Geometric properties of topography
(slope, curvatures) play a more important role in the evolution of the
pattern of soil detachment and net erosion/deposition than the
pattern of water flow. This type of flow is typical for areas with
good vegetation cover but also for a severely compacted, smooth soil
cover where compaction prevents soil detachment and formation of
rills. This type of flow is reflected by the lower value of exponent
m for the water term represented by the upslope area (Figure
1.).
Figure. 1 RUSLE3D LS
factor for prevailing sheet flow reflected by the value of m=0.1. In
this case the impact of steeper slope on the erosion pattern is
stronger than the impact of concentrated flow.
SUM = 334314
Number of cells: 735318
Minimum: 0
Maximum: 3.1872103214
Range: 3.18721
Arithmetic Mean: 0.454652
Variance: 0.137468
Standard deviation: 0.370766
|-----------------------------------------------------------------------------|
|MAP: 1.1*exp(house3.dsd*3./22.13,0.1)*exp(sin(sl3.rst50s2)/0.0896,1.3) |
|-----------------------------------------------------------------------------|
| Category Information | % | |
| #|description | cover| acres|
|-----------------------------------------------------------------------------|
|0-1|stable . . . . . . . . . . . . . . . . . . . . . . . . | 89.91|1476.09046|
|1-5|low. . . . . . . . . . . . . . . . . . . . . . . . . . | 9.62| 157.96604|
| *|no data. . . . . . . . . . . . . . . . . . . . . . . . | 0.47| 7.64897|
|-----------------------------------------------------------------------------|
If both types
of flow are present in the given area, which is usually the case due
to spatial variability in land cover and soil properties, the value
m=0.4 provides reasonable, averaged results (Figure 2). It has
also a theoretical foundation (Moore and Burch 1986) and is being
used, for example, by Engel (1999) This exponent balances the impact
of turbulent and sheet overland flow.
Figure 2. RUSLE3D LS
factor for averaged sheet, rill, and gully erosion including both
impact of concentrated flow and slope pattern using m=0.4.
SUM = 666252
Number of cells: 735318
Minimum: 0
Maximum: 24.6981582642
Range: 24.6982
Arithmetic Mean: 0.906074
Variance: 0.779073
Standard deviation: 0.882651
|-----------------------------------------------------------------------------|
|MAP: 1.4*exp(house3.dsd*3./22.13,0.4)*exp(sin(sl3.rst50s2)/0.0896,1.3) |
|-----------------------------------------------------------------------------|
| Category Information | % | |
| #|description | cover| acres|
|-----------------------------------------------------------------------------|
| 0-1|stable . . . . . . . . . . . . . . . . . . . . . . . | 68.45|1123.70468|
| 1-5|low. . . . . . . . . . . . . . . . . . . . . . . . . | 30.77| 505.07843|
| 5-10|moderate . . . . . . . . . . . . . . . . . . . . . . | 0.28| 4.51560|
|10-50|high . . . . . . . . . . . . . . . . . . . . . . . . | 0.05| 0.75779|
| *|no data. . . . . . . . . . . . . . . . . . . . . . . | 0.47| 7.64897|
|-----------------------------------------------------------------------------|
For
prevailing rill and gully erosion that appears on disturbed
land, with soils vulnerable to rilling and gully formation, creating
conditions for highly turbulent water flow, the impact of the water term
is much higher (Figure 3), reflected by the higher exponent m.
Figure 3. RUSLE3D LS
factor for prevailing rill and gully erosion reflected by the value
of m=0.6. In this case the impact of flow on the erosion pattern is
stronger than the impact of slope and overall erosion rates are much
higher.
SUM = 1112308
Number of cells: 735318
Minimum: 0
Maximum: 120.2604675293
Range: 120.26
Arithmetic Mean: 1.51269
Variance: 5.92545
Standard deviation: 2.43422
|-----------------------------------------------------------------------------|
|MAP: 1.6*exp(house3.dsd*3./22.13,0.6)*exp(sin(sl3.rst50s2)/0.0896,1.3) |
|-----------------------------------------------------------------------------|
| Category Information | % | |
| #|description | cover| acres|
|-----------------------------------------------------------------------------|
| 0-1|stable . . . . . . . . . . . . . . . . . . . . . . | 51.96| 853.10417|
| 1-5|low. . . . . . . . . . . . . . . . . . . . . . . . | 43.84| 719.65615|
| 5-10|moderate . . . . . . . . . . . . . . . . . . . . . | 3.12| 51.25830|
| 10-50|high . . . . . . . . . . . . . . . . . . . . . . . | 0.57| 9.30009|
|50-5000|severe . . . . . . . . . . . . . . . . . . . . . . | 0.04| 0.73779|
| *|no data. . . . . . . . . . . . . . . . . . . . . . | 0.47| 7.64897|
|-----------------------------------------------------------------------------|
If we assume
that a dense vegetation cover prevents creation of rills and keeps
water flow in dispersed, sheet flow while the loose, bare soil has an
opposite impact, increasing the flow turbulence and formation of
rills, we can use a spatially variable exponent based on the cover. In
the following example (Figure 4), we have used the exponents m=0.2 for forest,
m=0.4 for grass, m=0.5 for dormant sparse grass and m=0.6 for
disturbed areas. The result increases the negative impact of
disturbed areas and reduces the impact of vegetated areas.
Figure 4. RUSLE3D LS for
variable m exponent
SUM = 792530.019225
Number of cells: 735318
Minimum: 0
Maximum: 86.2473754883
Range: 86.2474
Arithmetic Mean: 1.07781
Variance: 1.7033
Standard deviation: 1.30511
|-----------------------------------------------------------------------------|
|MAP: (1.0+mvar)*exp(house3.dsd*3./22.13,mvar)*exp(sin(sl3.rst50s2)/0.089... (|
|-----------------------------------------------------------------------------|
| Category Information | % | |
| #|description | cover| acres|
|-----------------------------------------------------------------------------|
| 0-1|stable . . . . . . . . . . . . . . . . . . . . . . | 62.89|1032.51487|
| 1-5|low. . . . . . . . . . . . . . . . . . . . . . . . | 35.44| 581.81698|
| 5-10|moderate . . . . . . . . . . . . . . . . . . . . . | 1.06| 17.44907|
| 10-50|high . . . . . . . . . . . . . . . . . . . . . . . | 0.14| 2.22891|
|50-5000|severe . . . . . . . . . . . . . . . . . . . . . . | 0.00| 0.04667|
| *|no data. . . . . . . . . . . . . . . . . . . . . . | 0.47| 7.64897|
|-----------------------------------------------------------------------------|
Note, that RUSLE provides a variable
m exponent expressed as a function of
slope angle, which reflects the fact that planar hillslopes with low
slope have prevailing dispersed flow, while flow on steeper slopes
can be more turbulent. However, this exponent gives reasonable
results only for shorter slopes in RUSLE3d. The RUSLE equation for variable m
was developed for slope length and traditional field applications, so it
is not recommended for use with upslope area without evaluating the
results using field measurements. The slope-based formula for m can
result in values of 0.8 or greater on steeper slopes. For slopes hundreds of meters long,
or for concentrated flow, this exponent predicts extremely high
erosion rates when using upslope area in RUSLE3d.
A 
B
Figure 5. Full RUSLE3D
estimate with a) constant m=0.4; b) variable m. The result with
variable m predicts higher risk for gullies.
When different LS factors are used
in the full RUSLE3D equation, the quantitative results and pattern (Figure 5)
are as follows:
m=0.4
SUM = -6377646
Number of cells: 722067
Minimum: -734.1213378906
Maximum: -0
Range: 734.121
Arithmetic Mean: -8.83249
Variance: 328.88
Standard deviation: 18.135
m=variable
SUM = -9074719
Number of cells: 722067
Minimum: -3863.8823242188
Maximum: -0
Range: 3863.88
Arithmetic Mean: -12.5677
Variance: 1269.59
Standard deviation: 35.6313
USPED (Unit Stream Power - based Erosion Deposition) is a simple model that
predicts the spatial distribution of erosion and deposition rates for a steady
state overland flow with uniform rainfall excess conditions. It assumes
that the erosion process is transport capacity limited. The model is based on
the theory originally outlined by Moore and Burch (1986) with numerous
improvements. For the transport capacity limited case, we assume that the
sediment flow rate qs(r) is at the
sediment transport capacity T(r), r=(x,y) which is
approximated by (Julien and Simons 1985)
- |qs(r)| = T(r)
= Kt (r) |q(r)|m
sin b(r)n
where b(r) [deg] is slope, q(r)is water flow
rate, Kt(r) is transportability coefficient, which is
dependent on soil and cover; m, n are constants that vary according to
type of flow and soil properties. For overland flow the constants are usually
set to m=1.6, n=1.3 (Foster 1990). Steady state water flow can be
expressed as a function of upslope contributing area per unit contour
width A(r)[m]
|q(r)| = A(r) i
where i[m] is uniform rainfall intensity (note: approximation by
upslope area neglects the change in flow velocity due to cover). No experimental
work was performed to develop parameters needed for USPED, therefore we use the
USLE or RUSLE parameters to incorporate the approximate impact of soil and cover
and obtain at least a relative estimate of net erosion and deposition. We assume
that we can estimate sediment flow at sediment transport capacity as:
T
= R K C P Am (sin b)n
where R~im, KCP~Kt and LS=Am sin
b n, and m=1.0-1.6, n=1.0-1.3
Then the net erosion/deposition
is estimated as a change in sediment flow rate expressed by a divergence
in sediment flow:
ED = div (T . s) = d(T*cos
a)/dx + d(T*sin a)/dy
where a [deg] is aspect of the elevation surface (or direction
of flow, steepest slope direction, minus gradient direction).
Because
USPED computes divergence of sediment flow, the impact of the
exponents is more complex. The water flow term exponent here controls
the ratio between the extent of erosion and deposition as it is illustrated
by the following examples.
It reflects the fact that turbulent flow can carry
sediment farther and the impact of concentrated erosion will be wider
than if flow is dispersed by vegetation. The following figures (Figures 6, 7,
8,9,10) demonstrate the influence of the exponent m on the erosion/deposition
pattern and gully erosion risk estimates.
Figure 6. Spatial pattern
of topographic potential for erosion and deposition by USPED with
m=1, prevailing dispersed sheet flow, deposition extends high onto
hillslopes.
Figure 7. Spatial pattern
of topographic potential for erosion and deposition by USPED with
m=1.4, both sheet flow and rill flow influence erosion and deposition.
Deposition starts lower on the hillslope, gullies start farther towards the headwater
area and they are wider.
Figure 8. Spatial pattern
of topographic potential for erosion and deposition by USPED with
m=1.6, with prevailing rill and concentrated flow. Extent of
deposition is further reduced and the potential for gullies starts very
high in the headwater area, so gullies are even longer
and wider.
Figure 9. Spatial
pattern of topographic potential for erosion and deposition by USPED
with variable m.
Figure 10. Full USPED with
variable m
The RUSLE3d and USPED models as
described above were implemented in ArcGIS 8.1 as well as in ArcView3.x and GRASS
GIS. For ArcView, still the most widely used GIS on installations, we
developed sample Avenue scripts to make it easier to run the models. We
examined implementation in ArcGIS 8 using the ArcModel interface, but that
interface was not released with ArcGIS 8.1 and is reportedly undergoing major
revision with no release date set. Detailed procedures for running both
models in ArcGIS are provided in the on-line tutorial developed as part of this
work (see link below).
Using
Soil Erosion Modeling for Improved Conservation Planning: A GIS-based
Tutorial (http://www.gis.uiuc.edu/erosion) is a richly illustrated hypertext tutorial, available on-line and
on CDROM , produced as part of this contract. The tutorial
covers erosion theory, data collection and evaluation, methodology, running the
models using GIS, and interpreting results.
In the previous reports (Mitasova et al. 2000) we have applied
the models to a predominately natural watershed at Fort Hood (Owl creek). Here we focus
work on highly disturbed areas in the House Creek watershed. While the hydrology of this watershed has been investigated
using various models, high resolution analysis of erosion and deposition
by overland flow provides new insights about the distribution
of sediment sources and their relation to land cover and topography.
We obtained data from an NRCS erosion inventory for this area
and entered them into our database.
While the inventory data cannot be directly used for model calibration and
validation because of the approach that was used, it provides some indication of
the consistency of
both the estimates of input parameters, and erosion rates estimated
in the field at a few locations and from the GIS data.
The following is the database summary of 50 records with selected attributes observed at each inventory site which
were entered into the GIS database. The original data were paper forms
containing field measurements and observations for each inventory site. (Fort Hood data are courtesy Fred Schrank,
NRCS).
SITES FILENAME: erosionf
--------------
Header Information:
------------------
name erosion-house
description erosion inventory data for House creek at Ft. Hood
labels X|Y|#ID|%K%L%S%LS%C%P%ET%EA%EG%AG%ER
Number of DIMENSIONS: 2
--------------------
- - MIN - - - - MAX - -
dim 1 604010.000000 613355.000000 Easting
dim 2 3445785.000000 3452260.000000 Northing
Type of CATEGORY information: CELL_TYPE
----------------------------
- - MIN - - - - MAX - -
158 214 ID number
Number of DOUBLE attributes: 11
---------------------------
- - MIN - - - - MAX - -
dbl 1 0.2 0.39 K-factor
dbl 2 40 300 Slope Length [m]
dbl 3 1 14 Slope angle
dbl 4 0.12 1.45 LS
dbl 5 0.003 0.45 C-factor
dbl 6 1 1 P-factor
dbl 7 0 28
dbl 8 0 13 Soil loss (USLE)(t/a/y)
dbl 9 0 89 gully erosion (t/y)
dbl 10 0 0.5 area gully affected (acre)
dbl 11 0 152 road erosion (t/y)
TOTAL SITES COUNTED: 50
----------------------------------------------------------
The inventory data is also available as a GRASS sites database file
(http://www.gis.uiuc.edu/erosion/finalreport/erosionf.txt).
Notes about the soil erosion inventory data :
- USDA inventory data are for 2 acre sites - usually 150-200m long
while our results are for a reference point. We do not know where the
reference point for each plot is located (center, top).
- Note that each plot is 50-70 cells long
and 6-9 cells wide, so to really compare we would need to compute an estimate from all cells in that area,
however we do not know how that area is oriented on the slope.
- Our upslope area is from the top of the drainage to the site;
the observed slope length appears to be the length of the plot. (It should be measured from the top of
the hill but it is not clear whether it really was).
- It is not clear what units are used for slope; ours is in degrees.
| X |
Y |
ID |
K |
dsd/L |
S |
LS1.1 |
LS1.4 |
LS1.6 |
LSVAR |
C |
ET |
EA |
EG |
AG |
ER |
|
605462.
|
3451852
|
-
|
0.17
|
23
|
1.4dg
|
0.27
|
0.27
|
0.32
|
0.44 |
0.5 |
8/10 |
|
- |
|
|
| 605466 |
3451855 |
169 |
0.32 |
200 |
1.5 |
0.20 |
- |
- |
- |
0.1 |
4 |
1.9 |
0 |
0 |
0 |
|
605725
|
3452206
|
- |
0.17 |
14 |
1.9dg |
0.30 |
0.39 |
0.45 |
0.40 |
0.1 |
2/2 |
|
+ |
|
|
| 605721 |
3452202 |
160 |
0.32 |
200 |
3.5 |
0.46 |
|
|
|
0.1 |
9 |
4.3 |
24 |
0.2 |
32 |
|
605797
|
3452086
|
|
0.17 |
37 |
2.0dg |
0.38 |
0.85 |
1.42 |
1.1 |
0.1 |
4/5 |
|
+ |
|
- |
| 605799 |
3452086 |
162 |
0.32 |
150 |
4 |
0.47 |
|
|
|
0.2 |
17 |
8.2 |
39 |
0.5 |
3? |
| 606091 |
3452263 |
|
0.17 |
17 |
1.5 |
0.23 |
0.32 |
0.40 |
0.35 |
0.1 |
2 |
|
- |
|
- |
| 606102 |
3452260 |
158 |
0.2 |
300 |
1.5 |
0.23 |
|
|
|
0.1 |
3 |
1.4 |
7 |
0.1 |
9 |
| 606121 |
3451954
|
|
0.17 |
20 |
2.3 |
0.43 |
0.67 |
0.88 |
0.7 |
0.5 |
14/17 |
|
- |
|
- |
| 606132 |
3451953 |
164 |
0.2 |
200 |
4 |
0.53 |
|
|
|
0.4 |
28 |
13 |
86 |
0.4 |
0 |
| 606220 |
3451780
|
|
0.28 |
27 |
1.2 |
0.17 |
0.26 |
0.34 |
0.32 |
0.01 |
0.2 |
|
+ |
|
|
| 606232 |
3451784 |
171 |
0.32 |
200 |
3 |
0.35 |
|
|
|
0.2 |
13 |
6.3 |
1 |
0.1 |
0 |
From the point
of view of conservation measures, contour filter strips, spreaders
and, to some extent, grassed waterways, are effective in changing
the turbulent flow typical for rills and gullies to dispersed sheet
flow. Erosion prevention in areas with sheet flow should focus on
upper convex parts of the slope (that are prone to
increased net erosion by creating accelerated flow). Dense vegetation should be preserved in
these areas. Areas with more turbulent flow require flow dispersal
measures and prevention of concentrated flow development which can
cause severe and irreparable soil loss. Once
concentrated flow fully develops it is more difficult to control erosion and more expensive control measures, such as sedimentation ponds, need
to be added.
The first step in identifying erosion-prone areas
is to select a numerical threshold from the results that identifies "hot
spots", or areas to investigate for remediation (Figures 11, 12). Expert knowledge of
the study area and field observations should be compared to values obtained by
the model to help identify an appropriate threshold. Once a threshold
value (or a series of priority values) is chosen, the hot spot areas are
selected in the GIS. Appropriate vegetation may be added to the model in
selected areas within the hotspots by changing the C-factor for those
areas. The model run is then repeated and the results compared to those
from the initial condition. Repeating this process and visualizing the
results for multiple scenarios, using the GIS to analyze area, types, and cost
of remediation, should help in the development of a satisfactory remediation
plan (Figures 13, 14).
Figure 11. Hot spots from RUSLE3d
Figure 12. Hot spots from USPED
Figure 13. Current (dark green)
and needed new (light green) protective cover based on RUSLE3d
%area acres
sparse cover 76.35|1253.40376|
current forest 7.73| 126.97239|
proposed new forest/dense cover 13.66| 224.23337|
Figure 14. Current (dark green)
and needed new (light green) protective cover based on USPED.
% area | acres
sparse cover 79.58|1306.47097
current forest 6.94| 113.91671
new dense vegetation 8.78| 144.49688
The work summarized in this report provides a current application of
state-of-the-art modeling of erosion and sediment transport deposition
processes. Modeling has been improved by incorporating more accurate
representations of physical processes such as soil detachment, and by the
availability of higher resolution (and higher accuracy) data sources.
Because of the lack of appropriate field data, however, validation and
calibration of model parameters and results remains a research need.
The analysis of the RUSLE3D and USPED exponents explains how the flow term exponent
controls the magnitudes and spatial pattern of detachment and net erosion/deposition.
Because there are no extensive experimental data available to assign the values
of this exponent based on local conditions several approaches can be taken for
its determination. The most accurate results can be obtained if the exponent is
calibrated using spatially distributed measurements. If no field observations are available, it is possible to assign
a value to m
based on the land cover. C-factor reflects the impact of cover on soil
detachability, while m may incorporate the impact of land cover on
the type of flow. The same cover may have a different impact on C
and on m. Other models such as WEPP and SIMWE use the impact of
land cover in several parameters as well. Other indicators of type of flow, such as surface roughness,
propensity for seepage (which increases rilling and gullies, suggesting higher
m), and soil properties can also be used. For many applications, especially where
totals or averages are used, or the results are classified into a small number of classes,
it is sufficient to use m=1.4. Because the range of values for slope is much smaller
than the range of values for upslope area, the impact of its exponent on the results
is smaller, but still significant, so if the experimental data are available it is
useful to include a range of values into calibration.
The tutorial developed as part of this effort provides a more complete
discussion of work accomplished to date, as well as examples and instructions
for application.
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agricultural land, in Soil Erosion on Agricultural Land}, edited by J. Boardman,
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flow. Transactions of the ASAE, 28, 755-762.
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conservation strategies Report for USA CERL. University of Illinois,
Urbana-Champaign, IL.
Mitasova, H., Mitas, L., Brown, W. M., Johnston, D., 1999, Terrain modeling and
Soil Erosion Simulations for Fort Hood and Fort Polk test areas. Report for USA
CERL. University of Illinois, Urbana-Champaign, IL.
Mitasova, H., Mitas, L., Brown, W. M., Johnston, D., 1998, Multidimensional Soil
Erosion/deposition Modeling and visualization using GIS. Final report for USA
CERL. University of Illinois, Urbana-Champaign, IL.
Mitasova, H., Mitas, L., Brown, W. M., Johnston, D., 1997, Multidimensional Soil
Erosion/deposition Modeling. PART IV and V: Process based erosion simulation for
spatially complex conditions and its applications. Report for USA CERL.
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Moore, I.D., and Burch, G.J. (1986) Physical basis of the length-slope factor
in the Universal Soil Loss Equation. Soil Sciences Society America Journal, 50,
1294-1298.
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Fotheringham, eds.), Taylor and Francis, London: 83-108.
