Terrain modeling and Soil Erosion Simulation:


applications for evaluation and design of conservation strategies

   
   
 

Prepared by:

Geographic Modeling and Systems Laboratory, University of Illinois at Urbana-Champaign

Helena Mitasova, Lubos Mitas, William M. Brown, Douglas M. Johnston


 

for :

U.S. Army Construction Engineering Research Laboratories

Dr. Dick Gebhart


 
 
 
 
   
 
 
 
 
 

Annual Report

2000


 
 
 
 
 
 

Content


 
  

1. Introduction.............................................3

2. Methods..................................................4

   2.1 New generation of input data.........................5
       2.1.1 Digital elevation models (1-10m resolution)....6
       2.1.2 Land cover.....................................8
       2.1.3 Soils.........................................10

   2.2 Modeling at hierarchy of scales

       2.2.1 Effects at watershed - plot scales (see talk) 15
       2.2.2 Methods for multiscale........................18
       2.2.4 Path sampling ................................14
       2.2.5 whats new in RUSLE3d, USPED, SIMWE ...........15

   2.4 GIS erosion modeling and conservation design........14

       2.4.1 Design possibilities and limitations .........14
       2.4.2 Modeling effects of conservation measures.....18
             buffers, filter strips, grassways, hedges
       2.4.3 Examples (SedSpec, HydroPEDDS, TRAINER?)......16

3. GIS implementation in GRASS and ARC 

       3.1.1 ArcGIS8 versus ArcView implementation, ArcModel18
       3.1.2 Open source GIS implementation with new GRASS5.19

3. Applications............................................18

   3.1 Fort Hood overland and concentrated flow erosion....18
   3.2 Fort Polk sediment transport and wetlands...........21

4. Conclusion and future directions........................22

5. References..............................................23

6. Appendix................................................24

   6.1 Manuals and tutorials
   6.2 Data
   6.3 On-line documents

 
 
 
 
 
  Notes;

review all monthly reports

review all text written - GISEM chapter, proposals, etc

review all new results
 
 

1. Introduction


New mapping and environmental monitoring technologies generate large volumes of high resolution spatio-temporal data and offer unique opportunity to dramatically improve the land management, alleviate environmental pressures and preserve biodiversity. The major challenge for the rapidly evolving geographical information science is to provide methods and tools for effective use of these data for environmentally and economically sound land use management, with focus on proactive prevention rather than just remediation of environmental problems. It is therefore crucial to speed-up the relevant landscape modeling and management research so that it can provide the tools to evaluate the impact of changes before they are implemented and to support design of new, effective conservation and pollution prevention strategies.


The landscape-scale land management strategies are extremely difficult to test experimentally because of the cost and time requirements of such experiments and because of limited possibilities to evaluate their functioning under extreme conditions. Laboratory experiments, while useful, often behave quite differently from large-scale real-world systems, thus limiting the applicability of their results. Field experiments can support the studies of certain properties of conservation measures, however, they are constrained both spatially and temporally and cannot capture the long-range interactions typical for fluxes in complex landscapes. According to the National Research Council's report on "New strategies for America's watersheds, "one area of special promise to address these issues is simulation modeling which can give decision makers interactive tools for both understanding the system and judging how management actions might affect that system" (NRC, 1999)".


One of the key challenges in environmental research is the description of interacting physical processes with sufficient accuracy and efficiency. It is clear, that a rapid development of computer technology offers new opportunities to tackle extremely complex environmental problems. In fact, computational simulation and modeling is becoming a third way of doing scientific research complementing the traditional experiments and analytical theories. Computational approaches belong to "young" methodologies which were developed only over the past few decades and their progress is closely tied to the advances in computer capabilities. 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 geospatial data collection technologies and computer capabilities together with acute environmental problems have pushed the GIS applications into more sophisticated levels. Advanced geoscientific applications involve multidimensional phenomena (see chapter 6, also Mitasova et al 1995), dynamics (chapter 6, Mitas et al. 1997), supercomputing class simulations as well as real-time processing of huge amount of measured data (Catin and Fortin 2000). Nevertheless, the process-based modeling of the geospatial phenomena involves substantially more uncertainty than modeling in physics or chemistry. One of the key reasons is the above mentioned complexity of studied phenomena. The practical solutions then have to rely on the best possible combination of physical models, empirical evidence, intuition and available measured data. In physics, the 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, Schrodinger equation describes the 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 spatial simulations are "young" methodologies developed only over the past few decades and their progress is closely tied to the advances in computer performance. As such, they have their own rules, challenges, successes and limitations. The role of model development and verification, algorithms, data structures, advanced visualization and parallel computing are crucial and require close collaboration between traditional research disciplines and computational science. In spite of significant progress in Geographical Information Systems (GIS) technology and environmental modeling (Goodchild et al. 1993, 1996, 1997, GIS/EM4 2000, Boyle et al. 1998, Kellershohn et al. 1999, Mitasova et al. 1995, Wilson and Lorang 1999, Band et al. 2000) there are persistent problems with accuracy and reliability of spatial simulations (National Research Council 1999), preparation of data is often time consuming and running the models requires substantial expertise. Finally, the modeling efforts have focused on analysis of the current state and prediction, while their use for development of new land management and conservation strategies is largely unexplored.



  1. Methods

GIS supported model of the studied landscape will provide the basis for landscape monitoring, simulation and manipulation. Multi-resolution landscape model will be based on the combination of regional scale model with embedded high resolution models for monitoring of "hot spots" which will be implemented through nested grids (Mitas and Mitasova 1998b, DHI 2001). The low-resolution models will be created using available USGS data at 10 to 30 m resolution while rapidly changing areas will be mapped and monitored at much higher levels of detail using such state-of-the-art mapping technologies as LIDAR and real-time-kinematic (RTK) GPS, among others.



2.1 New generation of input data

Lot of new data available

consistency of data (different datums, coord. systems), resolution

show wake the differences, or Ft. hood - same area, different models, land cover etc.

Digital elevation data consistency of data (different datums, coord. systems), resolution,

artifacts in DEMs - quality of DEMs both resolution and interpolation

is better than 30m USGS DEM however several problems persist -

cutting off cm - 1m vertical resolution is not enough - not just for

flow routing but for any application which includes slope calculation -

it creates artificial slope pattern! In contracts the installations should request

cm precision (this has nothing to do with accuracy - even if accuracy is 5m

the precision in cm is needed.

dams in valeys are probably even worse than the pits in USGS DEM, this is

a known problem of TINs and contrled triangulation should be used!!!

Analysis of new DEM products suitability for erosion modeling is

being performed. This issue is important from the

point of view of landmanagers - our experience is that the DEMs

remain the main source of problems when applying the erosion models

at landscape scale.

We have continued with the analysis of the new USGS 10m DEM

and we have found that while the problem with "steps" due to integers

has been resolved new problems due to the interpolation method

that USGS uses were introduced: waves along contours, especially in

steep areas, leading to artificial erosion/deposition pattern;

excessive noise, especially in flat areas, leading to noisy and incosistent

contours; artificial dams in valleys due to triangulation. These

new problems would make the use of this DEM for erosion/depostion

modeling problematic - we have asked for the original contours and

we will test whether the RST method could produce better DEM.

From the point of view of LMS, the DEMs remain still a problem.

It would be useful to test other types of DEM which land managers

might have available to find out whether there is any type of

DEM which would not require any preprocessing for erosion modeling

from land managers.

the comparison of 30m and 10m DEM has also confirmed the importance

of selecting the proper resolution for land management applications -

while erosion from 30m DEM is mostly from sheet flow, 10m DEM reveals

much more concentrated flow with gully potential which would require

a different land management approach than the results from a 30m DEM.

Also erosion from concentrated water flow is not very well integrated

in the traditional models. Modified USLE and USPED can identify the areas

with this type of erosion, however for accurate quantitative estimates

calibartion using reliable field data would be needed.

2.1.2 Land cover.....................................8

2.1.3 Soils.........................................10



2.2 Modeling at a hierarchy of scales


Process-based modeling of geospatial phenomena is difficult and often involves a much higher level of uncertainty than, for example, simulations of microscopic systems in physics or chemistry which are based on virtually exact theories, such as quantum mechanics. Key reasons for these modeling difficulties are the complexity of landscape phenomena, the multitude of processes acting across a range of scales, non-equilibrium phenomena and/or lack of experimental data. The practical solutions rely on the best available combination of physical models, empirical evidence, experience from previous studies and available measurements. Landscape processes are therefore often described by a combination of physically-based and empirical models, and integration with field measurements using data adaptive simulation is especially important for increasing the reliability of predictions (e.g., Cantin and Fortin 2000). Typically, the physically-based models have the following components (Mitasova and Mitas, in press):



Many natural processes involve more than a single scale and exhibit multi-scale, multi-process phenomena (Steyaert 1993, Green et al. 2000). Problem of multiple scales permeates several scientific disciplines such as materials research, physics, and biology. Many multi-scale problems can be partitioned into a hierarchy of effective models which are nested in the direction from fine to coarse scales. At a given scale the model incorporates simplified or "smoothed out" effects coming from finer, more accurate levels. In the direction from coarse to fine scales, one develops a set of effective embeddings which determine boundary and/or external conditions for the processes at finer scales. The fine scale processes are then modeled at high resolution only in "hot spots" of the studied system (Mitas and Mitasova 1998b, Mitasova and Mitas in press).


FIGURE - Scheme for multiscale modeling


2.2.3 Path sampling


Spatial processes described by the governing partial differential equations are usually solved by

discretization techniques such as finite difference (e.g., Doe et al. 1996) and finite element methods (e.g., Vieux et al. 1996). Besides these approaches we also plan to explore other alternatives, in particular, path sampling method. Similar methods are known as path integrals in physics (Ceperley 1995) or random walks in stochastic processes (Karlin and Taylor 1981, Gardiner 1985). The path sampling is based on the fact that essentially any field can be represented by an ensemble of sampling points. For example, a scalar field can be determined by the distribution (spatial density) of sampling points in the corresponding region of space; conversely, ensembles of particles are often described by continuous fields. The duality of field <=> particle density is routinely employed in physics and helps to reformulate and solve complicated problems involving interacting systems with many degrees of freedom. The path sampling method has been successfully used in physics (Ceperley and Mitas 1996), chemistry (Hetenyi et al. 1999, Forsythe and Makri 1998), finance (Ingber 2000), etc., for linear or weakly nonlinear transport or time propagation problems which involve processes such as diffusion, advection, rate (proliferation/decay), reactions and others. It has several important advantages when compared with more traditional approaches. It is very robust, can be easily extended into arbitrary dimension, is mesh-free and is very efficient on parallel architectures including heterogeneous clusters of PCs and workstations. Path sampling is also rather straightforward to implement in a multi-scale framework with data adaptive capabilities. The method has been successfully used for distributed modeling of overland water and sediment flow and erosion/deposition studies (Mitas and Mitasova 1998a), including multi-scale applications (Mitas and Mitasova 1998b), and modeling of dissolved and suspended substances in lakes, estuaries and coastal areas (Dimou and Adams 1993, DHI 2000).


The method is based on duality between the particle and field

representation of spatially distributed phenomena.Within this concept,

density of particles in

space defines a field and vice versa, field is represented by

particles with corresponding spatial distribution of their densities. Using

this duality, processes can be modeled as evolution of fields or evolution

of spatially distributed particles (Fig. 1),

with the solution obtained as follows.


\begin{figure}[h]

\caption{Path sampling solution of the continuity equation for water

depth $h({\bf r})$ using duality between particle and field representation:

a) water depth at 1 minute, b) water depth after 24 minutes. The grid

is 416x430 cells at 10m resolution. See the {\sl CDROM} for animation.}

\centerline{\epsfig{file=llefig1.eps,width=1.\textwidth,clip= }}

\end{figure}


First a selected number of particles, also called walkers or sampling points,

is distributed according to the source

$ {\cal S}({\bf r'})$. These walkers are then propagated

according to the function $G({\bf r},{\bf r'},p)$, generating a number

of sampling paths. Averaging of these path samples provides

an estimation of the actual solution $ \gamma ({\bf r})$

with statistical accuracy proportional to $1/\sqrt{M}$ where $M$

is the number of walkers (Fig. 2). The solution is not restricted to the

steady state and the state of the modeled quantity at any given time

$p$ can be obtained by averaging the path samples at a given time $p$.


\begin{figure}[h]

\caption{Path sampling solution of the continuity equation for sediment

flow and net erosion/deposition:

a) results for 7000 walkers, b) results for 50 million walkers.

The grid is 280x250(70,000 cells) at 2m resolution. See the {\sl CDROM}

for animation.}

\medskip

\centerline{ \epsfig{file=llefig2.eps,width=1.\textwidth,clip= } }

\end{figure}


The path sampling technique has several unique advantages which

are becoming even more important due to new developments in

computer technology. Perhaps one of its most significant

properties is robustness which makes it possible to solve the equations

for complex cases, such as discontinuities in

the coefficients of differential operators (in our case, abrupt

slope or cover changes, etc). In addition, the independence of sampling points

makes the stochastic methods perfectly suited to

the new generation of computers as they provide scalability from

a single workstation to large parallel

machines and computers distributed over different types of networks.


SIMWE model was enhanced to output the temporal development

of water flow both for the uniform and multiscale version and the

output of real time was added. The dynamic version was tested for

Hohenfels and the results very clearly demonstrate the negative impact

of compacted soil by moving large amounts of water into the stream very

quickly, compared to slow movement of water within the forested

subwatersheds - while this is a known fact the animation from

the model may serve as a strong demonstration tool of how big

the difference is. I will include it into the annual report.


Put here the GISC2000 presentation and figures


      1. whats new in RUSLE3d, USPED, SIMWE ...........15


Models representing limiting cases of erosion are simple to compute

in GIS by combining the flow-tracing and topographic analysis functions with

map algebra. They can be applied to a single storm, monthly

and annual estimates of soil detachment and net erosion/deposition.

Caution should be used when interpreting the results from both

{\sl RUSLE3D and USPED} because the {\sl USLE/RUSLE}

parameters were developed for simple plane fields and detachment limited

erosion. Therefore, to obtain accurate quantitative predictions for

complex terrain and land cover conditions

they need to be re-calibrated, especially in areas of concentrated flow

(Foster, 1990; Mitasova et al., 1997 reply).

While the capabilities of both {\sl RUSLE3D and USPED}

to accurately predict the rates of

erosion and deposition at any point in the complex landscape are limited (fact

which is true about almost any erosion model),

they are useful tools for land management.

Both models use readily available parameters and

can provide valuable spatial information about: (i) the location of areas

with high erosion risk from both shallow overland and concentrated flow,

(ii) location of areas with deposition, and

(iii) relative estimates of erosion and deposition rates

for different land use alternatives and conservation strategies.

Locations identified as high risk from both {\sl RUSLE3D and USPED}

should be primary targets for field erosion inventory

(to validate the risk) and implementation of prevention/mitigation measures

(if the high risk is confirmed in the field). Computation of net erosion

and deposition is also useful for evaluation of the landscape's capacity

to deposit the eroded material before it can reach the streams.

Modified USLE and USPED have been further tested in

GRASS and ARCVIEW. The approach was comapred with Bernie Engels

methodology for modified USLE within ArcView - same problems

with the lack of full support for floating point data in ArcView

was observed.


2.4 GIS erosion modeling and conservation design........14


2.4.1 Design possibilities and limitations .........14

2.4.2 Modeling effects of conservation measures.....18

buffers, filter strips, grassways, hedges



Interaction between upland areas and areas along the streams

and water bodies was studied, including the assessment of

riparian buffer design. We have found that the current riparian

buffers are designed based only on the distance from the streams

neglecting the impact of topography, especially the areas

with convergent water flow.

Preliminary results from simplified models indicate that in areas

with complex terrain increased water and sediment flows from

intensively used uplands may get through the buffers because of

unprotected headwater areas and areas with no permanent streams

(more details will be provided in the report)

We did more work on riparian buffers - we used an Illinois

watershed as a test area as part of the leveraging project and

compared the riparian buffers 60ft, 100ft, buffers+slopes,

current land use and model based scenario with the results

at http://www2.gis.uiuc.edu:2280/modviz/courtcreek/lhu52/cctable.html

The results show that the buffers are too narrow and offer

little benefit for the stability of the entire area, at least

steep slopes and concentrated flow areas

should be added to vegetative protection

for the conservation areas to have any significant impact.

Similar analysis will be performed for the Fort Polk subwatershed

as soon as we are able to process the data.


We have studied the traditional designs of grassways,

compared them with the insights that we have gained from simulations

and identified issues which could lead to some improvements

in the designs.

Lot of info is on the web


{Concentrated flow erosion} Development of high erosion

in areas of concentrated flow was studied

by performing simulations of water flow and net erosion deposition

for an experimental field with uniform land cover

(350x270m, modeled at 2m resolution; Zhang, 1999).

For a short rainfall event ending before the flow has reached

steady state, the maximum erosion rate was on the

upper convex part of the hillslope and there was only

deposition in the center of the valley (Fig. 8a).

As the duration of the rainfall increased,

water depth in the center of the valley has grown

rapidly until it reached

a threshold when linear features with very high erosion rates

developed within the depositional area,

indicating potential for gully formation (Fig. 8b).

This effect is modeled by both USPED (Mitasova et al., 1996, 1999)

and SIMWE (Mitas and Mitasova, 1998), however, a smooth, high resolution

DEM without artifacts is needed to realistically capture

this commonly observed phenomenon (see Fig. 2c in Mitas and Mitasova, 1999).

% Increase in the roughness (Mannings

%n) in the field "delays" the onset of erosion in the center

%of the valley by making the water flow ridge wider and lower????

%(for n=0.01 the erosion occurs for XX rainfall intensity,

%for n=0.1 the erosion in the valley starts for XX rainfall)

This example also demonstrates that for a dynamic event modeling

incorporation of re-entrainment process is important and should be

incorporated into the {\sl SIMWE} model (Hairsine and Rose, 1992).


\begin{figure}[h]

\caption{ Water depth and net erosion/deposition pattern for

18mm/hr rainfall excess for a) short event, with only deposition

in the valley center, b) long event leading to steady state flow, with

both high erosion and deposition in the valley center,

indicating a potential for gully formation.

The 350x270m field is modeled at 2m resolution. See animation

on {\sl CDROM}.}

\centerline{\epsfig{file=llefig8.eps,width=1.\textwidth,clip= }}

\end{figure}


\bigskip

\paragraph {Grassed waterways} The common practice for prevention of erosion by

concentrated flow are grassed waterways. Their design is guided by the topographic

conditions and roughness within the grassed area, represented

by Mannings coefficient ({\sl SCS}, 1988). To investigate the impact

of a grassed waterway, the water and sediment flow as well as net

erosion/deposition pattern were simulated for a field within

the Scheyern experimental farm (Auerswald et al., 1996; Mitas and Mitasova, 1998)

for the bare soil conditions and after the installation

of grassed waterway with different values of roughness in the field.

For the bare field, there is a potential for gully formation

(Fig. 9a). After the installation

of grassed waterway the center of the valley

becomes a depositional area. However, if the roughness in the

field is several times smaller than in the grassed area, high

erosion develops around the waterway, potentially replacing one big

gully with two smaller ones. This "double

channeling" problem can substantially increase the cost of the waterway

maintenance (Fig. 9b). Increasing the roughness in the field

reduces the risk of double channeling and the transition

from erosion in the field to deposition in the grassed area is

relatively smooth (Fig. 9c). An alternative solution combines contour

filter strip on the upper convex part of the hillslope with grassed

waterway (Mitas and Mitasova, 1998).


\begin{figure}[h]

\caption{Impact of grassed waterway and differences in roughness

on sediment flow: a) bare field with gully potential in the center,

b) grassed waterway (light grey, n=0.1) and the bare field ( dark grey, n=0.01)

with sediment flow along the grassed waterway (double channeling),

c) grassed waterway (n=0.1) and the field with increased roughness

(n=0.05) without increase in sediment flow along the waterway

and smooth transition from erosion to deposition. See erosion/deposition

in color on {\sl CDROM}.}


Further research on hedges and other prevention measures

which require modeling of 2D water and sediment flow, as well as

impact of tillage and other human activities were discussed with

Seth Dabney from national Sedimentation Lab. The lab has extensive

field data which may be useful for calibrating, evaluating and further

development of our models.

1. We have started to enhance the Simwe model to support

the simulation of short term changes of terrain

due to erosion in landscape with spatially variable

land cover. The preliminary results show that this short term

terrain change can have a profound impact on effectivness of

conservation measures and erosion/deposition patterns

and should be therefore incorporated into erosion

models. For example, a uniform slope of 6% with hedges can change

it slope to a variable slope from 3% up to 9%i within 3 years.

Surprisingly besides the expected development of bench the model

developed also extensive rills over time for the case

when the overland flow had only small diffusive component.

Similarly as for grassways the simulations show

that if there is a great difference between roughnees in bare soil

area versus vegetated area, there is an increased erosion on the borderline

This indicates that a smooth transfer from bare soil into vegetated

area can be a better design than the "sharp" change

- we will explore this possibility.

With these new simulations we are getting new insights

into erosion processes due to small changes

in elevation (cm), which we believe are important for land management

and development of strategies for rehabilitation and erosion prevention


Recently, benching effect of hedges has gained an increased interest as a cost effective alternative to more complex terraces. Hedges are about 1-1.5m wide strips of dense vegetation installed along contour lines and the field data suggest that the combination of water erosion and tillage leads to natural creation of terraces along these hedges (Dabney et al., 2000). Modeling the impact of hedges poses a special challenge - the deposition is observed within and above the hedges which means that backwater effect is present, erosion is observed below the hedges due to the cleaner water coming from hedges. Moreover, increased deposition above the hedges is observed in swales with convergent flow and increased erosion is observed on noses with dispersal flow. Interaction of these complex phenomena make it difficult to predict these effects using the traditional approaches based on 1D flow over predefined hillslope segments and a 2D continuous diffusive wave approximation which incorporates also the terrain change is needed. We have used a time series of SIMWE (Mitas and Mitasova 1998) simulations which included the change of terrain due to erosion and deposition, to evaluate the suitability of the model for predicting the functioning of hedges. Terrain and erosion/deposition pattern development after 7 steady state events with uniform rainfall excess 36mm/hr, and Mannings n=0.2 (hedge) and n=0.15 (field) results in deposition above and within the hedge and erosion below the hedge (Figure 8.6). The impact of swales and noses is also correctly simulated due to the use of 2D flow in simulation. These results are preliminary and are used to further develop the model so that the dynamics during the event as well as the temporal change in terrain can be properly simulated



Recently, benching effect of hedges has gained an increased interest as a

cost effective alternative to more complex terraces.

Hedges are about 1-1.5m wide strips of dense vegetation installed

along contourlines and the field data suggest that the combination

of water erosion and tillage leads to natural creation of terraces

along the hedges. Figure 9 a,b shows the change in the topography

measured 3 years after the hedges were installed at a National Sedimentation

Laboratory's experimental field (Dabney et al, 2000).

We have used a time series of {\sl SIMWE} simulations including the

change of terrain due to the predicted erosion/deposition rates

to evaluate the suitability of the model for predicting

the functioning of hedges.

The presented results are preliminary and are being used to

extend the model capabilities.


Figure 9c,d,e illustrates some of the

results. As in grassways, the big difference in roughness between

the bare area and hedge causes sharp increase in water depth

within the hedge and consequent erosion both above and below the hedge.

In the field experiment, hay bales were placed each 9m perpendicular

to the hedge to prevent this negative effect.

Terrain and erosion/deposition pattern development for 7

steady-state events with uniform rainfall excess 36mm/hr, and

Mannings n=0.2(hedge) and n=0.15(field), with elevation

change proportional to predicted erosion/deposition rate

results in deposition above and within the hedge and

erosion below the hedge. Swales have increased deposition

while noses (convex areas) have increased erosion.


The third example (Figure 9e) shows the same simulation as

in (Figure 9d) with a reduced diffusion term and without

smoothing of the new elevation surface between the events

(which represents the smoothing effect of processes which

influence the terrain surface between the events).

After 7 events, rill-type features developed in the lower

part of the hillslope in swales.

While a closer look at the model is needed to analyze

the simulation process


Simulation of spatial distribution of water depth provides valuable information for identification of locations which require drainage to prevent negative impact of standing water on yields. Using a high accuracy DEM interpolated from rapid kinematic survey data by the RST method (Mitas and Mitasova 1999) within GRASS5.0, the water depth distribution was simulated for a typical rainfall for Midwestern agricultural fields (9mm/hr) under saturated conditions. The simplified water flow approximation by kinematic wave, e.g. by using the r.flow command in GRASS5.0 was not sufficient for a flat terrain with depressions (as explained by Figure 8.1b,c,d) and a two dimensional approximate diffusive wave simulation implemented in a GIS independent model SIMWE (Mitas and Mitasova 1998) had to be used. The gradual accumulation of water in depressions is shown by the 3 snapshots from the simulation during the uniform, steady rainfall (Figure 8.5a,b,c) and locations where water will stand several hours after the rainfall (taking into account also simplified infiltration) is in the Figure 8.5d. The resulting water depth maps were used to evaluate suitability of the locations of current drainage and to plan the location of new drainage network in the negatively affected field. While the model was very useful for evaluating and planning of spatial pattern of the drainage network, detailed soil data and more complex dynamic simulations with coupled surface and subsurface flow (Badiger et al., 2000) are necessary to optimize the size, depth, structure and other drainage network parameters.


2.4.3 Examples (SedSpec, HydroPEDDS, TRAINER?)......16


3. GIS implementation in GRASS and ARC


      1. ArcGIS8 versus ArcView implementation, ArcModel18


We were approved as a beta testing site for ArcInfo 8

and we have recieved and installed the sofwtare. We are preparing the

test data to evaluate the best approach to creating a easy to

use interface for erosion modeling - compared to ArcView3

Spatial Analyst the new Arc8 has more intuitive map algebra

making the implementation much easier. Also the problem

with floating point data being lost during the computation should

have been solved but we will test it thoroughly.


      1. Open source GIS implementation with new GRASS5.19


write here something about transformation of GRASS to opensource ...

IBM - opendx, LINUX stability and reliability


given the fact that GRASS is free, well mainatined it may be useful to add it to...

at least where UNIX or LINUX is used, although it runs on windows and Mac too.

Additionall capabilities developed all over the world could be available at no cost to army land managers -

examples of new tools???

take from proposals

The capabilities of this GIS are substantially enhanced by links to additional open source tools, such as R statistical language (Bivand and Neteler 2000) and PostgreSQL database system (PostgreSQL 2000).

international GRASS5 development team. It will contribute to the rapidly growing open source geospatial computing infrastructure and benefit from modifications and enhancements by international team of GIS developers.


Post processing - reports, color tables, ....., histogram, categories (find on NRCS website?)



3. Applications............................................18


    1. Fort Hood overland and concentrated flow erosion....18


Landscape/subwatershed scale - 10m resolution

Field/1st order watewrshed scale - 2m resolution gully prevention



We have explored the possibilities for multiscale representation

and modeling of a small watershed with gully at Ft. Hood, with watershed

represented at 2m resolution and gully at 0.4m resolution

using field measurements. We have identified the missing links in the

technology for succesful combination of data with different resolution

without gaps.


    1. Fort Polk sediment transport and wetlands.


We compared the vegetation map with the LCTA data, especially the

C-factor and computed average C-factor for each vegetation type.

The resulting C-factors are rather high compared to values for

the same vegetation cover given in literature. It is possible

that some of the LCTA sites have different actual land cover than

the one in the map so I would appreciate some feedback on

the procedures that CERL uses to determine the C-factor and on

relevance of LCTA C-factor to this study.

The following table shows the land cover categories that I have

and the C-factor based on LCTA and on literature.


land cover C-factor LCTA C-factor lit.

hardwood forest 0.01 0.0002

pine forest 0.01 0.0005

sparse pine 0.02 0.005

grass 0.02 0.001

thin grass 0.03 0.01

bare soil 0.2 0.5


Analysis of soil erosion and deposition by water for different conservation strategies



land use
legend

%agriculture (row crops)
Cfac legend

soil detachment:
total[1000t]/average[t/ay] legend[t/ay]

soil erosion/deposition: excess[1000t] legend[t/ay]

comments

bare

N/A

10,500  /  62

77

X

corn

100

  5,200  /  31

38

X

60 ft stream buffer

96

  4,800  / 28

19

only big streams have buffers

100 ft stream buffer

94

  4,600  / 27

19

only big streams have buffers

60ft stream buffer, forest on slopes > 10%

81

  2,000  / 12

15

X

current

63

  1,300  / 8

4

grains on steep slopes add to erosion - possible error in LU?

model-based, forest/dense grass where A>10

58

   200  /  1.2

0.1

almost the same % of agriculture as current, but some are in too small patches


1. This area has a very good, conservation oriented land use pattern (e.g. compare current land use with "buffers only" scenario).
2. Soil loss can be further reduced by focusing on headwater areas and areas with concentrated flow (possible gullies, interminent streams)
3. Rules for conservation areas which include both distance from stream and slope steepness are good, however they miss headwaters and concentrated flow.
4. Stream buffers based only on a distance from a stream do not provide sufficient protection - terrain configuration has to be considered.
5. Erosion/deposition model (USPED) shows significant deposition in valleys and hollows (more than what we usually get from the model) - it needs to be checked whether it is realistic or an artifact due to DEM.
6. Modeling spatial distribution of soil detachment and net erosion/deposition can provide valuable information for science based extension/enhancement of rules for conservation program if it is used with appropriate field observations.


water flow and erosion/deposition especially in streams if the fields in flat areas are a) drained (RST-based results and SIMWE indicates that substantial amount of water can be comming from there into the streams); b) without drainage with water accumulating and standing in depressions.

Preservation and restoration of wetlands is among the most

important and popular best management practices. Their success

depends on many factors, including

a sufficient supply of water.

The {\sl SIMWE} hydrologic submodel was used to identify

the locations within the Court Creek Pilot Watershed which have

topographic conditions favorable for wetlands.

Several simulations were performed for various rainfall intensities

and uniform land cover and soil conditions assuming that the flow

velocity is controlled only by the terrain gradient - the existing drainage

and channels were not considered.

Comparison of the resulting simulated water depth with existing

wetlands has shown that the areas with existing wetlands have accumulated depth

from one event overland flow of

at least 0.3m (except for wetlands in the upland areas which were

not captured by the 30m {\sl DEM} used in this study). Using this threshold,

a map for topographic potential for wetlands was

computed using map algebra (Figure 5).

While the simulation was very simplified,

the map can serve as a useful starting point for

identification of land owners with suitable land for wetlands

and for evaluation of the proposals for wetland locations.




  1. Conclusion and future directions


both examples demonstrate a need for linking this research with C-factor development

before accuracy of models is evaluated accuracy of parameters at landscape scale variability hsould be well

known - e.g. We should be able to say that we have C-fac 0.04+-0.005, or slope 11%+-1% (slope is known -

see gertner, unpublished Warren, etc


Just a few years ago, there were numerous efforts to built comprehensive modeling and problem solving environments which would provide essentially everything for doing both the routine processing and advanced modeling as well as development of new methods and technologies. The practice, however, seems to be going in other directions as well. The large, universal, thought-through, all-powerful systems which were expected to support almost every possible research or development need ("research cathedral" , Raymond, 1999) are, in fact, not practical. The maintenance of large software package is expensive, rigid and inefficient. More successful is a concept of cooperation between a number of smaller software units and tools, environments and program packages. This concept enables to create more independent smaller pieces of software with simplified interdependencies. It enables for a number of groups or individuals to contribute and work on various parts simultaneously. If some branch of development proves to be uninteresting or unproductive it rapidly dies out without necessity to go through decision hierarchies usually present in the other paradigm. In contrast to the "cathedral" such a framework creates a "research bazaar" which offers variety of combinations and provides in effect a market of tools which can be combined together or used for data processing, modeling, method development and their combination. This in many cases is more useful for new advances in scientific exploration as the most exciting and influential research breakthroughs happen through stepping outside the established routes.



5. References


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6. Appendix................................................24

owl10.cf = if(owl.lu<=8 && owl.lu>0||owl.lu==14,0.001,0.)\

+if(owl.lu==9||owl.lu==15,0.005,0.) \

+if(owl.lu==10,0.04,0.)+if(owl.lu==11,0.,0.) \

+if(owl.lu==12,0.9,0.)+if(owl.lu==13,0.003,0.)\

+if(owl.lu==16,0.,0.)

1:Juniper Forest

2:Live Oak Forest

3:Upland Deciduous Forest

4:North Slope Deciduous Forest

5:South Slope Deciduous Forest

6:Alluvial Deciduous Forest

7:Post Oak Forest

8:Maple Forest

9:Live Grassland/Herbaceous

10:Dormant Grassland/Herbaceous

11:Water

12:Bare Ground

13:Brush Piles

14:Urban Forest

15:Urban Grassland

16:Hardscape/Roads


r.report -n owl.lu units=a,p output=owllu.rep

---------------------------------------------------------------------

Processing of DEM


r.flow owl10.el dsout=owl10.dsd

r.slope.aspect owl10.el slop=owl10.sl asp=owl10.as dx=owl10.dx dy=owl10.dy


r.random owl10.el nsites=100000 sites_output=owl10.100K

s.surf.rst -t owl10.100K elev=owl10.rst80 ten=30 smo=0.2 npmin=350 dmin=20 slo=owl10sl.rst asp=owl10as.rst

r.flow owl10.rst80 dsout=owl10dsd.rst

r.slope.aspect owl10.rst80 dx=owl10rst.dx dy=owl10rst.dy

EROSION

----------------------------------------------------------------

R-factor is 280 in american units? check - this is according to NRCS

HUSLE

owhusle=0.3*-160*owl10.cf*1.6*exp(owl10.dsd*10/22.13,0.6)*exp(sin(owl10.sl/0.0896),1.6)

owhuslek=owl10.kf*-280*owl10.cf*1.6*exp(owl10.dsd*10/22.13,0.6)*exp(sin(owl10.sl/0.0896),1.6)

owlls.rst=1.6*exp(owl10dsd.rst*10/22.13,0.6)*exp(sin(owl10sl.rst/0.0896),1.6)

owhusle.rst=0.3*-160*owl10.cf*1.6*exp(owl10dsd.rst*10/22.13,0.6)*exp(sin(owl10sl.rst/0.0896),1.6)

owhuslek.rst=owl10.kf*-280*owl10.cf*1.6*exp(owl10dsd.rst*10/22.13,0.6)*exp(sin(owl10sl.rst/0.0896),1.6)


r.report -nC owhusle units=a,p output=owhusle.rep

r.report -nC owhuslek units=a,p output=owhusle.rep

r.report -nC owhusle.rst units=a,p output=owhuslerst.rep

r.report -nC owhuslek.rst units=a,p output=owhuslekrst.rep


USPED


p1, topo

oqs1x=owl10dsd.rst*10.*sin(owl10sl.rst)*cos(owl10as.rst)

oqs1y=owl10dsd.rst*10.*sin(owl10sl.rst)*sin(owl10as.rst)

r.slope.aspect oqs1x dx=oqs1x.dx

r.slope.aspect oqs1y dy=oqs1y.dy

owusped1to=(oqs1x.dx+oqs1y.dy)*10


p=1

oqs1x=owl10.kf*160*owl10.cf*owl10dsd.rst*10.*sin(owl10sl.rst)*cos(owl10as.rst)

oqs1y=owl10.kf*160*owl10.cf*owl10dsd.rst*10.*sin(owl10sl.rst)*sin(owl10as.rst)

r.slope.aspect oqs1x dx=oqs1x.dx

r.slope.aspect oqs1y dy=oqs1y.dy

owusped1=(oqs1x.dx+oqs1y.dy)*10.