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February 9 - 12, 1997 

# St. Petersburg, Florida, USA



# Conference Proceedings

### Prediction of Long-Term Erosion From Landfill Covers in the Southwest
Clifford E. Anderson1 and John C. Stormonf
Abstract
Erosion is a primary stressoroflandfill covers, especiallyforclimates

### with high intensitystorms and low native plant density. Rills and gulliesformed by discrete events can damage barrier layers and induce failure. Geomorphologic, empirical and physical modeling procedures are available to provide estimates ofsurface erosion, but numerical modeling requires accurate representation of the severe rainfall events that generate erosion. The National Weather Service precipitation frequency data and estimates of5, 10, 15, 30 and SO-minute intensity can be statistically combined in a numerical model to obtain long-term erosion estimates. Physically based numerical models using the KINEROS and AHYMO programs have been utilized to predict the erosion from a southwestern landfill orwaste containment site with 0.03, 0.05 and 0.08 meter per metersurface slopes. Results ofAHYMO modeling werewithin 15 percent ofaverage annual values computed with the empirical Universal Soil Loss Equation. However, the estimation of rill and gullyformation that primarily degrades cover systems requires quantifying single events. For southwestern conditions, a single 1 0-year storm can produce erosion quantities equal to three timesthe average annual erosion and a 100-year storm can produce fivetimes the average annual erosion.
Introduction
As a result of implementing new solid waste regulations for existing facilities, many communities nowfind it advantageousto close their existing landfills and use regional facilities. Consequently, many existing facilities are being closed and a smaller number of new landfills are being constructed. The regulations for closure of municipal waste landfills in New Mexico follow the Federal regulations established by the Resource Conservation and Recovery Act (RCRA, 1976, Subtitle D program, 40 CFR258) and require that thetop soil layer have a surface slope ofnot less than 0.02 meter per meter, and not greaterthan 0.05 meter per meter. This minimum slope is required because the surfaces of a landfill are subject to substantial local settlement due to the 

### normal process of decomposition of solid waste. Covers for hazardous waste remediation will generally have similarslopes. The application of minimum slope criteria provides fora surfacethat is relativelyfree oflocal depressions arid pond areas where excess precipitation can accumulate. Because surfaces ofwaste containment sites can have differential settlement depths ofone meter or more, the use of slopes steeper than 0.02 meter per meter will further minimize the future occurrence ofpending. It is common fordesigners and regulators to

### try to approach the 0.05 meter per meter limit. There has even been some discussion about changing the regulations to allow slopes of0.08 or0.10 meter per meter.
In many areas of the United States, the vegetative cover will form a protective blanket that effectively inhibits erosional processes onthe cover system. Butfor manyareas ofthe southwest United States, natural vegetation on covers is sparse, with vegetation covering only 10to 20percent ofthe surface. The native plants that have evolved forthis environment commonly establish root systemsthatcollect moisturefrom thesurrounding areas and store moistureduring drought periods. While it is possibleto provide revegetation with nonnative speciesto provide a higherplant density, such attemptsto modifythe native plant community arefrequently unsuccessful. In orderto sustain the nonnative species, supplemental watering may be required, but introduction ofwater is not



# ---------------------------------------------------------------------------------------- 1 University of New Mexico, Dept. of Civil Engineering, Albuquerque, NM 87131, (505) 277-4809, ceanders@unm.edu

2 University of New Mexico, Dept. Of Civil Engineering, Albuquerque, NM 87131, (505) 277-6063,jcstorm@unm.edu

389normally advisable for long-term post-closure operations. The most likely long-term success with reseeding will be found by restoring the native plant community and density

. Furthermore, some native plant species depend upon the naturally occurring depressions as a source of moisture. When conditions are created that minimize local depressions, some native plant species cannot be sustained. Ifthe native plant community is to be utilized for southwestern cover systems, there will likely be no opportunityto establish a continuous erosion blanket. Much ofthe land surface will be exposed to the impacts of rainfall and surface runoff, with the resulting transport of soil through erosion. Erosion has been shown to be greatly affected by the intensity of rainfall and the steepness ofthe terrain. The impactfrom raindrops initiates local soil movement, but it is the conveyance across slopes that causes the local soil movementto become erosion. A decision to create greatersurface slopes can have consequences for cover erosion thatwill become obvious only aftersevere rainfall events

. Erosion does not usually occur as a uniform lowering ofthe surface, but by the formation of rills orsmall gullies. Rills can be defined as the smallest channel, formed by runoff, that can be destroyed by minor surface disturbance, such as plowing. Gullies are the somewhat deeper channels, formed where no defined channel recently existed. The distinction between the two is not precise, and the terms are frequently used interchangeably. Rills and gullies havethe potential to cutthrough the top soil (erosion) layer ofa cover system and damagethe underlying barrier soil layerorliner. Such damage could compromise the protection that the cover system was designed to provide

. This paperwill assess the potential for erosion on a typical southwestern waste containment site and quantifythe relative impact ofslopes on erosion, and on rill or gully formation

. Forthis project a typical cover environment nearArtesia, New Mexico was selected. This example does not reflect the exact conditions ofany existing or proposed solid waste facility, butwas selected to provide a measurable example of physical phenomena. The estimation of erosion can be accomplished using three basic procedures described as (1) a geomorphologic evaluation, (2) an empirical equation or procedure and (3) mathematical modeling of the physical processes

. These three procedures will be applied to the Artesia example case. A Geomorphologic Procedure
A geomorphologic evaluation involves the consideration ofthe geology, hydrology and specific land forms ofa site, and comparing the results with similar conditions so thatthe analysis can be used to predict what will happen over time. It is the simplest in terms of mathematical procedure, but perhaps the most complex in terms offactors that must be considered and the level ofexpertise required to implementan evaluation

. 

### An example ofa geomorphologic approach is provided bythe US Bureau of Land Management publication 

### GullyErosion (Harvey, et al, 1985)

. This document may be applied to cover erosion to evaluate "(1) Threshold conditions for gully initiation (2) The stages ofgully evolution (3) Current stability ofa gullysystem (4) Long-term gully erosion rates and subsequentyields

."(Harvey, et al, 1985, p.3)
The 

### Gully Erosion report also describes the physical processes that are present in gully or rill formation

. Of particular notefor a cover system is the erosion initiated at points where there is an abrupt increase orconvex change in the longitudinal profile, called a nickpoint. Erosion is initiated at nickpoints, and the nickpoints migrate upstream. The upstream migration can continue until intercepted by a physical barrier or until the drainage basin is so small that runoff can no longer initiate erosion ofthe steepened section. Atwaste containment sites, the interface between the top surface and the steeper side slopes is a likely location forthe initiation of nickpoint erosion

. Among the geomorphologic thresholds that may be used to identify the potential for erosional processes, of particular note forthis study isthe shear stress threshold indicatorthat defines a line below which gully incision does notoccur. The equation forthe gully shear stress indicator is:



### 390S

# = 0.008 A.o
·26 (Harvey, et al, 1985, p.73) Where:
and A

# = area in square miles (1 square mile= 259 hectare) S = slope in meter per meter
This equation is based on work by J.C

. Brice in the Medicine Creek Drainage Basin, Nebraska, 1966. Only six watersheds from this study were below 250 ha (the smallest with 40 ha) and application to small waste containment sites may not be directlytransferrable. Nevertheless, Table 1 shows the results when the gully indicator equation is applied to very small drainage basins. Table 1. Application ofthe gully indicator equation Basin Area (ha or hm2) Threshold slope (meter/meter) 0.5 (1.24 acre) 0.041 2.0 (4.94 acre) 0.028
A figure showing a similar relationship between basin area and slope for gully erosion is included in 

### EngineeringAnalysis ofFluvialSystems (Simons, Li and Associates, 1982, p. 5.38). This figure was based on 1975 data from the Piceance Creek area in western Colorado. The data indicates that for drainage basins smaller than 1300 ha, the zone between gullied and ungullied is not precisely defined and both gullied and ungullied conditions are found for slopes between 0.025 and 0.065 meter per meter. Thus, a geomorphologic evaluation ofconditions similarto those found at waste containment surfaces raises concerns about gully erosion potential, and the formation of definitive conclusions requires a site specific geomorphologic investigation.

An Empirical Procedure: the Universal Soil Loss Equation An example of an empirical procedure applicable to cover system erosion evaluation is the Universal Soil Loss Equation (USLE). This equation is described in detail in 

### Predicting Rainfall Erosion Losses (Wischmeier and Smith, 1978). The USLE has been used throughoutthe United States and is particularly directed to the prediction of erosion from agricultural lands, but the procedure does include methodologyfor use in pasture, range and idle land, and forareas disturbed by construction. The USLE equation is: A=RKLSCP
where A is the computed soil loss per unit area, R is a rainfall runoff factor, K is a soil erodibility factor, L is a slope length factor (a ratio offield length to a 22.1 m test plot), S is a slope steepness factor (a ratio offield slopeto a 0.09 meter per meter slope), C is a vegetative or mulch cover and management factor, and P is a conservation practice factor. The USLE is limited to the determination of average annual erosion rates and cannot establish erosion from specific events and peak erosional years. The aerial extent and surfacing of manywaste containment sites provide similar conditions to those described in the procedures for USLE. However, there is no direct method within the USLE procedure to determine the depth or magnitude ofthe gully or rill erosion that may be an integral part ofthe erosion process. Mathematical Modeling with AHYMO and KINEROS
There is no known procedure that presents an accurate measure of the physical processes concerning erosion on a cover. However, there are several procedures that combine modeling of some physical processes along with application of empirical equations. One applicable methodology includes the determination of storm water runoff hydrographs using hydrologic modeling, the estimation of sediment wash loads (fine silts and clays that can be suspended in runoff) using the storm based Modified Universal Soil Loss Equation (Williams, 1975), and estimation of channelized sediment transport rates using regression equations developed from more complex sedimenttransport procedures. This computational methodology has been included inthe

### AHYMO ComputerProgram (Anderson, 1994)

. The sedimentvolumeforany storm event can



### 391be computed from the above procedure. All ofthe runoff events for a period can be accumula.ted to obtain an average annual volume

. An average annual sediment yield can also be computed by using a statistically weighted function ofthe 2, 5, 10, 25, 50 and 100-yearsediment yields.
As an alternative, the KINEROS, Kinematic Runoffand Erosion Model (Woolhiser, et al, 1990) provides a computerized procedure to compute rainfall hydrographs and erosion quantities. "KINEROS is a distributed, event-oriented, physically based model describing the processes of surface runoff and erosion from small agricultural and urban watersheds

.u(Woolhiser, et al, 1990, p

.3) The KINEROS model derives its name from the kinematic wave equations that are used to solve for overland flow and flowthrough channels, and from the capability to compute the quantity of erosion. Successful use of the model to simulate runoff and erosion at the Walnut Gulch Experimental Watershed in southern Arizona is described in the KINEROS documentation. Ofthe six sediment transport relations available with KINEROS, the tractive force relation (Meyer and Wischmeier, 1969) and the Bagnold/Kilinc relation (Kilinc and Richardson, 1973) were selected for this case study

. Estimates ofGullv and Rill Erosion
None ofthe procedures described above provide a method to compute the extent of rill or gully erosion, but the study by Kilinc and Richardson (1973) presents the results of erosion simulation tests made on a 1

.52 m by4.88 m watershed test bed. Artificially simulated rainfall using a sprinkler system was run using rainfall intensities that varied from 3

.175to 11.68 em per hour, using uniform bed slopes from 5.7 to40 meter per meter,with 24 one-hourtest runs

. Ofspecial interestforcover systems are the regression equations that quantify rill area, rill volume and rill depth as functions of slope and rate of runoff. Because the regression equations were established based on a constant rainfall and runoff rate for a one-hour simulation, it is not possible to directly apply the equations to a hydrograph computation. However, incremental hydrograph quantities can be combined with the regression equations in a spreadsheetto estimatethe volume of rill erosion for the peak one hour of runoff. Becausethe regression equations are based on a single 7.4 m2 test bed, there is no mechanism to accommodate dimensional scale effects with the computations. 

### Rainfall
Rainfall is the driving condition for most moisture and erosion that can impact a cover system

. While snow melt may also produce moisture, it is of lesser consequence for erosion throughout much ofthe southwest US. Forthe Artesia site, rainfall conditions for a one-hundred (100) year period were simulated with the US Army Corps of Engineers, Hydrologic Evaluation ofLandfl/1 Performance (HELP) Model (HELP3) (Schroeder, et al., 1994). This model uses probabilistic methods to identify the daily rainfall, or lack thereof, for every day in the 100-year period

. The HELP3 simulation for the Artesia area used synthetic precipitation data for the nearby city of Roswell, New Mexico. Forthe 100-year simulation, a total of 156 days had precipitation greater than 2.2 em; this isthe precipitation threshold expected to produce measurable runoff. The values computed by HELP3 were compared with the 24-hour precipitation amounts in the Precipitation FrequencyAtlas ofthe Western United States, Volume IV-New Mexico (NOAA Atlas) (National Weather Service, 1973). Itwas found thatthe values computed by HELP3 were substantially less than the NOAAAtlas values as shown on Table 2. Table 2. Precipitation frequency values for 24-hourstorm Return period From NOAAAtlas maps Computed by HELP3 2-year 10-year 100-year 4

.95cm 8

.13 em 12

.70 em

392 3.35cm 4.85cm 6.96cmForerosion event computation, extreme rainfall events must be accurately represented. Therefore, revised synthetic precipitation data was created using the Extreme Value Type I distribution and procedures described in AppliedHydrology(Chow, et al, 1988). This resulted in 137 daily events greater than 2.2 em for a 100-year simulation, and extreme event values in agreement with the NOAAAtlas.
The simulation ofrunoff and erosion required thatthe depth ofcumulative precipitation be simulated throughout a 24-hour period, with special concern aboutthe rainfall intensity during the peak hour. Review ofthe NOAAAtlas showed that the six-hour precipitation was approximately 80 percent of the 24-hour precipitation forthe Artesia, NM site. Similarly, the peak 1-hour precipitation was 60 percent of the 24-hour precipitation. The NOAA Atlas contains a table of adjustment factors to obtain 5, 10, 15 and 30-minute precipitation estimates as a ratio ofthe peak hourly precipitation. All ofthese factors can be combined to obtain a distribution ofa typical rainfall event based on the 24-hourvalue. Table 3 shows the ratios that were developed. The data from Table 3 was applied to each ofthe 137 daily rainfall quantities to form 137 rainfall distribution tables. Table 3. Rainfall distribution factors Time 24-hr 6-hr Percent of24-hour 100.0 80.0 Percent of 1-hour 166.67 125.0 1-hr 60.0 100.0 30-min 47.4 79.0 Establishing Input Parametersfor Erosion Simulations 15-min 

### 34.2 57.0 10-min 27.0 45.0 5-min 17.4 29.0

In orderto simulate runofffrom a typical cover system using numerical methods, itwas necessary to establish some physical parameters that would be used in the models. For this study the following values were selected: cover system area at 2.02 ha (5.0 acres), surface dimensions at 142.2 m by 142.2 m, a coarse sandy loam or loamy fine sand surface soil, median bed material gradation (050} of0.50 mm, and a ground coverwith 1Q-percent

- native grass cover. Surface slopes of0.02, 0.05 and 0.08 meterper meterwere used to simulatethree potential surface configurations.
Results from the Universal Soil Loss Equation Analysis
The average annual sedimentyield from the USLE istabulated in Table4. These values are based on a rainfall-runofffactor (R) of 80; a length-slope factor (LS) of 0.318 at a 0.02 meter per meter slope, 1.15 at a 0.05 meter per meter slope, and 2.14 at an 0.08 meter per meter slope; a soil erodibilityfactor 

### (I<) for coarse sandy loam or loamyfine sand of0.20; a coverfactorfor 10-percent native grass of0.35; and a conservation practice factorof 1.0. Table4 summarizes the resultsfrom the USLE. Table 4. Average annual sediment yields based on the USLE Slope

# = 0.02 m/m Slope

# = 0.05 m/m Slope

# = 0.08 m/m 4017 kg/ha/year 14437 kglha/year 26864 kg/ha/year 8128 kg/yearfor2.02 ha 29211 kg/yearfor2.02 ha 54359 kg/yearfor2.02 ha
Results from the AHYMO and KINEROS Modeling
When the 137 largest rainfall events of the 100-year period were evaluated 

### with the 

### AHYMO computer progr:am, only 115 events showed measurable runoff. A summary of the AHYMO sediment yields is contained in Table 5. The average annual sediment yields computed by the AHYMO program are within 15 percent ofthe values computed with the USLE.
The KINEROS model was also used to compute storm water runoffand sedimentyields forthe 137 largest rainfall events in the 1 OQ-year period. Because the KINEROS model uses more physically based parametersto compute runoff, different runoff rates are computed forthe 0.02, 0.05 and 0.08

393

### meter per meter slopes. Table 6 presents the sediment yields from the KINEROS model for the tractive force (Meyer and Wischmeier, 1969) and the Bagnold/Kilinc (Kilinc and Richardson, 1973) transport equations. The sediment yields computed by the KINEROS program are substantially largerthan the values computed with the USLE. Table 5. Sediment yield using AHYMO Event 24-hr Sediment (kg) at frequency rainfall (em) 0.02 m/m slope 100-year 12

.70 54848 1

0-year 8.13 23387 2-year 4

.95 6214 Average 9320

annual


### Table 6. Sediment yield using KINEROS Event 24-hr frequency rainfall (em) Tractive force equation: 100-year 12

.70 10-year 8.13 2-year 4.95 Average

annual Bagnold/Kilinc equation: 100-year 12.70 1

0-year 8.13 2-year 4.95 Average

annual
Results of

### Rill Erosion Evaluation Sediment (kg) at 0.02 m/m slope

69691 15961 1371 5515

49735 14293 1711 4939 Sediment (kg) at 0.05 m/m slope 184349 79415 21509 31841

Sediment (kg) at 0.05 m/m slope

293695 76335 8131 26310

247084 77420 11673 27568 Sediment (kg) at 0.08 m/m slope 326523 141648 38818 57018

Sediment (kg) at 0.08 m/m slope

439358 155589 19220 52429

456987 171734 29271 61564

The regression equations computed by Kilinc and Richardson (1973) were combined in a spreadsheet to estimate rill erosion. Two rainfall events were used forthis analysis: (1) a 1 00-year storm, and (2) a 1 0-year storm. The rainfall was simulated on the 1.52 m by 4.88 m area used with the original test setup. Table 7 is a summary ofthe spreadsheet computations. These values for rill depth, area and volume demonstrate the variability with changes in surface slopes. They can not be directly scaled to predict rill erosion for a specific site. Table 7. Estimates of Rill Erosion for a 1.52 m by 4.88 m test area. Surface Ave. rill depth Ratio: rill area to Ratio: volume rills to Volume slope surface area total sediment volume rills 100-yearstorm (24-hour precipitation=12

.70 em) 0.02 m/m 0.033 em 0.093 0.088 230 cm3 

### 0.05 m/m 0.182 em 0.108 0.122 1459 em3 

### 0.08 m/m 0.433 em 0.1 

### '17 0.144 3764 em3 

### 10-yearstorm (24-hour preeipitation=8.13 em) 0.02 m/m 0.011 em 0

.072 0.049 59cm3 

### 0.05 m/m 0.060 em 0.084 0.068 376 cm3 

### 0.08 m/m 0.144 em 0.091 0.080 970 cm3



### 394Conclusions
Thefunction ofany specifictop slope can be greatly impacted by construction measures designed to control erosion. While mitigative measures such as revegetation, application of mulches, construction ofterraces, placement ofgravely soils, construction of nickpoint barriers, and use of convex slopes may reduce erosion, it isthe combination ofslope, runoff and transportable soil that generates surface erosion. Some ofthetechniques presented inthis study mayserve as an outline for further erosion investigations at existing or proposed landfill and waste remediation sites.
The case study presented herein demonstratesthatthe slope of a final cover can have a profound impactonthe amount oferosion and on the size of rills or gullies. Geomorphologic considerations indicate that gullyformation may be initiated when slopes reach maximum regulatory levels. The average annual estimates of erosion using the USLE change substantially when slopes. are 

## increased above 0.02 meter per meter. In arid climates, asignificant percentage of erosion and formation of rills may comefrom single eventsthat are represented by 10-yearto 100-year storms. A single 10-yearstorm can to produce erosion quantities equal to threetimes the average annual erosion and a 100-year storm can produce five times the average annual erosion. A cover not experiencing problems for many years, may suddenly be compromised by a single catastrophic event. In order to predict the long-term performance of a cover, it is necessary to consider the major precipitation events that are likelyto occur overthe design life ofthe cover system.
References Anderson, C.E. (1994) AHYMO ComputerProgram Users Manual. Albuquerque, NM. Chow, V.T., D.R. Maidment and L.W. Mays. (1988) AppliedHydrology. Mc-Graw-Hill, NewYork, N.Y. Harvey, M.D., C.C. Watson and S.A. Schumm. (1985) GullyErosion. U.S. Department of Interior, Bureau of Land Management, Denver, CO. Kilinc, M. and E.V. Richardson. (1973) Mechanics ofSoilErosion from OverlandFlow GeneratedbySimulated Rainfall. Colorado State University, Hydrology Papers No. 63, Fort Collins, CO. Meyer, L.D. and W.H. Wischmeier. (1969) Mathematical simulation ofthe process ofsoil erosion by water. In Transactions ofthe American Societyof AgriculturalEngineers, 12(6), 754-762. National Weather Service. (1973) NOAA Atlas2, Precipitation-FrequencyAtlas ofthe Western United States, Volume IV-NewMexico. U.S. Department ofCommerce, National Oceanic and Atmospheric Administration, Silver Spring, MD. Schroeder, P.R., T.S. Dozier, P.A. Zappi, B.M. McEnroe, J.W. Sjostrom, and R.L. Peyton. (1994) The Hydrologic Evaluation ofLandfillPerformance (HELP) Model: Engineering Documentation for Version 3 and User's Guide for Version 3. EPA/600/9-94/168a and b, U.S. Environmental Protection Agency, Risk Reduction Engineering Laboratory, Cincinnati, OH. Simons, Li and Associates. (1982) EngineeringAnalysis ofFluvial Systems. Fort Collins, CO. Williams, J.R. (1975) Sediment-yield prediction with universal equation using runoff energyfactor. In Present andProspective TechnologyforPredicting Sediment Yields andSources, Proceedings 

### ofthe Sediment-Yield Workshop, USDA Sedimentation Laboratory, Oxford, Mississippi, November28-30,1972. ARS-S-40, U.S. Department ofAgriculture, Agricultural Research Service. Wischmeier, W.H. and D.O. Smith. (1978) Predicting Rainfall Erosion Losses- a Guide to Conservation Planning. Agriculture Handbook Number 537, U.S. Department of Agriculture, Science and Education Administration, Washington, DC. Woolhiser, D.A., R.E. Smith, and D.C. Goodrich. (1990) KINEROS, A Kinematic RunoffandErosion Model: Documentation and UserManual. U.S. Department of Agriculture, Agricultural Research Service, ARS-77, Springfield, VA.



### 395