DIVISION S-5-SOIL GENESIS, MORPHOLOGY,AND CLASSIFICATION

A Computer Simulation Model for Soil Genesis Applications1

ELISSA R. LEVINE AND EDWARD J. Cioucosz2

ABSTRACTA two horizon computer model was developed to simulate leaching

and acidification processes occurring in the solid phase of soils ofhumid, temperate climates. The model used linear equations to de-scribe processes of sulfate adsorption, cation exchange, mineralweathering, and precipitation and dissolution of sesquioxides withinthe soil. Standard soil characterization and precipitation chemistrydata were used as inputs to quantitatively predict changes in soilproperties over time. T-test analysis showed no significant differenceat the 0.01 level between field values and those predicted by themodel for base saturation, pH, and exchangeable cations. Two ex-amples illustrated the application of the simulation model for soilgenesis investigations. In the first example, acidic soils with lowbuffer capacities reached 60% base saturation (Ultic Hapludalfs)and 35% base saturation (Typic Hapludults) sooner than more basicsoils. Using regression analysis (excluding soils with high amountsof exchangeable bases), base saturation decreased at a rate of 0.01%/yr under deciduous forest, and 0.02%/yr under coniferous forest atprecipitation pH 4.1. At precipitation pH 5.5, the rate of changewas much slower. In the second example, a change in soil pH overtime was predicted at precipitation pH values of 4.1 and 5.5 underdeciduous and coniferous forest vegetation. In all simulations, a"steady state" soil pH level was predicted, with soils under coni-ferous forest reaching this level sooner than those under deciduousforest. The steady state pH was lower under precipitation pH 4.1than under pH 5.5.

Additional Index Words: soil chronofunction, steady state, soilacidification.

Levine, E.R., and E.J. Ciolkosz. 1986. A computer simulation modelfor soil genesis applications. Soil Sci. Soc. Am. J. 50:661-667.

ACCORDING TO JENNY (1941), a soil chronofunc-tion is defined by the equation: soil properties

= ./(time) with climate, organisms, relief, and parentmaterial held constant. This equation attempts toquantitatively describe soil properties that are solelydependent on varying lengths of time in which the soilforming factors affect soil development (Anonymous,

1 Authorized for publication as paper no. 7168 of the journal se-ries of the Pennsylvania Agricultural Experiment Station, Univer-sity Park, PA 16802. Received 29 Aug. 1985.

- Former Graduate Assistant and Professor of Soil Genesis andMorphology, Dep. of Agronomy, The Pennsylvania State Univ.,University Park, PA 16802.

1979). Attempts have been made to solve the chron-ofunction equation, generally with the use of simplestatistical analyses that explain the effects of a singlesoil parameter independent of interactions betweencomponents of the total system (Jenny, 1941; Yaalon,1971; Birkeland, 1974; Bockheim, 1979; Levine andCiolkosz, 1983). These statistical relationships are alsousually based on a single or theoretical date that hasnot been determined from the actual soil. Informationon soil properties is abundant in the form of profiledescriptions and laboratory characterization data col-lected by the National Cooperative Soil Survey. How-ever, isolating and accurately dating sites controlledfor soil forming factors, or resampling soils whose lo-cation and original sampling date is known is difficultin terms of resources and ability. Thus, the obstaclesin solving the chronofunction equation are not in theavailability of soils information, but rather in super-imposing and isolating the effects of time on the prop-erties of soils.

The use of "computer simulation models" to quan-titatively describe soil genesis processes has been sug-gested (Kline, 1973; Yaalon, 1975). Such a model usesa computer to interconnect various components of thesoil system and make repeated calculations to repre-sent changes in soil properties with time. The actualstructure of a model is as important as its analyticaluse in determining the type and validity of results pre-dicted. A model of the soil system must describe re-lationships between processes of addition, removal,transfer, and transformation as described by Simon-son (1959). These relationships interact in a "process-response" manner in which a change in a given pro-cess or property affects the behavior of another (Yaa-lon, 1975). The structure for a soil genesis model wasproposed by Kline (1973). This structure consists ofa single compartment, together with its input and out-put. The compartment consists of two basic param-eters: (i) the flow rate, and (ii) the content of the com-partment, which can be determined experimentally.The output of Kline's (1973) model is a series of equa-tions for each compartment showing the content ofthe compartment as a function of time that, if plotted,could be used to construct a soil profile for a givenpoint in time.

In this study, a soil genesis computer simulation

662 SOIL SCI. SOC. AM. J., VOL. 50, 1986

model was developed using Kline's (1973) compart-ment design to represent the zone above the soil sur-face, and an upper and lower soil horizon. The con-tents of each compartment are subject to processesthat are controlled and affected by each other. Eachcompartment is connected by the flow of ions fromupper to lower compartments. Using standard soilcharacterization information and precipitation data forinput to the model, changes in the original soil prop-erties to those at a given future time could thus bepredicted.

MODEL DESCRIPTIONA computer model was developed to simulate chemical

changes in the solid phase of soils over time in humid, tem-perate climates where natural processes of acidification andcation leaching are taking place. Two soil horizons are sim-ulated so that the chemistry predicted for the leachate fromthe upper horizon affects the lower horizon. The soil pro-cesses used in the model are primarily linear reactions in-volving exchange between acidic input and soil components.Data for average annual precipitation is used; thus, thesmallest increment of time in which changes occur withinthe simulation is 1 yr.

The precipitation input data required includes the annualamount of precipitation (cm) and precipitation chemistry(-log concentration (/tg/mL) of H+, SO2-, NO3-, NH4

+, Ca2+,Mg2+, K+, Na+, AP+, and Fe2+). The data available for themodel were not in SI units, thus the standard units indicatedwere used as input. The output can be given in either stan-dard (input units) or SI units. Input data for the two soilhorizons include their depth (cm) and the depth of the centerof each horizon (cm), bulk density (g/cc; Mg/m3), percentrock fragments, percent water flowing through large pores,percent water capacity at 16 bar (0.03 MPa tension, percentAl or Fe oxide, percent base saturation, percent organic C,percent clay, exchangeable Ca, Mg, K, and Na (meq/100 g;cmol (-t-)/kg), cation exchange capacity (CEC) (meq/100 g;cmol (+) kg), soil pH (0.01 M CaCl2 is preferred, but pHvalues determined by other methods can be substituted),and clay mineralogy (<2 pm) (percent chlorite, illite, ver-miculite, montmorillonite, and kaolinite). Additional soildata for input include CaCO3 (tons/ha; Mg/ha), evapotran-spiration (cm), and land use (deciduous or coniferous veg-etation, or bare soil).

Precipitation ModificationsA number of modifications can be made in the precipi-

tation data input to the model before it affects the soil pro-cesses. An option can be chosen to use dry deposition datafrom material deposited on plant and soil surfaces (J. Lynch,1983, personal communication) to modify the wet precipi-tation data. The model also accounts for the filtering effectof the forest canopy. The data of Abrahamsen et al. (1976),Mayer and Ulrich (1976), and Mollitor and Raynal (1982)for mean annual incident and throughfall precipitation fordeciduous and coniferous canopies was used for this pur-pose. In addition, data from Mollitor and Raynal (1982) andRibblett et al. (1982) Were used to represent the chemicalchanges in the precipitation as it moves through the soillitter layer (forest floor).

In the model, the amount of precipitation is modified be-fore it reaches the soil surface, and infiltrates into the soil,or percolates from the first into the second horizon. Equa-tions from Helvey and Patric (1965) for deciduous forests,and Helvey (1974) for coniferous forests were used to cal-culate the amount of throughfall during both the growingand the dormant seasons for these forest types. The amount

of runoff from the soil surface is predicted based on the soil'sslope and the percent organic matter in its surface horizon(Wischmeier and Mannering, 1965). Organic C data is con-verted to organic matter content using the equation of Ran-ney (1969). Gravitational water (held at <0.03-MPa ten-sion) is removed before chemical interactions take place withthe soil matrix. The gravitational water is used as the inputfor the next horizon. In addition, correction factors can beadded to the input to account for evapotranspiration andthe presence of large continuous pores.

Soil ProcessesThe major soil processes simulated in the model are af-

fected by the pH of the system and include H ion productionand H ion removal. The original soil pH is given in theinput data and, as the simulation proceeds, a new pH valueis calculated using the base saturation, which changes an-nually as acidification occurs. The pH values are calculatedfrom base saturation using an equation derived with datafrom The Pennsylvania State Univ. Soil Data Base (Cun-ningham et al., 1983). The equation for A horizons is:pH = (0.04 X base saturation) + 3.48 (r2 = 0.92)and for B horizons is:pH = (0.04 X base saturation) + 3.34 (r2 = 0.82).

Because soils have pH dependent exchange sites, the ac-tual (effective) cation exchange capacity (CEC) at low pHvalues is generally less than that determined at the higherpH of the standard leaching solution used in laboratory de-termination of CEC (1 M salt solution such as ammoniumacetate or sodium acetate, buffered at a neutral or alkalinepH) (Jackson, 1958). In order to account for this phenom-enon within the model, regression equations derived byHellings et al. (1964) are used to predict the effective CECat the pH of the soil during each year of the simulation.Additional sources of H ion within the model are CO2 hy-drolysis, hydrolysis of Al and Fe oxides, and nitrificationreactions. The amount of H ion added via these reactionsdepends on the soil pH and the amount of Al, Fe, andNH4 present within the system.

Systems of H ion uptake within the model serve as thebasis of acid buffering within the soil. These systems includethe adsorption of sulfate, cation exchange, mineral degra-dation, and the addition of CaCO3. The annual adsorptionof solution sulfate by the solid phase of the soil is predictedby the equation of Barrow (1970). After converting sulfateadsorption to a percentage (by weight of soil) basis, it isassumed that each sulfate ion adsorbed releases an equiva-lent amount of hydroxyl ions, which neutralizes an equiv-alent amount of H ions (Chang and Thomas, 1963). Aregression equation is used in the model to predict percentAl oxide content based on percent Fe oxide if a value forAl oxide is not available. The equation is as follows:Percent Al oxide = (1.14 X % Fe oxide)

- 0.06 (r2 = 0.74)This equation was derived from data for 84 samples fromthe northeastern USA from Levine (1981), Johnson and Chu(1984), and Cronce and Ciolkosz (1985), which containedanalyses of both Fe and Al oxides by the citrate-dithionite-bicarbonate method (Mehra and Jackson, 1960).

Sulfate adsorption is modified annually as the Al oxidecontent changes, and adsorption no longer occurs within themodel when the pH drops below 4.0, due to dissolution ofhydrous Al oxide coatings (Chao et al., 1964). When thesulfate adsorption system reaches its maximum adsorptionpotential or no longer functions, H ions neutralized by sul-fate adsorption remain available in solution to exchange withcations or take part in mineral degradation.

LEVINE & CIOLKOSZ: A COMPUTER SIMULATION MODEL FOR SOIL GENESIS APPLICATIONS 663

The H ions remaining in the soil solution after sulfateadsorption are partitioned, and allocated equally to the cat-ion exchange and the mineral degradation portion of themodel. In addition to H, other cations entering the soil ho-rizon are also considered in the cation exchange system ofthe model. The ease of removal of cations from the exchangesites is based on their concentration and their bindingstrength according to the lyotropic series as given by Sulli-van (1977). Without the benefit of soil solution data, themodel assumes those exchangeable cations in greatest con-centration on exchange sites are removed first. They con-tinue to be removed until a point is reached when all cationson exchange sites are equal in equivalents. At this point,cations are placed in the order Na > K > Mg > Ca >A1(H) (Sullivan, 1977). Exchange reactions are assumed tooccur rapidly, are reversible, and approach equilibrium aftereach year of simulation (Bohn et al., 1979). Sodium ions inthe soil solution are considered the first exchanger in themodel, followed by K, Mg, Ca, and H. While this order ofexchangers appears to be the reverse of what actually occursin the field, it is simulated within the model in this way inorder to account for the greater affinity for divalent cationson the exchange sites than for monovalent cations. Each ofthese solution cations in its turn replaces an equivalentamount of exchangeable cations and releases them into so-lution, thus changing the solution concentration (Fig. 1). Theexchange process continues until the equivalents of H al-located to the cation exchange portion of the model for thatyear have been utilized, or until all of the bases on exchangesites have been depleted. Cations remaining in solution asa result of exchange are used as the input for the next ho-rizon. Those basic cations remaining on the exchange sitesare summed to determine the new base saturation value forthe prediction of pH.

The H ions allocated to mineral degradation act on thecations within the minerals chlorite, illite, vermiculite,montmorillonite, and kaolinite. The quantity of these min-erals are put into the model in the input data. General for-mulas for these minerals were obtained from Lindsay (1979).Each equivalent of H replaces an equivalent of cation withina given mineral with the subsequent release of that cationinto solution. As each cation is removed, the total percent-age of that mineral in the solid phase of the soil decreases.In the model, the ease of cation replacement from mineralsoccurs according to the order: Ca > Mg > interlayer K >Fe(II) > Fe(III) > Si > Al (Bohn et al., 1979). To simulatethe difference in weatherability of minerals in the system,cations are removed from chlorite first, followed by illite,

vermiculite, montmorillonite, and kaolinite (Jackson andSherman, 1953). In addition, as each H ion replaces an ex-changeable cation on a colloidal surface, it is assumed thatthe H ion migrates into the tetrahedral and octahedral latticeof the minerals and an equivalent amount of Al is releasedthat, in turn, replaces H ions on exchange sites (Talibudeen,1981).

Additional sources of H ion buffering within the simula-tion model come from the dissolution of Al and Fe oxidesthat release hydroxyl ions to neutralize excess H ions. Also,Ca carbonate can be added in units of Mg/ha to the surfacehorizon, to act as a source of H ion neutralization.

OutputThe output produced by the simulation model consists of

a listing of the input to verify that it was entered correctlyand of the computed results including preliminary calcula-tions such as surface runoff (cm), infiltration (cm), predictedpercent Al oxide, and weight of 1000 cm2 of the horizon (g).Sulfate adsorption results are also printed and include thesulfate adsorption potential, whether or not it has beenreached during the time allowed for the simulation, and whatis the remaining potential. If CaCO3 was included in theoriginal input, the amount remaining (Mg/ha) is printed onthe output. The predicted CEC (cmol(+)/kg), percent basesaturation, determined by the sum of the exchangeable cat-ions divided by the predicted CEC, is printed, as well as theexchangeable acidity (predicted CEC — sum of the bases).In addition, the cmol(+)/kg of Ca, Na, Mg, and K presenton the exchange sites at the end of the simulation is printed.The percentage of each mineral remaining is given in theoutput as well. The amount of ions added to the soil solutionduring the simulation is also given in the output. These dataare useful as a method of balancing cation input and outputas well as determining the concentration of ions in waterpercolating through the soil, and entering the groundwater.However, without knowledge of the actual soil solution con-centration and thermodynamic relationships involved, theactivities of these ions can not be determined.

This description of the documentation of the model isvery brief due to the constraint of space. A more completediscussion of the model's documentation is given in Levine(1984).

TESTING OF THE SIMULATION MODELSamples and results of an acid mine water irrigation

study by Stein (1977) and Cronce et al. (1978) were

a)

d)

M9CaCaKNa

MgCaMgKNa

b)2Na, 2K, Mg, Ca, 2H

e)

2Ca, 2H

2K

MgCaNaNaKNa

c)

2K, Mg, 2Ca, 2H

MgCa -* —Ca 2H

Mg, Na, K

MgCaKKKNa

MgCaHHCa

Mg, 2Ca, 2H

I2Na

tCa

Fig. 1. Cation exchange process simulated in model for each year, (a) Na' ions in the soil solution exchange an equivalent amount of Ca2+,which is in the greatest concentration on exchange sites. (b)Ca2' exchanged by Na+ is added to the soil solution, and K+ ions in solutionreplace an equivalent amount of Na' ions, (c) Na' exchanged by K+ is leached into the next horizon, and Mg2+ in solution replaces anequivalent amount of K+ from the exchange sites, (d) K exchanged by Mg2' is leached into the next horizon, and Ca2+ in solution replacesan equivalent amount of Mg2+ from exchange sites, (e) Mg2+ Na+, and K+ exchanged by Ca2+ are leached into the next horizon and H+

in the soil solution replaces an equivalent amount of Ca2 * from exchange sites, (f) Ca2+ exchanged bj H leaches into the next horizon.

664 SOIL SCI. SOC. AM. J., VOL. 50, 1986

Table 1. Regression equations derived from simulation modelpredictions of change in percent base saturation (Y} over

time (years) (X) at precipitation pH 4.1 and 5.5 underconiferous and deciduous forest conditions, f

pH Coniferous forest Deciduous forest

Hagerstown (n = 25) (Typic Hapludalfs)4.1 Y = -0.02X + 67.7 0.81 Y = -0.01X + 75.7 0.925.5 Y = -0.006X + 76.1 0.94 y = -0.006X + 71.0 0.92

Edom (n = 25) (Typic Hapludalfs)4.1 Y = -0.002X + 96.2 0.76 y = -0.001X + 95.5 0.975.5 y = -0.0002X + 98.3 0.82 Y = -0.0003X + 97.0 0.82

Westmoreland (n = 25) (Ultic Hapludalfs)4.1 y = -0.02X + 53.3 0.84 y = -0.01X + 52.4 0.905.5 Y = -0.006X +55.2 0.87 y = -0.006X + 57.4 0.89

Morrison (n = 25) (Ultic Hapludalfs)4.1 y = -0.02X + 46.3 0.98 y = -0.01X + 49.3 0.925.5 y = -0.006X + 51.3 0.87 y = -0.006X + 52.5 0.82

t n = total no. of simulations run.

used to test the accuracy with which the simulationmodel predicts soil properties over time. These stud-ies were performed on 18, 10- by 10-m plots on a 0.18ha (20 by 90 m) research site located in Centre County,Pennsylvania, on the floodplain of Moshannon Creek.This creek was the source of the acid irrigation water(Stein, 1977). The soil on the floodplain was a Lindenvery fine sandy loam (coarse-loamy, mixed, mesicfamily of Fluventic Dystrochrepts). The entire site wasfertilized according to soil test recommendations, andone-half of the plots received 20.16 Mg ha~' of agri-cultural limestone. The other one-half received nolime. Acid water (pH 3.1-3.5) was applied at levels of0, 12.5, and 25 cm/week for 21 weeks (Cronce et al.,1978). Soil samples were taken from each plot fromthe Ap, Bwl, Bw2, and BC, and Cl horizons prior toirrigation, after irrigation (21 weeks) by Stein (1977),and 5 yr later by personnel of The Pennsylvania StateUniv. Soil Characterization Laboratory, UniversityPark, PA. The three sets of samples were analyzed atthe same time for pH, cation exchange capacity, basesaturation, exchangeable acidity, exchangeable cat-ions, and extractable Al, and the data are reported inLevine (1984). Standard soil characterization analysiswas also performed on a full soil profile located at oneend of the site (Stein, 1977). These data also suppliednecessary input to the model.

Results of soil analyses from the 21-week and 5-yrsamplings were used to compare actual changes thatoccurred in this soil under various treatments withchanges predicted by the model for 1 and 5 yr simu-lation runs. Soils data were put into the model for eachindividual plot so that treatments unique to that plotwere included in the input data. In addition, normalprecipitation amounts for Centre County, Pennsyl-vania were added to the amount and chemistry of theacid mine drainage water for each year of simulation.

The paired Mest analysis cited by Steel and Torrie(1980) was used to compare the actual field data foreach plot with results predicted by the simulationmodel. Data for each plot were compared with pre-dictions for that plot, and the results for the entirestudy area were combined for the final analysis. Thesoils data for the 1-yr simulation were analyzed by t-test separately from the 5-yr simulation results in or-

der to assess the ability of the model to predict soilconditions over different time periods.

Results of the Mest showed no significant differ-ences at the 0.01 level of significance for base satu-ration, pH, and exchangeable cations when the 5-yrsimulation period was compared to the actual fielddata. Results of the 1-yr simulation only showed asignificant difference when predicting levels of ex-changeable Mg and Na. Thus, the results of Mest anal-ysis indicate that the simulation model effectively pre-dicted the soil properties of a Linden very fine sandyloam soil under a given input of atmospheric depo-sition over 1- and 5-yr periods, and the longer simu-lation was a better predictor than the shorter simu-lation in determining the conditions of the soil in thefield.

Sulfate adsorption potential and clay mineralogywere not subjected to Mest analysis since these param-eters were not determined on the field samples. Pre-dictions of sulfate adsorption potentials from the sim-ulation model for A and B horizons of all plots of theLinden fine sandy loam soil ranged from 0.32 to 1.29cmol(—) sulfate adsorbed/kg of soil. These values werefound to be similar to results of sulfate adsorptionstudies from Ensminger (1954), Kamprath et al. (1956),and Chang and Thomas (1963). The model also pre-dicted that there would be no mineralogical changesin the soil after either 1- or 5-yr time periods, whichwould be expected, because these very short simula-tion periods would not be sufficient to significantlychange the nature of mineralogy in the soil. This con-tention is supported by work of Ciolkosz et al. (1979),Ciolkosz et al. (1983), and Lynn and Whittig (1966).Thus, although they were not analyzed statistically,sulfate adsorption and mineralogy predictions can beassumed to be reasonably accurate.

APPLICATIONS OF THE MODEL IN SOILGENESIS STUDIES

The computer model predicts changes in soil prop-erties over time with a given input of atmosphericdeposition. Therefore, it can be used to predict geneticchanges that take place in the soil. Two examples werechosen to demonstrate the application of the modelfor soil genesis. These were (i) the time required tochange the base saturation of soils, and (ii) pH changesin soils with time.

Determining the Time Required for Changes in BaseSaturation

According to Soil Taxonomy (Soil Survey Staff,1975), soils classified as Alfisols or Ultisols both haveargillic horizons (a subsurface zone containing an ac-cumulation of translocated clays). The major differ-ence between these two soil orders is that Alfisols havea higher base status than Ultisols (Soil Survey Staff,1975). The base saturation of Alfisols and Ultisols ismeasured at 125 cm below the top of the argillic ho-rizon, 1.8 m below the soil surface (whichever is shal-lower), or immediately above a lithic or paralithiccontact (Soil Survey Staff, 1975). If at this depth, thebase saturation (by the sum of the cations method) is>35%, the soil is classified as an Alfisol, and if it is

LEVINE & CIOLKOSZ: A COMPUTER SIMULATION MODEL FOR SOIL GENESIS APPLICATIONS 665

<35%, the soil is classified as an Ultisol (for addi-tional criteria used in classifying Alfisols or Ultisols,see Soil Survey Staff, 1975).

Using the simulation model and soils data from fourPennsylvania soils (Cunningham et al., 1983), basesaturation data from 25 simulation runs for yearsranging from 1 to 9000 yr were generated and used toderive regression equations showing the relationshipbetween base saturation and time (Table 1). For eachsimulation run, 85.5 cm of pH 4.1 precipitation (rep-resenting present day conditions in central Pennsyl-vania: Lynch, 1983, personal communication), and pH5.5 precipitation were used. A precipitation pH of 5.5was chosen because it represents the minimum pHthat can be obtained by "pure rain" at equilibriumwith normal levels of atmospheric carbon dioxide (U.S.EPA, 1980). This value may be more representativeof the pH of precipitation prior to the apparent aci-dification due to the burning of fossil fuels. Coniferousand deciduous forest conditions were also used.

Due to the two horizon limitation of the model, thecritical base saturation values were determined by thebase status of the second horizon. Data from all Ahorizons were combined and a weighted mean of eachsoil parameter was used in the model input for thefirst horizon. The depth of the first horizon was setequal to 23.5 cm, the mean A horizon depth of allfour soils, to correct for differences in leaching effectsdue to varying horizon thicknesses. The depth of thesecond horizon (weighted means of B horizon data)for each soil was set equal to 148.5 cm. Thus, thebottom of the second horizon (assumed to be the ar-gillic horizon) was at 125 cm below the bottom of theA horizon.

The slopes of regression equations for base satura-tion conditions for coniferous forest conditions wereconsistently greater than those for deciduous forestconditions for all soils under both precipitation pH4.1 and 5.5 (Table 1). This trend is the result of anincreased rate of leaching of bases in coniferous forestsdue to an increase in the acidity of the original pre-cipitation as the water passed through the coniferouscanopy and litter layer. Linear equations showed thebest fit to the data. Data within the model that ac-counted for the changes in chemistry as the precipi-tation passed through the forest canopy and litter layercame from literature values and may not reflect foresteffects at all locations. These data can and should bemodified within the model as more specific informa-tion becomes available.

The time required to change the base saturation val-ues predicted by the simulation model regressionequations are given in Table 2. Linear equations againgave the best fit to the data. Under coniferous forest,the change in base saturation of each soil was equalto or greater than under deciduous forest conditions,and the rate of change in base saturation was greaterunder precipitation pH 4.1 than under pH 5.5 precip-itation. While changes under coniferous forest con-ditions were more rapid under precipitation pH 4.1,the difference between results under the two foresttypes were less with precipitation pH 5.5. For exam-ple, (excluding the Edom soil) there was a mean dif-ference of 1375 yr to form Typic Hapludults under

Table 2. Time required to change the base saturation of Typicto Ultic Hapludalfs (60% base sat) and Typic Hapludults

(35% base sat), and Ultic Hapludalfs to TypicHapludults (35% base satl.f

Argillic B horizon

Basesatura-tion}

71.5

95.4

57.9

50.6

CEC}

cmol(+)kg-

23.3

47.0

18.5

9.5

Tex-turalclass

pH

Pre-cipita-tion

Ultic Hapludalfs Typic Hapludults

Conif-erousforest

Decid- Conif-uous erousforest forest

Decid-uousforest

HagerstownClay

Clayloam

Silty clayloam

Sandyclayloam

4.1 3855.5 1833

Edom4.1 >90005.5 >9000

Westmoreland4.15.5

Morrison4.15.5

•-

1570 16352683 6000

>9000 >9000>9000 >9000

9153367

5652717

40706850

>9000>9000

17403733

14302917

t At precipitation pH 4.1 and 5.5 under coniferous and deciduous forestconditions as predicted by the regression equations given in Table 1.

t Soil characteristics prior to first simulation.

precipitation pH 4.1, while under pH 5.1, there was amean difference of only 472 yr.

At the start of the simulation, the Westmorelandand Morrison soils were at <60% base saturation andthe CECs of these soils was less than that of the Edomand Hagerstown soils. Thus, these more acidic soilshad a lower buffer capacity, and reached the 35% basesaturation level in less time than the more basic soilsregardless of the vegetation type. The Edom soil, un-der both forest types, did not reach the 35 or 60% basesaturation limits within the 9000 yr of the simulation.The B horizon of the Edom soil, which had the highestoriginal level of base saturation and highest cation ex-change capacity, had the greatest resistance to basestatus change of all the soils. The rate of base satu-ration change of the Edom soil was also slowest of thesoils studied, as indicated by the slope of the regres-sion equations (Table 1).

In general, the time required to change Typic Ha-pludalfs to Ultic Hapludalfs, and Ultic Hapludalfs toTypic Halpludults depends on the pH of the precipi-tation, the total amount of exchangeable bases in theoriginal soil, and the vegetation type.

Changes in Soil pH Over TimeIn the second example, the change in soil pH over

time with 85.5 cm of precipitation at pH 5.5 and 4.1was studied. A series of simulations were run on Ha-zleton, Hagerstown, and Cavode soils using data fromThe Pennsylvania State Univ. Soil Data Base (Cun-ningham, 1983). Soil pH data for the time period from1 to 9000 yr were generated and regression equationswere derived for these relationships. These equationsare given in Table 3.

In all simulations, regardless of vegetation type orprecipitation pH, a constant soil pH level was reachedafter a period of time. This constant level can be con-

666 SOIL SCI. SOC. AM. J., VOL. 50, 1986

Table 3. Regression equations derived from simulation modelpredictions of change in pH (Y) over time (years) (X) under

coniferous and deciduous forest conditions atprecipitation pH 4.1 and 5.5. t

pH Hori- Coniferous Deciduous(Precip) zon forest r* forest i*

Hazleton (n — 16) (Typic Dystrochrepts)4.1

4.1

A

B

A

B

LOGy = -0.01LOGX 0.74+ 0.56

LOGy = -0.002LOGX 0.70+ 0.54

0.77LOGy = -0.01LOGX+ 0.59

LOGy = -0.005LOGX 0.70+ 0.56

Cavode (n = 20) (Aerie Ochraaquults)LOGy = -0.03LOGX 0.68

+ 0.68LOGy = -0.02LOGX 0.57

+ 0.65

LOGy = -0.03LOGX+ 0.70

LOGy = -0.02LOGX+ 0.68

0.71

0.56

Hagerstown (n = 25) (Typic Hapludalfs)4.1

5.5

A

B

A

B

LOGy =

LOGy =

LOGy =

LOGy =

-0.05LOGX+ 0.76-0.04LOGX+ 0.80-0.01LOGX+ 0.76-0.009LOGX+ 0.80

0.80

0.53

0.85

0.63

LOGy =

LOGy =

LOGy =

LOGy =

-0.05LOGX+ 0.78-0.04LOGX+ 0.81-0.01LOGX+ 0.78-0.008LOGX+ 0.81

0.77

0.56

0.92

0.72

t The Cavode and Hazleton soils simulated under pH 5.5 had straight line plotsfor the entire relationship, and thus, are not listed) (n = total number ofsimulations run.

sidered a steady-state pH, defined by Lavkulich (1969)as a condition in which reactions go on in the soil,but soil properties change too slowly to be measured,or they are not changing at all. Changes in the B ho-rizon pH did not occur until the A horizon reached asteady state and started to transmit acidity to the Bhorizon. The B horizon also reached a constant pHlevel that was generally lower than the steady state pHof the A horizon for each soil. All soils reached lowerequilibrium pH levels under coniferous forest thanunder deciduous forest conditions in both A and Bhorizons. As was the case in the base saturation ex-ample, the differences noted between the two foresttypes was due to a higher concentration of H ionsgenerated by the coniferous forest canopy and litterlayer.

In all soils, the steady state pH under precipitationpH 5.5 was consistently higher than at precipitation

pH 4.1 (Table 4). Thus, less leaching of bases occurredunder the higher precipitation pH before the soilreached its steady-state pH. Under deciduous forestconditions and pH 5.5, the A horizon of the Hazletonsoil was actually enriched with bases added to the pre-cipitation by the deciduous canopy and litter layer andits pH increased from 3.9 to 4.2. The pH of the Ha-zleton B horizon was not affected by the infiltratingwater, and it maintained its original pH throughoutthe simulation. The Cavode soil under deciduous con-ditions showed no pH change in either the A or Bhorizon under the same conditions. In addition, underconiferous forest conditions at pH 5.5, neither the Aor B horizons of the Hazleton soil changed from itsoriginal value. Thus, these data and the data for theHagerstown soil indicate that a change in precipitationpH changes soil conditions by affecting the amount ofcations leached as the soil establishes a new steadystate pH value. The final pH value and rate of pHchange depends on the proximity of the initial soil pHto the pH of precipitation after it passed through theforest canopy and litter layer, and the character of thesoil material.

COMMENTSThe natural soil is a complex and dynamic system,

involving the interaction of many processes and prop-erties. This model provides a simplified simulation ofthe soil system that must be interpreted based on thelimitations of the available data and structure of themodel. However, it is because of the model's abilityto combine many soil processes and make predictionsbased on standard soil characterization data that makesit unique and usable at the present time. In order toimprove this model and produce a soil simulation thatmore closely represents the real system, a number ofadditions can be made, assuming the appropriate databecomes available. These changes include improvingthe equations presently used to better fit a particulargeographic location, and adding equations to accountfor soil solution data, soil-root interactions, influencesof organic acids, microbiological effects, the migrationof clay, water flow through the soil profile, and esti-

Table 4. Time (years) required to change original pH to equilibrium pH under coniferous and deciduous forest conditionsat precipitation pH 4.1 and 5.5 as predicted by the equations given in Table 3.

Precipitation pH 4.1 Precipitation pH 5.5

Soil

Hazleton

Cavode

Hagerstown

Hazleton

Cavode

Hagerstown

Horizon

ABABAB

ABABAB

Texturalclass

LoamLoam

Silt loamSilty clay loam

Silt loamClay

CECt[cmol (+) kg

25.68.7

17.420.817.123.3

Initial-] pHtDeciduous forest

3.93.95.04.96.06.3

Coniferous forest3.93.95.04.96.06.3

EquilibriumpH

3.63.54.14.24.14.7

3.53.53.93.64.14.5

Years to reachequilibrium

250500500

100020002500

250250500

100020003000

EquilibriumPH

4.23.95.04.95.66.1

3.93.94.74.65.35.9

Years to reachequilibrium

250000

10002000

00

300750

12501500

t Soil characteristics prior to first simulation.

LEVINE & CIOLKOSZ: A COMPUTER SIMULATION MODEL FOR SOIL GENESIS APPLICATIONS 667

mate of soil loss due to erosion. In addition, morehorizons should be added to the simulation of a soilprofile in order to more accurately model the soil. Bothwet and dry atmospheric deposition data measuredover a long period of time is needed, as are improvedvalues for the change in throughfall precipitationchemistry after it passes through the forest canopy andlitter layer. Testing of the model should be continuedwhen appropriate data become available.