Journal of AridEnvironments

Journal of Arid Environments 72 (2008) 1012–1033

Modeling salt movement through a Mojave Desert soil

G.M. Marion�, P.S.J. Verburg, E.V. McDonald, J.A. Arnone

Division of Earth and Ecosystem Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA

Received 1 June 2007; received in revised form 23 November 2007; accepted 10 December 2007

Available online 31 January 2008

Abstract

Salt flux through soils can significantly influence local and global processes. For example, desert soils can atypically

concentrate NO�3 at depth in soil profiles. CaCO3 precipitation/dissolution can play significant roles as either sinks or

sources of global carbon. The objectives of this work were to develop a salt-flux model for long-term (41000 years)

simulations of desert soils and examine the consequences of climate, soils, system inputs, and land-use change on salt

movement in arid soils.

The field study was conducted at the Nevada Test Site in the northern Mojave Desert. New additions to the CALGYP

model allowing for site-specific parameterization included stochastic rainfall model, salt inputs, soil water-holding

capacities, and soil CO2 profiles. New ions added to the model included Na+, K+, Mg2+, Cl�, and NO�3 .

About 81% of Ca2+ input remained within the surface 1.0m of soil as CaCO3, which argues in favor of soil CaCO3

serving as a recalcitrant sink for global carbon. In contrast, E99.96% of Na+, K+, Mg2+, Cl�, NO�3 , and SO2�4 ions

leached to soil depths 41.0m and 94.3% leached to soil depths 42.0m. This is true despite only 1.64% of the rainfall

leached beyond 1.0m and 0.020% of the rainfall leached beyond 2.0m. The leachability of NO�3 and Cl� to soil depths 42.0m agrees with NO�3 and Cl� accumulations at depth in Mojave Desert soils (1.3–2.7m). Simulation of extreme events

and years with a stochastic rainfall model and accurate soil water-holding capacities are critical for modeling water and salt

flux through soils.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Desert soils; Salt flux; Biogeochemical model; Salt mineralogy; Calcite stability; Global carbon balance

1. Introduction

Salt flux through soils can significantly influence local and global processes. For example, salinity, per se,can limit plant growth and contaminate groundwater for human consumption (Buck et al., 2006; Nolan andStoner, 2000; Raven et al., 1986; Scanlon et al., 2005; Stokstad, 2003; Walvoord et al., 2003). On the otherhand, salts also provide essential nutrients for plant growth such as N, P, K, Ca, Mg, and S (Raven et al.,1986). Despite the fact that N is often the most limiting plant nutrient in terrestrial ecosystems, NO�3 in desertsoils often accumulates to highest concentrations at depth (1–5m) (Hartsough et al., 2001; Marion et al., inpress; Stokstad, 2003; Wallace et al., 1978; Walvoord et al., 2003), rather than near the surface (Jobbagy and

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0140-1963/$ - see front matter r 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jaridenv.2007.12.005

�Corresponding author. Tel.: +1 775 673 7349; fax: +1 775 673 7485.

E-mail address: giles.marion@dri.edu (G.M. Marion).

Jackson, 2001). Another example is the potential role that CaCO3 deposition may play in desert soils as a sinkfor and possibly as a source of atmospheric CO2 (Capo and Chadwick, 1999; Monger and Gallegos, 2000;Schlesinger, 1982; Serna-Perez et al., 2006).

Many numerical models have been designed to explore salt flux (especially CaCO3) through desertecosystems (e.g., Arkley, 1963; Bresler et al., 1982; Dudley et al., 1981; Marion, 1994; Marion and Schlesinger,1994; Marion et al., 1985; Mayer et al., 1988; McDonald et al., 1996; McFadden and Tinsley, 1985; McFaddenet al., 1991, 1998; Robbins et al., 1980). Models such as these have their strengths and weaknesses (Marion andSchlesinger, 1994). A few of these models deal with short-term (yearly) fluxes of highly soluble salts such asNa+, K+, Mg2+, and Cl� (e.g., Bresler et al., 1982; Dudley et al., 1981; Robbins et al., 1980). But none of thelatter soluble salt models deal with the long-term (41000 years) consequences of climate and soils on solublesalt accumulations in desert soils, which are critical to understanding why essential nutrients such as NO3-Naccumulate at depth in many desert soils.

The objectives of this work were to (1) develop a salt-flux model for long-term (41000 years) simulationsof desert soils, (2) parameterize the model for soils at a specific Mojave Desert site, (3) validate the model, and(4) examine the potential consequences of climate, soils, system inputs, and land-use change on salt movementin arid soils.

2. Methods and materials

2.1. Site location and environment

For the development and validation of the model, we used data from a field study conducted at the NevadaDesert Research Center (NDRC). NDRC is located at the Nevada Test Site (NTS) in the northern MojaveDesert, Nye County, Nevada (361490N, 1151550W). The site is 90 km northwest of Las Vegas at an elevation of960–975m, near Mercury, NV. The site of our work is in close proximity to ‘‘Frenchman Flat’’ (Frizzell andShulters, 1990; Romney et al., 1973). At NDRC, two ongoing experiments are assessing the impacts of climatechange on desert ecosystems. At the Mojave Global Change Facility (MGCF), experimental plots aremanipulated by increasing N deposition and summer precipitation. In addition, deserts crusts are disturbed tosimulate increased grazing. At the Nevada Desert Free Air CO2 Enrichment (FACE) facility (NDFF),atmospheric CO2 concentrations are increased.

The Mojave Desert experiences sporadic, low precipitation with an average annual rainfall ofapproximately 140mm. Winter rains are common and may last up to several days. Summer storms generallyoccur in July and August and are usually local, intense, and unpredictable. Relative humidity is low resultingin very high potential evaporation (Titus et al., 2002). Moisture is the primary limitation to plant growth in theMojave Desert (Smith et al., 1997; Turner and Randall, 1989). Temperatures vary from a minimum of �10 1Cin the winter to a maximum of 447 1C in the summer with large diurnal temperature fluctuation occurringthroughout the year (Bowers, 1987). Specific details on NTS climate are presented in our discussion of thestochastic precipitation model.

Soils are Entisols derived from (geomorphically) recent calcareous alluvium with textures ranging fromloamy sands in the surface horizon to coarse sands in subsoil horizons. The soils show very limiteddevelopment of diagnostic horizons and contain no layers or horizons that limit the downward flux of watersuch as calcretes or silcretes. Soils are characterized by spatial heterogeneity in nutrients, infiltration, andtexture (Romney et al., 1980). In this study, gravel contents (42mm) in 38 layers in the surface meter of eightsoil profiles adjacent to the MGCF and NDFF varied from 15.6% to 98.2% (mean ¼ 55.2%). Fig. 1 is anexample of one these profiles that is dominated by gravel (81% gravel between 14 and 120 cm).

The vegetation of the site is characteristic of the northern Mojave Desert and is dominated by the xerophyticshrubs Larrea tridentata (creosote bush) and Ambrosia dumosa (white bur-sage) (Ostler et al., 1999).

2.2. Model structure

The CALGYP (CALcite-GYPsum) model was developed to trace calcite/gypsum precipitation/dissolutionin aridland soils (Marion, 1994; Marion and Schlesinger, 1994; Marion et al., 1985). This model has five

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components: soil parameterization, chemical thermodynamic relations, a stochastic rainfall model, anevapotranspiration model, and subroutines that calculate water and salt fluxes. Compared with previousversions of the CALGYP model, this new version contains NTS site-specific parameterizations for rainfallinput, salt inputs, bulk density (BD), soil texture, water-holding capacity (WHC), and soil CO2 profiles. Inaddition, there are new chemical thermodynamic relations and several new highly soluble salts (Na+, K+,Mg2+, Cl�, and NO�3 ). The five components of the model are summarized below; for a more completediscussion of the model, see previous publications (Marion, 1994; Marion and Schlesinger, 1994; Marionet al., 1985).

2.2.1. Soil parameterization

CALGYP is a compartment model that can be parameterized to contain 1–50 soil layers. Model inputs foreach layer include layer thickness, BD, water contents initially and at soil water matric suctions of 0.01MPa(field capacity) and 8.0MPa (permanent wilting point; Schlesinger et al., 1987), initial soil calcite and gypsumcontents, initial concentrations of soluble ions (Na+, K+, Mg2+, Ca2+, Cl�, SO2�

4 ; and NO�3 ), and initialsoil pH. The model uses measured soil PCO2

as input, and it allows CO2 concentrations to vary spatially andseasonally (Fig. 2). Atmospheric inputs include the ion contents in rainfall and dust.

In most of our applications, we split the soil profile into ten, 10-cm soil layers. In one simulation, weextended the model to 3.0m using 30 10-cm soil layers. Each soil layer was assigned a BD of 1.644 g/cm3 basedon 12 whole soil samples. The average BD of six 0–5 cm soil layers was 1.628 g/cm3, which was notsignificantly different from the average BD of 1.660 g/cm3 of six 15–20 cm soil layers. These BD measurementsincluded sand, silt, clay, and gravel (42mm).

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Fig. 1. A Frenchman Flat soil profile. Note the knife for scale and the rooting distributions.

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–10331014

The soil water contents at 0.01 and 8.0MPa were estimated with the Rosetta program based on the sand,silt, and clay contents of the p2mm soil fraction (George E. Brown Jr., Salinity Laboratory in Riverside, CA;http://www.ars.usda.gov/Services/docs.htm?docid=8910). These p2mm estimates were adjusted to fieldconditions by assuming that the gravel (42mm) fraction has zero water content (Marcel Schaap, pers. comm.,2005). For example, if the water content at 0.01MPa is 15.0% based on the p2mm fraction with anassociated gravel content of 50%, then the field water content would be 7.5%. For the 38 soil samples fromeight soil profiles, the average water content at 0.01MPa was 4.2 (72.7)% and at 8.0MPa, 1.3 (70.6)%. Overa 6-year period (2001–2006) with 88 sampling dates, the surface horizons (0–20 cm) in four undisturbedMGCF plots ranged in soil moisture from 1.0% to 10.8%. Our model-calculated % moisture in the surfacehorizons of eight soil pits surrounding the MGCF plots ranged from 1.5% at 8.0MPa to 9.9% at 0.1MPa,which are in reasonable agreement with the field ranges.

Initial soil CaCO3 contents were based on measurements of the p2mm fractions (Dremanis, 1962). Thehigh gravel content of these soils (Fig. 1), and the fact that some of these gravels are limestone and dolomite(Frizzell and Shulters, 1990; Romney et al., 1973) made it difficult to accurately assess the CaCO3 content ofthe gravel. For our applications, we assumed that the p2mm CaCO3 percentages were representative of thetotal CaCO3 in the soil profiles. The average CaCO3 content of 38 soil samples from eight soil profiles was16.4(73.5)%. Initial concentrations of soluble soil ions were assigned arbitrary low values. For example, soilpH was assigned an initial value of 8.0 throughout the soil profile. These soil solution concentrations quicklybecome controlled by atmospheric inputs, chemical processes such as calcite solubility and CO2 equilibrium,and salt fluxes. Therefore, assumed initial soil ion concentrations are not critical for determining long-termpatterns in salt flux.

The partial pressures of CO2 (PCO2) in soil profiles are a function of plant respiration, microbial respiration,

and gas diffusion. In our model, soil PCO2is not explicitly simulated but rather used as input for the model. As

a reference point, atmospheric PCO2today is approximately 380 ppm. The PCO2

varied seasonally and with soildepths in these NTS soils and were based on monthly to bimonthly field measurements using gas wellsinstalled under shrubs and in intershrub locations (Fig. 2). The gas wells consisted of 0.5 in stainless steel tubesinstalled at 10, 40, or 90 cm depth. One set of wells was located underneath a Larrea shrub while a second setwas installed at least 1m away from a shrub in four control plots. The gas wells were installed by pre-drilling ahole to the desired depth using a 1m long 3/800 drill bit. The wells were then slowly pounded into the groundwith a sharpened steel pilot rod inside the well. When the desired depth was reached the pilot rod was pulledout of the well. The installation caused no visual damage to the surrounding soil and/or shrub. Prior tosampling, the well was flushed by removing a volume equal to the gas well using a plastic syringe to ensure that

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Fig. 2. The mean seasonal PCO2concentrations in the soil profiles at Frenchman Flat.

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–1033 1015

the well was filled with soil air. Gas samples for CO2 analysis were taken using a glass syringe. The sampleswere injected in evacuated Exetainers. The CO2 concentration was measured in the lab using a LI-COR 6251Infra Red Gas Analyzer. The seasons are winter (January–March), spring (April–June), summer(July–August), and fall (September–December). The data points at 10, 40, and 90 cm in Fig. 2 are themeans of 4, 8, 5, and 4 daily values for the winter, spring, summer, and fall seasons, respectively, measuredbetween 5/19/04 and 5/10/06. See Table A.1 in Appendix A for a listing of PCO2

values by date. The spring andsummer seasons show the highest PCO2

values, and the fall season shows the lowest PCO2values, most likely

reflecting seasonal patterns in belowground biological activity (Fig. 2). What we are calling the winter seasonis clearly a transition between the fall and spring/summer seasons. At times the PCO2

values at 90 cm in thewinter season were as high as the spring/summer seasons (wet winter), and at times as low as the fall season(dry winter).

Earlier work at a nearby Rock Valley site on the NTS by Terhune and Harden (1991) found seasonalaverage PCO2

values ranging from 340 to 1880 ppm at 10, 20, 40, and 60 cm soil depths, which are close to ourPCO2

values that ranged from 614 to 2759 ppm at 10, 40, and 90 cm soil depths. Terhune and Harden (1991)found that PCO2

values were close to atmospheric levels and varied randomly with depth during the summer,fall, and winter, and were highest in the spring. Our measurements were also high in the spring (Fig. 2), butnever close to atmospheric values during the other seasons. These seasonal differences may reflect sitevegetative species differences (Artemesia tridentia and Ephedra spp. at Rock Valley and L. tridentata andA. dumosa at Frenchman Flat) or differences in vegetation density. The increasing PCO2

values with increasingsoil depth with minimal values during the fall/winter seasons were similar to experimental measurements at 30,60, and 100 cm soil depths near Tucson, Arizona by Parada et al. (1983) that were used in previous versions ofthe CALGYP model (Marion 1994; Marion and Schlesinger, 1994; Marion et al., 1985). The major differencein soil PCO2

values between these two desert soils was that the Tucson site (Parada et al., 1983) had about twicethe PCO2

concentrations as the NTS site (Fig. 2). The major plants at Site 1 from which we derived theseasonal patterns of the Tucson study are saguaro (Carnegiea gigantea), creosote bush (L. tridentata), and paloverde (Cercidium microphyllum). Again, these differences in soil PCO2

values between sites probably reflectvegetative and microbial respiration differences, which, in turn, were likely due to annual rainfall differences(NTS ¼ 15.9 cm, Tucson ¼ 28.4 cm; Marion, 1994).

The atmospheric deposition of Ca2+, alkaline ions ðHCO�3 and CO2�3 Þ, and SO2�

4 were especiallyimportant for this study because of the prevalence of calcite and potentially gypsum in these soils. Our modelCaCO3 input of 1.685 g/m2/year and CaSO4 � 2H2O input of 0.345 g/m2/year were the means of two sites(8 and 10, Reheis and Kihl, 1995) near the Desert Rock site used in our stochastic rainfall model, which isonly 9 km from our NTS experimental site. These inputs included both dry and wet deposition (Reheis andKihl, 1995). Our input estimates of Na+ (1.85mg/l), K+ (0.52mg/l), Mg2+ (2.35mg/l), Cl� (1.65mg/l), andNO�3 (0.00–0.73mg N/l depending on season) were based entirely on wet deposition (rainfall) at Ely, NV(E290 km north of our NTS site) from the National Precipitation Sampling Network (Lodge et al., 1968).These rainfall soluble salt concentrations (Na+, K+, Mg2+, Cl�, and NO�3 ) multiplied by the average rainfallat our NTS site (15.9 cm/year) produced a total annual salt input of 1.06 g salt/m2/year, which is in reasonableagreement with measured total salt inputs of 0.75–0.79 g salt/m2/year at Sites 8 and 10 (Reheis and Kihl, 1995),which are near our NTS site. The CALGYP model as presently structured does not consider primary rockweathering or cation exchange reactions. The implicit assumptions were that in these low rainfallenvironments, primary rock weathering is minimal, and Ca2+ in these calcareous soils dominates theexchange reactions.

2.2.2. Chemical thermodynamic equations

Activities (ai) of ions having aqueous solution molal concentrations (mi) were estimated by

ai ¼ gimi, (1)

where gi is the single-ion activity coefficient, which was estimated with the Davies equation (Davies, 1962)

logðgiÞ ¼ �Az2i

ffiffiffiIp

1:0þffiffiffiIp� �� 0:3I

" #, (2)

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where zi is the ionic valence, I the ionic strength that is defined as

I ¼ 0:5X

miz2i (3)

and A is the Debye–Huckel constant that is given by

A ¼ 0:6303� 1:685095e� 3T þ 4:298701e� 6T2 (4)

for the temperature range from 273.15 to 313.15K (Pitzer, 1995).Critical to the performance of this model was the mechanism for estimating soil solution pH because of the

strong influence of pH on CaCO3 solubility. For a pure CaCO3–CaSO4 solution in the pH range of 7–8.5, thefollowing charge balance holds:

2½Ca2þ� ¼ ½HCO�3 � þ 2½CO2�3 � þ 2½SO2�

4 �, (5)

where brackets refer to concentrations. For a system in equilibrium with calcite, this equation can berewritten as

2K spðcalciteÞðHþÞ

2

gCaK2K1KHPCO2

¼KHK1PCO2

ðHþÞgHCO3

þ2KHK1K2PCO2

ðHþÞ2gCO3

þ 2½SO2�4 �. (6)

Given the equilibrium constants (see Appendix A), PCO2, activity coefficients, and the SO2�

4 concentration,Eq. (6) can be solved by successive approximations for (H+), which was used to control CaCO3 solubility.

Eqs. (5) and (6) are charge-balance equations. If the initial ion concentrations in these equations are notperfectly charge balanced, this algorithm will adjust the alkaline ion ðHCO�3 and CO2�

3 Þ concentrations tobring the overall ion concentrations into perfect charge balance. There are, of course, other ions besides Ca2+,HCO�3 ;CO

2�3 ; and SO2�

4 in these soils. As input in rainfall, the model also included Na+, K+, Mg2+, Cl�,and NO�3 ; these ions could be added to Eqs. (5) and (6). However, because the latter rainfall ions had ameasured excess of cations over anions, including these ions in Eqs. (5) and (6) led to an increase in alkalinityto balance the changes, and, as a consequence, to an increase in pH into the range of 9–10. Experimentalmeasurements of saturation extracts of the NTS soils had pH values of 7.6–8.3 (Marion et al., in press), whichare in good agreement with our model estimates using Eq. (6), which fell in the pH range of 7.6–8.1. Therefore,we chose Eqs. (5) and (6) to describe the pH of these soils. In another paper that examined the saturationextracts of these soils in greater detail (Marion et al., in press), it was clear that these calcareous soils aredominated by Ca and alkaline ions, which is another argument in favor of using Eqs. (5) and (6).

The specific equilibrium constants and their temperature dependence used in this study are listed inAppendix A.

2.2.3. Stochastic precipitation model

The stochastic rainfall model controlled input of water and was based on probability distributions forinterarrival times (the number of days between rainfall events) and the rainfall amounts for specific seasons atspecific sites. Fig. 3 contains the probability distributions for four seasons (winter, January–March; spring,April–June; summer, July–August; and fall, September–December) based on 25 years of records (1978–2003)at the Desert Rock WSMO (Weather Station Meteorological Office) station (Mercury, NV), which is locatedapproximately 9 km from our NTS site. See Table A.2 in Appendix A for a tabular listing of cumulativeprobabilities for rainfall amounts and interarrival times for the Desert Rock site. The most frequent rainfallsoccurred in the winter season (steepest ‘‘interarrival time’’ curve), and the least frequent rainfalls occurred inthe spring (least steep ‘‘interarrival time’’ curve) (Fig. 3). The most intense storms occurred in the summerwhere about 10% of the rainfalls were X2 cm/day (Fig. 3). A random-number generator was used to select theinterarrival times and rainfall amounts for each year from the cumulative probability distributions. Runningour rainfall model for 1000 years with five different ‘‘seeds’’ for the random-number generator led to15.8675.24 cm/year with 34.4 events/year. This model estimate compares to 15.9377.00 cm/year with35.6 events/year based on the 25-year history (1978–2003) that was used to generate our probabilitydistributions. The only potentially significant differences between the model and the historical record were thestandard deviations (SDs) (5.24 and 7.00 cm/year) that led to a smaller variability in annual rainfall for our

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model. In a previous paper, we compared calculated and measured rainfalls for seven southwest desert sites(Marion et al., 1985); in two cases, the SDs were essentially the same; in three cases, the model-calculated SDswere low; and in two cases, the model-calculated SDs were high. The reasons for these discrepancies areunclear, but could be due to covariance between interarrival times and rainfall amounts (Wang et al., 2006),which we ignored in our stochastic precipitation model development. Extreme events and extreme years arecritically important in assessing the movement of salts through soils as we discuss later in this paper.

2.2.4. Evapotranspiration model

Evapotranspiration is a measure of water loss from soils through soil surface evaporation and planttranspiration. The evapotranspiration model, which is primarily a function of temperature, controlled the lossof water and consisted of three steps. First, potential evapotranspiration was calculated using Thornthwaite’sequation (Marion et al., 1985; Thornthwaite, 1948). Second, Thornthwaite’s potential evapotranspiration wasconverted to pan evaporation using a derived, empirical relationship with temperature for southwesterndeserts (Marion, 1994; Marion et al., 1985). And third, actual evapotranspiration was calculated as a functionof soil moisture and pan evaporation between field capacity (0.01MPa) and permanent wilting point(8.0MPa). Calibration of the third step was based on field measurements from a L. tridentata site at theJornada Desert Long-Term Ecological Research site near Las Cruces, New Mexico (Marion et al., 1985).Water loss was assumed to occur at the pan evaporation rate in the upper 45.4% of the available moisturerange; in the lower 54.6% of the range, water loss was a linear function of soil moisture. See Marion et al.(1985) or Marion (1994) for more complete details on this evapotranspiration model. Mean monthly airtemperatures from the previously mentioned Desert Rock site (stochastic model) were used to drive ourevapotranspiration model.

2.2.5. Water and salt fluxes

A daily time step was used in our model to estimate the fluxes of water and salts through the soil. All rainfallwas assumed to enter the uppermost soil layer; the model ignored vegetative interception of rainfall andsurface runoff. Only saturated flow of water was considered in this model; this was a simplification that wasimportant in order to simulate soil processes for tens of thousands of years. If the water content of a layerexceeded field capacity, excess water moved into progressively deeper layers. Salts were assumed to move withthe mass of water. Water that entered a given layer was mixed with pre-existing water, and salts wereequilibrated chemically with the solid and gas phases. Therefore, the excess water that passed through a givenlayer contained an equilibrated concentration of solutes before passing to the deeper layers. During drying

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Fig. 3. The seasonal patterns of cumulative probability for interarrival days and daily precipitation at the Desert Rock site.

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–10331018

cycles, water was first extracted from the surface layer and then from progressively deeper layers using theevapotranspiration model previously discussed.

Because soil properties were only measured for the surface meter of soil (Fig. 1), most of our simulationswere restricted to the surface 1.0m. However, 1–2% of the rainfall leached beyond 1.0m. Therefore, weundertook a single simulation to the deepest depth predicted by our model for water flow (E3.0m). Becausewe had no measured soil properties below E1m, we assumed that deeper depths could be approximated byusing the properties of the 90–100 cm soil layer. The most critical soil properties affecting salt flux are WHCand PCO2

. Fig. 2 shows how PCO2increases with increasing soil depth up to 1.0m. Desert PCO2

has been shownto increase with depth up to 110m (Walvoord et al., 2005). Fig. 4 shows how WHC below the surface layerscan both decrease and increase with soil depth.

3. Results

3.1. Model simulations

In this paper, we separated the movement of salts through these Mojave Desert soils based on saltsolubilities. First, we examine the most insoluble salt of this study, which is CaCO3 (calcite). Second, weexamine CaSO4 � 2H2O (gypsum), which is moderately soluble. And finally, we examine highly soluble Cl�,NO�3 ; and SO2�

4 salts of Na+, K+, and Mg2+.

3.1.1. Calcium carbonate mobility

In Fig. 4, we depict the simulated movement of CaCO3 into two soil profiles based entirely on Ca2+ input indust and rainfall (1.685 g CaCO3/m

2/year over a 1000-year simulation). Also included are the twocorresponding soil profile WHCs, which are the difference between water contents at 0.01 and 8.0MPacorrected for gravel content. The WHC of the DWS pit was typical of our eight NTS soil profiles in that theWHC values were highest in the surface and declined with depth (Fig. 4); this was largely due to the higherconcentrations of gravel with soil depth. The WHC for pit CEI, on the other hand, actually showed anincreasing WHC below 23 cm, which was atypical of these NTS soils. In our following simulations, the WHCof the DWS profile was used as the site ‘‘standard.’’

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Fig. 4. The experimental water-holding capacity (WHC) of two Frenchman Flat soil profiles, and the simulated accumulation patterns for

CaCO3 deposition (assuming no initial CaCO3).

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–1033 1019

The WHC of these soils had a significant effect on the movement of CaCO3 through these soils. Because ofthe relatively high WHC of the DWS pit in the surface layers, the bulk of the added CaCO3 accumulated in the15–35 cm soil horizon (Fig. 4). In contrast, the much lower WHC of the CEI profile in the surface layers led toa deeper deposition of CaCO3 in the 45–65 cm horizon (Fig. 4). The point of this comparison is to demonstratethe sensitivity of relatively insoluble CaCO3 movement through soils caused by WHC.

In a paper based on analyses of California and Nevada soils, Arkley (1963) developed an equation relatingthe depth to the top of the BK or K horizons (horizons dominated by CaCO3) to annual precipitation. Usingthis equation with our average precipitation of 15.9 cm led to a depth of 25 cm to the top of a BK or K horizon.A similar equation relating precipitation to the mean depth of southwestern BK horizons led to 25 cm (Marionet al., 1985). This 25 cm depth fell in-between our model depths of 15 and 35 cm based on our site ‘‘standard’’WHC (DWS, Fig. 4). So while our model was consistent with these equations, the wide variation in CaCO3

depth of deposition (Fig. 4) clearly demonstrates the critical importance of WHC for specific profiles.While the WHC data in Fig. 4 were real, the CaCO3 accumulations were hypothetical because they assumed

no CaCO3 was originally present in these soils. In reality, these NTS soils formed on calcareous alluvium[soil CaCO3 content ¼ 16.4 (73.5)%]. All sampled horizons were supersaturated with respect to calcite(Marion et al., in press). Fig. 5 shows the CaCO3 profiles in the DWS and CEI soils. Both show a depletion ofCaCO3 in the surface horizon, as did the other six profiles (not shown). However, the DWS profile has anenrichment in the 55–75 cm layers, where the CEI profile shows a reduced CaCO3 in these layers. These majordifferences at depth are almost certainly due to different initial distributions of CaCO3 in these profiles, if forno other reason than the highly variable gravel content of these soils (Fig. 1).

Despite these limitations, we used an average CaCO3 content for the entire profile (Fig. 5, vertical dashedline) and simulated what would happen to such a uniform profile if it were subjected to our stochasticprecipitation model with an annual atmospheric input of 1.685 g CaCO3/m

2/year for the NTS site. By the endof 5000 years, 62% of the CaCO3 was removed from the surface 10 cm layer; by the end of 10,000 years, 100%of the CaCO3 was removed (Fig. 5). If these are reasonable assessments, then these soil profiles must beo5000years because the surface depletions from the DWS and CEI profiles were much less than 62% of the CaCO3

in the surface horizons. If we use the simulated 5000-year CaCO3 depletion as an age calibration tool andapply it to the CEI and DWS profile surface depletions, then these profiles are 948 and 1974 years old,respectively. Applying this dating tool to all eight of our soil profiles leads to an average age of 1636 (71272)years with a range from 176 to 3416 years. Another argument in favor of a young profile is the difference in thepatterns of CaCO3 accumulation between the two soil profiles and the simulations. The 5000 and 10,000-year

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Fig. 5. The CaCO3 content of two Frenchman Flat soil profiles, and the simulation of 0, 5000, and 10,000 years of leaching a hypothetical

soil profile.

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–10331020

simulations suggest a significant movement of surface CaCO3 leading to a distinct enrichment pattern at depth(BK horizon formation), which is not clearly present in the real soil profiles (Fig. 5). Gile et al. (1981) haveestimated that it would take 25,000–75,000 years for CaCO3 plugging of gravelly soils (K horizon formation)near Las Cruces, New Mexico. Based on lack of continuous pebble carbonate coatings and a lack of distinctsoil carbonate profile development (Fig. 1), these soil profiles represent Stage I carbonate accumulations,which places their age at o1000 years (McDonald et al., 2003), somewhat lower than our average estimateof 1636 years.

The bottom line for insoluble CaCO3 is that it moves slowly through soil profiles. This is in marked contrastto the more soluble salts that we examine next.

3.1.2. Calcium sulfate mobility

Fig. 6 shows the distribution of sulfate in saturation extracts in eight soil profiles [four beneath plantcanopies (can) and four in intershrub zones (inter)]. Also included in this figure is the distribution of solublesulfate predicted by the CALGYP model after a simulation of 10,000 years. We chose 10,000 years for oursimulation because we wanted a stable long-term outcome, which based on the infrequency of extreme eventstook thousands of years (see Section 4). Both the experimental data and the model simulation show thatsulfate, for the most part, only accumulates deep in these profiles.

In fact, the majority of the sulfate added in deposition (0.06424 g S/m2/year) was predicted to have leachedto depths 41.0m. At 1000 years, 96.93% of the added sulfate had leached below 1.0m. At 2000 years, thepercentage increased to 98.61%. Between 3000 and 10,000 years, the leached sulfate oscillated around99.7170.23%. So the accumulated sulfate modeled in the soil profile of Fig. 6 is only a small percentage of theadded sulfate. Furthermore, the magnitude of the modeled profile sulfate can vary widely at depth dependingon recent rainfall patterns; the overlap of the experimental and model concentrations in Fig. 6 is a merecoincidence. The rising percentages of leached sulfate in the first 3000 years is probably attributable to theinfrequency of rare, but extreme rainfall events that foster deep percolation through these soil profiles. Similarpatterns were found for soluble ions (see Section 4).

Applying an equilibrium thermodynamic model (FREZCHEM) to the saturation extract data (Marionet al., in press) showed that all samples were undersaturated with respect to gypsum. Only a single sample(the highest concentration in Fig. 6) was close to gypsum saturation. Similarly, the CALGYP model used inour simulations never led to gypsum precipitation. Apparently, the rainfall in combination with the WHC ofthese soils at this site were sufficient to leach virtually all sulfate to depths greater than 1.0m.

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Fig. 6. The sulfate concentration in saturation extracts from four soils beneath plant canopies (can) and four soils from intershrub areas

(inter), and the simulation of sulfate accumulation in the surface meter of soil after 10,000 years.

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–1033 1021

3.1.3. Soluble salt mobility

We included soluble Na+, K+, Mg2+, Cl�, and NO�3 in our model to show how soluble salts concentratedifferently at depth in the soil profiles (Figs. 7–11) compared with insoluble Ca2+ that precipitated as CaCO3

(Fig. 4). The soluble salt simulations show a zone of soluble salt concentration below 60 cm; the relative size ofthis salt bulge is simply a reflection of input concentrations and WHC. As pointed out previously with respectto sulfate (Fig. 6), the shape and magnitude of these salt bulges is coincidental with respect to the saturationextract data.

Virtually all inputs of Na+, K+, Mg2+, Cl�, NO�3 ; and SO2�4 leached beyond 1.0m depth in our

simulations. For example, 99.95–99.97% of these highly soluble salts leached beyond 1.0m in our 10,000-yearsimulation. This is in marked contrast to Ca2+ where only 19.3% of the dust and rainfall Ca leached pass the

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Fig. 7. The sodium concentration in saturation extracts from four soils beneath plant canopies (can) and four soils from intershrub areas

(inter), and the simulation of sodium accumulation in the surface meter of soil after 10,000 years.

Fig. 8. The potassium concentration in saturation extracts from four soils beneath plant canopies (can) and four soils from intershrub

areas (inter), and the simulation of potassium accumulation in the surface meter of soil after 10,000 years.

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–10331022

1.0m soil depth. The retention of most of the added Ca2+ within the surface meter of soil is important withrespect to carbon balance and soil profile development. Despite the near complete flushing of solublesalts to depths 41.0m, this phenomenon occurred with only 1.64% of the rainfall water leaching pass the1.0m depth.

The 10,000-year simulation for soluble salts (Figs. 6–11) was based on the WHC for the CEI profile, whichled to a significant difference in CaCO3 deposition compared with the DWS–WHC profile (Fig. 4). Runningthe 10,000-year simulation with the DWS–WHC profile led to 1.59% of the total rainfall leached beyond 1.0mcompared with 1.64% through the CEI profile. With the DWS–WHC profile, 18.9% of the Ca2+ inputleached beyond 1.0m compared with 19.3% through the CEI–WHC profile. The difference in leaching of the

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Fig. 9. The magnesium concentration in saturation extracts from four soils beneath plant canopies (can) and four soils from intershrub

areas (inter), and the simulation of magnesium accumulation in the surface meter of soil after 10,000 years.

Fig. 10. The chloride concentration in saturation extracts from four soils beneath plant canopies (can) and four soils from intershrub areas

(inter), and the simulation of chloride accumulation in the surface meter of soil after 10,000 years.

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–1033 1023

soluble ions (Na+, K+, Mg2+, Cl�, NO�3 ; and SO2�4 ) due to the different WHC profiles was 0.007%. While

WHC had a significant influence on insoluble CaCO3 mobility (Fig. 4), it had only a minor influence onsoluble ion mobility. It does not take much water to mobilize highly soluble ions.

We did a simulation to the model-calculated maximum depth of water penetration in this soil profile. SeeSection 2.2.5 for a discussion of deep soil profile assumptions. The maximum depth of water penetration wasE3.0m. The percentages of soluble salts that leached past the 2.0m depth were E94.3%; in contrast, only18.5% of the insoluble Ca leached past the 2.0m depth. Of the total rainfall of 157,527 cm over the 10,000-yearsimulation, 2580 cm (1.64%) leached past 1.0m, 31.7 cm (0.020%) leached past 2.0m, and 0.0 cm leached past3.0m. This implies that 94% of the added soluble salts would have accumulated between 2.0 and 3.0m,according to model calculations. Maximum model-calculated salt concentrations were at soil depths of2.2–2.5m. It did not take much water moving through these soil profiles to flush soluble salts to great depths.Previous work has demonstrated that Cl� and NO�3 peak accumulations in Mojave Desert soils (n ¼ 7)typically form at 1.3–2.7m soil depths with most around 2.0m (Hartsough et al., 2001; Walvoord et al., 2003),which agrees with our model simulation.

However, there are major limitations to application of the CALGYP model at high salinities. For example,the Davies activity coefficient model (Eq. (2)) is only valid to an ionic strength (I) (Eq. (3)) of 0.1molal(Nordstrom and Munoz, 1994). According to our model calculations, I reaches 0.14molal at 1.4m andincreases with increasing soil depth, peaking between 2.2 and 2.5m. Failure of our model to accuratelycalculate activity coefficients means that all chemical equilibrium calculations are invalid (see Eq. (6) andEqs. (A.1)–(A.9) in Appendix A).

On the other hand, if salts are not precipitating, then the model may still be reasonably estimating themovement of salts through the soil profile. We took the modeled concentrations of salts at 2.0m and evaluatedequilibria with minerals using the FREZCHEM model (Marion, 2001; Marion and Farren, 1999) that uses thePitzer approach (Pitzer, 1991, 1995) for modeling high salinities (Ip20molal), which is sufficient to describethe salinites of these soil profiles. We ran this simulation at two temperatures: 6.2 1C (December) and 29.2 1C(July). At 2.0m and T ¼ 6.2 1C, the only mineral that should be precipitating is calcite (CaCO3). As salts arefurther concentrated at depth, gypsum (CaSO4 � 2H2O), then epsomite (MgSO4 � 7H2O) should precipitate.Deeper still, nitratine (NaNO3), halite (NaCl), and niter (KNO3) are predicted to precipitate. At the peak ofsalt concentration (2.4–2.5m), kieserite (MgSO4 �H2O), anhydrite (CaSO4), bischofite (MgCl2 � 6H2O), andcarnallite (KMgCl3 � 6H2O) are predicted to precipitate. At 2.0m and T ¼ 29.2 1C, only calcite should be

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Fig. 11. The nitrate concentration in saturation extracts from four soils beneath plant canopies (can) and four soils from intershrub areas

(inter), and the simulation of nitrate accumulation in the surface meter of soil after 10,000 years.

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–10331024

precipitating. Deeper in the profile, first gypsum, then bloedite (Na2SO4 �MgSO4 � 4H2O) and anhydrite arepredicted to precipitate. Deeper still, hexahydrite (MgSO4 � 6H2O), halite, and kieserite are predicted toprecipitate. At the peak of salt concentration, carnallite, and bischofite are predicted to precipitate. Buck et al.(2006) found hexahydrite, bloedite, mirabilite (Na2SO4 � 10H2O), gypsum, thenardite (Na2SO4), and haliteforming in the Las Vegas Wash in Nevada. These systems include salt crusts and subsurface salt formations,which indicates a much more saline environment than our NTS site. Also, the groundwater table at the LasVegas Wash is only 1.5m below the surface. Nevertheless, similar, but not identical, salts are forming, or arepredicted to form, in the NTS and Las Vegas Wash Mojave Desert soils. Hamdi-Aissa et al. (2004) foundcalcite, gypsum, halite, and basanite (CaSO4 � 0.5H2O) forming in salt crusts in a hyperarid (4.1 cm ofrain/year) desert playa in North Africa. The FREZCHEM model does not include basanite. Had it includedthis mineral, there is a good chance that basanite rather then anhydrite would have precipitated in oursimulation. While the latter desert environment is far removed from those of the Mojave Desert, the mineralprecipitation sequence of calcite-gypsum-halite is identical.

There are also problems with the hydrologic model at high salinities. Diffusion of ions along concentrationgradients is likely to be important in the movement of salts at high salinities. Fortunately, the main emphasisof this study was on the surface 1.0m of the soil profile where soluble salts were mainly leached to deeperdepths. Clearly if the main focus of this study was on the layers of soluble salt accumulation, then moresophisticated hydrologic and chemical algorithms would be needed. But the bottom line is that despite theseuncertainties in modeling the hydrologic flow and chemical equilibria for high salinites, our calculated depthsof deposition (2.2–2.5m) are in excellent agreement with the literature (Hartsough et al., 2001; Walvoordet al., 2003).

According to model calculations, 19.2%, 2.5%, 70.8%, and 7.5% of the days (events) when leaching wasbeyond 1.0m occurred during the winter (January–March), spring (April–June), summer (July–August), andfall (September–December), respectively. Although winter rains are more frequent, summer rains are moreintense (Fig. 3), which led to more frequent deep leaching in the summer. In our 10,000-year simulation, therewere only 1715 days of leaching beyond 1.0m, which is equivalent to 1 day every 6 years. According to modelcalculations, 3.4% of the yearly rainfalls exceeded 26.3 cm [mean(15.9 cm)+2 SD (5.2 cm)]; 0.03% (3 years)exceeded 41 cm. So while these extreme rainfall events and years are infrequent, they almost certainly largelycontrol deep leaching of salts according to our simulations. Similar results for deep leaching of carbonaterelated to infrequent large-storm events was suggested by McDonald et al. (1996). In earlier work, Marion andSchlesinger (1994) demonstrated that a stochastic rainfall model led to much deeper leaching of saltscompared with a deterministic model based on a fixed yearly precipitation pattern. A stochastic rainfallmodel that allows extreme events and years is a necessary and powerful tool for simulating salt movementthrough soils.

We asked the question, how long would it take to supply the salts measured in the saturation extractexperiments (Figs. 6–11) based on our assumed inputs of salts? The calculated times are Cl� (32 years)oMg2+

(45 years)oNO�3 (55 years)oNa+ (78 years)oCa2+ (86 years)oSO2�4 (121 years)oK+ (513 years). The

value of Ca2+ needs to be qualified since the 86 years is based only on inputs of soluble Ca2+. If we includedthe inherited calcareous alluvium of these soils, then it would have taken 169,000 years to accumulate the soilCa2+associated with these alluvial deposits. The bulk of the CaCO3 in these soils was inherited with the parentmaterial and was not formed in situ.

Besides Ca2+, another cation that stands out is K+. Our model predicted that K+ should not beaccumulating in surface horizons, in contrast to our measurements (Fig. 8). The most probable explanationfor this discrepancy is that K+ is an essential macronutrient for plant growth. Plant–soil recyling may keepK+ near the soil surface where plant roots were concentrated (Fig. 1). Note in Fig. 8 that K+ concentrationsin the surface horizons were highest beneath plant canopies compared with intershrub zones providing furtherevidence that plants probably played an important part in the K+ distribution in the soil. The CALGYPmodel as currently structured (see previous discussion) does not include plants as a sink for salts. For plantmacronutrients such as K+ (Fig. 8) and Mg2+ (Fig. 9), this may need changing. However, NO3-N(a macronutrient) (Fig. 11) does not differ significantly from Na+ (Fig. 7) or Cl� (Fig. 10) that are onlyrequired in trace concentrations by plants (Raven et al., 1986). Before discussing the consequences of theseresults, we first briefly discuss the validation and limitations of our simulation model.

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3.2. Model validation and limitations

In the previous sections, we provided examples that help validate the model used to estimate salt movementthrough these NTS soils. Here is a summary of these points.

The stochastic precipitation model was able to simulate the short-term (25 years) average precipitation forthe NTS site. This stochastic model (Fig. 3) allows for random variation in both rain dates and rainfallquantities. As a consequence, this stochastic model leads to more realistic rainfall estimates than are generatedby deterministic precipitation models with fixed annual rain dates and rainfall quantities that are used overand over for multiple years (Marion and Schlesinger, 1994). With respect to salt flux, 2 years with 16 cm ofrainfall are not the same as two years with 8 and 24 cm of rainfall. The stochastic model allows for extremeevents and years, which are critical for the long-term simulation of salt movement through soils, as well as theavailability of water for plant growth (Mearns et al., 1997), which may be especially important for these water-limited desert ecosystems (Smith et al., 1997; Turner and Randall, 1989).

The equation used to model soil solution pH (Eq. (6)) (pH ¼ 7.6–8.1) was in excellent agreement withexperimental pH measurements of saturation extracts (Marion et al., in press) (pH ¼ 7.6–8.3). Accuratelymodeling soil pH was especially critical for modeling CaCO3 systems.

Our model predicted that these soils should be saturated with respect to calcite (Fig. 4), which agreed withexperimental measurements (Marion et al., in press). The depth of deposition of CaCO3 horizons (15–35 cm)using the ‘‘standard’’ WHC (DWS, Fig. 4) is in reasonable agreement with previously published regionalequations [Arkley, 1963; Marion et al., 1985 (E25 cm)]. Also our model predicted that these soils should beundersaturated with respect to gypsum in the surface 1.0m, which agreed with experimental measurements(Marion et al., in press). And finally, our model predicted that Na+, K+, Mg2+, Cl�, NO�3 ; and SO2�

4

should mostly leach beyond 2.0m, which was in excellent agreement with field and experimentalmeasurements (Hartsough et al., 2001; Marion et al., in press; Walvoord et al., 2003).

Our patterns of soil profile PCO2(Fig. 2) were in reasonable agreement with previous southwestern desert

studies (Parada et al., 1983; Terhune and Harden, 1991). Variations in absolute PCO2values among these

studies probably reflect vegetative/climatic differences (see previous discussion).While the stochastic precipitation model was listed above under ‘‘validation,’’ it also was a limitation,

especially for long-term simulations (41000 years). The fundamental problem was that this model was basedon an analysis of 25 years of rainfall data from the Desert Rock site. While this model may be reasonably validfor short-term simulations (a few hundred years), it was uncertain for long-term simulations (41000 years).The major problem was simulating extreme events and years that are critical for salt movement throughsoils. The maximum rainfall allowed by the model was the maximum rainfall that occurred in the 25-yearrecord (see the terminal points in Fig. 3 or Table A.2 in Appendix A). The terminal point for daily winterrainfall occurred at 5.54 cm, and the corresponding terminal point for daily summer rainfall occurred at8.94 cm (Fig. 3), which is the primary reason why our model predicted that most of the leaching beyond1.0m occurred in the summer. Also, the annual variability in rainfall [SD ¼ 5.24 cm/year (model) versus7.00 cm/year (weather record)] means that the model as presently structured exhibited less variability than isinherent in the weather record data. Again, this is a limitation because it will reduce the number of extremeyears, which are critical for accurate model simulations.

Another problematic limitation of the CALGYP model is how to cope with highly variable soil WHC,especially if one wants to develop a regional model. What constitutes a realistic regional WHC for sites thatcan vary from gravelly (Fig. 1) to sandy to clayey? Fig. 4 clearly shows that normal variations in WHC canlead to significant variations in CaCO3 distribution and, as a result, soil physical and chemical heterogeneity.

The three-step evapotranspiration model (see previous discussion) was explicitly developed as a ‘‘regional’’model for southwestern deserts (Marion et al., 1985). But the third step was calibrated based on field datafrom a L. tridentata site at the Jornada Desert Long-term Ecological Research site near Las Cruces, NewMexico. The validity of this calibration for other sites has never been tested.

The simple algorithms used for hydrology and chemical processes are clearly limited by the high salinitiesthat occurred at depths 41.4m in this study. If characterization of the soluble salt accumulations is a majorconsideration, then more sophisticated algorithms would be necessary for hydrology and chemistry. SeeSection 3.1.3 for a fuller discussion of this issue.

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4. Discussion

This new version of the CALGYP model has several site-specific parameterizations including measured soilCO2 profiles (Fig. 2), soil WHC (Fig. 4), a stochastic rainfall model (Fig. 3), and system salt inputs. This newversion also includes highly soluble ions such as Na+, K+, Mg2+, Cl�, and NO�3 . Estimates of systemequilibria with respect to salt movement were in reasonable agreement with respect to field and experimentaldata. See the previous discussion under ‘‘validation.’’ So despite the above ‘‘limitations,’’ these results lendsupport to our ability to speculate about the consequences of climate, soils, system inputs, and land-usechange on salt flux though this Mojave Desert soil.

The fact that 81% of the Ca2+ that enters the soil surface is retained within the surface meter of soil asCaCO3 argues in favor of CaCO3 acting as a sink for global carbon. Only a process, such as soil acidification,causing CaCO3 dissolution could cause carbonates to become a source of global carbon. Recently,Serna-Perez et al. (2006) concluded that exhumed petrocalcic horizons are not emitting significantly more CO2

than adjacent soils. Therefore, soil CaCO3 can be considered a recalcitrant reservoir for carbon sequestration,which is consistent with our modeling simulations. Based on our model simulations, C accumulation asCaCO3 is equivalent to an annual accumulation rate of 0.20 g/m2/year. This number is much lower thanmeasured annual C uptake through desert biotic processes of 100 g/m2/year (Jasoni et al., 2005) indicating thatcarbonates do not significantly contribute to net ecosystem C exchange. But, the much higher recalcitranceof inorganic soil CaCO3 compared with organic C is why desert soil CaCO3 contains more C than landvegetation (Schlesinger, 1997).

Plant uptake of nutrients is a process not presently included in the CALGYP model. Including plant uptakemight help explain existing soil patterns for essential nutrients such as K (Fig. 8) and Mg (Fig. 9) that showincreased concentrations in the surface beneath plant canopies. However, most likely plant uptake is smallrelative to leaching fluxes especially since less than 25% of the ground is covered with vegetation. According toPeterjohn and Schlesinger (1990), approximately 1.2% of the atmospheric input of N (2.99 kgN/m2) over10,000 years is stored in desert vegetation (0.036 kgN/m2) in the desert southwest.

But how do we explain NO�3 (Fig. 11) that exhibits patterns more similar to nonessential or trace nutrientssuch as Na+ (Fig. 7) and Cl� (Fig. 10). Peterjohn and Schlesinger (1990) have argued that 77% of atmosphericN inputs are lost from southwestern desert ecosystems to the atmosphere. However, this conclusion was basedon the assumption that vertical leaching of N was negligible below 1.0m. The latter assumption is strongly atvariance with our model estimates of 99.96% of atmospheric inputs of NO�3 leaching pass 1.0m and 94.4%leaching pass 2.0m, which agrees with field patterns of NO�3 accumulation in southwestern deserts (Hartsoughet al., 2001; Stokstad, 2003; Wallace et al., 1978; Walvoord et al., 2003). Water is the dominant factor limitingplant growth in these desert ecosystems (Smith et al., 1997; Turner and Randall, 1989). The NO�3 patterns maysimply reflect the modest N needs of these low productivity desert ecosystems. For example, our atmosphericinputs of NO�3 would take 853 years to supply the amount of N stored in southwestern desert vegetation[0.036 kgN/m2, Peterjohn and Schlesinger, 1990)]. Given that upper zones of NO�3 accumulations in thesedesert soils are within the rooting zone of plants, N may simply not be an important limiting factor for plantgrowth in these desert ecosystems. This is further evidenced by the fact that N additions at the MGCF causedvirtually no vegetative response (Barker et al., 2006).

Alternatively, the dissimilar patterns of essential macronutrient accumulations between cations (Figs. 8and 9) and anions (Figs. 6 and 11) could be due to the greater mobility of anions compared to cations, whichare somewhat limited in their mobility by exchange reactions with soil solid phases such as clay minerals.Furthermore, once NO�3 and SO2�

4 have leached to deeper soil depths (41m), these nutrients may berelatively unavailable for plant uptake due to low root mass at depth (Fig. 1) and a general lack of water atdepth that is necessary for plant uptake.

Land-use changes such as agricultural or housing developments could bring these zones of NO�3accumulations into prominence because of the increased and deeper flux of soil water associated with humandevelopments. Scanlon et al. (2005) found that recent replacement of rangeland with irrigated ecosystems wasdocumented through downward displacement of Cl� and NO�3 fronts; these thick unsaturated zones contain areservoir of salt that are readily mobilized under increased recharge related to land-use changes, potentiallydegrading groundwater quality. There is abundant evidence for the prevalence of these southwestern desert

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NO�3 accumulations (Hartsough et al., 2001; Marion et al., in press; Stokstad, 2003; Wallace et al., 1978;Walvoord et al., 2003). More than 15% of groundwater samples from 4 of 33 major aquifers commonly usedfor drinking water have NO3-N concentrations 410mgN/l, which is the maximum contamination level(Nolan and Stoner, 2000). The latter concentration is equivalent to 0.002m NO�3 (Fig. 11). Leaching of thesemassive accumulations of NO�3 into groundwater or ponds associated with housing developments, golfcourses, or agriculture could have a significant impact on water contamination, especially where land-usechanges increase the leaching of soil NO�3 , such as likely would occur with new agricultural or housingdevelopments.

Soil pH, soil PCO2, saturation with respect to calcite and gypsum, depth of calcite deposition, and deeper

leaching of soluble salts were in reasonable agreement with previous field and experimental studies. This workdemonstrated that site-specific biogeochemical models can provide reasonable approximations of water andsalt fluxes through desert soils, which may be especially important for predicting the consequences of futurehuman activity in desert ecosystems.

5. Conclusions

The main conclusions of this work are:

(1) To model long-term water and salt flux through soils critically requires:(a) a stochastic rainfall model that can simulate extreme events and years, and(b) accurate estimates of soil WHC, especially in dealing with insoluble minerals such as CaCO3.

(2) At the NTS, virtually all inputs of Na+, K+, Mg2+, Cl�, NO�3 ; and SO2�4 ions leached to soil depths

between 1 and 3m, despite only 1.64% of the rainfall leached beyond 1.0m.(3) Between 2.0 and 3.0m, a wide range of Cl�, NO�3 ; and SO2�

4 salts were predicted to precipitate, inreasonable agreement with other desert studies (Buck et al., 2006; Hamdi-Aissa et al., 2004).

(4) In contrast, 81% of the input Ca2+ was retained within the surface 1.0m, which argues for insolubleCaCO3 serving as a recalcitrant sink for global carbon.

(5) Anomalous NO�3 accumulation patterns in Mojave Desert soils (Hartsough et al., 2001; Stokstad, 2003;Wallace et al., 1978; Walvoord et al., 2003) were quantitatively modeled by CALGYP.

Acknowledgments

This work was supported by an NSF Grant (DEB0318646) on ‘‘Biotic and Abiotic Controls on SoilInorganic Carbon Dynamics in Arid Ecosystems: A Three Component Study to Evaluate the Effects ofGlobal Change in the Mojave Desert.’’ We thank Lisa Wable for help in preparing this manuscript and ToddCaldwell for help with the water physics. We also thank several anonymous reviewers and Associate Editor,Dr. Esteban Jobbagy for helpful suggestions that improved the paper. A FORTRAN version of the newCALGYP model and a model description (Marion, 1994) are available from G.M.M.

Appendix A

The chemical equilibrium equations used in this model included the following:

KH ¼ðCO2Þ

PCO2

, (A.1)

logðKHÞ ¼ 108:3865þ 0:01985076T � 6919:53=T � 40:45154 logðTÞ þ 669365=T2,

K1 ¼ðHþÞðHCO�3 Þ

ðH2OÞðCO2Þ, (A.2)

logðK1Þ ¼ �356:3094� 0:06091964T þ 21834:37=T þ 126:8339 logðTÞ � 1; 684; 915=T2

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K2 ¼ðHþÞðCO2�

3 Þ

ðHCO�3 Þ, (A.3)

logðK2Þ ¼ �107:8871� 0:03252849T þ 5151:79=T þ 38:92561 logðTÞ � 563713:9=T2,

K spðcalciteÞ ¼ ðCa2þÞðCO2�

3 Þ, (A.4)

log½K spðcalciteÞ� ¼ �171:9065� 0:077993T þ 2839:319=T þ 71:595 logðTÞ,

K spðgypsumÞ ¼ ðCa2þÞðSO2�

4 ÞðH2OÞ2, (A.5)

log½K spðgypsumÞ� ¼ 68:2401� 3221:51=T � 25:0627 logðTÞ,

K ipðCaCO03Þ¼ðCa2þÞðCO2�

3 Þ

ðCaCO03Þ

, (A.6)

log½K ipðCaCO3Þ� ¼ 1228:732þ 0:299444T � 35512:75=T � 485:818 logðTÞ,

K ipðMgCO03Þ¼ðMg2þÞðCO2�

3 Þ

ðMgCO03Þ

, (A.7)

log½K ipðMgCO3Þ� ¼ �0:9910� 0:00667T ,

K ipðCaSO04Þ¼ðCa2þÞðSO2�

4 Þ

ðCaSO04Þ

, (A.8)

log½K ipðCaSO4Þ� ¼ �3:5094þ 360:597=T ,

K ipðMgSO04Þ¼ðMg2þÞðSO2�

4 Þ

ðMgSO04Þ

, (A.9)

log½K ipðMgSO4Þ� ¼ �5:7051þ 994:374=T ,

where T is absolute temperature (K), and parentheses refer to activities. All numerical values for theseequations were taken from Nordstrom et al. (1990). Temperatures used to control model chemical equilibriawere based on mean monthly air temperatures from the Desert Rock site that we used to develop ourstochastic rainfall model (see Section 2.2.3) Table A.1 summarizes field PCO2

concentrations; Table A.2summarizes stochastic rainfall model probabilities.

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Table A.1

The average (n ¼ 8) soil CO2 partial pressures (ppm) at 10, 40, and 90 cm soil depths in control plots at the Nevada Test Site

Date Soil depth (cm)

10 40 90

5/19/2004 800 1229 1537

6/15/2004 638 781 1083

6/20/2004 615 849 1189

7/17/2004 711 1151 1528

7/20/2004 778 1207 1595

8/7/2004 769 1213 1695

8/16/2004 1134 1492 1731

9/29/2004 681 701 987

11/29/2004 632 760 823

2/17/2005 1019 1372 1322

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–1033 1029

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Table A.1 (continued )

Date Soil depth (cm)

10 40 90

3/28/2005 1005 1459 2123

4/28/2005 864 1528 2219

6/1/2005 945 1599 2759

6/27/2005 760 1190 2222

8/17/2005 894 1284 2173

9/28/2005 791 910 1298

11/29/2005 619 658 799

1/31/2006 614 647 708

3/9/2006 821 849 949

4/5/2006 906 1191 1125

5/10/2006 898 1301 1753

Table A.2

Stochastic model cumulative probabilities for rainfall amount and interarrival time by seasons at the Desert Rock Site

Season Rainfall Interarrival time

Amount (cm) Cumulative probability Days Cumulative probability

Winter 0.00 0.0000 0 0.0000

0.25 0.4944 1 0.4094

0.50 0.6955 2 0.5205

0.75 0.8018 3 0.6024

1.0 0.8736 5 0.6784

1.5 0.9569 10 0.7954

2.0 0.9856 15 0.8860

3.0 0.9971 20 0.9328

5.54 1.0000 30 0.9620

40 0.9854

54 1.0000

Spring 0.00 0.0000 0 0.0000

0.25 0.7116 1 0.2374

0.50 0.7949 3 0.4061

0.75 0.8782 5 0.4686

1.0 0.9295 10 0.6249

1.5 0.9551 20 0.7249

2.0 0.9872 30 0.8437

3.0 0.9936 40 0.9062

3.89 1.0000 50 0.9375

75 0.9875

115 1.0000

Summer 0.00 0.0000 0 0.0000

0.25 0.5478 1 0.4235

0.50 0.7396 3 0.5624

0.75 0.8012 5 0.6180

1.0 0.8492 10 0.6944

1.5 0.8972 20 0.8125

2.0 0.9041 30 0.8681

3.0 0.9657 40 0.9445

4.0 0.9794 50 0.9723

5.0 0.9863 69 0.9931

8.94 1.0000 113 1.0000

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–10331030

References

Arkley, R.J., 1963. Calculation of carbonate and water movement in soil from climatic data. Soil Science 96, 239–248.

Barker, D.H., Vanier, C., Naumburg, E., Charlet, T.N., Nielsen, K.M., Newingham, B.A., Smith, S.D., 2006. Enhanced monsoon

precipitation and nitrogen deposition affect leaf traits and photosynthesis differently in spring and summer in the desert shrub Larrea

tridentata. New Phytologist 169, 799–808.

Bowers, M.A., 1987. Precipitation and the relative abundances of desert winter annuals, a 6-year study in the northern Mojave Desert.

Journal of Arid Environments 12, 141–149.

Bresler, E., McNeal, B.L., Carter, D.L., 1982. Saline and Sodic Soils—Principles-Dynamics-Modeling. Springer, Berlin.

Buck, B.J., Wolff, K., Merkler, D.J., McMillan, N.J., 2006. Salt mineralogy of Las Vegas Wash, Nevada: Morphology and subsurface

evaporation. Soil Science Society of America Journal 70, 1639–1651.

Capo, R.C., Chadwick, O.A., 1999. Sources of strontium and calcium in desert soil and calcrete. Earth and Planetary Science Letters 170,

61–72.

Davies, C.W., 1962. Ion Association. Butterworths, London.

Dremanis, A., 1962. Quantitative gasometric determinations of calcite and dolomite by using Chittick apparatus. Journal of Sedimentary

Petrology 32, 520–529.

Dudley, L.M., Wagenet, R.J., Jurinak, J.J., 1981. Description of soil chemistry during transient solute transport. Water Resources

Research 17, 1498–1504.

Frizzell Jr., W.A., Shulters. J., 1990. Geological map of the Nevada Test Site, southern Nevada. Map I-2046, Misc. Investigations Series,

US Geological Survey, Denver, CO.

Gile, L.H., Hawley, J.W., Grossman, R.B., 1981. Soils and Geomorphology in the Basin and Range Area of Southern New Mexico—

Guidebook to the Desert Project. Memoir 39. New Mexico Bureau of Mines and Mineral Resources, Socorro, NM.

Hamdi-Aissa, B., Valles, V., Aventurier, A., Ribolzi, O., 2004. Soils and brine geochemistry and mineralogy of hyperarid desert playa,

Ouargla Basin, Algerian Sahara. Arid Land Research and Management 18, 103–126.

Hartsough, P., Tyler, S.W., Sterling, J., Walvoord, M., 2001. A 14.6 kyr record of nitrogen flux from desert soil profiles as inferred from

vadose zone pore waters. Geophysical Research Letters 28, 2955–2958.

Jasoni, R.L., Smith, S.D., Arnone III, J.A., 2005. Net ecosystem CO2 exchange in Mojave Desert shrublands during the eighth year of

exposure to elevated CO2. Global Change Biology 11, 749–756.

Jobbagy, E.G., Jackson, R.B., 2001. The distribution of soil nutrients with depth: global patterns and the imprint of plants.

Biogeochemistry 53, 51–77.

Lodge Jr., J.P., Pate, J.B., Basbergill, W., Swanson, G.S., Hill, K.C., Lorange, E., Lazrus, A.L., 1968. Chemistry of United

States Precipitation. Final Report on the National Precipitation Sampling Network, National Center of Atmospheric Research,

Boulder, CO.

Marion, G.M., 1994. CALGYP: a simulation model for calcite and gypsum precipitation–dissolution in soils. CRREL Special Report

94-19, Hanover, NH.

Marion, G.M., 2001. Carbonate mineral solubility at low temperatures in the Na–K–Mg–Ca–H–Cl–SO4–OH–HCO3–CO3–CO2–H2O

system. Geochimica et Cosmochimica Acta 65, 1883–1896.

Marion, G.M., Farren, R.E., 1999. Mineral solubilities in the Na–K–Mg–Ca–Cl–SO4–H2O system: a re-evaluation of the sulfate chemistry

in the Spencer–Møller–Weare model. Geochimica et Cosmochimica Acta 63, 1305–1318.

Marion, G.M., Schlesinger, W.H., 1994. Quantitative modeling of soil forming processes in deserts: the CALDEP and CALGYP models.

In: Bryant, R.B., Arnold, R.W. (Eds.), Quantitative Modeling of Soil Forming Processes. SSSA Special Publication 39, Madison, WI,

pp. 129–145.

ARTICLE IN PRESS

Table A.2 (continued )

Season Rainfall Interarrival time

Amount (cm) Cumulative probability Days Cumulative probability

Fall 0.00 0.0000 0 0.0000

0.25 0.5732 1 0.3103

0.50 0.7322 3 0.4409

0.75 0.8243 5 0.4899

1.0 0.8829 10 0.6328

1.5 0.9498 20 0.8001

2.0 0.9749 30 0.8736

3.0 0.9916 40 0.9307

3.76 1.0000 50 0.9674

83 0.9878

102 1.0000

G.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–1033 1031

Marion, G.M., Schlesinger, W.H., Fonteyn, P.J., 1985. CALDEP: a regional model for soil CaCO3 (caliche) deposition in southwestern

deserts. Soil Science 139, 468–481.

Marion, G.M., Verburg, P.S.J., Stevenson, B., Arnone, J.A., in press. Soluble element distributions in a Mojave Desert soil. Soil Science

Society of America Journal.

Mayer, L., McFadden, L.D., Harden, J.W., 1988. Distribution of calcium carbonate in desert soils: a model. Geology 16, 303–306.

McDonald, E.V., Pierson, F.B., Flerchinger, G.N., McFadden, L.D., 1996. Application of a soil–water balance model to evaluate the

influence of Holocene climate change on calcic soils, Mojave Desert, California, USA. Geoderma 74, 167–192.

McDonald, E.V., McFadden, L.D., Wells, S.G., 2003. Regional response of alluvial fans to the Pleistocene–Holocene climatic transition,

Mojave Desert, California. In: Yehouda, E., Wells, S.G., Lancaster, N. (Eds.), Paleoenvironments and Paleohydrology of the Mojave

and Southern Great Basin Deserts, Geological Society of America Special Paper, vol. 368, pp. 189–206.

McFadden, L.D., Tinsley, J.C., 1985. Rate and depth of pedogenic-carbonate accumulation in soils: formulation and testing of a

compartment model. In: Weide, D.G. (Ed.), Soils and Quaternary Geology of the Southwestern United States. Geological Society of

America Special Paper, vol. 203, Geological Society of America, Boulder, CO, pp. 23–41.

McFadden, L.D., Amundson, R.G., Chadwick, O.A., 1991. Numerical modeling, chemical, and isotopic studies of carbonate

accumulation in soils of arid regions. In: Nettleton, W.D. (Ed.), Occurrence, Characteristics, and Genesis of Carbonate, Gypsum, and

Silica Accumulations in Soils. Soil Science Society of America Special Publication 26, Madison, WI, pp. 17–35.

McFadden, L.D., McDonald, E.V., Wells, S.G., Anderson, K., Quade, J., Forman, S.L., 1998. The vesicular layer and carbonate collars of

desert soils and pavements: formation, age and relation to climate change. Geomorphology 24, 101–145.

Mearns, L.O., Rosenzweig, C., Goldberg, R., 1997. Mean and variance change in climate scenarios: methods, agricultural applications,

and measures of uncertainty. Climatic Change 35, 367–396.

Monger, H.C., Gallegos, R.A., 2000. Biotic and abiotic processes and rates of pedogenic carbonate accumulation in the southwestern

United States—relationship to atmospheric CO2 sequestration. In: Lal, R., Kimble, J.M., Eswaran, H., Stewart, B.A. (Eds.), Global

Climate Change and Pedogenic Carbonates. Lewis Publishers, Boca Raton, FL, pp. 273–289.

Nolan, B.T., Stoner, J.D., 2000. Nutrients in groundwaters of the conterminous United States, 1992–1995. Environmental Science and

Technology 34, 1156–1165.

Nordstrom, D.K., Munoz, J.L., 1994. Geochemical Thermodynamics. Blackwell Scientific Publications, Boston.

Nordstrom, D.K, Plummer, L.N., Langmuir, D., Busenberg, E., May, H.M., Jones, B.F., Parkhurst, D.L., 1990. Revised chemical

equilibrium data for major water-mineral reactions and their limitations. In: Melchior, D.D., Bassett, R.L. (Eds.), Chemical Modeling

in Aqueous Systems II. American Chemical Society, Symposium Series, vol. 93, pp. 398–413.

Ostler, W.K., Hansen, D.J., Hall, D.B., 1999. The classification of shrublands on the Nevada test site. In: McArthur, E.D., Ostler, W.K.,

Wambolt, C.L. (Eds.), Proceedings, Shrubland Ecotones, RMRS-P-11. USDA, Ogden, UT, pp. 137–147.

Parada, C.B., Long, A., Davis, S.N., 1983. Stable-isotope composition of soil carbon dioxide in the Tucson Basin, Arizona, USA. Isotope

Geoscience 1, 219–236.

Peterjohn, W.T., Schlesinger, W.H., 1990. Nitrogen loss from deserts in the southwestern United States. Biogeochemistry 10, 67–79.

Pitzer, K.S., 1991. Ion interaction approach: theory and data correlation. In: Pitzer, K.S. (Ed.), Activity Coefficients in Electrolyte

Solubions, second ed. CRC Press, Boca Raton, FL, pp. 75–153.

Pitzer, K.S., 1995. Thermodynamics, third ed. McGraw-Hill, Inc., New York.

Raven, P.H., Evert, R.F., Eichhorn, S.E., 1986. Biology of Plants, fourth ed. Worth Publishers, Inc., New York.

Reheis, M.C., Kihl, R., 1995. Dust deposition in southern Nevada and California, 1984–1989: relations to climate, source area, and source

lithology. Journal of Geophysical Research 100, 8893–8918.

Robbins, C.W., Wagenet, R.J., Jurinak, J.J., 1980. A combined salt transport—chemical equilibrium model for calcareous and gypsiferous

soils. Soil Science Society of America Journal 44, 1191–1194.

Romney, E.M., Hale, V.Q., Wallace, A., Lunt, O.R., Childress, J.D., Kaaz, H., Alexander, G.V., Kinnear, J.E., Ackerman, T.L., 1973.

Some characteristics of soil and perennial vegetation in northern Mojave Desert areas of the Nevada Test Site. US Atomic Energy

Commission Report UCLA #12-916, Office of Information Services, Springfield, VA.

Romney, E.M., Wallace, A., Kaaz, H., Hale, V.Q., 1980. The role of shrubs on redistribution of mineral nutrients in soil in the Mojave

Desert. Great Basin Naturalist Memoirs 4, 124–133.

Scanlon, B.R., Reedy, R.C., Stonestrom, D.A., Prudic, D.E., Dennehy, K.F., 2005. Impact of land use and land cover change on

groundwater recharge and quality in the southwestern US. Global Change Biology 11, 1577–1593.

Schlesinger, W.H., 1982. Carbon storage in the caliche of arid soils: a case study from Arizona. Soil Science 133, 247–255.

Schlesinger, W.H., 1997. Biogeochemistry: An Analysis of Global Change, second ed. Academic Press, San Diego.

Schlesinger, W.H., Fonteyn, P.J., Marion, G.M., 1987. Soil moisture content and plant transpiration in the Chihuahuan Desert of

New Mexico. Journal of Arid Environments 12, 119–126.

Serna-Perez, A., Monger, H.C., Herrick, J.E., Murray, L., 2006. Carbon dioxide emissions from exhumed petrocalcic horizons. Soil

Science Society of America Journal 70, 795–805.

Smith, S.D., Monson, R.K., Anderson, J.E., 1997. Physiological Ecology of North American Desert Plants. Springer, Berlin.

Stokstad, E., 2003. Unsuspected underground nitrates pose a puzzle for desert ecology. Science 302, 969.

Terhune, C.L., Harden, J.W., 1991. Seasonal variations of carbon dioxide concentrations in stony, coarse-textured desert soils of southern

Nevada, USA. Soil Science 151, 417–429.

Thornthwaite, C.W., 1948. An approach towards a rational classification of climate. Geographical Review 38, 55–94.

Titus, J.H., Nowak, R.S., Smith, S.D., 2002. Soil resource heterogeneity in the Mojave Desert. Journal of Arid Environments 52,

269–292.

ARTICLE IN PRESSG.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–10331032

Turner, F.B., Randall, D.C., 1989. Net production by shrubs and winter annuals in southern Nevada. Journal of Arid Environments 17,

23–36.

Wallace, A., Romney, E.M., Hunter, R.B., 1978. Nitrogen cycle in the northern Mohave Desert: implications and predictions. In: West,

N.E., Skujins, J. (Eds.), Nitrogen in Desert Ecosystems. Dowden, Hutchinson & Ross, Inc., Stroudsburg, PA, pp. 207–218.

Walvoord, M.A., Striegl, R.G., Prudic, D.E., Stonestrom, D.A., 2005. CO2 dynamics in the Amargosa Desert: Fluxes and isotopic

speciation in a deep unsaturated zone. Water Resources Research 41, WO2006, doi:10.1029/2004WR003599.

Walvoord, M.A., Phillips, F.M., Stonestrom, D.A., Evans, R.D., Hartsough, P.C., Newman, B.D., Striegl, R.G., 2003. A reservoir of

nitrate beneath desert soils. Science 302, 1021–1024.

Wang, J., Anderson, B.T., Salvucci, G.D., 2006. Stochastic modeling of daily summertime rainfall over the southwestern United States.

Part I: Interannual variability. Journal of Hydrometerorology 7, 739–754.

ARTICLE IN PRESSG.M. Marion et al. / Journal of Arid Environments 72 (2008) 1012–1033 1033