Forest Service Northern Research Station General Technical Report NRS-171

Site-specific Critical Acid Load 

Estimates for Forest Soils in the 

Osborn Creek Watershed, Michigan

Trevor Hobbs, Jason Lynch, and Randy Kolka

United States Department of Agriculture

July 2017
Manuscript received for publication 28 November 2016

Abstract

Anthropogenic acid deposition has the potential to accelerate leaching of soil cations, and in turn, deplete 

nutrients essential to forest vegetation. The critical load concept, employing a simple mass balance (SMB) 

approach, is often used to model this process. In an evaluation under the U.S. Forest Service Watershed 

Condition Framework program, soils in all 6th level watersheds on the Huron-Manistee National Forests (HMNF) 

in Michigan were assigned the lowest score of “3—Impaired Function” due to exceedance of the critical load 

of acidity as determined by national-scale estimates. The impetus for this research was to test the relevance of 

national-scale critical acid load estimates at the 6th level watershed scale by using site-specific field data in the 

SMB model where possible.

The Osborn Creek watershed on the HMNF served as a case study. Field data were collected to estimate soil 

mineral weathering rates, nutrient uptake rates, and forest growth characteristics at five sites containing 

sandy, nutrient-poor soils. Critical acid loads and exceedances were developed under “best” and “worst” case 

scenarios given the uncertainty in the SMB model. Despite the high likelihood of actual exceedance and some 

evidence for soil acidification across the watershed, base saturation remains excessively high (>100 percent) at 

most sites. Other field data suggest that these soils receive significant external inputs of base cations that may 

outweigh what is produced through weathering onsite within the rooting zone. Trees show no visible signs of 

decline.

Overall, the SMB approach may not adequately capture the complexity of nutrient cycling at all of the sample 

sites. The variability of soils, weathering estimates, and nutrient uptake rates between and within sites makes 

extrapolation of these results to other HMNF watersheds difficult to justify. Management programs aimed 

at improving our understanding of base cycling in complex glacial terrain, as well as mitigating the risks 

associated with nutrient depletion from frequent timber harvests and fuels reduction practices, are suggested.

Cover Art

A stylized fisheye photo looking up at the canopy of a sugar maple stand in the Osborn Creek watershed. 

Photo by Trevor Hobbs, Huron-Manistee National Forests.

The use of trade or firm names in this publication is for reader information and does not imply 

endorsement by the U.S. Department of Agriculture of any product or service.

Published by

U.S. FOREST SERVICE

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July 2017

For additional copies:

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Quality Assurance

This publication conforms to the Northern Research Station’s Quality Assurance Implementation Plan which 

requires technical and policy review for all scientific publications produced or funded by the Station. The 

process included a blind technical review by at least two reviewers, who were selected by the Assistant Director 

for Research and unknown to the author. This review policy promotes the Forest Service guiding principles 

of using the best scientific knowledge, striving for quality and excellence, maintaining high ethical and 

professional standards, and being responsible and accountable for what we do. 
Site-specific Critical Acid Load Estimates for Forest 

Soils in the Osborn Creek Watershed, Michigan

The Authors

TREVOR HOBBS is a soil scientist with the Huron-Manistee National Forests in Cadillac, MI. 

JASON LYNCH is an ecologist with the U.S. Environmental Protection Agency, Office of Air and 

Radiation, Office of Atmospheric Programs in Washington, DC. 

RANDY KOLKA is a research soil scientist with the U.S. Forest Service, Northern Research Station 

in Grand Rapids, MN. 

General Technical Report NRS-171
CONTENTS

INTRODUCTION...............................................................................................................................................................1

Watershed Condition Framework...............................................................................................................................1

Research Questions ....................................................................................................................................................2

Objectives...................................................................................................................................................................2

BACKGROUND AND MODELING APPROACH ......................................................................................................................2

Modeling Critical Acid Loads for Forest Soils...............................................................................................................2

Simple Mass Balance Method.....................................................................................................................................3

Input Data for the Simple Mass Balance Equation.....................................................................................................12

STUDY AREA .................................................................................................................................................................13

Geography................................................................................................................................................................13

Geology....................................................................................................................................................................13

Soils and Vegetation.................................................................................................................................................15

Surface Water and Groundwater ..............................................................................................................................15

Climate.....................................................................................................................................................................16

METHODS.....................................................................................................................................................................16

Field Methods...........................................................................................................................................................16

Laboratory Analysis..................................................................................................................................................18

Data Calculation Methods.........................................................................................................................................19

RESULTS AND DISCUSSION ............................................................................................................................................21

Site-specific Tree Uptake..........................................................................................................................................21

Soil Horizon Characteristics ......................................................................................................................................27

Weathering Results..................................................................................................................................................28

Critical Leaching of Acid Neutralizing Capacity.........................................................................................................32

Critical Acid Loads and Exceedances.........................................................................................................................34

MANAGEMENT RECOMMENDATIONS.............................................................................................................................36

Applicability of Results.............................................................................................................................................36

Watershed Condition Framework Rating..................................................................................................................36

Monitoring Soil Nutrients.........................................................................................................................................37

CONCLUSIONS...............................................................................................................................................................39

ACKNOWLEDGMENTS....................................................................................................................................................40

English Conversion Factors Applicable to Text, Tables, and Figures...........................................................................40

LITERATURE CITED ........................................................................................................................................................41
1

INTRODUCTION

Soils play a crucial role in maintaining healthy forest watersheds by providing substrate for 

forest growth and acting as a filter for precipitation entering lakes, streams, and groundwater 

aquifers. Acid deposition, primarily derived from nitrogen and sulfur compounds released 

into the atmosphere via combustion (automobile and industrial exhaust), and deposited via 

precipitation and dry deposition (gases, particles, and aerosols), has the potential to disrupt 

normal soil function. Acid deposition can leach soil cations, and in turn, deplete essential 

nutrients for healthy vegetation growth, affect the chemistry of nearby lakes and streams, and 

create toxic conditions for aquatic biota.

Watershed Condition Framework

The U.S. Forest Service classifies the health of watersheds according to the Watershed 

Condition Framework (WCF). The WCF is a nationwide U.S. Forest Service management 

program intended to establish a consistent, easy-to-compare, and credible process for 

evaluating and improving the health of watersheds on national forests and grasslands 

(Potyondy and Geier 2011). Under the WCF, each 6th level HUC (hydrologic unit code) 

watershed is assigned a score indicating its overall health. The score is based on 12 

environmental attributes, such as water quality, soils, ozone, and riparian vegetation. The WCF 

score is used to prioritize watershed restoration efforts and track the progress of watershed 

improvements.

All watersheds in the Huron-Manistee National Forests (HMNF; located in the northern 

Lower Peninsula of Michigan) received the lowest WCF score of “3—Impaired Function” 

for the soil contamination attribute (Table 1). This attribute was given a score of 3 because 

of supposed soil contamination due to excess acid deposition. This determination was based 

on exceedance of critical loads of acidity that were modeled at the national scale (McNulty 

and Boggs 2010; McNulty et al. 2007, 2013). As applied to soils, the critical load of acidity, 

hereafter called critical acid load (CAL), may be defined as the highest depositional load that 

will not cause chemical changes in soil leading to long-term harmful effects on ecosystem 

structure and function (Nilsson and Grennfelt 1988). To date, national-scale CAL estimates 

represent the only available CAL estimates for forest managers in Michigan. However, most 

CAL estimates modeled at the regional or national scale rely on broad assumptions and 

aggregation of coarse-resolution datasets, which may or may not accurately reflect actual soil 

Table 1.—Excerpt from Condition Rating Rule Set for soil condition indicator in USDA 

Watershed Condition Framework Technical Guide (adapted from Potyondy and Geier 2011: 29)a

Rating Description of soil contamination

Good (1)—

Functioning 

Properly

No substantial areas of soil contamination in the watershed exist. When atmospheric 

deposition is a source of contamination, sulfur and/or nitrogen deposition is more 

than 10 percent below the terrestrial critical load.

Fair (2)—

Functioning 

at Risk

Limited areas of soil contamination may be present, but they do not have a 

substantial effect on overall soil quality. When atmospheric deposition is a source 

of contamination, sulfur and/or nitrogen deposition is 0–10 percent below the 

terrestrial critical load.

Poor (3)—

Impaired 

Function

Extensive areas of soil contamination may be present. When atmospheric deposition 

is a source of contamination, sulfur and/or nitrogen deposition is above the 

terrestrial critical load.

a Other soil attributes considered in the overall soil score include soil productivity and soil erosion (not 

reproduced here).
2

conditions. As such, the impetus for this research was to develop site-specific CAL estimates 

using as many empirical data as possible. A 6th level HUC watershed on the HMNF, the 

Osborn Creek watershed, was used as a case study.

Research Questions

The following questions drive this research:

• What is the critical acid load for forest soils in the Osborn Creek watershed 

according to site-specific data analysis?

• Has the CAL been exceeded? If so, by how much?

• What management strategies can the U.S. Forest Service implement to monitor and 

mitigate forest soil contamination due to excess acid deposition?

Objectives

With these research questions in mind, the following objectives were developed to guide this 

project:

1) Deconstruct the process of deriving CAL estimates by describing key assumptions 

and sources of input data for each model parameter.

2) Develop a range of CAL estimates for forest soils in the Osborn Creek watershed 

using site-specific field data to the extent possible.

3) Compare the resulting site-specific estimates to national-scale modeled estimates for 

this watershed.

4) Determine if the WCF score of “3—Impaired Function” for the soil contamination

attribute is appropriate for the Osborn Creek watershed considering actual soil 

conditions.

5) Discuss the relevance of results in light of model uncertainties and suggest 

management considerations for maintaining soil nutrients.

BACKGROUND AND MODELING APPROACH

The topic of critical loads has a long history and wide literature base. For brevity, this report 

includes a modest amount of background information on the topic, with emphasis placed 

on new literature relevant to the ecosystems and soils found in the study area and literature 

published by the U.S. Forest Service. Most of the additional guidance comes from the UBA 

Critical Load Mapping Manual (UBA [Federal Environmental Agency] 2004).

Modeling Critical Acid Loads for Forest Soils

The critical load concept is a well-established method for determining environmental 

thresholds of anthropogenic pollutants, such as nitrogen deposition, acid rain, lead, and 

mercury, for various ecosystem compartments (e.g., lakes, streams, forests, and soils) (UBA 

2004). Federal land management agencies in the United States use critical loads as a tool to 

identify vulnerable ecosystem compartments and protect them from deleterious effects of 

anthropogenic pollutants (Burns 2011, Fenn et al. 2011, Porter et al. 2005). In the Midwest, 

nitrogen (N) and sulfur (S) compounds released into the atmosphere via combustion, as well 
3

as from agricultural fertilizer production and use and from agricultural waste, are the main 

contributors to anthropogenic soil acidification from deposition (Alewell 2003, Driscoll et 

al. 2001). Soil base cations (calcium, magnesium, potassium, and sodium), mainly derived via 

soil mineral weathering along with base cation deposition, are the primary buffering agent 

protecting soils against excessive acidic atmospheric deposition (Sverdrup and Warfvinge 

1993). When long-term acid deposition exceeds soil buffering capacity, the critical acid load 

(CAL) is exceeded. Critical load exceedance indicates an increased risk of damage to the 

ecosystem. Some consequences of increased soil acidity are leaching of essential soil nutrients 

(Schaberg et al. 2010), and aluminum toxicity (Cronan and Grigal 1995), both of which 

reduce forest growth (Ouimet et al. 2001) and acidify surface waters (McDonnell et al. 2012, 

Siemion et al. 2014).

Simple Mass Balance Method

Soil CALs are difficult to determine because their components are often hard to measure 

directly, causing values to vary depending on the approach and availability of environmental 

data (Pardo 2010, Rodríguez-Lado et al. 2007). The most common approach is the simple 

mass balance (SMB) model (also referred to as the “steady state method”) (Sverdrup and De 

Vries 1993). The SMB model considers inputs from atmospheric deposition and base cation 

weathering and outputs from leaching loss and biomass removal. If outputs exceed inputs, 

there will be a net loss of base cations from the soil, leading to increased acidification. This 

approach is often used for setting CALs on the national to regional level because it can be a 

first step to understanding impacts of soil acidification from deposition and because data are 

often not available at the site-specific scale (Nasr et al. 2010, Ouimet et al. 2006, Whitfield 

et al. 2010). By substituting as many site-specific data in the SMB equation as possible, the 

SMB model can potentially be made more accurate and reliable (Bosman et al. 2001, FreerSmith and Kennedy 2003, Whitfield et al. 2011).

Simple Mass Balance Model 

The simple mass balance equation is:

CAL(S+N) = BCdep – Cldep + BCw – BCu + Ni + Nu + Nde – ANCle,crit, (1)

Where

CAL(S+N) = the forest soil critical acid load for sulfur (S) and nitrogen (N),

BCdep = deposition of base cations: calcium (Ca2+), magnesium (Mg2+), potassium (K+), and 

sodium (Na+),

Cldep = chloride deposition, 

BCw = base cation weathering,

BCu = uptake of base cations (Ca2+, Mg2+, K+) by trees, 

Ni = nitrogen immobilization,

Nu = uptake of nitrogen by trees,

Nde = denitrification, and 

ANCle,crit = the acceptable forest soil acid-neutralizing capacity leaching rate. 
4

The critical acid load exceedance value is:

Ex(S+N)dep = (S+N)dep – CAL(S+N), (2)

Where

(S+N)dep = total sulfur (S) and nitrogen (N) deposition.

In the SMB equation (eqn. 1) and equation 2, all input parameters are expressed in 

equivalents per hectare per year (eq/ha/yr). (Please refer to the conversion factors on p. 40 to 

convert units from metric to English.)

N-Nutrient. In this study, a threshold for N-nutrient will not be considered when determining 

the total CAL. Easily applicable indicators that demonstrate ecosystem shifts caused by excess 

N-nutrient have not been developed for Michigan forests, nor has a consistent set of data on 

N dynamics emerged in the local forest ecology literature that could be reasonably adopted 

for this study. Furthermore, McNulty et al. (2007, 2013) and McNulty and Boggs (2010) did 

not assess the N-nutrient critical load in their national-scale estimates. Although data on N 

deposition will be obtained to represent certain parameters of the SMB model, the focus of 

this study is on the capacity for forest soils in the study area to neutralize anthropogenic acidic 

inputs, rather than on defining limits that prevent impacts of eutrophication.

Explanation and Comparison of Data Inputs Used in the Simple Mass Balance 

Equation

The following sections provide a synopsis of each input parameter used in the SMB equation. 

The purpose of this review is to deconstruct the model inputs, and identify sources of 

input data, assumptions, and inherent limitations of the SMB model. At the end of each 

section, the methods and data sources used to derive an input value for that parameter are 

described. It should be emphasized that the SMB model is a simplification of reality. A 

complete understanding of the complexity of acidification status in forest soils is almost never 

practical. Some input data will inevitably need to be modeled or assumed from the literature. 

Data obtained in this study potentially could lead to a different (and more accurate) CAL 

determination than national-scale estimates for soils in the Osborn Creek watershed.

Atmospheric Deposition of Sulfur+Nitrogen, Base Cations, and Chlorine. Atmospheric deposition 

of ions to soils occurs via wet deposition (i.e., rain and snow), dry deposition (i.e., particulates 

and gases), and cloud/fog inputs. Acidifying compounds such as nitrate (NO3

-

), nitric acid 

(HNO3), ammonium (NH4

+

), sulfur dioxide (SO2), and sulfate (SO4

2-), as well as the base 

cations calcium (Ca2+), sodium (Na+), magnesium (Mg2+), and potassium (K+), and chlorine 

(Cl), are some of the typical analytes measured in existing monitoring networks. Dry 

deposition data are more difficult to measure and model than wet deposition data because of 

the variety of local factors that influence deposition (i.e., elevation, slope, aspect, and canopy 

structure). Cloud and fog deposition exhibits significant spatial and temporal variation, and 

is generally excluded as a source of atmospheric deposition in SMB models for low-elevation 

regions. Acid deposition data are collected regularly at monitoring stations across the United 

States. Typically, data describing both wet and dry (particulate) forms are available. The 

two major sources of atmospheric deposition data in the United States are the National 

Atmospheric Deposition Program (NADP) and the Clean Air Status and Trends Network 

(CASTNET).
5

This study uses a new method for estimating total (S+N) deposition developed by the 

Total Deposition (TDEP) Science Committee and NADP that combines measured data 

from various monitoring stations with modeled data from the Community Multi-scale Air 

Quality (CMAQ) model. Monitoring data for the CMAQ model come from several sources: 

CASTNET, Ammonia Monitoring Network (AMoN), Southeastern Aerosol Research and 

Characterization (SEARCH), and National Trends Network (NTN). Data from each source 

span different years and undergo different transformations before being used as input to the 

CMAQ model. TDEP data combine monitoring data and CMAQ output by using inverse 

distance weighting interpolation methods, giving higher weight to measured data. The output 

contains annual total deposition data (S+N, wet and dry) at 4-km2 resolution. Further details 

of the methods used to derive deposition estimates can be found in Schwede and Lear (2014). 

Base Cation Weathering. Data accurately describing mineral weathering are necessary if 

the derived CALs are to be confidently relied upon for answering management questions 

at the local scale (McDonnell et al. 2010). Basic soil characteristics that influence mineral 

weathering rates include mineralogy, surface area (related to overall soil texture and the 

inherent surface roughness of different minerals), moisture content, temperature, carbon 

dioxide concentration, strength of organic acids, and pH (Sverdrup and Warfvinge 1993). 

Several methods for calculating mineral weathering have been developed. In general, simpler 

methods rely on more assumptions than complex methods, and as such, often result in greater 

uncertainty (Hodson and Langan 1999, Kolka et al. 1996). Common examples used in CAL 

studies include the soil texture approximation (STA) method (UBA 2004) and the PROFILE 

model (Warfvinge and Sverdrup 1992).

The STA method has the fewest data requirements, and therefore is the least expensive to 

implement. Weathering rates are assigned to soils based on a combination of soil texture 

and mineralogical “classes” (UBA 2004). In contrast, PROFILE is a kinetic soil chemical 

weathering model that combines transition-state theory, experimentally derived dissolution 

reaction data, and an independent database of soil geophysical data to develop base cation 

weathering rates under current deposition (Warfvinge and Sverdrup 1992). PROFILE 

requires more-detailed soils input data than the STA method. Although the underlying 

assumptions and accuracy of the PROFILE model have been questioned (Hodson et al. 

1997), over time it has become a standard to which other methods are compared. Some 

studies have evaluated other approaches to estimating mineral weathering and their impacts 

on critical load estimates (Whitfield et al. 2006, 2011). More recent studies have taken 

hybrid approaches by combining the STA and PROFILE methods, developing regression 

models that explain their relationship, and extrapolating those regressions to unmeasured soils 

(Whitfield and Watmough 2012).

Additional weathering rate calculation methods, such as the zirconium depletion method 

(Kirkwood and Nesbitt 1991) and the pedological mass balance (PMB) method (Elgi and 

Fitze 2000), use the relative concentrations of immobile (nonweatherable) elements and labile 

elements (base cations) in the rooting zone (E- through B-horizons) versus that of the soil 

parent materials (C-horizon). The PMB method, as revised by Elgi and Fitze (2000), adds 

another level of accuracy by incorporating net changes (positive and negative) in horizon 

volume due to weathering. Both of these methods rely on the underlying assumptions that 1) 

the original soil parent materials were mineralogically homogenous throughout the profile, 

and 2) the C-horizon represents unweathered soil (i.e., conditions at time zero). Researchers 

have not yet reached a consensus on the best method to determine mineral weathering. 
6

Each method presents advantages and disadvantages depending on how well underlying 

assumptions fit with actual soil conditions in the region under study.

The national-scale estimates to which site-specific CALs derived in this study are compared, 

use the STA method. The derivation of base cation weathering rate from the STA method 

involves first categorizing the soil into a basic texture class (Table 2) and mineral weathering 

rate class (Table 3) (UBA 2004). After soils are categorized, the weathering rate is derived as 

follows:

BCw = z × 500 × (WRc – 0.5) × exp � A

281 – A273 + T �, (3)

Where

z = soil depth (m),

WRc = weathering rate class (Table 3),

T = average annual soil temperature (0C), and 

A = 3600 (K).

Equation 3 determines a weathering rate for Ca+Mg+K+Na in eq/ha/yr. 

This study used the PMB method, as revised by Elgi and Fitze (2000). The PMB method 

has been successfully applied in long-term weathering studies of glacially derived silicate 

parent materials (Lichter 1998, Olsson and Melkerud 2000, Schroth et al. 2007). Input data 

for the PMB method include bulk density, elemental geochemistry, and horizon thickness 

throughout the soil profile. Long-term weathering rates are calculated as follows:

mmmmjjjj,ffffffffffffffff(zzzzwwww) = �CCCCjjjj,ppppρρρρpppp

nnnn

aaaa=1

� 1

εεεεiiii,wwww + 1

� ττττjjjj,wwww ∆zzzzwwww (4)

Table 2.—Soil texture classes as a function of clay and sand content (UBA 2004)

Texture class Name Definition

1 Coarse Clay <18% and sand ≥65%

2 Medium Clay <35% and sand >15%, but clay ≥18% 

if sand ≥65%

3 Medium fine Clay <35% and sand <15%

4 Fine 35% ≤ clay < 60%

5 Very fine Clay ≥60%

Table 3.—Weathering rate classes, ranging from low to high (1 to 6), as a function of parent 

material and texture classes (UBA 2004)

Texture class

Parent material 1 2 3 4 5

Acidic 1 3 3 6 6

Intermediate 2 4 4 6 6

Basic 2 5 5 6 6

Organic Class 6 for Oe and class 1 for other organic soils
7

εεεεiiii,wwww = �

ρρρρppppCCCCiiii,pppp

ρρρρwwwwCCCCiiii,wwww

� − 1 (5)

ττττjjjj,wwww = �

CCCCjjjj,wwwwCCCCiiii,pppp

CCCCjjjj,ppppCCCCiiii,wwww

� − 1,

 (6)

Where

mmmmjjjj,ffffffffffffffff = change in mass of element j (in this case, each of the major base cations) (kg/m2

),

zzzzwwww = weathered horizon, 

CCCCjjjj,pppp = concentration of labile element j in the parent material (kg/tonne), 

Ci,p = concentration of immobile element i in the parent material (kg/tonne),

Cj,w = concentration of labile element j in the weathered horizon,

Ci,w = concentration of immobile element i in the weathered horizon,

ρρρρpppp = bulk density of the parent material (tonne/m3

), 

ρρρρwwww = bulk density of the weathered horizon (tonne/m CCCCiiii,wwww 3),

εiiii,wwww = strain (volumetric changes due to pedogenic weathering with respect to the volume of 

the initial material), 

ττττjjjj,wwww = the open-system mass transport function for element j, and

∆zzzzwwww = the depth of the weathered horizon (m).

In this study, titanium (Ti) is used as the immobile element i, and Ca, K, Mg, and Na are used 

as the labile elements j. The open-system mass transport function (ττττjjjj,wwww ) (eqn. 6) for each of 

the major base cations (Ca2+, K+, Mg2+, and Na+) is calculated based upon their concentrations 

relative to the immobile element in the weathered horizon versus the parent materials. The 

strain calculation (eqn. 5) is incorporated into the mass flux equation to determine net gains 

or losses of each labile element j in the weathered horizons. Mass flux calculations for each 

horizon are then summed throughout the profile. See Theoretical Background section in Elgi 

and Fitze (2000) for further explanation of terms.

N Immobilization. In the SMB model, N immobilization (Ni) represents the long-term net 

retention of N in the soil root zone (under ambient N deposition) (UBA 2004). Nitrogen 

immobilization accounts for the amount of inorganic nitrogen converted to organic nitrogen 

and retained in soil N pools, primarily by microbes (aerobes, anaerobes, fungi, and bacteria). 

Nitrogen immobilization is counterbalanced by N mineralization, which is the release of 

inorganic forms of nitrogen as a by-product of detritus consumption by soil microbes (Paul 

2007). In N-limited environments (such as upland forest ecosystems), the amount of N 

immobilized depends on the amount and type of leaf litter, the productivity and efficiency 

of soil microbes, and, ultimately, soil temperature and soil moisture (with maximum N 

immobilization occurring at moderate levels of both moisture and temperature). Higher 

values for Ni would result in higher critical load estimates (the ecosystem is able to retain 

more N), whereas lower Ni values would result in lower critical load estimates (the ecosystem 

is able to retain less N). There is little consensus regarding acceptable Ni values for critical load 

assessments of forest soils, primarily because they are difficult to determine with certainty.
8

Most forest ecosystems where anthropogenic acidification is a concern are currently 

accommodating and adjusting to elevated levels of N deposition, making it impossible to 

measure in situ N retention under “long-term” (i.e., natural) conditions. Many studies rely 

on previously published or recommended values, most of which originate from Rosen et 

al. (1992), with minor adjustments and supporting justifications or assumptions (Table 4). 

Rosen et al. (1992) measured stable carbon-nitrogen compounds in a variety of soil profiles 

in Norway, and derived average N immobilization rates by dividing the N content in soils by 

landform age (i.e., the chronosequence approach). This approach is possible for landscapes in 

which the deglacial chronology is reasonably well constrained by absolute dates. Values for N 

ranging from 0.2 to 0.5 kg/ha/yr (14.3 to 35.7 eq/ha/yr) were reported (Rosen et al. 1992). 

These values have been adopted for use in other critical load studies of formerly glaciated 

environments dominated by Spodosols, and are suggested as the range of N immobilization 

for northern climates in the UBA mapping manual (UBA 2004). Other studies have used 

different values for Ni with a variety of supporting justifications (Table 4).

In this study, an approach similar to Whitfield et al. (2010) was used, primarily because 

there are no locally derived data that can be used as a meaningful substitute for long-term 

sustainable N immobilization in the study area. This approach also allowed the sensitivity of 

the Ni parameter to be tested under different deposition scenarios, and therefore allowed for 

more meaningful comparison of overall results to national-scale estimates.

Denitrification. In the SMB model, denitrification (Nde) represents the long-term loss of 

N from the soil to the atmosphere via reduction of nitrate (conversion to N gases) (UBA 

2004). Generally, denitrification caused by denitrifying bacteria occurs in soils when pore 

space saturation is greater than 60 percent (Paul 2007). Consequently, denitrification is 

Table 4.—Examples of commonly used values for nitrogen (N) immobilization in 

critical load studies that employed the simple mass balance approach

Value for N 

(kg/ha/yr) Justification Reference

0.2–0.5 Chronosequence study of Norwegian 

soils; recognized that values may be an 

underestimation.

Rosen et al. (1992)

0.0 Lack of site-specific data; assumption 

that production of N in humus is 

compensated for by N fixation.

Arp et al. (1996)

2.0 Lower end of the range suggested by 

Downing and others (1993).

Posch (1997)

0.0 Lack of data to suggest otherwise; 

study area characterized by disturbance 

processes (e.g., tree throw, fire) that 

would eventually export N from soils.

Oiumet et al. (2001)

2.0 Assumed, though reference was made 

to much higher N immobilization rates 

used in Europe (Posch et al. 2001) as 

justification as a precautionary value. 

Watmough and Dillon 

(2003)

0.0 and 8.8 To represent the worst- and best-case 

scenarios: 0.0 = all N leached, 8.8 = all N 

retained in ecosystem, respectively.

Whitfield et al. (2010)
9

more significant in wet soils than dry soils. In areas where soil acidification is a concern, 

denitrification can be an important mitigating process as it prevents excess NO3

-

 from 

entering groundwater (Wexler et al. 2014). However, in many such areas (e.g., well-drained, 

sandy upland forest soils), denitrification is often negligible, if not absent. Only in isolated 

pockets of poorly drained soils, in wetland soils, and along riparian areas would denitrification 

occur year-round (McLaughlin et al. 2011). Denitrification may play a minor role on other 

sites that experience periodic pulses of increased saturation throughout the year (i.e., during 

spring snowmelt, or because of a fluctuating water table). Higher values for Nde would result 

in higher critical load estimates (the ecosystem is able to process and release more N back to 

the atmosphere), whereas lower Nde values would result in lower critical load estimates (the 

ecosystem is less able to process and release N back to the atmosphere).

Most SMB critical load studies on well-drained upland forest sites assume that denitrification 

is insignificant, and therefore Nde values are set to zero (Aherne 2008, Pardo and Driscoll 

1996, Warfvinge et al. 1993, Watmough et al. 2006). Zero values for Nde are a precautionary 

approach to setting critical loads where no local data exist. In an SMB study of British 

Columbia soils, the availability of local data permitted Mongeon et al. (2010) to set Nde values 

to 0.035 kg/ha/yr. A meta-analysis of reported Nde values (from 25 studies) in forest soils by 

Barton et al. (1999) found that more than half of studies reported denitrification rates of <1 

kg N/ha/yr.

For this study, Nde values are assumed to be zero because the majority (78 percent) of soils 

in the watershed are excessively or well drained (Fig. 1). Furthermore, most of the U.S. 

Forest Service land in the watershed is occupied by red pine (Pinus resinosa; i.e., conifers). 

Therefore, considering the absence of site-specific measurements, and the fact that reported 

denitrification values for coniferous stands are generally <0.1 kg/ha/yr, a zero value for Nde is 

conservative and reasonable.

Figure 1.—Distribution, by location and proportion of the total, of A) soil drainage class and B) surface soil texture in the Osborn Creek 

watershed (Soil Survey Staff 1996).
10

Nitrogen Uptake and Base Cation Uptake. In the SMB model, Nu accounts for the amount of 

nitrogen that trees extract from the soil for growth. Similarly, BCu accounts for the quantity 

of base cations extracted from the soil for tree growth. When trees are harvested, both Nu and 

BCu represent long-term average removals of N and base cations from the ecosystem (UBA 

2004). The values are highly dependent on stand rotation period (Vadeboncoeur et al. 2014), 

stocking density, and harvesting practices (i.e., proportion of trees removed vs. left behind 

onsite) (Hazlett et al. 2014). Higher values for Nu effectively increase the CAL estimate; 

higher values for BCu effectively decrease the CAL estimate. In other words, N contributes to 

soil acidity, whereas base cations neutralize soil acidity. Values for Nu and BCu can be derived 

via site-specific measurements or published values.

The following equations were used in this study to calculate Nu and BCu (collectively termed 

“uptake”):

Uptakebark = Bark Volume × NCbark × SG (7)

Uptakebole = Bole Volume × NCbole × SG, (8)

Where

NC = nutrient concentration: nitrogen and base cation concentration in bark or bole, and

SG = specific gravity (density) of bark or bole wood (g/cm3).

The following sources of data were used to derive values for input into equation 7:

1) Species-specific volumetric equations for trees in the Lake States (Miles and Hill 

2010) were used to calculate total tree volume and volume of bark on a tree-by-tree 

basis, assuming stem-only harvest.

2) Base cation and nitrogen concentrations for bark and bole of individual species were 

calculated based on samples collected from the field.

3) Specific gravity values for bark and bole of individual species were calculated based on 

samples collected from the field.

In the SMB model, BC and N uptake rates represent nutrient loss via harvest over a typical 

rotation period. In this study, the BC and N uptake rates assume that trees are harvested at 

present, rather than specifying the length of a rotation period. In addition, species-specific 

formulas used to estimate bark and bole volume include terms that compensate for volume left 

behind according to typical harvest methods for that species. This approach was considered 

more appropriate than extrapolating BC and N concentration data forward to a typical 

rotation period for two reasons: 1) At best, rotation periods can only be estimated—often 

with a 20-year buffer—because, in practice, forest management activities are executed within 

a complex framework of overlapping priorities, socio-economics, and policy, which collectively 

determine when stands are actually harvested; and 2) many of the stands in this study are 

close to or at their intended age of harvest.

Critical Leaching Rate of Forest Soil Acid Neutralizing Capacity. In the SMB equation (eqn. 1), 

ANCle,crit represents the highest acceptable leaching rate of soil acid neutralizing capacity. This 

parameter of the SMB model determines the critical chemical criterion for adverse impacts 

to a receptor in the environmental compartment of concern, that is, damage to fine roots 
11

(receptor) in the soil (compartment). This parameter can be modeled in several different ways, 

so care must be taken in setting chemical criteria that are appropriate for the environment 

and receptor of concern. Choosing the right input values depends on whether the receptor 

is more sensitive to acidity (pH) or the toxic effects of high aluminum (Al) concentrations 

(UBA 2004). In upland forest ecosystems with low soil organic matter content, the 

relationship between available base cations in soil (soil nutrients) and levels of monomeric Al 

(which can be toxic to fine roots) is considered the most appropriate criterion to model. With 

these terms in mind, the following equation is used:

AAAAAAAACCCC(ffffllll,cccccccciiiicccc) = −QQQQ2

3 × �1. 5 ×

BBBBCCCCddddllllllll + BBBBCCCCwwww − BBBBCCCCffff

KKKKggggiiiigggggggg × �

BBBBCCCC

AAAAAAAA �

cccccccciiiicccc

�

1

3

−1. 5 × 

BBBBBBBBdddddddddddd + BBBBBBBBwwww − BBBBBBBBffff

�

BBBB

AAAAAAAA �

cccccccccccccccc

, (9)

Where

Q = precipitation surplus (in m3/ha/yr),

BCdep = base cation deposition,

BCw = base cation weathering, 

BCu = base cation uptake,

Kgibb = the gibbsite equilibrium constant, and

�

BBBBCCCC

AAAAAAAA �

cccccccciiiicccc

 = critical base cation-to-aluminum ratio.

The gibbsite equilibrium constant describes the relationship between hydrogen (H) and Al 

ions in solution. Common input values for Kgibb (Table 5) are based primarily on data from 

an intensive forest monitoring study in Europe (De Vries et al. 2003), and are widely used 

in regional and national-scale CAL estimates. The critical base cation-to-aluminum ratio 

(BC:Alcrit) is commonly set to 1 for coniferous forests and 10 for deciduous forests (UBA 

2004). These BC:Al values are the international standard considered to acceptably protect 

tree roots from Al toxicity.

Instead of using the default Kgibb constant in this study, we use a version of the ANCle,crit

equation that incorporates local soils data to describe the relationship between H ions and 

Table 5.—Ranges of the gibbsite equilibrium constant (Kgibb) as a function of soil 

organic matter content (UBA 2004)

Soil type

Organic 

matter (%) Kgibb (m6/eq2)

Mineral soils; C-horizon soils with low organic matter <5 950–9,500

Soils with low organic matter; B- and C-horizons 5–15 300–3,000

Soils with some organic matter; A- and E-horizons 15–30 100

Peaty and organic soils; organic layers >70 9.5
12

exchangeable Al in soil solution. This option more adequately reflects site-specific leaching 

processes in the soils under investigation (UBA 2004). The following equation was used:

[AAAAAAAA] = KKKKAAAAffffAAAAffff × [HHHH]aaaa, (10)

Where

Al = exchangeable aluminum (in eq/m3)

H = hydrogen ions in solution (in eq/m3).

Kalox (the equilibrium constant) and exponent a were derived by plotting the logarithmic 

forms of these data and inserting a power trendline (in the form of Y = K × Xa). These data 

were used instead of the Kgibb equilibrium constant in equation 9.

Input Data for the Simple Mass Balance Equation

Table 6 summarizes the SMB input data used in this study. Two input parameters were 

modeled based on actual data, four were assumed based on values reported in the literature, 

and six—including three of the most influential input parameters (BCw, BCu, and Nu)—were 

measured at the plot level (see the Methods section for more details). State-of-the-science 

Model parametera

This 

studyb Data source

Scale of input 

datac

Deposition data

(S+N)dep

(wet and dry)

MO Wet+dry: CMAQ (U.S. EPA 2014)

Years: 2000–2012

BCdep 4 km2

Cldep

Mineral weathering BCw

T ME Field Δ

M ME Lab Δ

Base cation uptake BCu

C ME Field/Lab Δ

SG ME Field/Lab Δ

Nitrogen immobilization Ni A/MO Wet+dry: CMAQ (U.S. EPA 2014)

Years: 2000–2012 4 km2

Nitrogen uptake Nu

C ME Field/Lab Δ

SG ME Field/Lab Δ

Denitrification Nde A -- NA

Leaching 

of acid 

neutralizing 

capacity, 

ANCle,crit

Discharge Q MO

(Michigan State University 

Extension 2014)

Years: 1981–2010

Δ

pH/pAl 

equilibrium 

constant

Kalox MO (NCSS, n.d.) NA

Critical base 

cation-toaluminum ratio

� � BC

Al crit A (UBA 2004) NA

a Abbreviations: S = sulfur, N = nitrogen, BC = base cation, Cl = chlorine, T= soil texture, M= soil mineralogy, C = content, 

SG = specific gravity, Al = aluminum.

b Abbreviations: MO = modeled, A = assumed, ME = measured.

c Abbreviations: Δ = site-specific, NA = not applicable. 

Table 6.—Summary of simple mass balance input data used in this study
13

Figure 2.—Location of Osborn Creek watershed (black outline) within the greater White River 

watershed (light gray shading). Approximate centroid (latitude/longitude) of Osborn Creek watershed: 

43.6252° N/–86.2083° W.

analysis tools were used to model input data wherever possible. Because we used the same 

modeling approach as McNulty and others (2007, 2013) and McNulty and Boggs (2010), the 

results of this work can be directly compared to results from national-scale estimates.

STUDY AREA

Geography

The Osborn Creek watershed is located in the southwestern portion of the Huron-Manistee 

National Forests, in central Oceana County, MI (Fig. 2). Elevations across the watershed 

range between 200 and 300 m above sea level. The area of the watershed is about 5,880 

ha. The watershed falls within Forest Service Management Area 4.4—Rural (U.S. Forest 

Service 2012). Rural areas are primarily managed for recreation, firewood, and moderate-tohigh volumes of softwood timber products. Only 8 percent of land within the watershed is 

federally owned. Private land in the watershed forms a patchwork of cover types including 

cultivated crops, deciduous and evergreen forests, herbaceous cover, and wetlands.

Geology

The surrounding region is characterized by gently rolling glacial hills of moderate relief and 

broad lowland outwash valleys (Schaetzl et al. 2013). Present landforms and drainage patterns 

in the study area owe their origin to former glacial ice margin positions and associated 

morphosequence deposits of the Lake Michigan Lobe (Farrand and Eschman 1974). In 

general, upland areas are primarily ground moraine or end moraine composed of a range 

of coarse-textured sediments. Lowland areas are primarily deep glacial outwash deposits 

composed of well-sorted medium-to-coarse sand (Farrand and Bell 1982, and personal 
14

observations 2014). Drift thickness ranges between 90 and 150 m in the study area. Road 

cuts and exposures of the underlying glacial stratigraphy are uncommon. Most geologic 

interpretation is therefore based on surface morphology and shallow soil boring.

The irregular topography of the watershed is typical of glaciated landscapes in the Great 

Lakes region (Fig. 3). A variety of ice-contact environments are evident among the 

landforms present in the watershed including kames, kettles, subglacial drainage networks 

(discontinuous eskers), and ice-contact outwash. The northern and northeastern parts 

of the watershed are locally referred to as “overwashed moraine” because clean sand of 

varying thickness and extent overlies sandy loam diamict at shallow depths; topographic 

roughness in such areas decreases markedly compared to the surrounding terrain (Fig. 3). 

Sand composes nearly all of the surficial deposits in the study area, indicating widespread 

meltwater processes during deglaciation. The lithology of surficial sediments in the study area 

reflects a wide-ranging mixture of subcropping bedrock units in the Michigan Basin, with 

greater concentrations of minerals that occur in locally subcropping bedrock. Underlying 

Figure 3.—Digital elevation 

model (10-m resolution) and 

transparent hillshade of Osborn 

Creek watershed (black outline) 

and surrounding area. Yellow 

and brown areas represent 

higher elevations. Blue and 

pale green areas represent 

lower elevations. Streams are 

light blue; wetlands and lakes 

are dark blue.
15

bedrock units in the area include the Michigan Formation (primarily composed of shale, with 

interbedded sandstone, dolostone, limestone, and evaporate) and the Marshall Formation 

(primarily composed of sandstone, with interbedded siltstone) (Milstein 1987). In general, 

more-resistant granitic minerals make up a greater proportion of the surficial deposits than 

less resistant (more weatherable) minerals. Indeed, recent mineralogical investigations by 

the U.S. Geological Survey (Smith et al. 2013) indicate that upland soils in nearby areas are 

primarily composed of quartz (~84 percent) with lesser amounts of potassium feldspar (~12 

percent) and plagioclase (~4 percent).

Soils and Vegetation

About 78 percent of the watershed is composed of excessively drained and well-drained 

soils; minor pockets (10 percent) of poorly drained and very poorly drained soils occur in 

swamps and riparian areas. Surface textures in the watershed are mostly sand (Fig. 1). On 

forested land within the watershed, cation exchange capacity (at pH 7.0) is estimated to 

be low, typically ranging between 1 and 4 meq/100 g; pH ranges from 5.0 (strongly acid) 

to 6.5 (slightly acid) (Soil Survey Staff 1996). The most common soils in the watershed are 

classified as Typic Udipsamments and Entic Haplorthods. Some of the soils classified as 

Entic Haplorthods are occasionally found overlying loamy till. Other soils contain loamy 

sand to sandy loam bands (lamellae or other nonpedogenic layers, or a combination) with 

total thickness of 5 to 15 cm within 1.56 to 4.5 m of the surface (Host et al. 1993). These 

substratum layers have been shown to significantly increase available moisture and nutrients 

for trees (Hannah and Zahner 1970, Schaetzl 1992).

Before European settlement, most of the vegetation in the study area was beech/sugar maple/

hemlock (Fagus spp./Acer saccharum/Tsuga spp.) forest (Comer et al. 1995). The southwestern 

portion of the watershed supported white pine (Pinus strobus)/mixed hardwood forest. All 

of the land in the watershed was most likely cleared of timber at some point in the mid-tolate 1800s. Areas of more fertile soil were put into agricultural production and remain as 

such to the present day. Most areas of nutrient-poor soils were cleared for agriculture and 

subsequently abandoned in the early-to-mid 1900s due to insufficient productivity or low 

profitability. These areas reverted to mixed hardwood regrowth or were planted with red 

pine throughout the early 20th century. Today, U.S. Forest Service land within the watershed 

primarily supports red pine, mixed oaks (white oak [Quercus alba], black oak [Q. velutina], 

and northern red oak [Q. rubra]), bigtooth aspen (Populus grandidentata), and minor areas of 

sugar maple.

Surface Water and Groundwater

Two creeks (Osborn Creek and Swinton Creek) flow through the Osborn Creek watershed 

(Fig. 3). Osborn Creek and Swinton Creek join the North Branch White River south of 

the watershed divide. These streams are all considered high-quality cold water systems 

supporting brook trout (Salvelinus fontinalis), Iowa darter (Etheostoma exile), and other 

species adapted to clean, cool water and a narrow pH range. Wetland communities compose 

about 10 percent (6.12 km2) of the total watershed area, and occur mostly in riparian zones. 

Within the watershed are five small lakes, of which four are internally drained seepage lakes 

(Fig. 3). Groundwater makes up a large portion of total stream flow in the Osborn Creek 

watershed. Regional models indicate that as much as 80 percent of total stream discharge in 

the surrounding White River watershed is attributed to groundwater, perhaps representing 
16

the most groundwater-dependent (base flow) fluvial system in the contiguous United States 

(Wolock 2003). Water table depths vary throughout the watershed, generally mimicking 

the topography, with typical values of 0 to 3 m in low areas and 24 to 30 m in higher areas. 

Factors that contribute to consistent groundwater flow include thick coarse-textured glacial 

drift, highly permeable soils, significant annual snowpack, and precipitation exceeding 

evapotranspiration in surrounding upland regions.

Climate

The region surrounding the Osborn Creek watershed has a humid warm continental climate 

with cold winters and warm summers. Proximity to Lake Michigan (~20 km to the west) 

provides a moderating effect on temperature throughout the year, and increases cloudiness 

and snowfall in the fall and winter. For this study climate statistics for 1981–2010 were 

derived from the weather station nearest Osborn Creek (Hart weather station, ID #3632, 

latitude 43.7366° N/longitude –86.3594° W) (Michigan State University Extension 2014). 

Average annual precipitation is 765 to 1079 mm (±1 standard deviation). Precipitation is well 

distributed throughout the year (Fig. 4). Average annual snowfall is 1128 to 3020 mm (±1 

standard deviation). Mean July temperature is 20.6 °C. Mean January temperature is –5.1 °C.

METHODS

Field Methods

Forest Sampling Sites

Five study sites on U.S. Forest Service land on the Huron-Manistee National Forests 

(HMNF) were chosen for detailed sampling of vegetation and soils (Fig. 5). Sandy soil types 

were preferentially chosen for sampling as they are generally considered more susceptible to 

acidification than finer-textured soils. A secondary consideration was to capture a range of 

typical forest cover types found on these soils across the watershed. Prior to the fieldwork, the 

approximate center of each forest stand was estimated on maps and marked with a point. The 

latitude/longitude coordinates of these points were calculated in ArcMap (Esri, Redlands, 

CA) and loaded onto handheld global positioning system (GPS) devices (Garmin Oregon®

550t; Garmin International, Olathe, KS) for field navigation. After navigating to the site in 

Figure 4.—A) Annual precipitation totals and B) average monthly precipitation at the Hart, MI weather station (located about 18 km from the 

center of the Osborn Creek watershed), 1981–2010.
17

the field, we adjusted the locations (if necessary) to avoid disturbed ground or stands having 

more than one dominant overstory species. When the final site location was established, a 

new GPS point was marked. The center of each site was flagged and a 20 m × 20 m square 

plot surrounding the center was measured and staked. The plot edges were aligned with 

the four major cardinal directions. Each plot was further subdivided into four quadrants 

(northeast, northwest, southeast, and southwest). All plots were at least 90 m from a road.

Vegetative Sampling

The dominant overstory and understory vegetative characteristics for each site were recorded. 

The number of dominant tree species was counted within each quadrant and summed for 

the entire plot. Five individual trees—one from each of the four quadrants, and one tree 

closest to the center of the plot—were selected within each plot for tissue sampling. Mature 

trees were preferentially chosen for sampling. At each sample tree, diameter at breast height 

(d.b.h., 1.37 m) and tree height (length of bole and total height) were measured. A bole 

sample was taken from each of the five trees with a 6.4-mm-diameter increment borer. Trees 

with rotten cull were not sampled. Bark samples were removed from the same five trees with 

a 5-cm-diameter hole saw, which was inserted into each tree at breast height.

Figure 5.—Location of the five forest soil sample sites in the Osborn Creek watershed.
18

Soil Sampling

Soil pits were located in the center of each 20 m × 20 m plot. Soil pits were excavated 

by shovel to a depth of about 120 to 150 cm. Soil profiles were described and samples 

collected from each horizon according to Natural Resources Conservation Service methods 

(Schoeneberger et al. 2012). The depth, character, and distribution of roots were recorded. 

Four bulk density samples were taken at each pit. Bulk density samples were gathered with 

a sharpened polyvinyl chloride pipe core (diameter: 7.6 cm; length: 9.7 cm) and sledge 

hammer, according to methods in Blake and Hartge (1986). The surface bulk density sample 

(A-horizon) was taken vertically (from above) next to the soil pit. The remaining three bulk 

density samples were carefully extracted horizontally from the pit face at different depths 

according to the horizon sequence: one sample each in the eluvial (E-) horizon, illuvial (B-) 

horizon, and C-horizon. All horizon and bulk density samples were transported from the field 

in waterproof sample bags.

Laboratory Analysis

Vegetation

Bark and bole samples were air dried, measured, and weighed in the geomorphology 

laboratory at the HMNF. Tree age was determined by visual inspection after taking highresolution photographs of bole samples with a mounted EF 200mm zoom lens (Canon, 

Tokyo, Japan) and counting rings between the inner bark and pith. Volume measurements 

of bark and bole were used along with sample weight to compute specific gravity of the 

samples. Bole sample volume was calculated using the standard equation for a cylinder. 

Bark sample volume was calculated by taking the average of 12 evenly spaced thickness 

measurements (mm) around the vertical edge of the sample and multiplying by the area of 

the bark sample. This method assumed that bark surface roughness was reasonably accounted 

for by the average of the 12 thickness measurements. Bark and bole samples were then 

packaged and shipped to the Cornell Nutrient Analysis Laboratory for geochemical analysis 

using inductively coupled plasma-atomic emission spectroscopy (ICP-AES), following nitric 

acid-perchloric acid-hydrofluoric (HNO3-HClO4-HF) acid digestion. Prior to ICP-AES, 

remaining bole wood was carefully removed from bark samples as necessary. 

Soils

Soil horizon and bulk density samples were weighed immediately upon return to the HMNF 

geomorphology laboratory. Samples were then air dried for several days and weighed again to 

calculate field moisture content. The bulk density of horizons that fell between bulk density 

sample depths was estimated by extrapolating the bulk density results throughout the profile 

and assigning a value corresponding to the depth of the midpoint of each horizon. This 

procedure assumes that bulk density changes linearly with depth between sample points. 

Horizon samples were lightly ground with a mortar and pestle and passed through a 2-mm 

sieve to remove and weigh coarse fragments. The remaining fine-earth fraction was then 

homogenized by running it through a sample splitter four times, ensuring a representative 

subsample (~50 g) for particle-size analysis. Samples were next shaken for 2 hours in a 

water-based solution with sodium hexametaphosphate as the dispersant. Then particle 

size was analyzed by using the Bouyoucos hydrometer method (Bouyoucos 1962). The 

remaining portion of horizon samples were repackaged and shipped to the Cornell Nutrient 

Analysis Laboratory for ICP-AES, following HNO3-HClO4-HF digestion. In addition, the 

extractable bases calcium (Ca2+), potassium (K+), and magnesium (Mg2+), and effective cation 
19

exchange capacity (ECEC), were measured in an unbuffered ammonium chloride (NH4Cl) 

solution. Base saturation was estimated as the sum of NH4Cl-extractable bases/ECEC ×

100. Soil pH (1:1 ratio with deionized water) and organic matter content (Loss on Ignition 

method) were also measured.

Data Calculation Methods

Deposition

For nitrogen (N) and sulfur (S) deposition data, raster grids for total N and total S deposition 

were downloaded from TDEP v. 2014.01 for each year from 2000 through 2012 (U.S. EPA 

2014). For base cation (BC) and chloride (Cl-) deposition data, raster grids of each BC 

analyte (Ca, Mg, K, and sodium [Na]) and Cl were downloaded for 2000 through 2012 

(NADP 2014). Both datasets were loaded into a new ArcMap document. Raster calculator 

was used to compute the average for each analyte over the period of analysis. Then, sitespecific values were obtained by extracting the averages at each study site. Data were 

converted from kg/yr to eq/ha/yr if necessary.

Mineral Weathering

Most studies that use the pedological mass balance (PMB) method for weathering rate 

calculations limit analysis to the rooting zone (E- through deepest B-horizons). In this study, 

the entire profile was analyzed (including the A-horizon down to the depth of sampling 

in the unweathered parent material). To account for base cations that are associated with 

organic matter, the concentration of exchangeable base cations (Ca, K, and Mg) at field pH 

is subtracted from the concentration of bases obtained through complete acid digestion 

of minerals, similar to the approach in Schroth et al. (2007). This correction was not done 

for Na (Na is assumed to be nonbiocycled). This correction procedure effectively limits the 

derivation of PMB mass flux calculations to nonexchangeable (solid) mineral phases. Base 

cations adsorbed to organic matter can accumulate at the soil surface due to preferential plant 

uptake and atmospheric deposition (Lichter 1998). For the purpose of obtaining a long-term 

weathering rate with the PMB method, these labile base cation enrichments to the surface 

and lower horizons are considered products of—or external inputs to—the soil weathering 

system and could drastically offset weathering losses from horizon flux calculations. Failing to 

make this adjustment could result in an underestimation of mass loss due to weathering. 

To determine a weathering rate, the results of the PMB method were then divided by the age 

of the landscape. Absolute ages have not yet been determined for specific geologic units in 

the study area. However, the morainic system surrounding the watershed very likely correlates 

to either the Inner Port Huron or Outer Port Huron Moraine. Blewett et al. (1993) report a 

minimum limiting radiocarbon age of the Inner Port Huron Moraine of 12,960 (±350), based 

on paleo-lake sediment samples taken next to the Inner Port Huron Moraine in Antrim 

County, MI. These radiocarbon dates calibrate to 15,077 (±449) calendar years before present. 

Therefore, as a conservative estimate, a value of 15,100 years is used for the age of soils in the 

Osborn Creek watershed.

Nitrogen and Base Cation Uptake

Gross volume for each tree was calculated by using field collected data for d.b.h. and tree 

height. Gross volume is the total volume of wood in the central stem of trees 12.7 cm or 

larger in diameter, from a 30-cm stump to a minimum 10.2-cm top diameter outside bark 

(d.o.b.), or where the central stem breaks into limbs, all of which are less than 10 cm in d.o.b. 
20

Gross volume equations do not account for rotten or missing form cull (Miles and Hill 

2010). Diameter at breast height was then converted to diameter inside bark (d.i.b.) using the 

species-specific bark ratio equations in Dixon and Keyser (2014). Bark volume was obtained 

by subtracting gross volume using d.b.h. from gross volume using d.i.b. This calculation 

assumes uniform bark thickness throughout the tree bole, and also assumes a stem-only 

harvest (leaves and branches are left onsite).

The concentrations of BC and N derived from ICP-AES analyses in bark and bole were 

multiplied by their respective volumes and specific gravity values to derive total BC and N 

content per sampled tree using equations 7 and 8. Total BC and N content was divided by 

tree age to derive an annual uptake rate per tree. BC and N tree uptake rates were averaged 

across data for sampled trees at the site (n=5). Then, uptake averages were multiplied by the 

number of trees within the 20 m × 20 m plot and converted to eq/ha/yr. The uptake rates for 

bark and bole were added to derive species-specific total uptake for each site.

Precipitation Surplus 

Precipitation surplus (Q) was estimated from daily precipitation and reference 

evapotranspiration data at the Hart weather station (Michigan State University Extension 

2014). The period of analysis was 1997 through 2013 as reference evapotranspiration data 

are not available before this time. Reference evapotranspiration data were subtracted from 

precipitation data on a daily basis. Precipitation exceeding evapotranspiration is considered 

to represent surplus soil moisture (unused by plants) that passes completely through the soil 

profile (i.e., infiltration). Daily precipitation surplus results were summed for each month, 

and monthly precipitation surplus was summed for each year. Finally, an annual average was 

calculated for the period of analysis (17-year average).

Critical Leaching of Acid Neutralizing Capacity 

For hydrogen (H+) and aluminum (Al) input into equation 10, lab data for Typic 

Udipsamments and Entic Haplorthods within Major Land Resource Area 94 (Michigan 

and Wisconsin Sandy Drift) were downloaded from the National Cooperative Soil Survey 

(NCSS) soils characterization database (NCSS, n.d.). Exchangeable Al (all unbuffered 

methods) and pH data for horizons with a pH of less than 5.5 were used (Al generally 

becomes insoluble at pH >5.5). Exchangeable Al was converted to equivalents, log10

transformed (defining pAl = 3 – log10([Al]/3), and plotted against pH for each group of 

sample horizons separately (A-, E-, B-, and C-horizons). In order for these data to be 

incorporated into equation 9, the formulas for ANCle,crit have to be adapted by replacing 

the exponent 3 by a (a modified version of the Kgibb constant, as presented in equation 10) 

and 1/3 by 1/a (UBA 2004). The KAlox and a values were inserted into equation 9 for each 

horizon group to determine which had the lowest ANCle,crit value. The lowest value was then 

adopted for the whole soil profile to protect the entire pedon from unacceptable leaching. 

This method assumes that the exchange of base cations between soil and soil solution are in 

steady-state.
21

RESULTS AND DISCUSSION

Site-specific Tree Uptake

On average, the base cation (BC) and nitrogen (N) uptake rates derived in this study 

(Table 7) were higher than values used by McNulty et al. (2007) in national-scale critical acid 

load (CAL) estimates by 76.1 and 167.9 eq/ha/yr, respectively (Table 8). White oak (site 3) 

exhibited the greatest difference in BC and N uptake rates between the two studies. Red pine 

(site 1) exhibited the least difference in BC uptake between the studies. Northern red oak 

(site 5) exhibited the least difference in N uptake between the studies. The BC and N uptake 

rates for sugar maple and white oak (sites 2 and 3) derived here were more than twice as high 

as those in McNulty et al. (2007).

The differences in uptake rates between the studies are very likely related to the method used 

to derive annual volume increment and nutrient concentration estimates. In McNulty et al. 

(2007), the volume of growth allocated to bark was assumed to be 15 percent for deciduous 

trees and 11 percent for coniferous trees. In this study, the average ratio of bark to bole 

volume was 16.2 percent for deciduous trees (n=15 samples) and 17.7 percent for coniferous 

trees (only red pine in this study, n=10) (calculated from data in Table 7).

If bark volume had been assumed in this study to be similar to that in McNulty et al. (2007), 

uptake rates for red pine bark would have been underestimated. This may partially explain 

the positive difference in uptake rates for red pine between the two studies (Table 8). The 

method used in this study to estimate nutrient uptake assumes that tissue concentrations 

in sampled trees adequately represent the entire population of trees across the stand (i.e., 

average data from sampled trees were extrapolated to unsampled trees). This assumption is 

based on the premise that nutrients and water in the soil are consistent across the site and 

not limiting, and therefore tissue concentrations are largely controlled by genetics (Alban 

1988). If this premise were true, then BC and N concentrations should exhibit a narrow 

range at the site level. Overall, samples from hardwood sites exhibited considerable variation 

in calcium (Ca), potassium (K), and magnesium (Mg) concentrations in bark and bole of 

sampled trees (Fig. 6). Sodium was omitted, as mentioned earlier, because it is not considered 

biologically important (Bailey et al. 2003). Sugar maple (site 2) exhibited the greatest range 

of bole wood BC concentrations, whereas red pine (site 4) exhibited the least. White oak 

(site 3) exhibited the greatest range of bark BC concentrations, and red pine (site 1) the least. 

Hardwoods had a greater range of tissue nutrient concentrations than conifers (red pine only) 

in this watershed (Fig. 6). This observation may be related to the growth characteristics of 

each stand. Stands with homogenous forest structure and closed canopy (such as red pine 

plantations: sites 1 and 4) may have more consistent tissue concentrations because light and 

space are consistently limited throughout the stand.

Significant intraspecies differences in nutrient concentrations occurred in red pine (sites 

1 and 4) in this watershed, even on sites with very similar soils and stand development 

(Table 9). To better understand intraspecies variability, we tested sites 1 and 4 for significant 

differences in Ca, K, Mg, and N concentrations in both bark and bole. A preliminary F-test 

for equality of variances indicated that the variances of sites 1 and 4 were not significantly 

different (data not shown). Therefore, an independent two-sample t-test (equal variance) was 

run (Table 9). The mean concentrations of bole Mg and bark N were significantly higher in 

site 1 than site 4. Mean concentrations of all other nutrients were not significantly different 

between the two sites.
22

Table 7.—Physical characteristics of the five trees sampled at each site (with 

number of trees in that species on the plot shown in parentheses)

Site and forest 

cover Tree age

Specific 

gravity of 

bole

Specific 

gravity of 

bark

Diameter 

at breast 

height

Tree 

height

Bole 

volume

Bark 

volume

yr --------g/cm3-------- cm m ---------m3---------

Site 1

Red pine

(38)

46 0.28 0.33 25.4 20 2.39 0.42

45 0.33 0.35 24.6 18 2.12 0.37

42 0.29 0.30 24.4 18 2.05 0.36

47 0.27 0.39 25.4 19 2.34 0.41

47 0.31 0.27 27.7 18 2.59 0.46

Site 2

Sugar maple

(9)

91 0.46 0.72 47.5 30 9.44 1.16

55 0.50 0.69 64.3 34 19.35 2.41

86 0.46 0.60 35.8 26 5.00 0.60

96 0.48 0.56 54.1 29 11.78 1.45

88 0.46 0.74 51.1 27 9.86 1.21

Site 3

White oak

(16)

82 0.47 0.50 47.2 31 11.37 2.11

95 0.52 0.64 45.5 26 8.90 1.63

79 0.48 0.48 40.1 26 7.20 1.31

79 0.50 0.58 44.5 24 8.18 1.49

99 0.49 0.43 43.7 25 8.05 1.47

Site 4

Red pine

(49)

48 0.28 0.27 32.3 20 3.55 0.64

46 0.28 0.35 23.4 21 2.12 0.37

50 0.28 0.24 29.0 19 2.87 0.51

46 0.28 0.27 27.2 16 2.27 0.40

42 0.28 0.36 26.9 18 2.41 0.43

Site 5

Northern red 

oak

(14)

111 0.45 0.49 47.2 31 8.16 1.49

114 0.49 0.71 45.5 26 6.40 1.15

109 0.47 0.67 40.1 26 5.14 0.91

106 0.43 0.37 44.5 24 5.97 1.07

117 0.40 0.50 43.7 25 5.92 1.06

Table 8.—Tree uptake values for each site compared to uptake values used for 

corresponding species in McNulty et al. (2007)

Nitrogen uptake

eq/ha/yr

Base cation uptake

eq/ha/yr

Site and forest cover

This 

study

From 

McNulty et al. 

(2007) Difference

This 

study

From 

McNulty et al. 

(2007) Difference

Site 1

Red pine

103.0 59.1 43.9 86.5 77.1 9.4

Site 2

Sugar maple

205.0 101.8 103.2 560.4 190.5 369.9

Site 3

White oak

273.4 129.7 143.7 599.9 599.9 386.1

Site 4

Red pine

148.9 59.1 89.8 108.3 108.3 31.2

Site 5

Northern red oak

129.3 129.7 –0.4 256.6 256.6 42.8

Average difference — — 76.1 — — 167.9
23

Figure 6.—Concentrations of base cations in A–C) bole samples and D–F) bark samples by site, Osborn Creek watershed. 

Note: The y-axis scale is different on each graph. The value for an individual tree sample is indicated by a dash.
24

Although the number of samples in each population (n=5) is small, these data suggest that 

tree nutrient demand may vary depending on factors other than genetics. When multiplied 

by growth characteristics from each stand (Table 7), these small differences in nutrient 

concentrations were compounded such that the final calculated uptake rates differed 

considerably between sites 1 and 4 (Table 8). On average, sites 2, 3, and 5 exhibited greater 

BC uptake than N uptake (Fig. 7). Nitrogen uptake exceeded BC uptake for sites 1 and 

4. Sugar maple, white oak, and northern red oak (sites 2, 3, and 5) exhibited much wider 

ranges of uptake rates at the site level for both BC and N than red pine (sites 1 and 4). Sites 

showing large differences in uptake rates between this study and the study by McNulty et al. 

Table 9.—Average concentrations of exchangeable base 

cations and nitrogen by component for sites 1 and 4 (red 

pine plantations) and results of paired t-test between 

variables (n=5)

Tree component 

and base cation

Site 1 Site 4

mg/kg mg/kg t-test p-value

Bole

Calcium 437.76 425.96 0.46 0.65

Potassium 92.38 88.32 0.97 0.35

Magnesium 85.83 75.75 3.10 0.01

Nitrogen 610.70 774.05 –1.01 0.34

Bark

Calcium 2,713.06 2,422.16 1.62 0.14

Potassium 139.26 139.41 –0.01 0.99

Magnesium 171.79 180.07 –0.18 0.85

Nitrogen 2092.32 1588.99 3.11 0.01

Figure 7.—Whole-tree base 

cation (BC) and nitrogen (N) 

uptake rates for sampled trees, 

Osborn Creek watershed. 

Hollow white dashes represent 

N uptake rates. Solid black 

dashes represent total uptake 

rates for Calcium+Potassium+ 

Magnesium. Note: Y-axis values 

are logarithmic to highlight 

smaller ranges in red pine 

values (sites 1 and 4).
25

(2007) (sites 2 and 3; Table 7) also exhibited greater variation in d.b.h., tree height, and tissue 

concentrations (Table 7, Fig. 6). Data from Figure 7 suggest that uptake rates assigned to 

hardwoods have greater uncertainty than uptake rates assigned to red pine in this study. More 

sample data would be needed to verify the relevance of uptake rates derived in this study for 

species exhibiting considerable BC and N variation at the site level. The final site-specific 

CAL estimate for hardwood sites should be interpreted in light of these observations where 

forest harvesting occurs.

The uptake rates used in McNulty et al. (2007) are based on species-group averages from 

the tree chemistry database (Pardo et al. 2005). For example, red pine was grouped with 

eastern white pine (P. strobus) and jack pine (P. banksiana) in that study, so the values used for 

comparison in Table 8 were actually the average for the three species. Although the dataset in 

Pardo et al. (2005) is larger than the dataset collected in this study, the inclusion of multiple 

species in uptake terms in McNulty et al. (2007) may dilute the precision and applicability 

of those estimates at the watershed scale. Nutrient concentration data in the tree chemistry 

database were compared to data in this study on an individual species basis (rather than 

species group) to better understand how data derived here fit with published values. With 

the exception of N concentrations in red pine, data derived here for all species were less than 

or within the range of values published in the tree chemistry database (Fig. 8). Given the 

combination of 1) lower nutrient concentrations derived here compared to data in the tree 

chemistry database and 2) higher uptake values derived here compared to McNulty et al. 

(2007), the most likely explanation for the differences in uptake between the studies (Table 8) 

involves differences in methods used to calculate volume. Overall, the considerable site-level 

variation in BC and N concentrations and uptake values in this study suggests that stand 

characteristics and soil factors have an inseparable connection to growth and uptake, and that 

assigning a single value to a species across a watershed (let alone a species-group across an 

entire continent) may not be appropriate. Nonetheless, site-specific data are always preferred 

over regional data when calculating critical acid loads (Pardo 2010). Future research aimed 

at characterizing the influence of edaphic factors, climate, and stand factors on intraspecies 

variability in nutrient uptake at multiple scales would be beneficial for critical load modelers.
26

Figure 8.—Comparison of concentration data for nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) from 

the tree chemistry database (Pardo et al. 2005) (hollow red dashes) and this study (solid black dashes). The three data 

points for each nutrient represent maximum, mean, and minimum values from each dataset. Note: The y-axis scale is 

different on each graph.
27

Soil Horizon Characteristics

Sampled soils on all sites had textures and horizon sequences typical of sandy, nutrient-poor 

soils found across the Huron-Manistee National Forests (Table 10). Mean clay content across 

all horizons was 1.6 percent. With the exception of the A-horizon at sites 1, 2, and 3, organic 

matter was less than 3 percent in all horizons. The pH of surface horizons ranged from 3.25 

(ultra acid) to 4.46 (extremely acid). All soils exhibited a gradual increase in pH with depth. 

The effective cation exchange capacity (ECEC) was very low across all horizons (average 0.45 

cmol/kg, n=24), reflective of a very low pH and clay content, and minimal organic matter 

content. Exchangeable pools of base cations (Ca, K, and Mg) at all sites (except site 4) were 

more than twice as great in surface horizons than the sum of all subsurface horizons, reflective 

of higher organic matter contents in surface horizons. Base saturation ranged from moderate 

to supersaturated (>100 percent) across all sites, reflective of very low ECEC. Base saturation 

was highest in surface horizons at all sites except site 3. Base saturation decreased in the 

rooting zones at sites 2, 3, 4, and 5, then slightly increased with depth. Base saturation was 

excessively high at site 2 (≥172 percent in all horizons).

Table 10.—Physical and chemical characteristics of soil horizons at the five sample sites

Site and

forest 

cover Horizon Depth Sand Silt Clay

Loss on 

ignition pH

Effective 

cation 

exchange 

capacity Calcium Potassium Magnesium

Base 

saturation

Inorganic 

carbona

cm --------------------Percent-------------------- --------------------------cmol/kg-------------------------- --------Percent--------

Site 1

Red pine 

A/E 0–25 97.4 0.5 2.1 9.26 4.46 2.82 2.53 0.14 0.38 108 <bdl

Apb/E 25–45 92.8 7.0 0.2 2.47 4.49 0.19 0.11 0.04 0.02 94 <bdl

BC 45–90 96.6 3.4 0.0 1.50 4.61 0.25 0.14 0.04 0.03 88 <bdl

C/E&Bt 90–140 96.1 1.0 2.9 0.14 4.84 0.31 0.17 0.04 0.03 77 <bdl

Site 2

Sugar 

maple

A 0–5 86.1 11.4 2.5 11.04 3.97 0.85 4.52 0.11 0.90 654 <bdl

E 5–10 88.7 10.3 1.1 0.89 4.11 0.34 0.46 0.02 0.10 172 <bdl

Bw 10–20 93.4 5.9 0.7 1.24 5.16 0.52 1.35 0.09 0.43 357 <bdl

Btw 20–60 94.4 4.1 1.5 0.73 5.01 0.32 0.82 0.07 0.25 357 <bdl

C 60–140 97.3 1.2 1.5 0.13 5.28 0.08 0.20 0.02 0.02 302 0.56

Site 3

White oak

A 0–6 86.7 7.6 5.7 25.80 3.25 1.25 1.23 0.28 0.88 192 0.38

E 6–12 95.3 2.5 2.1 1.35 3.94 0.28 0.09 0.18 0.04 113 <bdl

Bt 12–17 95.5 3.6 0.9 2.24 4.00 0.25 0.09 0.04 0.02 62 <bdl

Bw 17–80 96.4 3.6 0.0 1.43 4.24 0.23 0.08 0.03 0.01 48 <bdl

C 80–150 98.6 1.4 0.0 0.17 4.48 0.09 0.29 0.01 0.01 349 0.38

Site 4

Red pine 

A 0–5 95.5 3.8 0.7 1.87 3.78 0.26 0.19 0.04 0.02 100 <bdl

E 5–20 94.2 3.7 2.1 1.67 4.09 0.28 0.12 0.03 0.01 59 <bdl

Apb 20–40 90.9 7.7 1.3 1.53 4.09 0.33 0.13 0.03 0.01 52 0.38

Btw 40–60 92.6 5.4 2.1 1.49 4.24 0.26 0.09 0.02 0.00 43 <bdl

C 60–100 95.1 4.5 0.4 0.30 4.67 0.10 0.05 0.01 0.02 77 0.38

Site 5

Northern 

red oak

A 0–10 94.4 3.9 1.8 2.60 3.43 0.53 0.22 0.10 0.25 105 <bdl

E 10–24 92.1 4.0 4.0 1.00 3.67 0.36 0.06 0.03 0.03 31 <bdl

Btw 24–45 94.5 3.3 2.2 1.89 4.27 0.34 0.07 0.03 0.00 29 <bdl

Bw 45–110 94.3 4.3 1.4 0.70 4.53 0.18 0.06 0.02 0.01 49 <bdl

C 110–150 97.3 2.1 0.6 0.24 4.93 0.27 0.05 0.02 0.01 28 <bdl

a <bdl = below detectable limit.
28

Soils under red pine plantations (sites 1 and 4) contained a distinct buried plow (Apb) 

horizon with an abrupt lower boundary (Fig. 9), indicating that these two sites were farm 

fields before the U.S. Forest Service acquired the land in the 1930s. The original plow 

horizons were probably buried during site preparation prior to planting red pine in the early 

1960s. The Apb horizons at sites 1 and 4 were distinctly lighter colored than their overlying 

A-horizons (Munsell color 10YR 5/2 vs. 10YR 2/2). At site 1, an additional pH sample 

(not presented in Table 10) was taken from the middle of the Apb horizon near a large root 

where there was possible evidence of accelerated leaching (Fig. 9B). The pH near the large 

root was lower (4.06) than at the middle of the horizon (4.49). Together, these observations 

suggest that acidic conditions in the subsoil have facilitated the translocation of humus out of 

Apb horizons in a relatively short time span (~46 years since planting). This interpretation is 

supported by other studies demonstrating that conifer species produce aggressive weathering 

agents via root exudation (i.e., low-molecular-weight organic acids) (Augusto et al. 2002, 

Schroth et al. 2007), and may even facilitate accelerated weathering under nutrient stress in 

order to obtain necessary nutrients, especially during early stand development prior to canopy 

closure (Balogh-Brunstad et al. 2008, Shi et al. 2014).

Weathering Results

Bulk density ranged between 0.65 and 1.66 g/cm3 in all horizons of sampled soils, and 

gradually increased with depth (Table 11). Sites 1 and 4 (red pine plantations) exhibited higher 

surface bulk densities than sites 2, 3, and 5 (hardwoods), reflective of legacy soil compaction 

associated with site preparation. Major BC concentrations were lowest in A- and E-horizons 

at all sites, typical of soils dominated by podzolization. At all sites, Mg concentrations reached 

a maximum in the lowest B-horizons. At sites 1 and 2, Ca concentrations in surface horizons 

were about 3.5 times greater than in their respective C-horizons. Titanium (Ti) concentrations 

exhibited considerable variability with depth and across all sites.

Figure 9.—Soil pit at A) site 4 and B) site 1, Osborn Creek watershed. Note the lighter-colored buried plow (Apb) horizon 

between 20 and 40 cm compared to the overlying A-horizon in A). Red arrow in B) points to the portion of the Apb horizon 

that appears to be leached relative to the remainder of the horizon, as shown by C) the sharp decline to an extremely low pH 

within that horizon. Photos by Trevor Hobbs, Huron-Manistee National Forests.
29

In results from the strain analyses (Table 12), positive strain values suggest that a horizon 

has expanded in volume (dilation) since time zero, resulting in a decrease in Ti concentration 

in the weathered horizon relative to the parent material. Negative strain values suggest that 

a horizon has decreased in volume (collapse) since time zero, resulting in an increase in Ti 

concentration in the weathered horizon relative to the parent material. Sites 1 and 5 exhibited 

slightly positive strain values at the surface with gradually decreasing strain values at depth. 

Sites 2, 3, and 4 exhibited drastic increases in strain up-profile, generally reaching a maximum 

at or just below the surface. Because Ti is considered nonweatherable, positive strain values 

in weathered horizons are attributable to decreases in bulk density, inherent mineralogical 

heterogeneity of the original soil parent materials, or both. In a soil weathering study of 

successive beach ridges (dune sand), Lichter (1998) attributed up-profile decreases in bulk 

density to the accumulation of organic matter or bioturbation (confounding edaphic factors 

that introduce heterogeneity over time), or a combination thereof, and cited the correlation 

between Ti concentrations and bulk density as supporting evidence. In this study, soil horizon 

Site and 

forest 

cover Horizon

Horizon 

thickness

Bulk 

density

Calcium 

(Ca)a

Potassium 

(K)a

Magnesium 

(Mg)a

Sodium 

(Na)a

Titanium 

(Ti)a

m g/cm3 -------------------------------mg/kg----------------------------------

Site 1

Red pine

A/E 0.25 1.36 84.60 63.42 112.48 3.98 23.19

Apb/E 0.20 1.61 14.84 94.23 180.52 5.27 27.37

BC 0.45 1.65 35.75 116.04 366.67 6.46 40.16

C/E&Bt 0.50 1.54 18.92 96.36 314.89 5.54 32.22

Site 2

Sugar 

maple

A 0.05 0.67 420.66 306.54 221.69 5.39 7.76

E 0.05 0.76 71.09 260.34 79.88 1.89 6.86

Bw 0.1 1.02 221.74 288.32 369.76 3.31 6.97

Btw 0.4 1.55 147.16 242.64 333.70 2.32 10.10

C 0.8 1.66 121.52 249.45 233.94 3.66 20.22

Site 3

White

oak

A 0.06 0.65 14.63 286.89 199.10 10.32 11.91

E 0.06 0.81 20.83 176.55 85.15 0.75 4.54

Bt 0.05 0.98 43.49 254.32 165.05 1.47 8.64

Bw 0.63 1.44 47.57 260.64 203.16 2.01 5.84

C 0.7 1.62 19.86 250.90 163.03 0.07 18.89

Site 4

Red 

pine

A 0.05 1.28 25.60 260.35 162.07 0.91 8.01

E 0.15 1.32 31.35 263.90 177.00 1.35 6.28

Apb 0.2 1.42 39.79 262.27 179.81 2.97 8.25

Btw 0.2 1.46 56.23 292.25 334.39 2.90 9.74

C 0.8 1.72 59.27 261.92 323.68 1.60 13.76

Site 5

Northern

red

oak

A 0.1 0.88 0.08 212.28 61.44 1.33 5.27

E 0.14 1.13 20.86 231.12 70.64 0.36 8.34

Btw 0.21 1.41 42.30 267.62 212.97 2.92 6.65

Bw 0.65 1.46 27.04 236.51 187.32 1.02 9.12

C 0.4 1.61 46.42 268.59 256.06 1.12 7.85

a Base cation concentration data for Ca, K, and Mg presented here represent nonexchangeable quantities, derived by subtracting 

concentrations in extractable forms (from an ammonium chloride solution) from concentrations after nitric acid-perchloric acidhydrofluoric acid digestion. Data for Na and Ti are from nitric acid-perchloric acid-hydrofluoric acid digestion.

Table 11.—Select physical and chemical characteristics of soil profiles at the five sample sites
30

Ti concentrations exhibited a weak relationship to bulk density (data not shown). Therefore, 

soil horizons that exhibit drastic positive strain values (dilation) in Table 12 are likely to be 

more indicative of mineralogical heterogeneity of the parent materials than increases in bulk 

density up-profile.

This result is surprising given the close proximity and presumed similar genetic origin of these 

soils (i.e., outwash). Because the PMB method relies on the assumption that the original 

parent materials are homogenous, a common approach is to “normalize” parent material 

mineralogy data before calculating weathering losses. Parent material normalization effectively 

accounts for bioturbation. No such transformations were done with soil geochemical data 

in this study. We found no apparent significant correlation between Ti concentrations, bulk 

density, organic matter, or any other soil factors that would warrant transformation of the 

data in an attempt to produce more-accurate results. Furthermore, the sample size of parent 

materials across the study area was small (n=5). As such, the final mineral weathering rates for 

sites 2, 3, and 4 should be interpreted with caution.

Table 12.—Strain analyses and mass flux for each horizon and final weathering rate (WR) for the entire pedon at 

the five sample sites

Site and

forest cover Horizon

Horizon 

thickness Strain (εi,w)a Calcium Potassium Magnesium Sodium Base cation WRb

(m) ------------------------------------------eq/ha/yr-----------------------------------------

Site 1

Red pine 

A/E 0.25 0.57 8.0 -0.3 -21.1 0.0 –13.4

Apb/E 0.20 0.13 -0.1 0.7 -15.3 0.1 –14.6 –36.0

BC 0.45 -0.25 3.0 -0.5 -10.4 -0.1 –8.0

C/E&Bt 0.50 0.00

Site 2

Sugar maple

A 0.05 5.46 4.3 1.2 2.4 0.0 7.9

130.7

E 0.05 5.44 0.6 1.1 0.0 0.0 1.7

Bw 0.1 3.72 4.0 3.5 16.1 0.1 23.7

Btw 0.4 1.14 11.7 12.4 73.2 0.1 97.4

C 0.8 0.00

Site 3

White oak

A 0.06 2.95 0.0 0.9 2.1 0.1 3.1

126.3

E 0.06 7.32 0.3 1.0 1.2 0.0 2.5

Bt 0.05 2.61 0.6 1.2 2.4 0.0 4.2

Bw 0.63 2.64 12.4 28.1 75.5 0.5 116.5

C 0.7 0.00

Site 4

Red pine 

A 0.05 1.31 -0.2 1.2 -0.9 0.0 0.1

35.6

E 0.15 1.86 0.3 4.8 3.2 0.0 8.3

Apb 0.2 1.02 0.4 5.1 -2.2 0.2 3.5

Btw 0.2 0.66 1.4 5.3 16.8 0.2 23.7

C 0.8 0.00

Site 5

Northern 

red oak

A 0.1 1.73 -0.9 0.5 -5.3 0.0 –5.7

–101.7

E 0.14 0.34 -1.5 -1.5 -17.4 -0.0 –20.4

Btw 0.21 0.35 0.3 2.0 -0.6 0.2 1.9

Bw 0.65 -0.05 -8.4 -12.1 -56.9 -0.1 –77.5

C 0.4 0.00

a Positive strain values suggest that horizon volume has increased (dilation); negative values suggest that horizon volume has decreased 

(collapse). C-horizon strain values are always 0.000 by definition.

b Positive WR values indicate net gain of base cations and equate to a “negative” weathering rate.
31

The weathering rates for all soils ranged between -36. 2 and +130.7 eq/ha/yr (Table 12). Three 

of the soils (sites 2, 3, and 4) exhibited positive net fluxes of base cations throughout the 

profile and, therefore, “negative” weathering rates. Negative weathering rates calculated by the 

PMB method have primarily been attributed to inherent heterogeneity of immobile elements 

or major oxides throughout the profile (Whitfield et al. 2011). This explanation may be 

partially representative of conditions at sites 2, 3, and 4, which exhibited significant positive 

strain not attributable to bulk density decreases. However, the positive BC fluxes at sites 2, 

3, and 4 were disproportionately large compared to corresponding strain values (Table 12). 

Instead, net base cation gains may be indicative of significant external BC input at these sites. 

Possible external sources include atmospheric deposition of BC, preferential plant uptake 

from deeper soil layers containing high BC concentrations, or influxes of BC from shallow 

lateral groundwater flow during periods of heavy recharge. The latter explanation is supported 

by results for Mg concentrations, which showed the largest increase in the lowest B-horizon 

at sites 2, 3, and 4 (Table 12). Assuming that Mg held on clay exchange sites was sufficiently 

accounted for prior to calculating mass flux, BC concentration increases of such magnitude 

were probably not due to pedogenic translocation of clays, especially considering the low clay 

contents of these soils (<3 percent). Furthermore, at site 2, a clear lithologic discontinuity at 

a depth of about 140 cm was evident. The underlying diamict contained highly weathered 

chalk fragments that effervesced strongly in 10 percent hydrochloric acid (Fig. 10). These 

chalk fragments probably originated from underlying Paleozoic sedimentary carbonates in 

the Michigan Basin (many of which are dolomitized or high-Mg limestone) (Catacosinos et 

al. 2001) and subsequently would have been incorporated into the local till as ice advanced 

from the west. These nodules may be the source of elevated Mg and inorganic carbon in lower 

horizons at sites 2, 3, and 4 (Table 10); these elements would periodically mix with soil waters 

during periods of heavy rain or rapid spring snowmelt. Other soil weathering studies in 

areas with significant groundwater flow or sand over calcareous substrata, or both, document 

a transition from silicate- to carbonate-dominated weathering regimes below the primary 

rooting zone (Graf Pannatier et al. 2004; Jin et al. 2008, 2012). Such a scenario is suggested 

here at sites 2, 3, and 4.

Figure 10.—A) Bottom of soil pit at site 2 showing contact with underlying diamict and B) chalky nodule found in the 

diamict showing strong effervescence in hydrochloric acid. Red arrow shows where nodule was extracted. Photos by 

Trevor Hobbs, Huron-Manistee National Forests.
32

Critical Leaching of Acid Neutralizing Capacity

Precipitation Surplus

Average annual precipitation surplus at the Hart weather station for the period of analysis 

was 189 mm/yr. The highest soil infiltration occurred in November prior to soil freezing and 

as photosynthesis completely stops. Precipitation lags behind evapotranspiration in June and 

July, creating droughty conditions in the sandiest of soils (Fig. 11).

Relationship between pH and Exchangeable Aluminum

The relationship between pH and exchangeable aluminum (pAl) was generally weak across 

all horizon groups (Table 13). However, values derived for Kalox were still within the range 

suggested as default values using the Kgibb equilibrium constant for mineral soils with less than 

5-percent organic matter (Table 5) (UBA 2004). This finding supports the use of the Kalox

values derived here for calculating ANCle,crit in this study.

Figure 11.—Average moisture surplus by month at the Hart, MI weather station, 1997–2013. Blue areas 

represent periods when precipitation (P) exceeded reference potential evapotranspiration (PET). Pink area 

represents period when reference potential evapotranspiration exceeded precipitation.

Soil horizon 

group Kalox a

Number 

of samples 

analyzed r2

A 6,026 0.31 59 0.28

E 9,550 0.29 44 0.19

B 2,512 0.38 168 0.24

C 2,455 0.41 34 0.40

Table 13.—Equilibrium constant Kalox and exponent a, derived 

from the National Cooperative Soil Survey soil characterization 

database for Typic Udipsamments and Entic Haplorthods in 

Michigan and Wisconsin Sandy Drift, and results of regression 

analysis of pH and aluminum concentrations using data from 

the soil characterization database 
33

Site-specific Acid Neutralizing Capacity Rate

Site-specific acid neutralizing capacity rate (ANCle,crit) values are presented in Table 14. 

As a means of comparison, results of the ANCle,crit equation using both the standard Kgibb

equilibrium constant for sandy soils with less than 5-percent organic matter and the horizon 

group Kalox constants derived here from local soils data are presented. The formula for ANCle,crit

requires that the sum of BCdep and BCw exceed BCu, or that there is a minimum (nonnegative) 

amount of BC concentration in soil waters, below which base cations cannot be taken 

up by vegetation (UBA 2004). Sites 2, 3, 4, and 5 did not meet this requirement, due to a 

combination of negative weathering rates (sites 2, 3, and 4), and high uptake rates. Thus, for 

determining site-specific ANCle,crit, a minimum soil water BC concentration of 0.1 eq/m3 was 

used to calculate a default BC leaching term in the form of:

BCle = BCdep + BCw – (0.1 × [Q]), (11)

Where

BCdep = base cation deposition,

BCw = base cation weathering, and

Q = precipitation surplus (in m3/ha/yr) (UBA 2004).

This formula (eqn. 11) was used in place of the (BCdep + BCw – BCu) term in the ANCle,crit

equation (eqn. 9) for sites 2, 3, 4, and 5. Furthermore, site 4 was assigned the weathering 

rate of site 1 (red pine, 36.2 eq/ha/yr) and sites 2 and 3 (hardwoods) were assigned the 

weathering rate of site 5 (northern red oak, 101.8 eq/ha/yr). The use of the standard Kgibb

resulted in slightly lower ANCle,crit values than any of the horizon group Kalox values (Table 

14). Furthermore, within each Kalox horizon group, no differences between resulting ANCle,crit

values were observed (to two decimal places), with the exception of a minor difference in the 

C-horizon group as applied to site 5. The minimal difference between each horizon group is 

very likely due to the small Q used for all sites (the ANCle,crit equation appears sensitive to Q).

Kgibb Kalox

Site

(organic 

matter <5%)

AhorizonsEhorizonsBhorizonsChorizons

eq/ha/yr

1 130.2 147.9 147.9 147.9 147.9

2 26.3 37.5 37.5 37.5 37.5

3 26.3 37.5 37.5 37.5 37.5

4 351.4 375.7 375.7 375.7 375.7

5 357.6 381.9 381.9 381.9 381.9

Table 14.—Site-specific values for acid neutralizing capacity 

derived from standard gibbsite equilibrium constant (Kgibb) 

and the equilibrium constant Kalox for different horizon 

groups analyzed from National Cooperative Soil Survey soil 

characterization data
34

Critical Acid Loads and Exceedances

To calculate CAL estimates and exceedances for sample sites (Table 15), we used two 

scenarios for describing the fate of N in soils. In the worst-case scenario, Ni = 0; in other 

words, all Ndep that is not taken up by vegetation (Nu) is leached from the soil. In the bestcase scenario, Ni

 = Ndep – Nu (i.e., all Ndep that is not taken up by vegetation is retained in the 

soil system). Critical acid load estimates were calculated for each scenario using both Niworst

(CAL 1) and Nibest (CAL 2). Under the worst-case scenario, CALs ranged from –276.7 

to 32.0 eq/ha/yr. Under the best-case scenario, CALs ranged from 332.9 to 670.7 eq/ha/

yr. Negative CAL estimates for sites 2, 3, 4, and 5 under the worst-case scenario resulted 

from a combination of very low weathering rates and high tree uptake rates. Two scenarios 

for exceedance (corresponding to CAL 1 and CAL 2) are also presented in Table 15. 

Under scenario 1, exceedances ranged from 1,177.7 to 1,411.9 eq/ha/yr. Under scenario 2, 

exceedances ranged from 539.0 to 876.8 eq/ha/yr.

The site-specific CAL estimates under the worst-case and best-case scenarios were lower than 

national-scale CAL estimates by McNulty et al. (2010) for the same locations (Table 16). A 

critical acid load was not calculated for site 1 in McNulty et al. (2010), perhaps because only 

a minor amount of forest land was contained in that cell of their raster analysis, and therefore 

was excluded. For the other four sites, CALs under the worst-case scenario in this study were 

869.2 eq/ha/yr lower on average, and CALs under the best-case scenario in this study were 

335.0 eq/ha/yr lower on average, than those reported by McNulty et al. (2010). Exceedances 

under the worst-case scenario in this study were 975.1 eq/ha/yr higher on average than those 

reported by McNulty et al. (2010). Exceedances under the best-case scenario were 441.0 eq/

ha/yr higher on average than what was determined by McNulty et al. (2010). The CALs 

and exceedances under each scenario in this study were drastically different, demonstrating 

the uncertainty associated with long-term Ni. However, the fate of N is probably better 

represented by the worst-case scenario than the best-case scenario. Data from the Michigan 

Nitrogen Deposition Gradient Study support this interpretation (Zak et al. 2008, Zogg et al. 

2000). Zak et al. (2008) concluded that about 20 percent of anthropogenic N applied to their 

sites was assimilated into forest soil pools, with the remainder lost to leaching. Regardless 

of the fate of N at sites in this study, both scenarios suggest that soils are indeed at risk for 

acidification under current deposition rates, and perhaps more at risk than was predicted by 

national-scale CAL estimates (McNulty and Boggs 2010, McNulty et al. 2007).

Table 15.—Site-specific input parameters to the simple mass balance equation, critical acid loads (CALs), and 

exceedances for the five sample sites

Site BCdep Cldep BCw

a BCu Niworstb Nibestb Nu ANCle,crit S+Ndep CAL 1c CAL 2c Ex 1d Ex 2d

1 148.9 21.7 36.2 86.5 0 638.7 103.0 147.9 1,209.7 32.0 670.7 1,177.7 539.0

2 148.4 21.6 101.8 560.4 0 536.7 205.0 37.5 1,209.7 –164.3 372.4 1,374.0 837.3

3 148.4 21.6 101.8 599.9 0 468.3 273.4 37.5 1,209.7 –135.4 332.9 1,345.1 876.8

4 148.6 21.6 36.2 108.3 0 592.9 148.9 375.7 1,209.7 –171.9 421.0 1,381.6 788.7

5 152.8 22.1 101.8 256.6 0 538.6 129.3 381.9 1,135.2 –276.7 261.9 1,411.9 873.3

a Weathering results from the pedalogical mass balance method at sites 2, 3, and 4 were negative; therefore, the base cation weathering rate (BCw) 

from site 1 (red pine) was assigned to site 4 (also red pine), and BCw from site 5 (hardwood) was assigned to sites 2 and 3 (also hardwoods).

b Niworst represents the worst-case scenario and Nibest the best-case scenario for long-term nitrogen (N) retention in soils. The Niworst value of 0 assumes 

that all N is leached from the soil. The Nibest value is set to Ndep – Nu, representing a scenario where all deposited N (Ndep) that is not taken up by 

vegetation (Nu) is retained in the soil.

c CAL 1= critical acid load estimate using Niworst term; CAL 2= critical acid load estimate using Nibest term.

d Ex 1 = exceedance of critical acid load under CAL 1; Ex 2 = exceedance of critical acid load under CAL 2.
35

The estimated CALs and exceedances presented 

here are heavily influenced by BCu and Nu. 

Considerable variation in BCu and Nu was 

found across and within all sites in this study. 

To illustrate the influence of BC and N uptake 

in the simple mass balance model, CALs and 

exceedances were determined using the lowest 

and highest BC and N uptake data for individual 

trees at each site (Table 17). The lowest BC 

uptake combined with the highest N uptake 

represents the best-case scenario (highest 

CAL) for soil acidification status at each site. 

In contrast, the highest BC uptake combined 

with the lowest N uptake represents the worstcase scenario (lowest CAL) for soil acidification 

status at each site. Furthermore, because of the 

uncertainty associated with the fate of N at these 

sites, exceedance was calculated using only Sdep

in Table 17. Even under the best-case uptake 

scenario while using only Sdep (ignoring the 

potential for acidification from excess N), all sites 

were in exceedance of CALs.

In this study, we assume that BCu and Nu are 

representative of losses due to stem-only harvest. 

In reality, the BCu and Nu terms are accurate and 

applicable only when the sites are actually logged 

and nutrients are taken offsite. This method 

was used as a cautious approach to assess the 

potential for soil acidification assuming all trees 

will eventually be harvested. Although harvesting 

has not yet occurred, many of these stands are 

at or beyond their intended rotation age. The 

red pine stands in particular (sites 1 and 4) will 

probably be harvested more intensively than any 

of the other sites. Sites 2, 3, and 5 (hardwood 

stands) may or may not be harvested at all. 

Regardless of the intended rotation period or date 

of harvest, the CALs and exceedances presented 

here suggest that soils are at an increased risk for 

acidification if harvesting continues on these sites.

Although the estimated CALs and exceedances 

presented here are also influenced by BCw rates, 

the difference in weathering estimates calculated 

by different methods does not significantly 

influence the main conclusions of this study. 

National-scale estimates using the soil texture 

Table 16.—Critical acid loads (CALs) and exceedances 

as mapped by McNulty et al. (2010) at locations in this 

study and the difference (diff) in CALs and exceedances 

between this study and McNulty et al. (2010) for two 

nitrogen immobilization (Ni) scenariosa

Critical acid load

Site

From McNulty et al. 

(2010)

Diff scenario 

1t

Diff scenario 

2t

eq/ha/yr

1 0 0 0

2 764.9 –929.2 –392.5

3 768.0 –903.4 –435.1

4 770.5 –942.4 –349.5

5 424.9 –701.6 –163.0

Average difference –869.2 –335.0

Exceedance

1 0 0 0

2 320.9 1,053.1 516.4

3 314.3 1,030.8 562.5

4 313.0 1,068.6 475.7

5 664.1 747.8 209.2

Average difference 975.1 441.0

a Scenario 1 uses the Niworst term, which assumes that all N is leached from 

the soil, whereas scenario 2 uses the Nibest term, which assumes that all N 

not taken up by vegetation is retained in the soil.

Critical acid load

Best-case scenario Worst-case scenario

Site BCulow + Nuhigh BCuhigh + Nulow

1 68.2 1.7

2 243.7 –1,094.7

3 326.2 –509.4

4 –108.5 –253.4

5 –125.1 –469.9

Exceedanceb

1 399.8 466.3

2 224.2 1,562.6

3 141.8 977.4

4 576.4 721.4

5 592.4 937.8

a Scenarios differ in relative rate of base cation uptake (BCu) and of 

nitrogen uptake (Nu).

b Exceedances calculatedwith deposited sulfur only.

Table 17.—Critical acid loads and exceedances from 

empirical tree nutrient data in this study for two 

nitrogen immobilization scenariosa
36

approximation (STA) method predicted that mineral weathering in this location is 239.7 eq/

ha/yr (McNulty and Boggs 2010). Despite the difficulties that the pedological mass balance 

(PMB) method presented in estimating mineral weathering at these sites, the difference 

between BCw using the STA method and using the PMB method here would not result in a 

shift from exceedance to nonexceedance for any sites under either CAL scenario (calculations 

not shown).

MANAGEMENT RECOMMENDATIONS

Applicability of Results

The critical load concept is a useful tool for assessing the potential for adverse impacts caused 

by acid deposition and harvesting. Exceedances of critical acid loads (CALs) presented 

here may or may not actually indicate a specific ecosystem response, but rather suggest that 

adverse impacts to trees and soil nutrient pools are more or less likely. Even though typical 

ecosystem responses associated with CAL exceedance (such as fine root or foliar damage) 

were not specifically investigated during fieldwork, no gross signs of tree decline were noticed. 

The exceedances under best- and worst-case scenarios presented here suggest that soils in 

the Osborn Creek watershed are likely to undergo a reduction in capability of supporting 

healthy, vigorous stands if current rates of atmospheric deposition and presumed harvest 

scenarios continue. However, until more is known about N dynamics in these stands, and 

until specific dose-response relationships relating acid deposition to changes in a soil chemical 

indicator (and subsequent biological harm) are defined for different forest types in Michigan, 

quantifying the likelihood of adverse impacts will remain difficult.

Much recent work in Michigan, as part of the Michigan Nitrogen Deposition Gradient 

Study research program, has focused on understanding nitrogen (N) cycling and retention in 

forest soils, specifically with respect to impacts on soil fungal and microbial populations and 

carbon dynamics under elevated anthropogenic N deposition (Fisk et al. 2002; Hassett and 

Zak 2005; Zak et al. 2004, 2008; Zogg et al. 2000). Among important conclusions about soil 

acidification, the research program suggests that the influence of acid deposition in Michigan 

has been waning since passage of the 1990 amendments to the Clean Air Act of 1970, trees 

do not appear to be in decline, and soil base saturation remains high (Talhelm et al. 2012). 

These conclusions may be relevant to the Osborn Creek watershed as well. Base saturation 

remains high at all sites, with the exception of values approaching less than 30 percent in the 

subsoil at site 5 (Table 10). However, base saturation may not be an appropriate chemical 

indicator for this watershed. Soils in this region are predominantly sand textured, and as such, 

small differences in base cation concentration can have a large influence on base saturation 

calculations. Moreover, the Michigan Nitrogen Deposition Gradient study focuses solely on 

sugar maple stands, and therefore, conclusions from that research program may not be directly 

applicable to stands of other hardwoods and conifers in the Osborn Creek watershed.

Watershed Condition Framework Rating

Soils in all watersheds on the Huron-Manistee National Forests (HMNF) were given a score 

of “3—Impaired Function” in the Watershed Condition Framework (WCF) rating system. 

If it is important from a management perspective to adhere to the operational definitions 

associated with each WCF rating (Table 1), then a rating of 3 should be retained for this 

watershed. Indeed, sulfur (S) and N deposition are above the critical loads as determined in 
37

this study. However, exceedance of CALs has not yet produced observable negative impacts 

on forests in this watershed. If the observations and results from this study are considered in 

context, then a rating of “2—Functioning at Risk” is probably a more appropriate description 

of soil conditions across the Osborn Creek watershed.

Several factors complicate the widespread applicability of the results in this study. These 

factors include:

• The wide range of base cation and nitrogen uptake estimates within and across sites 

• The difficulty in estimating mineral weathering rates using empirical data 

• The influence of geologic (nonpedogenic) layers below the soil pedon, which may 

provide “external” nutrients to varying degrees depending on their lithology and the 

depth of rooting 

• The apparent chemical influence of groundwater in lower pedons at some sites 

• The excessively high base saturation in contrast to low pH in soils.

Biogeochemical cycling between soils and trees is highly spatially heterogeneous at the 

watershed scale. Given the wide range of CALs and exceedances presented here and the high 

degree of uncertainty associated with some parameters of the simple mass balance model—

despite detailed site-specific data—extrapolating the results of this research to assess the 

acidification status of soils in other watersheds on the HMNF is not recommended. Doing 

so would not provide any more-reliable insight than what has already been determined by 

national-scale CAL estimates for this area (McNulty and Boggs 2010, McNulty et al. 2007). 

In that sense, the national-scale CAL estimates are viewed as an excellent starting point to 

assess the potential for soil acidification at the regional scale, which can then be used to target 

specific watersheds for more detailed and site-specific modeling.

Monitoring Soil Nutrients

According to site-specific analysis, all sites in this study are likely to undergo CAL exceedance 

under presumed harvest scenarios. These results raise questions about the most effective 

management strategies to maintain soil nutrient pools and protect soils from increased 

acidification—especially in light of current fuels reduction and short-term rotation harvest 

practices common in some areas of the HMNF. Some national forests, particularly in the 

mid-Atlantic region, where acid deposition has historically been high relative to other parts 

of the country, have developed monitoring programs aimed at understanding and tracking the 

impacts of acid deposition on soils and forest health (Connolly et al. 2007, Farr et al. 2009). 

Those forests can easily demonstrate and justify the costs of conducting such work because of 

continued high acid deposition (especially N), and the observed effects directly related to acid 

deposition in both terrestrial and aquatic ecosystems (Driscoll et al. 2003, Siemion et al. 2014, 

Sullivan 2012). In the western Lower Peninsula of Michigan, however, acid deposition has 

historically been moderate; total (S+N)depwas about 17.9 kg/ha/yr from 2000 to 2012 in the 

Osborn Creek watershed.

Regardless of the impacts of acid deposition, forests in the region are already subject to a 

host of stresses from pests such as emerald ash borer (Agrilus planipennis) and gypsy moth 

(Lymantria dispar) and diseases such as oak wilt (causal agent: Ceratocystis fagacearum) and 

beech bark disease (a complex that includes the insect Cryptococcus fagisuga and the fungi 

Neonectria spp.) (Michigan Department of Natural Resources 2014). Groundwater across the 
38

region is generally considered to be well buffered due to high carbonate content inherited 

from underlying calcareous bedrock units in the Michigan Basin. Furthermore, the underlying 

glacial stratigraphy (and hence, surface water/groundwater interactions) is exceedingly 

complex and not mapped in detail. The sandiest of soils in Michigan, which are logically 

considered to be at risk of acidification, often contain an unpredictable arrangement of thin 

geogenic clayey layers (i.e., “textural banding”) that have been demonstrated to attract roots 

well below typical rooting depths (Hannah and Zahner 1970), and therefore very likely play 

a role in nutrient cycling and soil acid-base chemistry. In the Lower Peninsula, these features 

have been formally recognized and incorporated into county soil survey data only where U.S. 

Forest Service ecological mapping protocols were used (i.e., on U.S. Forest Service lands) 

(Host et al. 1993). Therefore, significant differences exist between map unit concepts across 

political boundaries in Michigan. These inconsistencies add more uncertainty to landscapescale CAL modeling efforts that rely on publicly available soils data.

From a broad management perspective, existing conditions may dampen the urgency for 

monitoring the impacts of acid deposition on the HMNF, especially in light of the inherent 

complexity of detecting direct links between acid deposition and ecosystem effects in this 

area. Moreover, new research suggests that climate change may have an impact on the 

factors that regulate CAL limits. For example, an increase in BC weathering and N uptake 

and retention due to warming temperatures and shifting precipitation patterns may benefit 

ecosystems susceptible to acidification (McNulty et al. 2013). Despite these uncertainties, we 

recommend an intensified soil nutrient management effort beyond what is already laid out 

in Forest Service Manual 2550 and the HMNF Land and Resource Management Plan (U.S. 

Forest Service 2012), specifically for ensuring long-term supply of soil base cations under 

current harvest and fuels reduction practices. An important first step would be to obtain 

more-sophisticated modeling tools to calculate mineral weathering (i.e., PROFILE), and 

plant uptake rates (i.e., the GrowUp model; Coordination Centre for Effects 2017) to reduce 

uncertainty related to those parameters of the SMB model. Further steps would involve 

using existing soil geochemical data from the U.S. Geological Survey (Smith et al. 2013) 

with additional field data to create Forest-wide maps that more accurately predict mineral 

weathering rates. Similarly, more field data should be collected to better understand the spatial 

variability and edaphic conditions that contribute to tree base cation (BC) and N uptake 

variability at the Forest-wide scale. At the site-specific scale, nondestructive methods for 

accurately and quickly measuring timber volume at monitoring plots (i.e., photogrammetry or 

LiDAR) could add greater certainty to BC and N uptake estimates.

These additional steps could be used to develop a Forest-wide nutrient sensitivity map, 

similar to what is in place on the Monongahela National Forest in West Virginia (Connolly 

et al. 2007), or to develop sustainable harvest interpretation ratings (applied to soil map 

units), similar to what is in place for Connecticut (Natural Resources Conservation Service 

2016). This map could then be used to develop guidelines for long-term, sustainable fuels 

reduction and harvest practices tailored to the capability of individual sites and soils to 

withstand repeated nutrient removals. Finally, testing and applying a soil chemical indicator 

that specifically relates acid deposition levels to a detectable symptom of biological damage 

for different tree species found across the HMNF would be informative. This effort ideally 

should be taken on by a government agency (such as the U.S. Forest Service), university, or 

other research organization that demonstrates a high level of expertise in both critical load 

modeling and the complexities of Michigan soils. The most commonly applied soil chemical 

indicators that relate deposition to biological damage (such as BC:Al ratios, Ca:Al ratios, 
39

and maintaining base saturation >20 percent) may or may not be relevant for some tree 

species in the northern Lower Peninsula, which have adapted over millennia to naturally 

acidic soils dominated by podzolization. Results of such efforts—combined with a U.S. Forest 

Service program to continually monitor base saturation before, during, and after harvests and 

plantings in areas determined to be at high risk—would greatly enhance the environmental 

analyses and disclosure of impacts to soils and aquatic resources associated with U.S. Forest 

Service timber management activities.

CONCLUSIONS

Site-specific critical acid loads were modeled for five sites in the Osborn Creek watershed 

using empirical data as much as possible and the simple mass balance (SMB) approach. On 

average, the base cation (BC) and nitrogen (N) uptake rates derived in this study were higher 

than values used in national-scale critical acid load (CAL) estimates by 76.1 and 167.9 eq/

ha/yr, respectively (McNulty and Boggs 2010; McNulty et al. 2007, 2013). Considerable 

variation in BC and N uptake rates was found between and within species in both bark and 

bole wood. Hardwood species exhibited a greater range of BC and N uptake than conifer 

species (red pine). Homogenous stands such as red pine plantations have more consistent 

nutrient values probably because light and space are consistently limited. Species-specific 

tree nutrient concentration data in this study are within or below the range of concentrations 

reported in the tree chemistry database (Pardo et al. 2005). Considered in combination, 

these observations suggest that values for BCu and Nu are heavily influenced by the method 

used to calculate tree volume. The method for calculating tree volume in this study is based 

on stem-only harvest and species-specific volumetric equations (Miles and Hill 2010) using 

field measurements at five trees per site. Overall, the considerable variation in BC and N 

concentrations and uptake values demonstrated at the site level in this study suggest that 

stand characteristics and soil factors have an inseparable connection to growth and uptake, 

and that assigning a single value to a species across a watershed (let alone a species-group 

across an entire continent) may not be appropriate. Future research aimed at characterizing 

the influence of edaphic factors, climate, and stand factors on intraspecies variability in 

nutrient uptake at multiple scales would be beneficial for larger scale CAL modeling efforts.

Our results point to signs of acidification in the soils studied. Surface horizons, which receive 

the bulk of atmospheric acid deposition, had pH values of 3.25 to 3.97 at four of the five 

sites. Buried plow horizons in red pine plantations (sites 1 and 4) exhibited clear signs of 

humus eluviation (lighter color and lower pH), suggesting that acid deposition—presumably 

in conjunction with low-molecular-weight organic acid production in the root zone—has 

facilitated the translocation of humus out of these horizons since the time of planting 

(~46 years ago). However, despite acidic soil conditions, base saturation remained high to 

super-saturated in most horizons. The combination of very low weathering rates, droughty 

conditions during summer months, very low cation exchange capacity, and significant external 

base cation inputs from a variety of potential offsite sources may be responsible for such high 

base saturation and exchangeable cation pools at these sites.

Empirical mineral weathering rates using the pedological mass balance method in this study 

for site 1 (36.2 eq/ha/yr) and site 5 (101.8 eq/ha/yr) were lower than what was determined by 

national-scale estimates using the soil texture approximation method for the same locations 

(239.9 eq/ha/yr) (McNulty and Boggs 2010; McNulty et al. 2007, 2013). Although three 

of the sites (2, 3, and 4) yielded negative weathering estimates, the positive net flux of base 
40

cations at these sites is not considered erroneous, but rather suggests that at present these soils 

may receive more nutritional support from external BC sources (i.e., atmospheric deposition 

or groundwater), which could outweigh what is produced through weathering onsite within 

the rooting zone alone. In such soils, applying the soil texture approximation method may 

overestimate actual mineral weathering rates. Overall, soils data collected in this study 

highlight the idea that the SMB model may not fully capture the nuances of nutrient cycling 

in forest soils with sandy-over-calcareous substrata, periodic base-rich groundwater influence, 

or a combination of both. CAL modeling in such areas should ideally incorporate into the 

SMB model additional parameters that estimate the flux of BC through the soil system from 

upslope sources delivering weathering products to the root zone, shallow groundwater fluxes, 

and pedogenic or geologic layers tapped by deep-rooted trees.

By using results from national-scale estimates, soils in all watersheds in the Huron-Manistee 

National Forests were assigned the lowest score of “3—Impaired Function” due to CAL 

exceedance in the Watershed Condition Framework rating system. This study found that 

exceedance occurred in soils in the Osborn Creek watershed under worst- and best-case 

scenarios of N retention, BC and N uptake, and mineral weathering estimates. Despite the 

high likelihood of exceedance, trees and roots exhibited no clear visual signs of biological 

damage. Furthermore, base saturation in soils remained high. As such, trees may not be at 

immediate risk of aluminum toxicity due to excess base cation depletion. However, if current 

deposition rates and harvesting practices continue in perpetuity, the likelihood of adverse 

impacts from acidification may increase.

ACKNOWLEDGMENTS

This work would not have been possible without the Geological Society of America 

GeoCorps program and the support of various staff members at the Huron-Manistee 

National Forests. Thanks to Terry Saarela, Rich Corner, Russ Sutton, Jonathon Meeks, 

Carol Young, and Danielle Roush for assistance with fieldwork. Thanks to Vince Archer for 

helpful suggestions at the onset of this research. Thanks to the Cornell Nutrient Analysis 

Laboratory for processing lab samples. Thanks to Mary Beth Adams and Steve McNulty, and 

two anonymous reviewers for valuable reviews of the manuscript. Thanks to Steve McNulty 

and Erika Cohen for supplying their data for comparison. Thanks to Jennifer Phelan (RTI 

International), Dave Smith (U.S. Geological Survey), and Julian Aherne (Trent University) 

for useful insight, data, and tools.

English Conversion Factors Applicable to Text, Tables, and Figures

When you know: Multiply by: Then add: To find:

centimeters (cm) 0.394 inches

cubic meters 35.3 cubic feet

degrees Celsius (0C) 1.8 × 0C 32 degrees Fahrenheit

hectares (ha) 2.47 acres

kilometers (km) 0.621 miles

meters (m) 3.28 feet

millimeters (mm) 0.0394 inches

square kilometers (km2) 0.386 square miles

tonnes 1.102 tons
41

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Hobbs, Trevor; Lynch, Jason; Kolka, Randy. 2017. Site-specific critical acid load estimates 

for forest soils in the Osborn Creek watershed, Michigan. Gen. Tech. Rep. NRS-171. 

Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research 

Station. 49 p. https://doi.org/10.2737/NRS-GTR-171.

Anthropogenic acid deposition has the potential to accelerate leaching of soil cations, and 

in turn, deplete nutrients essential to forest vegetation. The critical load concept, employing 

a simple mass balance (SMB) approach, is often used to model this process. In an evaluation 

under the U.S. Forest Service Watershed Condition Framework program, soils in all 6th level 

watersheds on the Huron-Manistee National Forests (HMNF) in Michigan were assigned 

the lowest score of “3—Impaired Function” due to exceedance of the critical load of acidity 

as determined by national-scale estimates. The impetus for this research was to test the 

relevance of national-scale critical acid load estimates at the 6th level watershed scale by 

using site-specific field data in the SMB model where possible.

The Osborn Creek watershed on the HMNF served as a case study. Field data were collected 

to estimate soil mineral weathering rates, nutrient uptake rates, and forest growth 

characteristics at five sites containing sandy, nutrient-poor soils. Critical acid loads and 

exceedances were developed under “best” and “worst” case scenarios given the uncertainty 

in the SMB model. Despite the high likelihood of actual exceedance and some evidence 

for soil acidification across the watershed, base saturation remains excessively high (>100 

percent) at most sites. Other field data suggest that these soils receive significant external 

inputs of base cations that may outweigh what is produced through weathering onsite 

within the rooting zone. Trees show no visible signs of decline.

Overall, the SMB approach may not adequately capture the complexity of nutrient cycling at 

all of the sample sites. The variability of soils, weathering estimates, and nutrient uptake rates 

between and within sites makes extrapolation of these results to other HMNF watersheds 

difficult to justify. Management programs aimed at improving our understanding of base 

cycling in complex glacial terrain, as well as mitigating the risks associated with nutrient 

depletion from frequent timber harvests and fuels reduction practices, are suggested.

KEY WORDS: soil acidification, critical load, simple mass balance, weathering rate, nutrient 

cycling

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To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD3027, found online at http://www.ascr.usda.gov/complaint_filing_cust.html and at any USDA office or write 

a letter addressed to USDA and provide in the letter all of the information requested in the form. To request 

a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: 

U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, 

SW, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov.
Northern Research Station

www.nrs.fs.fed.us