Spatial Distribution of Acid-Sensitive and Acid-Impacted Streams in Relation to Watershed Features in the Southern Appalachian Mountains

Water Air Soil Pollut (2007) 182:57–71 DOI 10.1007/s11270-006-9320-x

Spatial Distribution of Acid-sensitive and Acid-impacted Streams in Relation to Watershed Features in the Southern Appalachian Mountains

T. J. Sullivan & J. R. Webb & K. U. Snyder & A. T. Herlihy & B. J. Cosby

Received: 26 July 2006 /Accepted: 18 November 2006 / Published online: 19 December 2006 # Springer Science + Business Media B.V. 2006

Abstract A geologic classification scheme was com- sandstone and quartzite. Streamwater acid-base chem- bined with elevation to test hypotheses regarding istry throughout the region was also found to be watershed sensitivity to acidic deposition using associated with a number of watershed features that available regional spatial data and to delimit a high- were mapped for the entire region, in addition to interest area for streamwater acidification sensitivity lithology and elevation, including ecoregion, physio- within the Southern Appalachian Mountains region. It graphic province, soils type, forest type and watershed covered only 28% of the region, and yet included area. Logistic regression was used to model the almost all known streams that have low acid presence/absence of acid-sensitive streams throughout neutralizing capacity (ANC ≤20 μeq l−1) or that are the region. acidic (ANC ≤0). The five-class geologic classification scheme was developed based on recent lithologic Keywords acid neutralizing capacity . acidification . maps and streamwater chemistry data for 909 sites. Appalachian Mountains . geology. The vast majority of the sampled streams that had streamwater . watershed ANC ≤20 μeq l−1 and that were totally underlainby a single geologic sensitivity class occurred in the siliceous class, which is represented by such lithologies as 1 Introduction

Ongoing efforts to control the atmospheric emissions and deposition of sulfur (S) and nitrogen (N) near and upwind of the Southern Appalachian Mountains (SA) * : T. J. Sullivan ( ) K. U. Snyder region are aimed, in part, at reversing past acidifica- E&S Environmental Chemistry, Inc., P.O. Box 609, Corvallis, OR, USA tion and preventing future acidification of stream- e-mail: [email protected] waters (Brewer, Sullivan, Cosby, & Munson, 2000). : Sullivan et al. (2004) provided a model-based analysis J. R. Webb B. J. Cosby of future changes in streamwater acid-base chemistry Department of Environmental Sciences, University of Virginia, as part of an assessment by the Southern Appalachian Charlottesville, VA, USA Mountains Initiative (SAMI). The results of that analysis pertain primarily to numbers, percentages, A. T. Herlihy and extent of streams within the SAMI domain Department of Fisheries and Wildlife, Oregon State University, affected now or projected to be affected in the future Corvallis, OR, USA by acidic deposition. 58 Water Air Soil Pollut (2007) 182:57–71

Additional questions that might be deemed to be water acid-base status. For example, Lynch and Dise important, but that cannot be addressed using a (1985) found that watersheds in Shenandoah National quantitative, population-based analysis, could include: Park, VA that had a larger proportion of their area above 732 m elevation had lower streamwater ANC. & Where within the SAMI domain are the most acid- Such a gradient may have been due to the general sensitive streams located? observation that well-developed soils are less common & Are there features of the landscape that correlate with at higher elevation, where slopes are often steeper and current streamwater acid-base chemistry and/or sen- temperatures lower. Furthermore, slower weathering, sitivity to change in streamwater acid-base chemis- greater precipitation and base cation leaching, and try? If so, what are those features and how are they higher acidic deposition at higher elevation likely all distributed across the landscape? play significant roles in such observed relationships. The regional aquatic resource classification scheme Similar results have been found for Great Smoky presented here was developed to address questions Mountains National Park in North Carolina and such as these. Tennessee (Silsbee & Larson, 1982). The importance Acid neutralizing capacity (ANC) is an effective of elevation, elevational gradients, and landscape stratifying variable for evaluation of streamwater acid- position as controllers of drainage water acid-base base chemistry. ANC is both a measure of current acid- chemistry are well known (Johnson, Driscoll, Eaton, base status and a product of watershed processes that Likens, & McDowell, 1981). determine the presence of acidic and basic constituents The forest per se is not a major controlling variable in solution. We can expect future response to acidic for streamwater ANC. It can be, however, a useful deposition to differ among streams with different ANC indicator of the watersheds most likely to be sensitive levels. ANC has thus served as a primary variable for to, and/or impacted by, acidic deposition in the SAMI stratifying streams to be modeled for the SAMI aquatic region (Herlihy et al., 1993). Based on data from the effects assessment (Sullivan et al., 2002, 2004). Streams National Stream Survey (NSS), acidic streams are having ANC e 20 2eq l −1 are classified as sensitive to found only in forested watersheds in the northern chronic and/or episodic acidification with probable portion of the SAMI region, and streams with ANC adverse impacts on native brook trout (Salvelinus <50 2eq l−1 are more common in forested than fontinalis; Bulger, Cosby, & Webb, 2000). The unforested watersheds. In particular, there is a clear relationship between bedrock geology and the ANC of distinction in the Valley and Ridge Province between streams in the SA region has been well-recognized the more acid-sensitive streams of the forested ridges (Bricker & Rice, 1989; Cosby, Ryan, Webb, Hornberger, and the less acid-sensitive streams of the valleys, &Galloway,1991; DeWalle, Dinicola, & Sharpe, 1987; which contain mixed land use (Herlihy et al., 1993). Herlihy et al., 1993;Lynch&Dise,1985; Puckett & Acidic streams also tended to be located in smaller Bricker, 1992;Webbetal.,1994). Bedrock geology and, watersheds at higher elevation with steeper gradients. in glaciated terrain, surficial geology have been shown Acidic streams in the NSS were found almost to be important elsewhere (c.f., Norton, 1980;Melack, exclusively at elevations >300 m and in watersheds Stoddard, & Ochs, 1985; Peters & Driscoll, 1987;Clow less than 20 km2 (Herlihy et al., 1993). & Sueker, 2000). Bulger et al. (2000)demonstrated Ecoregions are depictions of ecosystem patterns that that classification of landscape by bedrock type can are created through a classification process that provide a basis for regionalizing the results of wa- captures the spatial distribution of relatively homoge- tershed acidification modeling in western Virginia. neous landscape areas at a specific scale (Bailey, 1995; These observations, plus the availability of recently- Bryce, Omernik, & Larsen, 1999; Omernik, 1995). developed geologic map coverages for most of the Ecoregions can be redefined in different ways at SAMI region, suggested that a useful regional different scales. The scale of application is important landscape classification scheme could be developed because the influence of specific landscape features based in part on the relationship between lithology (e.g., geologic class, vegetation type) on resource and streamwater ANC. integrity (e.g., forest health, streamwater acid-base In addition to geology, other landscape character- chemistry) is highly scale-dependent. For example, a istics have also been found to correlate with stream- stream located in a watershed that contains limestone, Water Air Soil Pollut (2007) 182:57–71 59 but that occurs in a region of largely granitic bedrock, Figure 1 presents the general scheme used to may have high ANC even though the region as a whole classify the non-carbonate lithologic units according may be best represented by low-ANC streams. Eco- to the properties expected to influence the ANC of regions were designed to provide a spatial framework associated streamwaters. Table 1 provides a list for ecological assessments, research, inventory, mon- indicating the assignment of individual lithologic itoring, and management. Watersheds within a given units to each of the five sensitivity classes. Note that ecoregion will tend to be somewhat similar to one lithologic map units with primary rock types defined another and different from those in other ecoregions by structure rather than composition were classified (Omernik & Bailey, 1997; Omernik & Griffith, 1991). based on secondary rock type or state-by-state Thus, a variety of watershed features are known or formation descriptions. Examples of lithologic map suspected to be associated with watershed and units in this category include: conglomerate, meta- drainage water acid-sensitivity. The objective of the sedimentary rock, breccias, and schist. research reported here was to test hypotheses suggest- ing that streamwater acid-base chemistry is controlled 2.2 Development of Landscape Classification System by geologic, edaphic, and topographic variables, using spatial data available at the regional scale. A regional spatial analysis was conducted to determine the extent to which streamwater ANC in the SAMI region correlated with landscape features that were (1) known or suspected to be important contrib- 2 Materials and Methods utors to surface water acidification sensitivity, and (2) available spatially for the entire study region. 2.1 Geologic Sensitivity Classification Such features included lithology, elevation, watershed area, watershed relief, forest vegetation type, A lithology-based five-unit geologic classification soil type, and ecoregion. system was developed for the SAMI region as an Streamwater composition data for the region were extension of the three-unit classification scheme compiled in support of the SAMI aquatic effects proposed by Peper et al. (1995) for use in the assessment (Sullivan et al., 2004). These data were Southern Appalachian Assessment (SAMAB, 1996). obtained from a number of national and regional The geologic map coverage that served as the basis databases, including the National Stream Survey for landscape classification was provided by the (NSS), Environmental Monitoring and Assessment Eastern Mineral Resources Team of the USGS, which Program (EMAP), Virginia Trout Stream Sensitivity has aggregated map units from available state Study (VTSSS), and a number of localized studies geologic maps (1:500,000 scale or better) to develop uniform multistate lithologic maps (Bruce Johnson, USGS, Reston, VA, pers. comm., 2000). A geographic information system (GIS) was used to calculate the percentage distribution of each of the USGS lithologic map units and the derivative landscape classes in each of the study watersheds. The geologic sensitivity map unit classification was generally based on composition and weathering properties of the primary rock type associated with each lithologic map unit. These units and examples of included rock types are: Siliceous: sandstone, quartzite Argillaceous: shale, siltstone Felsic: granite, gneiss Mafic: basalt, anorthosite Carbonate: limestone, dolomite Fig. 1 Conceptual scheme for lithological classification 60 Water Air Soil Pollut (2007) 182:57–71

Table 1 Classification of primary lithologies into five Class Primary lithology Class Primary lithology classes of geological sensitivity Siliceous Arenite Mafic Amphibole schist Chert Amphibolite Conglomerate Anorthosite Conglomerate (sandstone) Basalt Metasandstone Diabase Orthoquartzite Diorite Quartzite Dunite Sandstone Gabbro Felsic Alaskite Greenstone Augen gneiss Mafic gneiss Biotite gneiss Mafic metavolcanic rock Conglomerate (arkose) Meta-basalt Dacite Metavolcanic rock Felsic gneiss Norite Felsic metavolcanic rock Peridotite Felsic volcanic rock Schist (actinolite) Gneiss Ultramafic intrusive rock Granite Argillaceous Black shale Granitic gneiss Claystone Granodiorite Conglomerate (graywacke) Granulite Conglomerate (mudstone) Mica schist Conglomerate (shale) Migmatite Graywacke Mylonite Meta-argillite Orthogneiss Metasedimentary rock (graywacke) Quartz diorite Metasedimentary rock (meta- argillite) Quartz monzonite Metasedimentary rock (mica schist) Rhyolite Metasedimentary rock (phyllite) Sedimentary breccia Metasedimentary rock (arkose) Syenite Mudstone Carbonate Dolomite (dolostone) Phyllite Dolostone (dolomite) Schist Limestone Sedimentary breccia Marble Sedimentary breccia (mudstone clasts) Shale Siltstone Slate coordinated by the National Park Service, the USDA determined for others. Streamwater ANC, calculated Forest Service, and the Tennessee Valley Authority. from the charge balance, served as the primary Data quality was evaluated and screened, as described criterion for evaluation of the geologic classification by Sullivan et al. (2002). The total number of scheme. A single ANC value, measured in the late sampling sites used in the analysis was 909. Figure 2 1980s or 1990s from the charge balance, was shows the location of the available sites in relation to associated with each stream. In cases where multiple the SAMI region. sample values were available, samples most nearly Watershed boundaries were obtained from the US representing the spring of 1995 were selected. Spring EPA in digital or hard copy form for some sites (NSS season was chosen as an index of base flow and EMAP) and manually digitized or electronically chemistry, as was done in the US EPA’s National Water Air Soil Pollut (2007) 182:57–71 61

Fig. 2 Water chemistry sampling sites used for evaluation of landscape classification. The SAMI region boundary and state boundaries are also shown

Stream Survey, because it is generally reflective of the Forest Service’s Southern Forest Experiment Station lowest seasonal ANC values and because sensitive in 1992. Advanced Very High Resolution Radiometer life stages (eggs and young) are present for many fish (AVHRR) satellite data (1,000 m cell size) were used species (Kaufmann, Herlihy, Mitch, Messer, & Overton, as the primary source of vegetation information. 1991). Stream ANC has in this region changed little since the early 1990s (Webb et al., 2004). 2.3 Logistic Regression Analysis Landscape analyses were based on 30 m DEMs to determine sample location elevation. The ecoregional In order to more rigorously analyze the probability classification was based on analyses of Omernik of occurrence of acid-sensitive streams (ANC (1995). General soils types and forest types were <20 2eq l −1) in the Southern Appalachians, we used based on the STATSGO and USDA Forest Service logistic regression. It is similar to multiple linear vegetation databases, respectively. The State Soil regression techniques except that instead of modeling Geographic (STATSGO) data are administered by a continuous variable like ANC, logistic regression the Natural Resource Conservation Service and models binary (yes/no) data. Logistic regression is provide soils information at a scale of 1:250,000. often used in ecology to model fish species presence/ The forest vegetation map was created by the US absence (e.g., Porter, Rosenfeld, & Parkinson, 2000). 62 Water Air Soil Pollut (2007) 182:57–71

The logistic regression equation used to model the transforming the independent variables, but that did presence/absence of acid-sensitive sites had the form not result in any improvement to the model. We used of: the untransformed variables because they are easier to interpret. ðÞ¼ ðÞ=ðÞ Logit p Ln p 1 p A useful feature of the logistic regression model is ¼ þ þ þ þ A b1X1 b2X2 ... bnXn that the logit is a log odds function, and therefore the regression coefficients reflect an increase in the odds where p is the probability of obtaining an acid- of finding acid-sensitive sites per unit change in the X sensitive site, A is the logistic regression constant, b i variable (Ramsey & Schafer, 2002). For any explan- the regression coefficients, and X the explanatory i atory variable, the ratio of the odds of acid-sensitivity variables. Thus, the best-fit model can be used to at some value X , to the odds at some changed value estimate the probability of obtaining an acid-sensitive i (X +$X ) is given by, site for a given combination of explanatory variables. i i

Logistic regression can be performed using the same Odds Ratio ¼ exp ðÞbiðÞ$ Xi ; variety of procedures used in multiple linear regression. We used a stepwise variable selection procedure where b is equal to the regression coefficient for with a significance level of <0.05 as the criterion for variable i and assuming all other explanatory varia- variable entry or removal from the model, using all of bles are held constant. A 95% confidence interval (CI) the continuous watershed data we had available for all for the odds ratio can also be calculated using bi±1.96 909 sites (watershed area, elevation, geology and land times the standard error of the regression coefficient use composition) as the independent variables. Anal- in the above equation (Ramsey & Schafer, 2002). yses were done in SAS (ver. 8) using PROC LOGISTIC and we measured model quality using the C statistic (area under the Receiver Operating 3 Results and Discussion Characteristic curve). The C statistic is calculated by first forming all possible pairs of sites such that one 3.1 Geologic Sensitivity Classification site of the pair is observed to be acid-sensitive and the other is not. Then C equals the proportion of all such As indicated in Table 2, almost all of the acidic pairs for which the acid-sensitive site has the higher streams (ANC <0 2eq l −1) and most of the highly predicted probability of being acid-sensitive, accord- sensitive streams (ANC 0 to <20 2eq l −1) were ing to the model. A C-value of 0.5 indicates model associated with the siliceous landscape class. For the predictions that are equivalent to a random guess, portion of this analysis that evaluated the efficacy of while values >0.7 usually denote a worthwhile model the geologic sensitivity scheme, only those sites for (Hosmer & Lemenshow, 1989). which the entire watershed occupied a single geologic The final variables in the regression model were class were included (n=487). Subsequent analyses of checked for multicollinearity and none of the inde- relationships between watershed features and stream pendent variables used in the model were correlated ANC were based on analysis of all sites in the with each other (all Pearson and Spearman correlation database (n=909). All of the non-acidic streams in the coefficients <0.3). We also evaluated the option of log sensitive classes (ANC 0 to 20 2eq l −1 and 20 to

Table 2 Distribution of SAMI region sites grouped by lithology-based landscape class* in relation to ANC criteria

ANC (2eq l−1) Siliceous Argillaceous Felsic Mafic Carbonate

<0 15 1 0 0 0 0 to <20 14 6 4 0 0 20 to <50 18 11 17 0 0 50 to <150 17 36 38 58 9 >150 36 46 42 42 91

*The distributions include streams associated with single landscape classes Water Air Soil Pollut (2007) 182:57–71 63

50 2eq l −1) were associated with the siliceous, felsic, were not included if they showed indication of and argillaceous classes. All of the streams associated significant impacts on acid-base chemistry other than with the mafic and carbonate classes were relatively acidic deposition. The resulting database was inter- insensitive (ANC >50 2eq l −1). This information can nally consistent, showing good agreement between be used to indicate the geographic distribution of calculated (from the charge balance) and titrated acidic and sensitive streams throughout the SAMI ANC, with relatively little influence from natural region. organic acidity, and reasonable relationships between Because there was considerable overlap in ANC pH and ANC (Sullivan et al., 2002). The majority of values among the classes, the classification scheme the selected modeling sites had been sampled within does not provide a good basis for predicting the ANC large synoptic water chemistry surveys that had of individual streams. However, given the general substantial Quality Assurance/Quality Control (QA/ separation in ANC distributions among the classes, it QC) programs. We believe that the model input data is evident that the classification scheme will serve to are generally of high quality. Nevertheless, we expect indicate areas with high percentages of low-ANC that the laboratory analytical error for calculated ANC streams, as well as areas without low-ANC streams. is on the order of 13 2eq l −1, based on previous An additional issue concerns the discrimination unpublished analyses of National Surface Water between the felsic and argillaceous classes. Given that Survey data. the ANC distributions are similar, it might seem Stream water and soil solution chemistry are reasonable to represent these as a single class. temporally variable, mainly in response to changing However, these classes are different with respect to hydrological conditions and seasonal patterns in plant important acid-base properties that determine differ- and microbial activities. This uncertainty was consid- ences in response to acidic deposition. Examination of ered in the selection of ANC classes used for detailed data for a number of Virginia watersheds stratifying modeling sites and for presentation of the associated with these classes indicated differences in results. In other words, the interpretation of the model both base-cation availability and sulfur retention bet- projections of chronic chemistry allows for the ween these landscape types (Webb, 1999). Streams likelihood of additional episodic acidification. Al- associated with felsic geology have low base cation though the extent and magnitude of episodic acidifi- and sulfate concentrations. In contrast, streams asso- cation varies from site to site and with meteorological ciated with argillaceous geology have relatively conditions, some generalities are possible. For exam- higher base cation and sulfate concentrations. Al- ple, minimum measured episodic ANC values pub- though ANC, which is determined by the difference lished for Virginia streams were about 20% lower between base cations and acid anions (including than the median spring ANC (Webb et al., 1994). sulfate), is similar for streams associated with these Our correlational analyses were limited to the two geologic classes, this observation may not extend scale of geological data available for the entire to future responses to changes in acidic deposition. region (1:500,000). As a consequence of this scale Several sources of uncertainty affected the effort to limitation, these results are considered applicable at correlate ANC with regional lithology. These includ- the regional scale. More fine-scale watershed studies ed uncertainties associated with the water quality may find different associations and/or additional data, the delineation of watershed boundaries, past landscape variables that might also affect stream- and present land use, and geologic mapping. Such water chemistry. It is likely that an important errors and uncertainties are not additive, but rather uncertainty associated with available spatial geologic would be expected to some extent to offset each other. data is unreliable identification of carbonate rock Although it is not possible to rigorously quantify the distribution. Some streams associated with even the overall uncertainty in the assessment results, analyses siliceous (most-acidic) class have high ANC. This were conducted by Sullivan et al. (2002, 2004)to suggests the presence of unmapped, but chemically evaluate uncertainties and put them into perspective. significant, carbonate rock inclusions in geologic Rigorous screening criteria were adopted for regional formations that are primarily noncarbonate. In many modeling site selection, to ensure that the data for the cases carbonate rock types are indicated as second- modeling sites were internally consistent and that sites ary lithologies in descriptions of non-carbonate 64 Water Air Soil Pollut (2007) 182:57–71 formations. The presence of these secondary lithol- watershed area of most small, acid-sensitive streams ogies is geographically variable and efforts to within the region. account for their influence on the landscape classi- The five designated geologic sensitivity classes fication schemes were not successful. This problem, showed good ANC discrimination, with highest and perhaps other mapping problems, limits the use percentage of acidic and low-ANC stream sampling of the lithology-based classification schemes for points being located in the siliceous class. The acidic, prediction of ANC for particular streams. However, ANC 0 to 20 2eq l −1, and ANC 20 to 50 2eq l −1 the described lithology-based landscape classification streams were all most prevalent in the siliceous class. scheme does indicate the geographic distribution of The vast majority of the carbonate and unclassified acidic and sensitive streams throughout the SAMI sampling sites had high ANC (>150 2eq l −1). A total region. This will be useful for characterizing both of 1% of the region was not classified and this was the current and projected future acid-base status of mainly in South Carolina, where lithologic coverages streams within the region. The relationships identi- had not been completed by USGS at the time of this fied between streamwater ANC and geologic sensi- analysis. Only eight sampling sites occurred in un- tivity class were used as the foundation for a classified areas, and none of them had ANC e50 watershed sensitivity classification scheme, which is 2eq l −1. described below. Note that the correspondence between acidic streams and the siliceous geologic sensitivity class 3.2 Relationship between Streamwater ANC was substantially higher for the analysis restricted to and Landscape Characteristics streams for which the entire watershed was included within a single sensitivity class in the development of The relationships between streamwater ANC and the geologic sensitivity classification scheme than it landscape characteristics were examined using the was for the analysis based on all sampling point database of 909 streams located in the SAMI domain locations (Table 3). In the latter case, many sampling for which streamwater ANC data were available. The points were included for which the watershed was results of these analyses, coupled with the geologic occupied by multiple geologic sensitivity classes. sensitivity classes, were used as the basis for There was a clear pattern of increasing percentages development of an acidic deposition sensitivity of stream sampling sites having low ANC as site classification scheme. Several landscape variables elevation increased (Table 3). This pattern was very were found to correlate well with the percentage of apparent for acidic streams and streams having ANC sampled streams in various ANC classes (Table 3). In between 0 and 20 2eq l −1, both of which showed particular, certain landscape types were found to values <4% for sites located at elevations below contain high percentages of either acidic and low- 400 m, and values >11% for elevations >1,000 m. ANC streams or to contain high percentages of high- These relationships are shown graphically in Fig. 3. ANC streams. The data summarized in Table 3 are Over 53% of the low-elevation sites (<400 m) had based on analyses of landscape units that corre- high ANC (>150 2eq l −1), compared with only 14% sponded spatially with the sampling point locations. of the high-elevation sites (>1,000 m, Table 3). It must be recognized, however, that each sampling Certain forest types were commonly associated point location is, by definition, the lowest elevational with acidic streams: white-red-jack pine, maple- point within its respective watershed. The remainder beech-birch, and spruce-fir. In each of these types, of the watershed may, in some cases, lie within one or >11% of sampled streams were acidic. In contrast, more other landscape units located at higher eleva- only 3% of the non-forested sites were acidic and tion. Water chemistry at the sampling point integrates 75% had high ANC (>150 2eq l −1, Table 3). Only the characteristics that occur throughout the water- nine sites were sampled within the spruce-fir vegeta- shed, not just the characteristics present at the tion type, in part because spruce-fir covers only 0.3% sampling point. We chose to analyze landscape of the SAMI region and in part because, for many conditions at the stream sampling locations because watersheds that were partly covered by spruce-fir of the screening nature of this assessment and the low forests, the stream sampling location actually oc- resolution of geologic data compared with the curred below the elevational limit of this forest type. Water Air Soil Pollut (2007) 182:57–71 65

Table 3 Relationships between selected landscape variables and streamwater ANC for 909 stream sampling locations within the SAMI domain

Category Percent SAMI Number of Percent of sampled streams in ANC class region sampling covered by sites within Class 1 Class 2 Class 3 Class 4 Class 5 landscape unit unit (<0) (0–20) (20–50) (50–150) (>150)

Physiographic province Appalachian plateau 23.59 151 15.9 9.93 8.61 17.22 48.34 Valley and ridge 38.17 337 8.31 10.98 16.91 25.22 38.58 Blue ridge 23.97 422 1.42 6.87 16.82 45.5 29.38 Piedmont 14.27 16 0 0 6.25 31.25 62.5 Geologic sensitivity class Siliceous 23.35 274 15 15.69 20.07 21.17 28.1 Argillaceous 32.64 326 3.07 8.59 15.34 36.5 36.5 Felsic 19.86 195 0 3.08 14.87 46.67 35.38 Mafic 2.62 46 2.17 0 13.04 47.83 36.96 Carbonate 17.72 66 9.09 6.06 1.52 10.61 42.73 Unclassified 0.97 8 0 0 0 12.5 87.5 Elevation (m) <400 44.3 154 1.3 3.9 10.4 31.2 53.2 400–600 19.9 279 5.7 7.5 13.3 36.6 36.9 600–800 19 260 6.9 8.8 12.3 30.8 41.2 800–1,000 10.1 146 8.2 13 20.5 35.6 22.6 >1,000 6.5 87 11.5 13.8 31 29.9 13.8 Forest type Spruce-fir 0.29 9 11.1 33.33 0 55.56 0 Maple-beech-birch 3.29 47 17 19.15 23.4 19.15 21.28 White-red-jack pine 1.59 46 26.1 8.7 26.09 30.43 8.7 Loblolly-shortleaf pine 6.48 15 6.67 0 0 46.67 46.67 Oak-pine 17.15 149 6.04 5.37 16.78 53.02 18.79 Oak-hickory 42.45 509 4.52 11 17.29 33.01 34.18 Non-forest 27.74 149 2.68 0.67 4.03 17.45 75.17 Watershed area (km2) 0–5 N/A 470 8.1 11.1 16.2 33.4 31.3 5–10 N/A 165 9.1 9.1 20.6 31.7 26.7 10–20 N/A 126 4.8 6.3 14.3 32.1 42.9 20–40 N/A 78 0 9 11.5 32.1 47.4 >40 N/A 87 0 1.1 5.7 29.9 63.2 Soils type* Moomaw-Jefferson-Alonzville 0.96 29 13.8 17.24 13.79 17.24 37.93 Shottower-Laidig-Weikert 1.13 53 17 9.43 24.53 22.64 26.42 Edneyville-Tusquitee-Ashe 1.21 27 3.7 3.7 22.22 33.33 37.04 Catoctin-Myersville-Rock Outcrop 1.31 95 0 0 15.79 63.16 21.05 Gilpin-Dekalb-Ernest 2.17 26 7.69 11.54 11.54 30.77 38.46 Hayesville-Parker-Peaks 3.2 54 0 5.56 11.11 27.78 55.56 Frederick-Carbo-Timberville 4.01 28 0 0 3.57 10.71 85.71 Wallen-Dekalb-Drypond 4.17 146 10.3 19.86 21.92 30.82 17.12 Berks-Weikert-Laidig 1.31 81 3.7 7.41 16.05 45.68 27.16 Ecoregion* 66a-northern igneous ridges 1.53 118 0 0.85 15.25 66.95 16.95 66b-trap rock and conglomerate 0.47 45 17.8 35.56 24.44 11.11 11.11 uplands 66c-piedmont uplands 2.26 27 0 0 11.11 7.41 81.48 66d- southern igneous ridges and 6.49 87 1.15 4.6 17.24 37.93 39.08 66 Water Air Soil Pollut (2007) 182:57–71

Table 3 (continued)

mountains 66e-southern sedimentary ridges 1.79 55 0 10.91 25.45 47.27 16.36 66g-southern metasedimentary 4.08 48 0 6.25 27.08 47.92 18.75 mountains 67a-northern limestone/dolomite 2.84 27 3.7 7.41 0 11.11 77.78 valleys 67b-northern shale valleys 2.91 48 2.08 6.25 12.5 29.17 50 67c-northern sandstone ridges 3.21 40 7.5 12.5 20 27.5 32.5 67d-northern dissected ridges and 4.63 110 2.73 7.27 26.36 36.36 27.27 knobs 67h-southern sandstone ridges 4.21 52 26.9 23.08 13.46 23.08 13.46 69a-forested hills and mountains 5.85 84 27.4 13.1 7.14 13.1 39.29 69d-cumberland mountains 5.08 38 5.26 10.53 10.53 23.68 50 Watershed relief (m) 0–200 N/A 176 5.7 7.4 9.1 25 52.8 200–400 N/A 309 7.1 10.4 14.6 30.1 37.9 400–600 N/A 279 9.3 12.5 20.4 27.6 30.1 600–800 N/A 113 0.9 1.8 16.8 52.2 28.3 >800 N/A 49 0 2 10.2 65.3 22.4

*Includes only soil types and ecoregions for which there were 25 or more sampling sites.

Acidic and low-ANC streams were more prevalent There were 13 ecoregions within the SAMI region in small watersheds. There was a rather consistent that were represented by more than 25 sample increase in the percent of sampled streams in the locations. Most of those contained at least one acidic acidic and ANC 0 to 20 2eq l −1 categories, and a (9 of 13 ecoregions) and ANC 0 to 20 2eq l −1 (12 of decrease in the percent of sampled streams in the high 13 ecoregions) stream and each occupied less than 7% ANC (>150 2eq l −1) category, as watershed area of the SAMI domain. The ecoregion designation was decreased. About 8% of the streams that had water- therefore of limited utility as a classification param- sheds smaller than 20 km2 were acidic, compared eter to be added to geologic sensitivity. Nevertheless, with 0% of the streams with watersheds >20 km2 only four ecoregions (Forested Hills and Mountains, (Table 3). 69a; Southern Sandstone Ridges, 67h; Trap Rock and There were several STATSGO soils types that Conglomerate Uplands, 66b; and Northern Sandstone were associated with high percentages of acidic and Ridges, 67c) contained 81% of the known acidic low-ANC sampled streams, notably the Wallen- streams and 66% of the known streams having ANC DeKalb-Drypond, Moomaw-Jefferson-Alonzville, e 20 2eq l −1 in the SAMI domain, and these four and Shottower-Laidig-Weikert. Each of these was ecoregions only occupied 13% of the region. characterized by over 25% of the sampled streams There was a general tendency for watersheds having ANC e 20 2eq l −1. Each occupied a very having less than 600 m of vertical relief to have small component of the SAMI domain, however higher percentages of acidic and low-ANC streams (4.2, 1.0, and 1.1%, respectively). Their utility for than watersheds of greater relief. There was not a classification purposes was therefore somewhat consistent pattern observed, however, between relief limited. and streamwater ANC (Table 3). Water Air Soil Pollut (2007) 182:57–71 67

for which biological recovery may be possible under significantly reduced future deposition. The siliceous geologic sensitivity class was fairly restricted in area, covering 23% of the SAMI domain, and yet included the majority of the acidic (69%) and ANC e20 (59%) sampled streams. Examination of the locations of the acidic and low-ANC stream sampling sites relative to the location of lithologic coverages classified as siliceous revealed that many additional acidic and low-ANC stream sites were located outside, but in close proximity to, the siliceous class. Fig. 3 Scatter plot showing the relationship between stream- A 750 m buffer was therefore added in the GIS to the water ANC and sample site elevation for the 909 streamwater siliceous class coverage. Addition of this buffer sample site locations within the SAMI region for which ANC − substantially increased the extent to which acidic data were available. Sites having ANC >400 2eq L 1 or <−100 2eq l−1 were deleted from this figure. Reference lines and low-ANC streams were now included (97% and have been added to show the locations of the primary ANC 87%, respectively), with only moderate increase in the reference values that correspond with probable impacts on size of the defined area (to 35% of the SAMI region, 2 −1 brook trout (0 and 20 eq l ) and to show the locations of the Table 4). elevational references of 400 and 1,000 m. A very low percentage of the sites located below 400 m show ANC We identified two issues which likely contributed e20 2eq l−1. At higher elevations, larger numbers and higher significantly to the finding that many acidic and low- − percentages of streams having ANC e0 and e20 2eq l 1 are ANC stream sampling points were located just found outside the most acid-sensitive geologic class. First, many watersheds are partly or largely occupied by lithologic coverages of the siliceous type, and yet the Many of these variables are to some extent sampling location is actually somewhat downstream intercorrelated. For example, sampling sites that occur of the boundary between the siliceous class and at higher elevation generally have smaller watershed another class. If the distance between the sampling areas, and are more likely to be underlain by resistant point and the sensitivity class boundary is short, one bedrock and to be occupied by particular forest types, would expect streamwater chemistry in most cases to such as spruce-fir or maple-beech-birch. In contrast, be generally reflective of the upstream siliceous lower-elevation sites tend to have larger watershed geologic material. Second, geologic sensitivity classes areas, to be underlain by less resistant bedrock and to were designated using lithologic coverages of rather be covered by oak forest types or non-forest vegeta- coarse and variable scale. There is therefore some tion, including agricultural cover types. Furthermore, uncertainty regarding the exact location of lithologic the strength of these correlations does not necessarily boundaries. Thus, it is not surprising that the extent to imply cause-effect; other non-quantified factors may which the siliceous class included acidic and low- be underlying causes of the observed associations. ANC sites improved considerably upon addition of a buffer zone. 3.3 Final Classification Scheme for Evaluation Deletion of low-elevation areas (<400 m) dramat- of Watershed Sensitivity to Acidic Deposition ically reduced the size of the area of interest, to 22% of the SAMI domain, with little loss of acidic and/or A regional classification scheme was developed based low-ANC sites. It was also found that there were still on the geologic sensitivity classes described previ- a number of low-ANC stream sites excluded from the ously and elevation, to define an area that was area of interest, and most of these were found at high spatially limited, and yet contained the vast majority elevation (>1,000 m). All areas above 1,000 m of the acidic and low-ANC streams known to occur elevation that were not already included within the within the SAMI domain. These are the streams that siliceous class and associated buffer were therefore have been most adversely impacted by acidic depo- added to the area of interest. The resulting classifica- sition to date and to a large extent are also the streams tion scheme only covered 26% of the SAMI region, 68 Water Air Soil Pollut (2007) 182:57–71

Table 4 Inclusion of acidic and low-ANC streams (e20 2eq l−1) within landscape classification schemes defined on the basis of geologic sensitivity and elevation

Class Basis for classification Percent of SAMI region Percent of acidic and low- number included ANC streams included

ANC ANC e0 2eq l−1 e20 2eq l−1

1 Siliceous class 23 69 59 2 Siliceous class w/750 m buffer 35 97 87 3 Siliceous class w/750 m buffer minus elevation <400 m 22 93 82 4 Siliceous class w/750 m buffer minus elevation <400 m plus 26 95 88 elevation >1,000 m and yet contained 95% of the acidic sampled stream encompassed by this region is shown in Fig. 4.As sites and 88% of the ANC e20 2eq l −1 sites. This is shown on the map, all acidic and low-ANC streams the final scheme recommended for evaluation of were either within or in close proximity to the final acidic deposition sensitivities and effects. The area mapped area.

Fig. 4 Final area delim- ited by the acidification sensitivity classification scheme within the SAMI study area. The darkly shaded area includes the siliceous geologic sensitivity class surrounded by a 750 m buffer. In addition, all areas less than 400 m elevation have been deleted and areas greater than 1,000 m elevation have been added. The area thus circumscribed includes 95% of the known acidic streams and 88% of the known streams having ANC e20 2eq l−1 within the region. Furthermore, all known streams having ANC e20 2eq l−1 are in close proximity to the final mapped area Water Air Soil Pollut (2007) 182:57–71 69

Table 5 Logistic regression model for predicting the Logistic regression model SE values* probability of occurrence of acid-sensitive All sites (n=909, 134 yes/775 no)** − (ANC e20 2eq l 1) sites in LogitðÞ¼p 7:07 (SE=1.05) the Southern Appalachians þ 0:0282 % siliceous bedrock in watershed (SE=0.00286) þ 0:0250 % forest in watershed (SE=0.00998) ÀÁ 0:0245 watershed area km2 (SE=0.00990) þ 0:00234 elevationðÞ feet (SE=0.000438) C ¼ 0:86 Only Blue Ridge sites (n=425, 41 yes/384 no**) LogitðÞ¼p 4:92 (SE=0.652) þ 0:0281 % siliceous bedrock in watershed (SE=0.00424) þ 0:00187 elevationðÞ feet (SE=0.000652) C ¼ 0:85 Only Appalachian Plateau sites (n=132, 38 yes/94 no**) LogitðÞ¼p 14:5 (SE=3.82) þ 0:0901 % forest in watershed (SE=0.0346) þ 0:00674 elevationðÞ feet (SE=0.00144) C ¼ 0:89 Only Ridge and Valley sites (n=336, 55 yes/281 no**) *SE values are the standard LogitðÞ¼p 6:08 (SE=0.865) errors in the regression parameters þ 0:0391 % siliceous bedrock in watershed (SE=0.00573) **Yes indicates ANC þ 0:00354 elevationðÞ feet (SE=0.000981) −1 e20 2eq l ; No indicates C ¼ 0:86 ANC >20 2eq l−1

The four ecoregions indicated as 66b, 67c, 67h, sensitive sites (defined for this analysis as having and 69a can be used as an alternate classification ANC e20 2eq l −1) in the Southern Appalachians system, which included the majority of the most acid- using logistic regression: % siliceous bedrock in sensitive streams within the SAMI region, and yet watershed, % forested watershed, elevation, and only encompassed 13% of the region. 81% of the watershed area (Table 5). Each of these four variables known acidic streams and 66% of the known streams entered into the logistic regression equation with p< − having ANC e20 2eq l 1 were found within these 0.01 and the overall model was highly significant four ecoregions. Addition of ecoregions 66i (High (relative to the null hypothesis that all regression Mountains), 66e (Southern Sedimentary Ridges), and coefficients=0, likelihood ratio chi-square=198, p< 67d (Northern Dissected Ridges and Knobs) in- 0.0001). Regression models for individual physio- creased the area covered to 20% of the SAMI region. graphic provinces also found elevation to be a This area represented by seven ecoregions included significant predictor in all cases, along with either % 88% of the acidic streams and 82% of the streams siliceous bedrock or % forested land in the watershed. − having ANC e20 2eq l 1. Thus, this area represented The logistic models confirm the observations that by seven ecoregions constitutes an alternative basis acid-sensitive sites tend to be located in smaller, for focusing on the region of greatest interest. higher elevation, forested watersheds with base-poor bedrock. These models, however, also allow us to 3.4 Logistic Regression Model Results quantify the probability of encountering an acid- sensitive site. Based on the regional model in Across all sites, four variables were highly significant Table 5, the probability is 0.94 that a 5 km2, 100% in predicting the probability of occurrence of acid- forested watershed, at 2,000 ft elevation (615 m) with 70 Water Air Soil Pollut (2007) 182:57–71

100% siliceous bedrock will be acid-sensitive. On the southern Appalachian Mountains. In Proceedings of other hand, the probability is only 0.03 that a 50 km2, Annual Meeting of Air and Waste Management. Utah: Salt Lake City. 100% forested watershed, at 1,000 ft (308 m) eleva- Bricker, O. P., & Rice, K. C. (1989). Acidic deposition to tion with 0% siliceous bedrock will be acid-sensitive. streams. Environmental Science & Technology, 23, 379–385. Another way to look at these results is to examine the Bryce, S. A., Omernik, J. M., & Larsen, D. P. (1999). regression coefficients as odds ratios. Everything else Ecoregions: A geographic framework to guide risk characterization and ecosystem management. Environmen- being equal, a site with 100% siliceous geology has tal Practice, 1, 141–155. 17 times greater odds of being acid-sensitive than a Bulger, A. J., Cosby, B. J., & Webb, J. R. (2000). Current, site with 0% siliceous bedrock (95% CI=10–29). reconstructed past and projected future status of brook Similarly, a 100% forested site has 12 times greater trout (Salvelinus fontinalis) streams in Virginia. Canadian Journal of Fisheries and Aquatic Sciences, 57, 1515– odds of being acid-sensitive than a 0% forested site 1523. (95% CI=1.7–86). A change in elevation of 500 ft Clow, D. W., & Sueker, J. K. (2000). Relations between basin (154 m) increases the odds of being acid-sensitive by characteristics and stream water chemistry in alpine/ a factor of 3.2 (95% CI=2.1–5.0) and a decrease in subalpine basins in Rocky Mountain National Park, 2 Colorado. Water Resources Research, 36,49–61. watershed size of 50 km increases the odds by a Cosby, B. J., Ryan, P. F., Webb, J. R., Hornberger, G. M., & factor of 3.4 (95% CI=1.3–9.0). Galloway, J. N. (1991). Mountains of western Virginia. In Results of this study indicate that, at the broad D. F. Charles (Ed.), Acidic deposition and aquatic regional scale, acidic and low-ANC streams are ecosystems, regional case studies (pp. 297–318). Berlin Heidelberg New York: Springer. strongly associated with watershed lithology. Other DeWalle, D. R., Dinicola, R. S., & Sharpe, W. E. (1987). important variables associated with acid-sensitivity Predicting baseflow alkalinity as an index to episodic include elevation, % forested watershed, and water- stream acidification and fish presence. Water Resources shed area. These relationships allow identification of Bulletin, 23,29–35. Herlihy, A. T., Kaufmann, P. R, Church, M. R., Wigington, P. J. subregions where acid-sensitive streams are most Jr., Webb, J. R., & Sale, M. J. (1993). The effects of acid numerous. deposition on streams in the Appalachian Mountain and Piedmont region of the mid-Atlantic United States. Water Acknowledgments Funding for this research was provided Resources Research, 29, 2687–2703. by the Southern Appalachian Mountains Initiative (SAMI). We Hosmer, D. W., Jr., & Lemenshow, S. (1989). Applied logistic are especially grateful to the SAMI organization and to the regression. New York: Wiley. project manager, Pat Brewer, for assistance in carrying out this Johnson, N. M., Driscoll, C. T., Eaton, J. S., Likens, G. E., & research. Suggestions and constructive criticisms were provided McDowell, W. H. (1981). “Acid Rain,” dissolved alumi- throughout the duration of the project by members of the SAMI num and chemical weathering at the Hubbard Brook Effects Subcommittee: P. Brewer, W. Jackson, P. Muller, J. Experimental Forest, New Hampshire. Geochimica et Jansen, M. Teague, C. Huber, L. Moorhead, T. Elmore, N. Cosmochimica Acta, 45, 1421–1437. Nicholas, K. Eagle, R. Perhac, J. Renfro, D. Gasper, K. Kaufmann, P. R., Herlihy, A. T., Mitch, M. E., Messer, J. J., & Tonnessen, I. Silverberg, A. Rose, V. Sandeford, E. Haynes, Overton, W. S. (1991). Chemical characteristics of streams K. Kandasamy, and D. Neeley. Helpful suggestions were also in the Eastern United States: I. Synoptic survey design, provided by D. Johnson, J.D. Joslin, H. van Miegroet, and J. acid-base status and regional chemical patterns. Water Van Sickle. M. Adams, P. Brewer, F. Deviney, W. Edmunds, K. Resources Research, 27,611–627. Elliot, C. Huber, W. Jackson, A. Jenkins, S. Maben, A. Mays, Lynch, D. D, & Dise, N. B. (1985). Sensitivity of stream basins S. Moore, N. Nicholas, W.T. Price, J. Renfro, H. Van Miegroet, in Shenandoah National Park to acid deposition. Water- J. Vose, D. Wergowske, and F. Wood provided access to data or Resources Investigations Report 85–4115. Prepared in assisted with sample collection or analysis. O. Bricker, E. cooperation with University of Virginia Department of Campbell, T. Collins, C. Dicken, I. Duncan, C. Fievet, M. Environmental Sciences. Richmond, VA: US Geological Foose, T. Gathright, C. Huber, B. Johnson, G. Kappessar, J. Survey. Mueller, J. Peper, K. Rice, and C. Sherwood assisted in the Melack, J. M., Stoddard, J. L., & Ochs, J. A. (1985) Major development of regional landscape classification protocols. ion chemistry and sensitivity to acid precipitation of Sierra Nevada Lakes. Water Resources Research, 21, – References 27 32. Norton, S. A. (1980). Geologic factors controlling the sensitivity of aquatic ecosystems to acid precipitation. In D. S. Bailey, R. G. (1995). Descriptions of the ecoregions of the Shriner (Ed.), Atmospheric sulfur deposition (pp. 521– United States (2nd edn.). Misc. Publ. No. 1391. Wash- 531). Ann Arbor, MI: Ann Arbor Science. ington, DC: USDA Forest Service. Omernik, J. M. (1995.) Ecoregions: A spatial framework for Brewer, P. F., Sullivan, T. J., Cosby, J., & Munson, R. (2000). environmental management. In W. S. Davis & T. P. Simon Acid deposition effects to forests and streams in the (Eds.), Biological assessment and criteria: Tools for water Water Air Soil Pollut (2007) 182:57–71 71

resource planning and decision making (pp. 49–62). Boca Report. Report 2 of 5. Atlanta: USDA Forest Service, Raton, FL: Lewis Southern Region. Omernik, J. M., & Bailey, R. G. (1997). Distinguishing Silsbee, D. G., & Larson, G. L. (1982). Water quality of between watersheds and ecoregions. Journal of the streams in the Great Smoky Mountains National Park. American Water Resources Association, 33(5), 935– Hydrobiologia, 89,97–115. 949. Sullivan, T. J., Cosby, B. J., Webb, J. R., Snyder, K. U., Omernik, J. M., & Griffith, G. E. (1991) Ecological regions Herlihy, A. T., Bulger, A. J., et al. (2002). Assessment of versus hydrological units: Frameworks for managing the effects of acidic deposition on aquatic resources in the water quality. Journal of Soil and Water Conservation, Southern Appalachian Mountains. Report prepared for the 46(5), 334–340. Southern Appalachian Mountains Initiative (SAMI). Cor- Peper, J. D., Grosz, A. E., Kress, T. H., Collins, T. K., vallis, OR: E&S Environmental Chemistry, Inc. Kappesser, G. B., Huber, C. M., et al. (1995). Acid Sullivan, T. J., Cosby, B. J., Herlihy, A. T., Webb, J. R, Bulger, deposition sensitivity map of the Southern Appalachian A. J., Snyder, K. U., et al. (2004). Regional model Assessment Area: Virginia, North Carolina, South Caro- projections of future effects of sulfur and nitrogen lina, Tennessee, Georgia, and Alabama. US Geological deposition on streams in the Southern Appalachian Survey On-line Digital Data Series Open-File Report 95– Mountains. Water Resources Research, 40(2), W02101 810. doi http://dx.doi.org/10.1029/2003WR001998. Peters, N. E., & Driscoll, C. T. (1987). Hydrogeologic controls Webb, J. R. (1999). Synoptic stream water chemistry. In A. J. of surface-water chemistry in the Adirondack region of Bulger, B. J. Cosby, C. A. Dolloff, K. N. Eshleman, J. R. New York State. Biogeochemistry, 3,63–180. Webb, & J. N. Galloway (Eds.), Shenandoah National Porter, M. S., Rosenfeld, J., & Parkinson, E. A. (2000). Park: Fish in sensitive habitats (pp. 1–50). Project Final Predictive models of fish species distribution in the Report for US National Park Service, Volume II. Lackwater Drainage, British Columbia. North American Webb, J. R., Cosby, B. J., Deviney, F. A., Jr., Galloway, J. N., Journal of Fisheries Management, 20, 349–359. Maben, S. W., & Bulger, A. J. (2004). Are brook trout Puckett, L. J., & Bricker, O. P. (1992). Factors controlling the streams in western Virginia and Shenandoah National Park major ion chemistry of streams in the blue ridge and valley recovering from acidification? Environmental Science & and ridge physiographic provinces of Virginia and Mary- Technology, 38, 4091–4096. land. Hydrological Procedure, 6,79–98. Webb, J. R., Deviney, F. A., Galloway, J. N., Rinehart, C. A., Ramsey, F. L., & Schafer, D. W. (2002). The statistical sleuth. Thompson, P. A., & Wilson, S. (1994). The acid-base A course in methods of data analysis. Pacific Grove, CA: status of native brook trout streams in the mountains of Duxbery. Virginia; a regional assessment based on the Virginia trout SAMAB (Southern Appalachian Man and the Biosphere) (1996). stream sensitivity study. Charlottesville, VA: University of The Southern Appalachian Assessment Aquatics Technical Virginia.