Evaluation of changes to the quality of riparian forest buffers in the Watershed

Karen Stretton Geoenvironmental Research Paper M.S. Candidate Department of Geography and Earth Science Shippensburg University of

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Table of Contents Abstract p. 3 I. Introduction p. 3 II. Literature Review p. 4-9 A. Definition of riparian buffer B. Functions of riparian forests C. Buffer design D. Geospatial technology and riparian buffers E. Urbanization and riparian buffers in the CBW III. Study Area p. 9-10 IV. Purpose and Scope p. 10 V. Methodology p. 11-16 A. Land cover mapping methods B. Stream data C. Stream buffering methods VI. Results p. 16-20 VII. Discussion p. 20 VIII. Works Cited p. 22-24 IX. Appendix p. 24-30

Figures and Tables Figure 1. Flow of water through a riparian buffer p. 5 Figure 2. Three zone design of riparian buffers p. 8 Figure 3. Map of study area p. 10 Table 1. Reclassification scheme for land cover data p. 13 Table 2. Reclassification scheme for Omernik ecoregions p. 13 Figure 4. Map of 30 meter buffer quality values in 2006 p. 17 Table 3. Statistics on riparian health values p. 18 Figure 5. Map of changes in buffer quality, 1984 to 2006 p. 19 Table 4. Table of changes to buffer quality in Cumberland p. 20 County between 1984 and 2006

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Abstract

This research assesses changes to the quality of riparian buffers between 1984 and 2006 at the scale of subwatersheds of the Susquehanna River Basin (SRB). The methodology follows the Natural Lands Trust SmartConservationTM program. Geographic Information Systems (GIS) is used to incorporate land cover and stream order data to determine the quality of existing buffers as healthy riparian ecosystems. Thirty, 60 and 90 meter buffers are assessed on both sides of the water body. The analysis shows that there were minimal changes to buffer quality between 1984 and 2006. Additionally, buffer quality does not vary significantly between buffer widths.

I. Introduction

Riparian buffers have become an accepted way to mitigate the effects of agricultural and urban land uses on stream health. The reestablishment and protection of riparian buffers in the

Chesapeake Bay Watershed (CBW) are important components of the initiative to restore the health of the . In 2008, approximately 60 percent of the streams in the CBW had forested buffers (Sprague et al, 2006) The Geological Survey (USGS) estimates that 0.5 percent of buffers were cleared between 1996 and 2005 due to urbanization

(Chesapeake Bay Program, 2008). Riparian buffers are especially important in the Susquehanna

River Basin (SRB), which drains over 40 percent of the CBW and provides approximately half of the freshwater in the Bay (Horton, 2003). Healthier riparian ecosystems along to the Bay would have improved capabilities to reduce nutrient and sediment pollution, and consequently lead to an input of higher quality water into the Bay.

The goal of this research is to provide an assessment of changes to the quality of riparian buffers between 1984 and 2006 at the scale of subwatersheds of the SRB. The assessment will be based on the SmartConservationTM methodology devised by the Natural Lands Trust that incorporates land cover and stream order to determine the quality of existing buffers as healthy riparian ecosystems. Thirty, 60 and 90 meter buffers will be assessed on both sides of streams.

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II. Literature Review

A. Definition of riparian buffer

Due to the complexity of riparian systems, there are countless possible variations to the definition of riparian buffer. Critical components of the definition include the characteristics of being linear but with no definite boundaries, adjacent to and upgradient from water, and acting as a transition zone between aquatic and non-aquatic environments. The Chesapeake Bay Riparian

Handbook provides the concise definition of “an area maintained in permanent vegetation and managed to reduce the impacts of adjacent land use” (Palone, 1998, p. 1-10). For the purpose of this study, riparian buffers will include a zone on both sides of a water body.

B. Functions of riparian forests

Riparian forests provide critical functions that contribute to the health of hydrologic systems. Riparian forests comprise approximately five percent of the total land cover in the

CBW, but have a much larger role in maintaining healthy riparian systems (Sprague et al, 2006).

Although the environmental benefit of riparian buffers varies based on site characteristics, there are several primary functions that all buffers perform to some extent.

One critical function of riparian buffers is slowing the velocity of , which promotes the filtration of nutrient and sediment pollutants (Wagner, 2008). Early studies concluded that both grass and forest buffers were effective at removing sediments and nitrates, but were less effective at removing dissolved phosphates (Daniels and Gilliam, 1996; Lowrance et al, 1997). Figure 1 depicts a typical flow of water containing a nutrient and sediment load through a riparian buffer. Roberts and Prince (2010) performed research

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Figure 1. Flow of water through a riparian buffer, including nutrient removal processes . Source: Sprague et al, p.58

that specifically linked land cover in riparian buffers ranging from 31 to 1000 meters in the CBW to reductions of nutrient runoff to the Bay. Another important function of riparian buffers deals with the enhanced storage capacity for floodwater (Wagner, 2008).

A third function of riparian buffers is to create valuable habitat for the transition between aquatic and non-aquatic ecosystems. Healthy riparian ecosystems have been correlated with better stream water quality. In subwatersheds of the CBW in Montgomery County, , the amount of tree cover in riparian buffers was found to be the second most significant predictor of stream health. Only percent impervious surface area in the watershed had a bigger impact on water quality. In this study, the Index of Biotic Integrity (IBI), a common water quality index, was used to evaluate the diversity of fish and macro-invertebrate species. Data on temperature, dissolved oxygen concentration, and pH were also utilized (Snyder et al, 2005). Another study at the scale of the entire CBW found that tree cover within 30 meter riparian buffer zones was the second most important indicator of stream health, with impervious area in the watershed again being the primary indicator (Goetz et al, 2004). Research based in a southern Alabama

5 watershed also found that stream water quality was impacted by riparian vegetation and land use within a 30 meter buffer (Sawyer et al, 2004).

It is noteworthy that riparian functions are generally studied in established buffers.

Research on buffer restoration in the CBW in northern Virginia emphasizes the slow results of buffer restoration. A four year study of new riparian buffers showed an average improvement in

IBI, but positive water quality results were not present at all study locations (Teels et al, 2006).

Stream order is considered an important factor in the effectiveness of buffers, with lower stream orders considered more beneficial because of the higher amount of interaction between the water and the riparian land (Palone, 1998; Meyer et al, 2003). Stream order is a hierarchical means to classify stream networks. The Strahler method is a common way to determine stream order based on the number of tributaries that feed into a particular stream; therefore, stream order tends to increase when progressing downstream. All headwater streams are classified as first order. When two streams of the same order intersect, they become the next highest order (Kang and Lin, 2009). It’s estimated that first and second order streams represent 75 percent or higher of the total stream length in the United States (Meyer et al, 2003). Kang and Lin (2009) performed an analysis on riparian buffer zones in the East Watershed in east- central Pennsylvania, a CBW subwatershed, which examined differences in soil and landscape distribution for streams of different orders. Lower soil depth and available water capacity were found in first and second order streams, when compared to buffers of comparable widths in third through fifth order streams.

C. Buffer design

The width of a riparian buffer is a primary factor in determining its effectiveness. It is a basic assumption that a wider buffer tends to be more effective. Research on the Spring Creek

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Watershed in central Pennsylvania, a subwatershed of the CBW, provides an example of how narrow grass buffers of only three to four meters reduced suspended sediment loading (Carline,

2007). However a 30 meter buffer on both sides of the water body is a generally accepted standard in the United States (Goetz, 2006). The United States Environmental Protection

Agency (EPA) found that buffers over 50 meters are more reliable at removing substantial amounts of nitrogen (Mayer et al, 2005). It is also noteworthy that either fixed or variable width buffers underestimate the importance of areas further from the stream in improving water quality. These buffers that emphasize proximity to the stream can miss the complexity of local hydrology, which is strongly impacted by soil and geology (Qiu, 2009). One hundred meter buffers are considered at the wide range of buffer size (Mayer et al, 2005). This project will assess 30, 60 and 90 meter buffers on both sides of waterways. Thirty meter buffers were selected because it’s the minimum width that can be analyzed with Landsat data and also because it’s a standard minimum size. The 90 meter buffer was chosen because it’s approximately the maximum buffer size incorporated into land use policy at the state and federal level (Mayer et al, 2005). The 60 meter buffer size was selected because it’s the midpoint between 30 and 90.

An effective buffer design is determined by the characteristics of the surrounding landscape, particularly soil, topography and groundwater flow (Lowrance et al, 1997; Kang

2009). Nonetheless, a standardized but flexible criterion for implementing riparian buffers was developed in 1991 by the United States Department of Agriculture-Natural Resources

Conservation Service (NRCS) (Lowrance et al, 1997). The prominent characteristic of the

NRCS buffer is the designation of three zones. The Chesapeake Bay Program (CBP) also encourages a zonal design of riparian buffers (Palone, 1998). Figure 2 depicts the main

7 characteristics of each zone. Zone 1 and Zone 2 are the wooded core components of the buffer, and are recommended to be at least 100 ft, or approximately 30 meters, wide (NRCS 2003).

Zone 3 consists of grasses and other herbaceous plants, and doesn’t have a prescribed minimum width (Palone, 1998). Despite these findings, neither the Susquehanna River Basin Commission nor any of the states have specific requirements for creating riparian buffers or mandates of a particular width. Regulation is left up to local government, and typically is a voluntary choice of property owners.

D. Geospatial technology and riparian buffers

Landsat is a satellite monitoring system that has been gathering imagery of the earth since the launch of the Landsat 1 satellite in 1972. The 30 meter resolution of the Landsat 5 Thematic

Mapper ™ and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery is considered medium quality resolution that is relevant at regional scales. Landsat also provides useful temporal data because it exists for a lengthy time span; Landsat 5 has been in operation since

1984 and Landsat 7 was launched in 1999 (NASA, 2010) A frequent application of Landsat is to map land cover (Cohen, 2004).

Figure 2. The three zones design recommended for use in riparian buffers. Source: Lowrance (1997) 688

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Riparian land cover data derived from Landsat have furthermore been correlated with IBI and nitrate values in streams in case studies in Oregon (Lattin, 2004,) and the Chesapeake Bay

Watershed (Snyder et al, 2005; Goetz, 2006; Robert and Prince, 2010).

III. Study Area

The CBW is approximately 166,000 square kilometers in size and contains by far the largest land to water ratio in the world. This ratio dictates that an astounding 2,243 square kilometers of land drain into each cubic kilometer of water in the Bay. Land use and land cover throughout the CBW have a direct impact on the health of the Bay. Since land cover closest to streams and the Bay itself have an especially strong impact on water quality, maintaining riparian forests is a primary consideration (Horton, 2003). Only approximately 60 percent of the

321,000 kilometers of streams in the CBW have riparian buffers, and the quality of the buffers varies widely (Sprague et al, 2006).

The SRB has been selected as the study area for this research (Figure 3). The SRB is

71,250 square kilometers in area and contains 6 major sub-basins. The majority of the watershed is located in and Pennsylvania, and there is also a small portion in northeastern

Maryland. The SRB plays a particularly large role in the health of the Chesapeake Bay for a number of reasons. Susquehanna River contributes 45 percent of all freshwater to the Bay and drains 43 percent of the land in the CBW (Horton, 2003). Nutrient and sediment overloading are the primary sources of pollution in the Bay. The prevalence of agriculture in the SRB, containing over half of the farmland in the CBW, makes it a target for efforts to reduce nonpoint pollutants from entering waterways. Population growth in the SRB also increases nutrient pollution through increased sewage effluent and runoff from newly created impervious surfaces

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(Horton, 2003). Furthermore population growth can lead to the conversion of forested land near streams into urban or other less beneficial land uses.

Figure 3. Map of the SRB as a sub-basin of the CBW.

IV. Purpose and Scope

This research will examine the quality of 30, 60 and 90 meter riparian buffers in the

Susquehanna River Basin at a subwatershed scale. Land cover data from 1984 and 2006 are used to derive buffer quality. An evaluation is made to determine whether the quality of buffers as riparian ecosystems changes over the 22 year time period. The data are additionally evaluated to determine if there is a difference in the quality of 30, 60 and 90 meter buffers. Finally buffer quality is tested to determine if it varies significantly with buffer width.

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V. Methodology

The main methodology of this research was based in Geographic Information Systems

(GIS). The aim of the methodology was to perform a buffer quality analysis using a technique derived from the Natural Lands Trust SmartConservationTM program. The Natural Lands Trust is a regional conservation organization that has been working to preserve land in eastern

Pennsylvania and southern New Jersey since 1953 (Natural Lands Trust, 2010). Beginning in

1998, SmartConservationTM was developed to provide a means to assess the conservation value of landscapes in the Greater region in order to prioritize preservation. The buffer quality analysis is just one component of SmartConservationTM, and it’s intended to evaluate the quality of habitats within riparian buffer zones. The methodology of SmartConservationTM is based on land cover within buffers, where each land cover type is assigned an ecological value that is related to its role in enhancing or degrading stream health. Buffers are then weighted to reflect the stream order, with low order streams given a heavier weight (Natural Lands Trust,

2004).

A. Land cover mapping methods

Raster land cover data, derived from Landsat TM imagery, were obtained from the CBP for 1984 and 2006 (Irani and Claggett, 2010). The land cover data were derived from the

Landsat Thematic Mapper Program. The CBP datasets were developed at the same time with a consistent methodology, which is an advantage over the National Land Cover Datasets (NLCD).

The land cover data have a similar classification scheme as 2001 NLCD data. These data were used in two ways: 1) to identify large streams and waterbodies in order to create a more realistic depiction of wider streams, and 2) to identify buffer health by applying the SmartConservationTM methodology. To identify wide streams and waterbodies, the first step was to create a new layer

11 of only the open water classification using the reclassify tool to make this category have a value of one and all other categories have a value of NoData. This raster dataset was then converted into polygons. This dataset is used to provide a more realistic depiction of the width of larger streams and waterbodies in the SRB than can be obtained from the linear flowline data.

The CBP land cover was reclassified to fit the land cover scheme used by

SmartConservationTM and then were reclassified a second time to fit the scheme used for

SmartConservation’sTM ecological value classification. This second reclassification was from categorical land cover type to numerical ecological values (Natural Lands Trust, 2006) and is based on ecoregion. Table 1 portrays the final classification scheme from CBP land cover to ecological health values. The ecological health values were created by environmental professionals hired by Natural Lands Trust. The values range from 0 to 10, with 0 being the lowest quality riparian ecosystem and 10 being the highest quality.

The Natural Lands Trust created different reclassification values for three ecoregions, including the Piedmont and Atlantic Coastal Plain, Central Appalachian Forest and High

Allegheny Plateau. Since the Natural Lands Trust ecoregions were only available for

Pennsylvania and were not available as digital datasets, the Omernik Level III Ecoregions were downloaded from the EPA and fit to the Natural Lands Trust ecoregions (Level III Ecoregions,

2007). Omernik created widely used ecoregions for the contiguous United States and Level III is the highest level of detail available for the entire United States (Omernik, 1987). Table 2 depicts the classification of Omernik Level III ecoregions into the three Natural Lands Trust ecoregions.

Using a raster clip, the land cover data were separated into three datasets that correspond to the area of ecoregion. The new land cover datasets were then reclassified according to the schema for each ecoregion, which is included in Table 1. The final step was to perform a mosaic, which

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recombined the land cover data into a new raster containing the reclassified ecological health

values.

Table 1. Reclassifications required to transform CBP land cover data to fit the SmartConservationTM riparian buffer quality scheme.

Original Land Cover NLT Land Cover Piedmont Appalachian Allegheny Forest Plateau 11 - Open Water Water 0 1 1 21 - Developed Open Space Hay/pasture grass 5 5 4 22 - Developed Low Intensity Low intensity developed 0 1 0 23 - Developed Medium Intensity Moderate intensity developed 0 0 0 24 - Developed High Intensity High intensity developed 0 0 0 31 - Barren land Bare- past or present mining 2 0 0 32 - Unconsolidated shore N/A No Data No Data No Data 41 - Deciduous forest Deciduous forest 10 10 10 42 - Evergreen forest Evergreen forest 10 10 10 43 - Mixed forest Mixed deciduous and evergreen forest 10 10 10 52 - Scrub/shrub Hay/pasture grass 5 5 4 71 - Grassland/herbaceous Hay/pasture grass 5 5 4 81 - Pasture/hay Hay/pasture grass 5 5 4 82 - Cultivated crops Row crops 2 2 2 90 - Woody wetlands Forest (woody) wetland 10 10 10 95 -Emergent Herbaceous wetlands Emergent (herbaceous) wetland 10 10 10

Table 2. Table representing the transformation of Omernik Level III ecoregions into the ecoregions used by Natural Lands Trust.

Natural Lands Trust Piedmont and Atlantic Coastal Central Appalachian High Allegheny Plain Forest Plateau Northern Appalachian Omernik Middle Atlantic Coastal Plain Central Appalachians Plateau and Uplands North Central Level Northern Piedmont Appalachians Northeastern Highlands East Great Lakes and Hudson Western Allegheny III Lowlands Ridge and Valley Plateau Southeastern Plains

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B. Stream data

Vector data for surface water bodies in the CBW were downloaded from the National

Hydrography Dataset Plus (NHDP), an expanded and improved version of the National

Hydrography Dataset published by the USGS (Horizon Systems Corporation, 2010). NHDP is a public domain dataset that is supported by the Environmental Protection Agency (EPA) Office of

Water, with assistance from the USGS. NHDP is created by the Horizon Systems Corporation and is available through their website (NHDP, 2010). The most recent NHDP stream data from

2005 were downloaded at the scale of 1:100,000 for the extent of the CBW. The NHDP flowline data, which portray the general path of water under normal hydrologic conditions, were used for this analysis. NHDP data were selected over the original National Hydrography Dataset because it includes stream order. Prior to identifying the NHDP as the primary stream dataset to use for this work, a one arcsecond Digital Elevation Model of the SRB was created in an effort to extract stream networks and stream order; however, the accuracy of the data were insufficient for creating 30 to 90 meter buffers. Two other primary datasets were needed to perform this analysis. 11 digit Hydrologic Unit Code (HUC) data for the subwatersheds of the SRB were downloaded from the USGS (USGS, 2009). Additionally the outline of the SRB and its six sub- basins were downloaded from the Susquehanna River Basin Commission website (SRBC, 2010).

The next step in the methodology was to organize and buffer the stream data. First the

NHDP data were visually compared with aerial photography from the Burd Run Watershed, a small watershed of the CBW in south-central Pennsylvania, to ensure that stream location is depicted with reasonable accuracy. Next the NHDP streams and HUC11 watersheds were clipped to the SRB. A join by attributes was performed to link a data extension that includes

Strahler stream order to the NHDP flowline data. A spatial join was then used to extract the

14 stream order data from the NHDP flowlines to the open water polygons. Selection by attributes was used to create three categories of stream order for both the linear NHDP and polygon Open

Water datasets. The categories are low order streams from one to two, medium order streams from three to five, and high order streams from six to seven. Each category was then exported as a new layer.

C. Stream buffering methods

The next step in the methodology was to create 30, 60 and 90 meter buffers around each of the six waterway datasets. The Euclidean distance from all stream segments was calculated, with a maximum distance of 90 meters and cell size of 30 meters. To account for the three categories of stream order, linear NHDP data and polygon Open Water data, this step must be performed six times. The method for the 30 meter buffer is explained subsequently, and these steps were repeated for the 60 and 90 meter buffers. The output from the Euclidean distance analysis was reclassified to include only the distances from 0 to 30. This creates a new raster that is a 30 meter buffer on both sides of the waterways. This step is performed for the six waterway datasets. Buffers that were previously based on lines and polygons were merged, and separate buffer datasets for low, medium and high stream order were created. Then the buffers were combined with the reclassified ecological value data. This created a new raster that contained ecological values for areas within the buffers. This step had to be performed for each stream order category. Next a new field was added to the attribute table to contain the weighted ecological value. Weighted ecological value was obtained using map algebra, whereby the ecological value was multiplied by the stream order weight devised by SmartConservationTM.

Headwater streams have a weight of 0.625. Medium order streams are weighted 0.31, and high order streams are weighted 0.065 (Natural Lands Trust, 2006).

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The final portion of the methodology used Zonal Statistics to apply the weighted ecological values in the raster dataset to the vector HUC subwatersheds to summarize the riparian quality data by subwatershed. The Zonal Statistics attribute table was added to the HUC attribute table by an attribute join. A new field was then added to the HUC attribute table to contain the total weighted ecological value for each subwatershed. Finally the equation created by the Natural Lands Trust was used in the Field Calculator to obtain the average riparian buffer quality value by adding the values of the low, medium and high order streams for each subwatershed. The equation is as follows :[([Riparian Buffer 1-2]*0.625) + ([Riparian Buffer

3-5*0.31) + ([Riparian Buffer 6-12]*0.065)] (Natural Lands Trust, 2006). This entire analysis was performed for both the 1984 and 2006 datasets. A comparison of the results of this analysis will depict areas where the quality of riparian buffers changed over the 22 year study period.

The results will also determine whether riparian health varies between 30, 60 and 90 meter buffers. A one way analysis of variance (ANOVA) statistical test was also performed to determine whether there is a statistically significant difference between buffer quality and riparian buffer width. The ANOVA test compares variability between groups to variability within groups. If there is more variability between groups than within groups, the results are considered statistically significant. The ANOVA test was performed by entering the data into the analytical software program, Statistical Package for the Social Sciences (SPSS).

VI. Results

A primary result of this research is the creation of a map portraying patterns of riparian buffer quality throughout the SRB. Figure 4 portrays the distribution of quality for 30 meter buffers from the 2006 land cover data. Appendix 1 includes a list of the riparian buffer quality

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Figure 4. Map of riparian quality values for subwatersheds of the SRB in 2006. Note. A map was not included for the 1984 data because the results are extremely similar.

values for all 233 subwatersheds of the SRB for 1984 and 2006. A limitation of this methodology is that the 0 to 10 riparian buffer quality scale is unique to SmartConservationTM, and therefore can’t be directly compared with other evaluation methods. Nonetheless these values are relevant because of their direct correlation to land cover within the buffer zones.

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Riparian health values less than 5 have predominantly urban and agricultural land uses within buffers. Contrastingly, values that are closer to 10 have predominantly forest and wetland buffer land uses. These local waterways should have higher capabilities to mitigate the pollutants that enter waterways. Table 3 portrays the range, mean and standard deviation of each buffer size for

1984 and 2006. The mean value of buffers for all sizes in 1984 and 2006 ranges from 6.86 to

7.00. These values are in the upper middle portion of the range, and indicate that the overall health of riparian buffers in the SRB is good.

Table 3. Statistics on the riparian buffer health values for 30, 60 and 90 meter buffers in 1984 and 2006.

Buffer Size Standard Year (meters) Range Mean Deviation 1984 30 3.91 - 9.44 7.00 1.02 60 4.20 - 9.45 7.01 0.99 90 4.29 - 9.54 6.94 1.02

2006 30 3.88 - 9.43 6.98 1.02 60 4.20 - 9.44 7.00 0.99 90 4.29 - 9.36 6.86 1.03

Another interesting result is that the amount of change in the quality of the buffers between 1984 and 2006 is very small for all buffer sizes. Figure 5 is a map depicting watersheds in which the quality of buffers increased or decreased throughout the study period. Table 4 provides more detailed data about the amount of change in buffer health values for watersheds in

Cumberland County, Pennsylvania. Since the change is so small, it is likely due more to

differing Landsat classifications than actual changes in land cover and ecosystem health within buffer zones. The resolution of the data is therefore a major limitation to this project, particularly since many riparian reforestation projects will not be apparent until they become

18 better established. The resolution of stream data is also a limitation, in particular because many small and headwater streams aren’t part of the NHDP and therefore could not be considered in this analysis.

The results of the ANOVA test analyzing the values of 30, 60 and 90 meter buffers were not significant, indicating that there is no significant difference in buffer quality for the three buffer widths tested in this project.

Figure 5. Changes in riparian buffer health values between 1984 and 2006. Blue watersheds indicate small increases in buffer health, while red watersheds have decreased buffer health in 2006.

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Table 4. Amount of change in riparian buffer quality of four watersheds in Cumberland County, Pennsylvania. Positive values represent watersheds where riparian buffer quality increased, and quality decreased in watersheds with negative values.

Name of Watershed Change 1984 to 2006 -0.0168 Conodoguinet at Mount Rock Spring Creek 0.0170 Conodoguinet at West Fairview 0.0154 Conodoguinet at 0.0117

VII. Discussion

The lack of change in riparian buffer quality over the 26 year study period might indicate that the data and methods in this project were more suited for determining the current quality of riparian buffers, instead of comparing how it changed over time. The patterns of buffer quality in

Figure 4 mirror the patterns of land use in the SRB. The southeastern portion has the highest aggregation of lower quality buffers, which mirrors the higher population density and predominance of agriculture in these areas. The central and western portion of the watershed has higher quality riparian buffers, which corresponds to the predominance of forested land uses in these areas. These patterns indicate that land use closest to streams is similar to land use throughout the watershed, which runs counter to the purpose of riparian buffers to mitigate the harmful effects of intensive land uses.

The primary contribution of this research is the application to the entire SRB of

SmartConservationTM methodology to determine riparian buffer quality. This is noteworthy because consideration of stream order is often neglected in research on riparian buffers.

Additionally the aggregation of data at the subwatershed scale into an overall buffer quality value is a unique contribution of this project.

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VIII. Works Cited

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Daniels, R.B., and J.W. Gilliam. (1996). Sediment and chemical load reduction by grass and riparian filters. Soil Science Society of American Journal 60: 246-251.

Goetz, S.J, Jantz, C.A., Prince, S.D., Smith, A.J., Varlyguin, D. and R.K. Wright. (2004). Integrated analysis of ecosystem interactions with land use change: The Chesapeake Bay Watershed. Ecosystem Interactions with Land use Change. American Geophysical Union, Washington, D.C., p. 263-275.

Goetz, S.J. (2006). Remote sensing of riparian buffers: Past and future prospects. Journal of the American Water Resources Association 42(1): 133-143.

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Kang, S., and H. Lin. (2009). General soil-landscape patterns in buffer zones of different order streams. Geoderma 151: 233-240.

Lattin, P.D., Wigington Jr., P.J., Moser, T.J., Peniston, B.E., Lindeman, D.R., and D.R. Oeter. (2004). Influence of remote sensing imagery source on quantification of riparian land cover/land use. Journal of the American Water Resources Association 40: 215-227.

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Mayer, P.M., Reynolds Jr, S.K., Canfield, T.J., and M.D. McCutchen. (2005). Riparian buffer width, vegetative cover, and nitrogen removal effectiveness: A review of current science and regulations. National Risk Management Laboratory, Office of Research and Development, United States Environmental Protection Agency. p. 1-16.

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Natural Lands Trust. (2004). SmartConservationTM. Setting priorities for conservation in the Piedmont ecoregion of Pennsylvania. Natural Lands Trust, in conjunction with the Conservation Science Forum and the Southeastern PA Conservation Community.

NRCS. (2003). Code 391: Riparian forest buffer. Natural Resources and Conservation Service, Conservation Practice Standard.

Omernik, J.M. (1987). Ecoregions of the conterminous United States. Annnals of the Association of American Geographers 77(1): 118-125.

Palone, R. S., and A.H. Todd, ed. (1998). Chesapeake Bay Riparian Handbook: A guide for establishing and maintaining riparian forest buffers. United States Department of Agriculture Forest Services.

Qiu, Z., Hall, C., and K. Hale. (2009). Evaluation of cost-effectiveness of conservation buffer placement strategies in a river basin. Journal of Soil and Water Conservation 64(5): 293-302.

Roberts, A.D., and S.D. Prince. (2010). Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Chesapeake Bay. Ecological Indicators 10: 459-474

Sawyer, J.A., Stewart, P.M., Mullen, M.M., Simon, T.P., and H.H. Bennett. (2004). Influence of habitat, water quality, and land use on macro-invertebrate and fish assemblages of a southeastern coastal plain watershed, USA. Aquatic Ecosystem Health and Management 7(1): 85-99.

Snyder, M.N., Goetz, S.J., and R.K. Wright. (2005). Stream health rankings predicted by satellite derived land cover metrics. Journal of the American Water Resources Association 41(3): 659-677.

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Sprague, E., Burke, D., Claggett, S., and A. Todd, editors. (2006). The state of Chesapeake forests. The Conservation Fund. 1-114.

Susquehanna River Basin Commission. (2010). .

Teels, B.M., Rewa, C.A., and Myers, J. (2006). Aquatic condition response to riparian buffer establishment. Wildlife Society Bulletin 34(4): 927-935.

USGS. (2009). Hydrologic Unit Maps. National Water Information System, United States Geological Survey. < http://water.usgs.gov/GIS/huc.html>.

Wagner, M., (2008). Acceptance by knowing? The social context of urban riparian buffers as stormwater best management practices. Society and Natural Resources 21: 908-920.

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IX. Appendix 1

HUC Buffer Buffer 11 Name of HUC 11 Watershed Area in Km2 quality, quality, Code 1984 2006 80 Hoffman Brook 16.17 3.9085 3.8769 7 - Susquehanna Headwaters 241.79 4.0451 4.0449 31 Mud Creek 209.49 4.3065 4.2698 30 Otsdawa Creek 52.91 4.7527 4.7646 203 - Near Palmyra 200.12 4.8045 4.7981 218 - At Rock Hill 169.66 4.8933 4.9363 4 - Creek 264.60 4.9094 4.8558 62 Colonel Bills Creek 71.84 4.9463 4.9418 225 - Near Saginaw 416.41 4.9712 4.7292 209 Conestoga Creek- At Safe Harbor 696.72 5.0797 5.0695 South Branch - At 5.1111 5.0945 190.20 239 Newchester 194 Little - At Beverly Heights 256.87 5.1660 5.1651 69 Little Choconut Creek 61.18 5.2262 5.1850 92 Tracy Creek 22.81 5.2572 5.2572 73 Susquehanna River- Patterson Creek 41.66 5.2907 5.2868 103 Crooked Creek 341.87 5.3024 5.2762 271 Susquehanna River- Lower 76.31 5.3358 5.2861 207 - At Talmage 362.61 5.3364 5.3201 222 400.81 5.3695 5.4016 210 Chickies Creek- At Marietta 326.35 5.4582 5.4690 81 175.94 5.4653 5.4663 233 - Near Wrightsdale 124.38 5.5434 5.4649 153 289.40 5.6081 5.6274 230 East Branch Octoraro Creek- At Pine Grove 234.63 5.6473 5.5977 206 - At West Fairview 495.93 5.6651 5.6805 94 601.96 5.7158 5.4300 180 - At Lewistown 251.92 5.7543 5.7126 South Branch Codorus Creek- Near West 5.7716 5.7639 302.44 237 York 141 Susquehanna River- At Harvey Creek 449.10 5.7789 5.7191 240 Conowingo Dam- At Susquehanna Rivre 162.35 5.7971 5.7965 158 West Branch Susquehanna River 500.22 5.8788 5.8824 204 Yellow Creek- At Hopewell 248.38 5.9655 5.9189 44 Canacadea Creek 150.61 6.0419 6.1001 Little Conewago Creek- At Connewago 6.0510 6.0352 169.46 221 Heights

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Susquehanna River- At Haines Branch 6.0580 6.0104 731.45 216 (Lancaster County) Susquehanna River- Schenevus to Ouleout 6.0670 6.0515 234.38 29 Creeks Susquehanna River- to 6.1037 6.0634 172.54 15 13 582.92 6.1369 6.1365 Raystown Branch at - At 6.1469 6.1319 399.35 196 Ardenheim 181 Mahantango Creek- Near Paxton 226.90 6.1770 6.1794 Susquehanna River- to 6.1813 6.1710 502.42 43 Cascade Creek Susquehanna River – Cascade Creek to 6.2239 6.1955 367.11 79 6 - Upper 696.98 6.2476 6.2198 South Branch Tunkhannock Creek- At 6.2550 6.2572 254.54 126 Bardwell 164 Spring Creek- At Milesburg 378.26 6.2719 6.2029 197 Swatara Creek- At Middleton 524.53 6.2740 6.1911 107 Mill Creek 195.84 6.2888 6.2744 227 Conewago Creek- At Beaver Creek 378.59 6.3179 6.3094 220 - At Detters Mill 285.30 6.3219 6.3340 20 Great Brook 67.42 6.3443 6.3358 West Branch Susquehanna River – At 6.3485 6.3500 579.07 150 Northumberland 39 Dudley Creek 96.99 6.3557 6.3641 35 Fivemile Creek- Lower 35.86 6.3717 6.2474 41 Tioughnioga River- Lower 122.90 6.3810 6.3684 112 - At Upper Pittstown 899.75 6.3823 6.3045 86 Lower- Hendy Creek 41.64 6.3853 6.4029 109 East Branch - At Lawton 180.81 6.3865 6.3927 3 Chenango River- Upper 873.84 6.3915 6.3707 Unadilla River Lower and Butternut Creek 6.3916 6.3786 460.85 23 Lower Tioga River- to Chemung 6.3927 6.3473 45.74 74 River 21 Tioughnioga River- Middle 329.44 6.3987 6.2822 Susquehanna River- to 6.4079 6.4075 49.11 50 Unadilla River 102 264.41 6.4088 6.4153 87 Susquehanna River- Rockbottom Dam 11.90 6.4171 6.4171 42 Ouleout Creek 284.03 6.4302 6.4302 9 Otselic Creek- Upper 83.50 6.4325 6.4322

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Frankstown Branch Juniata River- At 6.4326 6.3980 329.35 198 Beaverdam Branch 171 Spruce Creek 282.90 6.4361 6.4212 47 Canisteo River- Upper Middle 85.28 6.4432 6.4914 19 - Upper 152.07 6.4907 6.4384 16 Elk Creek 85.76 6.5095 6.4842 8 Wharton Creek 241.20 6.5459 6.5275 90 Seeley Creek 255.27 6.5548 6.5753 34 Marsh Ditch 53.87 6.5580 6.5257 Frankstown Branch Juniata River- Near 6.5724 6.5452 470.08 186 Petersburg 110 Tunkhannock Creek 637.36 6.5741 6.5569 Conodoguinet Creek- At Mount Rock 6.5828 6.5998 537.30 205 Spring Creek 2 Unadilla River- Upper 325.91 6.5903 6.5405 172 - At Herndon 406.73 6.5969 6.5911 105 Susquehanna River- At Wyalusing Creek 378.36 6.6408 6.6572 70 Canisteo River- Lower 69.42 6.6480 6.5975 Susquehanna River- At Conewago Creek 6.6566 6.6022 871.92 192 (York County) Upper and South Branch of Tuscarora 6.6636 6.6786 176.10 71 Creek 217 Conewago Creek- At York Haven 311.49 6.6704 6.6480 67 North Branch and Lower Tuscarora Creek 156.27 6.6785 6.6992 36 Canisteo River- Upper 191.15 6.6853 6.6904 West Branch Susquehanna River- At Muncy 6.6923 6.6444 551.26 145 Creek 190 Beaverdam Branch- At Hollidaysburg 225.96 6.7024 6.6820 168 Susquehanna River- At Juniata River 602.67 6.7134 6.7221 57 Bennetts Creek 246.91 6.7212 6.7468 113 295.18 6.7616 6.7371 72 Chemung River Lower- Wynkoop Creek 279.68 6.7668 6.7484 246 Octoraro Creek 184.93 6.7699 6.7266 Susquehanna River- Hunts Creek and 6.7717 6.7862 90.74 85 Pumpelly Creek 88 Pierce Creek 15.58 6.7775 6.7775 11 Center Brook and Tallette Creek 117.70 6.7835 6.5814 40 Cohocton River- Upper Middle 121.41 6.7854 6.7816 100 Snake Creek 194.20 6.8064 6.7743 77 Chemung River- Upper and Caton Creek 113.24 6.8067 6.7926 56 Carrs Brook 76.89 6.8131 6.8117 27 Chenango River- Middle 568.18 6.8151 6.7944 215 Raystown Branch Juniata River- At Yellow 475.72 6.8181 6.7985 26

Creek Conodoguinet Creek- At Middle Spring 6.8209 6.8326 278.32 214 Creek 12 Butternut Creek- Upper 154.80 6.8491 6.8375 17 Canasawacta River 159.53 6.8648 6.8603 108 Sugar Creek at North Towanda 489.69 6.8662 6.4721 97 Bentley Creek 150.84 6.8734 6.8590 169 Creek- At Sunbury 354.32 6.8788 6.8553 83 Tioga River 794.73 6.8810 6.8546 61 Masonville Creek 103.19 6.9294 6.8918 101 Salt Lick Creek 136.48 6.9325 6.9474 120 Susquehanna River- Near Harding 776.57 6.9410 6.9331 East Branch Tunkhannock Creek- At 6.9561 6.9552 178.85 116 Glenwood 60 Castle Creek 78.33 6.9901 6.9849 111 Marsh Creek- At Ansonia 210.51 6.9975 6.9983 75 Susquehanna River- Little 92.86 7.0100 6.9984 154 Bald Eagle Creek- At Lock Haven 711.04 7.0345 7.0506 187 Juniata River- At Kishacoquillas Creek 342.42 7.0374 6.9851 189 Juniata River- At Raystown Branch 148.95 7.0397 7.0600 106 Wyalusing Creek 388.94 7.0401 7.0325 51 Canisteo River- Lower Middle 224.06 7.0480 7.0462 52 Nanticoke Creek 295.08 7.0535 7.0479 183 497.11 7.0648 7.0662 55 Cohocton River- Lower Middle 175.17 7.0666 7.0420 26 Cohocton River Upper- Twelvemile Creek 108.51 7.0795 7.0699 99 188.36 7.1185 7.0977 64 Newtown Creek 209.91 7.1326 7.1132 5 Beaver Creek 85.62 7.1336 7.0833 200 Sherman Creek- At Duncannon 633.40 7.1421 7.1474 Susquehanna River- Wappasening Creek to 7.1447 7.0947 104.18 98 Chemung Creek 14 282.68 7.1502 7.1197 18 Schenevus Creek 223.51 7.1924 7.1854 165 - Near Selinsgrove 739.56 7.2006 7.2036 68 Pipe Creek 119.63 7.2043 7.1967 48 Catatonk Creek 389.42 7.2073 7.2125 10 Cherry Creek 237.02 7.2571 7.2513 Raystown Branch- Juniata River at Dunning 7.2585 7.2508 417.84 219 Creek 144 Little - At Clarketown 211.93 7.2694 7.2521 78 Hoyt Creek 27.05 7.2713 7.2402

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22 Fivemile Creek- Upper 141.47 7.2741 7.2684 262 Broad Creek- On the Susquehanna River 104.35 7.2749 7.2764 95 Little Snake Creek 79.51 7.2749 7.2673 117 - At South Towanda 504.60 7.2841 7.2823 184 Juniata River- At Duncannon 394.75 7.2938 7.3029 33 Owego Creek 495.11 7.3093 7.2901 208 - At Cliffs 340.52 7.3102 7.3022 37 Cohocton River- Upper, Goff Creek 83.30 7.3339 7.3511 49 Kelsey Brook 108.34 7.3452 7.3379 59 Page Brook 90.31 7.3551 7.3540 148 - Near Bloomsburg 176.30 7.3877 7.3866 63 Chemung River- Upper, Sing Sing Creek 152.90 7.3945 7.3748 96 225.18 7.4260 7.3661 142 Fishing Creek- At Rupert 527.33 7.4339 7.4254 177 Mahantango Creek 224.89 7.4397 7.4752 93 South Creek 119.16 7.4557 7.4547 162 Pine Creek- At Coburn 242.62 7.4620 7.4839 Susquehanna River- Chenango River to 7.4668 7.4570 31.83 84 Vestal, New York 66 Chenango River- Lower 143.71 7.4673 7.4632 82 Susquehanna River- Ellis Creek 99.80 7.5024 7.5151 65 Cohocton River Lower, Cutler Creek 58.48 7.5078 7.4808 170 Middle Creek- Near Selinsgrove 452.15 7.5169 7.3686 182 - Near Millerstown 166.10 7.5358 7.5689 28 Tenmile Creek and Salmon Creek 82.22 7.5643 7.5673 54 Post Creek 89.49 7.5716 7.5502 193 Buffalo Creek- At Newport 185.95 7.6014 7.6137 174 Honey Creek- At Reedsville 242.82 7.6176 7.6039 38 366.19 7.6528 7.6277 166 Roaring Creek 227.58 7.6622 7.6354 128 Little - At Forksville 213.00 7.6685 7.6526 143 Huntington Creek- At Forks 294.00 7.6769 7.6798 25 Genegantslet Creek 271.83 7.6802 7.6745 202 - Near Allenport 398.60 7.6810 7.6774 188 Tuscarora Creek- At Port Royal 699.72 7.6812 7.7030 160 Buffalo Creek- At Lewisburg 345.99 7.6862 7.6708 179 Pine Creek- At Klingerstown 199.34 7.6872 7.6807 140 Susquehanna River- At 647.80 7.6893 7.6705 163 392.24 7.6895 7.6758 132 - At Eatonville 310.21 7.6975 7.6870 Yellow Breeches Creek- At New 7.7051 7.6883 565.86 213 Cumberland

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185 - At Millersburg 301.33 7.7314 7.7486 139 Muncy Creek 315.49 7.7402 7.7390 24 Charlotte Creek 455.18 7.7423 7.7334 178 Shaver Creek- At Petersburg 163.28 7.7791 7.7560 199 221.29 7.7812 7.7717 201 Bobs Creek- At Reynoldsdale 169.20 7.7902 7.7828 249 441.45 7.8035 7.7932 161 1019.18 7.8075 7.7876 76 Goldsmith Creek 105.71 7.8226 7.8273 West Branch Susquehanna River- At 7.8393 7.8390 759.53 156 Clearfield Creek 175 Juniata River- At Tuscarora Creek 351.91 7.8486 7.8453 191 Juniata River- At Aughwick Creek 285.64 7.8600 7.8664 58 Wylie Brook 65.25 7.8716 7.8598 130 318.48 7.8741 7.8919 173 - Near Petersburg 605.28 7.8765 7.8376 89 Choconut Creek 156.01 7.8987 7.9209 104 Pine Creek- At Marsh Creek 540.67 7.9122 7.8583 152 Anderson Creek- At Curwensville 201.62 7.9671 7.9646 136 230.81 8.1293 8.1295 45 Campbell Creek 101.93 8.1421 8.1635 115 First Fork - At Jericho 693.29 8.1795 8.1734 157 Fishing Creek- At Mill Hall 470.14 8.1850 8.1835 119 335.92 8.1869 8.1930 53 Stocking Creek 70.11 8.1932 8.1676 West Branch Susquehanna River- At 8.1943 8.1896 928.54 147 Sinnemahoning 195 - Near Orbisonia 187.90 8.2024 8.2026 211 Sideling Hill Creek- At Maddensville 250.64 8.2094 8.2172 91 Appalachin Creek 123.52 8.2317 8.2423 238 Muddy Creek- At Muddy Creek Forks 358.22 8.2512 8.2450 - At Huntingdon 8.2860 8.2798 343.04 176 Creek 46 Meads Creek 180.98 8.2933 8.2547 129 Loyalsock Creek- At Montoursville 728.76 8.3162 8.3025 Sinnemahoning Portage Creek- At 8.3486 8.3559 189.66 121 Emporium 226 Brush Creek- Near Everett 222.67 8.3667 8.3687 127 - At Waterville 467.36 8.3904 8.3687 32 Neils Creek 79.08 8.4002 8.4159 146 Mosquito Creek- At Karthaus 184.82 8.4117 8.3904 114 - At Galeton 185.68 8.4426 8.4122

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122 - At Powell 212.91 8.4938 8.4942 151 - At Allenwood 173.83 8.4992 8.4686 118 Pine Creek- At Jersey Shore 800.79 8.5225 8.5144 125 - At Williamsport 704.23 8.5268 8.5055 159 710.49 8.5518 8.5539 133 Loyalsock Creek- At 342.27 8.5523 8.5385 124 Driftwood Branch Sinnemahoning Creek 634.91 8.6212 8.6262 155 Nescopeck Creek 452.82 8.6673 8.6761 123 Kettle Creek- At Westport 638.47 8.8144 8.8173 167 - At Mahaffey 335.04 8.8322 8.8262 131 Young Woman’s Creek- At North Bend 227.96 9.0682 9.0533 149 Beech Creek 444.27 9.1657 9.1609 West Branch Susquehanna River- At Pine 9.1784 9.1722 826.83 137 Creek West Branch Susquehanna River- At Young 9.2197 9.2272 338.82 135 Womans Creek 138 Sinnemahoning Creek- At Keating 213.36 9.3424 9.3407 Bennett Branch Sinnemahoning Creek- At 9.4395 9.4348 949.44 134 Driftwood

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