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Published online 27 October 2006
Published in J Environ Qual 35:2123-2131 (2006)
DOI: 10.2134/jeq2006.0113
© 2006 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America
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TECHNICAL REPORTS

Landscape and Watershed Processes

Impacts of Land Cover on Stream Hydrology in the West Georgia Piedmont, USA

Jon E. Schoonovera,*, B. Graeme Lockabyb and Brian S. Helmsc

a Southern Illinois University Carbondale, Department of Forestry, 1205 Lincoln Drive, Carbondale, IL 62901-4411
b Auburn University, School of Forestry and Wildlife Sciences, Auburn, AL 36849
c Auburn University, Biological Sciences, Auburn, AL 36849

* Corresponding author (jschoon{at}siu.edu)

Received for publication March 20, 2006.

   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS AND DISCUSSION
 SUMMARY AND CONCLUSIONS
 REFERENCES
 
The southeastern United States is experiencing rapid urban development. Consequently, Georgia's streams are experiencing hydrologic alterations from extensive development and from other land use activities such as livestock grazing and silviculture. A study was performed to assess stream hydrology within 18 watersheds ranging from 500 to 2500 ha. Study streams were first, second, or third order and hydrology was continuously monitored from 29 July 2003 to 23 September 2004 using InSitu pressure transducers. Rating curves between stream stage (i.e., water depth) and discharge were developed for each stream by correlating biweekly discharge measurements and stage data. Dependent variables were calculated from discharge data and placed into 4 categories: flow frequency (i.e., the number of times a predetermined discharge threshold is exceeded), flow magnitude (i.e., maximum and minimum flows), flow duration (i.e., the amount of time discharge was above or below a predetermined threshold), and flow predictability and flashiness. Fine resolution data (i.e., 15-min interval) were also compared to daily discharge data to determine if resolution affected how streams were classified hydrologically. Urban watersheds experienced flashy discharges during storm events, whereas pastoral and forested watersheds showed less flashy hydrographs. Also, in comparison to all other flow variables, flow frequency measures were most strongly correlated to land cover. Furthermore, the stream hydrology was explained similarly with both the 15-min and daily data resolutions.

Abbreviations: IS, impervious surface • BI, baseflow index • RC, runoff coefficient • NCDC, National Climatic Data Center • CV, coefficient of variation


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS AND DISCUSSION
 SUMMARY AND CONCLUSIONS
 REFERENCES
 
LAND USE and/or land cover plays a crucial role in driving hydrological processes within watersheds. Vegetation removal, which reduces evapotranspiration, can cause increased overland flow and groundwater inputs into streams (Maidment, 1992). Silvicultural and agricultural activities that alter dominant vegetation communities within a watershed may also influence streamflow generation (Bormann et al., 1999; Xu et al., 2002). Perhaps the most prominent land use affecting hydrology is urban development (Dunne and Leopold, 1978; Imbe et al., 1997; Finkenbine et al., 2000; Lee and Bang, 2000; Bledsoe and Watson, 2001; Rose and Peters, 2001; Brezonik and Stadelmann, 2002).

Studies have shown that increases in a watershed's proportion of impervious surface (IS) to >10% may significantly impact stream hydrology (Hammer, 1972; Hollis, 1975). Hydrological effects of increased IS typically result in elevated quickflow generation and produce both high magnitudes and early peaks in storm hydrographs (Hirsch et al., 1990; Smith and Ward, 1998). These alterations in hydrology can have dramatic effects on ecological processes within stream ecosystems (Paul and Meyer, 2001). The impacts of IS on large order (e.g., >third order) streams are well documented, although fewer studies have assessed hydrological impacts from increasing levels of impervious surface in low order streams (Simmons and Reynolds, 1982; Ferguson and Suckling, 1990; Richter et al., 1997; Stewart et al., 1999; Frick and Buell, 1999; Rose and Peters, 2001; Rose, 2002; Schoonover et al., 2005).

Five important flow variables that affect ecological processes within streams have been shown to be influenced by IS: flow magnitude (amount of discharge), flow frequency (number of times a magnitude is exceeded), flow duration (the amount of time a discharge is exceeded), flow timing or predictability (overall variability of flows, coefficient of variation), and flashiness (rate of change of discharge) (Richter et al., 1996; Poff et al., 1997; Clausen and Biggs, 2000; McMahon et al., 2003; Helms, unpublished data, 2006). Typically, streams with increasing IS result in higher magnitudes and shorter return intervals of high flows, and streams also generally display shorter duration flows with high flashiness (Paul and Meyer, 2001). Thus, the preservation of land cover types such as forests and grasses are important when managing watersheds for flood prevention and the maintenance of habitat stability within streams.

The southeastern USA is clearly affected by rapid population growth as well as the conversion of land uses (U.S. Census Bureau, 2000). The rapidly growing city of Columbus, Georgia offered an excellent opportunity to explore the effects of land development on hydrology because there is a high abundance of relatively small watersheds within a variety of rather homogenous land covers. Columbus is also a much smaller city compared to those that many studies have investigated (e.g., Atlanta, GA, Phoenix, AZ, Portland, OR, and Baltimore, MD). Because of their vast number compared to larger metropolitan areas, smaller cities are critical areas to investigate to manage the impacts of urban expansion on lotic ecosystems.

In this paper the effects of land cover on hydrology of low order streams across an urban-rural gradient were investigated. The objectives of this study were twofold. First, the effects of land cover on an array of hydrologic measures were assessed. Specifically, the effects of land cover on flow duration, frequency, magnitude, variability, and baseflow were examined. Second, stage levels were recorded at two time intervals (15-min and daily) to allow comparisons between the sampling resolutions for their ability to characterize the stream hydrology among multiple land covers. The above objectives were investigated using data collected from 18 streams in which the watersheds were dominated by urban, developing, pastoral, unmanaged, or managed forest land covers.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS AND DISCUSSION
 SUMMARY AND CONCLUSIONS
 REFERENCES
 
Study Area
The southern extent of the study area is within Columbus, Georgia (32°18'29'' N, 84°31'29'' W) and extends northward to LaGrange, Georgia (33°01'29'' N, 85°01'13'' W) (Fig. 1). All watersheds ranged in size from 300 to 2500 ha and are subbasins of the Middle Chattahoochee Watershed within the Piedmont physiographic province. According to Strahler's stream classification system, the study streams ranged from first to third order (Strahler, 1952). Low order streams were used in this study since smaller watersheds tended to be dominated by a single land cover, whereas larger watersheds typically had mixed land covers that may have increased the difficulty of relating hydrologic changes with land cover.


Figure 1
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  		Fig. 1. Locations of study watersheds (depicted by polygons) within the Middle Chattahoochee Watershed of west Georgia. Stars represent rainfall stations used in the study; circles represent study watersheds.

 
Dominant land covers within the study area were classified as unmanaged forest, managed forest, urban, developing, and pastoral (Table 1). Aerial photographs (1-m resolution) were taken during leaf-off in March 2003 to facilitate land cover classification. The first effort in the 1-m image analyses was to generate an IS percentage for each watershed. Impervious surface is a widely accepted and reliable indicator of urbanization and its impacts on natural resources, particularly for water resources (Schueler, 1995; Arnold and Gibbons, 1996). The remaining land classes were then digitized using both unsupervised and supervised classification methods. The image processing methods used in this assessment are described in detail by Lockaby et al. (2005).


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  		Table 1. Summary of land cover percentages within the 18 study watersheds of western Georgia. Numbers in parentheses represent the number of watersheds in each category.

 
Elevation ranges of the Piedmont are between 152 and 457 m above mean sea level and the topography is gently rolling to steep. Udults dominate the area, which have clayey or loamy subsoil, a thermic temperature regime, a udic moisture regime, and a kaolinitic or mixed mineralogy (Soil Survey Staff, 2003). The soils are underlain by acid crystalline and metamorphic rocks. Historical cotton farming has eroded approximately 18 cm of the topsoil in many localities, leaving clayey subsoil exposed (Trimble, 1974). Stream channel substrates were predominantly composed of unconsolidated materials in size classes <2 mm.

Forest cover types within the study area range from intensively managed pine plantations to bottomland hardwood forests. Many of the uplands are either in pasture, which is used for grazing or growing hay, or in pine plantations. Plantations are both nonindustrial privately owned and industry owned lands under several management prescriptions. Loblolly pine (Pinus taeda L.) is the predominate species that is commercially harvested and exists in various rotation stages (i.e., mature, clearcut, thinned, or planted). Many of the watershed lowlands have intact riparian corridors composed of bottomland hardwoods such as sweetgum (Liquidambar styraciflua L.), oaks (Quercus spp.), tulip poplar (Liriodendron tulipifera L.), and magnolias (Magnolia spp.).

Field Methods
Eighteen watersheds were instrumented with InSitu MiniTroll pressure transducers to record stream stage at fixed sampling locations near the point of outflow from the watersheds (InSitu, Boulder, CO). Pressure transducers were housed in 10-cm, schedule 40 PVC pipe, which were perforated along the portion extending into the stream. The PVC tubes served as temporary stilling wells, prevented damage to the units, and provided a stable water surface to increase the accuracy of stage readings. Pressure transducers were programmed to record a stage reading at 15-min intervals, which allowed for detailed storm hydrographs. The period of record was from 29 July 2003 through 23 Sept. 2004.

Stream stage readings were correlated with discharge readings that were measured during all seasons of the year and at various stages. To ensure that rating curves were represented at a variety of stages, discharge was also recorded during several high flow events. All streams were measured for morphometry characteristics at baseflow near the gauging stations and the data were used to calculate discharge utilizing Manning's equation (Maidment, 1992) for extreme events that were unsafe to sample. Instantaneous discharge was determined by measuring the velocity and cross-sectional areas of subsections across the stream channel. Generally, at least 20 subsections were measured across the stream channel, which complies with USGS stream gauging guidelines (Rantz, 1982). A Marsh-McBirney (1990) flowmeter was used to measure the velocity within each subsection. The mean velocity (typically, measured below the stream's surface at 60% of the total depth) at each subsection or point was multiplied by the area of that subsection, and the results from all the subsections were summed to obtain total discharge (L s–1) for the transect.

Daily precipitation data available from the National Climatic Data Center (NCDC) (2005) was used to calculate runoff coefficients. Four NCDC stations were monitored to ensure that sufficient coverage was available across the study area. Weather stations used in this study were located at the Columbus Metropolitan Airport (#092166/93842), Mulberry Grove (096148), West Point (099291), and Woodbury (099506) (Fig. 1). Historical monthly averages were based on the Columbus, West Point, and Woodbury stations; Mulberry Grove was excluded due to its recent installation (1997), whereas the others had historic data from 1948, 1931, and 1931, respectively.

Indicators of Hydrologic Alteration
Software developed by the Nature Conservancy, Indicators of Hydrologic Alteration (IHA), was used to analyze daily median flows. The software is a free downloadable version (The Nature Conservancy, 2006) that has shown potential for quantifying hydrologic characteristics important to stream biota (Richter et al., 1996). Further, the daily sampling inputs in the software may help reduce data analysis time and sampling costs. Daily median flow values were calculated from the 15-min discharge collected from the 18 watersheds. Only complete annual datasets (29 July 2003 through 28 July 2004) were used, which avoided the potential introduction of errors from estimates using the missing data options of the software.

Data Analysis
Rating curves for stage and/or discharge relationships were developed using Table Curve 2D software based on the 15-min discharge data (Systat, 2002). A logistic dose response equation was the simplest equation that accurately explained the rating curve data of all streams, without overfitting the data while maintaining a high r2 (0.95–0.99).

Hydrograph analyses were performed using SAS software (SAS Institute, 1999). Hydrology data were separated into five categories: baseflow, magnitude (i.e., a measure of extreme or low flow events), frequency (i.e., the number of times a predetermined flow magnitude is exceeded), duration (i.e., the time flow is above or below a given magnitude), and predictability and flashiness (i.e., the rate and/or amount of change in flow). The specific magnitudes used for frequency analysis were based on the variables used by Clausen and Biggs (1997, 2000). The variables are not necessarily suggestive of a particular stream stage or have any specific biological importance but are used in correlative analyses to compare relative flows from multiple streams. Thirty-two hydrologic variables were calculated for each stream (Table 2) and were developed to be comparable to variables generated in IHA software outputs (i.e., both the 15-min data and IHA data have variables representing baseflow, flow magnitude, flow frequency, flow duration, and flashiness measures). Dependent variables (i.e., hydrologic parameters) were tested to determine if they differed significantly among land cover classes (independent variables) using Kruskal-Wallace tests ({alpha} = 0.05). Nonnormal dependent variables were log-transformed to meet assumptions of normality before performing analyses (Sokal and Rohlf, 2000).


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  		Table 2. Hydrologic variables by dominant land cover for all western Georgia watersheds.

 
Baseflow Hydrology
Baseflow was predicted for each stream using a 5-d smoothed minima technique (Gustard et al., 1992). A brief outline of the baseflow separation method follows:
  1. Mean daily flow data was divided into nonoverlapping blocks of 5 d and then the minimum flow for each block was determined.
  2. The minimum flow value was multiplied by a constant of 0.9 (Gustard et al., 1992). If the product is less than both the previous 5-d block minima and next 5-d block minima then the value was used as an estimate of baseflow.
  3. A daily value of baseflow was estimated for the entire data set using linear interpolation between each predicted baseflow.
  4. If the actual observed flows were lower than predicted flows, then the baseflow estimate was equal to the observed flow.

Predicted baseflow values were then summed and divided by the sum of the observed values, resulting in an estimate of baseflow index (BI). The BI is the proportion of water contributing to a stream as groundwater inputs vs. surface runoff, where high BI values indicate significant groundwater inputs (less flashy hydrographs) and low values indicate higher surface water inputs (flashy flows). Previous investigations have shown BI to be less variable than other low flow variables (Gustard et al., 1992).

IHA Analyses
The IHA software offers user-defined thresholds for identifying criteria for extreme low flows, high flow pulses, and large and small flood events (The Nature Conservancy, 2005). For the following analyses, extreme low flows were defined as those that fell below the lowest 10% of flows for the entire sampling period. High flow pulses were initiated when the flow increased by >25% per day or exceeded 75% of all flows for the period of record. A high flow pulse ended once the flow decreased by <10% per day or to a value <50% of all daily flows. Small and large flood events were identified as the flows in which high flow pulses had a recurrence interval of at least 2 and 10 yr, respectively. The IHA software calculated 32 parameters that were organized into five comparable groups as the 15-min data: (1) magnitude, (2) magnitude and duration of annual extreme conditions, (3) timing of annual extreme conditions, (4) frequency and duration of high and low flow pulses, and (5) rate and frequency of flow change (Richter et al., 1996). Individual parameters of IHA are discussed in detail by Richter et al. (1996 and 1997).


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS AND DISCUSSION
SUMMARY AND CONCLUSIONS
 REFERENCES
 
Precipitation
The 30-yr average rainfall total was 132 cm yr–1 for western Georgia, and fell predominantly as rain (NCDC, 2005). In 2003, annual rainfall was 23.3 cm above average, whereas in 2004 the annual rainfall was 5.5 cm below normal. In 2003, most above average rainfall fell between February and August, with the remainder of the year being below average. In 2004, late winter through early spring (i.e., January through May) had below average precipitation totals whereas the early fall (specifically September) experienced higher rainfall than average.

Stormflow Hydrology
Flow magnitude, frequency, duration, and flashiness variables calculated from 15-min data are summarized and defined in Table 2. The number of readings in which particular magnitudes (i.e., the frequency variables) were exceeded was considerably higher in urban watersheds than in watersheds with other land covers. Specifically, the 3xMed (the number of times discharge exceeded three times the median flow), 5xMed (the number of times discharge exceeded five times the median flow), and the >99th percentile (the number of times discharge exceeded the 99th percentile) variables were higher in the urban watersheds. Additionally, when both the urban and developing land use categories were combined and compared to other land covers there were significant differences with regards to the three aforementioned variables and the 7xMed flow frequency variable (the number of times the discharge exceeded seven times the median flow) (Table 3). Urban watersheds also experienced the highest peak discharges (i.e., Max) for magnitude (L s–1) (Table 2). However, duration of flows above the magnitude was similar to, or lower than, other land covers.


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  		Table 3. Land cover comparisons using Kruskal-Wallace tests for flow variables calculated from 15-min discharge data.

 
Watersheds dominated by forestland (both managed and unmanaged combined), without an urban component, have significantly lower mean discharges per area (p = 0.05) as well as lower maximum discharges (p = 0.02) than all other land covers combined. Forested watersheds also had significantly lower minimum flows, which was likely due to high evapotranspiration losses from the trees. Median flows were negatively correlated with the percent of unmanaged forest cover in watersheds (Fig. 2).


Figure 2
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  		Fig. 2. Median (± 1SE) stream discharge relationships with percent unmanaged forest in western Georgia watersheds.

 
Watersheds with large proportions of pasture (i.e., >30%) had higher BI than other land covers, suggesting that groundwater inputs provide a significant input to stream recharge. Infiltration rates greatly depend on soil condition and soils with dense grass cover have been shown to promote homogeneous infiltration and storage of soil waters (Williamson et al., 2004). Grasses also produce dense rooting networks, which affect infiltration capacities and in drying soils uniformly (through evapotranspiration), thus facilitating soils' ability for high infiltration and runoff storage during subsequent events (Hino et al., 1987). High infiltration would also explain the significant groundwater contribution to streamflow generation (i.e., high BI). Streams draining pastureland also rarely exceeded the 5x and 7x median flows, which resulted in less flashy hydrographs throughout the study period. Similarly, watersheds dominated by either unmanaged or managed forests had high BIs. Deep rooting networks and established litter layers in forest soils generally promote high infiltration and groundwater recharge (Fisher and Binkley, 2000).

Watersheds with an IS of >20% were positively correlated with flow frequency variables, whereas forested areas were generally negatively correlated with frequency variables (Table 4). The higher flow frequencies and flow magnitudes in the urban watersheds likely resulted from high runoff volumes and fast conveyance of water by ISs such as parking lots, rooftops, driveways, and sidewalks (Carter, 1961; Leopold, 1968; Tourbier and Westmacott, 1981). By contrast, forested watersheds generally have very high infiltration capacities, which lead to longer lag times between rainfall and increases in stream flow (Whitehead and Robinson, 1993; Fisher and Binkley, 2000).


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  		Table 4. Pearson's correlation coefficients for 15-min hydrology variables by the dominant land cover within 18 western Georgia streams.

 
Several approaches are commonly used to estimate runoff generation and lag times for different land cover types, including the rational method, Soil Conservation Service (SCS) method, and runoff coefficients (Kirpich, 1940; Soil Conservation Service, 1972; Ward et al., 1980). Runoff coefficients (C) were calculated for the three urban watersheds in this study; however, remaining watersheds were not located close enough to NCDC rainfall stations to calculate reliable C values. Runoff coefficient values for the urban watersheds were 0.69, 0.65, and >1.0. The urban watershed with the C value >1 indicated that streamflow generation exceeded precipitation volume; streamflow was likely elevated here because discharge originated from sources other than rainfall, such as leaky sewage pipes, septic drain fields, or excessive landscape irrigation (Ferguson and Suckling, 1990). Runoff coefficient values reported by Erie and Niagara Counties Regional Planning Board (1981) ranged from 0.47 to 0.69 for high density residential areas, and up to 0.90 in industrial areas. The American Society of Civil Engineers and Water Pollution Control Federation report similar C values, up to 0.95 in downtown urban areas (Maidment, 1992). Although topography and soils are similar, C values reported by Rose and Peters (2001) for Peachtree Creek near Atlanta, Georgia were considerably less than those measured in downtown Columbus, Georgia. Differences in effective impervious surfaces (i.e., those sources of discharge directly connected to streams by pipes) (Booth and Jackson, 1997) may have been higher in Columbus watersheds.

Stormflow hydrology also had significant effects on physicochemical and habitat variables within the study streams, which may have impacted stream biota (Schoonover et al., 2005; Helms et al., 2005). Specifically, fish assemblages displayed a shift from sensitive minnow-based assemblages in forested watersheds to tolerant, sunfish-based assemblages in developing and urban watersheds. This suggests that in these lowland streams, altered physicochemical conditions, induced by impervious surface and altered hydrology, may be a strong driver of fish assemblage structure. With regards to invertebrate assemblages, streams experiencing flashy flows and increased flow frequency experienced shifts from intolerant species (e.g., those representing EPT taxa) toward more tolerant species (e.g., Chironomidae and Oligochaete worms) (Helms, unpublished data, 2006).

Baseflow Hydrology
Median base flow ranged from 13.1 L s–1 in unmanaged forested watersheds to 201.3 L s–1 in pastoral watersheds. Median base flow in pastoral watersheds was significantly higher than watersheds with other dominant land covers (p = 0.04). High base flow in the pastoral watersheds was likely due to the high infiltration capacities of pastures, which may lead to groundwater recharge. Additionally, the lack of forest cover may reduce high transpiration losses by woody vegetation (Whitehead and Robinson, 1993). Baseflow levels of urban streams have been reported as being lower than in rural areas due to the high impervious surface coverage, which reduces infiltration, and sanitary sewerage (Sulam and Ku, 1977; Simmons and Reynolds, 1982). Groundwater inputs likely contribute to streamflow generation in both managed and unmanaged forested watersheds throughout the year, as opposed to urban and developing watersheds where, possibly because of lower evapotranspiration, discharge did not increase during the winter. However, baseflow levels were maintained throughout the year in urban streams, perhaps low flows may be supplemented by leaky sewage pipes, septic drainage, or excess irrigation (Simmons and Reynolds, 1982; Ferguson and Suckling, 1990).

Baseflow index for each land cover is illustrated in Fig. 3 and exhibited relatively tight correlations with several hydrologic parameters. The BI was highest in pastoral and forested streams; however, MU2, a managed forest stream, and MU1 and FS3, which are pastoral streams, do not follow the trend. MU2 was recently clearcut, which would reduce evapotranspiration and potentially lead to higher overland flow inputs from the areas with exposed mineral soil, thus ultimately reducing the BI. For the two pastoral streams, the cause of the lower BI (~0.30) was not identifiable.


Figure 3
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  		Fig. 3. Baseflow indices for 18 streams in western Georgia (text symbols represent watershed ID).

 
Figures 4 and 5 illustrate the relationships between BI and CV (coefficient of variation) and the number of times the 7xMedian flow was exceeded. Watersheds with high BIs showed less flashiness (i.e., lower %CVs) whereas watersheds with low BIs showed higher peak discharges and higher occurrences of flows exceeding greater magnitudes. Jordan et al. (1997) found similar trends in baseflow; watersheds with low BIs were clearly dominated by brief high discharge episodes, and watersheds having high BIs experienced more constant flows through time.


Figure 4
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  		Fig. 4. Relationship between baseflow index and coefficient of variation (%) for stream discharge in 18 western Georgia streams.

 

Figure 5
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  		Fig. 5. Relationship between baseflow index and the number of times that the 7x median flow was exceeded in 18 western Georgia streams.

 
IHA Analysis
The IHA software calculated hydrologic parameters based on daily median flow values. Urban watersheds (>24% IS) were compared to watersheds with little urban development (Table 5). IHA Group 2 variables (i.e., magnitude variables) included both maximum and minimum flow values. Urban watersheds had both the highest 1-d and 90-d minimum flows. Minimum flows from the 15-min data were not significantly different among land cover classes. However, low flows in the forested watersheds were significantly lower than those for other land covers, likely because of transpiration losses during the summer months (Whitehead and Robinson, 1993). Both low and high pulse counts and rise and fall rates were highest in urban watersheds, suggesting that urban streams experience not only high spate flows, but also rapid hydrograph rise and fall. Forested land cover percentages within the watersheds were negatively correlated (r = –0.52, p = 0.03) with the number of high pulses, reiterating the less flashy character of stream flows in forested watersheds.


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  		Table 5. Results from the IHA software (Richter et al., 1996) for 18 streams across an urban-rural land cover gradient in western Georgia. Data are based on daily median flows and only presented for variables with significant results (i.e., p ≤ 0.05).

 
Parameters tested in the 15-min data set and those for the IHA software were not identical. However, the five dominant flow categories showed general trends between the two methods. Both the IHA and the 15-min data showed higher magnitude flows, frequency of flows, and higher flashiness in urban watersheds than watersheds with <5% IS coverage. The two methods appear to show similar agreement among land covers. However, if analysis of individual storm hydrographs is deemed critical, then the 15-min data has greater utility in characterizing individual storm hydrographs.


   SUMMARY AND CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS AND DISCUSSION
 SUMMARY AND CONCLUSIONS
REFERENCES
 
This paper addressed how hydrologic variables related to land cover within 18 watersheds draining the Piedmont of western Georgia. Thirty-two variables that characterized the magnitude, duration, frequency, and flashiness of flows were calculated for each watershed. Two scales of flow measurement were also compared (a 15-min interval discharge data set vs. daily discharge) in terms of their utility for assessing stream hydrology. In both the 15-min and daily data sets, flow frequency variables were most tightly correlated to land cover. High flow pulses and elevated peak discharges were more frequent in urban watersheds (>24% IS) than any other land cover, and baseflow (i.e., groundwater) inputs in urban streams were lower than other watersheds. Urban streams baseflow discharge deviated little between growing and dormant seasons, suggesting that vegetation had minor to no effect on groundwater contributions to streamflow. Also, BIs suggested that quickflow contributed up to 90% of the flow reaching urban streams and between 65 and 90% of flow in streams associated with developing watersheds. Conversely, watersheds with high forest or grass cover had higher contributions from groundwater inputs. Furthermore, the proportion of unmanaged forest within the watersheds was negatively correlated with stream discharge. Runoff coefficients were similar to the ranges reported for residential and commercial areas. However, measured runoff coefficients were considerably higher than those reported near downtown Atlanta, Georgia, perhaps due to greater proportions of effective impervious surfaces.

Urban development has a strong influence on the hydrologic character of low order Piedmont watersheds. Consequently, hydrologic modification caused by development may threaten channel morphometry and/or stream biota. The scale at which the hydrologic character is measured is dependent on sampling period and the desired data output. The analysis of the western Georgia streams suggested that both the 15-min and daily discharge data provided comparable hydrologic characterization of the streams on an annual basis. However, if individual storm analyses are necessary, the 15-min data would provide greater hydrograph resolution, which would provide data for detailed hydrograph analyses.


   ACKNOWLEDGMENTS
 
Funding for this research was provided by the Center for Forest Sustainability through the Peaks of Excellence program at Auburn University. Additional funds were provided by Auburn University's Environmental Institute. The authors thank Shufen Pan for providing land cover data for the study watersheds and Jackie Crim, Summer Simpson, and Don Vestal for their hard work in the field.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS AND DISCUSSION
 SUMMARY AND CONCLUSIONS
 REFERENCES
 




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