Unc-Wrri-94-284 Effects of Urban I

UNC-WRRI-94-284

EFFECTS OF URBAN IZATION AND LAND-USE CHANGES ON LOW STREAMFLOW

by Jack B. Evett with Contributions from Margaret A. Love and James M. Gordon Department of Civi 1 Engineering College of ~ngineering The University of North Carolina at Char1 otte Charlotte, North Carolina 28233

The research on which this report is based was financed in part by the Department of the Interior, U.S. Geological Survey, through the Water Resources Research Institute of The University of North Carol ina. The contents of this publication do not necessarily reflect the views and policies of the Department of the Interior, nor does mention of trade names or commercial products constitute their endorsement by the United States government.

Agreement No. 14-08-0001 -G2O37 USGS Project No. 14 (FY93) WRRI Project No. 70122 .One hundred fifty copies of this report were printed at a cost of $1,981 -50 or $13.21 per copy. ACKNOWLEDGEMENT

The research on which this report is based was financed in part by the United States Department of the Interior, Geological Survey, through the N.C. Water Resources Research Institute. Margaret A. Love, a graduate student in civil engineering supported financially by the project, contributed significantly to all phases of the study. James M. Gordon, also a graduate student in civil engineering, was instrumental in developing, as part of his master's thesis, some of the statistical methodology used herein. Ms. Love contributed to the writing of the "Introduction" section; Mr. Gordon contributed to the writing of both the "Introduction" and the "Procedures" sections. Civil engineering graduate students William L. Saunders, Jr. and Scott A. Robidoux also provided assistance in conducting the project. Specid. thanks go to Robert Mason of the Water Resources Division of the U.S.Geological Survey in Raleigh, NC, for providing streamflow data and helpful comments, and also to the government documents library staff of Atkins Library at The University of North Carolina at Charlotte for their generous efforts in assisting the project. College of Engineering Dean R. D. Snydex and Civil Engineering Department Chairman L. Ellis King of The University of North Carolina at Charlotte provided personal as well as facilities support.

ABSTRACT

Historical low-streamflow data were analyzed for a number of gaging stations on streams in and around various urban areas in North Carolina in an attempt to find and document effects of urbanization and land-use changes on low streamflows. Records for streams within each urban area were compared with streams outside (but nearby) the urban area by two statistical methods. It was concluded from the study that there is some support for the premise that urbanization causes a decrease in low streamflows over time, but statistically the results are inconclusive. It appears more likely that most small streams--both urban and rural--are experiencing decreasing low flows over time. (Key words: low streamflows, urbanization, land-use change, urban hydrology)

TABLE OF CONTENTS

Page Acknowledgement Abstract

List of Figures v1 .. . List of Tables Vlll

Summary and Conclusions ix Recommendations Introduction Purpose and Objectives Procedures One Sample Run Test for Randomness Test for Equality of Slopes Applications to Various Urban Areas Asheville Greensboro Raleigh Charlotte Goldsboro-kin ston Rocky Mount-Tarboro Other Attempts Results and Discussion References

Glossary Appendix A 7Q Flows (annual and five-year mean values), precipitation. and population data for stations in the Asheville area Appcfidix B 74Rows (annual and five-year mean values), precipitation, and

population data for stations in the Greensboro area 85 Appendix C 74mows (annual and five-year mean values), precipitation, and population data for stations in the Raleigh area 96 Appendix D 7Q Rows (annual and five-year mean values), precipitation, and population data for stations in the Charlotte area 105 Appendix E 74Flows (annual and five-year mean values), precipitation, and population data for stations in the Goldsboro-Kinston area 116 Appendix F 7Q Flows (annual and five-year mean values), precipitation, and population data for stations in the Rocky Mount-Tarboro area 123 Appendix G Equality of slopes test applied to Swannanoa River at Biltmore and Mills River near Mills River 130

LIST OF FIGURES

Page Location Map of Stations in the Asheville Area 9

5-Year Averaged 7-Day Low Flows for Study Stations in the Asheville Area 11

Annual Precipitation for Study Stations in the Asheville Area 12

Area Population for Study Stations in the Asheville Area 13

Low-Flow Trends for Swannanoa River at Biltmore 14

Low-Flow Trends for Mills Fbver near Mills River 15

Location Map of Stations in the Greensboro Area 18

5-Year Averaged 7-Day Low Flows for Study Stations in the Greensboro Area 20

Annual Precipitation for Study Stations in the Greensboro Area 2 1 Area Population for Study Stations in the Greensboro Area 22 Low-Flow Trends for North Buffalo Creek near Greensboro 23 Low-Flow Trends for East Fork Deep River near High Point 24 Low-Flow Trends for Reedy Fork near Oak Ridge 25 Low-Flow Trends for Little Yadkin River at Dalton 26 Low-Flow Trends for Deep River at Ramseur 27 Low-Flow Trends for Hunting Creek near Harmony 28 Location Map of Stations in the Raleigh Area 30 5-Year Averaged 7-Day Low Flows for Study Stations in the Raleigh Area 32 Annual Precipitation for Study Stations in the Raleigh Area 33 Area Population for Study Stations in the Raleigh Area 34 Low-Flow Trends for Middle Creek near Clayton 35 L,ow-Flow Trends for Little Fishing Creek near White Oak 36 Low-Flow Trends for Flat River at Bahama 37 Low-Flow Trends for Little River near Pnnceton 38

Low-Flow Trends for Deep River at Moncure Location Map of Stations in the Charlotte Area 5-Year Averaged 7-Day Low Flows for Study Stations in the Charlotte Area Annual Precipitation for Study Stations in the Charlotte Area Area Population for Study Stations in the Charlotte Area Low-Row Trends for Indian Creek near Laboratory Low-Flow Trends for Long Creek near Bessemer City Low-Flow Trends for Big Rear Creek near Richfield Low-Flow Trends for Irwin Creek near Charlotte Low-Flow Trends for McAlpine Creek at Sardis Road near Charlotte Low-Flow Trends for Twelve Mile Creek near Waxhaw Low-Row Trends for Long Creek near Paw Creek Low-Flow Trends for McMullen Creek at Sharon View Road near Charlotte

Location Map of Stations in the Goldsboro-Kinston Area 5-Year Averaged 7-Day Low Flows for Study Stations in the GoIdsboro-Kinston Area Annual Precipitation for Study Stations in the Goldsboro-Kinston Area Area Population for Study Stations in the Goldsboro-Kinston Area

Low-Row Trends for Nahunta Swamp near Shine Low-Flow Trends for Contentnea Creek near Lucama Location Map of Stations in the Rocky Mount-Tarboro Area 5-Year Averaged 7-Day Low Flows for Study Stations in the Rocky Mount-Tarboro Area Annual Precipitation for Study Stations in the Rocky Mount-Tarboro Area Area Population for Study Stations in the Rocky Mount-Tarboro Area

vii

LIST OF TABLES

Page Stations in the Asheville Area 8 Results of One Sample Run Test for Stations in the Asheville Area 16

Stations in the Greensboro Area 19 Results of One Sample Run Test for Stations in the Greensboro Area 29 Stations in the Raleigh Area 29

Results of One Sample Run Test for Stations in the Raleigh Area 3 1

Stations in the Charlotte Area 40 Results of One Sample Run Test for Stations in the Charlotte Area 53 Stations in the Goldsboro-Kinston Area 54 Results of One Sample Run Test for Stations in the Goldsboro-Kinston Area 01 Stations in the Rocky Mount-Tarboro Area 61 Summary of Results of the Equality of Slopes Test 67

SUMMARY AND CONCLUSIONS

Historical low-streamflow data were analyzed for a number of gaging stations on streams in and around various urban areas in North Carolina in an attempt to find and document effects of urbanization and land-use changes on low streamflows. The urban areas included Asheville, Greensboro, Raleigh, Charlotte, Goldsboro-Kinston, and Rocky Mount-Tarboro. Records for streams within each urban area were compared with streams outside (but nearby) the urban area by two statistical methods--the One Sample Run Test and the Equality of Slopes Test. The former test checked on the randomness of data; the latter compared slopes of low-flow trends for various pairs of urban versus rural stations. The hypothesis was that low streamflows in North Carolina would exhibit a decreasing trend as urbanization progressed, with the proviso that a similar decreasing trend would not be present for nearby nonurban (rural) areas. In the Asheville area, two rivers were considered--one urban, the other rural. Both exhibited a negative (downward) trend in low flow over time with the urban station's slope being more negative, but statistically they did not differ significantly. In the Greensboro area, two of the three urban areas had positive trends; the third was slightly negative. All of the rural stations exhibited negative trends. This outcome in itself is contrary to the project's hypothesis (that rural slopes should be greater than urban ones). The statistical analyses were, however, inconclusive. In the Raleigh area, trends of all stations were negative, but the lone urban station did have the greatest negative slope; and the statistical analyses did tend to uphold the project's hypothesis. In the Charlotte area, trends for all stations except one--a rural one--were negative. The statistical analyses were not entirely favorable to the project's hypothesis, but the overall results did tend to uphold the hypothesis. In the Goldsboro-Kinston and Rocky Mount-Tarboro areas, the analyses were abandoned because of problems with the data. It was concluded from the study, therefore, that there is some tendency to support the premise that urbanization causes a decrease in low streamflows over time, but statistically the results are inconclusive. It appears more likely that most small streams--both urban and rural--are expzriencin? decreasing low flows over time, more so than would result from decreasing trends in precipitat~onalone.

It is hoped that the results of this study will prove useful to water resource planners and regulators. The indication that low streamflows in and around urban areas appear to be decreasing with time should be helpful in future permitting actions. Additionally, proposed land-use changes within drainage basins can be considered in light of the impact the changes may have on streams which may experience decreasing low flows over time. Further study is suggested to try to collect actual field data (as opposed to using already- collected data, as was done in this project) to develop parameters to describe the "urban" condition. Further study is also suggested to consider additional factors that might affect low streamflows. Such factors might include: time of occurrence of low flows each year; precipitation during, or immediately preceding, the time of occurrence; maximum temperature during, or immediately preceding, the time of occurrence; stream slopes; evapotranspiration; soil sample analyses; riparian zones; and groundwater levels. It is felt in particular that the relationship between maximum temperature during, or immediately preceding, the time of occurrence of annual low flows should be studied further.

INTRODUCTION

Most hydrological research into streamflow rates has concentrated on flood events rather than low-flow regimes. This is to be expected since uncontrolled flooding can have catastrophic consequences, which are readily apparent to the public. However, even though low-flow issues are not as visible as their high-flow counterparts, low-flow conditions play a major role in water quality and capacity problems. Low streamflows are a matter of concern for several reasons--notably in consideration of the adequacy of a stream to supply municipal and industrial water requirements, to receive wastes (i.e., provide dispersion and dilution), to provide supplemental irrigation, and to maintain aquatic life. It appears that much research on low streamflow has been done to quantify historical low flows rather than to identify and document specific causes of low-flow variations over time or to predict low flows (Giese and Mason 1991, Loaiciga and Marino 1988, Reynolds 1982, Riggs and Hanson 1969, Stallings 1967, Caffey et al. 1980, Vladimirov and Chebotarev 1974, Vogel and Kroll 1989). In some cases, such research has dealt with quantifying low streamflows by frequency analysis or streamflow-duration curves (Loaiciga and Marino 1988, Stallings 1967, Vladimirov and Chebotarev 1974, Vogel and Kroll 1989). Other articles deal with effects of urbanization on streamflows in general without much consideration given specifically to effects on low streamflows (Beard and Chang 1979, Hajas et al. 1978). Yet others deal with low-flow forecasting (Fish 1968, Miller and Wenzel 1985, Riggs and Hanson 1969), and others on the effects of drought on streamflow (Anderson and McCall 1968, Chang 1990, Horn 1989). Streamflow during low-flow periods and the range of variability in low flows over time are affected by many factors but primarily are determined by a watershed's geology, climate, aild topo_praphy (Riggs 1976). These three factors all contribute to flow patterns, but one may domnate, depending on particular basin characteristics. Soil type influences base-flow conditions by controlling infiltration and aquifer recharge. with high clay content causing more runoff (Giese and Mason 1991). Schneider (1965) has determined that geology may be more important in determining low-flow rates than precipitation. Streams underlain by limestone and dolomite have the greatest variability, while streams underlain by shale produce the lowest flows and streams underlain by sandstone produce the highest consistent low flows. Many researchers have noted that urbanization contributes to decreasing low flows. For example, Leopold (1 968) reported that urbanization causes an increase in runoff and less recharge. Reynolds (1982) found a 25 percent decrease in low flows after large-scale paving of open land within a drainage basin. Fok and associates (1975) found that base flow may be reduced by as much as 30 percent by urbanization. Singh and Stall (1974) fcund that urban development caused streamflow to be reduced during low-flow periods, but not during very low flows. James (1965) based much of his work on a computer model that has inputs of land use, slope, soils, drainage density, and climatological data. The advantage of this model is that it does not rely on hydrological data, which may not always be available. James reported that base flow dropped with urbanization to about 7/10 of its rural value over a 10-year period. Also, according to James, each hydrological effect of urbanization stems from the reduced role of soil moisture storage as urbanization restricts the contact of rainfall with the soil. This reduction is magnified during periods of low flows, when soil moisture is a major factor in streamflow, but has a smaller effect when mid-winter brings higher precipitation. Urbanization was thus found to affect both high and low flows but to be most influential in increasing the peaks of smaller floods. On the other hand, some cases of increasing low streamflows as a result of urbanization have been reported. Spieker (1970) reported on a rapidly-urbanizing drainage area, in Salt Creek at Western Springs in Northeastern Illinois, where the 7-day, low flow exhibited a pronounced upward trend with time. Apparently the increase in impervious area, which would be expected to decrease low flow, was counteracted by other factors, such as sewer leakage and addition of wastewater effluent to the stream, both of which increase with increasing population. Additionally, low flows may be increased substantially due to other types of development, such as golf courses which may need frequent irrigation (and provide return flow to a stream from irrigation) (Crippen 1965). Miller (1966) reported that, under extreme low-flow conditions, as much as two-thirds of the flow of Assunpink Creek in central New Jersey originates at a sewage treatment plant servicing an area that obtains its water fiom outside the basin drained by the creek. He also found that the return flow from 15 industrial plants along the creek totaled 2 cfs more than the 38 cfs withdrawn. Riggs (1963), however, concluded that urbanization has little effect on low flows of streams because most drainage basins are large enough that impervious area is a small percentage of the total land area. Riggs (1965) also stated that low flows during summer and fall are better maintained in basins having a large proportion of cleared land than in those that are largely timbered. He went on to say that the influence of cleared land is greatest when the general level of discharge is lowest and that the difference becomes negligible at a high discharge level. Several studies have looked at the impact of watershed vegetation on low flows, particularly focusing on the role of evapotranspiration in water uptake (Caffey et al. 1980, Chang and Boyer 1977, Johnson and Meginnis 1960). One of the more extensive studies was conducted at Coweeta Hydrological Laboratory in North Carolina. Swank and Douglass (1974) found that streamflow during all flow regimes was substantially reduced following plantiqg of white pines due to increased uptake of water during the growing season and increased evapotranspiration. A similar but less dramatic effect was seen after conversion of hardwood forest to white pines, as well as following conversion of a hardwood-covered watershed to pass. Also at Coweeta, another study involved cutting trees in the riparian zone while leaving the remainder of the drainage basin forested. This produced an increase in strearnflow of 20 percent during the late summer period although less than 12 percent of the total watershed had been cut (Croft and Hoover 1951). These effects were found to be more pronounced in sunny (south-facing) slopes than in shady (north-facing) slopes at Coweeta (McNaughton and Jarvis 1983). Clearing of land in the riparian zone produces greater effects oil streamflow than clearing away from the seeam- side areas and may affect low flows even more thari higher flows (Riggs 1965). In addition, these riparian zones serve as nutrient filters between urban and agricultural areas and the stream, enhancing water quality as it travels toward the stream (Petersen et al. 1987, Hill 1990, Lowrance et al. 1984). Johnson and Kovner (1956) found that strearnflow was increased by 4 percent due to decreased evapotranspiration after removal of only the rhododendron and laurel understory in forested areas. This finding was corroborated by Ward (1951) who found that evapotranspiration increased with vegetation height. Evapotranspiration probably has a greater efkct on the percentage of precipitation which will eventually reach the water table than any other factor, including soil conditions (LeGrand and Mundorff 1952). The type of soil in a drainage basin and the land uses within that basin combine to determine the irxdtration rate within the watershed. Among the factors controlling infiltration rates are soil texture and stmcture, moisture content, vegetation density, and organic content of surface materials (Dunne 1983). Forested areas will usually have high infiltration rates as will sandy areas such as the Sandhills region of North Carolina. Within residential areas of urbanized regions, lawns have been shown to have infiltration rates approximately one-sixth that of forests due to compaction (Fok et al. 1975). In areas with large clay contents, changes in land use toward increased imperviousness will not affect the infiltration rate as dramatically as changes from sandy soils to pavement (Riggs 1976). In the riparian zone, preservation of the infiltration capacity can be assured by development of greenbelts and protection of wetlands which serve as groundwater recharge areas (Singh 197 1). The size of the drainage basin has been significantly linked to flow rates, as would be expected, with larger basins sustaining higher flows during low-flow conditions. Larger basins produce streams which are more entrenched and thus more likely to maintain a streambed below the seasonal water table, ensuring continuous-flow conditions (Singh 197 1, Singh and Stall 1974, Chiang and Johnson 1976). Larger basins will intercept more precipitation, especially during summer storms when spatial variation is greatest (Singh 197 1). Many researchers have attempted to determine which factors have the most significant impact on streamflow during low-flow conditions. Riggs (1976) found that anything which affects evapotranspiration rates will have the largest effect on low flows, including removal of riparian vegetation. Friel and associates (1989) found that only streamflow and size of drainage basin were statistically significantly correlated at the 95 percent level. Another study by Gebcrt (1978) showed that the most significant factors in a regression analysis where low flow was the dependent variable were drainage area, forest cover, soil infiltration rates, and base flow. Huff and Changnon (1963) studied low streamflow in Illinois and found that precipitation data explained more than 70 percent of the low-flow variation. In the Ohio River basin, Chang and Boyer (1977) produced statistically significant results between 7410 (10-year recunence intenral for annual minimum of average of 7-day mean flows) values and drainage area, elevation, percentage of forest cover, soil type, and mean annual snowfall. In southeastern Massachusetts, Tasker (1972) found that 742 (2-year recurrence interval for annual minimum of average of 7- day mean flows) and 7410 were significantly related to drainage area and groundwater levels. However, z study in Missouri (Skelton 1974) using 7410 and drainage area in a regression analysis produced a standard error of 480 percent, while a multiple regression which included additional variables such as slope and length of basin, surface storage, annual precipitation, elevation, percentage of forest cover, rainfall intensity, and infiltration rate reduced the standard error to 250 percent. It is clear from some of these studies that one or even several factors cannot accurately predict streamflow patterns during low-flow conditions when many areas are analyzed covering a vast region. PURPOSE AND OBJECTIVES

The primary purpose of this project was to identify and document effects of urbanization and land-use changes on low streamflows in North Carolina, with results to be used to predict changes in low flows in areas subject to future effects of urbanization and land-use changes. Benefits would be in using the information to plan future modifications of regulations for affected areas. The primary focus of the project was on analyzing historical low-streamflow data, with consideration given to geology, climate, and topography. Based on information gleaned from the literature review (see preceding section), it was hypothesized that low streamflows in urban areas in North Carolina would exhibit a decreasing trend as urbanization progressed, with the proviso that a similar decreasing trend would not be present for nearby nonurban (rural) areas. Thus, the overall objective of this project was, in effect, to prove or disprove this hypothesis. PROCEDURES

The project was initiated by reviewing maps of North Carolina and selecting key U.S. Geological Survey continuous-record, stream-gaging stations, for which low-streamflow data were available for a substantial period of time. More specifically, stations were selected from each of six "urban areasn--Asheville, Greensboro, Raleigh, Charlotte, Goldsboro-Kinston, and Rocky Mount-Tarboro. In each case, stations were identified both within and outside the immediate urban area so that urban-rural comparisons could be made. As a general rule, at least 30 years of continuous record were required in order for a station to be included. For each station, mean-daily flow rates for all years of record were analyzed, and "74 values" were obtained for each year of record. 74 refers to the minimum average value of mean-daily streamflows for any seven consecutive days. As suggested by the American Society of Civil Engineers (ASCE), a water year from April 1 through March 31 was used in this study, the reason being to capture in a single year the entire period of late summer and early fall during which low flow is likely to occur (Loganathan et al. 1985). Annual 74 values were then plotted on a time graph for all stations in each respective urban area. Also, precipitation and population data were obtained and plotted on a time graph for each station (Figures 3, 9, 19,28,40 & 46 and 4, 10, 20, 29,41 & 47 respectively). It was clear early on, however, that annual plotting of 74values yielded a graph that was subject to significant variation from year to year, which would make visual analysis difficult. To make analyses easier but still show the low-flow trends (in other words, to "smooth" the data), successive ten-year means of 7Q values were plotted. Thus, the mean of the ten 74 values from 1960 through 1969 was plotted, followed by the mean of the ten 74values from 1961 through 1970, then fiom 1962 through 1971, and so on. The resulting plot was much "smoother" and easier to use, but it shortened the time span of available data on the plot. (For example, 30 years of record from 196 1 through 1990 provide only 21 plotted points from 1961- 1970 through 198 1- 1990.) As something of a compromise between plotting annual values and plotting ten-year means, successive five-year means of 74 values were plotted. It was felt that this plot smoothed the data sufficiently without reducing the span of available data too much. (Thrty years of record from 196 1 through 1990 provide 26 plotted points from 196 1- 1965 through 1986-1990.) Accordingly, all remaining plots were done using successive five-year mean values. With plots using successive five-year mean values available for all stations in each respective urban area, a review of the stations identified for each urban area was made, resulting in the elimination of a few stations. Some were rejected because their low flows were judged to be too great to be affected by urbanization. Others were eliminated because their flows were subject to regulation, such as by a power plant dam. It was felt that such stations would not reflect naturally-occurring low-flow conditions. On the other hand, stations with flows having diversions of water to or from them were generally not eliminated, since it was felt that such diversions were themselves quite likely manifestations of the urbanization process. Another critical judgment was required at this point. Sicce the basis of the analysis to follow was a comparison of urban versus ma1 stations, it was essential to label each station as being either "urban" or "rural." For many stations, this designation was obvious; but for some. it was not. In fact, some stations might be in transition fiom rural to urban, but the process required that each station be classified one way or the other. Initially, it was proposed to use population data as an indicator of urban or rural, the premise being that urban areas would exhibit significantly-increasing pop~lationover time while rural areas would not. Hence, population data were initially gathered for each drainage area represented by each station. This exercise, however, turned out to be of little value, since practically all stations--ones obviously urban and others obviously rural--showed significantly-increasing population over time. Other population analyses were tried, such as using percentage increases in population rather than actual head counts and using population densities, but none proved useful. Accordingly, rural/urban judgments were made on a station-by-station basis, considering primarily the general location of each station and the basin draining into the station. At this point, 74values and precipitation data were plotted individually for each station and a "best straight line," as determined by regression analysis, was placed on each graph to indicate trends over time. This line is referred to hereafter as the "trend line." (For two reasons-- consistency and the feeling that the most rapid urbanization has generally occurred since around 1960--these plots were made for the last 30 years or so, rather than for the entire period of record.) Stations in each urban area were then studied and analyzed to try to detect effects of urbanization on low streamflows. Two statistical methods were utilized in the analyses: the "One Sample Run Test for Randomness" and the "Test for Equality of Slopes." These nonparametric methods are described below.

ONE SAMPLE RUN TEST FOR RANDOMNESS (McCuen 1993) Many statistical methods assume data are values of a random variable that are in sequence, but with independence between the measured values. The "One Sample Run Test" can be used to test a sample for randomness. The null hypothesis is a statement that the data represent a sample of a randomly-distributed variable. The alternative hypothesis is that one cannot conclude that the occurrence of the sample elements is not fiom an independent process. Zf the null hypothesis is rejected, the acceptance of nonrandomness does not indicate the type of nonrandomness in the sequence; but the One Sample Run Test may detect a trend or an episodic change, or ir may suggest that there is serial correlation between the measurements. The One Sample Run Test was used to attempt to detect whether or not urban development caused a decrease in the 7-day low flow during the period of record. If a watershed undergoes urbanization during a period of record, one might expect the flow trend to decrease more than if the urbanization had not taken place. An increase in urbanization would appear as a downward trend in flow values, which represents 2 serial correlatio~?between the discharges in the annual series of low-flow data. The One Sample Run Test was applied to the low-flow trends (straight-line plots described previously) for all stations in each urban area studied to test the following null (&) and alternative (HA)hypotheses: Ho: The 7-day low flows are randomly distributed and thus there is no signifiicant trend.

HA:There is a significant trend in the 7-day, low-flow data, since the annual data are not randomly distributed. For the run test, the flow series is represented by a series of "+" and "-" symbols. The index that is used to indicate a "+" or "-" event is the median flow (the flow for which 50 percent of the years experienczd an event that is larger than that value and 50 percent experienced a smaller event). If urbanization has caused a decrease in flow rates, then the series should have more "-" symbols in the part of the data corresponding to greater urbanization. This influence reduces the randomness of the data, which is what the test is measuring. When the data move from one side of the median value to the other side, it is called a "run." The lower the number of runs. the less random the data, which indicates outside influence has occurred. Critical values upon which hypothesis acceptance or rejection is based are calculated numbers that are based solely on the number of years of data that are being tested. These critical values are calculated by:

Upper value U(R) = (n + 2)/2 (1)

Lower value L(R) = n(n - 2)/[4(n - I)] (2) where n is the number of years being tested. The null hypothesis should be rejected if the number of runs in the sample is less than or equal to the lower critical value or greater than or equal to the upper critical value.

TEST FOR EQUALITY OF SLOPES (R. C. Tiwari, professor of statistics, UNCC, pers. com. 1992) While the One Sample Run Test gives credibility to the hypothesis that urbanization did (or did not) result in lower streamflows for the stations studied, it was felt that additional analysis was needed to try to confirm that there was (or was not) a significant difference between the streamflow trends for urban versus rural stations. With the assistance of Tiwari, a nonparametic method for testing equality of slopes in a simple linear model was developed and applied. The purpose of the method is to test, to a certain level of significance, two hypotheses:

Ho: The two comparative stations (urban versus rural) have the same slope (B 1 = B2). HA: One station has a greater slope than the other one (B 1 > B2). If the null hypothesis is accepted to a high level of significance, then the indication would be that there was little or no effect on the 7-day low flow when an area becomes urbanized. If the alternative hypothesis is accepted, then an indication of correlation between urbanization and 7- day low flow exists to an indicated level of significance. To carry out the nonparametric model for slope comparison, a "2 value" is calculated; and a Z table is used to determine the significance level indicated by the calculated Z value. The Z value is calculated by the following formula: N 2 0.5 Z = {T-N1[(N+1)/2]]/{NIN~/[N(N- 1)] Z R -~1~2(N+1)2/[4(~- I)]) 1 where Z == Z -.due T = sum of ranked numbers N1 = number of slopes in one graph N2 = number of slopes in other graph N=N1+N

This nonparametric test is facilitated by a computer-generated output that calculates every possible slope between two points on each graph. After all slopes are calculated, they are ranked and given a rank number; and after the rank numbers are given, they (the slopes) are separated back into the watershed from which they came while still maintaining their rank numbers. When N is large, the formula used for calculating the Z value takes on a standard normal distribution, in which case the slopes are taken and analysis is made using the Central Limit Theorem. This theorem simply takes the sum of the ranked numbers, denoted by T, subtracts the expected value (E), which is represented by

and divides it by the standard deviation ( a ) indicated by

Based on computed Z values, the statistical significance of accepting the alternative hypothesis (one station has a greater slope than the other one) can be determined from a Z table.

APPLICATIONS TO VARIOUS URBAN AREAS As indicated at the beginning of this section, stations were selected from each of six "urban areas" in North Carolina--Asheville, Greensboro, Raleigh, Charlotte, Goldsboro-Kinston, and Rocky Mount-Tarboro--for low-flow analyses. Specific details of each of these analyses follow. Asheville Six stations were chosen initially for study in the Asheville area. They are listed in Table 1, and their approximate locations are shown in Figure 1.

Table 1. Stations in the Asheville Area. Drainage Area Urban/ USGS No. Statim Rural 03439000 French Broad River at Rosman 67.9 Rural 03443000 French Broad River at Blantyre 296 Rural 0345 1500 French Broad River at Asheville 945 Urban 03453500 French Broad River at Marshall 1332 Rural 0345 1000 Swannanoa River at Biltmore 130 Urban 03436000 Mills River near Mills River 66.7 Rural

The French Broad River originates in the Blue Ridge Mountains of North Carolina near the South Carolina border and flows more or less northward by stations at Rosman, Blantyre, Asheville, and Marshall. Clearly the station at Asheville would be classified as an urban station; the others, rural. The Swannanoa River at Biltmore station is located on the southern side of the city of Asheville; it flows from the east along a relatively built-up area along Interstate 40. A significant amount of water is diverted from the Swannanoa for water supply, some of which is returned to the French Broad River below the station. The Mills River near Mills River station is located some 15 miles south of Asheville just west of Interstate 26; it drains iargely a rural mountainous ma. It seems cleu that Swannanoa River should be classified for purposes of this Figure 1 Location Map of Stations in the Asheville Area study as urban and Mills River, rural; and because of their close proximity, meaning similar geologic, topographic, and climatic factors, an urban versus rural comparison of low flows is appropriate. Appendix A lists the 7Q flows (annual and five-year mean values), precipitation, and population data for the Asheville-area stations, and Figures 2 through 4 exhibit the time variations for these properties. Examination of the four French Broad River stations reveals that their low-flow patterns more or less mimic each other as well as their precipitation records. The only exception is that the low-flow record for the Blantyre station appears to not decline as much as its three counterparts for the period from 1970-75 through 1985-90. This could possibly be the result of logging operations in the area. However, because of the relatively large flow rates involved (on the order of 800 cfs for the station at Asheville), it was felt that any urbanization effects on the French Broad River would be negligible at best. Hence, no further consideration was given to the four French Broad stations. The low-flow patterns for Swannanoa and Mills Rivers (Figure 2) both exhibit downward trends for the overall period of record. Figure 5 gives a more detailed plot of flow for Swannanoa River as well as a plot of precipitation for the period of record (1962- 199 1). In each plot, a trend line is shown. The slope of the trend line for flow (-0.0178) is in units of "fractional increase (or decrease) per year." {Approximately, [(28 - 50)/50]/25 yr = -0.0 176) The slope of the trend line for precipitation (-0.2644) is in units of "inches increase (or decrease) per year." Figure 6 gives similar information for Mills River. Inasmuch as Swannanoa River was classified as urban and Mills River as rural, it would be expected, in accord with the hypothesis stated in the "Purpose and Objectives" section, that the low-flow trend line for Swannanoa River (Figure 5) would exhibit a downward trend while the one for Mills River (Figure 6) would be more nearly "flat." Or, at least the trend line for Swannanoa River would exhibit a more downward trend than the one for Mills River. In fact, the trend line for each station is downward, with Swannanoz River (slope = -0.0178) being somewhat more downward than Mills River (slope = -0.0 126)(see Figures 5 and 6). Similarly, the precipitation trend lines are both downward, with Swannanca River (slope = -0.2644) being slightly more downward thail Mills River (slope = -0.25). While both stations exhibited decreasing precipitation during the period, it would appear that the decline in low flow for both stations significantly exceeds that which might be attributed to decreasing precipitation alone. The flow trend lines were first analyzed by the One Sample Run Test. Table 2 lists the annual 74flows for Swannanoa River and Mills River. Median values are 35.30 cfs and 52.40 cfs, respectively. The columns in Table 2 after each 74 column include a "+" or 'I-" sign, depending on whether a given 74value is greater than or less than the median, respectively. The number of runs (when the data move from one side of the median value to the other) as determined from these columns is 11 for Swannanoa and 15 for Mills River. Critical values upon which hypothesis acceptance or rejection is based were calculated using Equations (1) and (2), where n, the number of years being tested, is 29.

Since the number of runs for Swannanoa (1 1) and Mills (15) is in each case less than U(R) and greater than U(L), the null hypothesis should be accepted in each case, indicating the flows are randomly distributed and thus there is no significant trend. nisconclusion is contrary to the overall hypothesis stated in the "Purpose and Objectives" section. Figure 2 5-Year Averaged 7-Day Low Flows for Study Stations in the Asheville Area

period of record

period of record Figure 3 Annual Precipitation for Study Stations in the Asheville Area

period of record I

period of record I Figure 4 Area Population for Study Stations in the Asheville Area

tm 0 s 80,000 60,000

40,000

20,000

0 1960 1970 1980 1990 period of record Figure 5 Low-Flow Trends for Swannanoa River at Biltmore

#03451000 - Swannanoa River at Biltmore slope = -0.01 78

#03451000 Swannanoa River at Biltmore slope = -0.2644 annual precipitation, inches (5 yr avg) flow, cfs (5 yr avg) Table 2. Results of One Sample Run Test for Stations in the AsheviUe Area

Swannanoa Mills year 7Q sign 70 sign 62-63 50.80 + 47.60 63-64 24.10 32.40 64-65 28.70 60.00 + 65-66 43.70 + 52.10 66-67 35.30 + 54.60 + 67-68 65.10 + 101.60 + 68-69 34.00 63.80 + 69-70 52.80 + 104.30 + 70-71 28.60 41.70 71 -72 38.80 + 66.60 + 72-73 47.70 + 46.80 73-74 53.40 + 55.70 + 74-75 64.30 + 72.10 + 75-76 56.00 + 78.10 + 76-77 44.00 + 54.30 + 77-78 31.40 49.80 78-79 35.30 + 43.00 79-80 58.1 0 + 78.30 + 80-81 36.00 + 45.30 81 -82 13.80 28.70 82-83 28.70 48.80 83 -84 35.30 54.40 + 84-85 33.30 64.10 + 85-86 28.60 46.80 86-87 11.30 28.30 87-88 23.00 52.40 88-89 11.10 27.80 89-90 47.1 0 + 113.60 + 90-91 38.40 + 40.10

#0345lOOO U(R) = 15.5 Swannanoa River at Biltmore L(R) = 7.0 median = 35.30 # runs = 11

#03446000 Mills River near Mills River median = 52.40 #runs = 15 (It should be noted that the One Sample Run Test as well as the Equality of Slopes Test, which follows, are performed using annual low-flow data. These tests should not be contemplated with reference to Figures 5 and 6, which are plots of 5-yr average low flows.) The flow trend lines were next analyzed by the Equality of Slopes Test. As stated previously, the first step in this test is to calculate every possible slope between two points on each graph. Then all slopes are ranked and given a rank number; and after the rank numbers are given, they (the slopes) are separated back into the watershed from which they came while still maintaining their rank numbers. These computations and rankings for Swannanoa and Mills Rivers are given in Appendix G. The first group of values in Appendix G gives the ranked slopes for Mills River, the second gives the same for Swannanoa River. The last group lists the slopes separated back into the watershed from which they came while st111 maintaining their rank number. The expected value can now be computed by Equation (4). In this equation, N1 is 406 and N is 406 + 406, or 8 12. Hence,

The standard deviation can be calculated next using Equation (5). In this equation, N2 is 406 and

From Appendix G, the value of T (sum of the ranked numbers) for Mills River is 161,855.00. Hence,

With a Z value of -0.95, from a Z table, available in almost any statistics book, the statistical significaxe of accepting the alternative hypothesis (that the rural station has a greater slope than the urban one) is 65.8 percent. Greensboro Ten stations were chosen inj tially for study in the Greensboro area. (The Greensboro area includes Greensboro, Winston-Salem, and High Point, as well as other smaller urban locations.) They are listed in Table 3, and their approximate locations are shown in Figure 7. Urban/rural classification of these stations is somewhat difficult, as none of them is located in a really heavily-urbanized area. The North Buffalo Creek near Greensboro, Reedy Fork near Gibsonville, and Reedy Fork near Oak Ridge stations are all located on the northern side of Greensboro within ten or so miles from the center of Greensboro; they are considered as urbzn for this study. The Haw River at Haw River station is located on the eastern side of Burlington and is also considered as urban. The Deep River near Randleman and East Fork Deep River near High Point stations are also considered as urban for this study. The former is located south of Greensboro and east of High Point: the latter, west of Greensboro and north of High Point. The

Table 3. Stations in the Greensboro Area. Drainage Area Urban/ USGS No. Station !km Rural North Buffalo Creek near Greensboro Urban Reedy Fork near Gibsonville Urban Deep River near Randlernan Urban East Fork Deep River near High Point Urban Reedy Fork near Oak Ridge Urban Little Yadkin River at Dalton Rural Haw River at Haw River Urban South Yadkin River near Mocksville Rual Deep River at Ramseur Rual Hunting Creek near Harmony Rural remaining four stations are clearly rural. Little Yadkin River at Dalton is located some 15 miles northwest of Winston-Salem and far from Greensboro; South Yadkin River near Mocksville, 25 miles southwest of Winston-Salem; Hunting Creek near Harmony, 30 miles west of Winston- Salem; and Deep River at Ramseur, 25 miles south of Greensboro. Appendix B lists the 74 flows (annual and five-year mean values), precipitation, and population data for the Greensboro-area stations, and Figures 8 through 10 exhibit the time variations for these properties. South Yadkin River near Mocksvllle and Haw River at Haw River were excluded at this point because of their relatively large and varying low flows and became of diversions and/or regulation. Reedy Fork near Gibsonville and Deep River near Randleman were also eliminated because of their significant regulation by several lakes upstream. Figures 11 through 16 give more detailed plots of flow and precipitation for the remaining six stations for approximately the last 30 years. The slopes of the low-flow trend lines for the first three of these (North Buffalo Creek near Greensboro, Reedy Fork near Oak Ridge, and East Fork Deep River near High Point), all urban stations, are 0.00675,0.00498, and -0.0008, respectively. For the last three (Little Yadkin River at Dalton, Hunting Creek near Hmony, and Deep River at Ramseur), which are rural stations, the slopes are -0.00237, -0.0024, and -0.0039. Increasing flows for two of the three urban areas may be attributable in part to increasing precipitation during the period, although two of the three rural stations showed decreasing flows despite increasing precipitation. On the face of it, however, these results, like those for the Asheville area, appear to be contrary to the overall hypothesis stated in the "Purpose and Objectives" section. .4nalysis of these stations was performed by the One Sample Run Test, with the results shown in Table 4. The Equality of Slopes Test was applied selectively to the Greensboro-area stations. First the slope of the low-flow trend line for Reedy Fork near Oak Ridge was compared to that of Little Yadkin River near Dalton. With a resulting Z value of -0.59, the statistical significance of accepting the alternative hypothesis (in this case, that the urban station has a greater slope than the rural one) is 44.5 percent. Also, the slope of the low-flow trend line for East Fork Deep River near High Point was compared to that of Hunting Creek near Harmony. With a computed Figure 8 5-Year Averaged ?-Day Low Flows for Study Stations in the Greensboro Area

- - period of record

Rimr nmr High -wFmkrru Oak Ridge --P-yy.L. Rim u Oritan Point Figure 9 Annual Precipitation for Study Stations in the Greensboro Area

.. - period of record

period of record

I -Eastf%rkt)cq ~Pnk~u-Lh*Yra~ Rim na- Hi& Osk Ridge Rim .tD*lUn 1 I Point Figure 10 Area Population for Study Stations in the Greensboro Area

25,000

20,000 t" 0 g IS,OOO a rcl 0

5,000 fh LA LA LI

0 1 I 1960 1970 1980 1990 period of record

I1aw Rivcr .i llaw Rivcr ---- South Yadkh River - Dap River U bu HmhgM - MocLNillc - -kmy -

period of record I

V ~ut~acp Pxk ncu in^ YM River ncu High -OetE Ridge -Riw uDJtm Poult #02095500 North Buffalo Creek near Greensboro slope = 0.00675

#02095500 North Buffalo Creek near Greensbcro slope = 0.20 annual precipitation, inches (5 yr avg) flow, cfs (5 yr avg) #02093800 Reedy Fork near Oak Ridge slope = 0.00498

#02093800 Reedy Fork near Oak Ridge slope = 0.20 annual precipitation, inches (5 yr avg) flow, cfs (5 yr avg) annual precipitation, inches (5 yr avg) flow, cfs (5 yr avg) Figure 16 Low-Flow Trends for Hunting Creek near Harmony

Hunting Creek near Harmony slope = -0.0024

#02118500 Hunting Creek near Harmony slope = 0.01 95

I Table 4. Results of One Sample Run Test for Stations in the Greensboro Area. Urban/ Trend/ Rural U(R) U(L) No. Runs No Trend North Buffalo Creek near Greensboro Urban 16.5 7.49 .5 no trend East Fork Deep River near High Point Urban 17.5 7.99 S no trend Reedy Fork near Oak Ridge Urban 17.5 7.99 .8 trend Little Yadkin River at Dalton Rural 16.5 7.49 .3 no trend Deep River at Ramseur Rural 17.5 7.99 .8 trend Hunting Creek near Harmony Rural 17.5 7.99 .4 no trend

Z value of 1.85, the statistical significance of accepting the altemative hypothesis (again, that the urban station has a greater slope than the rural one) is 93.6 percent. Finally, the slope of the low- flow trend line for East Fork Deep River near High Point was compared to that of Deep River at Ramseur. With a computed Z value of 3.45, the statistical significance of accepting the altemative hypothesis (again, that the urban station has a greater slope than the rural one) is 99.9 percent. Raleigh Eight stations were chosen initially for study in the Raleigh area. (The Raleigh area includes Raleigh, Durham, and Chapel Hill.) They are li sted in Table 5, and their approximate locations are shown in Figure 17.

Table 5. Stations in the Raleigh Area. Drainage Area Urban/ USGS No. Station Rural Middle Creek near Clayton 37.1 Urban Little Fishing Creek near White Oak 177 Rural Flat River at Bahama 149 Rural Tar River at US 401 at Louisburg 427 Rural Little River near Princeton 232 Rural Contentnea Creek near Lucama 161 Rural Cape Fear River at Lillington 3464 Rural Deep River at Moncure 1434 Rural

Of these stations, only one--Middle Creek near Clayton--can be arguably classified as urban. Although located some 15 miles south of the center of Raleigh, it is in a growing area. All other stations are classified as rural. Tar River at US 401 at Louisburg and Little Fishing Creek near White Oak are located approxirnztely 25 miles and 50 miles (respectively) northeast of Raleigh. Contentnea Creek near Lucama lies 25 miles east of Raleigh and Little River near Princeton, 30 miles southeast. Cape Fear River at Lillington and Deep River at Moncwe are some 30 miles south and 25 miles southwest of Raleigh, respectively. Finally, Flat River at Bahama is located 25 miles northwest of Raleigh and 10 miles north of Durham. Appendix C lists the 74 flows (annual and five-year mean values), precipitation, and population data for the Raleigh-area stations, and Figures 18 through 20 exhibit the time variations for these properties. Cape Fear River at Lillington was eliminated from further consideration at this point because of its relatively large flow and also because of its regulation--particularly by B. Everett Jordan Lake since 1981. Also excluded were Tar River at US 401 at Louisburg and Contentnea Creek near Lucama because of less than 30 years of record. The latter also experiences some regulation at low flows. Figures 21 through 25 give more detailed plots of flow for the remaining five stations for approximately the last 30 years. The slope of Middle Creek near Clayton, the lone urban station, is -0.0334. The slopes of the remaining four stations--Little Fishing Creek near White Oak, Flat River at Bahama, Little River near Princeton, and Deep River at Moncure--are -0.00914, -0.0124, -0.0266, and -0.00945, respectively. While all of these slopes are negative (downward), at least the urban station has the greatest negative slope, which fact tends to support the overall hypothesis stated in the "Purpose and Objectives" section. With regard to precipitation, it appears (from Figure 21) that the decreasing low flow for the urban station greatly exceeds that which might be attributable to decreasing precipitation. For the most part, the rural stations exhibit relatively small changes in precipitation during the period. These five stations were analyzed by both the One Sample Run Test and the Equality of Slopes Test. Results of the former are shown in Table 6.

Table 6. Results of One Sample Run Test for Stations in the Raleigh Area. Urban/ Trend/ Station Rural U(R) UIL) No. Runs No Trend Middle Creek near Clayton Urban 17.5 7.99 13 notrend Little Fishing Creek near White Oak Rural 17 7.74 18 trend Flat River at Bahama Rural 17.5 7.99 16 notrend Little River near Princeton Rural 17.5 7.99 10 notrend Deep River at Moncure Rural 17.5 7.99 18 trend

The Equaiiiy of Slopes Test was applied selectively to the Raleigh-area stations. The slope of the low-flow trend line for the lane urban station--Middle Creek near Clayton--was compared separately to that of Flat River at Bahama, Little River near Princeton, and Deep River at Moncure. The Middle Creek cear Clayton-Rat River at Bahama comparison yielded a Z value of 2.21; the statistical significance of accepting the alternative hypothesis (in this case, that the rural station has a greater slope than the urban one) is 97.3 percent. For the Middle Creek near Clayton-Little River near Princeton and the Middle Creek near Clayton-Deep hver at Moncure comparisons, the Z values were -5.14 and -0.20 and the statistical significances of accepting the alternative hypothesis are 100 and 15.9 percent, respectively. Figure 18 5-Year Averaged 7-Day Low Flows for Study Stations in the Raleigh Area Figure 19 Annual Precipitation for Study Stations in the Raleigh Area

period of record

period of record Figure 20 Area Population for Study Stations in the Raleigh Area

35,000 -

30,000 - 4 t m 25,000 Ef g, 20,000 csr 0 e 7 --- 4 2 15,000 8 Ea = 10,000

5,000 -

0 I 1960 1970 1980 1990 period of record

16,000

14,000

12,000 r-

10,000

8,000

6,000 X 4,000

2,090

0 - I 1960 1970 1980 1990 period of record Figure 2 1 Low-Flow Trends for Middle Creek near Clayton

#02088000 Middle Creek near Clayton slope = -0.0334

#02088000 Middle Creek near Clayton slope = -0.1 6 Figure 22 Low-Flow Trends for Little Fishing Creek near White Oak

#02082950 Little Fishing Creek near White Oak slope = -0.0091 4

#02082950 Little Fishing Creek near White Oak slope = 0.065 #02085500 Flat River at Bahama slope = -0.01 24

#02085500 Flat River at Bahama slope = G.07 Figure 24 Low-Flow Trends for Little River near Princeton

#02088500 Little River near Princeton slope = -0.0266

#02088500 Little River near Princeton slope = -0.36 annual precipitation, inches (5 yr avg) flow, cfs (5 yr avg) Charlotte Ten stations were chosen initially for study in the Charlotte area. They are listed in Table 7, and their approximate locations are shown in Figure 26. (The two Long Creek stations are on different streams.)

Table 7. Stations in the Charlotte Area.

Drainage Area Urban/ USGS No. Station (I-& Rur a1 Rocky River near Norwood Rural Indian Creek near Laboratory Rural Long Creek near Bessemer City Rural Big Bear Creek near Richfield Rural Zrwin Creek near Charlotte Urban McAlpine Creek below McMullen Creek near Pineville Urban McAlpine Creek at Sardis Road near Charlotte Urban Twelve Mile Creek near Waxhaw Urban Long Creek near Paw Creek Urban McMullen Creek at Sharon View Road near Charlotte Urban

Unlike the Raleigh area where only one urban station could be identified, the Charlotte area has many. In fact, it was difficult to find appropriate rural statioi~sfor comparison purposes. Four such rural stations identified were Rocky River near Norwood, Indian Creek near Laboratory, Long Creek near Bessemer City, and Big Bear Creek near Richfield. Rocky River near Norwood is about 40 miles east of Charlotte; Indian Creek near Laboratory, 25 miles northwest of Charlotte; Long Creek near Bessemer City, 20 miles west of Charlotte; and Big Bear Creek near Richfield. 30 miles northeast of Charlotte. Although the Indian Creek near Laboratory station is not far south of the city of Lincolnton, it is felt that all these stations are properly classified as rural for this study. Long Creek near Paw Creek is located on the northwest side of Charlotte; while Irwin Creek near Charlotte, which drains the central Charlotte urban area, is on the west side. Twelve Mile Creek near Waxhaw is located some 10 miles south of the center of Charlotte, but still in a rapidly-urbanizing area. The three other stations listed in Table 7 are loczted on the south or southeast side of Charlotte; they generally drain an area that was formerly agricultural but is now urban. Appendix D lists the 74 flows (annual and five-year mean values), precipitation, and population data for the Charlotte-area stations, and Figures 27 through 29 exhibit the time variations for these properties. Rocky River near Norwood was eliminated from further consideration at this point because of its relatively large flow, and McAlpine Creek below McMullen Creek near Pineville was excluded because its record goes back only 14 years. Figures 30 through 37 give more detailed plots of flow for the remaining stations for approximately the last 30 years. The slopes of the three rural stations--Indian Creek near Laboratory, Long Creek near Bessemer City, and Big Bear Creek near Richfield--are -0.0193, -0.01 8 1, and 0.00387, respectively. The slopes of the remaining Eve stations--Irwin Creek near Charlotte, McAlpine Creek at Sardis Road near Charlotte, Twelve Mile Creek near Waxhaw, Long Creek near Paw Creek, and Mchldlen Creek at Sharon View Road near Chalotte--are

Figure 27 5-Year Averaged 7-Day Low Flows for Study Stations in the Charlotte Area

period of record

period of record 43- wrl POD-N -T=Q-l-, ==7-3mtFI- --?li@JH- I Figure 29 Area Population for Study Stations in the Charlotte Area

1970 1980 period of record

Mchfullcn Get& at kinCruk near McAlpirc Creek bclow McALpk Qadt at -Shon View Road mw -Q~dottc -McUullen Creek near -SudirRod~ I I Cl1ulonc rimvillc Qurioltc Figure 30 Low-Flow Trends for Indian Creek near Laboratory

#02143500 Indian Creek near Laboratory slope = -0.01 93 #02144000 Long Creek near Bessemer City slope = -0.01 81

#02 144000 Long Creek near Bessemer City slope = -0.07 annual precipitation, inches (5 yr avg) flow, cfs (5 yr avg) annual precipitation, inches (5 yr avg) flow, cfs (5 yr avg) Figure 34 Low-Flow Trends for McAlpine Creek at Sardis Road near Charlotte

#02146600 McAlpi-ne Creek at Sardis Road near Charlotte slope = -0.00832

#02146600 McAlpine Creek at Sardis Road near Charlotte slope = -0.01 43 annual precipitation, inches (5 yr avg) flow, cfs (5 yr avg) #02142900 Long Creek near Paw Creek slope = -0.021 6

#02142900 Long Creek near Paw Creek slope = -0.20 annual precipitation, inches (5 yr avg) flow, cfs (5 yr avg)

IUOPU1 PPPPPP PPPPPP 000000 0-IUOPrn 000000 000000 -0.01843, -0.00832, -0.02075, -0.0216, and -0.008727. With the exception of Big Bear Creek near Richfield (rural), all of these stations--rural and urban--exhibit a negative (downward) slope for the flow trend line. For the most part, all stations in this area show relatively little variation in precipitation during the period. These stations were analyzed by both the One Sample Run Test and the Equality of Slopes Test. Results of the former are shown in Table 8.

Table 8. Results of One Sample Run Test for Stations in the Charlotte Area. Urban/ Trend/ Station Rural U(R) U(L) No. Runs No Trend Indian Creek near Laboratory Rural 17.5 7.99 no trend Long Creek near Bessemer City Rural 17 7.74 no trend Big Bear Creek near Richfield Rural 17.5 7.99 trend Irwin Creek near Charlotte Urban 15.5 6.99 no trend McAlpine Creek at Sardis Road near Charlotte Urban 15.5 6.99 no trend Twelve Mile Creek near Waxhaw Urban 16.5 7.49 no trend Long Creek near Paw Creek Urban 13.5 5.99 trend McMullen Creek at Sharon View Road near Charlotte Urban 15.5 6.99 no trend

The Equality of Slopes Test was applied selectively to the Charlotte-area stations. The slope of the low-flow trend line for rural station Long Creek near Bessemer City, which is located 20 miles west of Charlotte, was compared separately to that of urban stations Long Creek near Paw Creek and Irwin Creek near Charlotte, both of which are on the west side of Charlotte. The Long Creek near Bessemer City-Long Creek near Paw Creek comparison yielded a Z value of 4.72; the statistical significance of accepting the alternative hypothesis (that the ma1 station has a greater slope than the urban one) is 100 percent The Long Creek near Bessemer City-Irwin Creek near Charlotte comparison yielded a Z value of 1.50, indicating a statistical significance of accepting the alternative hypothesis of 86.6 percent.

Additionally, the slope of the low-flow trend line for rural station Big Bear Creek near Richfield, which is located 30 miles northeast of Charlotte, was compared separately to those of urban stations Twelve Mile Creek near Waxhaw, McAlpine Creek at Sardis Road near Charlotte, and McMullen Creek at Shar~nView Road near Charlotte, all of which are on the south or southeast side of Charlotte. Computed Z values of 2.34, -0.37, and -1.67 and statistical signi.fkances of accepting the alternative hypothesis of 98.1,28.9, and 90.5 percent, respectively, were determined. In each of these cases, the alternative hypothesis is that the rural station has a greater slope than the urban one. Goldsboro-Kinston An attempt was made to study the Goldsboro-Kinston "urban area." Six stations were chosen for study in this area. They are listed in Table 9, and their approximate locatioils are shown in Figure 38. The primary basis for picking these stations was that the three Neuse River stations would be considered as urban, since they are located at or near the cities cf Smithfield, Goldsboro, and Table 9. Stations in the Goldsboro-Kinston Area. Drainage Area Urban/ USGS No. Station hiq Rural Neuse River at Smithfield 1206 Urban Neuse River near Goldsboro 2399 Urban Neuse River at Kinston 2692 Urban Nahunta Swamp near Shine 80.4 Rural Little River near Princeton 232 Rural Contentnea Creek near Lucama 161 Rural

Kinston; while the other three would clearly be considered as rural. Nahunta Swamp near Shine is located some 12 miles northeast of Goldsboro and 20 rniles northwest of Kinston; Little River near Princeton, 10 miles east of Smithfield and 15 miles northwest of Goldsboro; and Contentnea Creek near Lucama, 12 rniles north of Little River near Princeton. Appendix E lists the 74 flows (annual and five-year mean values), precipitation, and population data for the Goldsboro-Kinston-area stations, and Figures 39 through 41 exhibit the time variations for these properties. Because of the relatively high flows of the Neuse River (on the order of 300 to 400 cfs), it was felt that any effects of urbanization on low streamflows would be negligible; hence, the three Neuse River stations were eliminated from further consideration. This action, of course, left no possibility for urban-rural comparisons, since the remaining stations were all rural. However, because these three rural stztions' flow patterns on Figure 39 appear to show a downward trend over the last tlurty years or so, it was decided to consider them further. Figures 42 and 43 give more detailed plots of flows for Nahunta Swamp near Shine and Contentnea Creek near Lucama, respectively. Corresponding plots for Little River near Princeton were given previously (Figure 24). Interestingly, all of these rural stations exhibit definite downward flow trend lines, which appear generally to decline more than might be attributed to their corresponding declines in precipitation over the same time. It is also interesting to compare the long-term, low-flow pattern for Little River near Princeton with those for the Neuse River stations near Goldsboro and at Kinston (Figure 39). Little River near Princeton's low-flow pattern very closely mimics those for the two Neuse River stations from 1930-35 through about 1957-61. Thereafter, for an almost-equal amount of time (1957-61 through 1987-91), the Newe River stations continue with roughly the same trends as before, while Little River near Princeton exhibits a rather drastic decrease in low flow. It was thought that perhaps some kind of land-use change might have occurred in the Little River basin during the late 1950s that could account for the significant change in low-flow pattern. However, a spokesman for the U.S. Geological Swey in Raleigh (Bobby Ragland, pers. corn. 1993) indicated that no such land-use change had occurred. He said this area has always been primarily agricultural. He also stated that the Survey was aware of this unusual trend, had studied it quite a bit, and had no explanation for it One Sample Run Tests were applied to these three stations, with the results summarized in Table 10. It is apparent from these results that, according to this test, there are no significant trends for these stations. Figure 38 Location Map of Stations in the Goldsboro-Kinston Area Figure 39 5-Year Averaged 7-Day Low Flows for Study Stations in the Goldsboro-Kinston Area

period of record

Ne~uRiw at SrnW=ld - -New River nuGoIdsbo~o -*- Nucac RimY Kinsta~

period of record Figure 40 Annual Precipitation for Study Stations in the Goldsboro-Kinston Area

period of record - s- h'eusc River at Smill~cid S Ncuse River mar Goldsboro -N- Rim at Ki

period of record Figure 41 Area Population for Study Stations in the Goldsboro-Kinston Area

25,000 7

20,000

V) E: I5,OOO ag Ccl C & 10,ooo J E k

i L L1 4A 5,000 fs

0 I 1960 1970 1980 1990 period of record -Nahunta Suarnp mar Shine -Little Rwa ru.?btm -Gn~cnmcr Gd nrr Lu- Figure 42 Low-Flow Trends for Nahunta Swamp near Shine

#02091000 Nahunta Swamp near Shine slope = -0.0206

#O2O9lOOO Nahunta Swamp near Shine slope = -0.74 annual precipitation, inches (5 yr avg) flow, cfs (5 yr avg) - Table 10. Results of One Sample Run Test for Stations in the Goldsboro-Kinston Area. Urban/ Trend/ Station -Rural U(R) UOJ No. Runs No Trend Nahunta Swamp near Shine Rural 17.5 7.99 15 no trend Little River near Princeton Rural 17.5 7.99 10 no trend Contentnea Creek near Lucama Rural 14 6.24 11 no trend

Rocky Mount-Tarboro An attempt was also made to study the Rocky Mount-Tarboro (Tar River) "urban area." Six stations were chosen for study in this area. They are listed in Table 1 1, and their approximate locations are shown in Figure 44.

Table 11. Stations in the Rocky Mount-Tarboro Area. Drainage Area Urban/ USGS No. Station W) Rural 02083500 Tar River at Tarboro 2183 Urban 02082506 Tar River below Tar River Reservoir near Rocky Mount 777 Urban 02082585 Tar River at NC 97 at Rocky Mount 925 Urban 02083800 Conetoe Creek near Bethel 78.1 Rural 02082950 Little Fishing Creek near White Oak 177 Rural 02081740 Tar River at US 401 at Louisburg 427 Rural

The first three Tar River stations fisted in Table 11 were characterized as urban because of their proximity to either Tarboro or Rocky Mount; the other three were judged to be rural. Conetoe Creek near Bethel is located approximately 10 miles southeast of Tarboro; Little Fishing Creek near White Oak, 15 miles north of Rocky Moun$ and Tar River at US 401 at Louisbarg, 30 miles northwest of Rocky Mount. Appendix F lists the 74flows (annual and five-year mean values), precipitation, and population data for the Rocky Mount-Tarboro-area stations, and Figures 45 through 47 exhibit the time variations for these properties. Examination of the flow pmems in Figure 45 suggest that these data are not helpful in trying to make the urban/rural comparisons that were done previously for other areas. Tar River below Tar River Reservoir near Rocky Mount, Tar River at NC 97 at Rocky Mount, and Tar River at US 401 at Louisburg all have periods of record of 14 years or fewer. The low flows for Tar River at Tarboro are probably too high (on the order of 200 to 250 cfs) to be influenced significantly by urbanization. This leaves Conetoe Creek near Bethel and Little Fishing Creek near White Oak, both of which are rural and both of which appear without further analysis to exhibit no significant trends either upward or downwvd for the period plotted. Accordingly, no further consideration was given to the stations in the Rocky Mount- Tarboro area. Figure 44 Location Map of Stations in the Rocky Mount-Tarboro Area Figure 45 5-Year Averaged 7-Day Low Flows for Study Stations in the Rocky Mount-Tarboro Area

period of record

Tu Rim at NC 97 SI RDcLy Mount Tu Kivcr at Tab 'Tar River klow Tax River Hcrrrvoir -~lcar Rocky hlount - 1 Figure 46 Annual Precipitation for Study Stations in the Rocky Mount-Tarboro Area

period of record I

period of record Figure 47 Area Population for Study Stations in the Rocky Mount-Tarboro Area

period of record

d-Tm Kkcr at Tar hno Tar River bcIow Tar River Re-oir Tu River at NC 97 at RoJ;). Mwnt rcu Rocky Mount -

8,000

6,000

r-l C H 4,000

2,000 4 A w A 0 T 1950 1970 1980 1990 period of record OTHER ATTEMPTS Several other attempts to find effects of urbanization and land-use changes on low streamflows were made during the course of this study--all as yet yielding no useable results. One such attempt was to try to correlate low-flow data from a continuous-record station in a rural area with data fiom a partial-record station in a nearby urban station. Another attempt was to try to locate one or more areas of significant size that had undergone rather drastic land-use change (such as forested area to farmland or farmland to military base) within the last thirty years or so which had adequate streamflow data available to try to investigate effects of land-use change on low streamflows. No such areas were found. Yet another attempt was to study the five stations along the Yadkin River, beginning with Yadkin River at Patterson just north of Lenoir in the foothills of the Blue Ridge Mountains and continuing to Yadkin River at Yadkin College just west of High Point. The main purpose of the endeavor was to seek trends in low flows that might be attributable to land-use changes in the area. These stations were studied in the same manner as other stations previously discussed, but nothing of significance was observed. One final attempt was to plot annual 74values of urban sites against concurrent 74 values of corresponding rural sites for sub-periods of record to seek significant differences in such relationships for different time periods (for example, before urbanization and after urbanization). This endeavor too proved to be fruitless. RESULTS AND DISCUSSION

The results of this project, when analyzed with respect to the hypothesis stated in the "Purpose and Objectives" section, might best be described as a "mixed bag." That hypothesis (hereafter referred to as the "project's hypothesis") was stated as: low streamflows in urban areas in North Carolina would exhibit a decreasing trend as urbanization progresses, with the proviso that a similar decreasing trend would not be present for nearby nonurban (rural) areas. The discussion that follows considers each urban area analyzed. In the Asheville area, two rivers were considered--Swannanoa River at Biltmore and Mills River near Mills River, the former considered as urban, the latter rural. Results of the One Sample Run Test (Table 2) were that for both stations, the flows are randomly distributed and thus there is no significant trend. Furthermore, the Equality of Slopes Test indicated there is only a 65.8 percent significance that the rural station has a greater slope than the urban one. (Results of the Equality of Slopes Tests for Asheville and other areas are summarized in Table 12.) Hence, one must conclude that, while both stations exhibit a negative (downward) trend with the urban station's trend being more negative (more downward) than the rural one, statistically they do not differ significantly; and the project's hypothesis is not proved.

Table 12. Summary of Results of the Equality of Slopes Test. Percent Urban Station Rural Station Z value Significance Swannanoa River at Biltmore Mills River near Mills River East Fork Deep River near High Point Hunting Creek near Harmony East Fork Deep River near High Point Deep River at Ramseur Reedy Fork near Oak Ridge Little Yadkin River near Dalton Middle Creek near Clayton Flat River at Baharna Middle Creek near Clayton Little River near Princeton Middle Creek near Clayton Deep River at Moncure Long Creek near Paw Creek Long Creek near Bessemer City Irwin Creek near Charlotte Long Creek near Bessemer City Twelve Mile Creek near Waxhaw Big Bear Creek near Richfield Mc Alpine Creek at Sardis Road Big Bear Creek near Richfield McMullen Creek at Sharon View Road Big Bear Creek near Richfield

In the Greensboro area, two of the three urban stations had positive slopes; the third was negative, but virtually zero. All of the rural stations exhibited negative slopes. This outcome in itself is contrary to the project's hypothesis (that rural slopes should be greater than urban ones). The One Sample Run Test (Table 4) indicated that one urban station and one rural station exhibited trends (are not randomly distributed) while all others (two urban and two rural) did not. According to the Equality of Slopes Test, the separate comparisons of East Fork Deep River near High Point (urban) with two rural stations (Hunting Creek near Harmony and Deep River at Ramseur) both indicated a significant difference in slopes at a statistical significance of at least 90 percent. This outcome was, however, contrary to the project's hypothesis, since the rural slopes were in each case greater than the urban ones. (These comparisons may be somewhat compromised by the fact that the low flows for both of the rural stations are considerably higher than those of the urban station.) The other comparison of rural versus urban stations by the Equality of Slopes Test indicated there is only a 44.5 percent significance that there is a difference between slopes of the paired urban and rural stations. Hence, while it might be concluded that the project's hypothesis is rejected and that the opposite is true, a better conclusion would probably be that the results are inconclusive (a "mixed bag"). The Raleigh-area stations appear to give somewhat more credibility to the project's hypothesis. As noted previously (in the "Procedures" section), slopes of all stations are negative (downward), but the lone urban station does have the greatest negative slope. The One Sample Run Test (Table 6)' however, again gave somewhat mixed results. The urban station--Middle Creek near Clayton--resulted in "no trend," while the rural stations were evenly split with two indicating "trend" and two, "no trend." Of the three urban versus rural comparisons (with Middle Creek near Clayton being the urban station in each comparison) by the Equality of Slopes Test, two indicated a significant difference in slopes at a statistical significance of at least 90 percent The third comparison yielded a statistical significance of only 15.9 percent. However, in this last comparison, the low flows for the rural station are considerably higher than those of the urban station; hence, this comparison may be flawed. (In the other two comparisons, the low flows are all of comparable values.) All things considered, while the results of the One Sample Run Test are not entirely favorable to the project's hypothesis, it is felt that the overall results do tend to uphold the project's hypothesis. In the Charlotte area, slopes of all stations except one (Big Bear Creek near Richfield, which is rural) were negative. Results of the One Sample Run Test (Table 8) were again inconclusive, with one of the three rural stations (Big Bear Creek near Richfield) and one of the five urban stations (Long Creek near Paw Creek) indicating "trend" and all others, "no trend." Results of the Equality of Slopes Test were somewhat conclusive. Comparisons were made between Long Creek near Bessemer City (rural) and two urban stations and between Big Bear Creek near Richfield (rural) and three urban stations. One of the Long Creek near Bessemer City comparisons and two of the Big Bear Creek near Richfield comparisons indicated a significant difference in slopes at a statistical significance of at least 90 percent. As with the Raleigh-area stations, it is felt that, while the results of the One Sample Run Test are not entirely favorable to the project's hypothesis, the overall results do tend to uphold the project's hypothesis. As indiczted in the preceding section, urban versus rural comparisons were not forthcoming fiom the Goldsboro-Kinston area or the Rocky Mount-Tarboro area. To summarize the above discussion that considers each urban area analyzed:

(1) In the Asheville area, while both stations--one urban, the other rural--exhibit a negative (downward) trend with the urban station's trend being more negative (more downward) than the rural one, statistically they do not differ significantly; and the project's hypothesis is not proved.

(2) In the Greensboro area, the tendency appears to be opposed to the project's hypothesis, since slopes for rural stations are generally less than those of urban ones. However, all things considered, the results are probably inconclusive.

(3) In the Raleigh area, whiie the results of the One Sample Run Test are not entirely favorable to the project's hypothesis, it is felt that the overall results do tend to uphold the project's hypothesis.

(4) In the Charlotte area, as with the Raleigh-area stations, it is felt that, while the results of the One Sample Run Test are not entirely favorable to the project's hypothesis, the overall results do tend to uphold the project's hypothesis. Hence, two of the four urban areas do tend to uphold the project's hypothesis, while the other two tend not to uphold it. In an attempt to understand this difference, consideration was given to the physiographic characteristics of the respective areas. Asheville is in the "Western Piedmont and Mountains Physiographic Area" (HA 10) of North Carolina. [HA10 and other similar designations as well as other information here are from (Giese and Mason 1991).] The other three urban areas are, for the most part, all located in the "Eastern and Central Piedmont Physiographic Area." The latter can be broken down further, however. Greensboro is located in the "Carolina Slate Belt" hydrologic area (HA7), which consists predominantly of metavolcanics and metaigneous rocks. Raleigh is located in the "Raleigh Belt" hydrologic area (HA5), which consists predominantly of felsic metaigneous, felsic gneiss, and schist rock types. Charlotte is located in the "Charlotte Belt and Milton Belt" hydrologic area (HAg), which consists predominantly of igneous, metaigneous, and metavolcanic rocks. In terms of "potential to sustain low flow," the HA10 area is rated "high"; HA7, "low"; and HA5 and HA9, "intermediate." The fact that the Raleigh and Charlotte areas are both rated intermediate with respect to their potential to sustain low flow may help explain why those two areas are the ones whose o~erallresults do tend to uphold the project's hypothesis, whereas the other areas are rated either high or low and do not tend to uphold the project's hypothesis. It is not clear, however, exactly what that explanation is. It is also interesting to note that of all stations examined in this project, including the Goldsboro- Kinston ones, 21 out of 24 exhibit a downward trend over the approximately 30 years of record. Of these, 8 of the 10 urban ones and 13 of the 14 rural ones exhibit a downward trend. Hence, it would appear that almost all stations--both urban and rural alike--are exhibiting downward trends. The fact in itself that most urban stations exhibited downward trends suggests that the effect of urbanization is to decrease low streamflows over time. However, the fact that most rural stations in the same general areas also exhibited downward trends over the same time period suggests that perhaps it was not urbanization that caused the decrease in low streamflows. In other words, for whatever reasons, almost all relatively small streams seem to be decreasing. Review of those references previously cited (in the "Introduction" section) which claim that urbanization produces a decrease in low streamflows over time reveals that in many cases they apparently considered only urban streams in their studies and did not compare their results with nearby rural streams, as was done in this study. Had they done so, perhaps they would have found that most small streams seem to be decreasing. Or,had the current study not included nearby ma1 stations, the results would be overwhelming that urbanization causes a decrease in low streamflows. (Actually, some of the other studies cited appear to have their conclusions based more on supposition than on technical analysis.) The conclusion to all of this is, again, that the results are inconclusive. It is acknowledged that the trend trying to be detected by this project is elusive. It is almost certainly going to occur gradually over a rather long period of time, thereby requiring streamflow data over long periods of time. This study was severely limited by the fict that it depended solely on streamflow data already collected and available from the U.S. Geological Survey; hence, available data-source locations were aot always strategically located for the purposes of the study. In some cases, the study necessarily had to focus on relatively large streams of mixed land use. It would, of course, be much more desirable to select initially the locations at which data would be collected for this particular study and then to collect the data there. While that could be done (i.e.. locations selected initially), it would then reqllire waiting for a long period of time while data were collected at those locations. Any legitimate trend may also be relatively small and therefore difficult to detect. The statistical methods used in this study may not be powerful enough to detect such small trends. Also, as previously acknowledged, any low-flow trends. if detected, may be influenced by factors other than urbanization and/or land-use change. All things considered, it is concluded that there is a tendency from the results of this project to support the premise that urbanization causes a decrease in low streamflows, but statistically the results are inconclusive. It actually appears more likely that most small streams--both urban and mal--are experiencing decreasing low flows over time, more so than would result from decreasing trends in precipitation alone. Further study is suggested to try to collect actual field data (as opposed to using already- collected data, as was done in this project) to develop parameters to describe the "urban" condition of a basin. For example, concurrent streamflow records could be collected in very small, highly urbanized basins and nearby natural areas, such as small streams that drain parks or woodlands and nearby streams that drain parking lots or roads. If differences are detected between the two sets, comparison of drainage area, percent impervious area, stream channel incision, and possibly other parameters could be made. Additionally, further study might be focused on smaller "urban" areas, rather than the largest urban areas in the state as was done in this study. Many smaller cities and towns are undergoing rapid growth--in residential areas and shopping centers, for example--and study of this growth on low streamflow in small streams in such areas might prove fruitful. Further study is also suggested to consider additional factors that might affect low streamflows. Such factors might include time of occurrence of low flows (74 values) each year; precipitation during, or immediately preceding, the time of occurrence (rather than annual precipitations that were included in this study); maximum temperature during, or immediately preceding, the time of occmence; stream slopes; evapotranspiration; soil sample analyses; and groundwater levels. In her recently-completed thesis, Love (1 993) found a significant correlation between maximum temperature during and preceding the time of occurrence of 7Q values. REFERENCES

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Loganathan, G. V., P. Mattejat, C. Y. Kuo, and M. H. Diskin. 1986. Frequency analysis of low flows: Hypothetical distribution methods and a physically based approach. Nordic Hyciroloqy 17: 129- 150. Love, M. A. 1993. Variation in low streadow based on watershed characteristics. MSCE Thesis, The University of North Carolina at Charlotte. Lowrance, R. R., R. L. Todd, and L. E. Asmussen. 1984. Nutrient cycling in an agricultural watershed. 11. Streamflow and artificial drainage. J Environ Oual 13:27-32. Matalas, N. C. 1963. Probability distribution of low flows. U.S.Geological Survey Professional Paper #434-A, p. A- 11. McCuen, R. H. 1993. Statistical Hydrology Englewood Cliffs NJ: Prentice Hall. McMahon, T. A. 1976. Low flow analyses of streams: Details of computational procedures and annotated bibliography. Research Repon #5, Monash Univ Dept of Civil Eng, Clayton, Australia. McNaughton, K. G., and P. G. Jarvis. 1983. Predicting effects of vegetation changes on transpiration and evaporation. Water Deficits and Plant Growth 7: 1-47. . 1985. Analysis and simulation of low flow hydraulics. J Hydraulic Eng Div ASCE 1 11: 1429- 14-46. Miller, E. G. 1966. Effect of urbanization on low flow. Geological Survey Research, U.S. Geological Survey Professional Paper #550, p. A 166. Motayed, A. K. 1980. Discussion of Urbanization impact on streamflow. J Hydraulics Div ASCE 106:627-628. Nemec, J. 1986. Hyirological Forecasting. Dordrecht, Holland: D. Reidel Pub Co.

Novoa, J. I., and L. M. Salcedo. 1980. Discussion of Urbanization impact on streamflow. J Hydraulics Div AS CE 106: 1 139-1 141. Orsborn, J. F. 1973. Predicting low flows and floods from ungaged drainage basins. Hydraulic Engineering and the Environment. ASCE, Proc 21st Ann Hydraulics Div Spec Conf. . 1975. Predicting ungaged low flows in diverse hydrologic provinces using river basin geomorphic characteristics. International Assoc of Hydrological Sciences pub#l17, pp. 157-167. Page, L. V., and L. C. Shaw. 1977. Low-flow characteristics of Pennsylvania streams. U.S. Geological Survey Bulletin #12. Petersen, R. C., B. L. Madsden, M. A. Wilzbach, C. H. D. Magadza, A. Paarlberg, A. Kullberg, and K. W. Cummins. 1987. Stream management: emerging global similarities. Ambio 16:166-179.

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Abbreviations (units of measurement) cubic feet per second square miles

Year

Terms Evapotranspiration: Evaporation plus transpiration Riparian Zone: Area adjacent to a sBeam ZQ: Minimum average value of mean-daily streamflows for any seven consecutive days 702: 2-year recurrence interval for annual minimum of average of 7-day mean flows 7010: 10-year recurrence interval for annual minimum of average of 7-day mean flows APPENDIX A

74 Rows (annual and five-year mean values), precipitation, and population data for stations in the Asheville area (pp. 79-84)

APPENDIX B

74 Flows (annual and five-year mean values), precipitation, and population data for stations in the Greensboro area (pp. 86-95)

Fast Fork Oee~River near Hiah Poirlt

River at Haw rive^

71 18500. H- Creek near Harmonv APPENDIX C

7Q Flows (annual and five-year mean values), precipitation, and population data for stations in the Raleigh area (pp. 97- 104) ek near Cfavton

#0?085500. Flat River at Rahu

0380. Contemea Creek mrI ucm

APPENDIX D

7Q Flows (annual and five-year mean values), precipitation, and population data for stations in the Charlotte area (pp. 106- 1 15)

Note: For Irwin Creek near Charlotte, McAlpine Creek below McMullen Creek near Pineville, McAlpine Creek at Sardis Road near Charlotte, and McMullen Creek at Sharon View Road near Charlotte, population data were unavailable for the specific subbasins for the entire period. Hence, data for Mecklenburg County were used for these stations (Figure 29).

143500. Indian Creek near I &2&xy

rn Creek near Charlow

80?146600. Mc/Ugine Creek at Sardis Road near Ch&& #03146900. Twelve b& CreekmWax& on Vtew Rdnear Chi&& APPENDIX E

74Flows (annual and five-year mean values), precipitation, and population data for stations in the Goldsboro-Kinston area (pp. 117-122) . . 803087570. Neuse River at SM

vear go~l~lation 1960 136325 1970 152266 1980 197425 i990 24041 o

Contentnea Creek near APPENDIX F

7Q Flows (annual and five-year mean values), precipitation, and population data for stations in the Rocky Mount-Tarboro area (pp. 124- 129) #03C)83500.Tar River at TarbpLg 087506. Tar River below Tar giver Res-ckv Mount 3585. Tar River at NC 97 at Rockv Mom 7083800.Conetoe Creek near Bethel Fisba Creek near White O&

APPENDIX G

Equality of slopes test applied to Swannanoa River at Biltmore and Mills River near Mills River (pp. 131-146) STREAM RANK SLOPES Mills Mills 1 .oo -85.50 Mills Mills 2.00 -85.30 Mills Mills 3.00 -54.90 Mills Mills 4.00 -21 -20 Mills Mills 5.00 -71.90 Mills Mills 6.00 -71.90 Mills Mills 7.00 -70.50 Mills Mills 8.00 -59.20 Mills Mills 9.00 -58.30 Mills Mills 10.00 -66.20 Mills Mills 1 1 .oo -66.80 Mills Mills 12.00 -56.00 Mills Mills 13.00 -54.50 Mills Mills 14.00 -63.80 Mills Mills 15.00 -61.50 Mills Mills 16.00 -51 20 Mills Mills 17.00 -59.30 Mills Mills 18.00 -59.20 Mills Mills 19.00 -53.00 Mills Mills 20.00 -57.50 Mills Mills 21 .oo -56.70 Mills Mills 22.00 -54.00 Mills Mills 23.00 . -53.50 Mills Mills 24.00 -52.20 Mills Mills 25.00 -49.50 Mills Mills 26.00 -49.70 Mills Mills 27.00 -49.50 Mills Mills 28.00 -49.50 Mills Mills 29.00 -47.30 Mills Mills 30.00 -47.00 Mills Mills $1 .OO -45.30 Mills Mills 32.00 -45.70 Mills Mills 33.00 -44.30 Mills Mills 34.00 -41.50 Mills Mills 35.00 -4; .so Mills Mills 37.00 -4'5.50 Mills Mills 39.00 -39.70 Mills Mills 40.90 -36.50 Mills Mills 41 .OO -36.40 Mills Mills 47.00 -35.50 Mills Mills 48.00 -35.40 Mifis Mills 49.00 -35.30 Mills Mills 50.00 -35.30 Mills Mills 51.00 -34.20 Mills Mills 55.00 -31.70 Mills Mills 56.00 -31.50 Mills Mills 57.00 -3i .40 Mills Mills 58.00 -37.30 Mills Mills 59.00 -30.70 Mills Mills 60.00 -30.50 Mills Mills 61 .OO -30.40 Mills Mills 63.00 -28.50 Mills Mills 70.00 -27.60 hdiiis Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Milis Mills Mills Mills Milis Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Miils Mills Mills Mills Mills Mills Mills Mills Mills Mills Miils Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mi!ls Mills hlills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Milis Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Miils Miils Mills Mills Mills Mills Mills Mills Mills Mills Mills tdills Mills Mills Mills Mills Mills Mills 49.90 Mills Mills 50.00 Mills Mills 50.00 Mills Mills 50.30 Mills Mills 50.50 Mills Mills 51.80 Mills Mills 51 .go Mills Mills 52.80 Mills Mills 54.50 Mills Mills 54.80 Mills Mills 54.80 Mills Mills 55.50 Mills Mills 56.30 Mills Mills 57.50 Mills Mills 57.50 Mills Mills 58.60 Mills Mills 59.00 Mills Mills 59.90 Mills Mills 61.30 Mills Mills 61.50 Mills Mills 62.60 Mills Mills 64.20 Mills Mills 72.90 Mills Mills 73.30 Mills Mills 73.50 Mills Mills 73.80 Mills Mills 75.60 Mills Mills 76.00 sumranks Mills Mills 76.50 161 855.00 Mills Swannanoa -41.OO Mills Swannanoa -40.20 Mills Swarinanoa -36.40 Mi!ls Swannanoa -36.80 Mills Swannanoa -35.60 Mills Swannanoa -35.70 Mills Swannanoa -35.50 Mills Swannanoa -24.00 Mills Swannanoa -33.30 Mills Swannanoa -31.30 Mills Swannanoa -30.30 Miils Swannanoa -29.80 Mills Swannanoz; -29.50 Mills Swannanoa -29.40 Mills Swannanoa -29.30 Mills Swannanoa -29.00 Mills Swannanoa -28.71? Mills Swannanoa -27.40 Mills Swannanoa -27.30 Mills Swannanoa -27.30 Mills Swannanoa -27.' 0 Mills Swannanoa -26.70 Mills Swannanoa -25.50 Milis Swannanoa -24.80 Mills Swannanoa -24.70 Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swarmnoa Swannanoa Swannanoz Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannacoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoe Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannan~a Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanca Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa 789.00 53.20 Swannanoa Swannanoa 790.00 53.80 sumranks Swannanoa Swannanoa 791 .OO 54.00 16821 4.00 Swannanoa Swannanoa Milis River near Mills River --Swannanoa River near BiRmore Swannanoa expected value = 165039.00 Swannanoa stand dev = 3341.80 Swannanoa Z( Mills ) = 0.95 Swannanoa Z( Swannanoa ) = -0.95 Swannanoa Swannarroa Swannanoa Swanzanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swsnnanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa SLOPES STREAM RANK Swannanoa -85.80 Mills 1 .oo Mills -85.30 Mills 2.00 Mills -84.90 Mills 3.00 Mills -8i 20 Mills 4.00 Mills -71.30 Mills 5.00 Mills -71.9O Mills 6.00 Mills -70.60 Mills 7.00 Mills -69.20 Mills 8.00 Swannanoa -68.30 Mills 9.00 Swannanoa -56.80 Mills 10.00 Swannanoa -56.80 Mills 1 1 .oo Swannanoa -66.00 Mills 12.00 Swannanoa -64.00 Mills 13.00 Swannanoa -53.80 Mills 14.00 Swannanoa -51 .SO Mills 15.00 Mills -61.20 Mills 16.00 Mills -53.30 Mills 17.00 Swannanoa -59.20 Mills 18.00 Swannanoa -59.30 Mills 19.00 Swannanoa -57.90 Mills 20.00 Swannanoa -56.70 Mills 21 .oo Swannanoa -54.00 Mills 22.00 Mills -53.60 Mills - 23.00 Mills -52.20 Mills 24.00 Mills -49.80 Mills 25.00 Swannanoa -49.70 Mills 26.00 Mills -49.50 Mills 27.00 Mills -49.50 Mills 28.00 Swannanoa -47.00 Mills 29.00 Swannanoa -47.00 Mills 30.00 Swannanoa -45.90 Mills 31 .OO Mills -45.70 Mills 32.00 Mills -44.30 Milis 33.00 Swannanoa -41.GO Mills 34.00 Swannanoa -41 .SO Mills 35.00 Swannanoa -41 -00 Swannanoa 36.00 Mills -40.50 Mills 37.00 Mills -40.20 Swannanoa 38.00 Mills -39.70 Mills 39.00 Swannanoa -36.60 Mills 40.00 Mills -36.40 Mills 41 .OO Mills -36.40 Swannanoa 42.00 Swannanoa -36.00 Swannanoa 43.00 Swannanoa -35.80 Swannanoa 44.00 Swannanoa -35,70 Swannanoa 45.00 Mills -35.60 Swannanoa 46.00 Mills -35.50 Mills 47.00 Mills -35.40 Mills 48.00 Mills -35.30 Mills 49.00 Mills -35.30 Mills 50.00 Swannanoa -34.20 Mills 51 .OO Mills -34.00 Swannanoa 52.00 Swannanoa -33.30 Swannanoa 53.00 Swanrianoa Mills Swannanoa Swannanoa Mills Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Mills Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Mills Mills Swannanoa Mills Mills Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Mills Mills Mills Swannanoa Swannanoa Mills Mills Swannanoa Swannanoa Mills Mills Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Mills Swannanoa Mills Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Mills Mills Swannanoa Mills Mills Mills Mills Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Mills Mills Mills Mills Mills Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Mills Mills Swannanoa Mills Swannanoa Swannanoa Mills Mills Swannanoa Swannanoa Swannanoa Mills . Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Mills Swannanoa Mills Mills Swannanoa Mills Mills Mills Mills Mills Mills Swannanoa Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Swannanoa Mills Swannanoa Swannanoa Mills Mills Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Mills Mills Swannanoa Mills Swannanoa Swannanoa Mills Mills . Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Mills Swannanoa Mills Swannanoa Mills Mills Mills Swannanoa Swannanoa Mills Mills Swannanoa Swannanoa Mills Swannanoa Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Mills Swannanoa Mills Mills Swannanoa Swannanoa Mills Swannanoa Mills Mills Swannanoa Swannanoa Mills Mills Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Mi!ls Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Mills Mills Swannanoa. Swannanoa Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Swannanoa Mills Swannanoa Mills Mills Mills Swannanoa Swannanoa Mills Mills Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Mills Swannanoa Swannanoa Mills Mills Mills Swannanoa Mills Mills Mills Mills Milts Swannanoa Mills Mills Swannanoa Swannanoa Mills Mills Mills Swannanoa Mills Miils Mills Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Milis Mills Swannanoa Mi!ls Swannanoa Milts Swannanoa Swannanoa Mills Swannancja Mills Swannanoa Swannanoa Mills Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Mills Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Mills Swannanoa Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Mills Mills Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Mills Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swmnanoa Mills Swannanoa Mills Swannanoa Mills Mills Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Swannanoa Mills Mills Miils Mills Mills Swannanoa Mills Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Swannanoa Mills Swannanoa Mills Swannanoa Mills Miils Swannanoa Mills Swannanoa Mills Swannanoa Swannanoa Mills Mills Swannanoa Mills Swannanoa Mills Swannanoa Mills Mills Mills Swannanoa Mills Swannanoa Mills Mills Swannanoa Mills Mills Swannanoa Mills Mills Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Mills Mills Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Swannanoa Mills Mills Mills Swannanoa Miils Swannanoa Mills Mills Mills Swannanoa Mills Mills Mills Swannanoa Swannanoa Mills Swannanoa Swannanoa S-~annanoa Mills Swannanoa Swannanoa Miils Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Swannanoa Mills Swannanoa Swannanoa Mills Swannanoa Swannanoa Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills 81 0.00 Swannanoa Mills 81 1.OO Mills Mills 812.00 Swannanoa Mills Mills Mills Swannanoa Swannanoa Swannanoa Swannanoa Mills Mills Mills !dills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills Mills