Landscape, Hydrological and Social Factors Affecting Water Quantity and Quality in Urban Headwater Streams of Central Iowa Jiayu Wu Iowa State University

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2013 Landscape, hydrological and social factors affecting water quantity and quality in urban headwater streams of central Iowa Jiayu Wu Iowa State University

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Landscape, hydrological and social factors affecting water quantity and quality in urban headwater streams of central Iowa

Jiayu Wu

A dissertation submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Major: Environmental Science

Program of Study Committee: Janette R. Thompson, Major Professor Timothy W. Stewart Monica A. Haddad Kristie J. Franz John C. Tyndall

Iowa State University Ames, Iowa 2013

TABLE OF CONTENTS

ABSTRACT iv

CHAPTER 1: GENERAL INTRODUCTION 1 Study Questions 6 Dissertation Organization 8 References 9

CHAPTER 2: QUANTIFYING IMPERVIOUS SURFACE CHANGES USING TIME SERIES PLANIMETRIC DATA FROM 1940 TO 2011 IN FOUR CENTRAL IOWA CITIES, U.S.A. 13 Abstract 13 Introduction 14 Materials and Methods 18 Results 23 Discussion 27 Conclusion 33 Acknowledgements 35 References 35 Tables 40 Figures 45

CHAPTER 3: USING THE STORM WATER MANAGEMENT MODEL TO PREDICT URBAN HEADWATER STREAM HYDROLOGICAL RESPONSE TO CLIMATE AND LAND COVER CHANGE 52 Abstract 52 Introduction 53 Methods 57 Results 64 Discussion 67 Conclusions 72 Acknowledgements 74 References 74 Tables 78 Figures 83

CHAPTER 4: WATERSHED FEATURES AND STREAM WATER QUALITY: A CAUSAL ANALYSIS OF LINKAGES IN MIDWEST URBAN LANDSCAPES, U.S.A. 88 Abstract 88 Introduction 89 Methods 91 Results 97 Discussion 100 iii

Conclusions 104 Acknowledgements 105 References 105 Tables 111 Figures 124

CHAPTER 5: URBAN STREAM WATER QUALITY AS A PERFORMANCE MEASURE OF THE NPDES STORM WATER PHASE II PERMIT PROGRAM 129 Abstract 129 Introduction 131 Methods 133 Results 138 Discussion 140 Conclusions 144 Acknowledgements 145 References 145 Tables 150 Figures 156

CHAPTER 6: GENERAL CONCLUSIONS 158 References 161

ACKNOWLEDGEMENTS 162

ABSTRACT

Urban landscapes are complex and dynamic systems, and in areas of strong human population growth urban areas are undergoing rapid expansion. Increases in impervious surface area (e.g. roads, buildings, and parking lots) are typically associated with urban expansion, and usually represent irreversible change on the landscape. These impervious surface areas provide services to urban residents, but also have environmental consequences, including alteration of urban stream hydrology and stream water quality. This dissertation documents research conducted to quantify urban expansion, and to explore the complex relationships among terrestrial landscape features, urban stream hydrology, social system factors, and water quantity and quality variables in 20 urban stream watersheds located in five central Iowa cities. First, urban land cover change was quantified for four cities using planimetric data applied to aerial photo images from 1940 to 2011. Impervious surfaces accounted for about 1.5% of the area in four study cities in 1940, and increased to 16.8% in 2011, a 1,020 % increase. Much of the expansion of impervious surface was contributed by buildings and parking lots. High, medium, and low percent impervious surface zones were dominated by parking lots, buildings, and roads, respectively. Second, both climate change and land cover change were predicted and simulations were performed using the Storm Water Management Model to determine their potential effects on stream hydrology (with unit-area peak discharge, flashiness index, and runoff ratio as response variables) in five selected study watersheds. Simulation of climate change predicted effects on unit-area peak discharge, while simulation of land cover change predicted changes in all three response variables. Different distributions of additional impervious surface simulated within a single watershed had a greater effect on timing of delivery than on total amount of v discharge. Third, the direct, indirect and total effects of human system, terrestrial landscape features, and stream hydrology variables on water quality outcome variables were assessed using path analysis. Stream water conductivity, total nitrogen concentration, and total phosphorus concentration were strongly influenced by road density, percent of crop land, and percent of residents with college-level education, respectively. Finally, a semi-quantitative analysis of the implementation of the National Pollutant Discharge Elimination System (NPDES) permitting program was linked to water quality outcomes in 20 urban streams in five cities to examine the effectiveness of these regulatory requirements. Cities with higher ratings for completeness of responses to the NPDES stormwater program also had better stream water quality. Based on multiple regression analysis, a strong relationship was detected between the quality of municipal responses to the NPDES program and stream water concentrations of phosphate, total phosphorus, total suspended solids and turbidity. Evidence presented in this dissertation indicates that urban stream hydrology and water quality are related to characteristics of urban populations, landscapes, and that impacts of changes in these variables on urban stream water quality and quantity in central Iowa are likely to be more pronounced over time. The information provided by this research should be helpful for civic officials in these and other cities to mitigate against potential negative impacts of these changes to urban streams.

CHAPTER 1: GENERAL INTRODUCTION

The world’s human population has grown tremendously in recent decades. Globally, there were about 2.5 billion people in 1950; which grew to 6.9 billion in 2010, and is expected to rise to 9.3 billion by 2050 (United Nations, 2011). In 2010, the three most populated countries included China (1.3 billion), India (1.2 billion), and the United States (0.3 billion). Recent population growth has contributed more to urban populations than to those in rural areas. For example, on a global basis, only about 29% of the world’s population was urban in 1950; but by

2010 the percent of urban residents was close to 52%, and by 2050 it is expected to be about

67% (United Nations, 2011).

The population of Iowa has increased more gradually over time, growing from 2.6 million people in 1950 to 3.0 million in 2010 (SDCI, 2010). However, migration to urban centers in the state has led to a more dramatic rural-urban shift compared to global trends. Urban residents accounted for only 18% of Iowa’s population in 1950, but by 2010 the population of the state was approximately 64% urban. Among the Metropolitan Statistical Areas (MSAs) in the state, the Des Moines-West Des Moines MSA has been characterized by the greatest population growth and has become Iowa’s most populated area. From 1950 to 2010, the population in this area increased approximately 114% and now accounts for about 19% of total population of the state. Within this MSA, populations in the ring of communities around the City of Des Moines itself (including the cities of Altoona, Ankeny, Clive, Johnston, Norwalk, Pleasant Hill,

Urbandale, and West Des Moines) have increased very rapidly (SDCI, 2010).

Urban population growth typically leads to conversion of agricultural landscapes

(especially in the Midwest) and natural areas (e.g. forests, grasslands) to urban land uses. For example, Bowman et al. (2012) found that urban land area increased between 28% and 80% in 2 four Iowa cities between 1992 and 2002 leading to decreases in both agricultural and natural areas in the peri-urban landscape. Often, the conversion of land to urban uses is disproportionately larger than that caused by population growth alone, a phenomenon described as “urban sprawl” (Catalan, Sauri, & Serra, 2008; Seto, Fragkias, Guneralp, & Reilly, 2011).

One way to assess urban expansion is to quantify areas of impervious surface (IS), which include “materials that prevent the infiltration of water into the soil” (Arnold & Gibbons, 1996).

Impervious surfaces include a wide variety of features, such as roads, buildings, and parking lots

(Tilley & Slonecker, 2007). In 2000, about 0.4% of the global terrestrial surface was covered by

IS. Countries which had the most IS included China (87,182 km2), the United States (83,881 km2), and India (81,221 km2) (Elvidge et al., 2007). In addition to impervious surface features, urban areas are also characterized by impervious underground features, such as the storm sewer systems used to convey stormwater to nearby streams.

Because rapid urban expansion is occurring worldwide, recent studies have been conducted to accurately assess the locations and extent of urban growth. Two main categories of technology have been used, including satellite imagery and aerial photo imagery. At a global scale, relatively coarse spatial resolution satellite imagery data has been used. For example,

Schneider, Friedl, & Potere (2009) used Moderate Resolution Imaging Spectroradiometer

(MODIS) images with 500-meter spatial resolution to study global distribution of urban land cover. In more local-scale studies, moderate spatial resolution satellite imagery has commonly been used, such the Landsat Thematic Mapper (TM)-derived National Land Cover Database

(NLCD), which has 30-meter spatial resolution (Fry et al., 2011). Aerial photo-based imagery can provide much higher resolution (one meter or less), and has often been used to study city- or watershed-scale areas (e.g. Tilley & Slonecker, 2007). Using such land cover data, spatial 3 metrics can be applied to quantify spatio-temporal patterns of urban land cover change, including shape, edge, and connectivity characteristics in urban landscapes (McGarigal, Cushman, & Ene,

2012; Seto & Fragkias, 2005). However, because of their scarcity, spatial metrics have not frequently been applied to high spatial resolution land cover data (e.g. those derived from aerial photo-based imageries). More research is needed to validate the applicability of existing spatial metrics for use with high spatial resolution land cover data.

Spatiotemporal changes in urban landscapes have been used by researchers to investigate complex interactions among humans, infrastructural elements of the urban environment, and natural systems (Deng, Huang, Rozelle, & Uchida, 2008; Seto, Guneralp, & Hutyra, 2012).

Although urban areas account for less than 2.4% of the global terrestrial surface, the “ecological footprint” emanating from urban areas is much larger than the cities themselves because of material and energy inputs and pollutant outputs (Rees & Wackernagel, 1996; Grimm et al.,

2008). In this context, urban streams are important both as a natural feature but also because of their role in conveying urban drainage (along with pollutants) to downstream areas.

Impairments of urban streams that stem from human and urban system outputs have been investigated, including hydrological alterations and water quality degradation (Arnold &

Gibbons, 1996; Walsh et al., 2005; Wenger et al., 2009). Changes to urban stream hydrology often lead to stream bank erosion and disturbances to aquatic habitat (Fox et al., 2012; Wang,

Lyons, & Kanehl, 2001). This, in turn, can result in biological consequences including eutrophication, decreases in biodiversity, and shifts in taxonomic composition to assemblages dominated by pollution-tolerant species (Violin et al., 2011; Walsh et al., 2005).

During precipitation events, impervious surfaces in urban areas block the infiltration of water into soil. Thus, water is often delivered to lakes and streams via stormwater conveyance 4 systems such as storm sewers. Because of this, urban areas typically generate more runoff than natural areas, and it is delivered more rapidly into nearby streams. This causes changes in hydrological response patterns, including increases in total discharge volume (Arnold & Gibbons,

1996), peak discharge (Nelson et al., 2009), and flashiness (Nagy, Lockaby, Kalin, & Anderson,

2012) for urban streams. Further, anticipated changes in climate could also cause changes in stream hydrology (Denault, Millar, & Lence, 2006; Jha, Pan, Takle, & Gu, 2004). Because urban land cover change and climate change have simultaneous impacts on urban streams, their individual hydrological consequences can be difficult to assess. One approach that has been adopted to characterize these effects is the use of computer-based hydrological models.

Commonly used hydrological models include the Storm Water Management Model (SWMM)

(Rossman, 2010), the Hydrological Simulation Program-Fortran model (HSPF) (Bicknell,

Imhoff, Kittle, Donigian, & Johanson, 1997), and the Soil and Water Assessment Tool (SWAT)

(Neitsch, Arnold, Kiniry, Srinivasan, & Williams, 2002). Of these, SWMM is particularly suitable for evaluation of urban hydrology because it simulates hydraulic dynamics in urban storm sewers. Although some research has been conducted examining these dynamics, more studies are needed to elucidate the effects of climate change and land cover change on hydrology in urban watersheds with different degrees of development.

In addition to impacts on hydrology, effects on urban stream water quality are another important consequence of human population growth and urban expansion. Human activities such as motor vehicle travel, food production/consumption, use of fossil fuels, and application of fertilizer can drastically alter fluxes of carbon, nitrogen, and phosphorus in urban environments

(Fissore et al., 2011). These macro-elements can be easily conveyed to urban streams during precipitation events and become stream water pollutants. Although in Iowa agricultural activities 5 are common sources of pollutants (e.g. sediment, nutrients; Hatfield et al., 1999; Schilling &

Libra, 2000), urban pollutants have also been identified, such as application of road deicing materials (Nixon, Kochumman, Qiu, Qiu, & Xiong, 2007). A number of techniques have been used to link stream water quality outcomes to urban features, including computational models

(e.g. HSPF; Im, Brannan, & Mostaghimi, 2003; Tong, Sun, Ranatunga, He, & Yang, 2012) and statistical techniques (e.g. linear regression analysis, structural equation modeling; Nagy et al.,

2012; Riseng, Wiley, Black, & Munn, 2011). Because of the complexity of the urban system, more studies are needed to explore the comprehensive influences of human system, terrestrial landscape, and urban stream hydrology on water quality outcome variables.

River and stream water quality problems have long been recognized in the United States, and led to passage of the Clean Water Act in 1972. This legislation, meant to control water pollution, was further amended in 1987 and led to creation of the National Pollutant Discharge

Elimination System (NPDES) Storm Water permit program, which regulates urban stormwater discharges. This program has been administered by the U.S. Environmental Protection Agency at the federal level, and is coordinated in Iowa by the Iowa Department of Natural Resources. The program applies to municipalities above a certain population threshold that have “municipal separate storm sewer systems”, and requires them to annually report their activities to address specific minimum control measures (MCMs). Although some research has been conducted to assess the quality of muncipal responses in terms of their intentions to manage stormwater (e.g. their statements in their “Notice of Intent”; White and Boswell, 2006) few studies have provided direct quantitative evidence about how implementation of the Phase II program affects local urban stream water quality.

Study Questions

The overall goal of this research was to assess hydrological and water quality parameters for 20 streams located in five central Iowa cities, and to determine the relative importance of effects from the social and biophysical aspects of urban expansion, climate change, and government regulation. Specific study objectives were to: 1) quantify the patterns of urban land cover change over a 70-year period for four central Iowa cities; 2) simulate the potential hydrological consequences of predicted future climate and land cover change in five watersheds;

3) assess the complex relationships among social system, terrestrial landscape, stream hydrology and stream water quality variables in 20 watersheds; and 4) examine the relationship between stream water quality and municipal responses to the NPDES program for five cities. The specific study questions for the following chapters were:

1. What are the spatiotemporal characteristics of urban land cover change in four central Iowa cities?

The availability of high spatial resolution aerial photograph imagery with urban land cover data offers the ability to more accurately quantify the location and characteristics of impervious surfaces (Tilley & Slonecker, 2007). Being able to accurately quantify distribution, growth, and patterns of IS can provide fundamental information which can then be used to determine the effects of IS on urban streams. In addition, traditional spatial metrics have not previously been applied to assess changes in urban land cover over time. Further, investigation is needed to determine how information provided by such analyses could best be used for land use policy and planning, and to mitigate against the potential hydrological consequences of land cover changes.

2. How will urban headwater streams respond to predicted climate and land cover changes?

Both climate change and land cover change have been documented as drivers for urban stream hydrological alterations (Denault et al., 2006; Nagy et al., 2012; Nelson et al., 2009).

Generally, these and other studies have concluded that climate change and land cover change increased total discharge, peak discharge, and flashiness in urban streams. However, questions remain about the relative importance of these changes and how they might affect urban watersheds with different initial percent IS. In addition, we lack information on the effects of specific spatial distributions of IS within watersheds. Simulation studies could help civic officials understand, predict, and mitigate stream dynamics in relation to future scenarios for climate and IS change in their cities.

3. How strong are the relative effects of social system, landscape, and stream hydrology variables on stream water quality outcomes for urban headwater streams?

Interactions between landscape characteristics and urban streams have been the subject of a number of studies, but many questions about the potential drivers for the hydrological and ecological condition of urban streams remain (e.g. Wenger et al., 2009). Several recent studies have focused on linkages between two components of these systems, for example, between population and road density (Quinn, 2013), or between impervious surface and water quality

(Nagy et al., 2012). More comprehensive analyses linking social system, landscape and hydrological variables to water quality outcomes will enable researchers, urban engineers, and policy makers to devise effective strategies and policies for improving urban stream water quality.

4. What does water quality in urban streams indicate about the effectiveness of the NPDES

Phase II Storm Water permitting requirements?

Although the quality of municipal responses (e.g. “Notice of Intent”) to the NPDES

Phase II Storm Water permitting program has been studied for a limited number of municipalities (e.g. White and Boswell, 2006, 2007), questions remain about direct measures of program effectiveness in terms of water quality in receiving water bodies. Concurrent examination of municipal responses to the NPDES permitting program coupled with assessment of water quality in urban streams would allow an analysis of the relative importance of regulatory requirements for specific water quality outcomes and comparison to other factors also known to influence urban stream water quality. Information generated by such analyses could be used to assist city staff with planning and decision-making for stormwater management that would better protect and improve urban stream water quality.

Dissertation Organization

This dissertation explores these study questions in six chapters: Chapter 1, General introduction; Chapter 2, Quantifying impervious surface changes using time-series planimetric data from 1940 to 2011 in four central Iowa cities, U.S.A; Chapter 3, Using the Storm Water

Management Model to predict urban headwater stream hydrological response to climate and land cover change; Chapter 4, Watershed features and stream water quality: a causal analysis of linkages in Midwest urban landscapes, U.S.A.; Chapter 5, Urban stream water quality as a performance measure of the NPDES Storm Water Phase II permit program; and Chapter 6,

General conclusions.

Data collection, data analysis, and manuscript preparation were the responsibility of the candidate. Dr. Janette Thompson provided assistance with study design, data collection and 9 analysis, and manuscript editing for all studies. Dr. Timothy Stewart assisted with study design, analysis, and manuscript editing for Chapters 3, 4, and 5. Dr. Randy Kolka assisted with study design, analysis, and manuscript editing for Chapters 3, 4, and 5. Dr. Kristie Franz assisted with study design, analysis, and manuscript editing for Chapters 3 and 4. Anna Whipple (City of Des

Moines) provided planimetric data for Polk County, IA used in Chapters 2 and 3. Marissa Moore,

Ph.D. candidate in Sustainable Agriculture, assisted with evaluation of municipal annual reports for NPDES permits that was used in Chapter 5.

References

Arnold, C. L., & Gibbons, C. J. (1996). Impervious surface coverage - The emergence of a key environmental indicator. Journal of the American Planning Association, 62(2), 243-258. Bicknell, B. R., Imhoff, J. C., Kittle, J. L., Jr., Donigian, A. S., Jr., & Johanson, R. C. (1997). Hydrological Simulation Program--Fortran, user's manual (Version 11). Athens, Georgia: U.S. Environmental Protection Agency, National Exposure Research Laboratory. Available from: http://water.usgs.gov/software/HSPF/. Accessed 27 January 2013 Bowman, T. A., Thompson, J. R., Tyndall, J. C., & Anderson, P. F. (2012). Land cover analysis for urban foresters and municipal planners: examples from Iowa. Journal of Forestry, 110(1), 25-33. Catalan, B., Sauri, D., & Serra, P. (2008). Urban sprawl in the Mediterranean? Patterns of growth and change in the Barcelona Metropolitan Region 1993-2000. Landscape and Urban Planning, 85(3-4), 174-184. Denault, C., Millar, R. G., & Lence, B. J. (2006). Assessment of possible impacts of climate change in an urban catchment. Journal of the American Water Resources Association, 42(3), 685-697. Deng, X. Z., Huang, J. K., Rozelle, S., & Uchida, E. (2008). Growth, population and industrialization, and urban land expansion of China. Journal of Urban Economics, 63(1), 96-115. Elvidge, C. D., Tuttle, B. T., Sutton, P. C., Baugh, K. E., Howard, A. T., Milesi, C., Bhaduri B. L., & Nemani, R. (2007). Global distribution and density of constructed impervious surfaces. Sensors, 7(9), 1962-1979. Fissore, C., Baker, L. A., Hobbie, S. E., King, J. Y., McFadden, J. P., Nelson, K. C., & Jakobsdottir, I. (2011). Carbon, nitrogen, and phosphorus fluxes in household ecosystems in the Minneapolis-Saint Paul, Minnesota, urban region. Ecological Applications, 21(3), 619- 639. 10

Fox, D. M., Witz, E., Blanc, V., Soulie, C., Penalver-Navarro, M., & Dervieux, A. (2012). A case study of land cover change (1950-2003) and runoff in a Mediterranean catchment. Applied Geography, 32(2), 810-821. Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., & Wickham, J. (2011). Completion of the 2006 National Land Cover Database for the Conterminous United States. Photogrammetric Engineering & Remote Sensing, 77(9), 858-864. Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J. G., Bai, X. M., & Briggs, J. M. (2008). Global change and the ecology of cities. Science, 319(5864), 756-760. Hatfield, J. L., Jaynes, D. B., Burkart, M. R., Cambardella, C. A., Moorman, T. B., Prueger, J. H., & Smith, M. A. (1999). Water quality in Walnut Creek watershed: Setting and farming practices. Journal of Environmental Quality, 28(1), 11-24. Im, S. J., Brannan, K. M., & Mostaghimi, S. (2003). Simulating hydrologic and water quality impacts in an urbanizing watershed. Journal of the American Water Resources Association, 39(6), 1465-1479. Jha, M., Pan, Z. T., Takle, E. S., & Gu, R. (2004). Impacts of climate change on streamflow in the Upper Mississippi River Basin: A regional climate model perspective. Journal of Geophysical Research-Atmospheres, 109(D9), 12. McGarigal, K., Cushman, S., & Ene, E. (2012). FRAGSTATS(4.x): Spatial pattern analysis program for categorical and continuous maps. University of Massachusetts, Amherst. Available from: http://www.umass.edu/landeco/research/fragstats/fragstats.html. Accessed 27 January 2013 Nagy, R. C., Lockaby, B. G., Kalin, L., & Anderson, C. (2012). Effects of urbanization on stream hydrology and water quality: the Florida Gulf Coast. Hydrological Processes, 26(13), 2019-2030. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Srinivasan, R., & Williams, J. R. (2002). Soil and Water Assessment Tool user's manual (Version 2000). College Station, Texas: Texas Water Resources Institute. Available from: http://swat.tamu.edu/media/1294/swatuserman.pdf. Accessed 27 January 2013 Nelson, K. C., Palmer, M. A., Pizzuto, J. E., Moglen, G. E., Angermeier, P. L., Hilderbrand, R. H., Dettinger, M., and Hayhoe, K. (2009). Forecasting the combined effects of urbanization and climate change on stream ecosystems: from impacts to management options. Journal of Applied Ecology, 46(1), 10. Nixon, W. A., Kochumman, G., Qiu, L., Qiu, J., & Xiong, J. (2007). Evaluation of deicing materials and corrosion reducing treatments for deicing salts. Iowa City, Iowa: Iowa Highway Research Board,University of Iowa. Quinn, P. (2013). Road density as a proxy for population density in regional-scale risk modeling. Natural Hazards, 65(3), 1227-1248. Rees, W., & Wackernagel, M. (1996). Urban ecological footprints: why cities cannot be sustainable - and why they are a key to sustainability. Environmental Impact Assessment Review, 16(4-6), 223-248. 11

Riseng, C. M., Wiley, M. J., Black, R. W., & Munn, M. D. (2011). Impacts of agricultural land use on biological integrity: a causal analysis. Ecological Applications, 21(8), 3128-3146. Rossman, L. A. (2010). Storm Water Management Model user’s manual (Version 5.0). Cincinnati, Ohio: U.S. Environmental Protection Agency. Available from: http://www.epa.gov/nrmrl/wswrd/wq/models/swmm/epaswmm5_user_manual.pdf. Accessed 27 January 2013 Schilling, K. E., & Libra, R. D. (2000). The relationship of nitrate concentrations in streams to row crop land use in Iowa. Journal of Environmental Quality, 29(6), 1846-1851. Schneider, A., Friedl, M. A., & Potere, D. (2009). A new map of global urban extent from MODIS satellite data. Environmental Research Letters, 4(4), 11. SDCI. (2010). The State Data Center of Iowa. Available from: http://www.iowadatacenter.org. Accessed 27 January 2013 Seto, K. C., & Fragkias, M. (2005). Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landscape Ecology, 20(7), 871-888. Seto, K. C., Fragkias, M., Guneralp, B., & Reilly, M. K. (2011). A meta-analysis of global urban land expansion. Plos One, 6(8), 9. Seto, K. C., Guneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences of the United States of America, 109(40), 16083-16088. Tilley, J. S., & Slonecker, E. T. (2007). Quantifying the components of impervious surfaces (Open File Report 2007-1008). Reston, Virginia: U.S. Geological Survey, Eastern Geographic Science Center. Available from: http://pubs.usgs.gov/of/2007/1008/ofr2007- 1008.pdf. Accessed 27 January 2013 Tong, S. T. Y., Sun, Y., Ranatunga, T., He, J., & Yang, Y. J. (2012). Predicting plausible impacts of sets of climate and land use change scenarios on water resources. Applied Geography, 32(2), 477-489. Uinted Nations. (2011). Revision of world urbanization prospects (the 2011 Revision). New York: Department of Economic and Social Affairs, Population Division. Available from: http://esa.un.org/unpd/wup/index.htm. Accessed 27 January 2013 Violin, C. R., Cada, P., Sudduth, E. B., Hassett, B. A., Penrose, D. L., & Bernhardt, E. S. (2011). Effects of urbanization and urban stream restoration on the physical and biological structure of stream ecosystems. Ecological Applications, 21(6), 1932-1949. Walsh, C. J., Roy, A. H., Feminella, J. W., Cottingham, P. D., Groffman, P. M., & Morgan, R. P. (2005). The urban stream syndrome: current knowledge and the search for a cure. Journal of the North American Benthological Society, 24(3), 706-723. Wang, L. Z., Lyons, J., & Kanehl, P. (2001). Impacts of urbanization on stream habitat and fish across multiple spatial scales. Environmental Management, 28(2), 255-266. Wenger, S. J., Roy, A. H., Jackson, C. R., Bernhardt, E. S., Carter, T. L., Filoso, S., Gibson, C. A., Hession, W. C., Kaushal, S. S., Marti, E., Meyer, J. L., Palmer, M. A., Paul, M. J., 12

Purcell, A. H., Ramirez, A., Rosemond, A. D., Schofield, K. A., Sudduth, E. B., & Walsh, C. J. (2009). Twenty-six key research questions in urban stream ecology: an assessment of the state of the science. Journal of the North American Benthological Society, 28(4), 1080-1098. White, S. S., & Boswell, M. R. (2006). Planning for water quality: implementation of the NPDES Phase II Stormwater Program in California and Kansas. Journal of Environmental Planning and Management, 49(1), 141-160. White, S. S., & Boswell, M. R. (2007). Stormwater quality and local government innovation. Journal of the American Planning Association, 73(2), 185-193.

CHAPTER 2: QUANTIFYING IMPERVIOUS SURFACE CHANGES USING TIME SERIES PLANIMETRIC DATA FROM 1940 TO 2011 IN FOUR CENTRAL IOWA CITIES, U.S.A.

A manuscript formatted for submission to Landscape and Urban Planning

Jiayu Wu, Janette R. Thompson

Abstract

Rapid expansion of urban areas has been identified as a significant challenge for management of natural systems in and around cities and for the people who dwell in them. As a starting point, urban expansion must be quantified before it can be associated with other natural or social processes. Although spatial metrics have been used previously to quantify urban expansion, they have only rarely been applied to planimetric data. Using multi-year high- resolution planimetric data, a percent impervious surface-based zoning strategy, and six spatial metrics, we quantified impervious surface changes in four rapidly growing cities in central Iowa,

U.S.A. from 1940 to 2011. We documented increases in impervious surface area over time, largely due to increases in building and parking lot areas, especially in the most recent 20 years.

Rates of impervious surface expansion declined in all four cities between 2002 and 2011.

Application of a new spatial metric, percent connected impervious surface area, indicated increasingly connected impervious surfaces during the period of study. Application of a second new metric, road equivalent width, indicated gradual increases in road width over time. Distinct spatial patterns of change for impervious surface classes were identified in three % IS zones, with roads dominating low % IS zones, buildings dominant in medium % IS zones, and parking lots dominant in high % IS zones. Land use policies, planning tools, and management practices could be strategically implemented to mitigate the impacts of impervious surfaces by targeting specific IS classes that dominate different city zones. 14

Keywords: Urban growth; urbanization; spatial metrics; land use patterns; impervious surface zones; urban hydrology

Introduction

The human population in urban areas throughout the world has increased dramatically in recent years. In 2011, just over half of the world’s population (52.1%) resided in urban areas, and in the United States (U. S.) the proportion of urban dwellers is much higher, at 82.4% (United

Nations, 2011). The rapid growth of urban populations in most cases has been associated with improved living conditions, but has also led to rapid expansion of urbanized areas (e.g. Deng,

Huang, Rozelle, & Uchida, 2008; Elvidge et al., 2007; Guest, 1973). The dynamics of urban expansion associated with urban population growth have been studied by a number of researchers, particularly in the conterminous U.S. (e.g. Brown, Johnson, Loveland, & Theobald,

2005; Hammer, Stewart, Winkler, Radeloff, & Voss, 2004). Changes associated with urbanization are important because they have great influence on other natural systems, including climate (Kalnay & Cai, 2003), stream hydrology (Arnold & Gibbons, 1996; Schueler, 1994), stream water quality (Booth & Jackson, 1997; Walsh, Roy, et al., 2005), and aquatic ecosystems

(Alberti et al., 2007; Wenger et al., 2009).

One fundamental aspect of studies of urban growth has to do with the ability to accurately quantify the extent of urban areas and their characteristics. An obvious characteristic of urban areas is the construction of "materials that prevent the infiltration of water into the soil", known as impervious surface (IS) (Arnold & Gibbons, 1996). Quantification of IS and identification of trends of change in this parameter are important because conversion of land to impervious cover is usually a permanent change, and retrospective analyses can identify 15 consistent trajectories and cumulative effects of those changes. This information has wide applicability to enable evaluation of current development patterns and support implementation of approaches that limit expansion of IS where loss of natural areas, protection of heritage features, and/or prevention of environmental degradation are concerns (e.g. Bowman, Thompson, Tyndall,

& Anderson, 2012; de Noronha Vaz, Caetano, & Nijkamp, 2011). For example, further increases in IS could be mitigated through the use of planned unit developments, low-impact development

(LID) techniques, or conservation/heritage conservation design (CSD) approaches (Arendt, 2004,

Dietz, 2007; de Noronha Vaz, et al., 2011; Pejchar, Morgan, Caldwell, Palmer, & Daily, 2007;

Rohe, 2009). Assessment of IS is particularly useful for planners and public works personnel responsible for stormwater management, given that increases in IS generally lead to greater stormwater production (Meierdiercks, Smith, Baeck, & Miller, 2010).

A variety of methods have been developed to quantify IS based either on satellite-based remote sensing data (e.g. Chabaeva, Civco, & Hurd, 2009; Civco, Chabaeva, & Hurd, 2006) or aircraft-based air photos (e.g. Hodgson, Jensen, Tullis, Riordan, & Archer, 2003; Phinn,

Stanford, Scarth, Murray, & Shyy, 2002). Satellite-based images are used frequently because of their ready availability, low cost, and ease of use (Tilley & Slonecker, 2007). Computer-based spectral pattern recognition techniques have been developed to estimate percent impervious surface (% IS), using, for example, the Landsat Thematic Mapper (TM) and Enhanced Thematic

Mapper Plus (ETM+) images (Civco, Hurd, Wilson, Arnold, & Prisloe, 2002; Wilson, Hurd,

Civco, Prisloe, & Arnold, 2003). Most IS data in this category are characterized by 15-m or 30-m spatial resolution, such as the % IS layer in 2006 National Land Cover Database (Fry et al.,

2011). 16

Recently, more precise IS data have been derived from high-resolution digital orthorectified images (e.g. Tilley & Slonecker, 2007). Use of rectified air photo mosaics is a particularly good approach for the study of long-term trends because equivalent satellite images do not exist for earlier decades, and because air photos typically have much greater spatial resolution (e.g. 1 m) and accuracy (e.g. Weng, 2012). These images have been used as a reference to assess the accuracy of satellite-derived IS data (Chabaeva et al., 2009; Civco et al.,

2006) or used directly as an indicator of urban sprawl (Irwin & Bockstael, 2007). However, the use of high-resolution IS data is limited by the requirement for appropriate remote sensing images, and relatively labor-intensive image interpretation (Slonecker, Jennings, & Garofalo,

2001).

A further aspect of studies of urban growth is related to the methods used to quantify the spatial and temporal patterns of change for urban land. Standard landscape metrics (McGarigal,

Cushman, & Ene, 2012) have been applied to quantify urban land cover changes over time (e.g.

Seto & Fragkias, 2005; Solon, 2009). Some researchers have proposed use of the term “spatial metrics” (in lieu of “landscape metrics”) to clarify that they are being used to measure spatial patterns of urban features rather than ecological features (Aguilera, Valenzuela, & Botequilha-

Leitao, 2011). Typically, some set of metrics are used together in order to explicitly examine different aspects of urban landscape characteristics such as composition, configuration, and anisotropy (see for example O'Neill et al., 1988). In order to better assess the spatial heterogeneity of urban features, researchers have also used techniques to subdivide urban study areas for closer examination, including transects (Luck & Wu, 2002), bands (Zhou, Huang,

Pickett, & Cadenasso, 2011), and concentric rings (Seto & Fragkias, 2005). However, relatively 17 few studies of urban growth patterns have been conducted using high-resolution IS images for multiple cities at a whole-city scale, largely because of scarcity of this form of data.

Because it is an important and unique characteristic of urban land use, % IS has been widely used by researchers to assess environmental impacts of urbanization, particularly with respect to generation of stormwater (Arnold & Gibbons, 1996; Steuer, Bales, & Giddings, 2009).

For example, more surface runoff is generated in high % IS areas, because water infiltration is decreased in those areas. Further, the runoff generated by these surfaces is increased where they are highly interconnected (e.g. driveways connected to roads with gutters) and is often transported directly to streams via storm sewers (Meierdiercks, et al., 2010). These hydrologically-connected IS areas have been identified as “effective impervious surface” (EIS), and may lead more directly to diminished water quality as well as increased flashiness of urban streams (Walsh, Fletcher, & Ladson, 2005).

We were interested in characterizing urban landscape change for a set of rapidly growing cities using high-resolution planimetric data that allowed us to identify different types of impervious surfaces and the connections among them. We used spatial metrics to analyze patterns of change for IS in four such cities over approximately 70 years. This study examines the following questions: 1) What can planimetric data reveal about spatio-temporal changes in total impervious surface area (TIS) and IS class areas, and how can this information be used in local policy and planning? 2) How applicable are existing landscape metrics for quantifying changes of IS in urban areas? and 3) How can detailed information on IS class changes be used to identify practices that could be used to mitigate the potential hydrological consequences of those changes?

Materials and Methods

Study area

Our study includes four cities in central Iowa, U.S., a landscape matrix otherwise dominated by intensive agricultural land use. The four cities (Altoona, Ankeny, Johnston and

Pleasant Hill) are located in Polk County near Des Moines, the capital of Iowa (Fig. 1). These four cities were selected because they were part of a larger project examining water quality, quantity, and aquatic ecology in urban headwater streams in central Iowa. In recent years these four cities have experienced significant increases in population (Table 1), have become predominantly suburban/exurban, and typical of many metro-fringe areas in the U.S., have been expanding rapidly due to automobile commuting, land availability and lax zoning controls.

Between 2000 and 2010 their population, on average, increased by 70.4%, compared to 2.4% for the City of Des Moines, and 4.1% for Iowa as a whole (SDCI, 2010).

Altoona (population 14,541 in 2010) is located 8.6 km northeast of Des Moines and immediately south of U.S. Interstate Highway 80. Commercial facilities are concentrated along the western edge of the city, and include an amusement park, a casino and a horse-racing track.

Ankeny (population 45,582 in 2010) is located adjacent to U.S. Interstate Highway 35, about 8.3 km north of downtown Des Moines. Within the city boundaries there is agricultural land (at both the northern and southern edges), residential land (centrally located), and “big-box” commercial enterprises located south of the main residential area. Johnston (population 17,278 in 2010) is located just north of the intersections of U.S. Interstate Highways 35 and 80, about 8 km north and west of downtown Des Moines. Developed areas are located in the eastern and western parts of the city, which is divided by green space along Beaver Creek which runs through the center of

Johnston. Pleasant Hill (population 8,785 in 2010) is situated at the intersection of State 19

Highway 163 and U.S. Highway 65, directly adjacent to the eastern boundary of Des Moines.

Developed areas are concentrated in the western part of the city, with open space and agricultural land along Fourmile Creek on the eastern side. Five-year average (2006-2010) per capita income for the four cities ranges from U.S. $29,000 to $40,000 (USDC Census Bureau, 2010). More than 90% of working residents in these four cities commute approximately 20 minutes to work

(USDC Census Bureau, 2010).

Data preparation

Planimetric data for IS based on the 2006 National Agriculture Imagery Program (NAIP) aerial photography mosaic were obtained from the City of Des Moines GIS department (A.

Whipple, personal communication, June 17, 2010). Total impervious surface area (TIS) was classified as roads, driveways, parking lots, buildings, airports, bridges, paved drains, and miscellaneous structures. Sidewalks were not included because they were too narrow to delineate at the spatial resolution of our images. We collapsed the last four categories (airports, bridges, paved drains, and miscellaneous structures) into a single class, named “other”. The resulting five classes provided comprehensive coverage for analysis of IS in the four cities.

The original planimetric data were stereo-digitized by City of Des Moines personnel using the 2006 NAIP aerial photography mosaic. In order to create a multiple-year planimetric data set, we downloaded the 1930s digital georectified mosaic (created between 1936 and 1941), the 1961 digital orthorectified mosaic, the 1990 orthophoto mosaic, the 2002 Color-Infra-Red aerial photo mosaic, and the 2011 NAIP aerial photo mosaic from the Natural Resources

Geographic Information Systems Library (NRGIS, 2012). The original data sources for these images were different. The 1936-41 and 1961 mosaics were originally photographed at 1:20,000, and were digitally scanned at 600 dpi from hardcopy prints to provide raster-format images at 1- 20 m resolution. To simplify calculations, we assumed the 1930s image was made in 1940. The

1990 and 2002 mosaics were created by scanning film diapositives from images photographed at

1:40,000 using a precision image scanner with original resolution of < 1.28 m resampled to 1 m.

The 2011mosaic was based on digital aerial photos provided at 1-m resolution.

We modified the 2006 planimetric data by manual heads-up digitizing in ArcGIS 10

(ESRI, 2010) using 2011 city limits for each city as spatial boundaries for all study years.

Planimetric data for 1940, 1961, 1990, 2002 and 2011 were created on respective images working forward (2011) or backward (2002, 1990, 1961, 1940) from the original planimetric data (Fig. 2). Manual digitizing was conducted by the same technician for all cities and time periods. Accuracy was evaluated using 30 sample points that were randomly generated for each city and year using HawthsTools (Beyer, 2004). Within 50-m buffers for sampling points, the average digitizing accuracy for the 20 images (four cities, five time periods) was 98.9%.

Zone delineation

To simplify application of spatial metrics given the heterogeneity of IS within each city, three % IS zones were delineated according to percent total IS (TIS) based on 2011 planimetric data: low % IS (<20% IS), medium % IS (20%-40% IS), and high % IS (>40% IS). The % IS criteria we imposed allowed us to 1) highlight city nuclei where IS was concentrated, 2) balance the area of each zone within each city and among the four cities, and 3) identify typical land uses associated with different proportional levels of IS. The zones were created using a grid composed of 16-ha hexagon-shaped cells (e.g. Griffith, Martinko, & Price, 2000).

Impervious surface area

Total impervious surface area (TIS) was summarized for each city and year. Percentages of TIS and IS class areas were calculated by dividing IS areas by the corresponding 2011 total 21 city area. The change in percent TIS was quantified by estimating annual percent TIS change

(Equation 1):

(TIS −TIS ) I = end begin (1) area n where Iarea is the annual percent TIS change (%), TISbegin is the percent TIS (%) at the beginning of a time period, TISend is the percent TIS (%) at the end of a time period, and n is the length of a time period (years).

We also calculated annual TIS change rate (Equation 2):

n Aend I% = ( √ − 1) ∗ 100 (2) Abegin where I% is the annual TIS change rate (%), Abegin is the TIS area (ha) at the beginning of a time period, Aend is the TIS area (ha) at the end of a time period, and n is the length of a time period

(years).

Application of spatial metrics

Two new metrics and four standard metrics were used in this study. The two new spatial metrics were created to quantify percent connected impervious surface area (PCISA, which we compared to the standard landscape metric connectance index, McGarigal et al., 2012) and road equivalent width (REW), which were applied at a whole-city scale. Three additional standard spatial metrics were used to quantify spatial patterns in the three % IS zones (Table 2): class area

(CA), mean patch size (MPS), and area-weighted mean shape index (AWMSI) (McGarigal et al.,

2012).

PCISA characterizes the percentage of TIS within each city that is physically connected to nearby IS, and can range from 0 to 100. A higher value means a greater proportion of IS areas are connected. We hypothesized that PCISA values would increase as TIS areas in all classes 22 expanded and merged over time. For this metric, IS areas for all classes combined that are less than 1 m from each other (the spatial resolution of our data) in edge to edge distance are identified as connected (Equation 3):

∑ A PCISA = conn ∗ 100 (3) ATIS where PCISA is percent connected TIS area (%), Aconn is the area (ha) of IS that has at least one other patch within a 1-m radius, and ATIS is TIS (ha) in a specific city and year.

We used the 1-m distance to avoid “false positives” that could occur with wider buffers.

We developed PCISA to compare to the standard landscape connectance index metric

(CONNECT, McGarigal et al., 2012), which has been used to quantify percentage of maximum possible connections among patches and can range from 0 to 100. CONNECT is calculated as

(Equation 4):

n ∑m ∑ i i=1 j=1 cij CONNECT = [ n (n −1)] ∗ 100 (4) ∑m i i i=1 2 where CONNECT is the connectance index (%), cij indicates whether the patch (j) is joined to other patches within the same class (i) (cij is equal to one if there is at least one other patch in the same class within a 1-m radius, it is zero otherwise), ni is number of patches for a particular IS class (i), and m is the total number of IS classes.

Road equivalent width (REW) is a ratio that estimates the area-weighted average road surface width (m) within the study area (sidewalks were not included) and can range from 0 to infinity. REW provides an index of the prevalence of highways and major arterial streets versus smaller neighborhood-scale streets. We hypothesized that REW would increase over time as cities enhanced their transportation systems to accommodate increased traffic and to improve the 23 flow of traffic. REW is calculated by dividing total road area by half of the total road edge

(Equation 5):

A REW = road (5) (Eroad/2)

2 where REW is road equivalent width (m), Aroad is total road area (m ), and Eroad is the total road edge (m) in a specific city and year.

Class area (CA) divides TIS (ha) into different types referred to as IS classes, and ranges from 0 to infinity. A greater value means the IS area for the class (e.g. buildings) is greater.

Mean patch size (MPS) characterizes the mean area (m2) of all the patches in a single IS class, and ranges from 0 to infinity. A greater value means patches in a particular class are larger on average. Because our planimetric data had relatively high spatial resolution and represents relatively small areas, we use m2 rather than ha to characterize MPS (McGarigal et al., 2012).

Area-weighted mean shape index (AWMSI) quantifies the mean shape of all patches in a specific IS class. This metric is area-weighted, which means the shape of a bigger patch will have a correspondingly larger influence on the value. These metric ranges from 1 to infinity, with an AWMSI equal to 1 indicating that all patches in the class are perfect circles. A greater value indicates that patches in an IS class have shapes that are more complex than perfect circles.

All of the spatial calculations were performed on vector-based shapefiles using ArcGIS

10 (ESRI, 2010) automated by Python 2.6.5.

Results

Whole-city scale

We identified four key findings related to IS change at the whole-city scale from 1940 to

2011. First, overall increases in TIS and percent TIS occurred for all four cities, although during the most recent time period (2002 - 2011) the rate of increase was declining. Second, roads were 24 the largest IS class for all cities for the duration of the study, although the contributions of buildings and parking lot areas to TIS increased markedly over time. Third, IS was more connected in 2011 than in 1940. Finally, we detected a gradual increase in road equivalent width over time.

Total impervious surface and impervious surface class area

TIS increased consistently in all cities from 1940 (on average 63.18 ha) to 2011 (729.42 ha) (Fig. 3A). In 2011, Ankeny had the most TIS (1436.18 ha), followed by Johnston (654.98 ha), Altoona (529.97 ha), and Pleasant Hill (296.57 ha). Percent TIS also increased consistently over the 71-year period (Fig. 3B). In 2011, Altoona had the greatest percent TIS (22.08%), followed by Ankeny (18.92%), Johnston (13.89%), and Pleasant Hill (12.11%).

Average annual percent TIS change for the four cities was 0.05% from 1940 to 1961

(Fig. 3C). This increased to 0.51% in the most recent (2002-2011) period. Annual percent TIS change was greatest for Ankeny (0.65%) during the most recent period, compared to Altoona

(0.64%), Johnston (0.41%) and Pleasant Hill (0.34%). Average annual TIS change rate across the four cities increased from 2.42% (1940-1961) to 4.17% (1990 - 2002) (Fig. 3D). For the most recent period, the average annual TIS change rate was 3.62%. The greatest annual TIS change rates for Altoona (4.11%, 1961 - 1990), Ankeny (4.19%, 2002 - 2011), Johnston (5.48%, 1990 -

2002), and Pleasant Hill (4.28%, 1990 - 2002) occurred during different time periods (Fig. 3D).

Overall, roads were the largest IS class for all cities (Fig. 4). Road area on average accounted for 1.24% of the area for the four cities in 1940 (81.18% of the TIS). Over the entire study period (1940 to 2011), areas occupied by buildings increased the most, with an average of

221.43 ha for each of the four cities, followed by parking lots (181.34 ha), roads (174.10 ha), driveways (75.14 ha), and other surfaces (14.24 ha). In 2011, roads on average accounted for 25

5.26% of area in the four cities (31.38% of the TIS), followed by buildings (5.05% of area,

30.18% of TIS) and parking lots (4.30% of area, 25.65% of TIS) (Fig. 4).

Percent connected impervious surface areas and connectance index

Percent connected impervious surface areas (PCISA, the percentage of IS area that is physically connected to nearby IS) increased gradually from 1940 to 2011(Table 3). In 1940,

PCISA ranged from 86.08% (Pleasant Hill) to 91.74% (Johnston). PCISA increased most rapidly during different time periods: 1940 to 1961 for Altoona and Ankeny, 1990 to 2002 for Johnston, and 1961 to 1990 for Pleasant Hill. In 2011, PCISA ranged from 94.82% (Pleasant Hill) to

96.80% (Ankeny). Calculation of the connectance index indicated very low connectance (0.01% to 0.02%) and consistent decreases over the period of study in the proportion of all possible connections among impervious surfaces within each class that were connected (Table 3).

Road equivalent width

Road equivalent width (REW) increased gradually in all four cities between 1940 and

1990 (Table 4). REW was greatest in Altoona and Ankeny in 1990, at 9.96 m and 9.86 m, respectively. For Johnston, the greatest REW (8.36 m) occurred in 2011, and for Pleasant Hill the maximum was 9.21 m in 2002.

Zones-within-city scale

Visual inspection of images indicated that low % IS zones were primarily composed of low-density residential and commercial areas, forests, water bodies, agricultural lands, and grasslands. Medium % IS zones were composed of high-density residential areas and low- density commercial areas. High % IS zones were primarily composed of high-density commercial areas. Each of the three zones had distinct patterns of change for IS in terms of class 26 area, class mean patch size, and area-weighted mean patch size. Generally, in low % IS zones, IS class areas for the four cities increased at similar rates between 1940 and 2011. In medium % IS zones the greatest increases occurred for building CA, and parking lot AWMSI. In high % IS zones, greatest increases occurred for parking lot CA, and building and parking lot MPS.

% IS zones and class area within zones

The largest zone in each of the four cities was the low % IS zone, followed by the medium % IS and high % IS zones (Table 5). In 1940, the largest CA in all three % IS zones and in all four cities was contributed by road surfaces (Fig. 5). In low % IS zones, roads comprised the largest area during the entire study period. In medium % IS zones, buildings emerged as the largest IS class in all cities between 2002 and 2011. In high % IS zones, parking lots emerged as the largest IS class between 1990 and 2002. In 2011, the dominant IS class was consistent for all four cities for low (roads), medium (buildings) and high (parking lots) % IS zones.

Class mean patch size

Two general patterns emerged over time for class mean patch size (MPS) in all % IS zones: 1) driveways were the only IS class for which MPS decreased; and 2) parking lots had considerably larger MPS than all other classes (Fig. 6). More specifically among these general patterns, driveway MPS decreased markedly (by 75.82% in medium % IS zones to 79.52% in low % IS zones) for all four cities from 1940 to 2011. Increases in building MPS (165.80%) and parking lot MPS (988.79%) were greatest in high % IS zones over the period of study compared to those in the other two % IS zones.

Area-weighted mean shape index

Area-weighted mean shape index had three consistent patterns across all % IS zones: 1) driveway shapes became simpler over time; 2) shapes of the remaining classes became more complex, and 3) “other” surfaces in Ankeny increased markedly between 1990 and 2002 in all three % IS zones (Fig. 7). In addition to these general patterns, in medium % IS zones, the four- city average increase for parking lot AWMSI was 48.88% for the entire study period, compared to a 3.09% decrease for low % IS zones, and a 25.80% increase for high % IS zones.

Discussion

We used high-resolution spatial planimetric data to conduct an in-depth analysis of changes in TIS and among IS classes for four cities. We used a zoning strategy in our application of metrics to simplify the degree of spatial heterogeneity in IS and to develop a detailed assessment of spatial and temporal change for these cities between 1940 and 2011. We evaluated applicability of four existing landscape metrics and developed two new spatial metrics that capture meaningful trends in IS change over this time period. We suggest that such analyses of impervious surface characteristics over time could be used to inform land use policy and planning decisions and to identify appropriate stormwater management interventions.

Use of planimetric data to assess spatio-temporal changes in TIS and IS class areas and to inform land use policy and planning We found consistent increases in TIS for all four cities over the study period (Fig. 3), similar to reports of other researchers (e.g. Lu, Moran, & Hetrick, 2011; Yang, Xian, Klaver, &

Deal, 2003). We also detected variation in the rate of TIS change, which was greatest between

1990 and 2002, and declined in the most recent study period (2002-2011). This decrease in TIS 28 change rate was likely related to declines in construction activity associated with the recent economic downturn.

Within TIS, we identified roads as the dominant IS class in each of the four cities for the entire study period. As of 2011, however, buildings (30.2%) and parking lot areas (25.7%) were nearly as prevalent as roads (31.4%). These results were very similar to findings of Tilley and

Slonecker (2007), who reported that buildings were the dominant IS class (29.1%), followed by roads (28.3%) and parking lots (24.8%) in their study of six urban/suburban watersheds distributed across the continental U.S. Our results confirm, at a whole-city scale and for multiple municipalities, the emergence of buildings and parking lots as substantial components of IS.

Visual inspections of images revealed that “big box” retail developments (buildings with large footprints associated with large parking lots) were driving some of these changes, especially in

Altoona and Ankeny, cities with relatively large % IS and greater rates of change in recent years

(Figs. 3 and 4). For communities in which concern has been expressed about the impacts of rapidly expanding IS, land use policy/planning instruments that include careful scrutiny of proposals for such development and use of incentives to limit the area of new IS and promote use of LID approaches in new commercial developments would be helpful. Additionally, communities could identify opportunities and incentives to support retrofit projects in areas with high % IS by replacing them with more pervious alternatives (e.g. Dietz, 2007). For new residential developments, communities could also develop policies to encourage use of LID/CSD approaches that would limit additional IS (Bowman, Thompson, & Tyndall, 2012).

Our delineation of different percent IS zones (% IS) revealed predominance of somewhat different land uses in these areas and a multi-nucleated morphology for high percent IS zones in each of the four cities (similar to that described by Seto & Fragkias, 2005). In our case, multiple 29 city nuclei may exist because these areas have different urban functions. In Altoona, for example, one nucleus is a cluster of civic structures (e.g. city hall and library), whereas another is comprised of the amusement park, casino and horse-racing track, and a third is a commercial center. Our analyses of CA for all four cities indicated consistent dominance of roads in low %

IS zones over time, and the emergence of a relatively high proportion of buildings in medium %

IS areas and parking lots in high % IS zones over time. Our visual inspection of the images for these zones indicated that medium % IS zones typically contained primarily residential and scattered commercial areas, and high % IS zones were primarily occupied by commercial centers. Thus, changes in IS classes are linked to distinct land uses within these zones, information which could be used to identify target areas for application of incentives and policy changes.

Applicability of existing landscape metrics and new spatial metrics for quantifying IS change The availability of detailed planimetric data allowed us to directly adapt the landscape metric CA as a spatial metric to describe different classes of IS data and track changes in those classes over time, information which could be very useful in policy and planning decisions as previously noted. We also developed two new metrics, PCISA and REW. Using PCISA, we determined that IS in all four cities was highly connected and gradually became even more connected during the study period (which would be expected as new IS patches are linked to previously existing ones, e.g. Olivera & DeFee, 2007). This finding is contrary to our result from application of the standard landscape connectance index metric (which indicated decreases in connectivity over time, Table 3). Since CONNECT was created to identify connections among patches of the same class, when applied in a strict sense to planimetric data (as we did), it does not include connections between different IS classes. In terms of the processes that occur in 30 urban areas, connections between different patch types (e.g. between roads and parking lots) are very important, for example, for generation and delivery of stormwater runoff. Thus, the

CONNECT metric would underestimate potential stormwater contributions if used in conjunction with hydrological analyses. In addition, the denominator of Equation 4 measures the sum of all possible connections within each class. For analyses of urban landscapes, however, it is unlikely that one parking lot would be connected with all other parking lots. As an alternative, the PCISA metric measured connections among different kinds of patches and without sensitivity to the total number of patches within a class. Efforts to mitigate the effects of IS could thus be focused on incentives and policies to decrease connectivity of IS via retrofit projects in existing developments, or as part of site plans for newly developing areas.

We developed another new metric, REW, to examine changes in road width over time.

This metric is conceptually similar to the landscape metric edge density (ED, which we did not calculate) of McGarigal et al. (2012), but is calculated as half of the reciprocal of ED and only applies to roads. Unlike ED, REW has direct physical meaning related to the width of roads. The largest increases in REW corresponded with construction of major highways within city boundaries in three of the four communities, including I-80 and US-6 between 1940 and 1961 for

Altoona, I-35 between 1961 and 1990 in Ankeny, and IA-163 between 1961 and 1990 and US-

65 between 1990 and 2002 for Pleasant Hill. Johnston was characterized by lower REW than the other cities, partly because it is the only city without a major highway within its boundaries, although a gradual trend of increasing REW in Johnston overall reflects widening of arterial roads at a relatively consistent rate over time. In some cases (e.g. Altoona and Ankeny between

1990 and 2002, Pleasant Hill between 2002 and 2011) REW declined slightly, due to a shift toward construction of narrow neighborhood roads which have relatively smaller area and 31 greater edge. The significance of roads as an IS class in this and previous assessments (e.g.

Tilley & Slonecker, 2007) is thus related to the number and width of road surfaces, which both merit consideration in planning and policy development for mitigation of potential impacts.

Direct application of the landscape metrics MPS and AWMSI was useful and revealed that patches of most IS classes (other than driveways) became larger and more complex over time (Figs. 6 and 7). This indicates that policies and practices used to mitigate negative impacts of these surfaces may need to be scaled up to be effective. In addition, the different % IS zones had distinct patterns of change for these two metrics. We observed large increases for parking lot

MPS for all four cities in high % IS zones, although changes in AWMSI for these surfaces were more gradual. We also found decreases for driveway MPS and AWMSI over the entire study period, which could be related to more compact neighborhood designs with smaller house setbacks (Gordon & Richardson, 1997, p. 99). A large increase in AWMSI across all three % IS zones for the “other” surface category in Ankeny between 1990 and 2002 was a result of establishment of the Ankeny Regional Airport in 1993 which straddled all three % IS zones.

Using detailed information on impervious surface class changes to identify practices that could be used to mitigate potential hydrological consequences Our overall findings of increased TIS and increased connectivity among IS classes over time have implications for stormwater management. The impacts of IS on hydrological responses have been studied for both TIS and some IS classes, including roads and buildings. In early work, researchers identified a threshold of 10 % TIS (Arnold & Gibbons, 1996; Schueler and

Holland, 2000) above which distinct hydrological impacts on urban streams can be observed.

More recent work in small stream urban stream systems indicates that hydrological impacts may occur at even lower % TIS (e.g. Nagy, Lockaby, Kalin, & Anderson, 2012; Yang, Bolwing,

Cherkauer, Pijanowski, & Niyogi, 2010). Thus, by 2011 the % TIS in all four communities we 32 studied was sufficient to cause disruptions to natural stream systems embedded in their landscapes. In situations for which impacts to natural streams are of concern to municipalities, use of policy and planning tools to carefully manage any additional impervious surface area is imperative.

Further, for a given precipitation event, two watersheds that have similar % TIS may have different hydrological responses depending on the arrangement and type of IS. For example, a watershed or city zone with a greater percentage of road IS may generate more stormwater discharge, since roads have a relatively high runoff to rainfall ratio (Ragab, Bromley,

Rosier, Cooper, & Gash, 2003; Ragab, Rosier, Dixon , Bromley, & Cooper, 2003). Given the preponderance of roads in low % IS zones, managing stormwater discharge by focusing on mitigation of road runoff in these areas could alleviate impacts to local waterways. Parking lots also generate relatively high quantities of stormwater discharge (e.g. Arnold & Gibbons, 1996).

We observed increases in parking lot MPS over time (especially in high % IS zones), suggesting that nearby streams could be impacted by increased runoff. Closer examination of, parking lots in the aerial photos, however, indicated that, “islands” containing turf and trees were contributing to increased AWMSI and may in fact capture some stormwater, depending on curb configurations.

Finally, information on IS composition and characterization of hydrological parameters for each IS class could improve the ability of hydrological models to estimate and predict future stormwater discharge. Metrics such as CA and PCISA could also be useful to quantify impervious surfaces in such models. For example, the U.S. E.P.A.’s Storm Water Management

Model (SWMM) uses effective impervious surface (EIS, or hydrologically connected IS, Walsh,

Fletcher et al., 2005) as a parameter to model urban hydrological processes (Gironás, Roesner, & 33

Davis, 2009). In cases where storm sewer data are unavailable, it may be possible to use IS class and PCISA data as a proxy for EIS. Our overall findings of increased TIS and increased connectivity among IS classes over time suggest that degradation of habitat and water quality in aquatic systems in these four cities is likely exacerbated by these trends (e.g. Booth & Jackson,

1997; Lussier, Enser, Dasilva, & Charpentier, 2006).

Our analysis of the spatial distribution of different classes of IS could also be helpful to identify specific mitigation approaches to limit impacts on streams in urban areas. For example, we identified parking lots as the most prominent IS in high % IS zones, and a trend for dramatically increased size of parking lot areas in recent years. In these zones, mitigation activities should focus on encouraging application of practices such as installation of pervious pavement materials, sand filters, and bioswales that can be effective at the scale of these constructed surfaces (e.g. Collins, Hunt, & Hathaway, 2008). For medium % IS zones which are dominated by buildings, use of distributed site-level low-impact design strategies and technologies such as green roofs would apply (e.g. Dietz, 2007; Mentens, Raes, & Hermy, 2006).

Finally, in low %IS zones, methods for mitigating road impacts, as previously mentioned, would likely provide the greatest benefit (e.g. Tilley and Slonecker, 2007).

Conclusion

Using multi-year high-resolution spatial planimetric data and a % IS-based zoning strategy, we were able to accurately quantify IS changes in four cities in central Iowa over approximately 70 years. We found four general patterns of change for IS in these settings. First,

TIS increased consistently (although the rate of change varied) over the study period (1940-

2011), and impervious surfaces became more connected over time. Second, road width gradually increased over time. Third, buildings and parking lots were the greatest contributors to growth of 34

TIS and their areas increased more in medium % IS zones (buildings) and high % IS zones

(parking lots). Finally, each % IS zone had unique patterns of change for class mean patch size and area-weighted mean shape index. These results could be used to enhance local land use policy and planning by focusing efforts on specific areas and targeting specific practices to specific IS classes to reduce impacts by decreasing connectivity of existing IS or minimizing creation of additional IS in newly developing areas.

A limited number of studies have applied spatial metrics to detailed planimetric data. A few such studies that have been reported include efforts to monitor urban expansion

(Charbonneau, Morin, & Royer, 1993), to create reference data for assessing the accuracy of other techniques to develop estimates of IS (Chabaeva et al., 2009), and to explain environmental degradation in a more general sense (Arnold & Gibbons, 1996). Of the six spatial metrics we evaluated, five metrics (CA, PCISA, REW, MPS, and AWMSI) enabled us to detect relatively nuanced changes in IS, which could ultimately help direct on-the-ground actions to mitigate their effects on natural systems. We also determined that one landscape metric (connectance index) was not applicable using this type of data. Because these metrics have not been widely used in analyses of planimetric data, more studies to evaluate performance of additional spatial metrics, confirm that such metrics have meaning when applied to urban planimetric data, and further explore development of new metrics to quantify patterns and processes would be useful.

The spatiotemporal patterns of change in IS revealed in this study corroborate other evidence of serious and widespread challenges for maintaining urban environmental quality, in cities both large and small. The methods we used for quantifying different classes of IS could be used to enhance the performance of computational or statistical hydrological models, and to more effectively target mitigation strategies by municipal personnel. 35

Acknowledgements

We would like to thank Anna Whipple, City of Des Moines, for access to land cover data, and city staff members in Altoona, Ankeny, Johnston, and Pleasant Hill for their help in obtaining storm sewer data. This research was supported by the Chinese Scholarship Council, the

Center for Global and Regional Environmental Research at the University of Iowa, the State of

Iowa, and McIntire–Stennis funding.

References

Aguilera, F., Valenzuela, L. M., & Botequilha-Leitao, A. (2011). Landscape metrics in the analysis of urban land use patterns: A case study in a Spanish metropolitan area. Landscape and Urban Planning, 99(3-4), 226-238. doi - 10.1016/j.landurbplan.2010.10.004 Alberti, M., Booth, D., Hill, K., Coburn, B., Avolio, C., Coe, S., & Spirandelli, D. (2007). The impact of urban patterns on aquatic ecosystems: An empirical analysis in Puget lowland sub- basins. Landscape and Urban Planning, 80(4), 345-361. doi - 10.1016/j.landurbplan.2006.08.001 Arendt, R. 2004. Linked landscapes: Creating greenway corridors through conservation subdivision design strategies in the Northeastern and Central United States. Landscape and Urban Planning 68, 2-3, 241-269. Arnold, C. L., & Gibbons, C. J. (1996). Impervious surface coverage - The emergence of a key environmental indicator. Journal of the American Planning Association, 62(2), 243-258. doi - 10.1080/01944369608975688 Beyer, H. L. (2004). Hawth's Analysis Tools for ArcGIS (Version 3.27). Available from http://www.spatialecology.com/htools. Accessed 27 January 2013 Booth, D. B., & Jackson, C. R. (1997). Urbanization of aquatic systems: Degradation thresholds, stormwater detection, and the limits of mitigation. Journal of the American Water Resources Association, 33(5), 1077-1090. doi - 10.1111/j.1752-1688.1997.tb04126.x Bowman, T. A., Thompson, J. R., & Tyndall, J. C. (2012). Resident, developer, and city staff perceptions of LID and CSD subdivision design approaches. Landscape and Urban Planning, 107(1), 43-54. doi - 10.1016/j.landurbplan.2012.04.011 Bowman, T. A., Thompson, J. R., Tyndall, J. C., & Anderson, P. F. (2012). Land cover analysis for urban foresters and municipal planners: Examples from Iowa. Journal of Forestry, 110(1), 25-33. doi: 10.5849/jof.10-045 Brown, D. G., Johnson, K. M., Loveland, T. R., & Theobald, D. M. (2005). Rural land-use trends in the conterminous United States, 1950-2000. Ecological Applications, 15(6), 1851-1863. doi - 10.1890/03-5220

Chabaeva, A., Civco, D. L., & Hurd, J. D. (2009). Assessment of impervious surface estimation techniques. Journal of Hydrologic Engineering, 14(4), 377-387. doi - 10.1061/(asce)1084- 0699(2009)14:4(377) Charbonneau , L., Morin, D., & Royer, A. (1993). Analysis of different methods for monitoring the urbanization process. Geocarto International, 8(1), 17-25. doi - 10.1080/10106049309354395 Civco, D. L., Chabaeva, A., & Hurd, J. D. (2006). A comparison of approaches to impervious surface characterization. International Geoscience and Remote Sensing Symposium, Vols 1-8 (pp. 1398-1402). doi - 10.1109/IGARSS.2006.361 Civco, D. L., Hurd, J. D., Wilson, E. H., Arnold, C. L., & Prisloe, M. P. (2002). Quantifying and describing urbanizing landscapes in the Northeast United States. Photogrammetric Engineering and Remote Sensing, 68(10), 1083-1090. Collins, K. A., Hunt, W. F., & Hathaway, J. M. (2008). Hydrologic comparison of four types of permeable pavement and standard asphalt in eastern North Carolina. Journal of Hydrologic Engineering, 13(12), 1146-1157. doi - 10.1061/(asce)1084-0699(2008)13:12(1146) de Noronha Vaz, E., Caetano M., & Nijkamp P. (2011). A multi-level spatial urban pressure analysis of the Giza pyramid plateau in Egypt. Journal of Heritage Tourism, 6(2), 99-108. doi - 10.1080/1743873X.2011.553285 Deng, X. Z., Huang, J. K., Rozelle, S., & Uchida, E. (2008). Growth, population and industrialization, and urban land expansion of China. Journal of Urban Economics, 63(1), 96-115. doi - 10.1016/j.jue.2006.12.006 Dietz, M. (2007). Low impact development practices: A review of current research and recommendations for future directions. Water Air and Soil Pollution, 186 (1-4), 351-363. doi - 10.1007/s11270-007-9484-z Elvidge, C. D., Tuttle, B. T., Sutton, P. C., Baugh, K. E., Howard, A. T., Milesi, C., Bhaduri B. L., & Nemani, R. (2007). Global distribution and density of constructed impervious surfaces. Sensors, 7(9), 1962-1979. doi -10.3390/s7091962 Environmental Systems Research Institute (ESRI). (2010). ArcGISTM (Version 10.0). Redlands, California, Available from http://www.esri.com/. Accessed 27 January 2013 Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., & Wickham, J. (2011). Completion of the 2006 National Land Cover Database for the conterminous United States. Photogrammetric Engineering & Remote Sensing, 77(9), 858-864. Gironás, J., Roesner, L. A., & Davis, J. (2009). Storm water management model applications manual (EPA/600/R-09/000). Cincinnati, OH - U.S. Environmental Protection Agency, National Risk Management Research Laboratory Office Of Research And Development. Retrieved from http://www.epa.gov/nrmrl/wswrd/wq/models/swmm/epaswmm5_apps_manual.zip. Accessed 27 January 2013 Gordon, P., & Richardson, H. W. (1997). Are compact cities a desirable planning goal? Journal of the American Planning Association, 63(1), 95-106. doi - 10.1080/01944369708975727 Griffith, J. A., Martinko, E. A., & Price, K. P. (2000). Landscape structure analysis of Kansas at

three scales. Landscape and Urban Planning, 52(1), 45-61. doi - 10.1016/S0169- 2046(00)00112-2 Guest, A. M. (1973). Urban growth and population-densities. Demography, 10(1), 53-69. doi - 10.2307/2060750 Hammer, R. B., Stewart, S. I., Winkler, R. L., Radeloff, V. C., & Voss, P. R. (2004). Characterizing dynamic spatial and temporal residential density patterns from 1940-1990 across the North Central United States. Landscape and Urban Planning, 69(2-3), 183-199. doi - 10.1016/j.landurbplan.2003.08.011 Hodgson, M. E., Jensen, J. R., Tullis, J. A., Riordan, K. D., & Archer, C. M. (2003). Synergistic use of LIDAR and color aerial photography for mapping urban parcel imperviousness. Photogrammetric Engineering and Remote Sensing, 69(9), 973-980. Irwin, E. G., & Bockstael, N. E. (2007). The evolution of urban sprawl: Evidence of spatial heterogeneity and increasing land fragmentation. Proceedings of the National Academy of Sciences of the United States of America, 104(52), 20672-20677. doi - 10.1073/pnas.0705527105 Kalnay, E., & Cai, M. (2003). Impact of urbanization and land-use change on climate. Nature, 423(6939), 528-531. doi - 10.1038/nature01675 Lu, D. S., Moran, E., & Hetrick, S. (2011). Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier. Journal of Photogrammetry and Remote Sensing, 66(3), 298-306. doi - 10.1016/j.isprsjprs.2010.10.010 Luck, M., & Wu, J. G. (2002). A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landscape Ecology, 17(4), 327-339. doi - 10.1023/a:1020512723753 Lussier, S. M., Enser, R. W., Dasilva, S. N., & Charpentier, M. (2006). Effects of habitat disturbance from residential development on breeding bird communities in riparian corridors. Environmental Management, 38(3), 504-521. doi - 10.1007/s00267-005-0088-3 McGarigal, K., Cushman, S., & Ene, E. (2012). FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Amherst, Massachusetts. Available from http://www.umass.edu/landeco/research/fragstats/fragstats.html. Accessed 27 January 2013 Meierdiercks, K. L., Smith, J. A., Baeck, M. L., & Miller, A. J. (2010). Analyses of urban drainage network structure and its impact on hydrologic response. Journal of the American Water Resources Association, 46(5), 932-943. doi - 10.1111/j.1752-1688.2010.00465.x Mentens, J., Raes, D., & Hermy, M. (2006). Green roofs as a tool for solving the rainwater runoff problem in the urbanized 21st century? Landscape and Urban Planning, 77(3), 217-226. doi - 10.1016/j.landurbplan.2005.02.010 Nagy, R. C., Lockaby, B. G., Kalin, L., & Anderson, C. (2012). Effects of urbanization on stream hydrology and water quality: the Florida Gulf Coast. Hydrological Processes, 26(13), 2019- 2030. doi - 10.1002/hyp.8336 Natural Resources Geographic Information Systems Library (NRGIS). (2012). Available from http://www.igsb.uiowa.edu/nrgislibx/. Accessed 27 January 2013

Olivera, F., & DeFee, B. B. (2007). Urbanization and its effect on runoff in the Whiteoak Bayou watershed, Texas. Journal of the American Water Resources Association, 43(1), 170-182. doi - 10.1111/j.1752-1688.2007.00014.x O'Neill, R. V., Krummel, J. R., Gardner, R. H., Sugihara, G., Jackson, B., DeAngelis, D. L., Milne, B. T., Turner, M. G., Zygmunt, B., Christensen, S. W., Dale, V. H., & Graham, R. L. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153-162. doi - 10.1007/bf00162741 Pejchar, L., Morgan, P. M., Caldwell, M. R., Palmer, C. and Daily, G. C. 2007. Evaluating the potential for conservation development: Biophysical, economic, and institutional perspectives. Conservation Biology 21, 1, 69-78. Phinn, S., Stanford, M., Scarth, P., Murray, A. T., & Shyy, P. T. (2002). Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques. International Journal of Remote Sensing, 23(20), 4131-4153. doi - 10.1080/01431160110114998 Ragab, R., Bromley, J., Rosier, P., Cooper, J. D., & Gash, J. H. C. (2003). Experimental study of water fluxes in a residential area: 1. Rainfall, roof runoff and evaporation: the effect of slope and aspect. Hydrological Processes, 17(12), 2409-2422. doi - 10.1002/hyp.1250 Ragab, R., Rosier, P., Dixon, A., Bromley, J., & Cooper, J. D. (2003). Experimental study of water fluxes in a residential area: 2. Road infiltration, runoff and evaporation. Hydrological Processes, 17(12), 2423-2437. doi - 10.1002/hyp.1251 Rohe, W. (2009). From local to global: One hundred years of neighborhood planning. Journal of the American Planning Association, 75(2), 209-230. Doi - 10.1080/01944360902751077 State Data Center of Iowa (SDCI). (2010). Available from http://www.iowadatacenter.org/. Accessed 27 January 2013 Schueler, T. R. (1994). The importance of imperviousness. Watershed Protection Techniques, 1(3), 100 - 111. Schueler, T., & Holland, H. (2000). The Practice of Watershed Protection: Techniques for Protecting our Nation’s Streams, Lakes, Rivers, and Estuaries. Ellicott City, Maryland - Center for Watershed Protection. Seto, K. C., & Fragkias, M. (2005). Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landscape Ecology, 20(7), 871-888. doi - 10.1007/s10980-005-5238-8 Slonecker, E. T., Jennings, D. B., & Garofalo, D. (2001). Remote sensing of impervious surfaces: A review. Remote Sensing Reviews, 20(3), 227-255. doi - 10.1080/02757250109532436 Solon, J. (2009). Spatial context of urbanization: Landscape pattern and changes between 1950 and 1990 in the Warsaw metropolitan area, Poland. Landscape and Urban Planning, 93(3-4), 250-261. doi - 10.1016/j.landurbplan.2009.07.012 Steuer, J. J., Bales, J. D., & Giddings, E. M. P. (2009). Relationship of stream ecological conditions to simulated hydraulic metrics across a gradient of basin urbanization. Journal of the North American Benthological Society, 28(4), 955-976. doi - 10.1899/08-157.1 Tilley, J. S., & Slonecker, E. T. (2007). Quantifying the components of impervious surfaces

(Open File Report 2007-1008). Reston, Virginia - U.S. Geological Survey, Eastern Geographic Science Center. Retrieved from http://pubs.usgs.gov/of/2007/1008/ofr2007- 1008.pdf. Accessed 27 January 2013 United Nations. (2011). World urbanization prospects, the 2011 revision (United Nations publications ESA/P/WP/224). New York City, New York. Available from http://esa.un.org/unpd/wup/index.htm. Accessed 27 January 2013 U.S. Department of Commerce Census Bureau (USDC Census Bureau). (2010). 2006 - 2010 American community survey 5-year estimates. Available from http://www.census.gov/acs/www/. Accessed 27 January 2013 Walsh, C. J., Fletcher, T. D., & Ladson, A. R. (2005). Stream restoration in urban catchments through redesigning stormwater systems: looking to the catchment to save the stream. [Article; Proceedings Paper]. Journal of the North American Benthological Society, 24(3), 690-705. doi - 10.1899/0887-3593(2005)024\[0690:sriuct\]2.0.co;2 Walsh, C. J., Roy, A. H., Feminella, J. W., Cottingham, P. D., Groffman, P. M., & Morgan, R. P. (2005). The urban stream syndrome: current knowledge and the search for a cure. Journal of the North American Benthological Society, 24(3), 706-723. doi - 10.1899/0887- 3593(2005)024\[0706:TUSSCK\]2.0.CO;2 Weng, Q. H. (2012). Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment, 117, 34-49. doi - 10.1016/j.rse.2011.02.030 Wenger, S. J., Roy, A. H., Jackson, C. R., Bernhardt, E. S., Carter, T. L., Filoso, S., Gibson, C. A., Hession, W. C., Kaushal, S. S., Marti, E., Meyer, J. L., Palmer, M. A., Paul, M. J., Purcell, A. H., Ramirez, A., Rosemond, A. D., Schofield, K. A., Sudduth, E. B., & Walsh, C. J. (2009). Twenty-six key research questions in urban stream ecology: an assessment of the state of the science. Journal of the North American Benthological Society, 28(4), 1080-1098. doi - 10.1899/08-186.1 Wilson, E. H., Hurd, J. D., Civco, D. L., Prisloe, M. P., & Arnold, C. (2003). Development of a geospatial model to quantify, describe and map urban growth. Remote Sensing of Environment, 86(3), 275-285. doi - 10.1016/s0034-4257(03)00074-9 Yang, G. X., Bowling, L. C., Cherkauer, K. A., Pijanowski, B. C., & Niyogi, D. (2010). Hydroclimatic response of watersheds to urban intensity: An observational and modeling- based analysis for the White River basin, Indiana. Journal of Hydrometeorology, 11(1), 122- 138. doi - 10.1175/2009jhm1143.1 Yang, L. M., Xian, G., Klaver, J. M., & Deal, B. (2003). Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering and Remote Sensing, 69(9), 1003-1010. Zhou, W. Q., Huang, G. L., Pickett, S. T. A., & Cadenasso, M. L. (2011). 90 years of forest cover change in an urbanizing watershed: spatial and temporal dynamics. Landscape Ecology, 26(5), 645-659. doi - 10.1007/s10980-011-9589-z

Tables

Table 1. Geographic (area) and demographic (total population) characteristics of Altoona, Ankeny, Johnston, and Pleasant Hill in central Iowa. Population data are from U.S. Census Bureau, 2012. Altoona Ankeny Johnston Pleasant Hill Area Total Area (ha) 2399.02 7581.89 4705.80 2447.57 Total Population 1940 640 779 no data no data 1960 1,458 2,964 no data 397 1990 7,242 18,482 4,702 3,671 2000 10,345 27,117 8,649 5,070 2010 14,541 45,582 17,278 8,785

Table 2. Six spatial metrics used in this study to quantify spatial characteristics of impervious surface area, connectivity, size and shape. Metrics Scale Units Range Percent of connected impervious surface areas (PCISA) City Percent 0 - 100 Connectance Index (CONNECT) City Percent 0 - 100 Road equivalent width (REW) City Meters 0 - ∞ Class area (CA) Zone Hectares 0 - ∞ Mean patch size (MPS) Zone Square Meters 0 - ∞ Area-weighted mean shape index (AWMSI) Zone None 1 - ∞

Table 3. Percent of connected impervious surface area (PCISA, %) and connectance index (CONNECT, %) in 1940, 1961, 1990, 2002, and 2011 for four study cities in central Iowa. Statistics City 1940 1961 1990 2002 2011 Altoona 89.56 92.67 95.47 95.87 96.39 Ankeny 87.90 94.25 95.69 96.06 96.80 PCISA Johnston 91.74 90.09 92.51 95.07 95.07 Pleasant Hill 86.08 86.05 91.87 94.80 94.82 Altoona 0.13 0.10 0.02 0.02 0.01 Ankeny 0.06 0.04 0.01 0.01 0.01 CONNECT Johnston 0.10 0.09 0.03 0.02 0.01 Pleasant Hill 0.21 0.16 0.04 0.03 0.02

Table 4. Road equivalent width (REW, m) in 1940, 1961, 1990, 2002, and 2011 for four study cities in central Iowa. City 1940 1961 1990 2002 2011 Altoona 8.06 9.69 9.96 9.79 9.95 Ankeny 8.44 9.13 9.86 9.44 9.69 Johnston 7.71 7.99 7.84 8.00 8.36 Pleasant Hill 7.22 7.20 8.40 9.21 9.05

Table 5. Land area within three percent impervious surface zones for four study cities in central Iowa. Altoona Ankeny Johnston Pleasant Hill Zone Areas (ha) High (>40) 287.99 781.84 206.68 78.90 Medium (20% - 40%) 991.74 2395.57 1209.27 396.42 Low (<20%) 1119.29 4404.48 3289.85 1972.25 Percentage (%) High (>40) 12.00 10.31 4.39 3.22 Medium (20% - 40%) 41.34 31.60 25.70 16.20 Low (<20%) 46.66 58.09 69.91 80.58

Figures

Fig. 1. Locations, boundaries and percent impervious surface zones for Altoona, Ankeny, Johnston, and Pleasant Hill in central Iowa.

Fig. 2. An example of digitizing impervious surface change using planimetric data for Altoona in 1940 (left) and 2011 (right).

Fig. 3. Total impervious surface area and changes from 1940 to 2011 for four study cities in central Iowa: A. total impervious surface (TIS) area (ha); B. total percent impervious surface area (%); C. annual percent TIS change (%); and D. annual TIS change rate (%).

Fig. 4. Percent impervious surface class area based on planimetric data from 1940, 1961, 1990, 2002 and 2011 for four study cities in central Iowa.

Fig. 5. Class area (ha) for low (left column), medium (middle column) and high % IS zones (right column) for four study cities in central Iowa (top four rows) and on average for all cities (bottom row) from 1940 to 2011.

Fig. 6. Class mean patch size (m2) for low (left column), medium (middle column) and high % IS zones (right column) for four study cities in central Iowa (top four rows) and on average for all cities (bottom row) from 1940 to 2011.

Fig. 7. Area-weighted mean shape index for low (left column), medium (middle column) and high % IS zones (right column) for four study cities in central Iowa (top four rows) and on average for all cities (bottom row) from 1940 to 2011.

CHAPTER 3: USING THE STORM WATER MANAGEMENT MODEL TO PREDICT URBAN HEADWATER STREAM HYDROLOGICAL RESPONSE TO CLIMATE AND LAND COVER CHANGE

Modified from a paper accepted by Hydrology and Earth System Sciences-Discussion

Jiayu Wu, Janette R. Thompson, Randy K. Kolka, Kristie J. Franz and Timothy W. Stewart

Abstract

Streams are natural features in urban landscapes that can provide ecosystem services for urban residents. However, urban streams are increasingly stressed by multiple anthropogenic impacts, including increases in human population and associated impervious surface area, and accelerated climate change. The ability to anticipate these changes and better understand their effects on streams is important for developing and implementing strategies to mitigate potentially negative effects. In this study, streamflow was monitored during April-November 2011 and

2012, and the data were used to apply the Storm Water Management Model (SWMM) to five urban watersheds in central Iowa, U.S.A. collectively representing a gradient of percent impervious surface (IS, ranging from 5.3% to 37.1%). A set of three scenarios was designed to quantify hydrological responses to independent and combined effects of climate change (18% increase in precipitation), and land cover change (absolute increases in IS between 5.2% and

17.1%, based on separate projections for the five watersheds) for the year 2040 compared to a current condition simulation. An additional set of three scenarios examined stream response to different distributions of land cover change within a single watershed. Hydrological responses were quantified using three indices: unit-area peak discharge, flashiness (R-B Index), and runoff ratio. Stream hydrology was strongly affected by watershed percent IS. For the current condition simulation, values for all three indices were five to seven times greater in the most developed watershed compared to the least developed watershed. The climate change scenario led to a

53 predicted 20.8% increase in unit-area peak discharge on average across the five watersheds compared to the current condition simulation. The land cover change scenario resulted in large increases for all three indices: 49.5% for unit-area peak discharge, 39.3% for R-B Index, and

73.9% for runoff ratio, on average, for the five watersheds. The combined climate and land cover change scenario resulted in even greater increases for all three indices: 80.1% for unit-area peak discharge, 43.7% for R-B Index, and 74.5% for runoff ratio, on average, for the five watersheds.

The scenarios for different distributions of land cover change within one watershed resulted in changes for all three indices, with an 18.4% increase in unit-area peak discharge for the midstream scenario, and 17.5% (downstream) and 18.1% (midstream) increases in R-B Index, indicating sensitivity to the location of potential additions of IS within a watershed. Given the likelihood of increased precipitation in the future, land use planning and policy tools that limit expansion of impervious surfaces (e.g. by substituting pervious surfaces) or mitigate against their impacts (e.g. by installing bioswales) could be used to minimize negative effects on streams.

Keywords: Central Iowa, hydrological modeling, impervious surface, unit-area peak discharge, R-B index, runoff ratio

Introduction

The hydrology of urban streams is responsive to human activities in the surrounding landscape (Arnold and Gibbons, 1996; Walsh et al., 2005; Wenger et al., 2009). Compared to streams in more natural settings, urban streams are located in landscapes associated with less infiltration and more surface runoff, often leading to greater peak discharge and shorter peak discharge lag times (Anderson, 1970; Arnold and Gibbons, 1996). Declines in stream water quality and overall ecological condition result from increased concentrations of nutrients and other pollutants (e.g. Hatt et al., 2004; Pekarova and Pekar, 1996), and shifts in organismal

54 assemblages to species tolerant of eutrophic conditions (e.g. Black et al., 2011; Walsh et al.,

2001) have commonly been reported for urban streams. Accelerated climate change (Denault et al., 2006), land use and land cover change (Grimm et al., 2008), and combinations of such changes (Nelson et al., 2009; Tong et al., 2012) are thought to be among the major driving factors leading to rapid degradation of urban stream systems.

Computer-based hydrological models have been used to better understand urban stream responses to potential stressors, such as projected changes in climatic conditions and land cover.

Frequently used models include the Storm Water Management Model (or SWMM; Rossman,

2010; US EPA, 2011), the Hydrological Simulation Program-Fortran model (HSPF; Bicknell et al., 1997), and the Soil and Water Assessment Tool (SWAT; Neitsch et al., 2002), which can be used to predict hydrological responses to user-designed scenarios at relatively low cost. Of these models, SWMM has been applied in studies of urban streams because of its ability to simulate the hydraulic dynamics of artificial drainage systems that are prevalent in urban areas (e.g.

Denault et al. 2006; Hsu et al., 2000; Meierdiercks et al., 2010). SWMM was developed to enable appropriate design of drainage systems (e.g. sizing for detention features, evaluating effectiveness of different runoff control strategies) and can be used to simulate dynamics of single events or for modeling on a continuous basis (US EPA 2011). The model incorporates precipitation data to simulate surface runoff and pollutant outputs for sub-catchment areas which are then conveyed to the watershed outlet by a user-designated drainage system (US EPA 2011).

Studies using hydrological models to predict stream response to changes in precipitation amounts and delivery patterns have used a variety of techniques to generate future scenarios. For example, in assessments of streamflow responses to climate change, Global Climatic Models

(GCMs) and Regional Climate Models (RCMs) have been used (e.g. Jha et al. 2004; Nelson et

55 al., 2009; Takle et al. 2010; Quilbe et al., 2008). However, the grid scales commonly used in

GCMs (hundreds of km; Boyle, 1998) and RCMs (~50 km; Takle et al. 2010) and their time intervals (≥ hourly; Kendon et al., 2012) are not suitable for predictions at finer spatio-temporal scales. Other approaches that have been used at more local scales and for shorter time intervals include linear regression based on historical precipitation records (Denault et al., 2006; Takle and Herzmann, 2010) or projections based on likely proportional changes, for example, ± 20% of current precipitation (Tong et al., 2012).

Similarly, a variety of approaches have been used to create projections for land cover change, including those based on Markov-chain probability models that generate both quantity and location of additional impervious surfaces (such as the software package Land Change

Modeler, Eastman, 2012, as used by Bowman et al., 2012). Alternatively, logistic regression- based methods that incorporate historical land cover analyses combined with socioeconomic

(population, land value) driver variables have also been used (Guneralp et al., 2012; Serneels and

Lambin, 2001). A third approach that has been used to predict land cover change is based on simple regression of historical changes in percent of developed land projected to a specified time in the future (Tu, 2009). Unlike the first two methods, land cover change using the third method is influenced only by the historical land cover characteristics, such as percent impervious surface

(IS).

Some of the previously described prediction methods have been applied to investigate hydrological responses to climate change alone (Denault et al., 2006; Jha et al. 2004; Takle et al.

2010), land cover change alone (Meierdierks et al., 2010; Nagy et al., 2012; Rose and Peters,

2001) or to combined climate and land cover changes (e.g. Cuo et al., 2009; Hamdi et al., 2011;

Tong et al., 2012; Yang et al. 2010). In general, these researchers reported greater variability in

56 discharge (flashiness) and greater pollutant loading in response to increases in IS and/or precipitation. In a growing number of studies, urban stream responses to climate and/or land cover change have been examined for multiple watersheds. For example, using the ArcView

Generalized Watershed Loading Function (AVGWLF) model, Tu (2009) predicted future climate and land cover changes and examined responses for streamflow and nitrogen loads at seven study sites in and near Boston, Massachusetts. Model outputs indicated that the greatest impacts from climate and land cover change were related to the seasonal distribution of discharge rate and nitrogen loading in the future, which were projected to be greater in fall and winter and lower in the summer, rather than affecting the average total annual amounts. In another study, Nagy et al. (2012) observed hydrological and water quality differences among 13 small watersheds located along the Florida Gulf Coast with different watershed percent IS. These researchers reported increases in peak flow, flashiness, pH, and specific conductance as impervious surface in the watershed increased (Nagy et al., 2012).

To date, however, relatively few studies have been conducted in Midwest U.S.A. to quantify responses of multiple urban streams to potential changes in both climate and land cover using a hydrological model specifically designed for use in urban environments (e.g. SWMM) and to examine a suite of variables to describe stream responses. In contrast to earlier studies conducted in the eastern U.S., this study was conducted in the Midwest, an area with generally less precipitation, different seasonal distribution of precipitation, generally slower stormwater infiltration, and different land cover characteristics that include a more intensively managed agricultural matrix and somewhat less intensively developed cities. In the research described in this paper, we collected water quality and water quantity data for headwater streams in 20 watersheds over two years, 2011 and 2012. Five watersheds were purposefully selected from

57 among these 20 to represent a gradient of percent IS, ranging from 5.3% to 37.1%, for application of the model. Climate and land cover change were projected to the year 2040 using regressions based on historical data. We then used SWMM to create hydrological models for these watersheds to answer the following questions:

1) What hydrological differences can be detected among the five urban headwater streams

along a % IS gradient?

2) What are the hydrological responses to projected climate change, land cover change, and

combined climate and land cover change for these stream systems?

3) How might different distributions of land cover change within a watershed (e.g.

downstream near the outflow point, or upstream, distant from it) affect urban headwater

stream hydrology?

Methods

Study area

Five headwater stream watersheds located in Polk County, Iowa, U.S.A. were included in this study (WS1 to WS5, Fig. 1). These watersheds were within the corporate boundaries of four cities: Altoona, Ankeny, Johnston and Pleasant Hill. These cities are located close to Iowa’s state capital (Des Moines), and have experienced rapid population growth in recent years. Between

2000 and 2010, populations in Altoona, Ankeny, Johnston, and Pleasant Hill increased by 41%,

68%, 100%, and 73%, respectively, compared to a 4% increase for Iowa as a whole (State Data

Center of Iowa, 2012). The five watersheds were located within the Upper Midwest climatic region, with average annual precipitation of 838 mm (National Climatic Data Center, 2012).

About 75% of annual precipitation typically occurs between April and September. The watersheds were located along the southern edge of Des Moines Lobe landform region, a

58 recently glaciated area (14K ybp) in which stream network development is ongoing. The watersheds were approximately 280 m above sea level and had an average slope of about 8%

(Table 1).

Among the five watersheds, watershed area ranged from 61.8 ha to 269.8 ha, % IS ranged from 5.3% to 37.1%, and average slope ranged from 5.4% to 10.5% (Table 1). Two of the watersheds (WS1 and WS2) were located in Pleasant Hill (Fig. 1). Land cover in WS1 included residential development (clustered in the upstream area), agricultural land (midstream area), and pasture land (downstream area). The second watershed (WS2) contained a segment of U.S.

Highway 65 and was otherwise dominated by agricultural and forested areas. The third watershed (WS3) was in northeastern Altoona, along the eastern boundary of the city, and it contained residential, commercial, and agricultural land. The fourth watershed (WS4) was located in Johnston, and the fifth (WS5) was in Ankeny. Both WS4 and WS5 contained primarily residential areas. Although their drainage densities were similar, WS4 was characterized by a lower % IS than WS5.

Stream monitoring methods and other data sources

Flow rates were measured twice per month from April to October at defined channel cross-sections near each stream outlet using a FLO-MATE 2000 Water Current and

Flowmeter™ (Hach Company, Loveland, CO). Discharge was then determined using the cross- section method of Rantz (1982). Area of the defined channel cross-section was determined by measuring stream depth at evenly distributed points (varying from one to seven based on stream width) and multiplying by width. The maximum distance between adjacent measuring points was

0.5 m.

In addition, stream stage was continuously recorded at 5-minute intervals from mid-May to October at each cross-section using HOBO U20 water level data loggers (Onset Computer

Corporation, Inc., Pocasset, MA). An additional data logger was used to measure barometric pressure for correction of stream stage data. Continuous stage data were converted to discharge using a stage-discharge rating curve developed for each stream.

Digital Elevation Models (DEMs) at one-meter resolution were generated from Light

Detection and Ranging (LiDAR) data available from the Iowa LiDAR Mapping Project

(GeoTREE, 2011). Impervious surface cover for each watershed was manually digitized based on 2011 aerial photo images. Storm sewer GIS layers were obtained from staff members in the four cities. Five-minute interval precipitation data were obtained from Iowa SchoolNet system

(Iowa Environmental Mesonet, 2012). Three weather stations were selected based on proximity to the five watersheds, “SDRI4” in Des Moines (for WS1, WS2, and WS3), “SGRI4” in

Johnston (for WS4), and “SAKI4” in Ankeny (for WS5).

Calibration and validation of the Storm Water Management Model

The Storm Water Management Model (SWMM Version 5.0.022; Rossman, 2010; US

EPA 2011) was used to simulate current and projected watershed conditions in this study.

SWMM is designed to simulate rainfall-runoff dynamics. It is a distributional hydrological model that accounts for geological characteristics influencing rainfall collection in sub- catchments and in delivering runoff via conduits to specified outflow points.

Sub-catchments were delineated to collect precipitation and the kinematic wave method

(Rossman, 2010) was used in this study to route water through designated channels or pipes. The model simulates runoff (e.g. depth and volume) generated within each subcatchment, and flow dynamics within each channel or pipe (e.g. flow rate, flow depth, discharge rate) leading to the

60 outflow point. These data can then be used to calculate whole-watershed hydrological patterns such as peak discharge, flashiness, and runoff ratio. SWMM also includes a groundwater module which allows calculation of stormwater infiltration depth, which is then subtracted from the surface water discharge system.

Individual models were constructed for each of the five watersheds using five-minute interval precipitation data to simulate surface runoff and channelized discharge in road gutters, storm sewers, and surface channels. The embedded groundwater module was activated for all models using the same precipitation event (6.4 mm delivered to each watershed 5 hours before each simulation) to “recharge” groundwater. Because we were interested in flow dynamics associated with single precipitation events, the effects of evaporation were not included (Gironás et al., 2009). Infiltration processes were simulated using Horton’s equation within the model

(Green, 1986).

Model parameters were obtained in three different ways. The first set of parameters were defined based on existing data for sub-catchment and drainage structure parameters, % IS, and slope. The second set of parameters were calibrated using available discharge observations, sub- catchment width, coefficients for groundwater equations, and Manning’s roughness coefficient

(n) for impervious surfaces, pervious surfaces, and channels. All other parameters were set to default values or values suggested by the SWMM application manual (Gironás et al., 2009). For example, the initial infiltration rate was 100 mm hour-1, the constant infiltration rate was 7 mm hour-1, and the decay constant was 3.5 hour-1 for Horton’s infiltration equation.

Precipitation events (26 June 2011) recorded at the three weather stations were chosen to use for the calibration process. Precipitation amounts were 8.9 mm (WS1, WS2, and WS3), 10.4 mm (WS4), and 12.7 mm (WS5). The models were then manually calibrated for best fit to the

61 continuous discharge data derived from field monitoring for the same events. Precipitation events from the three weather stations on another date (25 May 2011) were used to validate the models. Precipitation amounts for the validation procedure were 21.3 mm (WS1, WS2, and

WS3), and 23.1 mm (WS4 and WS5).

Model performance was quantified using the coefficient of determination (R2) and Nash–

Sutcliffe model efficiency coefficient (NSE). The coefficient of determination ranges from 0 to

1, where greater values indicate a closer relationship between predicted and observed values for discharge. The NSE statistic has a range of -∞ to 1. A greater value indicates a better prediction of discharge, shown in Eq. (1):

2 ∑(Qo,t−Qm,t) NSE = 1 − 2 (1) ∑(Qo,t−Q̅̅̅o̅)

3 where NSE is the Nash–Sutcliffe model efficiency coefficient, Qo,t is the observed discharge (m

-1 3 -1 s ) at time t, Qm,t is the modeled discharge (m s ) at time t, and Q̅̅̅o̅ is the average for the observed discharge (m3 s-1).

Climate change projection

A precipitation event occurring on 10 June 2011 was used as the current climate condition for all five watersheds. This event delivered 16.8 mm of precipitation in one hour. To create a future (2040) precipitation event, annual precipitation for the region was obtained for the period 1895 to 2011 (National Climatic Data Center, 2012). Using linear regression (similar to method of Denault et al., 2006; Takle and Herzmann, 2010), annual precipitation in 2040 was projected to be 18% more than it was during 2011. This proportional increase is consistent with results reported for this region of study by previous researchers (e.g. Jha et al. 2004; Takle et al.

2010). The projected precipitation event was assumed to have the same duration and time distribution as the event on 10 June 2011, but with 18% more precipitation.

Land cover change projection

Percent IS in 2011, calculated by dividing IS area within each watershed by the corresponding total watershed area, was used as the current land cover condition. Land cover was projected to the year 2040 using separate regression models based on quantification of total

IS areas for each of the four cities (Altoona, Ankeny, Johnston, and Pleasant Hill) in 1940, 1961,

1990, 2002, and 2011 (method adapted from Tu, 2009). Binomial curves provided the best fit for the four cities, with coefficients of determination (R2) of 0.997 (Altoona), 0.984 (Ankeny),

0.973(Johnston), and 0.994 (Pleasant Hill). The increase of IS area within each watershed (Table

1) was assumed to be the same as that for each city, unless the projected % IS for the watershed exceeded 45% (which we set as a maximum according to the % IS for other fully developed residential areas in our study area). For the land cover change simulation that included all five watersheds, increases in IS area were evenly distributed across each watershed.

SWMM simulations with independent and combined effects of climate and land cover change

We conducted current condition SWMM simulations using a single precipitation event

(10 June 2011, 16.8 mm precipitation in one hour) and 2011 land cover data in all of the calibrated SWMM models (Table 2). A set of three different climate and land cover change scenarios were designed for this part of the study. In the first scenario, we used a precipitation event projected for 2040 with actual 2011 land cover. In the second scenario, we used the actual

2011 precipitation events and the projected land cover for 2040. In the third scenario, we used

2040 projections for both climate and land cover.

SWMM simulations with different distributions of land cover change in a single watershed

To assess effects of different distributions of future land cover changes, one watershed

(WS4) was divided into three sections (Fig. 2). The three sections were downstream, midstream, and upstream areas within the watershed, and standardized for size and initial % IS (2011). In this watershed, existing impervious surfaces (2011) were relatively evenly distributed between

10% and 95% distances from the stream origin to the watershed outlet. The same increase in urban land cover (to cause a 16.7% absolute increase for each section) was applied in separate simulations for each of the three sections, changing the impervious surface distribution from upstream to downstream. The projected precipitation event for 2040 in WS4 was used for each simulation in this set of scenarios.

Quantifying hydrological indices

To quantify hydrological responses, three indices were calculated for current condition and scenario simulations, including unit-area peak discharge, Richards-Baker Index (hereafter R-

B Index; Baker et al., 2004), and runoff ratio. Unit-area peak discharge is the quotient of peak discharge divided by watershed area, and indicates the greatest amount of discharge generated by a unit area in a single precipitation event. A greater value of peak discharge indicates greater potential for flooding (Huong and Pathirana, 2013). The R-B Index measures oscillations in discharge relative to total discharge, also referred to as “flashiness”. A higher value of the R-B

Index indicates a greater difference between high and low flows, which may be linked to changes in channel morphology, water quality and habitat structure of stream ecosystems (Shields et al.,

2010; Violin et al., 2011). We calculated the R-B Index based on 5-minute interval discharge data, as per Eq. (2):

푛 ∑푖=1 |푞푖−푞푖−1| R-B Index = 푛 (2) ∑푖=1 푞푖

th where n is the total number of discharge records, and qi is the i measured discharge of a stream,

th and qi-1 is the i-1 measured discharge of a stream.

The runoff ratio is the total discharge depth divided by total precipitation depth, which indicates the proportion of precipitation that is discharged in surface channels. A higher value of runoff ratio indicates increase of surface runoff and may result in decreases in groundwater level because of less infiltration (Foster and Chilton, 2004).

Results

Calibration and validation of the Storm Water Management Model

Calibrated model parameters (Table 3) were used to generate hydrographs for each watershed (Fig. 3). Generally, calibration and validation hydrographs demonstrated acceptable fit between observed and simulated discharge, although some differences in timing (WS5, calibration) and/or magnitude (WS1, validation) were observed. For calibration simulations, coefficients of determination ranged from 0.73 to 0.89, and NSEs ranged from 0.25 to 0.80 (Fig.

3). Coefficients of determination for validation simulations ranged from 0.39 to 0.92, and NSEs ranged from 0.23 to 0.91. Among the models, validation statistics were somewhat lower

(although still acceptable as per Moriasi et al., 2007) for watershed WS1.

Current condition simulation

Current condition simulations describe existing (2011) hydrological dynamics for the five watersheds (Table 4). Compared to other watersheds, WS1 was characterized by the lowest values for unit-area peak discharge (0.29 × 10-6 m s-1), R-B Index (flashiness; 0.015), and runoff ratio (0.053). As % IS increased, watershed simulations generated consistently greater values for

65 all three indices. In WS5, the three indices were 2.33 × 10-6 m s-1, 0.130, and 0.355, respectively, five to seven times greater than those for WS1.

Independent and combined effects of climate and land cover change simulations

In the climate change scenario, an 18% increase in precipitation generated increases in all three hydrological indices compared to the current condition (e.g. Fig. 4 for WS4). Specifically, unit-area peak discharge for the five watersheds ranged from 0.35 × 10-6 m s-1 to 2.82 × 10-6m s-1, an increase of 20.8% on average compared to current condition values (Table 4). The change in unit-area peak discharge was greatest in WS3 (23.4%). Increases in R-B Index and runoff ratio compared to current condition values were much smaller than those for unit-area peak discharge.

On average, the R-B Index increased 3.7%, ranging from 0.015 to 0.136, with the greatest proportional increase occurring in WS4 (8.3%). Watersheds with higher % IS generally had greater values for R-B Index. Runoff ratio increased 0.4% on average, ranging from 0.053 to

0.358, with the greatest proportional increases occurring in WS4 and WS5 (0.7%).

The land cover change scenario led to a greater hydrological response compared to the current condition than did the climate change scenario (e.g. Fig. 4 for WS4). All three response indices increased to a greater degree than they did in the climate change scenario (Table 4). Unit- area peak discharge ranged from 0.58 to 2.80 (an average increase of 49.5%). The greatest increase compared to the current condition (99.7%) occurred in WS1. Increases in the R-B Index ranged from 0.019 to 0.148, with an average increase of 39.3%. The greatest increase (68.4%) occurred in WS3. Runoff ratio ranged from 0.105 to 0.430, a 73.9% average increase, with the greatest increase (97.9%) detected for WS2.

The combined effects of climate and land cover changes generated the largest changes in the hydrograph (e.g. Fig. 4 for WS4) and for all three indices (Table 4). Unit-area peak discharge

66 increased from 0.69 to 3.40, with an average increase of 80.1%. The greatest increase (138.0%) occurred in WS1. The R-B Index ranged from 0.019 to 0.156 (an average increase of 43.7%), with the largest increase (66.9%) in WS3. The runoff ratio ranged from 0.105 to 0.433 (an average increase of 74.5%), with the largest increase (98.9%) in WS2.

Simulations of different distributions of land cover change for WS4

For different distributions of land cover change in WS4, we observed consistent increases but different patterns of change for each of the hydrological indices (Table 5). The unit-area peak discharge responded most strongly to additional % IS in the midstream area, increasing by

18.4%, compared to the other two scenarios (16.1% and 15.6%; Table 5). The response of the R-

B Index to greater % IS in the upstream area was much lower (12.0%) compared to the other two scenarios (17.5% and 18.1%). Increases in runoff ratios were similar for the three scenarios

(ranging from 19.3% to 20.2%; Table 5).

When urban land cover was added to the downstream portion of the watershed, the three indices increased (16.1%, 17.5%, and 19.3%, respectively) compared to the current condition.

When urban land cover increased in the midstream area of the watershed, the unit-area peak discharge and R-B Index increased the most of the three scenarios tested (18.4% and 18.1%, respectively). When urban land cover increased in the upstream portion of the watershed, runoff ratio increased the most among the three scenarios, from 0.268 to 0.322 (a 20.2% increase).

Hydrographs for the initial climate change scenario and the three different distributions of land cover change (Fig. 5) indicated that: 1) discharge rates increased more rapidly during the early stages (at 0.33 hour) of the downstream scenario (Box A), and discharge recession was slower for the upstream scenario (Box E); 2) lower peak discharge occurred for the upstream scenario at the first (Box B) and the third peak (Box D), linked to smaller increases in R-B

Index; and 3) the greatest overall unit-area peak discharge occurred during the second peak for all three scenarios, among which the midstream scenario generated the greatest unit-area peak discharge (Box C).

Discussion

In this study, we calibrated and validated SWMM for each of five urban headwater stream watersheds that represent a gradient in % IS. Calibration and validation of the five models demonstrated acceptable fit between measured and simulated discharge. The current condition simulation illustrated the influence of increased % IS among these watersheds. Of the response metrics we analyzed, simulations of climate change had the greatest effect on unit-area peak discharge. Simulations of land cover change led to relatively large increases in unit-area peak discharge, R-B Index, and runoff ratio. Simulations for combined climate and land cover change caused greater changes to the three hydrological indices than either climate or land cover change alone. Unit-area peak discharge and R-B Index were the most sensitive to different spatial distributions of additional IS.

Calibration and validation of the storm water management model

Calibration and validation results indicated that SWMM was able to simulate hydrological processes in the small watersheds we studied, and was sensitive to the gradient in initial % IS among them (Fig. 3). Of the five watersheds, WS1 and WS3 were separated by the greatest distance from a weather station (Table 1), which resulted in time lags between recorded and actual precipitation (Fig. 3) which led to the generally lower (but still acceptable) validation statistic values for these watersheds.

Current condition simulations

Current condition hydrological responses indicated that unit-area peak discharge, R-B

Index, and runoff ratio all increased along the % IS gradient that characterized these watersheds

(Table 4), a result that corroborates the hypotheses and work of a number of researchers (e.g.

Arnold and Gibbons, 1996; Nagy et al., 2012; Schueler, 1994, Yang et al., 2010).

Among much earlier studies documenting the effects of % IS on urban streams, distinct hydrological impacts were reported to occur when watershed % IS reached a threshold of 10%

(Arnold and Gibbons, 1996; Booth and Jackson, 1997; Schueler and Holland, 2000). We found important differences for all three hydrological response indices that were detectable between

5.30% IS (WS1) and 7.96% IS (WS2), especially for unit-area peak discharge (Table 4). These results indicate that important hydrological responses can occur below the often-cited threshold, in our case below 8% IS. Other researchers have also recently reported hydrological changes at relatively low % IS in lower-order watersheds, suggesting threshold criteria of about 5% IS

(based on 13 watersheds studied by Nagy et al., 2012 along the Florida Gulf Coast), or even 3%

IS (based on 16 watersheds examined by Yang et al., 2010 in the White River Basin in Indiana).

It may be that focusing on small headwater streams allows detection of these effects at lower %

IS thresholds, or that these relatively small stream systems are more sensitive to % IS.

A number of other factors important to urban stream hydrology include slope, distribution of IS, and storm sewer system density and structure (Booth and Jackson, 1997;

Dingman, 2008; Meierdiercks et al., 2010; Mejia and Moglen, 2010). Given variation in these watershed parameters among the five study watersheds (Table 1), the consistent trend of changes in the three hydrological response indices corroborates other evidence that % IS can be a robust

69 indicator of the effects of urban land cover on stream hydrology (as earlier suggested by Paul and

Meyer, 2001; Schueler et al., 2009).

Climate change simulations

In previous studies, hydrological impacts have been interpreted by determining changes in continuous discharge over relatively long time scales (e.g. Franczyk and Chang, 2009; Jha et al., 2004). In this study, however, we used single precipitation events to examine more detailed stream hydrological responses to climate change. Our single event simulations predicted an increase in precipitation intensity, although they did not allow us to investigate other possible variations in precipitation frequency and/or duration. However, given the uncertainty associated with modeling the characteristics of future precipitation, the scenarios we tested did reveal plausible changes in urban stream hydrology. The SWMM simulations we conducted indicate that climate change (increased precipitation with other factors held constant) will have greatest effects on unit-area peak discharge (20.8% average increase) compared to R-B Index (a 3.7% average increase) or runoff ratio (a 0.4% average increase). Thus, for already developed watersheds, stormwater mitigation strategies that address peak discharge rates should be a priority. A possible strategy that addresses peak discharge is stormwater system retrofitting to delay delivery (e.g. Karamouz et al., 2011), such as integration of wet ponds in a stormwater treatment train (Villarreal and Bengtsson, 2004). Increased precipitation resulted in greater response for all three indices along the gradient of % IS from WS1 (5.30% IS) to WS3 and WS4

(18.21 to 28.28% IS). Thus, at higher initial % IS, stormwater management strategies to increase infiltration to mitigate potential increases in flashiness and runoff ratio become more important.

Land cover change simulations

Our watershed simulations for land cover change indicated that, compared to the climate change scenario, land cover change resulted in increased values for all three hydrological indices. Our findings are generally consistent with previous reports for increases in peak discharge (from 30% to 100%, e.g. Rose and Peters, 2001), runoff ratio (from 21% to 45%, but more sensitive to watershed slope; Rose and Peters, 2001) and R-B Index (15%, Yang et al.,

2010) in response to greater amounts of impervious land cover. In their examination of small watersheds along the Gulf Coast, Nagy et al. (2012) reported greater effects of increasing % IS on peak discharge, but lesser effects on R-B Index. It is likely that the relative importance of changes in climate and land cover on stream hydrology are subject to the specifics of scenario designs (e.g. differing interpretations offered by Hamdi et al., 2011; Tong et al. 2012; Tu, 2009).

Notwithstanding, our land cover change simulations provide further support for the contention that important hydrological changes occur at thresholds below 10% IS, based on consistent increases in all three response indices for the predicted impervious surface change from 5.30 to

10.47% in WS1 and from 7.96 to 15.74% in WS2.

Combined climate and land cover change simulations

The combined climate and land cover change simulations led to changes in response indices that were slightly greater than additive effects for climate and land cover change considered independently (Table 4). Although previous work has documented additive effects of combined climate and land cover changes (e.g. Nelson et al., 2009; Tong et al., 2012; Tu, 2009), conclusions about which influence might be stronger (either climate or land cover change) have been inconsistent, possibly owing to differences in prediction methods. For example, Tong et al.

(2012) reported that climate change (assuming a 2% increase in temperature, and 20% increase

71 in precipitation) led to a greater change in mean daily flow than did land cover change when both were projected to 2050. However, when their climate change assumption was altered (to a 4% increase in temperature with associated increases in evaporation and evapotranspiration, and

20% increase in precipitation), the increase in mean daily flow due to climate change was less than that due to land cover change.

In our study, we projected a 1-hour precipitation event with an 18% increase from a 16.8 mm (current condition) event to 19.8 mm. The projected 2040 precipitation event was equivalent to a 1-hour, 4-month recurrence interval event in this region at the present time (Huff and Angel,

1992). We selected and projected this event relatively conservatively to avoid flooding during the simulations, which would preclude detection of changes in the response variables. Thus, although it appears that land use change has a stronger influence in our case, it may be only because the climate change scenario was constrained by the relatively small change to predicted precipitation.

Simulations of different distributions of land cover change in WS4

The differentially allocated land cover change scenarios for WS4 indicated that, although all three hydrological indices increased from the initial climate change scenario, only unit-area peak discharge and R-B Index appeared to respond differently to increases in % IS applied in the downstream, midstream, or upstream areas of the watershed. Thus, it appears that the location of

IS additions primarily affects the timing, rather than amount, of discharge conveyed to the watershed outlet (e.g. WS4 curve in Fig. 5, where Box A indicates greater unit-area discharge early in the storm event for the downstream scenario due to the short path to the watershed outlet, and Box E indicates a slower recession for the upstream scenario due to a longer flow path).

Assuming a given increase of % IS in a watershed, any scenario that minimizes increases in peak discharge and flashiness would be better for the in-stream environment, since greater values for these indices are related to stream dynamics that can cause bank erosion and lead to poorer quality aquatic habitat (Walsh et al., 2005; Wenger et al., 2009). Thus, given the existing

IS distribution and stormwater conveyance structures, addition of impervious surfaces in the upstream section of WS4 would likely have less impact on stream hydrology and ecology. Using an urban hydrological model (e.g. SWMM in our study) to assess scenarios for future urban development in a range of watershed types could provide land use planners with critical information for decision making to better protect urban streams.

Conclusions

We used SWMM to simulate hydrological responses of five headwater streams to increases in precipitation and urban land cover based on projections to the year 2040. We conclude that:

1. The current condition simulations indicated that watersheds that have greater % IS are also characterized by greater values in unit-area peak discharge, R-B Index, and runoff ratio. Given variation in other important characteristics among the study watersheds (e.g. average slope, watershed size, and drainage density) % IS was a reliable indicator of impacts on urban stream hydrology. Efforts to mitigate negative impacts to stream hydrology in urban areas should include specific attention to strategies that limit additional IS and that minimize connectivity among existing impervious surfaces. We also detected important hydrological responses below the often-cited 10% IS threshold, in our case below 8% IS.

2. The climate change scenario in this study had strongest effects on unit-area peak discharge in these watersheds. All three hydrological indices were affected by the land cover change

73 scenarios used in this study. The combined climate and land cover change scenarios resulted in slightly more than additive effects from climate and land cover change alone. These findings confirmed that urban stream hydrology, especially for unit-area peak discharge, is highly sensitive to expected changes in climate and land cover. The capacity of existing stormwater drainage/infiltration systems should be continuously evaluated and incorporated in comprehensive planning at municipal and regional levels.

3. Simulations for different distributions of land cover change demonstrated that the location of

IS additions has a greater effect on the timing of delivery than on total amount of discharge. The ability to detect hydrological impacts associated with specific placement of impervious surfaces indicates that this simulation method could be very useful in identifying locations for development that would minimize stream degradation in small urban watersheds.

The result that hydrological responses of these streams to projected land cover changes were much greater than those generated by projected climate change is due, in part, to the conservative approach to climate change that we used to develop the models. In spite of this limitation, simulations for this set of watersheds indicated that plausible changes in both climate and land cover will have strong effects on urban stream hydrology. In addition, simulations for different distributions of IS in WS4 were also constrained by pre-existing IS and stormwater conveying systems in that watershed. In spite of these limitations, this study extends our knowledge of the hydrological dynamics of lower-order urban streams, and elucidates several potential cause and effect relationships that can be used to manage urban landscapes to reduce negative impacts to urban stream ecosystems.

Acknowledgements

Center for Global and Regional Environmental Research at the University of Iowa, the State of

Iowa, and McIntire–Stennis funding.

References

Anderson, D. G.: Effects of urban development on floods in Northern Virginia, United States Government Printing Office, Washington, DC, 1970. Arnold, C. L., and Gibbons, C. J.: Impervious surface coverage - The emergence of a key environmental indicator, J. Am. Plann. Assoc., 62, 243-258, 1996. Baker, D. B., Richards, R. P., Loftus, T. T., and Kramer, J. W.: A new flashiness index: Characteristics and applications to midwestern rivers and streams, J. Am. Water Resour. As., 40, 503-522, 2004. Bicknell, B. R., Imhoff, J. C., Kittle, J. L., Jr., Donigian, A. S., Jr., and Johanson, R. C.: Hydrological Simulation Program - Fortran, User's manual for version 11. EPA/600/R- 97/080, U.S. Environmental Protection Agency, National Exposure Research Laboratory, Athens, GA, 1997. Black, R. W., Moran, P. W., and Frankforter, J. D.: Response of algal metrics to nutrients and physical factors and identification of nutrient thresholds in agricultural streams, Environ. Monit. Assess., 175, 397-417, 2011. Booth, D. B., and Jackson, C. R.: Urbanization of aquatic systems: Degradation thresholds, stormwater detection, and the limits of mitigation, J. Am. Water Resour. As., 33, 1077-1090, 1997. Bowman, T. A., Thompson, J. R., Tyndall, J. C., and Anderson, P. F.: Land cover analysis for urban foresters and municipal planners: examples from Iowa, J. Forest., 110, 25-33, 2012. Boyle, J. S.: Evaluation of the annual cycle of precipitation over the United States in GCMs: AMIP simulations, J. Climate, 11, 1041-1055, 1998. Cuo, L., Lettenmaier, D. P., Alberti, M., and Richey, J. E.: Effects of a century of land cover and climate change on the hydrology of the Puget Sound basin, Hydrol. Process., 23, 907-933, 2009. Denault, C., Millar, R. G., and Lence, B. J.: Assessment of possible impacts of climate change in an urban catchment, J. Am. Water Resour. As., 42, 685-697, 2006. Dingman, S. L.: Physical hydrology, Waveland Pr Inc, Long Grove, Illinois, 2008.

Eastman, J. R., IDRISI Selva, Computer software program produced by Clark University, Worcester, MA, 2012. Foster, S. S. D., and Chilton, P. J.: Downstream of downtown: urban wastewater as groundwater recharge, Hydrogeol. J., 12, 115-120, 2004. Franczyk, J., and Chang, H.: The effects of climate change and urbanization on the runoff of the Rock Creek basin in the Portland metropolitan area, Oregon, USA, Hydrol. Process., 23, 805- 815, 2009. GeoInformatics Training, Research, Education, and Extension (GeoTREE), data available at: http://www.geotree.uni.edu/extensions/iowa-lidar-mapping-project/ (last access: : 10 April 2012), 2011. Gironás, J., Roesner, L. A., and Davis, J.: Storm water management model applications manual. U.S. Environmental Protection Agency, Cincinnati, OH, 2009. Green, I. R. A.: An explicit solution of the modified Horton equation, J. Hydrol., 83, 23-27, 1986. Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., and Briggs, J. M.: Global change and the ecology of cities, Science, 319, 756-760, 2008. Guneralp, B., Reilly, M. K., and Seto, K. C.: Capturing multiscalar feedbacks in urban land change: a coupled system dynamics spatial logistic approach, Environ. Plann. B., 39, 858- 879, 2012. Hamdi, R., Termonia, P., and Baguis, P.: Effects of urbanization and climate change on surface runoff of the Brussels Capital Region: a case study using an urban soil-vegetation- atmosphere-transfer model, Int. J. Climatol., 31, 1959-1974, 2011. Hatt, B. E., Fletcher, T. D., Walsh, C. J., and Taylor, S. L.: The influence of urban density and drainage infrastructure on the concentrations and loads of pollutants in small streams, Environ. Manage., 34, 112-124, 2004. Hsu, M. H., Chen, S. H., and Chang, T. J.: Inundation simulation for urban drainage basin with storm sewer system, J. Hydrol., 234, 21-37, 2000. Huff, F. A., and Angel, J. R.: Rainfall frequency atlas of the Midwest, Midwestern Climate Center, NOAA, Champaign, Illinois, 1992. Huong, H. T. L., and Pathirana, A.: Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam, Hydrol. Earth Syst. Sc., 17, 379-394, 2013. Iowa Environmental Mesonet, data available at: http://mesonet.agron.iastate.edu/ (last access: 10 April 2012), 2012. Jha, M., Pan, Z. T., Takle, E. S., and Gu, R.: Impacts of climate change on streamflow in the Upper Mississippi River Basin: A regional climate model perspective, J. Geophys. Res.- Atmos., 109, 12, 2004. Karamouz, M., Hosseinpour, A., and Nazif, S.: Improvement of urban drainage system performance under climate change impact: case study, J. Hydrol. Eng., 16, 395-412, 2011. Kendon, E. J., Roberts, N. M., Senior, C. A., and Roberts, M. J.: Realism of rainfall in a very high-resolution regional climate model, J. Climate, 25, 5791-5806, 2012.

Meierdiercks, K. L., Smith, J. A., Baeck, M. L., and Miller, A. J.: Analyses of urban drainage network structure and its impact on hydrologic response, J. Am. Water Resour. As., 46, 932- 943, 2010. Mejia, A. I., and Moglen, G. E.: Impact of the spatial distribution of imperviousness on the hydrologic response of an urbanizing basin, Hydrol. Process., 24, 3359-3373, 2010. Moriasi, D., Arnold, J., Van Liew, M., Bingner, R., Harmel, R., and Vieth, T.: Model evaluation guidelines for systematic quanitfication of accuracy in watershed simulations, T. ASABE, 50, 885-900, 2007. Nagy, R. C., Lockaby, B. G., Kalin, L., and Anderson, C.: Effects of urbanization on stream hydrology and water quality: the Florida Gulf Coast, Hydrol. Process., 26, 2019-2030, 2012. National Climatic Data Center, available at: http://www.ncdc.noaa.gov/temp-and-precip/time- series/index.php?parameter=pcp&month=12&year=2011&filter=12&state=13&div=5 (last access: 10 April 2012), 2012. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Srinivasan, R., and Williams, J. R.: Soil and Water Assessment Tool user's manual, TWRI Report TR-192, Texas Water Resources Institute, College Station, TX, 2002. Nelson, K. C., Palmer, M. A., Pizzuto, J. E., Moglen, G. E., Angermeier, P. L., Hilderbrand, R. H., Dettinger, M., and Hayhoe, K.: Forecasting the combined effects of urbanization and climate change on stream ecosystems: from impacts to management options, J. Applied Ecology, 46, 154–163, 2009. Paul, M. J., and Meyer, J. L.: Streams in the urban landscape, Annu. Rev. Ecol. Syst., 32, 333- 365, 2001. Pekarova, P., and Pekar, J.: The impact of land use on stream water quality in Slovakia, J. Hydrol., 180, 333-350, 1996. Quilbe, R., Rousseau A. N., Moquet, J. S., Savary, S., Ricard, S., and Garbouj, M. S.: Hydrological responses of a watershed to historical land use evolution and future land use scenarios under climate change conditions, Hydrol. Earth Syst. Sc., 12, 101-110, 2008. Rantz, S. E.: Measurement and computation of streamflow, volume 1, Geological Survey Water- Supply Paper 2175, Washington, DC, 1982. Rose, S., and Peters, N. E.: Effects of urbanization on streamflow in the Atlanta area (Georgia, USA): a comparative hydrological approach, Hydrol. Process., 15, 1441-1457, 2001. Rossman, L. A.: Storm Water Management Model user’s manual, U.S. Environmental Protection Agency, Cincinnati, OH, 2010. Schueler, T. R.: The importance of imperviousness, Watershed Protection Techniques, 1, 100- 111, 1994. Schueler, T. R., and Holland, H.: The Practice of watershed protection: techniques for protecting our nation’s streams, lakes, rivers, and estuaries, Center for Watershed Protection, Ellicott City, MD, 2000. Schueler, T. R., Fraley-McNeal, L., and Cappiella, K.: Is impervious cover still important? A review of recent research, J. Hydrol. Eng., 14, 309-315, 2009.

State Data Center of Iowa, data available at: http://www.iowadatacenter.org/ (last access on 10 April 2012), 2012. Serneels, S., and Lambin, E. F.: Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model, Agr. Ecosyst. Environ., 85, 65-81, 2001. Shields, F. D., Lizotte, R. E., Knight, S. S., Cooper, C. M., and Wilcox, D.: The stream channel incision syndrome and water quality, Ecol. Eng., 36, 78-90, 2010. Takle, E. S., and Herzmann, D., Future climates for pavement performance analysis, Climate Science Program Report, Iowa State University, Ames, 2010. Takle, E. S., Jha, M., Lu, E., Arritt, R. W., Gutowski, W. J., and the NARCCAP Team: Streamflow in the upper Mississippi river basin as simulated by SWAT driven by 20th- century contemporary results of global climate models and NARCCAP regional climate models, Meteorol. Z., 19, 341-346, 2010. Tong, S. T. Y., Sun, Y., Ranatunga, T., He, J., and Yang, Y .J.: Predicting plausible impacts of sets of climate and land use change scenarios on water resources, Appl. Geogr, 32, 477-489, 2012. Tu, J.: Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts, USA, J. Hydrol., 379, 268-283, 2009. U.S. Environmental Protection Agency (US EPA), Storm Water Management Model (SWMM) Version 5.0.022, available at http://www.epa.gov/nrmrl/wswrd/wq/models/swmm/ (last access 10 May 2012), 2011. Villarreal, E. L., and Bengtsson, A.: Inner city stormwater control using a combination of best management practices, Ecol. Eng., 22, 279-298, 2004. Violin, C. R., Cada, P., Sudduth, E. B., Hassett, B. A., Penrose, D. L., and Bernhardt. E. S.: Effects of urbanization and urban stream restoration on the physical and biological structure of stream ecosystems, Ecol. Appl., 21, 1932-1949, 2011. Walsh, C. J., Roy, A. H., Feminella, J. W., Cottingham, P. D., Groffman, P. M., and Morgan, R. P.: The urban stream syndrome: current knowledge and the search for a cure, J. N. Am. Benthol. Soc., 24, 706-723, 2005. Walsh, C. J., Sharpe, A. K., Breen, P. F., and Sonneman, J. A.: Effects of urbanization on streams of the Melbourne region, Victoria, Australia. I. Benthic macroinvertebrate communities, Freshwater Biol., 46, 535-551, 2001. Wenger, S. J., Roy, A. H., Jackson, C. R., Bernhardt, E. S., Carter, T. L., Filoso, S., Gibson, C. A., Hession, W. C., Kaushal, S. S., Marti, E., Meyer, J. L., Palmer, M. A., Paul, M. J., Purcell, A. H., Ramirez, A., Rosemond, A. D., Schofield, K. A., Sudduth, E. B., and Walsh, C. J.: Twenty-six key research questions in urban stream ecology: an assessment of the state of the science, J. N. Am. Benthol. Soc., 28, 1080-1098, 2009. Yang, G. X., Bowling, L. C., Cherkauer, K. A., Pijanowski, B. C., and Niyogi, D.: Hydroclimatic response of watersheds to urban intensity: an observational and modeling-based analysis for the White River Basin, Indiana, J. Hydrometeorology, 11, 122-138, 2010.

Tables

Table 1. Geographic characteristics, drainage density, distance to nearest weather station, and projected percent impervious surface (IS, 2040) for five urban watersheds (WS1 to WS5) in central Iowa representing a percent impervious surface gradient. Total drainage density is the sum of road gutter, storm sewer and surface channel densities. WS1 WS2 WS3 WS4 WS5 Area (ha) 194.9 61.8 269.8 89.5 92.0 Current percent IS 5.3 8.0 18.2 28.3 37.1 Average slope (%) 10.3 5.4 5.6 10.5 8.8 Total drainage density (km km-2) 3.2 10.7 5.1 9.0 8.8 Road gutter density (km km-2) -- -- 0.6 0.3 0.3 Storm sewer density (km km-2) 0.2 -- 1.3 6.7 6.5 Surface channel density (km km-2) 3.0 10.7 3.3 2.0 1.9 Distance to nearest weather station (km) 10.4 5.8 15.0 5.3 3.1 Land cover projection Percent IS in 2040 10.5 15.7 35.3 45.0 45.0 Absolute change (%) 5.2 7.8 17.1 16.7 7.9 Relative change (%) 97.6 97.7 93.7 59.1 21.2

Table 2. Current and predicted scenarios for SWMM model simulation using current (2011) and future (2040) climate and land cover conditions for five watersheds in central Iowa. The different distributions of land cover change (partial) were only applied to the fourth watershed (WS4). Watersheds Scale of simulation Land cover Climate year included year Current condition All Whole watershed 2011 2011 Predicted scenarios Climate All Whole watershed 2011 2040 Land cover All Whole watershed 2040 2011 Climate and land cover All Whole watershed 2040 2040 Land cover (partial) WS4 Subsection, downstream area 2040 2040 Land cover (partial) WS4 Subsection, midstream area 2040 2040 Land cover (partial) WS4 Subsection, upstream area 2040 2040

Table 3. Calibrated SWMM model parameters for five watersheds (WS1 to WS5) in central Iowa. A1, B1, A2, and B2 are coefficients for the groundwater equation in SWMM. Parameters and Statistics WS1 WS2 WS3 WS4 WS5 Number of sub-catchments 29 32 49 52 60 Average width of sub-catchments (m) 704 259 324 101 123 Manning’s n (impervious surfaces) 0.017 0.05 0.05 0.05 0.05 Manning’s n (pervious surfaces) 0.18 0.5 0.2 0.4 0.4 Manning’s n (channels: natural) 0.05 0.05 0.04 0.05 0.04 Manning’s n (channels: storm sewer) -- -- 0.012 0.012 0.012 Manning’s n (channels: gutter) 0.015 -- 0.015 0.015 0.015 Parameters for groundwater equation A1 0.0005 0.050 0.0070 0.050 0.008 B1 1.00 1.00 2.00 1.00 1.00 A2 0.0005 0.007 0.0010 0.050 0.008 B2 1.00 1.00 1.00 1.00 1.00

Table 4. Hydrological response characteristics for current condition, climate change, land cover change, and combined scenarios for five watersheds (WS1 to WS5) in central Iowa. The relative changes in percent for climate, land cover and combined scenarios are calculated compared to the current condition. Scenarios Indices WS1 WS2 WS3 WS4 WS5 Current condition Unit-area peak discharge 0.29 0.88 1.37 1.62 2.33 (10-6 m s-1) R-B Index 0.015 0.027 0.060 0.061 0.130 Runoff ratio 0.053 0.077 0.181 0.266 0.355 Climate Unit-area peak discharge 0.35 1.02 1.69 1.97 2.82 (10-6 m s-1) Change (%) 21.1 16.8 23.4 21.8 21.1 R-B Index 0.015 0.027 0.061 0.066 0.136 Change (%) 1.7 2.0 2.0 8.3 4.7 Runoff ratio 0.053 0.077 0.181 0.268 0.358 Change (%) 0.0 0.6 0.1 0.7 0.7 Land cover Unit-area peak discharge 0.58 1.17 2.03 2.37 2.80 (10-6 m s-1) Change (%) 99.7 33.7 47.8 46.4 20.1 R-B Index 0.019 0.037 0.102 0.091 0.148 Change (%) 27.2 38.5 68.4 48.5 14.1 Runoff ratio 0.105 0.152 0.350 0.424 0.430 Change (%) 97.8 97.9 93.6 59.1 21.0 Climate and land cover Unit-area peak discharge 0.69 1.37 2.46 2.92 3.40 (10-6 m s-1) Change (%) 138.0 56.9 79.8 80.0 46.0 R-B Index 0.019 0.038 0.101 0.098 0.156 Change (%) 29.9 40.9 66.9 60.0 20.7 Runoff ratio 0.105 0.153 0.351 0.427 0.433 Change (%) 97.8 98.9 93.8 60.2 21.9

Table 5. Hydrological response characteristics for different distributions of land cover change in three sections (downstream, midstream, and upstream areas) of one watershed, WS4. The three simulations use the predicted (2040) climate parameters, percent changes for indices are compared to the initial climate change scenario for that watershed. Initial Climate Downstream Midstream Upstream Change Scenario Unit-area peak discharge 1.97 2.29 2.33 2.28 (× 10-6 m s-1) Change (%) 16.1 18.4 15.6 R-B Index 0.066 0.078 0.078 0.074 Change (%) 17.5 18.1 12.0 Runoff ratio 0.268 0.320 0.321 0.322 Change (%) 19.3 19.6 20.2

Figures

Fig. 1. Five headwater stream watersheds located in four cities (one each in Altoona, Ankeny, and Johnston, and two in Pleasant Hill) in Polk County, central Iowa. Shaded areas represent impervious surface in 2011, watershed areas for WS1 through WS5 are delineated with cross- hatching.

Fig. 2. Boundaries of the three sections for the different distributions of impervious land cover scenario for WS4. The three sections have similar area and percent impervious surface in 2011 (initial % IS). The downstream section includes 0 to 40% of flow length to the outlet, the midstream section includes 40 to 70%, and the upstream section includes 70 to 100% of flow length to the outlet.

Fig. 3. Hydrograph segments for calibration (left column) and validation (right column) of the five models (WS1 to WS5). Discharge was standardized by watershed area.

Fig. 4. Precipitation and hydrographs for the current condition, climate change, land cover change and combined climate and land cover change scenarios for WS4.

Fig. 5. Precipitation amounts and hydrographs for different distributions of land cover change for WS4. Discharge was standardized by watershed area. Box A illustrates greater discharge for the downstream scenario; Box B and Box D indicate lower peak discharge for the upstream scenario; Box C illustrates greatest peak discharge for the midstream scenario; and Box E indicates greater discharge for the upstream scenario.

CHAPTER 4: WATERSHED FEATURES AND STREAM WATER QUALITY: A CAUSAL ANALYSIS OF LINKAGES IN MIDWEST URBAN LANDSCAPES, U.S.A.

A manuscript formatted for submission to Urban Ecosystems

Jiayu Wu • Timothy W. Stewart • Janette R. Thompson • Randall K. Kolka • Kristie J. Franz

Abstract

Streams are important natural features in urban landscapes, but stream water quality can be negatively affected by human activities in urban settings. Because of the complexity of the urban environment, causal relationships among variables that may lead to urban stream degradation are difficult to isolate and describe. We collected data for social system and terrestrial landscape variables from 20 urban headwater stream watersheds in central Iowa,

U.S.A. We also measured hydrology and water quality parameters for streams within each watershed. We used path analysis to quantify the direct, indirect, and total effects of causal variables on response variables. Path analysis models suggested that stream water conductivity increased with increasing human population density and road density, and declined with increases in baseflow ratio. Models also indicated total nitrogen concentration increased with population density and percent of watershed covered by crop land. The path model for total phosphorus concentration indicated that it declined as a function of percent of residents with college-level education and percent impervious surface in the watershed. Overall, these models provided important insights on the direct and indirect effects of social, landscape, and hydrological variables on urban stream water quality. Knowledge of likely causal variables for different kinds of urban stream water quality impairment could be used to guide mitigation decisions by city staff.

Keywords: Coupled natural and social systems, path analysis, urban hydrology, urban streams, stream water conductivity, stream water nutrient concentration

Introduction

In 2011, more than half of the world’s population (52.1%) resided in urban areas, and in the United States (U.S.) the proportion of urban dwellers was much greater, at 82.4% (United

Nations 2011). Additionally, growth of the urban population in the U.S. is increasing at a faster rate (12.1% between 2000 and 2010) than the overall population growth rate (9.7% during the same period; U.S. Census Bureau 2012). Further, the expansion of urban areas is disproportionately greater than population growth alone would cause (Liu 2005; Seto et al.

2012). Finally, changes associated with conversion to urban land use are profound and often permanent (McGranahan and Satterwaite 2003; Seto et al. 2011). Thus, understanding the relationships among humans, their activities, and natural systems in urban landscapes is increasingly important.

Urban landscapes also represent complex systems with tight coupling between human and natural elements (Grimm et al. 2008; Liu et al. 2007). Addition of infrastructure, such as roads, buildings, and storm sewers often causes damage to natural landscapes that is difficult to repair or mitigate. For example, declines in the size and quality of natural areas embedded in urban and peri-urban landscapes have strong negative effects on biodiversity (e.g. McKinney

2002; 2008). Also, daily human activities within urban systems, such as motor vehicle use and application of lawn fertilizer, lead to high concentrations of potential pollutants (Fissore et al.

2011).

Conditions in urban streams reflect changes in the upland landscape (Sun et al. 2013;

Walsh et al. 2005), including changes in hydrology (Arnold and Gibbons 1996; Carvalho et al.

2010; Olivera and DeFee 2007) and water quality (DiDonato et al. 2009; Hatt et al. 2004; Nagy

90 et al. 2012). For example, increases in total discharge, peak discharge, and flashiness have been reported in urban streams as urban (impervious) land cover increases within a watershed (Nelson et al. 2009; Schoonover et al. 2006). Elevated concentrations of nutrients (Deemer et al. 2012), toxic metals (Hatt et al. 2004) and sediments (Grayson et al. 1996) have also been reported for urban streams. For example, phosphorus and nitrogen from fertilizer applied to lawns (Fissore et al. 2011), sediment and salts from roads (Adachi and Tainosho 2005) and increased runoff from building roofs (Ragab et al. 2003) delivered to streams rapidly via storm sewers (Negishi et al.

2007) have been identified as contributors to stream degradation in urban areas. Given changes in urban stream hydrology and water quality, biological consequences for aquatic organisms also occur, related to changes in habitat structure and characteristics (Walsh et al. 2005).

The spatial heterogeneity of urban systems makes study of the causal mechanisms driving changes in urban streams complex (Cadenasso et al. 2007). Path analysis is an approach that can be used to study these mechanisms within and among several categories of variables (Wright

1934). Path analysis estimates the direct effect (DE) of one variable on another variable using a path coefficient (or standardized regression coefficient, β). The indirect effect (IE) of a variable, mediated by at least one intervening variable, can also be measured. The total effect (TE) of a variable on another variable is determined as the sum of its direct and indirect effects. By measuring DE, IE, and TE, complex causal relationships among variables can be quantified.

Path analysis has been applied, for example, to examine causal relationships among land cover, storm characteristics, stream hydrology, and water quality variables in Phoenix, Arizona,

U.S.A. (Lewis and Grimm 2007). In their study, path models indicated that impervious surface was an important contributor to nitrogen exports. Although this approach clearly has promise for furthering understanding of complex relationships in urban systems, we are not aware of such

91 studies that have specifically included social system variables as factors that both directly and indirectly influence urban stream water quality.

In this study, social system, terrestrial landscape, stream hydrology and water quality variables were measured for 20 urban watersheds in five central Iowa cities. Social system variables included population, educational attainment, and socio-economic variables. Landscape variables included road density, average slope, and proportions of different land cover types.

Hydrological variables included discharge rates, stream flashiness, and baseflow characteristics.

Water quality variables included conductivity, dissolved oxygen, pH, temperature, and measures of nutrient and suspended solids concentrations. The objective of this research was to quantify the complex causal relationships among variables within social, terrestrial and hydrological systems on water quality variables in urban streams. Specifically, we were interested in describing how conductivity, total nitrogen, and total phosphorus were affected by these variables, and the relative strength of direct and indirect effects of these variables on stream water quality parameters.

Methods

Study area

The study area consisted of four headwater stream watersheds in each of five cities located in Polk County, Iowa, U.S., including Altoona, Ankeny, Des Moines, Johnston, and

Pleasant Hill (Fig. 1). The watersheds were located within the Upper Midwest climatic region, with average annual precipitation of 838 mm (NCDC 2012), of which 75% occurred between

April and September. The mean elevation of these watersheds was 284 m above sea level. Four of these cities (Altoona, Ankeny, Johnston, and Pleasant Hill) experienced rapid population

92 increases (between 40.6% and 99.8%) from 2000 to 2010 (SDCI 2010). The City of Des Moines had much lower population growth (2.4%) during the same time period (SDCI 2010).

To delineate watershed boundaries we generated digital elevation models (DEMs) at one- meter resolution from Light Detection and Ranging data (Iowa LiDAR Mapping Project,

GeoTREE 2011) using ArcHydro Version 2.0 (Environmental Systems Research Institute, ESRI,

Redlands, California, U.S.A.) associated with ArcMap Version 10.0 (ESRI, Redlands,

California, U.S.A.). Watershed area ranged from 0.5 km2 to 20.5 km2. The 20 watersheds represented different degrees of urban development, with percent impervious surface area that ranged from 4% to 44%. Other major land cover types included open space (mostly lawns), crop land, and small areas of forest. Social system and terrestrial landscape variables were quantified within each watershed.

Data structure

To quantify the complex relationships among causal variables on water quality outcomes, multiple variables were measured within each of four categories, including social system, terrestrial landscape, stream hydrology, and stream water quality (Table 1).

Social system

Social system variables were used to quantify effects of human populations and their activities (Table 1). Census block-group population and education data were extracted from 2010 census data (U.S. Census Bureau 2012). These data were matched to watershed boundaries and used to calculate an area-weighted total population and number of residents with specific education levels in ArcMap (ESRI, Redlands, California, U.S.A.). Population density was determined by dividing estimated watershed population by watershed area. Percent of watershed residents having high school- or college-level education was determined by dividing numbers

93 from each group by estimated watershed population. Average home value was obtained from

Polk County Assessor’s records (Polk County Assessor 2013). We chose these variables in the social system because previous research has suggested relationships between population level and stream hydrology that are mediated by percent impervious surface (Nagy et al. 2012), and between residents’ level of education and pro-environmental behaviors (e.g. use of phosphorus- free fertilizers, Akhtar, et al. 2007), which will influence water quality.

Terrestrial landscape

Terrestrial landscape variables were used to quantify infrastructure and land cover characteristics. Road surface and slope data were downloaded from the Natural Resources

Geographic Information Systems Library (NRGIS 2011). Road density and average slope were determined for areas within watershed boundaries using ArcMap. Impervious surface data were obtained from the City of Des Moines GIS department (A Whipple personal communication

June 17 2010). Additional land cover data (percent water, lawn, forest, and crop land) were extracted from the 2006 National Land Cover Dataset (NLCD; Fry et al. 2011).

Stream hydrology

We measured stream hydrological variables to quantify discharge and patterns of discharge. For each of the 20 streams, flow rate was measured using a FLO-MATE 2000 Water

Current and Flowmeter™ (Hach Company, Loveland, Colorado, U.S.) twice a month at defined channel cross-sections near the stream outlet between April and October, 2011 (9 to 15 measurements per stream depending on amount of flow). Stream channel area was determined by measuring stream depth for evenly distributed cells (varying from one to seven depending on stream width) and multiplying by cell width. The maximum distance between adjacent

94 measuring points was 0.5 m. Stream discharge was then determined using the cross-section method of Rantz (1982).

One HOBO U20 water level data logger (Onset Computer Corporation Inc., Pocasset,

Massachusetts, U.S.) was installed in each of the 20 streams at the sampling location to record stream stage at 5-minute intervals. These measurements were used in conjunction with flow rate data to create a stage-discharge rating curve, which was then used to transform stream stage data to discharge. Richards-Baker Flashiness Index (R-B index; Baker et al. 2004) and baseflow ratio

(Dingman 2008) were calculated based on estimated discharge. The R-B Index measures oscillations in discharge relative to total discharge, also referred to as “flashiness”; a greater value of the R-B index indicates more pronounced differences between high and low flows.

Baseflow ratio measures the ratio of baseflow depth to total precipitation amount. A digital filter

(Eckhardt 2005) was applied using the Web-based Hydrograph Analysis Tool (WHAT; Lim et al. 2005) to separate baseflow from total flow. Means for each variable for each stream were calculated for use in subsequent analyses.

Stream water quality

We measured stream water quality variables using portable meters and by collecting grab samples during each time flow rate measurements were made (April to November, 2011).

Conductivity, dissolved oxygen, pH, and temperature were determined on-site using a portable

Hach HQ40d™ meter (Hach Company, Loveland, Colorado, U.S.) and turbidity was measured on-site using an Oakton Model T-100™ turbidimeter (Oakton Instruments, Vernon Hills,

Illinois, U.S.). All stream water grab samples were collected in acid-washed polypropylene bottles. Samples for determination of total phosphorus, phosphate, and TSS were collected in

1000-ml bottles. Samples for determination of total nitrogen and nitrate were collected in an

95 acidified 50-ml bottle. All samples were transported in a cooler and analyzed in the Riparian

Management Systems laboratory in the Department of Natural Resource Ecology and

Management, Iowa State University, Ames, Iowa. Water samples were analyzed to determine concentrations of nitrate, total nitrogen, phosphate, and total phosphorus, using standard methods

(U.S. EPA methods 353.2, 365.1, and 365.3; U.S. EPA 1978; 1993a; 1993b). Total suspended solids were determined using filtration (Eaton et al. 2005). The mean of each variable (from 9 to

15 measurements) for each stream was calculated for use in subsequent analyses.

Statistical Approach

Conceptual model

A conceptual model was constructed to illustrate hypothesized causal relationships among the four categories of variables (Fig. 2). Urban stream water quality parameters were the outcome (dependent) variables. The theoretical basis for the model was based on previous analyses of urban stream systems (e.g. Arnold and Gibbons 1996; Quinn 2013; Rose and Peters

2001; Walsh et al. 2005; Wenger et al. 2009). First, we expected social system variables to have direct effects on both terrestrial landscape characteristics and stream water quality. For example, we expected population density would influence road density, because greater population density would necessitate greater transportation capacity (Quinn 2013). Population density could also directly influence stream water quality (e.g. total nitrogen concentration) through activities such as fertilizer application on lawns (e.g. Sun et al. 2013). Second, we expected that characteristics of the terrestrial landscape would affect both stream hydrology and water quality. For example, peak discharge has been reported to be as much as 30% to 100% greater in watersheds with high percent urban area (Rose and Peters 2001). We also expected stream hydrology to have a direct effect on water quality, because stream flashiness (oscillations in flow rate) contributes to

96 instream erosion and suspension of pollutants in stream water (Booth et al. 2002). Finally, we also expected to find indirect relationships among variables across the four categories. For example, it is likely that road density would indirectly influence conductivity, because road density typically decreases the baseflow ratio (Arnold and Gibbons 1996; Earon et al. 2012).

Data analyses

Descriptive statistics (mean, minimum, maximum, and standard deviation) were calculated for each variable. Prior to statistical analysis, data were transformed using ln (x+1) or arcsine-square root, in order to obtain greater linearity and normality, and reduced heteroscedasticity (Cohen et al. 2003). Using transformed data, correlation analyses were conducted within each variable category (e.g. social system, terrestrial landscape, stream hydrology, and stream water quality) to reduce the number of variables selected for path analyses. Based on the conceptual model and correlation results, variables were selected using two criteria: 1) within each of the four categories, only one variable was selected from among groups of highly correlated variables; and 2) among each of the four categories, variables with greater likelihood of strong causal relationships with other variables (based on earlier studies) were given selection priority. We used SPSS Version 19.0 (IBM Corp., Armonk, New York,

U.S.) to conduct all calculations and analyses.

Multiple regression analyses were conducted for preliminary exploration of causal relationships among the four categories of variables. Then, path analyses were conducted to quantify the direct and indirect causal relationships among the variables in the four categories

(Wright 1934; Garson 2008). One path model was built for each of three outcome variables, including stream water conductivity, and total nitrogen and total phosphorus concentrations. A path coefficient (standardized regression coefficient, β) was used to quantify the causal

97 relationships between two linked variables. The path analyses were conducted using AMOS

Version 17.0 (AMOS Development Corp., Crawfordville, Florida, U.S.), a software package designed for SEM and path analysis (Byrne 2010). We report statistical significance at p ≤ 0.10 and p ≤ 0.05.

Because no single goodness-of-fit statistic is widely accepted (Byrne 2010), multiple statistics were used to evaluate the performance of different path models, including the Chi- square (χ2) test, with associated degree of freedom and probability values, the comparative fit index (CFI), and root mean square error approximation (RMSEA). The χ2 test measures the discrepancy between the observed covariance matrix and the implied covariance matrix. A greater p-value associated with the χ2 test indicates a better fit of the hypothetical model to the data (Bollen 1989). The CFI is a goodness-of-fit statistic for studies with a small sample size

(Bentler 1990). The model is considered a good fit when the CFI value (ranging from 0 to 1) is greater than 0.9 (Bentler 1992). The RMSEA also measures how well the hypothetical model fits the observed data; an RMSEA value less than 0.05 indicates a good fit (Browne and Cudeck

1992).

Results

Variable description

Social system, terrestrial landscape, stream hydrology and water quality features varied widely among the 20 sites (Table 2). For example, population density ranged from 41 to 2,318 persons km-2, and 9 % to 39 % of the population had attained a college-level education. For terrestrial landscape variables, road density ranged from 1.7 to 12.9 km km-2 and impervious surface area ranged from 4.2 % to 44.3 %. In addition to impervious surface, lawn (from 8.2% to

37.1%) and crop land (from 0% to 70.4%) were also important land cover categories. For stream

98 hydrology variables, mean discharge ranged from 0.002 to 0.072 m3 s-1 km-2, and R-B index ranged from 0.017 to 0.118. Baseflow ratio was more consistent among watersheds, with a mean of 0.87. For water quality variables, conductivity ranged from 615.9 to 1128.5 uS cm-1, total nitrogen concentrations ranged from 0.9 to 6.7 mg L-1, and total phosphorus concentrations ranged from 96 to 275 μg L-1.

Variable relationships and selection

For social system variables, four significant correlations were found: average home value was positively related to college education and negatively related to high school education; total population was positively related to population density; and the two education attainment variables were negatively related to each other (Table 3). For terrestrial landscape variables, eight significant correlations were detected: road density was positively related to percent impervious surface and negatively related to crop land; average slope was positively related to percent of forest and negatively related to percent of crop land; percent impervious surface was positively related to percent of lawn and negatively related to percent of crop land; percent of water surface was positively related to percent of lawn; and percent of lawn was negatively related to percent of crop land (Table 4). For stream hydrology variables, four significant correlations were found: maximum discharge was positively related to mean discharge and R-B index, and negatively related to baseflow ratio, and R-B index was negatively related to baseflow ratio (Table 5). Among water quality variables, 13 significant correlations were detected: dissolved oxygen was negatively related to phosphate, total phosphorus and total suspended solids; nitrate was negatively related to temperature and positively related to total nitrogen; phosphate was positively related to total phosphorus, total suspended solids and turbidity; temperature was negatively related to total nitrogen; total phosphorus was positively related to

99 total suspended solids and turbidity; and total suspended solids was positively related to turbidity

(Table 6). Based on water quality correlation results and our variable selection criteria, we chose conductivity, and total nitrogen and total phosphorus concentration as the outcome variables for three independently generated path models. We used correlation results within the other categories to limit selection of causal variables to subsequently test in path analysis.

Multiple regression and path analysis models

Regression analyses were conducted based on the general conceptual model and to determine possible causal relationships for the path models (Tables 7, 8 and 9). Population density, road density, and runoff ratio were significantly related to stream water conductivity.

Population density, watershed percent crop land, and mean discharge were significantly related to total nitrogen concentration. Percent of residents with college-level education, watershed percent impervious surface, and R-B index (stream flashiness) were significantly related to total phosphorus concentration.

We developed three path models using single variables from each category to quantify the direct and indirect effects on water quality outcome variables. For the first model

(conductivity as outcome variable), all direct, indirect and total effects were statistically significant (p < 0.05) (Fig. 3). Population density had a strong positive effect on road density, which itself negatively affected baseflow ratio. Conductivity was positively affected by road density and negatively affected by baseflow ratio. Conductivity was also indirectly affected by population density and road density (Table 10). The total effects of population density, road density and baseflow ratio on conductivity were 0.45, 0.56 and -0.36, respectively (Table 10).

For the nitrogen concentration model, watersheds with greater population density had lower percentage of crop land (Fig. 4, Table 11). Total nitrogen concentration was influenced by

100 percent of crop land and population density and moderately influenced by mean discharge. Total nitrogen was also indirectly affected by total population (Table 11). The total effects of population density, percent crop land and mean discharge on total nitrogen were 0.10 (p=0.809),

0.63 (p=0.072) and 0.33 (p=0.161), respectively (Table 11).

For the total phosphorus concentration model, watersheds with residents having higher percentage of college education attainment had significantly greater percent impervious surface

(Fig. 5, Table 12). Total phosphorus was directly and negatively influenced by R-B index percent impervious surface, and college education. Total phosphorus was also indirectly influenced by college education and percent impervious surface (Table 12). The total effects of college education, impervious surface and R-B index on total phosphorus concentration were -

0.54 (p=0.053), -0.29 (p=0.025) and -0.11 (p=0.700), respectively (Table 12).

All three path models were characterized by acceptable values for goodness-of-fit statistics (Table 13). Specifically, the p-values for χ2-test were 0.782 for the conductivity model,

0.749 for the total nitrogen concentration model, and 0.429 for the total phosphorus concentration model. All three models had CFI = 0.999 and RMSEA = 0.001, indicating that the hypothetical models fit the observed data well (Bentler 1990; Bollen 1989; Brown and Cudeck

1992).

Discussion

We evaluated social system, terrestrial landscape, stream hydrology, and water quality variables for 20 urban headwater stream watersheds in central Iowa. Conductivity, total nitrogen concentration, and total phosphorus concentration were selected as outcome variables for path analyses to quantify the direct, indirect and total effects from these categories of variables. The

101 path models provided evidence that the causal variables we investigated affect water quality and also indicated their relative importance for this set of urban headwater streams in the Midwest.

Because of our relatively small sample size (n = 20 streams), the total number of variables that could be included in each of the three path models was limited (Byrne 2010). We carefully selected these variables based on preliminary analyses, previous studies, and our knowledge of the study area to quantify the major cause and effect relationships in these models.

The models presented here reflect the strongest causal relationships we detected as determined by path coefficients. There were distinct sets of causal variables that emerged as influential for the three different stream water quality outcome variables. These findings indicate that selecting mitigation strategies to address multiple stressors or an approach using multiple mitigation strategies may be necessary for improvement of urban stream water quality.

Conductivity model

The model for stream water conductivity indicates that population density, road density, and baseflow ratio were important causal factors. Specifically, population density had a strong and positive direct effect on road density, which corroborates other evidence for this linkage (e.g.

Owens et al. 2010; Quinn 2013). Road density had a strong and positive direct effect on conductivity. We suspect road salt is one of the more important sources of ionic solutes (Perera et al. 2013) that could be impacting these streams. For example, approximately 200,000 tons of rock salt are used annually in Iowa for highway deicing operations (Iowa Department of

Transportation 2013). Other pollutants contributing to conductivity may include sediment

(Adachi and Tainosho 2005) and heavy metals (Herngren et al. 2006) that could be delivered to urban streams via surface runoff or ground water discharge. (Earon et al. 2012; Perera et al.

2013).

102

Because roads limit infiltration and contribute to more surface discharge, greater road densities also usually lead to lower baseflow ratios (e.g. Arnold and Gibbons 1996). In our analysis, we also documented a negative effect of road density on baseflow ratio, which in turn was linked negatively to conductivity. However, the total effect of road density on conductivity was stronger than the total effect of baseflow (Table 10). Thus, for this set of watersheds, roads were the most important contributor to stream water conductivity. To mitigate conductivity levels in urban streams, civic officials should consider actions that minimize addiitonal road surface area, provide “treatment trains” for road run-off that can filter salts and other pollutants

(Earon et al. 2012), and consider alternatives to application of road salt (Nixon et al. 2007).

Total nitrogen model

In the total nitrogen model, population density, watershed percent of crop land, and stream mean discharge were included as causal variables. Among these variables, percent of crop land had the strongest direct and total effect on stream total nitrogen concentration (Table 11).

The negative effect of population density on percent of crop land was expected since crop land is located in low population density areas (e.g. at the periphery of the city). This model identified crop land as a more important contributor to stream water nitrogen concentration even when compared to population density. Intermixing of urban land and crop land is typical for cities in the Midwest Cornbelt region, and this analysis provides evidence that streams in this landscape receive pollutants from both agricultural and urban sources (Carpenter et al. 1998; Dinnes et al.

2002; Hatfield et al. 1999; Herringshaw et al. 2011). Percent crop land also had a negative effect on mean discharge, consistent with findings of other researchers (e.g. Dabney 1998; Nangia et al.

2010; Rost et al. 2008). Thus, for cities embedded in an agricultural matrix, attention to mitigating against potential negative effects of agricultural activities could be important to

103 manage nitrogen inputs to streams. Several strategies, such as vegetated buffer strips (e.g.

Osborne and Kovacic 1993; Schultz et al. 2004) or nitrogen bioreactors (e.g. Christianson et al.

2012; Shih et al. 2011) could be adapted for these settings.

Total phosphorus model

Unlike the other two models, college education was more influential than population density in the total phosphorus concentration model. Percent impervious surface and R-B index were the other two causal variables related to stream total phosphorus. Specifically, college education had a negative direct effect on total phosphorus, and a positive direct effect on impervious surface. With respect to the direct negative effect of education on total phosphorus, it is possible that more highly educated residents are more aware of the environmental problems associated with lawn fertilizers containing phosphorus, and that these residents may be more likely to use phosphorus-free labeled products (Lehman et al. 2011). This strong positive effect suggests that community education delivered across a wider spectrum of socioeconomic status could be very important in future total phosphorus mitigation programs. With respect to the positive relationship between education level and impervious surface, it is possible that more well-educated residents live in areas with wider roads and larger homes. This is contrary to results of at least one prior study in the Midwest (Li and Weng 2007), perhaps indicating that the relationship between education level and impervious surface might vary by specific location.

Prior studies of the relationship between percent impervious surface and total phosphorus in streams have not generated consistent results. For example, Nagy et al. (2012) reported increased levels of total phosphorus when percent impervious surface increased among study watersheds, but Roberts et al. (2009) reported an opposite pattern. In our analysis, percent impervious surface had a direct negative effect on total phosphorus concentration. Because

104 impervious surfaces act as a part of the stormwater conveyance system (via hard-surfaced road gutters and drains), reductions in overall watershed soil erosion could limit total phosphorus concentrations detected in these streams (e.g. Carvalho et al. 2010).

Conclusions

Urban landscapes are complex systems for which identification of linkages and causal mechanisms driving change in natural systems can prove difficult. We conducted this study examining 20 Midwestern urban headwater streams using path analysis to quantify direct, indirect and total effects among social system, terrestrial landscape, and stream hydrology variables on stream water quality outcomes including conductivity, total nitrogen, and total phosphorus concentrations. This approach allowed us to integrate system complexity in small, well-characterized landscape units in an effort to identify important causal factors and the relative magnitude of their effects.

Stream water conductivity was positively affected by population density and road density, and negatively affected by baseflow ratio. Stream water total nitrogen concentration was positively affected by population density and percent of crop land. Stream water total phosphorus concentration was negatively influenced by percent of residents with college-level education and watershed percent impervious surface. In terms of total effects, road density, percent of crop land, and percent of residents with college education were the most important variables affecting stream conductivity, total nitrogen and total phosphorus, respectively. Goodness-of-fit statistics indicate that the models describing these relationships are relatively robust.

Differences among the variables linked to specific water quality outcomes indicate that mitigation strategies should be focused on different actions for these response variables. For example, actions to limit road salt and other road-related pollutant delivery to streams will be

105 important to decrease stream water conductivity. For total nitrogen concentration, crop land was a primary driver for the set of streams we studied. This phenomenon may be common in areas where municipalities are embedded in agricultural landscapes similar to those in the Midwest, particularly where considerable crop land exists within urban political boundaries. To address elevated nitrogen levels in stream water, strategies such as vegetated buffer strips or incorporation of nitrogen-reducing bioreactors may be applied. For total phosphorus, our study streams were strongly affected by overall education level of watershed residents, suggesting that more broad-spectrum community education about causes and consequences of elevated stream phosphorus levels should be carried out.

Acknowledgements

We would like to thank the stream sampling team (J. Bolton, M. Gerken Golay, P. Bice,

R. Manatt, and A. Olson) for their help with stream monitoring, sample collection and analyses.

We also appreciate technical assistance from city staff members in the five communities, and we thank them as well as local residents for permission to access stream sampling locations. This research was supported by the Chinese Scholarship Council, the Center for Global and Regional

Environmental Research at the University of Iowa, the State of Iowa, and McIntire – Stennis funding.

References

Adachi K, Tainosho Y (2005) Single particle characterization of size-fractionated road sediments. Appl Geochem 20(5): 849-859 Akhtar MS, Oki Y, Adachi T, Murata Y, Khan MHR (2007) Relative phosphorus utilization efficiency, growth response, and phosphorus uptake kinetics of Brassica cultivars under a phosphorus stress environment. Commun Soil Sci Plant Anal, 38(7-8), 1061-1085. Arnold CL, Gibbons CJ (1996) Impervious surface coverage - The emergence of a key environmental indicator. J Am Plan Assoc 62(2): 243-258.

106

Baker DB, Richards RP, Loftus TT, Kramer JW (2004) A new flashiness index: Characteristics and applications to midwestern rivers and streams. J Am Water Resour Assoc 40(2): 503-522 Bentler PM (1990) Comparative fit indexes in structural models. Psychol Bull 107(2): 238-246 Bentler PM (1992) On the fit of models to covariances and methodology to the bulletin. Psychol Bull 112(3): 400-404 Bollen KA (1989) Structural equations with latent variables. Wiley, New York Booth DB, Hartley D, Jackson R (2002) Forest cover, impervious-surface area, and the mitigation of stormwater impacts. J Am Water Resour Assoc 38(3): 835-845 Browne MW, Cudeck R (1992) Alternative ways of assessing model fit. Sociol Methods Res 21(2): 230-258 Byrne BM (2010) Structural equation modeling with AMOS : basic concepts, applications, and programming. Lawrence Erlbaum Associates, Inc., New Jersey Cadenasso ML, Pickett STA, Schwartz K (2007) Spatial heterogeneity in urban ecosystems: Reconceptualizing land cover and a framework for classification. Front Ecol Environ 5(2): 80-88 Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN, Smith VH (1998) Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol Appl 8(3): 559-568 Carvalho O, Guimaraes R, Freitas L, Comes-Loebmann D, Gomes RA, Martins E, Montgomery DR (2010) Urbanization impacts upon catchment hydrology and gully development using mutli-temporal digital elevation data analysis. Earth Surf Process Landf 35(5): 611-617 Christianson L, Bhandari A, Helmers M, Kult K, Sutphin T, Wolf R (2012) Performance evaluation of four field-scale agricultural drainage denitrification bioreactors in Iowa. Trans ASABE 55(6): 2163-2174 Cohen J, Cohen P, West SG, Aiken LS (2003) Applied multiple regression/correlation analysis for the behavioral sciences. Lawrence Erlbaum Associates, New Jersey Dabney SM (1998) Cover crop impacts on watershed hydrology. J Soil Water Conserv 53(3): 207-213 Deemer B, Goodwin K, Lee T, Birchfield MK, Dallavis K, Emerson J, Freeman D, Henry E, Wynn L, Harrison J (2012) Elevated nitrogen and phosphorus concentrations in urbanizing southwest Washington streams. Northwest Sci 86(4): 237-247 DiDonato GT, Stewart JR, Sanger DM, Robinson BJ, Thompson BC, Holland AF, Van Dolah RF (2009) Effects of changing land use on the microbial water quality of tidal creeks. Mar Pollut Bull 58(1): 97-106 Dingman SL (2008) Physical hydrology (2nd edition ed.). Waveland Pr Inc., Illinois Dinnes DL, Karlen DL, Jaynes DB, Kaspar TC, Hatfield JL, Colvin TS, Cambardella CA (2002) Nitrogen management strategies to reduce nitrate leaching in tile-drained midwestern soils. Agron J 94(1): 153-171 Earon R, Olofsson B, Renman G (2012) Initial effects of a new highway section on soil and groundwater. Water Air Soil Pollut 223(8): 5413-5432

107

Eaton AD, Clesceria LS, Rise EW, Greenburg AE (2005) Standard methods for the examination of water and wastewater. American Public Health Association Publications, Washington, DC Eckhardt K (2005) How to construct recursive digital filters for baseflow separation. Hydrol Process 19(2): 507-515 Fissore C, Baker LA, Hobbie SE, King JY, McFadden JP, Nelson KC, Jakobsdottir I (2011) Carbon, nitrogen, and phosphorus fluxes in household ecosystems in the Minneapolis-Saint Paul, Minnesota, urban region. Ecol Appl 21(3): 619-639 Fry J, Xian G, Jin S, Dewitz J, Homer C, Yang L, Barnes C, Herold N, Wickham J (2011) Completion of the 2006 National Land Cover Database for the Conterminous United States. Photogramm Eng Remote Sens 77(9): 858-864. Garson D (2008) Path analysis. North Carolina State University, Raleigh, North Carolina. http://hbanaszak.mjr.uw.edu.pl/TempTxt/Garson_2008_PathAnalysis.pdf. Accessed 27 January 2013 GeoTREE (2011) Iowa LiDAR Mapping Project. http://www.geotree.uni.edu/extensions/iowa- lidar-mapping-project/. Accessed 27 May 2013. Grayson RB, Finlayson BL, Gippel CJ, Hart BT (1996) The potential of field turbidity measurements for the computation of total phosphorus and suspended solids loads. J Environ Manag 47(3): 257-267 Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu JG, Bai XM, Briggs JM (2008) Global change and the ecology of cities. Sci 319(5864): 756-760 Hatfield JL, Jaynes DB, Burkart MR, Cambardella CA, Moorman TB, Prueger JH, Smith MA (1999) Water quality in Walnut Creek watershed: Setting and farming practices. J. Environ Qual 28(1): 11-24. Hatt BE, Fletcher TD, Walsh CJ, Taylor SL (2004) The influence of urban density and drainage infrastructure on the concentrations and loads of pollutants in small streams. Environ Manag 34(1): 112-124 Herngren L, Goonetilleke A, Ayoko GA (2006) Analysis of heavy metals in road-deposited sediments. Anal Chim Acta 571(2): 270-278 Herringshaw C, Stewart T, Thompson J, Anderson P (2011) Land use, stream habitat, and benthic invertebrate assemblages in a highly altered Iowa watershed. Am Midl Nat 165:274- 293 Iowa Department of Transportation (2013) Winter operations. http://www.iowadot.gov/maintenance/materials.html. Accessed 27 January 2013. Lehman JT, Bell DW, Doubek JP, McDonald KE (2011) Reduced additions to river phosphorus for three years following implementation of a lawn fertilizer ordinance. Lake Reserv Manag 27(4): 390-397 Lewis DB, Grimm NB (2007) Hierarchical regulation of nitrogen export from urban catchments: Interactions of storms and landscapes. Ecol Appl 17(8): 2347-2364 Li G, Weng Q (2007) Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data. Int J Remote Sens 28(1-2): 249-267

108

Lim KJ, Engel BA, Tang ZX, Choi J, Kim KS, Muthukrishnan S, Tripathy D (2005) Automated web GIS based hydrograph analysis tool, WHAT. J Am Water Resour Assoc 41(6): 1407- 1416 Liu J (2005) Household dynamics in space and time: implications for urbanization and the environment. Proc. Urban-Rural Interface Conf. Atlanta, GA: 335 Liu J, Dietz T, Carpenter SR, Alberti M, Folke C, Moran E, Pell AN, Deadman P, Kratz T, Lubchenco J, Ostrom E, Ouyang Z, Provencher W, Redman CL, Schneider SH, Taylor WW (2007) Complexity of coupled human and natural systems. Sci 317(5844): 1513-1516 McGranahan G, Satterthwaite D (2003) Urban centers: an assessment of sustainability. Annu Rev Environ. Resour 28:243-274. McKinney M (2002) Urbanization, biodiversity, and conservation. BioSci. 52: 883-890 McKinney M (2008) Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst 11:161-176 Nagy RC, Lockaby BG, Kalin L, Anderson C (2012) Effects of urbanization on stream hydrology and water quality: the Florida Gulf Coast. Hydrol Process 26(13): 2019-2030 Nangia V, Mulla DJ, Gowda PH (2010) Precipitation changes impact stream discharge, nitrate- nitrogen load more than agricultural management changes. J Environ Qual 39(6): 2063-2071 NCDC (2012) National climatic data center. http://www.ncdc.noaa.gov/. Accessed 27 January 2013 Negishi JN, Noguchi S, Sidle RC, Ziegler AD, Nik AR (2007) Stormflow generation involving pipe flow in a zero-order basin of Peninsular Malaysia. Hydrol Process 21(6): 789-806 Nelson KC, Palmer MA, Pizzuto JE, Moglen GE, Angermeier PL, Hilderbrand RH, Dettinger M, Hayhoe K (2009) Forecasting the combined effects of urbanization and climate change on stream ecosystems: from impacts to management options. J Appl Ecol 46(1): 154-163 Nixon WA, Kochumman G, Qiu L, Qiu J, Xiong J (2007) Evaluation of deicing materials and corrosion reducing treatments for deicing salts. Iowa Highway Research Board, Iowa NRGIS (2011) Natural resources geographic information systems library. http://www.igsb.uiowa.edu/nrgislibx/. Accessed 27 January 2013 Olivera F, DeFee BB (2007) Urbanization and its effect on runoff in the Whiteoak Bayou watershed, Texas. J Am Water Resour Assoc 43(1): 170-182 Osborne LL, Kovacic DA (1993) Riparian vegetated buffer strips in water-quality restoration and stream management. Freshw Biol 29(2): 243-258 Owens PM, Titus-Ernstoff L, Gibson L, Beach ML, Beauregard S, Dalton MA (2010) Smart density: a more accurate method of measuring rural residential density for health-related research. Int J Health Geogr 9: 8. doi: 10.1186/1476-072x-9-8 Perera N, Gharabaghi B, Howard K (2013) Groundwater chloride response in the Highland Creek watershed due to road salt application: A re-assessment after 20 years. J Hydrol 479: 159-168

109

Polk County Assessor (2013) http://web.assess.co.polk.ia.us/cgi- bin/web/tt/infoqry.cgi?tt=home/index. Accessed 27 January 2013 Quinn P (2013) Road density as a proxy for population density in regional-scale risk modeling. Nat Hazards 65(3): 1227-1248 Ragab R, Bromley J, Rosier P, Cooper JD, Gash JHC (2003). Experimental study of water fluxes in a residential area: 1. Rainfall, roof runoff and evaporation: the effect of slope and aspect. Hydrol Process 17(12): 2409-2422 Rantz SE (1982) Measurement and computation of streamflow, volume 1. USGS Water-Supply Paper 2175, U.S. Geological Survey, Washington, DC Roberts AD, Prince SD, Jantz CA, Goetz SJ (2009) Effects of projected future urban land cover on nitrogen and phosphorus runoff to Chesapeake Bay. Ecol Eng 35(12): 1758-1772 Rose S, Peters NE (2001) Effects of urbanization on streamflow in the Atlanta area (Georgia, USA): a comparative hydrological approach. Hydrol Process 15(8): 1441-1457 Rost S, Gerten D, Bondeau A, Lucht W, Rohwer J, Schaphoff S (2008) Agricultural green and blue water consumption and its influence on the global water system. Water Resour Res 44(9): W09405. doi: 10.1029/2007WR006331 Schoonover JE, Lockaby BG, Helms BS (2006) Impacts of land cover on stream hydrology in the west Georgia piedmont, USA. J Environ Qual 35(6): 2123-2131 Schultz RC, Isenhart TM, Simpkins WW, Colletti JP (2004) Riparian forest buffers in agroecosystems - lessons learned from the Bear Creek Watershed, central Iowa, USA. Agrofor Syst 61-2(1): 35-50 SDCI (2010) The state data center of iowa. http://www.iowadatacenter.org. Accessed 27 January 2013 Seto KC, Fragkias M, Guneralp B, Reilly MK (2011) A meta-analysis of global urban land expansion. Plos One 6(8): e23777. doi: 10.1371/journal.pone.0023777 Seto KC, Guneralp B, Hutyra LR (2012) Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc Natl Acad Sci USA109(40): 16083-16088 Shih R, Robertson WD, Schiff SL, Rudolph DL (2011) Nitrate controls methyl mercury production in a streambed bioreactor. J Environ Qual 40(5): 1586-1592 Sun RH, Chen LD, Chen WL, Ji YH (2013) Effect of land-use patterns on total nitrogen concentration in the upstream regions of the haihe river basin, China. Environ Manag 51(1): 45-58 United Nations (2011) Revision of world urbanization prospects, the 2011 Revision edn. Department of Economic and Social Affairs, Population Division, New York U.S. Census Bureau (2012) American fact finder. http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml. Accessed 27 January 2013 U.S. EPA (1978) Method 365.3: phosphorus, all forms (colorimetric, ascorbic acid, two reagent). United States Environmental Protection Agency, Washington, DC

110

U.S. EPA (1993a) Method 353.2: determination of nitrate-nitrite nitrogen by automated colorimetry, revision 2.0. United States Environmental Protection Agency, Washington, DC U.S. EPA (1993b) Method 365.1: determination of phosphorus by semi-automated colorimetry, revision 2.0. United States Environmental Protection Agency, Washington, DC Walsh CJ, Roy AH, Feminella JW, Cottingham PD, Groffman PM, Morgan RP (2005) The urban stream syndrome: current knowledge and the search for a cure. J North Am Benthol Soc 24(3): 706-723 Wenger SJ, Roy AH, Jackson CR, Bernhardt ES, Carter TL, Filoso S, Gibson CA, Hession WC, Kaushal SS, Marti E, Meyer JL, Palmer MA, Paul MJ, Purcell AH, Ramirez A, Rosemond AD, Schofield KA, Sudduth EB, and Walsh CJ (2009) Twenty-six key research questions in urban stream ecology: an assessment of the state of the science. J North Am Benthol Soc 28(4): 1080-1098 Wright S (1934) The method of path coefficients. Ann of Math Stat 5(3): 161–215

Tables

Table 1 Variables and units of measurement included within four categories of variables (social system, terrestrial landscape, stream hydrology, and stream water quality) that were measured in 20 urban watersheds in central Iowa. Social system Terrestrial landscape Stream hydrology Stream water quality Population (number of persons) Road density (km km-2) Minimum discharge (m3 s-1 km-2) c Conductivity (uS cm-1) Population density (persons km-2) Average slope (%) Maximum discharge (m3 s-1 km-2) c Dissolved oxygen (mg L-1) a b 3 -1 -2 c High school education (%) Impervious surface (%) Mean discharge (m s km ) pH College education (%) a Water (%) b R-B index Temperature (℃) Average home value (US Dollar) Lawn (%) b Baseflow ratio Nitrate (mg L-1) Forest (%) b Total nitrogen (mg L-1) Crop land (%) b Phosphate (μg L-1) Total phosphorus (μg L-1) Total suspended solids (mg L-1) Turbidity (NTU) a Education level variables were quantified as percent of total population within a watershed 111 b Land cover variables were calculated as percent of total area of a watershed c Discharge variables were standardized by watershed area

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Table 2 Descriptive statistics (mean, minimum, maximum, and standard deviation) for measured variables in 20 urban headwater stream watersheds in central Iowa. Mean Minimum Maximum Standard deviation Population (number of persons) 3,082 47 17,136 4,718 Population density (persons km-2) 8 90 41 2,318 653 High school education (%) 26.2 12.8 42.5 9.9 College education (%) 25.8 8.8 39.2 9.2 Average home value (US Dollar) 138,870 83,011 234,230 36,533 Road density (km km-2) 7.0 1.7 12.9 2.8 Average slope (%) 6.7 4.4 10.9 2.0 Impervious surface (%) 25.4 4.2 44.3 10.0 Water (%) 0.1 0 1.5 0.3 Lawn (%) 20.7 8.2 37.1 8.3 Forest (%) 5.6 0 25.7 8.7 Crop land (%) 13.5 0 70.4 18.1 Minimum discharge (m3 s-1 km-2) 0 0 0.005 0.001 Maximum discharge (m3 s-1 km-2) 2.587 0.339 9.167 2.726 3 -1 -2 Mean discharge (m s km ) 0.023 0.002 0.072 0.018 R-B index 0.057 0.017 0.118 0.031 Baseflow ratio 0.87 0.77 0.96 0.06 Conductivity (uS cm-1) 764.8 615.9 1128.5 119.6 Dissolved Oxygen (mg L-1) 8.9 7.8 10.7 0.7 Nitrate (mg L-1) 2.4 0.9 6.7 1.7 pH 8.2 7.9 8.5 0.1 Phosphate (μg L-1) 133.3 91.9 182.6 26.4 Temperature (℃) 18.8 16.1 22.1 1.5 Total nitrogen (mg L-1) 2.5 0.9 6.7 1.6 Total phosphorus (μg L-1) 150.1 88.6 275.1 44.0 Total suspended solids (mg L-1) 33.5 17.5 105.2 19.5 Turbidity (NTU) 13.9 3.6 51.2 11.2

Table 3 Pearson correlation coefficients (r) among variables in the social system category. Average home value Population Population High College density school education (%) education (%) Average home value -- Population -0.39* -- Population density -0.05 0.78** -- High school education (%) -0.55** 0.22 0.02 -- College education (%) 0.64** -0.28 -0.02 -0.94** -- * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 4 Pearson correlation coefficients (r) among variables in the terrestrial landscape category. Road density Average slope Impervious Water (%) Lawn (%) Forest (%) Crop land (%) surface (%) Road density -- Average slope 0.22 -- Impervious surface (%) 0.53** -0.12 -- Water (%) 0.00 -0.20 0.33 -- Lawn (%) 0.31 0.02 0.46** 0.45** -- Forest (%) -0.11 0.84** -0.36 -0.26 -0.04 -- Crop land (%) -0.57** -0.46** -0.70** -0.33 -0.48** -0.25 -- * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 5 Pearson correlation coefficients (r) among variables in the stream hydrology category. Minimum discharge Maximum discharge Mean discharge R-B index Baseflow ratio Minimum discharge -- Maximum discharge -0.17 -- Mean discharge 0.01 0.57** -- R-B index -0.23 0.55** 0.11 -- Baseflow ratio 0.35 -0.62** -0.05 -0.89** -- * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 6 Pearson correlation coefficients (r) among variables in the stream water quality category. Total Dissolved Total Total Conductivity Nitrate pH Phosphate Temperature. suspended Turbidity. Oxygen nitrogen phosphorus solids Conductivity --

Dissolved oxygen 0.18 --

Nitrate 0.08 0.07 -- pH 0.13 0.24 -0.34 --

Phosphate 0.07 -0.60** 0.20 -0.07 --

Temperature -0.29 -0.34 -0.74** 0.29 0.09 --

Total nitrogen 0 0.07 0.98** -0.39* 0.24 -0.70** --

Total phosphorus 0 -0.59** 0.13 -0.37 0.85** 0.05 0.18 --

Total suspended solids -0.08 -0.47** 0.32 -0.38* 0.60** -0.24 0.31 0.79** --

* * ** ** ** Turbidity -0.34 -0.44 0.07 -0.43 0.53 -0.01 0.10 0.75 0.89 -- 116 *

Statistically significant at 0.10 level ** Statistically significant at 0.05 level

Table 7: Regression statistics for causal relationships evaluated in path analysis to describe effects on conductivity. Dependent Variable Independent Variable(s) Coefficient S.E. p-value Model adjusted R2 Model p-value Road Density Intercept 1.27 0.14 0.63 <0.001 Population density 0.37** 0.06 <0.001 Baseflow ratio Intercept 1.43 0.11 0.16 0.048 Road density -0.11** 0.05 0.048 Conductivity Intercept 7.01 0.49 0.35 0.010 Road density 0.15* 0.08 0.072 Baseflow ratio -0.57* 0.33 0.100 * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 8: Regression statistics for causal relationships evaluated in path analysis to describe effects on total nitrogen. Dependent Variable Independent Variable(s) Coefficient S.E. p-value Model adjusted R2 Model p-value Crop land (%) Intercept 0.58 0.15 0.13 0.064 Population density -0.14* 0.07 0.064 Mean discharge Intercept 0.03 0.01 0 0.337 Crop land (%) -0.01 0.02 0.337 Total nitrogen Intercept 0.38 0.27 0.34 0.022 Population density 0.17* 0.10 0.100 Crop land (%) 0.99** 0.30 0.004 Mean discharge 7.22 4.30 0.113 * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 9: Regression statistics for causal relationships evaluated in path analysis to describe effects on total phosphorus. Dependent Variable Independent Variable(s) Coefficient S.E. p-value Model adjusted R2 Model p-value Impervious surface Intercept 0.32 0.14 0.06 0.155 (%) College education (%) 0.39 0.26 0.155 R-B index Intercept 0.03 0.03 0 0.312 Impervious surface (%) 0.06 0.05 0.312 Total Phosphorus Intercept 5.95 0.33 0.24 0.059 College education (%) -1.16* 0.56 0.054 Impervious surface (%) -0.58 0.49 0.254 R-B index -1.09 1.98 0.590 * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 10 Direct (DE), indirect (IE), and total effects (TE) of independent variables on dependent variables in path analysis to describe effects on stream water conductivity. Population density effect Road density effect Baseflow ratio effect Dependent variable DE IE TE DE IE TE DE IE TE Road Density 0.81** 0.81** p-value 0.041 0.041 Baseflow ratio -0.36** -0.36** -0.45** -0.45** p-value 0.019 0.019 0.017 0.017 Conductivity 0.45** 0.45** 0.40** 0.16** 0.56** -0.36** -0.36** p-value 0.025 0.025 0.035 0.017 0.023 0.024 0.024 * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 11 Direct (DE), indirect (IE), and total effects (TE) of independent variables on dependent variables in path analysis to describe effects on stream water total nitrogen concentration. Dependent variable Population density effect Crop land (%) effect Mean discharge effect DE IE TE DE IE TE DE IE TE Crop land (%) -0.42* -0.42* p-value 0.084 0.084 Mean discharge 0.10 0.10 -0.23 -0.23 p-value 0.243 0.243 0.429 0.429 Total nitrogen 0.36* -0.27* 0.10 0.70* -0.07 0.63* 0.33 0.33 p-value 0.055 0.065 0.809 0.051 0.238 0.072 0.161 0.161 * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 12 Direct (DE), indirect (IE), and total effects (TE) of independent variables on dependent variables in path analysis to describe effects on stream water total phosphorus concentration. Dependent variable College Impervious R-B index education effect surface effect effect DE IE TE DE IE TE DE IE TE Impervious surface (%) 0.33** 0.33** p-value 0.028 0.028 R-B index 0.08 0.08 0.24 0.24 p-value 0.185 0.185 0.305 0.305 Total phosphorus -0.44** -0.10** -0.54* -0.26* -0.03 -0.29** -0.11 -0.11 p-value 0.026 0.026 0.053 0.069 0.463 0.025 0.700 0.700 * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 13 Goodness-of-fit statistics for stream water conductivity, total nitrogen concentration, and total phosphorus concentration path models. Model χ2 (df) p-value CFI RMSEA Conductivity 0.492 (2) 0.782 0.999 0.001 Total Nitrogen 0.102 (1) 0.749 0.999 0.001 Total Phosphorus 0.627 (1) 0.429 0.999 0.001

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Figures

Fig. 1: Location of 20 headwater stream watersheds included in this study in five cities, central Iowa, U.S.A.

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Social system:  Total watershed population  Watershed population density  Education level  Average home value

Terrestrial landscape:  Road density  Impervious surface  Land cover

Stream hydrology:  Discharge  R-B index  Baseflow ratio

Stream water quality:  Conductivity  Dissolved oxygen  pH  Nitrogen concentration  Phosphorus concentration  Temperature  Total suspended solids  Turbidity

Fig. 2: Hypotheses of relationships among four categories of variables: social system, terrestrial landscape, stream hydrology, and stream water quality. Arrows represent hypothesized causal relationships among variables tested in this study and their direction.

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Population

density

0.81**

Road density

** -0.45

Baseflow ratio 0.40**

-0.36**

Conductivity

Fig. 3: Path model of direct and indirect effects of social (population density), landscape (road density), and hydrology (baseflow ratio) variables on a water quality variable (conductivity). Each value associated with an arrow is a path coefficient (standardized regression coefficient) and is equivalent to the direct effect of the independent variable on the dependent variable. Two asterisks denote statistical significance at p<0.05.

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Population

density

-0.42*

Crop land

-0.23 0.36*

0.70* Mean discharge

0.33

Total nitrogen

Fig. 4: Path model of direct and indirect effects of social (population density), landscape (percent of crop land), and hydrology (mean discharge) variables on a water quality variable (total nitrogen concentration). Each value associated with an arrow is a path coefficient (standardized regression coefficient) and is equivalent to the direct effect of the independent variable on the dependent variable. One asterisk denotes statistical significance at p<0.10.

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College education

0.33**

Impervious surface

** 0.24 -0.44

* -0.26 R-B index

-0.11

Total phosphorus

Fig. 5: Path model of direct and indirect effects of social (percent of college education), landscape (percent of impervious surface), and hydrology (R-B index) variables on a water quality variable (total phosphorus concentration). Each value associated with an arrow is a path coefficient (standardized regression coefficient) and is equivalent to the direct effect of the independent variable on the dependent variable. One asterisk denotes statistical significance at p<0.10, two asterisks denote statistical significance at p<0.05.

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CHAPTER 5: URBAN STREAM WATER QUALITY AS A PERFORMANCE MEASURE OF THE NPDES STORM WATER PHASE II PERMIT PROGRAM

A manuscript formatted for submission to Journal of Environmental Management

Jiayu Wu, Janette R. Thompson, Timothy W. Stewart, Randall K. Kolka and Kristie J. Franz

Abstract

The United States Environmental Protection Agency has administered the National

Pollutant Discharge Elimination System (NPDES) storm water program since 1990. This program was created to reduce pollutant delivery into receiving water bodies such as rivers and streams. Although in some cases local municipal responses to the NPDES permit program itself have been examined, previous studies have not been undertaken to examine relationships between municipal responses to permit requirements and surface water quality in urban streams.

In this study, data for social and landscape characteristics were collected for five municipalities, and stream hydrology and water quality were monitored for four headwater streams in each one.

We tested for differences in stream water quality (based on measurements of seven parameters) among the five municipalities, and examined relationships between stream water quality and each municipality’s response (based on activities addressing the six required “Minimum Control

Measures” described in their annual reports) to the NPDES program. We also examined the relative importance of the other social, landscape, and hydrological variables in relation to

NPDES responses with respect to water quality outcome variables. We detected differences between municipalities for total phosphorus, phosphate, total suspended solids, and turbidity in stream water, but no differences among them for conductivity, total nitrogen, or nitrate in streams. Based on semi-quantitative analyses of their reports, municipalities receiving higher scores on their responses to the NPDES program had consistently lower levels of nitrate,

130 phosphate and total suspended solids in streams. Among other factors that affected stream water quality, population density and percent crop land were associated with higher concentrations of nitrate and total nitrogen. Phosphate and total phosphorus were negatively related to proportion of watershed residents with a college education. Evidence of linkages between the responses of municipalities to storm water regulations and stream water quality point toward effectiveness of the NPDES permitting program and could be used to provide additional guidance to municipal officials about activities associated with their storm water management programs.

Keywords: Stream nutrient concentrations, stream water conductivity, total suspended solids, notice of intent, minimum control measures, storm water management

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Introduction

Nonpoint source pollutants in urban areas have long been identified as a threat to urban stream water quality and ecological integrity (Brown et al., 2009; Carpenter et al., 1998; Wenger et al., 2009) by causing eutrophication and reducing biodiversity (Violin et al., 2011; Walsh et al., 2005). Commonly reported urban stream pollutants include sediment (Adachi & Tainosho,

2005), metal ions (Herngren, Goonetilleke, & Ayoko, 2006), and nutrients (Brett et al., 2005).

These pollutants are usually associated with human activities, such as application of lawn fertilizer (Fissore et al., 2011), road construction (Earon, Olofsson, & Renman, 2012), or improper waste disposal (Oyekale & Ige, 2012). These pollutants are then delivered to urban streams as “pulses” in surface runoff during and immediately following precipitation events

(Roberts & Prince, 2010).

Although sources and mechanisms of urban storm water-related problems have been studied (Hatt, Fletcher, Walsh, & Taylor, 2004; Meierdiercks, Smith, Baeck, & Miller, 2010;

Smith et al., 2005; Walsh et al., 2005), they are difficult to mitigate because of the complexity of the urban environment. Municipal-level storm water mitigation efforts generally take one of two forms, either by implementing engineered solutions (and/or bio-engineered solutions) or by setting policy. With respect to engineered solutions, a number of practices have typically been used for management of storm water quantity and quality, including structural elements such as sand filters, infiltration trenches, and infiltration basins (Mays, 2004), and permeable/pervious pavement (Fassman & Blackbourn, 2010). Bio-engineering solutions that have been applied for urban storm water mitigation include constructed ponds/wetlands, bioswales, and riparian buffers

(Hottenroth, Harper, & Turner, 1999; Meierdiercks et al., 2010; Osborne & Kovacic, 1993;

Vymazal, 2007).

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With respect to policy, the amendments made in 1987 to the Clean Water Act of 1972 led to development of a tiered strategy for implementation of the National Pollutant Discharge

Elimination System (NPDES) storm water program. The primary intention of the NPDES permitting program has been to “preserve, protect, and improve the Nation’s water resources from polluted storm water runoff” (U.S. EPA, 2000b). Federally, the NPDES permitting program has been administered by the U.S. Environmental Protection Agency (EPA). In Iowa, the program has been coordinated at the state level by the Iowa Department of Natural Resources

(IDNR). The program applies primarily to municipalities, which are required to submit a Notice of Intent (NOI) to apply for an NPDES permit that governs their activities for a five-year period.

In Iowa, municipalities are also required to submit reports about storm water management activities that address their NOI on an annual basis. The annual report must address the six minimum control measures (MCMs) as specified in the NPDES Rule for management, including

(1) public education and outreach, (2) public participation/involvement, (3) illicit discharge detection and elimination, (4) construction site runoff control, (5) post-construction runoff control, and (6) pollution prevention/good housekeeping (U.S. EPA, 2000b).

The NPDES program was implemented in two distinct phases. Phase I was implemented in 1990 and applied to municipal separate storm sewer system (“MS4s”) serving populations of

100,000 or more, construction activity disturbing five or more acres of land, and ten categories of specific industrial activities (U.S. EPA, 2000b). In Iowa, only two municipalities were subject to

Phase I. The Storm Water Phase II Final Rule (Phase II hereafter) was initiated in 1999 and included 43 additional municipalities throughout Iowa.

In two previous studies, researchers examined implementation of the NPDES Phase II program in California and Kansas, with particular attention given to how the program led to local

133 government actions (White & Boswell, 2006; 2007). They found that implementation of the

Phase II program was positively affected by education level of residents, local officials’ perceptions of the federal policy, and whether the local government was engaged with other municipalities in cooperative planning efforts related to storm water management. They also found that the impetus provided by the Phase II program requirements were responsible for the majority (62%) of BMP adoptions, and that municipalities responding before program deadlines had higher quality responses to those requirements.

Because the goals of the NPDES permitting program are to improve the quality of water resources, we contend that measurement of water quality parameters is a good direct approach to assess municipal performance in response to NPDES criteria. However, we are not aware of previous studies examining relationships between water quality outcomes and NPDES requirements, or to responses provided by municipalities in their NPDES NOI/annual report documents. In this study, we examined water quality parameters for four urban streams in each of five municipalities to investigate the following questions:

1) Are there differences in stream water quality between the five study municipalities?

2) Are municipal responses to implementation of the NPDES permitting program (as

indicated in annual reports based on their NOI) related to water quality in their streams?

3) What is the relative importance of different variables among social, landscape, and

hydrological factors that affect stream water quality in these municipalities?

Methods

Study area

This study included five municipalities located in Polk County, Iowa, U.S.A., including

Altoona, Ankeny, Des Moines, Johnston, and Pleasant Hill (Figure 1). All of these municipalities

134 are regulated by the NPDES permitting program; Des Moines was included in Phase I, and the other four were added during implementation of Phase II. Within each city, we studied four headwater stream watersheds, for a total of 20 streams. Watershed boundaries were delineated for each of these watersheds using LiDAR-generated Digital Elevation Models with 1-meter spatial resolution (GeoTREE, 2011). The area of the watersheds ranged from 0.5 km2 to 20.5 km2. The major land cover types represented in the watersheds included urban land cover

(impervious surfaces), open space (mostly lawns), crop land, and small areas of forest.

Data structure

Four categories of variables were measured for each watershed, including social, landscape, hydrology, and water quality variables that were selected based on results of prior studies (Table 1). Social variables included population density and college education; landscape variables included road density, percent impervious surface, and percent crop land; hydrological variables included mean discharge, flashiness (R-B index), and baseflow ratio; and water quality variables included conductivity, total suspended solids, turbidity and concentrations of total nitrogen, nitrate, total phosphorus and phosphate. We also rated responses to the six minimum control measures of the NPDES permitting program by evaluating annual report content for each municipality.

Social variables

Block-group level population and education data were extracted from 2010 census data

(U.S. Census Bureau 2012) and matched to watershed boundaries. An area-weighted total population and number of residents with specific education levels was then calculated using

ArcMap Version 10.0 (Environmental Systems Research Institute, ESRI, Redlands, California,

U.S.). Population density was determined by dividing total population by watershed area, and

135 percent of residents with college education was obtained by dividing the number of residents with college-level education by total watershed population. In addition to population density, we chose education level as a social system because previous research has indicated positive relationships between residents’ level of education and pro-environmental behaviors (e.g. use of phosphorus-free fertilizers) and between residents’ educational attainment and municipal responses to NPDES requirements (White and Boswell, 2006).

Landscape variables

Road data were downloaded from the Iowa Natural Resources Geographic Information

Systems Library (NRGIS, 2011). Road density was determined by dividing the total length of roads in each watershed by watershed area. Impervious surface data was obtained from the City of Des Moines GIS department (A. Whipple, personal communication, June 17, 2010) and was originally digitized based on 2006 aerial photos. We updated these data using 2011 aerial images with 1-meter spatial resolution. Percent impervious surface was determined as total impervious surface area in a watershed divided by watershed area. Crop land data was obtained from the

2006 National Land Cover Database (Fry et al., 2011). Percent of crop land was determined by dividing the area of cultivated crop land by total watershed area.

Hydrological variables

For each of the 20 streams, stream monitoring and water sampling were conducted twice a month at a common point near the stream outlet from April to October, 2011 (9 to 15 sample events per stream depending on stream hydrological conditions). Stream discharge was determined using the method described by Rantz (1982). The mean discharge was calculated based on all observed discharge rates for the watershed and then standardized by area of the watershed. One HOBO U20 water level data logger (Onset Computer Corporation, Inc.,

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Pocasset, MA) was installed in each of the 20 streams to record stream stage at 5-minute intervals, which was then converted to discharge using a stage-discharge rating curve for each stream. These discharge data were used to calculate Richards-Baker Flashiness Index (R-B index) (Baker, Richards, Loftus, & Kramer, 2004) and baseflow ratio. The R-B index measures oscillations in discharge, and a greater value indicates a flashier stream. Baseflow ratio was calculated using a digital filter (Eckhardt, 2005) which estimated the amount of baseflow as a proportion of total discharge.

Water quality variables

During each field visit to each stream, conductivity was determined on-site at the defined stream cross-section using a portable Hach HQ40d™ meter (Hach Company, Loveland, CO).

Turbidity was also determined on-site using an Oakton Model T-100™ Turbidimeter (Oakton

Instruments, Vernon Hills, IL). For each stream, two grab samples were collected using acid- washed polypropylene bottles: one sample (1000-ml bottle) was collected to determine phosphate, total phosphorus and total suspended solids, and another sample (in an acidified 50- ml bottle) was collected to determine nitrate and total nitrogen. All samples were stored in a cooler before being processed in the Riparian Management Systems laboratory in the

Department of Natural Resource Ecology and Management, Iowa State University, Ames, Iowa.

Concentrations of nitrate, total nitrogen, phosphate, and total phosphorus were measured using standard methods (U.S. EPA method 353.2 for nitrogen; 365.1 and 365.3 for phosphorus; U.S.

EPA 1978; 1993a; 1993b) Total suspended solids were determined using filtration (Eaton et al.

2005). We calculated a mean value based on all samples for each of the seven water quality variables for each of the streams.

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Scoring procedures for annual report responses related to the six minimum control measures

We obtained the two most recent annual reports addressing the NOI from each of the five municipalities. The reports were evaluated independently by two researchers using a rising eight- point scale (from 0 to 7) to rate the quality and completeness of responses for each of the six minimum control measures (MCMs) (scoring system adapted from White & Boswell, 2006). On this scale, a score of “0” was given if the MCM was not addressed; a score of 3 was given if the response minimally met EPA requirements and the community’s NOI; and a score of 7 indicated that the response substantially exceeded EPA requirements. After independently evaluating the reports, the two researchers reached consensus on a single score for each MCM for each community and report year. We then calculated an average score for each MCM across the two years and added all six scores to create a total score for each municipality.

Statistical analysis

Minimum, maximum, mean, and standard deviation were calculated for the social, landscape, hydrological and water quality variables of the 20 watersheds, and for each of the

MCM scores for the five municipalities. We then used the Kruskal-Wallis test to examine water quality differences among the five municipalities (Kruskal & Wallis, 1952). The Kruskal-Wallis test is a non-parametric analogue of analysis of variance (ANOVA) that performs on ranked data and does not require normality and homoscedasticity. This test was conducted for all water quality variables to determine if there were differences in stream water parameters among the five municipalities (data aggregated for each group of four streams). The null hypothesis for this test was that the mean ranks of the water quality variables for the five municipalities were the same.

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Linear regression analyses were conducted to examine relationships between total MCM scores and the seven water quality variables. We then used the Jonckheere trend test to determine whether increases in total MCM scores were related to trends for the water quality variables we measured (Jonckheere, 1954). With this approach, we could test whether a municipality with a higher total MCM response score had lower stream water pollutant concentrations. The null hypothesis for the Jonckheere trend test was that the distribution of measured water quality parameters would not differ along a gradient of increasing total MCM scores.

For each of water quality outcome variables, a multiple regression analyses was then conducted to assess the relative importance among the variables in the other four (social, landscape, hydrological, and MCM ratings) categories. For each regression, we selected one variable (that was likely to be important based on previous research) from each of the categories in Table 1. We used SPSS Version 19.0 (IBM Corp., Armonk, NY) to conduct all calculations and statistical tests. We report statistical significance at p< 0.10 and p< 0.05.

Results

Variable descriptions and differences among municipalities for water quality variables

Social, landscape, hydrological, and water quality variables

The 20 watersheds had a mean population density of 890 persons km-2, and 25.8 % of residents had a college-level education (Table 2). For landscape variables, mean road density for the 20 watersheds was 7.0 km km-2. Percent of impervious surface ranged from 4.2 % to 44.3 % with a mean of 25.4 %. Percent of crop land ranged from 0 % to 70.4%, with a mean of 13.5%.

For hydrological variables, the mean discharge of the 20 streams ranged from 0.002 to 0.072 m3 s-1 km-2, and the R-B index ranged from 0.017 to 0.188. The streams had a mean baseflow ratio of 0.87. For water quality variables, the mean for conductivity was 764.8 μS cm-1, for nitrate was

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2.4mg L-1, and for total nitrogen was 2.5 mg L-1. The mean for phosphate concentration was133.3 μg L-1, and for total phosphorus was 150.1 μg L-1. The mean for total suspended solids was 33.5 mg L-1, and for turbidity was 13.9 NTU.

Differences among municipalities for water quality variables

The Kruskal–Wallis tests for phosphate (p = 0.04), total phosphorus (p = 0.05), and total suspended solids (p = 0.05) indicated differences among the municipalities for these water quality outcomes (Table 3). Differences in turbidity (p = 0.08) were less pronounced. We did not detect differences for conductivity, nitrate, or total nitrogen concentrations in stream water.

MCM scores and their relationships with water quality variables

MCM scores

Mean scores for the six MCMs were between 4.8 and 6.0 (Table 4). All five communities had relatively high scores for public education and outreach with a mean score of 6.0 on an eight-point (0 to 7) scale. Mean scores for public participation/involvement (5.1) and illicit discharge detection and elimination (4.8) were somewhat lower, because of lower minimum scores (3.5 for public participation/involvement, and 2.8 for illicit discharge detection and elimination). Construction site runoff control had a mean score of 5.6, post-construction runoff control had a mean score of 5.9, and pollution prevention/good housekeeping had a mean score of 5.6. Total MCM scores varied from 27.5 to 36.5 among the five municipalities.

Relationships among total MCM scores and water quality variables

As total MCM scores increased, pollutant levels decreased for phosphate, total phosphorus, total suspended solids, and turbidity. The Jonckheere trend tests for nitrate (p =

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0.06), phosphate (p = 0.07) and total suspended solids (p = 0.10) point toward decreases in pollutant levels in streams along a gradient of increasing mean total MCM scores (Table 5).

Multiple regression analysis

Five of the seven regression models demonstrated acceptable goodness-of-fit (model p- value less than 0.10), including the models for conductivity, nitrate, total nitrogen, phosphate, and total phosphorus (Table 6). However, the conductivity model was characterized by relatively low p-values for independent variable coefficients. The total suspended solids and turbidity models were also limited by weak overall model goodness-of-fit. The effect of total MCM score on water quality variables was significant (at p< 0.10) for six of the seven models.

The nitrate model demonstrated that percent of crop land was strongly related to stream nitrate concentration (standardized coefficient = 0.760, p < 0.001), followed by population density, total MCM score, and mean discharge (Table 6). Percent of crop land was also the strongest variable in the total nitrogen concentration model (standardized coefficient = 0.791, p <

0.001). In the phosphate model, total MCM score had the strongest effect (standardized coefficient = -0.472, p = 0.014), followed by impervious surface, proportion of watershed residents with college education and R-B index. Stream water total phosphorus concentration was strongly related to the proportion of residents with college-level education (standardized coefficient = -0.430, p = 0.044) and total MCM score (standardized coefficient = -0.420, p =

0.039), followed by impervious surface and R-B index.

Discussion

We monitored four urban streams in each of five municipalities, and evaluated annual reports (based on their Notice of Intent) from each of the municipalities to characterize their responses to the six MCMs included in the NPDES permitting program. We quantified social,

141 landscape, hydrology and water quality variables for the 20 watersheds associated with these streams. Stream water quality variables consistently decreased as total MCM scores increased.

Four water quality variables showed differences among the five municipalities, including phosphate, total phosphorus concentration, total suspended solids, and turbidity. Further, when total MCM scores increased, concentrations for three water quality variables decreased, including nitrate, phosphate, and total suspended solids. Finally, relative importance of social, landscape, hydrological, and municipal response effects on the seven water quality variables was quantified.

Differences in stream water quality between the five study municipalities

We detected differences in stream water concentrations of phosphate, total phosphorus, total suspended solids and turbidity among the five municipalities. Thus, although these streams were located in a small geographic area within a similar geological context, their water quality signatures were different, possibly because sources and amounts of pollutant delivery may be different for these communities. Some commonly reported phosphorus pollutant sources include lawn fertilizer (Fissore et al., 2011; Lehman et al., 2011) and soil erosion (Im, Brannan, &

Mostaghimi, 2003). For total suspended solids and turbidity, previous studies have also identified soil erosion as well as impervious surfaces as influential factors (Brodie & Dunn,

2010; Carvalho et al., 2010; Earon et al., 2012). We detected variations for impervious surface and road density among these watersheds, which could be related to these water quality differences.

We also note that total nitrogen concentrations, total phosphorus concentrations, and turbidity levels in these streams at times exceeded EPA water quality criteria for Ecoregion VI

(recommended levels are 2.18 mg L-1 for total nitrogen, 76 μg L-1 for total phosphorus, and 6.36

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NTU for turbidity; U.S. EPA, 2000a). For total nitrogen and turbidity streams only occasionally exceeded these criteria. For total phosphorus concentration, however, even the minimum level detected in our samples exceeded the criteria, and the maximum was about 3 times greater.

However, it should also be noted that many streams, including those in undisturbed landscapes may exceed these criteria, particularly for phosphorus (e.g. Ice and Binkley, 2003).

Relationships between MCM scores and stream water quality

Municipalities with higher total MCM scores generally had lower concentrations of all pollutants in their streams, notably so for phosphate, nitrate, and total suspended solids in the trend tests we conducted. It seems likely that strategies documented by the municipalities that qualitatively distinguished their reports did positively affect stream water quality, such as use of phosphorus-free fertilizer on park lawns, holding public education events, and adopting and enforcing ordinances to control illicit discharge and construction site stormwater runoff. Linear regression also indicated relationships between municipal MCM scores and concentrations of phosphate, total phosphorus, total suspended solids, and turbidity. We did not detect relationships between municipal scores and stream water pH or conductivity. Although nutrient concentrations and turbidity in these streams could be related to NPDES responses, they still often exceeded EPA ambient water quality criteria, thus municipalities should continue to pursue additional strategies to limit delivery of these pollutants to streams.

For each of the MCMs, scoring of annual reports indicated that municipal responses almost always met, and often exceeded regulatory requirements as described in Phase II Final

Rule documents (U.S. EPA, 2000b). There was strong evidence that all five municipalities were engaged in public education and outreach, post-construction runoff control, and pollution prevention/good housekeeping. We observed more variation in responses to construction site

143 runoff control and public participation/involvement, MCMs for which some municipalities marginally met the criteria. Municipalities with relatively low scores for these two MCMs could improve their NPDES performance by developing ordinances that require use of specific measures on construction sites, such as compost socks, cover crops, or limited site grading to reduce erosion and runoff from these areas (USDA NRCS, 2004). Public participation and involvement could be increased by creating opportunities for collaborative learning and action to improve stream water quality (e.g. Herringshaw, Thompson, & Stewart, 2010). Scores also were relatively low for illicit discharge detection and elimination, which might have been more related to limited evidence included in the reports rather than lack of on-the-ground activity to pursue this measure. For municipalities that need to better address this requirement, low-cost visual reconnaissance techniques have been described for pollutant source identification in storm sewer systems (e.g. Irvine, Rossi, Vermette, Bakert, & Kleinfelder, 2011).

Relative importance of social, landscape, hydrological and policy responses for water quality outcomes Water quality regression models provided evidence of the relative influence of the social, landscape, and hydrological variables we included in the models compared to municipal responses on annual reports. Among the five models with acceptable goodness-of-fit, models for nitrate and total nitrogen concentration in stream water indicated a strong influence from percent of crop land in these watersheds. Although all 20 streams in our study were located within urban political boundaries, because of the characteristics of Midwest landscape, these watersheds still contained areas of crop land (Table 2). Nitrogen delivery from crop land has been identified as a major source of pollution at both global (Vitousek et al., 1997) and local (Johnson, Richards,

Host, & Arthur, 1997) scales. Because nitrogen-related pollutants exceed water quality criteria in

144 these streams (U.S. EPA, 2000a), controlling inputs from agricultural activities is important even within a municipal setting.

For models describing phosphate, total suspended solids, and turbidity, total MCM response scores were strongly related to stream water quality. This confirms the importance of urban stormwater management for these water quality variables, and indicates that strategies being implemented by these municipalities are effective in protecting urban streams. In these models and the total phosphorus model, percent of residents with college-level education was also an important factor. Among other possible effects, college-educated residents are probably more aware of the environmental impact of phosphorus-containing lawn fertilizers and they may be more likely to avoid purchasing or applying these products (Lehman et al., 2011). Additional public education and public involvement in urban stream-related activities could lead to more widespread adoption of practices that limit delivery of pollutants to streams.

Conclusions

We monitored urban stream hydrology and water quality parameters, and evaluated annual reports of five municipalities in response to the NPDES permitting program. We examined potential relationships between stream water quality and the degree to which municipal responses were sufficient in meeting regulatory requirements. We conclude that:

1. There were differences in stream water quality for our study municipalities, including levels of phosphate, total phosphorus, total suspended solids, and turbidity. Total phosphorus, total nitrogen, and turbidity also at least occasionally exceeded ambient water quality standards for ecoregion VI (U.S. EPA, 2000a), and total phosphorus exceeded this criterion even in the least polluted streams we observed. Continued efforts to improve urban stream water quality should

145 specifically target sources of phosphorus pollutants and focus public education and engagement accordingly.

2. The quality of municipal responses to the NPDES permitting program was related to water quality outcomes for nitrate, phosphate, total phosphorus, total suspended solids, and turbidity.

This provides direct evidence of positive water quality outcomes in urban streams that emanate from municipal efforts to address these imposed regulatory requirements.

3. Among a group of selected variables representing social, landscape, hydrological and NPDES response categories, nitrate and total nitrogen concentrations in stream water were most strongly related to percent of crop land in the watershed, while phosphorus and phosphate concentrations, total suspended solids, and turbidity were strongly related to total MCM score and percent of population with college-level education. Efforts to broaden opportunities for public education and outreach and public participation/ involvement may lead to improved water quality outcomes for these pollutants.

Acknowledgements

We would like to thank collaborators (particularly city staff members) and residents in the five municipalities included in this study for permission and access to sample the streams and for sharing their annual reports. We also thank Marissa Moore for assistance with evaluation of documents. This research was supported by the Chinese Scholarship Council, the Center for

Global and Regional Environmental Research at the University of Iowa, the State of Iowa, and

McIntire – Stennis funding.

References

Adachi, K., & Tainosho, Y. (2005). Single particle characterization of size-fractionated road sediments. Applied Geochemistry, 20(5), 849-859.

146

Baker, D. B., Richards, R. P., Loftus, T. T., & Kramer, J. W. (2004). A new flashiness index: Characteristics and applications to midwestern rivers and streams. Journal of the American Water Resources Association, 40(2), 503-522. Brett, M. T., Arhonditsis, G. B., Mueller, S. E., Hartley, D. M., Frodge, J. D., & Funke, D. E. (2005). Non-point-source impacts on stream nutrient concentrations along a forest to urban gradient. Environmental Management, 35(3), 330-342. Brodie, I. M., & Dunn, P. K. (2010). Commonality of rainfall variables influencing suspended solids concentrations in storm runoff from three different urban impervious surfaces. Journal of Hydrology, 387(3-4), 202-211. Brown, L. R., Cuffney, T. F., Coles, J. F., Fitzpatrick, F., McMahon, G., Steuer, J., Bell, A. H., & May, J. T. (2009). Urban streams across the USA: lessons learned from studies in 9 metropolitan areas. Journal of the North American Benthological Society, 28(4), 1051-1069. Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., & Smith, V. H. (1998). Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological Applications, 8(3), 559-568. Carvalho, O., Guimaraes, R., Freitas, L., Comes-Loebmann, D., Gomes, R. A., Martins, E., & Montgomery, D. R. (2010). Urbanization impacts upon catchment hydrology and gully development using mutli-temporal digital elevation data analysis. Earth Surface Processes and Landforms, 35(5), 611-617. Earon, R., Olofsson, B., & Renman, G. (2012). Initial effects of a new highway section on soil and groundwater. Water Air and Soil Pollution, 223(8), 5413-5432. Eaton, A. D., Clesceria, L. S., Rise, E. W., & Greenburg, A. E. (2005). Standard methods for the examination of water and wastewater. American Public Health Association Publications, Washington, DC Eckhardt, K. (2005). How to construct recursive digital filters for baseflow separation. Hydrological Processes, 19(2), 507-515. Fassman, E. A., & Blackbourn, S. (2010). Urban runoff mitigation by a permeable pavement system over impermeable soils. Journal of Hydrologic Engineering, 15(6), 475-485. Fissore, C., Baker, L. A., Hobbie, S. E., King, J. Y., McFadden, J. P., Nelson, K. C., & Jakobsdottir, I. (2011). Carbon, nitrogen, and phosphorus fluxes in household ecosystems in the Minneapolis-Saint Paul, Minnesota, urban region. Ecological Applications, 21(3), 619- 639. Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., & Wickham, J. (2011). Completion of the 2006 National Land Cover Database for the Conterminous United States. Photogrammetric Engineering & Remote Sensing, 77(9), 858-864. GeoTREE. (2011). Iowa LiDAR mapping project. Available from: http://www.geotree.uni.edu/extensions/iowa-lidar-mapping-project/. Accessed 27 January 2013 Hatt, B. E., Fletcher, T. D., Walsh, C. J., & Taylor, S. L. (2004). The influence of urban density and drainage infrastructure on the concentrations and loads of pollutants in small streams. Environmental Management, 34(1), 112-124.

147

Herngren, L., Goonetilleke, A., & Ayoko, G. A. (2006). Analysis of heavy metals in road- deposited sediments. Analytica Chimica Acta, 571(2), 270-278. Herringshaw, C. J., Thompson, J. R., & Stewart, T. W. (2010). Learning about restoration of urban ecosystems: a case study integrating public participation, stormwater management, and ecological research. Urban Ecosystems, 13(4), 535-562. Hottenroth, D., Harper, C., & Turner, J. (1999). Effectiveness of integrated stormwater management in a Portland, Oregon, watershed. Journal of the American Water Resources Association, 35(3), 633-641. Ice, G. & Binkley, D. (2003). Forest stream water concentrations of nitrogen and phosphorus: A comparison with EPA’s proposed water quality criteria. Journal of Forestry 101: 21-28. Im, S. J., Brannan, K. M., & Mostaghimi, S. (2003). Simulating hydrologic and water quality impacts in an urbanizing watershed. Journal of the American Water Resources Association, 39(6), 1465-1479. Irvine, K., Rossi, M. C., Vermette, S., Bakert, J., & Kleinfelder, K. (2011). Illicit discharge detection and elimination: Low cost options for source identification and trackdown in stormwater systems. Urban Water Journal, 8(6), 379-395. Johnson, L. B., Richards, C., Host, G. E., & Arthur, J. W. (1997). Landscape influences on water chemistry in Midwestern stream ecosystems. Freshwater Biology, 37(1), 193. Jonckheere, A. R. (1954). A distribution-free k-sample test again ordered alternatives. Biometrika (41), 133-145. Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583-621. Lehman, J. T., Bell, D. W., Doubek, J. P., & McDonald, K. E. (2011). Reduced additions to river phosphorus for three years following implementation of a lawn fertilizer ordinance. Lake and Reservoir Management, 27(4), 390-397. Mays, L.W. (2004). Urban Stormwater Management Tools. New York: MacGraw Hill. Meierdiercks, K. L., Smith, J. A., Baeck, M. L., & Miller, A. J. (2010). Analyses of urban drainage network structure and its impact on hydrologic response. Journal of the American Water Resources Association, 46(5), 932-943. NRGIS. (2011). Natural resources geographic information systems library. Available from: http://www.igsb.uiowa.edu/nrgislibx/. Accessed 27 January 2013 Osborne, L. L., & Kovacic, D. A. (1993). Riparian vegetated buffer strips in water-quality restoration and stream management. Freshwater Biology, 29(2), 243-258. Oyekale, A. S., & Ige, R. F. (2012). Urban households' willingness to pay for environmental safety in Ibadan metropolis, Nigeria. Asia Life Sciences, 69-85. Rantz, S. E. (1982). Measurement and computation of streamflow, volume 1. Geological Survey Water-Supply Paper 2175. Washington, DC. Roberts, A. D., & Prince, S. D. (2010). Effects of urban and non-urban land cover on nitrogen and phosphorus runoff to Chesapeake Bay. Ecological Indicators, 10(2), 459-474.

148

Smith, J. A., Baeck, M. L., Meierdiercks, K. L., Nelson, P. A., Miller, A. J., & Holland, E. J. (2005). Field studies of the storm event hydrologic response in an urbanizing watershed. Water Resources Research, 41(10), 18. U.S. Census Bureau. (2012). United States Census Bureau, American fact finder. Available from: http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml. Accessed 27 January 2013 USDA NRCS. (2004). Strategies for Growing Communities, Available from: http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_011640.pdf. Accessed 27 January 2013 U.S. EPA. (1978). Method 365.3: phosphorus, all forms (colorimetric, ascorbic acid, two reagent). Washington, DC: United States Environmental Protection Agency. U.S. EPA. (1993a). Method 353.2: determination of nitrate-nitrite nitrogen by automated colorimetry, revision 2.0. Washington, DC: United States Environmental Protection Agency. U.S. EPA. (1993b). Method 365.1: determination of phosphorus by semi-automated colorimetry, revision 2.0. Washington, DC: United States Environmental Protection Agency. U.S. EPA. (2000a). Ambient Water Quality Criteria Recommendations Rivers and Streams in Nutrient Ecoregion VI (Vol. EPA 822-B-00-017). Washington, DC: United States Environmental Protection Agency. U.S. EPA. (2000b). Stormwater Phase II final rule fact sheet series. Available from: http://cfpub.epa.gov/npdes/stormwater/swfinal.cfm. Accessed 27 January 2013 Violin, C. R., Cada, P., Sudduth, E. B., Hassett, B. A., Penrose, D. L., & Bernhardt, E. S. (2011). Effects of urbanization and urban stream restoration on the physical and biological structure of stream ecosystems. Ecological Applications, 21(6), 1932-1949. Vitousek, P. M., Aber, J. D., Howarth, R. W., Likens, G. E., Matson, P. A., Schindler, D. W., Schlesinger, W. H., & Tilman, D. (1997). Human alteration of the global nitrogen cycle: Sources and consequences. Ecological Applications, 7(3), 737-750. Vymazal, J. (2007). Removal of nutrients in various types of constructed wetlands. Science of the Total Environment, 380(1-3), 48-65. Walsh, C. J., Roy, A. H., Feminella, J. W., Cottingham, P. D., Groffman, P. M., & Morgan, R. P. (2005). The urban stream syndrome: current knowledge and the search for a cure. Journal of the North American Benthological Society, 24(3), 706-723. Wenger, S. J., Roy, A. H., Jackson, C. R., Bernhardt, E. S., Carter, T. L., Filoso, S., Gibson, C. A., Hession, W. C., Kaushal, S. S., Marti, E., Meyer, J. L., Palmer, M. A., Paul, M. J., Purcell, A. H., Ramirez, A., Rosemond, A. D., Schofield, K. A., Sudduth, E. B., & Walsh, C. J. (2009). Twenty-six key research questions in urban stream ecology: an assessment of the state of the science. Journal of the North American Benthological Society, 28(4), 1080-1098. White, S. S., & Boswell, M. R. (2006). Planning for water quality: Implementation of the NPDES Phase II Stormwater Program in California and Kansas. Journal of Environmental Planning and Management, 49(1), 141-160.

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White, S. S., & Boswell, M. R. (2007). Stormwater quality and local government innovation. Journal of the American Planning Association, 73(2), 185-193.

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Tables

Table 1: List of variables and units of measurement for the four categories (social, landscape, hydrology, and water quality) included in analyses of 20 streams in five central Iowa cites. Categories Variables Social Population density (persons km-2) College education (%) a Landscape Road density (km km-2) Impervious surface (%) b Crop land (%) b 3 -1 -2 c Hydrology Mean discharge (m s km ) R-B index Baseflow ratio Water quality Conductivity (uS cm-1) Nitrate (mg L-1) Phosphate (μg L-1) Total nitrogen (mg L-1) Total phosphorus (μg L-1) Total suspended solids (mg L-1) Turbidity (NTU) MCMs Six minimum control measure scores Total minimum control measure score a College education was quantified as percent of total population of a watershed. b Land cover variables were calculated as percentages of total area of a watershed. c Mean discharge rate was standardized by watershed area.

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Table 2: Descriptive statistics (mean, minimum, maximum, and standard deviation) for measured variables in 20 urban headwater stream watersheds in central Iowa, U.S.A. Mean Minimum Maximum Standard deviation Population density (person km-2) 890 41 2318 653 College education (%) 25.8 8.8 39.2 9.2 Road density (km km-2) 7.0 1.7 12.9 2.8 Impervious surface (%) 25.4 4.2 44.3 10.0 Crop land (%) 13.5 0 70.4 18.1 3 -1 -2 Mean discharge (m s km ) 0.023 0.002 0.072 0.018 R-B index 0.057 0.017 0.118 0.031 Baseflow ratio 0.87 0.77 0.96 0.06 Conductivity (uS cm-1) 764.8 615.9 1128.5 119.6 Nitrate (mg L-1) 2.4 0.9 6.7 1.7 Phosphate (μg L-1) 133.3 91.9 182.6 26.4 Total nitrogen (mg L-1) 2.5 0.9 6.7 1.6 Total phosphorus (μg L-1) 150.1 88.6 275.1 44.0 Total suspended solids (mg L-1) 33.5 17.5 105.2 19.5 Turbidity (NTU) 13.9 3.6 51.2 11.2

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Table 3: Kruskal–Wallis test statistics for seven water quality variables among the five municipalities. The test evaluated whether water quality variables were statistically different among the five municipalities. Kruskal–Wallis test Kruskal–Wallis test Variables Variables χ2 p-value χ2 p-value Conductivity 2.41 0.66 Total phosphorus 10.27** 0.04 Nitrate 4.59 0.33 Total suspended solids 9.59** 0.05 Phosphate 9.79** 0.04 Turbidity 8.33* 0.08 Total nitrogen 4.50 0.34 * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 4: Descriptive statistics (mean, minimum, maximum, and standard deviation) for scores of responses to the six minimum control measure of the NPDES permitting program in five cities in central Iowa, U.S.A. Minimum control measure Mean Minimum Maximum Standard deviation Public education and outreach 6.0 5.5 6.3 0.3 Public participation/involvement 5.1 3.5 6.8 1.2 Illicit discharge detection and elimination 4.8 2.8 6.3 1.8 Construction site runoff control 5.6 3.0 6.5 1.5 Post-construction runoff control 5.9 5.0 7.0 0.8 Pollution prevention/good housekeeping 5.6 5.0 6.3 0.5 MCM total 32.8 27.5 36.5 3.6

Table 5: Jonckheere trend tests for seven water quality variables along an increasing total MCM score gradient for the five municipalities. The test evaluated whether municipalities with a higher total MCM score also had better water quality. Jonckheere trend test Jonckheere trend test Variables Variables Standard statistic p-value Standard statistic p-value Conductivity -1.33 0.18 Total phosphorus -1.26 0.21 Nitrate -1.86* 0.06 Total suspended solids -1.66* 0.10 Phosphate -1.79* 0.07 Turbidity -1.13 0.26 Total nitrogen -1.53 0.13 * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Table 6: Multiple regression analysis examining relationships among social, landscape, hydrology, and MCM scores with seven water quality variables. For each independent variable, regression coefficient, standard deviation (S.E.), standardized coefficient, and p-value were used to quantity their relationships with the corresponding water quality outcome variable. Adjusted coefficient of determination (R2) and p-values were used to evaluate goodness-of-fit for each model. Dependent Standardized Model Model Independent Variable(s) Coefficient S.E. p-value Variable Coefficient adjusted R2 p-value Conductivity Intercept 1547.465** 459.381 0.004 0.306 0.048 Population density 2.870 7.137 0.157 0.693 Road density 13.171 15.785 0.304 0.417 Baseflow ratio -702.098 424.689 -0.353 0.119 Total MCM score -8.879 7.609 -0.248 0.261 Nitrate Intercept 5.176* 2.619 0.067 0.586 0.001 Population density 0.112** 0.045 0.443** 0.025 Crop land (%) 0.070** 0.015 0.760** <0.001 Mean discharge 28.776* 14.320 0.308* 0.063 Total MCM score -0.163* 0.079 -0.327* 0.058 Total Intercept 5.455** 2.487 0.044 0.594 0.001 nitrogen Population density 0.106** 0.043 0.434** 0.026 Crop land (%) 0.070** 0.015 0.791** <0.001 Mean discharge 19.977 13.598 0.223 0.162 Total MCM score -0.162** 0.075 -0.339** 0.049 Phosphate Intercept 0.305** 0.045 0.000 0.480 0.007 College education (%) -0.001* 0.001 -0.317* 0.095 Impervious surface (%) -0.001* 0.000 -0.361* 0.069 R-B index -0.027 0.145 -0.032 0.855 Total MCM score -0.004** 0.001 -0.472** 0.014 Total Intercept 0.414** 0.082 <0.001 0.372 0.025 phosphorus College education (%) -0.002** 0.001 -0.430** 0.044 Impervious surface (%) -0.001 0.001 -0.206 0.323 R-B index -0.098 0.266 -0.070 0.718 Total MCM score -0.006** 0.002 -0.420** 0.039 Total Intercept 133.628** 43.209 0.007 0.117 0.218 suspended College education (%) -0.712 0.494 -0.335 0.170 solids Impervious surface (%) -0.098 0.466 -0.051 0.835 R-B index -39.161 139.632 -0.063 0.783 Total MCM score -2.348* 1.281 -0.403* 0.087 Turbidity Intercept 75.153** 23.884 0.007 0.183 0.136 College education (%) -.495* .273 -.405* 0.090 Impervious surface (%) .009 .257 .008 0.974 R-B index -.956 77.181 -.003 0.990 Total MCM score -1.484* .708 -.443* 0.054 * Statistically significant at 0.10 level ** Statistically significant at 0.05 level

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Figures

Figure 1: Locations of the 20 headwater stream watersheds in five municipalities in central Iowa, U.S.A.

157

Figure 2: Linear regression for total MCM and water quality variables. Regression lines are associated with corresponding coefficients of determination (R2) and p-value (based on Analysis of Variance).

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CHAPTER 6: GENERAL CONCLUSIONS

This dissertation documents an investigation of the relative importance of landscape, hydrological, and social factors on urban headwater streams in central Iowa. This study focused on five cities, including Altoona, Ankeny, Des Moines, Johnston, and Pleasant Hill, and examined changes in human population and associated changes in urban impervious surface (IS) area and composition in these cities. Previous research has documented the role of IS in causing urban stream hydrological alterations (Arnold & Gibbons, 1996; Tilley & Slonecker, 2007).

Climate change has also been identified as a potential influential factor likely to impact urban stream hydrology (Denault, Millar, & Lence, 2006). Because of the complexity of urban systems, it has been difficult to ascertain which factors among landscape, hydrological, and social variables most strongly influence urban stream water quality. While efforts to protect and improve urban stream water quality have been ongoing for a number of years, research to describe the expansion and extent of urban land cover, and to identify causal linkages among a number of other factors affecting urban streams, will provide much needed information to guide and improve strategies for managing these resources.

What are the spatiotemporal characteristics of urban land cover change in four central Iowa cities?

Availability of detailed planimetric data provided the opportunity to explore the dynamics of urban expansion in four central Iowa cities (Chapter 2). In 1940, impervious surface accounted for about 1.5% of total land cover within the city limits of Altoona, Ankeny, Johnston, and Pleasant Hill. By 2011, the average percent IS for the four cities was 16.8%, much of which was contributed by buildings and parking lots. During this 70-year period, impervious surfaces

159 also became more interconnected, likely to lead to rapid stormwater drainage to nearby streams

(e.g. Han and Burian, 2009). Application of existing as well as newly developed spatial metrics revealed that (1) high, medium, and low percent IS areas were dominated by parking lots, buildings, and roads, respectively, (2) road width has gradually increased over time, and (3) buildings and parking lots have been the biggest contributors to growth of IS over time. These results could be used to enhance local land use and development policies to mitigate potential negative effects on urban streams.

How will urban headwater streams respond to predicted climate and land cover changes?

The Storm Water Management Model was used to simulate stream responses (in terms of unit-area peak discharge, flashiness index, and runoff ratio) to increased precipitation, increased

IS cover, and simultaneous increases in both for five study watersheds selected along a gradient of initial % IS (Chapter 3). The climate change scenario caused increases in unit-area peak discharge for all five study watersheds. The urban land cover change scenario led to increases in all three hydrological indices. The combined climate and land cover change scenarios resulted in slightly more than additive effects from either change alone. Finally, different distributions of land cover change simulated for a single watershed had a greater effect on the timing of delivery than on the total amount of discharge. Information on urban stream hydrological response to predicted climate and land cover changes should be useful to guide municipal-level decision- making for urban stormwater management that effectively integrates these important and dynamic phenomena.

How strong are the relative effects of social system, landscape, and stream hydrology variables on stream water quality outcomes for urban headwater streams?

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Path analysis was used to quantify direct, indirect, and total causal effects of social system, landscape, stream hydrology, on stream water quality (conductivity, total nitrogen concentration, and total phosphorus concentration) outcomes (Chapter 4). Stream water conductivity was positively affected by human population density and road density (strongest effect in the model), and negatively affected by baseflow ratio. Stream water total nitrogen concentration was positively affected by human population density and percent of crop land

(strongest effect). Stream water total phosphorus concentration was negatively influenced by percent of residents with a college-level education (strongest effect) and watershed percent impervious surface. Identification of influential variables for specific stream water impairments could be useful to civic officials in designing efficient mitigation strategies for these pollutants.

What does water quality in urban streams indicate about the effectiveness of the NPDES Phase

II Storm Water permitting requirements?

Relationships between social variables, landscape variables, stream water quantity variables, stream water quality variables, and municipal responses (in annual reports for the

NPDES permitting program) were examined for five central Iowa municipalities (Chapter 5).

Overall, annual report documents indicated adequate responses by each community to the

NPDES requirements. Municipalities with better qualitative responses to the NPDES permitting program were also characterized by lower levels of pollutants (nitrate, phosphate, and total suspended solids) in stream water. Among social, landscape, and hydrological variables examined, quality of municipal response to the NPDES permitting program was the most important variable in multiple regression models predicting pollutant levels for phosphate concentrations, total suspended solids, and turbidity. Linkages between NPDES permitting

161 program responses by municipalities and stream water quality provides direct evidence of the effectiveness of the program.

References

Arnold, C. L., & Gibbons, C. J. (1996). Impervious surface coverage - The emergence of a key environmental indicator. Journal of the American Planning Association, 62(2), 243-258. Denault, C., Millar, R. G., & Lence, B. J. (2006). Assessment of possible impacts of climate change in an urban catchment. Journal of the American Water Resources Association, 42(3), 685-697. Han, W. S., & Burian, S. J. (2009). Determining effective impervious area for urban hydrologic modeling. Journal of Hydrologic Engineering, 14(2), 111-120. Tilley, J. S., & Slonecker, E. T. (2007). Quantifying the components of impervious surfaces (Open File Report 2007-1008). Reston, Virginia: U.S. Geological Survey, Eastern Geographic Science Center. Available from: http://pubs.usgs.gov/of/2007/1008/ofr2007- 1008.pdf. Accessed 27 January 2013

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ACKNOWLEDGEMENTS

This dissertation is dedicated to all of the people who supported me through the past four years.

My family: Mom and Dad – Your belief in me was the strongest motivation to help me go this far.

My major professor: Dr. Thompson –I would like to thank you for your dedication to my studies, papers, funds, and all of the other help. I cannot think of anyone that can do better than you for your students. What you offered me was not only the knowledge and capacity for doing research, but also the scientific spirit and noble personality. I would like to express my sincerest respect to you with the most sparing and the noblest phrase in my native language:

(Professor, thank you!).

My committee and professors: Dr. Franz, Dr. Haddad, Dr. Kolka, Dr. Schultz, Dr.

Stewart, and Dr. Tyndall – Thank you for your great advice and for helping me through the

Ph.D. and GIS certificate programs. I could not reach this point without your help.

My lab mates: Michaeleen Gerken Golay, Zak Keninger, Alister Olson, Marissa Moore,

Joe Bolton, Rob Manatt, and Troy Bowman – Thanks for your assistance and company during the past four years.

My sponsors: This research was supported by the Chinese Scholarship Council, the

Center for Global and Regional Environmental Research at the University of Iowa, the State of

Iowa, and McIntire – Stennis funding.