Impacts of urbanization on ecosystem goods and ... - land cover change

Ecosystems DOI: 10.1007/s10021-012-9519-1 Ó 2012 Springer Science+Business Media, LLC

Impacts of Urbanization on Ecosystem Goods and Services in the U.S. Corn Belt Annemarie Schneider,1* Kelly E. Logan,1 and Christopher J. Kucharik1,2 1 Center for Sustainability and the Global Environment, Nelson Institute for Environmental Studies, University of Wisconsin-Madison, 1710 University Avenue, Madison, Wisconsin 53726, USA; 2Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Drive, Madison, Wisconsin 53726, USA

ABSTRACT In this study, three cities located in the U.S. Corn Belt are evaluated for impacts of past (1992–2001) and projected (2001–2030) urban expansion on ecosystem goods and services, with a specific focus on changes in energy balance, hydrology, and productivity. Scenarios for high-, medium- and low-density urban areas are simulated using a dynamic agro-ecosystem model (Agro-IBIS), by incorporating new parameterizations for impervious surfaces and turf grass. Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m albedo data and remote sensing-derived 30-m resolution maps from the U.S. National Land Cover Database are used as model input to simulate biogeochemical, thermodynamic, and hydrological cycles for a range of land-cover types in each region. The results show that the expanding urban areas have a significant impact on each city’s capacity to regulate climate and flooding. High-density urban areas, for instance, have soil surface temperatures up to 6°C higher than soils within natural and managed ecosystems. Expansion of turf grass in residential areas could require an additional 8–105 million m3 of water use annually, which increases

runoff by 15–48% and reduces the capacity to respond/adapt to flooding. Finally, the analysis shows that net primary productivity (NPP) decreases as expected due to the removal of cropland, forests, and grasslands in favor of development, but increased urban turf grass provides an annual offset of 40–210 g C m-2. Urban expansion through 2030 is estimated to lower total annual crop production by 8.1, 8.6, and 16.7% for the Madison, Peoria, and Indianapolis regions, respectively. Given current projections for city growth to exceed 2–3% per year in the north-central U.S., urban expansion across a nine-state region in the Corn Belt could potentially take an additional 210,000–310,000 ha of farmland out of production annually at a time when demand for food, fuel, and fiber is increasing. Because conversion of cropland to urban uses is nearly always unidirectional, any changes to ecosystem goods and services due to urbanization are likely to be permanent and irreversible. Key words: ecosystem services; land use; sprawl; modeling; cities.

urbanization;

INTRODUCTION Received 7 April 2011; accepted 22 November 2011 Author Contributions: All three authors contributed to the design and implementation of the study. *Corresponding author; e-mail: [email protected]

With over 50% of the global population now living in cities and towns (United Nations 2011), urbanization is quickly surfacing as an important environmental issue. Though cities currently make up

A. Schneider and others only 1–2% of global land (Schneider and others 2009), they represent the majority of human activity and experiences. It is estimated that more than 70% of global economic activity is centered in urban spaces, with nearby lands often converted to industrial, commercial, and residential uses when cities expand their economic base to compete in the global marketplace (World Bank 2008). This land conversion transforms the landscape in profound ways, altering local climates, precipitation regimes, energy balance, hydrological and biogeochemical cycles, and ecosystem productivity (Alberti 2005). Despite growing recognition of the role that cities and their populations have on ecosystem and agricultural productivity (Ellis and Ramankutty 2008), urban areas remain one of the most under-studied and least understood of all ecosystems (Mills 2007). In recent years, there has been an emerging focus on urban ecosystems, with particular emphasis on the goods and services that are provided both within the city (for example, vegetative cover for cooling) and from nearby landscapes (for example, cropland for water filtration, flood regulation) (Kreuter and others 2001; Shen and others 2008). These services, defined as ‘‘the conditions and processes through which natural ecosystems, and the species that comprise these systems, sustain and fulfill human life’’ (Brown and others 2007), include provisioning services (mostly ‘‘goods’’), supporting services (prerequisites for delivery of other services), regulating services (those responsible for keeping ecosystem functioning bounded) and cultural services (those that enrich human existence) (Table 1, MEA 2005). Although humanmodified systems such as cropland and green spaces within cities clearly contribute a range of ecosystem services, it is widely accepted that urbanization—especially wholesale, low-density urban land expansion—has a significant negative impact on these services at local and possibly regional scales Table 1. (2005)

(Sanchez-Rodriguez and others 2005). Often, this impact is due to the location of cities in highly productive agricultural areas (Bairoch 1988; Imhoff and others 1997), because expansion takes the most fertile soils permanently out of production. The exact impacts of urbanization are often difficult to isolate and quantify, however. As city leaders look for ways to be green and environmentally sustainable (for example, initiatives such as Greening America’s Capitals 2011), many are beginning to recognize the role that nearby agroecosystems play in maintaining services. To provide policy-relevant information for effective land management, it is therefore critical that we develop ways to measure current and future impacts of urbanization on ecosystem services across cities that vary by size, location, and rates/pattern of growth. In this regard, simulation modeling has become a powerful approach for isolating different environmental variables that may simultaneously and interactively affect ecosystem responses (Canham and others 2003; Milesi and others 2005). The objective of this research was to understand the impacts of current and projected urbanization and land-cover change on ecosystem services in three cities in the U.S. Corn Belt region. We integrated a dynamic, process-based agro-ecosystem model (Agro-IBIS; Kucharik 2003) capable of simulating biogeochemical, thermodynamic and hydrological cycles with urban-to-rural gradient analysis (McDonnell and others 1997) to investigate the underlying ecosystem processes present in the urban environment, and to evaluate different hypotheses and scenarios, including future scenarios to 2030. This study focused on three critical aspects of urbanization: energy balance, including partitioning of net radiation into sensible and latent heat; the hydrological cycle, particularly infiltration and runoff; and ecosystem productivity, including net primary

Critical Ecosystem Goods and Services, Adapted from the Millennium Ecosystem Assessment

Ecosystem goods and services Provisioning and supporting

Regulating

Cultural

Food, fuel, fiber Net primary production Crop yield Fresh water Infiltration Soil Erosion control Nutrient cycling

Flooding Surface runoff Climate Albedo Carbon sequestration Disease Climate (humidity, temperature)

Recreational Open space Aesthetic Open space Educational

Impacts of Urbanization on Ecosystem Goods and Services productivity (NPP) and crop yield reduction due cropland loss. This study significantly improves upon previous work on urban ecosystems (Milesi and others 2003, 2005; Shen and others 2008) by: (1) assessing a range of land-cover types across the urban-to-rural interface, including high-, medium-, and lowdensity urban areas; (2) directly incorporating remote sensing observations of albedo (Schaaf and others 2002), regional maps of land-cover change (Fry and others 2009), and projections of urban expansion through 2030 (Pijanowski and others 2005) into an agro-ecosystem modeling framework to extend analysis through space and time; (3) exploiting an agro-ecosystem model calibrated and validated to simulate both natural vegetation and major row crop systems (for example, corn, soybean) to study coupled carbon, water, and energy balance, and updated to include specific parameterizations for urban areas based on concrete and turf grass; and (4) focusing on small- and mid-sized cities in the U.S. Midwest, a region where urban expansion is expected to exceed 2–3% per year. In the following sections, we review the environmental impacts of urbanization, introduce the methodology, and present our findings. The article concludes with a discussion of the direct and indirect impacts of urbanization on a wider range of ecosystem goods and services, and the importance of maintaining these services for current and future generations.

ENVIRONMENTAL IMPACTS URBANIZATION

OF

Although cities may be an efficient way to concentrate human impacts, they are also the source of negative environmental impacts that cross scales and city boundaries. Urban environments are primarily characterized by the nexus of buildings, managed vegetation, and impervious surfaces, all of which affect local climate and ecosystem productivity (Oke 1987; Mills 2007). One specific way in which urban land alters the surrounding environment is the pattern of altered energy cycles created around cities (for example, Oke 1982, 1987; Offerle and others 2006). Perhaps the best known impact is the urban heat island (UHI), whereby the near-surface air temperature of a city is higher than surrounding land (usually 3–10°C warmer at night) (Oke 1987). The energy balance in its simplest form is Rnet ¼ LE þ H þ G

ð1Þ

where Rnet is net radiation (that is, the balance of incoming solar radiation and outgoing terrestrial radiation), LE is latent heat flux (that is, energy released or absorbed by changing the phase of water), H is sensible heat flux (that is, energy released or absorbed that causes a change in temperature), and G is stored energy in the system, usually determined as a residual (Piringer and others 2002). Changes in land-cover albedo alter the total amount of energy available to the system (altering Rnet) by changing the amount of solar radiation reflected. Increased imperviousness of land cover increases surface runoff and decreases infiltration, lowering the LE of the system. A decrease in the area or amount of vegetation decreases evapotranspiration, also resulting in lower LE (Piringer and others 2002; Offerle and others 2003). Naturally, in the total balance of the system (equation 1) this lowered LE must correspond with increases in either H or G, meaning increases in the near-surface sensible heating and surface heat felt. Alterations of emissivity, thermal conductivity, specific heat capacity, and density of surface layers from an increase in built-up materials modify the H and G of the system. Moreover, anthropogenic heat emissions from buildings, metabolism, and vehicles also add to the total energy, by increasing the Rnet (Sailor and Lu 2004). Overall, the characteristic surface energy balance of an urban system can be described as higher heat uptake during the day and a greatly increased ratio of sensible to latent heat flux (Souch and Grimmond 2006). Increases in regional temperatures can in turn have negative impacts on human health and comfort (Patz and others 2005; Fouillet and others 2006). Urban areas have impacts not just on the local temperature, but on many other aspects of the regional environment. Alterations of the local energy system have been known to enhance storms (Bornstein and Lin 2000) or change precipitation patterns (Shepherd 2005). Impervious surfaces, by definition, have negligible infiltration and therefore cause increased surface runoff, which has implications for flood control and pollutant runoff (Brabec and others 2002). Pollutant runoff is particularly problematic within U.S. residential areas due to the abundance of highly managed, fertilized turf grass (Robbins and Birkenholtz 2003). Turf grass also requires a significant amount of water, and has been estimated to be the largest irrigated ‘‘crop’’ in the U.S. (Milesi and others 2005). A change in vegetation type or amount is also known to alter total NPP, a metric that is often used to estimate the value of ecosystem services (Imhoff and others 2004;

A. Schneider and others Bjorklund and others 1999). Decreases in NPP not only reflect a loss of food and fiber available for human consumption but also result in decreased carbon sequestration in the soil, which has clear implications for regional to global climate patterns (Solomon and others 2007). Finally, urban areas have a direct and lasting impact on biodiversity (McKinney 2002). Expansion of built-up land may result in a decrease of local species and loss of species richness, or may lead to the introduction of nonnative species (Knapp and others 2008).

Study Areas Three study areas were chosen within the Midwestern U.S. to represent different climate characteristics, population sizes, and rates/patterns of urban expansion: Madison, Wisconsin, Peoria, Illinois, and Indianapolis, Indiana (Figure 1). All three are small- to mid-sized cities: Peoria’s Metropolitan Statistical Area (MSA) is 376,000 inhabitants, Madison’s MSA has 570,000 and Indianapolis has over 1.7 million (U.S. Census 2009). The cities all have cold, snowy winters and warm, humid

summers (Figure 2A, B), but Madison is cooler throughout the year than the other regions (average annual minimum temperature of 2.44°C, compared to 4.94 and 5.94°C in Peoria and Indianapolis, respectively). Madison also receives the most snowfall, with 125 cm per year whereas Peoria and Indianapolis receive 66.8 and 67.8 cm, respectively. Peoria experiences a generally warmer and wetter climate than Madison, whereas Indianapolis’s more southern location contributes to its higher annual average temperature (11°C) and greater annual precipitation. All three cities are surrounded almost exclusively by cropland (63–75% of each study area, Table 2), primarily maize and soybean crops and pastures, whereas 9–13% of each area is categorized as urban land. Urban extent varies by region: Indianapolis has 3,000 km2 of urban land, compared to 1,000 km2 for Peoria and 850 km2 for Madison, circa 2001. The NLCD classification provides a breakdown of urban areas into four classes that reveal their composition (Figure 3): high-density (>80% impervious surface), medium-density (50–79% impervious surface), and low-density urban areas (20–49%

Figure 1. Maps of each study region derived from the National Land Cover Data set circa 2001 (Homer and others 2004) for A Madison, Wisconsin; B Peoria, Illinois; and C Indianapolis, Indiana.

Impacts of Urbanization on Ecosystem Goods and Services Figure 2. Distribution of A mean monthly temperature and B precipitation for each study area (averaged for 1971– 2000) (Mitchell and Jones 2005).

impervious surface), as well as open space (80% impervious surface; medium-density urban areas, 50– 79% impervious surface; low-density urban areas, 20– 49% impervious surface; and open space, 5°C and 5-day running mean temperature is >10°C2, with three possible approaches: Non-irrigated: no irrigation Smart irrigation: irrigation occurs during growing season when soil AWC 60% of land cover, grown continuously with contemporary management including fertilizer Lands where soybean crops comprise >60% of land, grown continuously with contemporary management including fertilizer

Soybean PFT with fertilizer based on historical application rates Cultivar selection and planting date based on regional climate

Unmanaged ecosystems based on competition between all PFTs C3 and C4 grass parameters for PFTs Canopy height restricted to 110 mm or lower to mimic animal disturbance Maize PFT with fertilizer based on historical application rates Cultivar selection and planting date based on regional climate Maize PFT with irrigation during the growing season when soil AWC 5°C and 5-day running mean temperature >10°C2 and soil AWC 5°C and 5-day running mean temperature >10°C2

Model details

3

2

Land-cover classes correspond directly to the National Land Cover Data set (NLCD, Homer and others 2004). Defined by Kucharik and Brye (2003). Impervious surface is derived from the National Land Cover Data set (Yang and others 2002). 4 U.S. Department of Agriculture crop statistics (USDA 2010).

1

Soybean

Irrigated maize

Maize

Pasture

Natural vegetation

Lands dominated by deciduous broadleaf woody vegetation with percent cover >60% and height exceeding 2 m Lands dominated by woody and herbaceous vegetation with percent cover >60% Lands with herbaceous vegetation (grasslands) >60%, typically used for grazing of livestock Lands where maize crops comprise >60% of land cover, grown continuously with contemporary management including fertilizer

Lands with turf grass grown in patterns typical for lawns with irrigation driven by soil moisture deficit

Turf grass—smart irrigation

Vegetated classes Forest

Lands with turf grass grown in patterns typical for lawns, non-irrigated

Lands with concrete impervious surface and no vegetative cover

Turf grass

Urban classes Concrete

Description

Cultivated croplands, with weighted average of maize and soybean based on relative percentage of maize to soy in each study region4

Pasture, hay

Grassland, herbaceous

Deciduous forest

High-, medium- and low-density urban areas, with weighted average of concrete impervious surface and turf grass based on percentage of each cover type within the study region.3 Turf grass coverage was assumed to be 1—impervious surface cover

Corresponding land cover1

Agro-IBIS Model Simulations Conducted During Analysis and Their Corresponding Land-Cover Classes

Scenario name

Table 4.

Impacts of Urbanization on Ecosystem Goods and Services

A. Schneider and others (Figure 4, ‘‘data preparation’’). First, the 2001 NLCD classification scheme was restructured into seven generalized land-cover types to correspond with ten Agro-IBIS model scenarios (Table 4). The coarse resolution (500-m) grid from the MODIS data was then overlaid on each 30-m land-cover map, and the mean percent coverage of each landcover type was assigned to each 500-m grid cell. For cells that contained greater than 90% of a given class, the corresponding black- and white-sky albedo for these cells was averaged for each study region and used as input to the Agro-IBIS model runs. Note that the albedo for high-density urban areas was used in the concrete surface scenario, and the albedo for low-density urban areas was used for the turf grass scenario. The remote sensing-derived NLCD land-cover maps for 1992 and 2001 were also used to spatially distribute and interpret the results of the model runs (Figure 4, ‘‘analysis’’). In each region, the land-cover composition follows a natural gradient outward from the city center: high-, medium-, and low-density urban, followed by urban open space, agriculture and natural vegetation (forest and grassland). This continuum provided an urbanto-rural gradient in each study area to test the response of different environmental variables across space and time. Specifically, model outputs of concrete and turf grass were combined in weighted averages according to the NLCD impervious surface percent cover map for each city and each category of urban land. In addition, the NLCD cropland category was considered to be a weighted average of maize and soybean crops, calculated by region according to 2001 U.S. Department of Agriculture state statistics (USDA 2010). The study regions also coincided with projected land-use maps (Pijanowski and others 2005), so future land scenarios were available to extend the implications of this study to 2030. The land-use projections were developed using a neural network-based land-use model that exploited landcover maps derived from Landsat imagery, base maps from WiscLand and the Illinois Natural History survey, aerial photography, county- and state-level population projections, and linear growth models (Pijanowski and others 2005; Mishra and others 2010). The projected land-use maps were checked against the NLCD maps, and then combined with the corresponding model simulation to predict the future impact of urbanization on ecosystem goods and services (Figure 4, ‘‘analysis’’). Although this study investigated projected impacts of urbanization through 2030, it should be noted that the

potential confounding effects of future climate change were not addressed. The climate variables, atmospheric carbon dioxide levels, albedo values, and crop management techniques were all representative of contemporary times, circa 2001. Recent studies have shown that increased CO2 can increase UHI effects (McCarthy and others 2010), and both negatively and positively impact vegetation growth/ productivity (see Fuhrer 2003 for a review). Therefore, future study should consider how land use and climate change may interact to impact ecosystem services in coming decades.

Agro-IBIS Model Simulations For each scenario, Agro-IBIS was run for the period of 1750–1849 as potential (natural) vegetation, 1850–1939 as non-irrigated maize with historical fertilizer rates, and 1940–2002 as the chosen model run. This step insures that each run begins with contemporary levels of soil carbon and nitrogen (Kucharik and others 2000). Results were averaged for 1972–2002, allowing the scenario 32 years to reach equilibrium before considering results. The model runs (Table 4) were conducted at a resolution of 0.5°, and results were then averaged over each study area to develop scenarios representative of each region’s soil profiles and climate inputs. Unfortunately, one drawback to this approach is that adjacent grid cells do not exchange numerical information during model runs, a common shortcoming in DGVMs. Thus, this study was unable to characterize interactions or feedbacks between land-cover types, and some potential influences of urban land remain unanalyzed. The urban scenarios were run for each city with a variety of human management techniques for turf grass scenarios. In addition, the cropland scenarios maize, soybean, and irrigated maize included crop PFTs and land management that represent the majority of agricultural practices in the study regions. Pasture also makes up a substantial portion of land (22–70%), but was not a previously included scenario in Agro-IBIS. To represent pasture, only C3 and C4 grass PFTs were allowed to grow, and plant height was restricted to 110 mm to mimic pasture disturbance. Finally, it is important to note that the dominant PFT in the natural vegetation scenario varied across study regions. The PFTs effectively represent the potential vegetation or biomes that should exist in a given area in the absence of human management. Natural vegetation for Peoria and Indianapolis was dominated by forest, and Madison by C4 grasses.


RESULTS Energy Balance Soil Temperature near the Surface To characterize the impact of albedo alterations and differences in material properties on surface heating, surface soil layer temperatures were assessed (Figure 6). The differences between land-cover categories were similar for each city, so the results for Indianapolis are shown as representative of all three regions. The results indicate that high-density urban areas experience significantly higher soil surface temperatures (on average, 3.5°C higher than natural vegetation) throughout the year as a result of the large amount of concrete. Modeled differences in surface soil temperatures between urban areas and other vegetated covers were greatest during spring-summer, coinciding with maximum incident solar radiation and increases in day length and seasonal air temperature. The model results agree well with observational studies that measured surface soil temperature in the field. Using samples with a depth of 10 cm from an urban-to-rural gradient near Baltimore (an area with similar latitude and temperate continental conditions as our study areas), two separate investigations reported maximum and minimum soil surface temperatures (21–27°C during summer months, 0–5°C during the winter) similar to the Agro-IBIS model results (George and others 2007; Savva and others 2010). Each study found seasonal trends comparable to the model results, with temperature differences of 0.5–4°C between urban and rural land-cover types. Although turf grass soil temperature was on average less than 1°C warmer than other vegetation,

Figure 6. Average monthly surface-layer soil temperature for land-cover types in Indianapolis, Indiana.

irrigation contributed to a significant difference in seasonal temperature profiles (Figure 7). The results suggest that any amount of irrigation lowers the soil temperature throughout most of the year (March–November). The difference in soil temperature between no irrigation and the other two scenarios is greatest in late summer, which coincides with the end of the growing season when soil moisture is typically lowest. During late summer, evapotranspiration will often exceed precipitation and soil moisture recharge, leading to a gradual decline in stored soil water. Therefore, differences in soil water content and soil temperature for irrigated and non-irrigated scenarios are likely maximized in each region during the August–September period. To understand the spatial pattern of added soil heat storage, average July soil surface temperatures for model scenarios were cross-walked to the 2001 NLCD maps (Figure 8). The maps illustrate that

Figure 7. Mean monthly soil surface temperature difference between non-irrigated turf grass and turf grass with smart or continuous irrigation applied for A Madison, Wisconsin; B Peoria, Illinois; and C Indianapolis, Indiana.

A. Schneider and others

Figure 8. Average July near-surface soil temperature estimated by the Agro-IBIS model for each circa 2001 land-cover category for A Madison, Wisconsin; B Peoria, Illinois; and C Indianapolis, Indiana (land-cover categories are described in Table 3).

urban centers are several degrees Celsius warmer than surrounding land-cover types. Madison’s urban center is the coolest of the three study areas (27.2°C), but the region as a whole has lower soil surface temperatures, so the difference between the city and its surroundings is still large. The Peoria region has the highest overall soil surface temperatures (23.8–29.8°C, compared to 21.0–27.2°C in Madison and 23.5–29.2°C in Indianapolis), and the greatest difference in the absolute range of temperatures between high-density urban areas and surrounding land-cover categories. Finally, the magnitude of differences in soil temperatures was calculated for each month, whereby the control scenario, non-irrigated cropland, was subtracted from each land-cover type (Figure 9A–D). Interestingly, surface soils across the range of urban densities are estimated to remain warmer than agricultural land throughout the year in all cities, although increasing amounts of turf grass cause this difference to drop in early spring and fall (Figure 9C). Soil surface temperatures in high-density urban areas are up to 6.0°C warmer than surrounding cropland, in fact, compared to a difference of only 2.0°C for low-density urban areas. In contrast, natural vegetation is cooler than cropland throughout the year, with the exception of winter months.

Energy Flux To help explain UHI signatures, fluxes of latent and sensible heat between the land surface and atmosphere were assessed (Table 5). Here, results are presented for the vegetated classes and for the components of urban areas (concrete, turf grass) to illustrate absolute differences across surface types (impervious vs. pervious) and irrigation schemes. The biophysical characteristics of concrete, including decreased albedo, lack of vegetation transpiration and soil evaporation, contributed to a much higher Bowen ratio (the ratio of sensible to latent heat flux over a surface) of 2.62. Compared to concrete, all of the vegetation classes have much lower Bowen ratios (0.20–0.40). Turf grass categories have slightly lower Bowen ratios than the natural vegetation and cropland categories, and the ratio is lower for more intense irrigation schemes. Because urban expansion typically occurs as a mixture of concrete and vegetation, the conversion of cropland or natural vegetation would result in either an increase in the Bowen ratio, as in the case of concrete, or a decrease, as in the case of turf grass. However, the increase in sensible heat caused by conversion to an impervious surface was much greater than the decrease for conversion to turf grass. As a result, expansion of urban land is


Figure 9. Results of the Agro-IBIS model showing the difference between the mean monthly soil surface temperature for a given land-cover type and non-irrigated crops (top row), and the difference between the mean monthly surface runoff (in mm/day) for a given land-cover type and non-irrigated crops (the control) for each region (bottom row). The urban density classes (A–C, E–G) are modeled using a weighted average of concrete and turf grass based on the composition within each study area, whereas natural vegetation (D, H) is dominated by trees and grassland.

estimated to greatly increase the proportion of net radiation dissipated as sensible heat at the surface, which in turn contributes to an expansion of UHI signatures. In fact, an urban area consisting of 10% impervious surfaces and 90% turf grass with smart irrigation would have an average sensible heat flux slightly higher than that of non-irrigated maize cropland.

Hydrological Cycle Runoff Analysis of the runoff ratio (total annual surface runoff divided by annual precipitation) reveals that urban land use has a significant negative impact on infiltration into soils, as expected (Table 5). The runoff ratio is 58% higher for concrete than for natural vegetation, and depending on management, 10–47% higher for turf grass than for natural vegetation. In the continuous irrigation scenario, turf grass was watered regardless of the soil AWC, and therefore any precipitation likely occurred on saturated soils, increasing runoff. In addition, smart irrigation did not produce more surface runoff than non-irrigated turf grass, implying that the vegetation efficiently utilized the extra water and that evapotranspiration occurred at the potential rate.

In general, all grass categories (pasture, turf grass) had lower LAI and productivity, and higher rates of runoff across all three cities. Somewhat surprisingly, pasture had greater runoff than nonirrigated turf grass or turf grass with smart irrigation, despite the fact that it had similar parameters in place (namely, altered canopy height). Closer assessment reveals that this difference is likely due to the intensive management parameters incorporated in the turf grass scenarios: the LAI of turf grass was capped to mimic lawn mowing, and thus, more soil surface was exposed and intercepted shortwave radiation increased, allowing increased drying and an increased capacity of the soil to allow more infiltration. Other than the pasture case, the results confirm that conversion from natural vegetation or managed agriculture to any intensity of urban land use would lead to greater rates of runoff, regardless of irrigation scheme. It should be noted that small-scale differences in elevation and slope can directly impact rates of runoff and infiltration. Agro-IBIS is not able to account for the movement of water across adjacent grid cells, so our results should therefore be considered a first step toward understanding potential impacts of urbanization on the water cycle. Work to link Agro-IBIS to hydrologic routing models (Donner

1

0.39 0.31 – 0.53 -2.22 -30.30

0.20 0.50 – 0.3 1.40

0.81

Bowen ratio1 Runoff ratio2 Irrigation (mm y-1) NPP (kg m-2 y-1) Change in regional NPP 1992–2001 (%)3 Change in regional NPP 2001–2030 (%)3 -7.05

0.38 0.38 -0.69

–

0.30 0.54

Pasture Peo

0.35 0.47 – 0.26 –

Mad

-4.37

0.25 0.57 – 0.41 -0.79

Ind

0.31 0.47 – 0.31 –

Peo

Turf grass

-7.05

0.36 0.36 – 0.82 -0.50

Mad

0.26 0.49 – 0.32 –

Ind

Ind

-0.69

0.35 0.38 – 0.89 0.38

-4.37

0.30 0.39 – 0.94 -0.79

74.20

0.21 0.51 60.41 0.34 6.82

Ind 0.27 0.77 313.46 0.25 –

Mad

–

0.31 0.40 58.13 0.83 –

–

0.25 0.41 90.61 0.96 –

–

0.22 0.43 81.56 0.98 –

-7.05

0.37 0.37 – 0.62 -0.50

Mad

0.17 0.72 320.37 0.33 –

Ind

-0.69

0.36 0.38 – 0.56 0.38

-4.37

0.31 0.40 – 0.58 -0.79

Soybean Peo Ind

0.18 0.76 360.69 0.32 –

Peo

Turf grass—continuous irrigation

Irrigated maize Mad Peo Ind

40.97

36.76 Maize Peo

0.23 0.49 60.13 0.33 3.78

Peo

0.34 0.49 20.39 0.26 1.23

Mad

Turf grass—smart irrigation

Note: results are presented for the components of the urban density classes (concrete, turf grass) and for the vegetated land cover classes (for example, maize, pasture, natural vegetation). Mad = Madison; Peo = Peoria; Ind = Indianapolis; NPP = net primary productivity. 1 The Bowen ratio is defined as the ratio of sensible to latent heat flux. 2 The runoff ratio is defined as the ratio of surface runoff to mean annual precipitation. 3 Change in total NPP is the result of changes in land cover composition in each study area. Because projections of specific crop types were not available, the change in regional NPP 2001–2030 is reported as the aggregate cropland change for maize, soybean, and pasture.

-33.38

0.36 0.54 – 0.31 -0.50

Mad

2.32 0.91 – 0 –

Ind

0.31 0.33 – 0.57 0.24

Natural vegetation Mad Peo Ind

2.65 0.91 – 0 –

Peo

Scenario

3.01 0.91 – 0 –

Mad

Concrete

Results from the Agro-IBIS Simulations for Madison, Peoria, and Indianapolis

Bowen ratio Runoff ratio2 Irrigation (mm y-1) NPP (kg m-2 y-1) Change in regional NPP 1992–2001 (%)3 Change in regional NPP 2001–2030 (%)3

Scenario

Table 5.

A. Schneider and others

Impacts of Urbanization on Ecosystem Goods and Services and others 2002) holds promise for more detailed assessment of the amount and location of flooding and pollutant runoff in urban areas. Although annual patterns in runoff do not vary appreciably across the study areas, seasonal runoff patterns show some variability across the three cities (Figure 9E–H). In Madison, the difference in surface runoff for urban areas relative to cropland (the control) follows the same pattern as monthly precipitation (Figure 2B). In Peoria and Indianapolis, urban areas have a similarly large difference in runoff in summer months (25–89 mm day-1 depending on amount of concrete), but the difference in runoff begins earlier (March) and peaks again in November. Surface runoff is highest in winter in Peoria and Indianapolis, because these areas receive the greatest amount of precipitation during winter months. However, these cities receive approximately 50% less snowfall than Madison. Therefore, increased wintertime runoff in Peoria and Indianapolis is likely due to a combination of increased precipitation as well as a higher likelihood that the precipitation falls as rain or freezing rain instead of snow. Finally, the seasonal patterns also show that increasing amounts of turf grass result in less runoff. However, even lowdensity urban areas still lead to increased runoff of 5–40 mm day-1 relative to cropland. The spatial differences in surface runoff (Figure 10) illustrate that runoff for the urban

classes was an order of magnitude greater than for vegetation outside each city (300–800 mm y-1 compared to 20–40 mm y-1 for cropland and natural vegetation). There were also substantial differences in runoff across urban density classes for all three cities. Peoria had greater runoff than Madison in general, with higher peak runoff (536– 763 mm y-1) in medium- to high-density urban areas, whereas Indianapolis had the highest runoff (>840 mm y-1) of all cities in its high-density urban areas. In total, the average annual runoff (1972–2002) predicted for the Madison region was 626 million m3 of water; this amount is on par with the capacity of the city’s northern-bounding Lake Mendota, which contains 505 million m3 (Kitchell 1992). The Peoria region had a similar annual average total runoff of 639 million m3, whereas Indianapolis totaled nearly three times that amount, at 1.99 billion m3 y-1. Irrigation and Water Use Similar to the results for runoff, management practices had a considerable impact on water consumption for irrigation in each study area (Table 5). Typical annual water use for turf grass with smart irrigation was 20 mm for Madison, and 60 mm for Peoria and Indianapolis; these amounts are only slightly less than modeled irrigation requirements for maize over the same area

Figure 10. Average annual surface runoff estimated by the Agro-IBIS model for each circa 2001 land-cover category for A Madison, Wisconsin; B Peoria, Illinois; and C Indianapolis, Indiana (land-cover categories are described in Table 3).

A. Schneider and others (58–91 mm). We note that, however, only a small fraction of corn and soybean area is irrigated in these regions because water use during the growing season is mostly supported by precipitation. Summed across each study area, these amounts translate to water of 12 million m3 y-1 in Madison, 44 million m3 y-1 in Peoria, and 133 million m3 y-1 in Indianapolis, circa 2001. These results agree well with expected water consumption in each city. For example, monthly water use statistics for Madison show that approximately 8.3 million m3 are pumped per year for outdoor activities (City of Madison 2008). According to the U.S. Environmental Protection Agency (EPA 2008), it is safe to assume that half of outdoor water use is for lawn watering. This back-of-the-envelope calculation suggests that, conservatively, around 4.1 million m3 of water per year is likely used for turf grass irrigation. This amount is lower than the modeled results (12 million m3), because not all residences water their lawns (that is, the modeled values would result if all turf grass was watered regularly), and our study region included several small urban centers outside the City of Madison. Annual water use for turf grass under the continuous irrigation scenario of 25.4 mm per week causes water consumption to increase 5–15 times in each study region, jumping to 313–361 mm. These results suggest that 198, 264, and 703 million m3 y-1 of water would be required to continuously irrigate all lawns in Madison, Peoria, and Indianapolis, respectively. These numbers are striking, and likely represent an extreme case scenario. Nevertheless, the model results agree well with reported annual irrigation of north-central U.S. golf courses. According to survey data, turf grass irrigation averages 365.8 mm y-1 (Throssell and others 2009). Water use of this magnitude would no doubt have a significant negative effect on many aspects of ecosystems, including surface soil temperatures and surface runoff, and would likely lead to other environmental problems.

Ecosystem Productivity Total annual NPP is reported for all vegetation types in Table 5. The results illustrate that human management of crop systems has indeed increased annual NPP substantially, as evident in higher NPP values for maize and soybean (up to 980 g C m-2 for irrigated maize) than for forest, pasture, or turf grass cover types (250–570 g C m-2). Turf grass NPP (250–340 g C m-2) is lowest by comparison, with some variability by location and irrigation technique.

Land-cover changes between 1992 and 2001 have already led to a loss of potential crop production and as well as differences in regional NPP (Table 5). Urban expansion resulted in total losses of a potential 37,000 metric tons of soybean yield and 121,000 metric tons of maize grain across all study areas over the 10-year period. When averaged for each region, annual NPP decreased by only 0.1% in Madison and Indianapolis, and increased by 0.1% in Peoria due to agricultural expansion into forest. These low rates reflect the partial offset in NPP caused by turf grass expansion: total NPP of turf grass increased 1.2, 3.8, and 6.8% in Madison, Peoria, and Indianapolis, respectively, due to expansion of low- and medium-density urban areas. Moreover, these results represent a change in production from sustenance (maize) to decorative crops (turf grass) that is not captured in the measure of total NPP change. The offset in total NPP caused by turf grass is evident when differences between land-cover types are plotted on a monthly basis (Figure 11). Over the course of the year, the difference in NPP when urban areas replace maize resulted in losses of up to 300 g C m-2 per month in all three cities. However, a small positive increase in monthly NPP is apparent in September–October, corresponding to continued turf grass production after crops were harvested. Similarly, the difference between natural vegetation and maize also produced a positive increase in monthly NPP in spring and fall.

2030: Projected Impacts of Land-Use Change Based on rates and patterns observed from 1992 to 2001 in each city, urban areas were projected to grow by 60, 62, and 79% in Madison, Peoria, and Indianapolis, respectively, for the 2001–2030 period (2–3% annually). Indianapolis had the greatest amount of expected expansion, totaling 2,343 km2, whereas Peoria was expected to add 632 km2, and Madison, 517 km2 (Table 2). Because economic, demographic and policy-based factors could cause an increase or decrease in these projections, these numbers should be considered a ‘‘businessas-usual’’ scenario that is subject to change. Conversion of land to urban uses could have large and far-reaching impacts on many aspects of the surrounding ecosystem. Areas of increased surface temperatures would expand. When combined with reduced infiltration due to increased impervious surface area (thereby increasing surface runoff), these changes would impact the total energy balance and its relative components. Results


Figure 11. Mean monthly difference in NPP between a given land-cover type and non-irrigated crops (the control) for each region. The urban density classes are modeled using a weighted average of concrete and turf grass based on the land-cover composition within each study area, whereas natural vegetation is dominated by trees and grassland.

suggest that urban expansion could greatly increase sensible heat flux and decrease latent heat fluxes, which in turn would be felt near the surface as an increase in air temperature. The projected expansion of impervious surface area would also cause a 15–48% increase in annual runoff across each region: annual runoff would increase by 96 million m3 in Madison, 147 million m3 in Peoria, and 952 million m3 in Indianapolis. Another change to the water cycle could be an increase in irrigation that would occur to accommodate turf grass in newly developed residential, commercial and industrial areas. If the amount of new urban land was added in similar proportion to current density levels in each city (Figure 3), smart irrigation of all new turf grass would result in annual increases of 8 million,

27 million, and 105 million m3 of water use in Madison, Peoria, and Indianapolis, respectively. If we assume that all new urban land is low density (