Modeling the Effects of Land Use on the Quality of Water , Air , Noise , and Habitat for a Five-County Region in Georgia

A computer simulation model, the Regional Simulator (RSim), was constructed to project how landuse changes affect the quality of water, air, noise, and habitat of species of special concern. RSim was designed to simulate these environmental impacts for five counties in Georgia that surround and include Fort Benning. The model combines existing data and modeling approaches to simulate the effects of land-cover changes on: nutrient export by hydrological unit; peak 8-h average ozone concentrations; noise caused by small arms and blasts; and habitat changes for the rare Red-cockaded Woodpecker (Picoides borealis) and gopher tortoise (Gopherus polyphemus). The model also includes submodules for urban growth, new urbanization influenced by existing roads, nonurban land cover transitions, and a new military training area under development at Fort Benning. The model was run under scenarios of business as usual (BAU) and greatly increased urban growth for the region. The projections show that the effects of high urban growth will likely differ from those of BAU for noise and nitrogen and phosphorus loadings to surface water, but not for peak airborne ozone concentrations, at least in the absence of associated increases in industry and transportation use or technology changes. In both scenarios, no effects of urban growth are anticipated for existing populations of the federally endangered Red-cockaded Woodpecker. In contrast, habitat for gopher tortoise in the five-county region is projected to decline by 5 and 40% in the BAU and high urban growth scenarios, respectively. RSim is designed to assess the relative environmental impacts of planned activities both inside and outside military installations and to address concerns related to encroachment and transboundary influences.


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A regional approach to environmental impact assessments (Munns 2006) provides 31. the opportunity to examine the extent and spatial interactions of key drivers 32. and processes affected by land-use change. Because these drivers and the 33. factors influencing these processes change over space because of variations in 34. such features as topography, climate, and human activities, it is important to 35. consider their influence in a spatial context in order to understand the full 36. range and extent of causes and implications of environmental change. Such 37. analyses can be of assistance to regional planning and hence foster 38. sustainability by allowing potential environmental repercussions to be a part 39. of planning.

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Furthermore, there is a need to examine how environmental impacts can change 41. across several stressors, environmental media, and sectors (e.g., water, air, 42. noise, and habitats for species of special concern). Although environmental 43. laws typically segregate these impacts both in the ways they are reported and 44. managed, such an artificial division can lead to inadequate understanding and, 45. hence, management problems. For example, contrary incentives can arise if one 46. sector gains at the expense of another. In other situations, inappropriate 47. management actions can result from the focus on only one sector and not the 48. consideration of all aspects of the environment that might be affected.

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As a major driver of environmental change, it is critical to understand how 50. land-use activities affect the landscape. For example, human use can degrade or 51. ameliorate soil properties, enhance or reduce runoff, and aggravate or alleviate 52. drought. In turn, land use can be constrained by environmental conditions, such 53. as topography, slope, exposure, soil conditions, and climate.

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With the recent advent of geographic information systems and the field of 55. landscape ecology (Turner et al. 2001), it has been possible for such a spatial 56. approach to environmental change to be conducted. Undertaking a regional and 57. cross-sectorial approach to the study of environmental change requires 58. determination of the appropriate spatial and temporal scales of resolution and 59. consideration of potential feedbacks across sectors. One of the goals in such a 60. multisector approach is to provide a way to fully understand the key components 61. of the system, including possible cumulative impacts.
62. This paper proposes a regional, cross-sectorial approach to examining land-use 63. change and its effects and presents an example of its application to a 64. five-county region in west central Georgia. We focus on the region in Georgia 65. around and inclusive of Fort Benning for three reasons: (1) large quantities of 66. data are available; (2) the region will be undergoing dramatic changes in the 67. future as the military training activities and the many people supporting them 68. now at Fort Knox, Kentucky, are moved to Fort Benning; and (3) the military 69. land (on which urban growth is restricted) serves as a control against which 70. changes on private lands can be compared. The Regional Simulator model (RSim) 71. has been developed for this five-county region and includes the ability to 72. project future changes in the quality of water, air, noise, and habitat (Dale 73. et al. 2005). The spatially explicit simulation model is structured so that the 163. region to predict the annual flux of N and P from terrestrial watersheds. The 164. noise module uses GIS data layers of military noise exposure developed by the 165. U.S. Army Center for Health Promotion and Preventive Medicine (CHPPM) as part 166. of the Fort Benning Installation Environmental Noise Management Plan (IENMP). 167. RSim builds upon noise guideline levels developed by the military under the 168. Army's Environmental Noise Program [ENP] (U.S. Army. Army Regulation 169. 200-1. 1997). RSim contains noise contour maps resulting from artillery, as 170. projected by the DoD noise simulation model BNOISE, because artillery is the 171. greatest source of noise at Fort Benning. The approach produces noise contours 172. that identify areas where noise levels are compatible or incompatible with 173. noise-sensitive land uses outside of Fort Benning. The Army's 174. Environmental Noise Program's guidelines define zones of high noise and 175. accident potential and recommend compatible uses in these zones. Local planning 176. agencies are encouraged to adopt these noise guidelines. The air-quality module 177. of RSim estimates the impact of emissions changes on ozone air quality using 178. sensitivity coefficients available from the Fall Line Air Quality Study 179. (http://cure.eas.gatech.edu/faqs/index.html). The measure of ozone air quality 180. is based on the U.S. Environmental Protection Agency (EPA) Clean Air 8-hour 181. Ground-level Ozone rule, an EPA action designating areas whose air quality does 182. not meet the health-based standards established in 1997 for ground-level ozone 183. pollution (http://www.epa.gov/ozonedesignations/). This policy-based 184. designation lets the public know whether air quality in a given area is healthy 185. and is not designed to convey effects on plant physiology or productivity or at 186. different temporal resolutions. The module predicting habitat for red-cockaded 187. woodpecker was developed on the basis of spatial data of long leaf pine in the 188. region (as described in Appendix 2). The module that predicts habitat for the 189. gopher tortoise (Gopherus polyphemus) was developed on the basis of analysis of 190. locations of gopher tortoise burrows at Fort Benning and was tested for the 191. larger five-county region (Baskaran et al. 2006B).

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Numerous future scenarios can be modeled using RSim. These include both civilian 193. and military land-cover changes. The current implementation of RSim includes 194. four specific types of scenarios, along with their impacts on environmental 195. conditions over the next decades: (1) urbanization (conversion of nonurban land 196. cover to low-intensity urban and conversion of low-intensity to high-intensity 197. urban), (2) planned road expansion plus modeled urbanization, (3) a new 198. training area at Fort Benning, and (4) hurricanes of various intensities. 199. Low-intensity urban land cover includes single-family residential areas, 200. schools, city parks, cemeteries, playing fields, and campus-like institutions. 201. High-intensity urban land cover includes paved areas with buildings and little 202. vegetation, power substations, and (occasionally) grain storage buildings.

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For the case considered in this study, RSim was run under conditions meant to 204. simulate "business as usual" (BAU) urbanization for 40 years into 205. the future from 1998, as compared to great increases in urban growth (see 206. Appendix 2 for input conditions). The BAU case includes typical urbanization 207. for the region as based on regional growth patterns from 1990 to 1998, the new 208. training area at Fort Benning (which is already under construction), and road 209. expansion according to the Governor's plans for development of four-lane 210. highways in the region. The high-growth scenario is identical except for an 211. increase in urban growth starting in 1998. This scenario is meant to simulate 212. changes in urban growth of the region that may result from the transfer of 213. training from Fort Knox, Kentucky, to Fort Benning. Although many changes in 214. the region are anticipated (Dale et al. 2005), no one has yet published an 215. analysis of how these changes might affect land cover and other environmental 216. conditions.

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On the basis of the conditions and scenarios selected, the projected changes in 220. land cover are depicted in Figure 3. Graphs of the changes in land cover for 221. the two scenarios are in Figures 4 and 5

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Forest cover also changes in the BAU scenario ( Figure 5A). Both mixed forest and 235. forested wetlands decline from 32,145 ha to 12,775 ha and from 27,933 ha to 236. 14,310 ha, respectively. Deciduous forest and evergreen forests both increase 237. in area from 106,439 ha to 118,880 ha and from 144,905 ha to 191,419 ha, 238. respectively.

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Compared to the BAU case, forest cover has a quite different pattern of change 240. over the next 40 years for the high urban growth scenario (compare figures 5A 241. and 5B). In the latter case, all the common forest categories decline, with 242. mixed forest changing from 32,145 ha to 10,765 ha, forested wetlands from 243. 27,933 ha to 10,561 ha, deciduous forest from 106,439 ha to 42,488 ha, and 244. evergreen forests from 144,905 ha to 70,911 ha.

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The water-quality module projects large differences in the amount and location 247. of major nitrogen (N) and phosphorus (P) export for the BAU scenario as 248. compared to the high urban growth scenario. The BAU case results in the 249. watershed containing the city of Columbus [Hydrological Unit Code (HUC) 30104] 250. exhibiting the greatest changes in N and P exports. In contrast, the high urban 251. growth scenario projects that the watershed northeast of Columbus (HUC 21206) 252. has the greatest changes in these exports. The overall change in N export for 253. the RSim region was 1,002,406 kg and 1,609,560 kg, respectively, for the BAU 254. and high urban growth scenarios. The overall change in P export was 164,703 kg 255. and 374,600 kg, respectively, for the BAU and high-growth scenarios.

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In both scenarios, the peak 8-hour ozone concentration over the five-county 258. region increased from 71 ppbv (parts per billion by volume) in 1998 to about 90 259. ppbv in 2038. Thus, when comparing results of the two scenarios against one 260. another, the additional changes in the high urban growth scenario, which are 261. over and above the BAU scenario, did not yield any additional changes to the 262. estimated change in peak 8-hour ozone concentration over the five-county 263. region. It should be noted, however, that peak 8-hour ozone concentration is 264. but one measure of air quality. Other metrics, for example those that measure 265. dose or the temporal or spatial distribution of ozone, might, in fact, show 266. differences in air quality between the two scenarios. As it is, the increase 267. over 40 years of peak 8-hour ozone concentrations from 71 ppbv to 90 ppbv is 268. caused by the projected growth in industrial, commercial, and transportation 269. activities. Growth in both scenarios, though, is untempered by any future 270. regulatory controls, technological innovations, or air-quality-management 271. decisions. For context, peak 8-hour ozone concentrations actually observed in 272. the five-county region in 1998 ranged up to 104 ppbv.

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For both the BAU and high urban growth scenarios, RSim projects that by model 276. year 2038, 150% of the original clusters of red-cockaded woodpecker will exist 277. in the five-county region. Most of these clusters will be located in evergreen 278. forest within the boundaries of Fort Benning that mature to the stage in which 279. they can support red-cockaded woodpecker by the end of the 40-year model run.

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RSim projects that by model year 2038 there will be 181,288 ha and 113,639 ha of 285. potential area of suitable gopher tortoise habitat for the BAU and high urban 286. growth scenarios, respectively. These projections compare to 190,918 ha of 287. gopher tortoise habitat in the five-county region at the beginning of the 288. simulation. The 5% and 40% reduction in potential area that can support gopher 289. tortoise burrows reflects changes in land cover for the BAU and high urban 290. growth scenarios, respectively. The probability of having suitable gopher 291. tortoise habitat increases when more land cover is in pasture, clear-cuts, 292. forest, transportation corridors, row crop, or utility swaths.

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For the two scenarios, the land-cover changes combine to produce different 295. patterns of risk from noise (compare Figures 6A and 6B). There is a moderate 296. risk of noise complaints from areas outside Fort Benning of 6,334 ha and 93,448 297. ha area, for the BAU and high urban growth scenarios, respectively. The areas 298. likely to experience a high risk of noise complaints are relatively small in 299. both scenarios, with 9 ha and 61 ha being likely by 2038 for the BAU and growth 300. scenarios, respectively. RSim predicts that, by 2038, 8,335 ha and 38,773 ha 301. for the BAU and high-growth scenarios, respectively, of land outside of Fort 302. Benning will be in land uses that are incompatible with noise produced from 303. military activities.

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Projected changes in land cover under the two scenarios are quite different 306. (Figures 4 and 5). The BAU case has only small changes in the urban land cover 307. types. A sharp decline in clearcut land and a more gradual decline in row crops 308. occur as pasture and urban land covers increase in area. At the same time, 309. evergreen and deciduous forest land increases in the region. In contrast, the 310. sharp increase in high-intensity urban lands under the high urban growth 311. scenario is associated with a decline in all of the other land cover types 312. mentioned above. These alterations in land cover types set the stage for 313. changes in some of the other environmental conditions discussed below.

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Changes in N and P export to streams over the 40-year projection are dramatic 315. for both scenarios. For the BAU case, the watershed containing the city of 316. Columbus has more N and P export after 40 years than any other watershed in the 317. region because it continues to be the center of high urban intensity. The city 318. is currently the largest in the five-county area and in 1998 had the greatest 319. concentration of urban land cover in the region. The high proportion of urban 320. lands in Columbus increases the paved areas, which allow runoff as well as 321. industrial inputs of N and P into the water system. Over the 40-year 322. projection, no land-cover changes in the rural or forested landscape are great 323. enough to overcome the large influence of Columbus on the water quality of the 324. region. These results suggest that current and future attention to the effects 325. of N and P export should concentrate on the city of Columbus under the BAU 326. case. However, under the high-growth scenario, the intense urban development 327. shifts to the northeast of Columbus (i.e., to HUC 21206). This difference in 328. results for the two scenarios suggests that the region needs to be prepared to 329. support infrastructure needs and increases in N and P export for a larger 330. region than just the Columbus area.

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Under both scenarios, air-quality changes projected from land-cover changes in 332. the five-county region are similar. There are two principal ways that forest 333. cover can affect air quality, and both are represented in RSim. First, forests 334. emit reactive hydrocarbons that are involved in the chemistry that forms 335. ground-level ozone. In the southeastern United States, biogenic hydrocarbons 336. are ubiquitous, and stoichiometrically speaking, the region is saturated with 337. hydrocarbons. Removing anthropogenic sources of hydrocarbons under any 338. conceivable scenario (or adding more for that matter) has no significant effect 339. on ozone concentrations. For this reason, projected changes in the local forest 340. cover have a negligible effect on extant hydrocarbon emissions and thus ozone 341. concentrations. The second way that forests can affect ground-level ozone is 342. via emissions of nitrous oxide (NOx), either from burning activities in the 343. forest or from activities associated with logging or otherwise managing or 344. using the forest (e.g., chainsaws, trucks, and all-terrain vehicles). Estimates 345. of all these contributions are included in RSim's current emissions 346. inventory. However, forest-related emissions are only a small part of the total 347. emissions inventory, and they have scant impact on the "peak" ozone 348. concentration in the region (which is what RSim calculates and the variable 349. that is generally related to human health and vegetation growth). Further, 350. unless the changes in the forest emissions are collocated with the place where 351. the peak ozone concentration occurs (which is likely because the peak pollutant 352. concentrations tend to occur more near the urban areas where the more-intense 353. emissions sources are located), an effect on ozone concentrations is unlikely. 354. Lastly, forest emissions are distributed over a large area, so the effect is 355. diluted at any one location. Even though all of these factors are included in 356. the air-quality module of RSim, there is little effect on regional air quality 357. as calculated in the form of peak 8-hour ozone concentrations produced by 358. land-cover changes. Conversely, it is expected that air quality does affect 359. land-cover. Though this direct feedback loop has not yet been implemented in 360. RSim, users should be aware that, for both scenarios, the model projects that 361. concentrations of ozone exceed the secondary-ozone standard that is protective 362. of vegetation for 34 years of the 40-year projection period. Consequently, 363. adverse vegetative impacts could be assumed.

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The habitats for the two species included in RSim respond in quite different 365. ways to projected changes in land cover from the BAU and high-growth scenarios. 366. The number of clusters of red-cockaded woodpecker has few differences in the two 367. scenarios because the clusters are almost all located in military lands that are 368. not subject to urban expansion. In contrast, the habitat of gopher tortoise is 369. strongly affected by the increased high urban growth scenario because that case 370. instigates a change in several land-cover types that are suitable for gopher 371. tortoise. Under the BAU case, the clearcut lands undergo a steady decline from 372. 44,735 ha to 20,317 ha; whereas in the high-growth scenario, these clearcut 373. lands decline to about 10,963 ha. At the same time, pasture lands are projected 374. to increase from about 22,890 ha to 27,150 ha in the BAU scenario and decline to 375. 7,800 ha in the high-growth scenario. The decline in both clearcut and pasture 376. lands that results from the high urban growth reduces the area suitable for 377. gopher tortoise habitat.

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The projected risk from noise under the two scenarios is very different (Figure  379. 6). The BAU case is associated with a slight increase in the lands with 380. moderate risk from noise and incompatible land use. In contrast, the high level 381. of urban growth projects dramatic increases in the area of land with moderate 382. risk from noise and incompatible land use. Both of these scenarios display a 383. local peak in risk from noise that occurs just before model year 2008 [when the 384. area of land in high-and low-intensity urban categories are approaching similar 385. values (Figure 4)]. Before 2008, both urban types contribute to the noise risks, 386. but the declining area of residential home lands after 2008 causes the noise 387. risk to also decline for a short period until the influence of the rising 388. high-intensity urban land causes another rise in the noise risk. The location 389. of these new urban lands near the boundary of Fort Benning ( Figure 3) and 390. within the range of noise impacts is another factor affecting the sharp rise in 391. risk from noise.
392. This regional, cross-sectorial analysis of environmental influences of land-use 393. change in west central Georgia illustrates some of the benefits of using such a 394. holistic approach to land-use planning. A broader understanding of potential 395. effects of land-use changes can be achieved. This information can be used to 396. streamline management activities by allowing potential effects to be considered 397. before a decision is made and it promotes discussion and planning for 398. on-the-ground repercussions of decision making. In addition, the simulation 399. model identifies conditions under which cross-sectorial effects should be 400. considered (or not considered). For example, in the scenarios presented here, 401. impacts on air quality are negligible. At least in the absence of large changes 402. in dominant emissions factors such as might be associated with increases in 403. industrial and transportation use or in technology changes, the effects of 404. land-use change on air quality are small. Use of the RSim model enhances 405. understanding of interactions between environmental effects (feedbacks and 406. cumulative impacts) and therefore allows for greater understanding of the 407. conditions necessary to sustain several environmental amenities of the region.

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The use of RSim to explore regional changes in west central Georgia projects 410. that high urban growth can have dramatic impacts upon water and noise quality 411. and upon the habitat of one species of special concern (gopher tortoise) but 412. not another (red-cockaded woodpecker). Hence, this example illustrates where 413. management attention might be focused in order to promote environmental 414. sustainability of the region. However, only a limited set of conditions were 415. considered in this example. The ongoing and regular use of this type of model 416. in a planning environment is the most effective way to make use of the 417. approach. Both the counties and the military lands in Georgia require regular 418. updates to their planning activities, and the use of a land-use planning model 419. in such reporting would permit the model to include both the most recent data 420. and scenarios relevant to recent activities. Simulation models offer a 421. cost-effective and efficient means to explore potential outcomes of resource 422. management and land use. This analysis shows that modeling, understanding, and 423. managing for effects of land-use change on several sectors (air, water, noise, 424. and habitat) require attention to the spatial and temporal scale at which each 425. sector operates and how the factors influencing the sectors interact.    Fig. 6. Land area at moderate or high risk to noise complaints and having incompatible land uses for projected noise risks for the (A) business as usual (BAU) scenario and (B) the high urban growth scenario over the 40 year RSim projection period.

A.1 Modeling urbanization in RSim
RSim simulates changes to urban pixels for land-cover maps for the five-county region around Fort Benning. Urban-growth rules are applied at each iteration of RSim to create new urban land cover. The subsequent RSim modeling step then operates off a new map of land cover for the five-county region. The computer code (written in Java) has been built from the spontaneous, spread-center, and edgegrowth rules of the urban growth model from Sleuth (Clarke et al. 1996, Clarke and Gaydos 1998, Candau 2002. The urban-growth submodel in RSim includes both spontaneous growth of new urban areas and patch growth (growth of preexisting urban patches). RSim generates low-intensity urban areas (e.g., singlefamily residential areas, schools, city parks, cemeteries, playing fields, and campus-like institutions) and high-intensity urban areas. Three sources of growth of low-intensity urban pixels are modeled: spontaneous growth, new-spreading-center growth, and edge growth. First, an exclusion layer is referenced to determine those pixels not suitable for urbanization. The exclusion layer includes transportation routes, open water, the Fort Benning base itself, state parks, and a large private recreational resort (Callaway Gardens). Spontaneous growth is initiated by the selection of n pixels at random, where n is a predetermined coefficient. These cells will be urbanized if they do not fall within any areas defined by the exclusion layer. New-spreading-center growth occurs by selecting a random number of the pixels chosen by spontaneous growth and urbanizing any two neighboring pixels. Edgegrowth pixels arise from a random number of nonurban pixels with at least three urbanized neighboring pixels.
Low-intensity urban pixels become high-intensity urban cells according to different rules for two types of desired high-intensity urban cells: (1) central business districts, commercial facilities, and highly impervious-surface areas (e.g., parking lots) of institutional facilities that are created within existing areas with a concentration of low-intensity urban cells; and (2) industrial facilities and commercial facilities (malls) that are created at the edge of the existing clumped areas of mostly low-intensity urban cells or along four-lane roads.
For the first high-intensity category, land-cover changes occur in a manner similar to changes in lowintensity growth, as described above: a spontaneous-growth algorithm converts random low-intensity pixels to high-intensity pixels, and an edge-growth algorithm converts random low-intensity urban pixels with high-intensity urban neighbors to high-intensity pixels. The second type of conversion from low-intensity to high-intensity urban land use is road-influenced growth and is described in the next section.
The user can influence the pattern and rate of urban growth via changes to several parameters: • Dispersion (Low): Influences the number of randomly selected cells for possible lowintensity urbanization. For dispersion (low) coefficient dL, a new value DL is computed as DL = (dL * 0.005) * sqrt(r 2 + c 2 ), where r and c refer to the number of rows and columns in the land-cover map, respectively. During each time step, DL pixels will be selected at random for attempted low-intensity urbanization. For this and all other rules defining the creation of new low-intensity urban cells, only previously nonurban cells lying outside of urban exclusion zones may be changed to low-intensity urban.
• Dispersion (High): Influences the number of randomly selected cells for possible high-intensity urbanization. For dispersion (high) coefficient dH, a new value DH is computed as DH = (dH * 0.005) * sqrt(r2 + c2), where r and c refer to the number of rows and columns in the land-cover map, respectively. During each time step, DH pixels will be selected at random for attempted high-intensity urbanization. For this and all other rules defining the creation of new high-intensity urban cells, only previously low-intensity urban cells lying outside of urban exclusion zones may be changed to high-intensity urban.
• Breed (Spread): The probability that a spontaneously created (by the above dispersion rule) low-intensity urban cell is chosen to become a potential new spreading center. For each such cell, two of its neighboring cells are randomly selected for new lowintensity urbanization, if possible. A patch of three or more urban cells is considered a spreading center, and is eligible for edge growth, as described below.
• Spread (Low): The probability that a low-intensity urban cell within a spreading center will spawn a new neighboring low-intensity urban cell during any time step. Such growth is also termed edge growth.
• Spread (High): The probability that a high-intensity urban cell within a spreading center will spawn a new neighboring high-intensity urban cell during any time step.
The Sleuth model has been applied to more than 32 urban areas around the world, and the parameters for these model runs are stored at the application web site (http://www.ncgia.ucsb.edu/projects/gig/v2/About/ applications.htm). RSim was calibrated to the five-county region surrounding and including Fort Benning by running the model in a hind-cast mode and comparing projections to the U.S. census data.
A.2 Modeling the effects of roads on urban growth in RSim The road-influenced urbanization module of RSim consists of growth in areas near existing and new roads by considering the proximity of major roads to newly urbanized areas. The new-road scenario makes use of the Governor's Road Improvement Program (GRIP) data layers for new roads in the region. Upon each iteration (time step) of RSim, some number of nonurban pixels in a land-use-landcover map are tested for suitability for urbanization according to spontaneous-and patch-growth constraints. For each pixel that is converted to urban land cover, an additional test is performed to determine whether a primary road is within a predefined distance from the newly urbanized pixel. This step is accomplished by searching successive concentric rings around the urbanized pixel until either a primary-road pixel is found or the coefficient for a road search distance is exceeded. If a road is not encountered, the attempt is aborted.
Assuming the search produces a candidate road, a search is performed to seek out other potential pixels for urbanization. Beginning from the candidate road pixel, the search algorithm attempts to move a "walker" along the road in a randomly selected direction. If the chosen direction does not lead to another road pixel, the algorithm continues searching around the current pixel until another road pixel is found, aborting upon failure. Once a suitable direction has been chosen, the walker is advanced one pixel, and the direction selection process is repeated.
In an effort to reduce the possibility of producing a road trip that doubles back in the opposite direction, the algorithm attempts at each step of the trip to continue moving the walker in the same direction in which it arrived. In the event that such a direction leads to a nonroad pixel, the algorithm's search pattern fans out clockwise and counterclockwise until a suitable direction has been found, aborting upon failure. Additionally, a list of road pixels already visited on the current trip is maintained, and the walker is not allowed to revisit these pixels.
The road-trip process continues until it must be aborted because of the lack of a suitable direction or because the distance traveled exceeds a predefined travel limit coefficient. The latter case is considered a successful road trip. To simulate the different costs of traveling along smaller two-lane roads and larger four-lane roads, each single-pixel advancement on a two-lane road contributes more toward the travel limit, allowing for longer trips to be taken along four-lane roads, such as the GRIP highways.
Upon the successful completion of a road trip, the algorithm tests the immediate neighbors of the final road pixel visited for potential urbanization. If a nonurban candidate pixel for urbanization is found, it is changed to a low-intensity urban type, and its immediate neighbors are also tested to find two more urban candidates. If successful, this process will create a new urban center that may result in spreading growth as determined by the edge-growth constraint.
Roads also influence the conversion of low-intensity urban land cover to high-intensity urban land cover. For the second high-intensity urban subcategory (industry and malls), the RSim code selects new potential high-intensity-urbanized cells with a probability defined by a breed coefficient for each cell. Then, if a four-lane or wider road is found within a given maximal radius (5 km, which determines the road_gravity_coefficient) of the selected cell, the cells adjacent to the discovered four-lane or wider road cell are examined. If suitable, one adjacent cell is chosen for high-intensity urbanization. Hence, the new industry or mall can be located on the highway, within 5 km of an already high-intensity urbanized pixel.
A.3 Modeling changes in land-cover types other than urban in RSim Changes within land-cover types other than urban in the RSim region can affect the potential for cells to be urbanized. Therefore, a brief description of that change process is included here. The annual nonurban land-cover trend was determined by using change-detection procedures that identify changes from one land-cover type to another. Changes to and from urban classes were not considered in the results because they were being dealt with using different growth rules. Based on the land-cover changes happening over a period of time, the annual rate of change was calculated. These nonurban changes were incorporated in the form of a transition matrix from which the transition growth rules were derived. Because forest management activities are different within Fort Benning and the surrounding private lands, the transition rules were calculated separately for Fort Benning and for the area outside Fort Benning. Outside Fort Benning, National Land Cover Datasets (NLCD) of 1992 and 2001 were used. The 2001 data set covers only the northern part of the RSim study region. The data for the remaining regions is yet to be released. Hence currently, the changes observed in the northern portion are assumed to be representative of changes in all of the five-county study region outside Fort Benning. Within Fort Benning, land-cover data sets from 2001 and 2003 were used to derive the annual transition rules for nonurban land-cover changes.

B. Modules for environmental effects in RSim
RSim was designed to focus explicitly on how changes in land cover affect and are affected by environmental conditions. As such, the following environmental interactions are an integral part of the RSim package.

B.1 The air-quality module
The air-quality module (AQM) of RSim estimates how demographic and economic growth, technology advances, activity change, and land-cover transformations affect ground-level ozone concentrations in the Columbus-Fort Benning area. The AQM is largely based on air-quality computer modeling completed during the Fall Line Air Quality Study (1999)(2000)(2001)(2002)(2003)(2004) (Chang et al. 2004). Unlike the Fall Line Air Quality Study models, though, the design of the AQM removes the computational load of traditional air-quality modeling while remaining flexible enough for the user to test various future scenarios. The RSim AQM estimates the relative change in the concentration of ground-level ozone in the Columbus area caused by changes in transportation, business and industry, construction, military operations, and other human activities. In addition, the AQM simulates effects on vegetation.
RSim draws on the extensive, state-of-the-art, and thoroughly reviewed ozone air quality model simulations of the Fall Line Air Quality Study (FAQS). Therein, an air-quality model was created that accurately represents a historical ozone episode for the Columbus/Fort Benning area in the year 2000. In RSim, future-year changes in human activities (sources) are used together with the FAQS base case to estimate future-year changes in ozone air quality: ozone t = ozone 2000 + (∂ozone/∂source) ∆Source t-2000 In the above equation, sources may change relative to how they were in the year 2000 (∆Source t-2000 ), for example, from economic growth in the region or changes in transportation patterns, and these can be controlled by the RSim user. The term ∂ozone/∂source is a sensitivity coefficient that is unique to the source and quantifies how a change in the source, ∂source, affects changes in the concentrations of ozone, ∂ozone. These sensitivity coefficients were calculated outside of RSim and cannot be modified by the user. The description above assumes only one source changes during any given period. As implemented in RSim, the AQM really accounts for multiple changes in many sources throughout the e missions inventory, some of which may exasperate poor air quality and some of which may mitigate poor air quality. Selection of the Default RSim scenario creates a future in which relative changes in emissions sources (∆Source t-2000 ) are estimated with growth factors from the U.S. EPA's Emissions Growth Analysis System (EGAS) (US EPA 2004a and2004b).
Ozone can cause foliar damage in trees, crops, and other vegetation as well as other effects. RSim simul ates effects of ozone on vegetation by using the secondary standard for ozone to simulate relative likelihoods of effects of ozone on vegetation. This standard is meant to protect crops and vegetation as well as other aspects of public welfare. The secondary standard for ozone is equivalent to the primary standard, which states that the fourth highest 8-hr ozone concentration cannot exceed 0.08 ppm (parts per million).

B.2 Water quality and nitrogen and phosphorus export
The water-quality module predicts changes in annual nitrogen (N) and phosphorus (P) exports from watersheds within the five-county (Harris, Muscogee, Marion, Chattahoochee, and Talbot) RSim region surrounding Fort Benning. It is widely established that land use and land cover are principal determinants of nutrient export from terrestrial ecosystems to surface receiving waters (Beaulac and Reckhow 1982). The water-quality submodel predicts total (kg yr -1 ) and normalized (kg ha -1 yr -1 ) losses of N and P from 48 watersheds within the region over the time frame of RSim scenarios by using export coefficients (Johnes 1996, Mattikalli and Richards 1996. Calculations of annual N and P export are performed for the 48 12-digit hydrologic units (HUC) that are included within the RSim region. The method is based on land-cover area (ha) within each watershed and annual nutrient-export coefficients (kg element ha -1 ) specific to each of the eight land-cover types. The area (ha) of each land-cover category is multiplied by its respective export coefficient, and the products are summed for all land covers to estimate the annual flux (kg element yr -1 ) of N or P from each watershed. The exports (kg yr -1 ) are also normalized for the size (ha) of the watershed to yield an area-normalized N or P export (kg element ha -1 yr -1 ). The 48 12-digit HUCs range in size from approximately 3,200 to 12,000 ha.
RSim predictions of N and P exports (kg element yr -1 ) over time vary depending on the changing patterns of land cover within each watershed. Trial runs with the water-quality submodel indicate that the annual fluxes of both N and P exhibit a significant (P ≤ 0.001) positive correlation with size of the hydrologic unit (r = 0.80 and r = 0.48, respectively). However, size of a watershed, the types of land cover within a watershed, and the export coefficients selected for different land covers all influence predicted N and P exports.
B.3 Species of special concern RSim considers effects on the two rare species in the vicinity of Fort Benning: red cockaded woodpecker (Picoides borealis) and gopher tortoise (Gopherus polyphemus). RSim simulates changes in red cockaded woodpecker (RCW) clusters based on land-cover changes. These clusters primarily occur in mature long leaf pine forest so as land changes from evergreen forest it becomes unsuitable for RCW. In the five-county region, most of the clusters are found within Fort Benning. In December 2005, there were 212 known active and 96 inactive RCW clusters at Fort Benning. According to the FWS biological opinion and the installation RCW management plan, Fort Benning's goal is set at 361 active breeding clusters. RSim reports how this goal is affected by land-cover changes for every year of the projection.
The gopher tortoise habitat module in RSim computes the probability of a suitable gopher tortoise habitat in a region according to a logistic regression model described by Baskaran et al. (2006). The gopher tortoise habitat module of RSim uses land-cover variables, distance to stream and road variables, and clay variables as inputs to derive the probability of finding a gopher tortoise. RSim gives the user the option to further define habitat suitability based on habitat patch size [identified within RSim by using a modification of the Hoshen-Kopelman algorithm (Berry et al. 1994, Constantin et al. 1997]. Outputs from this module are  • NOISEMAP calculates contours resulting from aircraft operations using such variables as power settings, aircraft model and type, maximum sound levels and durations, and flight profiles for a given airfield.
• BNOISE projects noise impacts around military ranges where 20-mm or larger weapons are fired and takes into account both the annoyances caused by both impulsive noise and vibration caused by the low-frequency sound of large explosions.

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The Small Arm Range Noise Assessment Model (SARNAM) projects noise impacts around small-arms ranges and accounts for noise attenuated by different combinations of berms, baffles, and range structures. In the implementation of RSim in the region of Fort Benning, noise contour maps represent blast noise simulated with BNOISE, as well as the negligible noise from small arms, but not aircraft noise. RSim uses these contours to estimate human annoyance and to recommend compatible land uses [i.e., residential development and other land uses associated with low-intensity urban land cover are not compatible with blast noise above 115 dBP (peak decibels)].