Projections of Watershed Pollutant Loads Using a Spatially Explicit, Agent-Based Land Use Conversion Model: A Case Study of Berkeley County, West Virginia

  • Nazia N. Arbab
  • Alan R. Collins
  • Jamison F. Conley
Article

Abstract

This research presents a methodology to make projections of land use conversions in Berkeley County, West Virginia and then utilizes these projections to estimate water quality impacts on the Opequon Creek in Berkeley County. Empirical estimates for factors that influence the land use conversion probability are captured using parameters from a spatial logistic regression (SLR) model. Then, an agent-based, probabilistic land use conversion (APLUC) model is used to explore the impacts of policies on land use conversion decisions using estimates from actual land use change from 2001 to 2011 in SLR model. Three policy scenarios are developed: (1) no policy implementation, (2) a 15.24 m (50 ft) buffer zone policy of no development applied to all streams, and (3) 15.24 m buffer policy applied only on critical source area (CSA) watersheds. The projected land use patterns in the APLUC model are driven by individual land conversion decisions over 50 model runs of 10 iterations each under each policy scenario. The results show that with no policy scenario, most conversions occurred near existing residential land use and urban centers. Residential land use conversions are greatly reduced in a 15.24 m buffer policy around all streams in watershed. Spatial patterns generated under a 15.24 m buffer policy in CSAs only showed that future projected land use changes occurred close to major highways and shifted the residential development to the northern part of the Opequon Creek. Finally, the impacts of these three policies on water quality are estimated using an ArcSWAT model, a graphical user interface for SWAT (Soil and Water Assessment Tool). This model indicates that the 15.24 m buffer policy in CSAs is most effective among the three policies in reducing the pollutant loads. This study suggests that carefully designed policies which discourage residential land use conversions in CSAs, result in less pollutant loads by shifting the location of residential conversions to less critical areas where agricultural land is dominant in the watershed.

Keywords

Spatially explicit land use Agent-based Watershed pollutants ArcSWAT 

References

  1. Alig, Ralph J. (2010). Economic modeling of effects of climate change on forest sector and mitigation options: a compendium of briefing papers. USDAGoogle Scholar
  2. Almeida, C. M., Batty, M., Monteiro, A. M. V., Câmara, G., Soares-Filho, B. S., Cerqueira, G. C., et al. (2003). Stochastic cellular automata modeling of urban land use dynamics: empirical development and estimation. Computers, Environment and Urban Systems, 27(5), 481–509.CrossRefGoogle Scholar
  3. Alonso, W. (1964). Location and land use. Cambridge: MA. Harvard University Press.CrossRefGoogle Scholar
  4. Arbab, Nazia N. (2014). Application of a Spatially Explicit, Agent-Based Land Use Conversion Model to Assess Water Quality Outcomes under Buffer Policies. (PhD dissertation). West Virginia University.Google Scholar
  5. Atkinson, P. M., & Massari, R. (1998). Generalised linear modelling of susceptibility to land sliding in the central Apennines, Italy. Computers & Geosciences, 24, 373–385.CrossRefGoogle Scholar
  6. Balzter, H., Braun, P. W., & Köhler, W. (1998). Cellular automata models for vegetation dynamics. Ecological Modelling, 107(2–3), 113–125.CrossRefGoogle Scholar
  7. Batty, M. (2012). A generic framework for computational spatial modeling. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 19–50). New York, NY: Springer.CrossRefGoogle Scholar
  8. BBER (2014). Population trends in West Virginia through 2030. Morgantown, WV: Bureau of Business and Economic Research, College of Business and Economics, West Virginia University.Google Scholar
  9. Benenson, I., & Torrens, P. (2004). Geosimulation: Automata-based modeling of urban phenomena. West Sussex: Wiley.CrossRefGoogle Scholar
  10. Berkeley County Development Authority. (2014) “Facts and Figures” Retrieved from http://www.developmentauthority.com/
  11. Berkeley County Planning Commission. (2006). Berkeley County, comprehensive plan update. Retrieved from http://www.berkeleycountycomm.org/docs/2006BCCompPlan.pdf
  12. Berkeley County Planning Commission. (2009): Subdivision ordinance (2009). Draft ordinance with county commission approved changes. Retrieved from http://www.berkeleycountycomm.org/docs/draft_subreg0409.pdf
  13. Bhaduri, B., Minner, M., & Tatalovich, S. H., J. (2001). Long-term hydrologic impact of urbanization: a tale of two models. Journal of Water Resources Planning and Management, 127, 13–19.Google Scholar
  14. Bockstael, N. E. (1996). Modeling economics and ecology: the importance of a spatial perspective. American Journal of Agricultural Economics, 78(5), 1168–1180.CrossRefGoogle Scholar
  15. Bockstael, N. E., & Bell, K. P. (1998). Land use patterns and water quality: the effect of differential land management controls. In R. Just & S. Netanyahu (Eds.), International water and resource economics consortium, conflict and cooperation on trans-boundary water resources (pp. 169–191). Norwell, MA: Kluwer Academic Publishers.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. Clark Labs. (2012). IDRISI Selva, Clark University http://www.clarklabs.org/
  18. Clarke, K. C., & Gaydos, L. J. (1998). Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12(7), 699–714.CrossRefGoogle Scholar
  19. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple Regression/Correlation analysis for the behavioral sciences. Mahwah, New Jersey: Lawrence Erlbaum Associates, Publishers.Google Scholar
  20. Corbett, C. W., Wahl, M., Porter, D. E., Edwards, D., & Moise, C. (1997). Nonpoint source runoff modeling. A comparison of a forested watershed and an urban watershed on the South Carolina coast. Journal of Experimental Marine Biology and Ecology, 213(1), 133–149.CrossRefGoogle Scholar
  21. Coutu, G. W., & Vega, C. (2007). Impacts of land use changes on runoff generation in the east branch of the Brandywine creek watershed using a GIS-based hydrologic model. Middle States Geographer, 40, 142–149.Google Scholar
  22. Dosskey, M. G., Vidon, P., Gurwick, N. P., Allan, C. J., Duval, T. P., & Lowrance, R. (2010). The role of riparian vegetation in protecting and improving chemical water quality in streams. JAWRA Journal of the American Water Resources Association, 46(2), 261–277.CrossRefGoogle Scholar
  23. DOT (Department of Transportation) Maryland Transit Administration. (2014) Retrieved from: http://www.mdot.maryland.gov/
  24. Duan, S., Kaushal, S. S., Groffman, P. M., Band, L. E., & Belt, K. T. (2012). Phosphorus export across an urban to rural gradient in the Chesapeake Bay watershed. Journal of Geophysical Research: Biogeosciences, 117(G1), − G01025.Google Scholar
  25. EPA (United States Environmental Protection Agency). (2007). Multi-resolution land characteristics consortium (MLRC). Retrieved from http://www.epa.gov/mrlc/definitions.html
  26. ESRI. (2014). ArcGIS Help 10.2. Retrieved from: http://resources.arcgis.com/en/help/main/10.2/index.htmlGoogle Scholar
  27. Fragkias, M., & Seto, K. C. (2007). Modeling urban growth in data-sparse environments: a new approach. Planning and Design, 34(5), 858–883.CrossRefGoogle Scholar
  28. Gimblett, R. H. (2002). Integrating geographic information systems and agent-based modeling techniques for stimulating social and ecological processes. USA: Oxford University Press.Google Scholar
  29. Goetz, S. J., Wright, R. K., Smith, A. J., Zinecker, E., & Schaub, E. (2003). IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid Atlantic region. Remote Sensing of Environment, 88, 195–208.CrossRefGoogle Scholar
  30. Goodspeed, R. (2007). Leapfrog' sprawl in West Virginia. Retrieved from http://goodspeedupdate.com/2007/2104
  31. Hagerstrand, T. (1965). A Monte Carlo approach to diffusion (pp. 43–67). VI: Archive of European Sociology.Google Scholar
  32. Hatten, M., Lapp, J., Bennett, D., & Stottlemyer, D. (2011). WV stream and wetland valuation (SWVM) metric development. Lexington, KY: Appalachian Stream Mitigation Workshop.Google Scholar
  33. Heppenstall, A. J., & Crooks, A. T. (2012). In Batty M., See L. M. (Eds.), Agent-based models of geographical systems Springer.Google Scholar
  34. Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., et al. (2007). Completion of the 2001 national land cover database for the conterminous United States. Photogrammetric Engineering and Remote Sensing, 73(4), 337–341.Google Scholar
  35. HUD (U.S. Department of Housing and Urban Development). (2012). Comprehensive housing market analysis: Hagerstown-Martinsburg, Maryland-West Virginia. (Research No. U.S. Department of Housing and Urban Development). Retrieved from http://www.huduser.org/publications/pdf/HagerstownMD_comp_12.pdf
  36. Hunsaker, C. T., & Levine, D. A. (1995). Hierarchical approaches to the study of water quality in rivers. Bioscience, 45(3, Ecology of Large Rivers), 193–203.Google Scholar
  37. Irwin, E. G. (2010). New directions for urban economic models of land use change: incorporating spatial dynamics and heterogeneity. Journal of Regional Science, 50(1), 65–91.CrossRefGoogle Scholar
  38. Irwin, E. G., & Bockstael, N. E. (2002). Interacting agents, spatial externalities and the evolution of residential land use patterns. Journal of Economic Geography, 2(1), 31–54.CrossRefGoogle Scholar
  39. Irwin, E. G., & Bockstael, N. E. (2004). Land use externalities, open space preservation, and urban sprawl. Regional Science and Urban Economics, 34(6), 705–725.CrossRefGoogle Scholar
  40. Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., & Xian, G. (2013). A comprehensive change detection method for updating the national land cover database to circa 2011. Remote Sensing of Environment, 132(0), 159–175.Google Scholar
  41. Johnson, L., Richards, C., Host, G., & Arthur, J. (1997). Landscape influences on water chemistry in Midwestern stream ecosystems. Freshwater Biology, 37(1), 193–208.CrossRefGoogle Scholar
  42. Jung, K. W., Lee, S. W., Hwang, H. S., & Jang, J. H. (2008). The effects of spatial variability of land use on stream water quality in a costal watershed. Paddy and Water Environment, 6, 275–284.CrossRefGoogle Scholar
  43. Karigomba, W. (2009). A spatial optimization approach to watershed water quality management: A case of the Opequon watershed. (PhD dissertation). West Virginia University.Google Scholar
  44. Kaushal, S. S., Groffman, P. M., Band, L. E., Elliott, E. M., Shields, C. A., & Kendall, C. (2011). Tracking nonpoint source nitrogen pollution in human-impacted watersheds. Environmental Science & Technology, 45, 8225–8232.CrossRefGoogle Scholar
  45. Kitamura, R., Mokhtarian, P. L., & Laidet, L. (1997). A micro-analysis of land use and travel in five neighborhoods in the San Francisco bay area. Transportation, 24, 125–158.CrossRefGoogle Scholar
  46. Koontz, T. M. (2001). Money talks—but to whom? Financial versus nonmonetary motivations in land use decisions. Society and Natural Resources, 14, 51–65.Google Scholar
  47. Le, Q. B., Park, S. J., & Vlek, P. L. G. (2010). Land use dynamic simulator (LUDAS): a multi-agent system model for simulating spatio-temporal dynamics of coupled human–landscape system: 2. Scenario-based application for impact assessment of land-use policies. Ecological Informatics, 5(3), 203–221.CrossRefGoogle Scholar
  48. Lee, S. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing, 26(7), 1477–1491.CrossRefGoogle Scholar
  49. Lee, S., Hwang, S., Lee, S., Hwang, H., & Sung, H. (2009). Landscape ecological approach to the relationships of land use patterns in watersheds to water quality characteristics. Landscape and Urban Planning, 92(2), 80–89.CrossRefGoogle Scholar
  50. Li, Y. L., Liu, K., Li, L., & Xu, Z. X. (2012). Relationship of land use/cover on water quality in the Liao river basin, China. Procedia Environmental Sciences, 13(0), 1484–1493.Google Scholar
  51. Liu, Y., Kong, X., Liu, Y., & Chen, Y. (2013). Simulating the conversion of rural settlements to town land based on multi-agent systems and cellular automata. PloS One, 8(11), e79300.CrossRefGoogle Scholar
  52. Louis, J. S., & Raines, L. G. (2003). Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity, Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, p.515, November 03–05, 2003Google Scholar
  53. Manson, S. M. (2001). Simplifying complexity: a review of complexity theory. Geoforum, 32(3), 405–414.CrossRefGoogle Scholar
  54. Mejıa, A., Daly, E., Rossel, F., Jovanovic, T., & Gironas, J. (2014). A stochastic model of stream flow for urbanized basins. Water Resources Research, 50Google Scholar
  55. Memarian, H., Balasundram, S. K., Talib, J. B., Sung, C. T. B., Sood, A. M., & Abbaspour, K. (2012). Validation of CA-markov for simulation of land use and cover change in the Langat basin, Malaysia. Journal of Geographic Information System, 4, 542–554.CrossRefGoogle Scholar
  56. Menard, S. (1995). Applied logistic regression analysis. Sage University Paper Series on Quantitative Applications in Social Sciences, 106, 98.Google Scholar
  57. Mills, E. S. (1967). An aggregative model of resource allocation in a metropolitan area. American Economic Review, 57(2), 197–210.Google Scholar
  58. Muth, R. F. (1969). Cities and housing. Chicago: University of Chicago Press.Google Scholar
  59. Natural Resource Analysis Center (NRAC) (2007). Watershed characterization modeling system for ArcGIS 9.2, Release 1.0. Natural Resource Analysis Center: West Virginia University, Morgantown, WV.Google Scholar
  60. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2005). Soil and water assessment tool - theoretical documentation – version 2005. Grassland, Soil and Water Research Laboratory, Agricultural Research Service and Blackland Research Center, Texas Agricultural Experiment Station, Temple, Texas. Retrieved from http://swat.tamu.edu/media/1292/swat2005theory.pdf
  61. Niraula, R., Kalin, L., Srivastava, P., & Anderson, C. J. (2013). Identifying critical source areas of nonpoint source pollution with SWAT and GWLF. Ecological Modelling, 268(0), 123–133.Google Scholar
  62. Olson, R. K., & Olson, A. H. (1999). Farmland loss in America. In R. K. Olson & T. A. Lyson (Eds.), Under the blade: The conversion of agricultural landscapes (pp. 15–52). Boulder, CO.: Westview Press.Google Scholar
  63. Osborne, L. L., & Wiley, M. J. (1988). Empirical relationships between land use/cover and stream water quality in an agricultural watershed. Journal of Environmental Management, 26, 9–27.Google Scholar
  64. Ozah, A. P., Dami, A., & Adesina, F. A. (2012). A deterministic cellular automata model for simulating rural land use dynamics: a case study of Lake Chad basin. Journal of Earth Science & Engineering, 2(1), 22.Google Scholar
  65. Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: a review. Annals of the Association of American Geographers, 93(2), 314–337.CrossRefGoogle Scholar
  66. Peterson, B. J., Wollheim, W. M., Mulholland, P. J., Webster, J. R., Meyer, J. L., Tank, J. L., et al. (2001). Control of nitrogen export from watersheds by headwater streams. Science, 292(5514), 86–90.CrossRefGoogle Scholar
  67. Pionke, H. B., Gburek, W. J., & Sharpley, A. N. (2000). Critical source area controls on water quality in an agricultural watershed located in the Chesapeake basin. Ecological Engineering, 14(4), 325–335.CrossRefGoogle Scholar
  68. Polhill, J. G., Parker, D., Brown, D., & Grimm, V. (2008). Using the ODD protocol for describing three agent-based social simulation models of land-use change. Journal of Artificial Societies and Social Simulation, 11(2), 3.Google Scholar
  69. Polyakov, M., & Zhang, D. (2008). Population growth and land use dynamics along urban–rural gradient. Journal of Agricultural and Applied Economics, 40(2), 649–666.Google Scholar
  70. Pontius Jr., R. G., & Neeti, N. (2010). Uncertainty in the difference between maps of future land change scenarios. Land use and Ecosystems, 5, 39–50.Google Scholar
  71. Poudyal, N. C., Cho, S., Strickland, J. D., & Hodges, D. G. (2008). Socio-demographic and market forces of forest land use change on the northern Cumberland Plateau, Tennessee. International Journal of Ecological Economics & Statistics, 10(W08), 53–62.Google Scholar
  72. Pozzi, F., & Small, C. (2005). Analysis of urban land cover and population density in the United States. Photogrammetric Engineering and Remote Sensing, 71, 719–726.CrossRefGoogle Scholar
  73. Qian, Z. (2010). Without zoning: urban development and land use controls in Houston. Cities, 27(1), 31–41.CrossRefGoogle Scholar
  74. Roe, B., Irwin, E. G., & Marrow-Jones, H. A. (2004). The effects of farmland, farmland preservation, and other neighborhood amenities on housing values and residential growth. Land Economics, 80(1), 55–75.CrossRefGoogle Scholar
  75. Rosenberger, R. S., Gebremedhin, T. G., & Hailu, Y. G. (2002). An economic analysis of urbanization of agricultural land in West Virginia. (Research Paper No. 8).West Virginia Regional Research Institute.Google Scholar
  76. Schueler, T., Fraley-McNeal, L., & Cappiella, K. (2009). Is impervious cover still important? Review of recent research. Journal of Hydrologic Engineering, 14(4), 309–315.CrossRefGoogle Scholar
  77. Serneels, S., & Lambin, E. F. (2001). Proximate causes of land-use change in Narok district, Kenya: a spatial statistical model. Agriculture, Ecosystems and Environment, 85, 65–81.CrossRefGoogle Scholar
  78. Shirzadi, A., Saro, L., Joo, O. H., & Chapi, K. (2012). A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Natural Hazards, 64(2), 1639–1656.CrossRefGoogle Scholar
  79. Sliva, L., & Williams, D. (2001). Buffer zone versus whole catchment approaches to studying land use impact on river water quality. Water Research, 35(14), 3462–3472.CrossRefGoogle Scholar
  80. Sohn, K. T., & Park, S. M. (2008). Guidance on the choice of threshold for binary forecast modeling. Advances in Atmospheric Sciences, 25(1), 83–88.CrossRefGoogle Scholar
  81. Strager, M. P., Fletcher, J. J., Strager, J. M., Yuill, C. B., Eli, R. N., Todd Petty, J., et al. (2010). Watershed analysis with GIS: the watershed characterization and modeling system software application. Computers & Geosciences, 36(7), 970–976.CrossRefGoogle Scholar
  82. Tayyebi, A., Delavar, M. R., Yazdanpanah, M. J., Pijanowski, B. C., Saeedi, S., & Tayyebi, A. H. (2010). A spatial logistic regression model for simulating land use patterns, a case study of the shiraz metropolitan area of Iran. In Chuvieco, E., Li, J., Yang, X. (Ed.), Advances in earth observation of global change, Springer Press.Google Scholar
  83. Tong, S. T. Y., & Chen, W. (2002). Modeling the relationship between land use and surface water quality. Journal of Environmental Management, 66(4), 377–393.CrossRefGoogle Scholar
  84. U.S. Census Bureau. (2000). Population density by census tract, 2000. Retrieved from http://www.census.gov/
  85. U.S. Department of Transportation (1997): Bureau of Transportation Statistics. http://www.rita.dot.gov/bts/home
  86. U.S. Geological Survey. (2014). National land cover database (NLCD). Retrieved from http://www.mrlc.gov/index.php
  87. Valbuena, D., Verburg, P., Bregt, A. K., & Ligtenberg, A. (2010). An agent-based approach to model land-use change at a regional scale. Landscape Ecology, 25, 185–199.CrossRefGoogle Scholar
  88. Von Thünen, J. H. (1826). In P.G. Hall e. (Ed.), Die isolierte staat in beziehung auf landwirtshaft und nationalökonomie. (Wartenberg C M in 1966 Trans.). Pergamon Press, New York.Google Scholar
  89. VT CTMDLWS. (2006). Opequon creek watershed TMDL implementation plan. Virginia tech center for TMDL and watershed studies. Retrieved from http://www.tmdl.bse.vt.edu/uploads/File/pub_db_files/Opequon%20Creek%20TMDL%20IP%2007-12-06.pdf
  90. Water Resources and TMDL Center. (2008). Total maximum daily loads for selected streams in the Potomac direct drains watershed, West Virginia. Prepared for: West Virginia Department of Environmental Protection Division of Water and Waste Management Watershed Branch, TMDL section.Google Scholar
  91. WVDEP (West Virginia Department of Environmental Protection), Canaan Valley Institute, & the Opequon Creek Project Team, Inc. (2008). Watershed based plan for mill creek “A tributary of Opequon creek, in the Potomac direct drains watershed” Berkeley County, WV. Retrieved from http://www.dep.wv.gov/WWE/Programs/nonptsource/WBP/Documents/WBP/MillCreekOpequon_WBP.pdf
  92. Weng, Q. (2001). Modeling urban growth effects on surface runoff with the integration of remote sensing and GIS. Environmental Management, 28(6), 737–748.CrossRefGoogle Scholar
  93. White, R., & Engelen, G. (1993). Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment & Planning A, 25(8), 1175–1199.CrossRefGoogle Scholar
  94. White, E. M., Morzillo, A. T., & Alig, R. J. (2009). Past and projected rural land conversion in the US at state, regional, and national levels. Landscape and Urban Planning, 89(1–2), 37–48.CrossRefGoogle Scholar
  95. Wu, F. (2002). Calibration of stochastic cellular automata: the application to rural-urban land conversions. International Journal of Geographical Information Science, 16(8), 795–818.CrossRefGoogle Scholar
  96. Zeeb, C. N., & Burns, P. J. (1998). A comparison of failure probability estimates by Monte Carlo sampling and Latin hypercube sampling. (Technical Report). Sandia National Laboratories.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Nazia N. Arbab
    • 1
  • Alan R. Collins
    • 2
  • Jamison F. Conley
    • 3
  1. 1.Center for Resilient Landscapes, Department of Ecology, Evolution, and Natural Resources, School of Environmental and Biological SciencesRutgers, The State University of New JerseyNew BrunswickUSA
  2. 2.Agricultural and Resource Economics ProgramWest Virginia UniversityMorgantownUSA
  3. 3.Department of Geology and GeographyWest Virginia UniversityMorgantownUSA

Personalised recommendations