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Optimal Site Selection of Watershed Hydrological Monitoring Stations Using Remote Sensing and Grey Integer Programming

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Abstract

Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are all intimately related with each other to form water balance dynamics on the surface of the Earth. To monitor change in hydrological systems with minimum effort, however, hydrological monitoring networks at the watershed scale should be deployed at critical locations to advance the monitoring and sensing capability. One of the science questions is how to develop an optimum arrangement/distribution strategy of those monitoring platforms with respect to hydrological components subject to technical and resources constraints. While the complexities arise from the integration of highly heterogeneous data streams in the hydrological cycle under uncertainty, there is an acute need to develop a site screening and sequencing procedure permitting a cost-effective search for final site selection. This paper purports to develop such an approach to address the optimal site selection strategy by integrating satellite remote sensing images with a grey integer programming (GIP) model. The approach uses spatial information on the range of likely values temporally encountered for a number of biophysical descriptors in support of the optimization analysis under uncertainty. Practical implementation was assessed by a case study in a semi-arid watershed—the Choke Canyon Reservoir watershed, south Texas. GIS-based GIP modeling technique successfully supports the screening and sequencing mechanism based on the composite satellite images, which smoothly prioritizes the relative importance and provides the rank order scores across all candidate sites. With the aid of such a synergistic approach, seven locations out of 563 candidate sites were eventually selected and confirmed by a field investigation.

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References

  1. Baghdadi, N., King, C., Chanzy, A., & Wigneron, J. P. (2002). An empirical calibration of the integral equation model based on SAR data, soil moisture and surface roughness measurement over bare soils. International Journal of Remote Sensing, 23(20), 4325–4340. doi:10.1080/01431160110107671.

    Article  Google Scholar 

  2. Basak, I. & Satty, T. (1993). Group decision making using the analytical hierarchy process. Mathematical and Computer Modelling, 17(4–5), 101–109. doi:10.1016/0895-7177(93)90179-3.

    Article  Google Scholar 

  3. Bellman, R. & Zadeh, L. A. (1970). Decision-making in fuzzy environment. Management Science, 17, 141–164. doi:10.1287/mnsc.17.4.B141.

    Article  Google Scholar 

  4. Belton, V. & Gear, T. (1983). On a short-coming of Satty’s method of analytic hierarchies. Omega—International Journal of Management Science, 11, 228–230.

    Article  Google Scholar 

  5. Ben-Jemaa, F., Marino, M. A., & Loaiciga, H. A. (1995). Sampling design for contaminant distribution in lake sediments. Journal of Water Resources Planning and Management, 121(1), 71–79. doi:10.1061/(ASCE)0733-9496(1995)121:1(71).

    Article  Google Scholar 

  6. Chang, N. B., Chen, Y. L., & Wang, S. F. (1997). A fuzzy interval multi-objective mixed integer programming approach for the optimal planning of metropolitan solid waste management system. Fuzzy Sets and Systems, 89(1), 35–60. doi:10.1016/S0165-0114(96)00086-3.

    Article  Google Scholar 

  7. Chang, N. B., Ning, S. K., & Chen, J. C. (2006). Multi-criteria relocation strategy of offsite radioactive monitoring network for a nuclear power plant. Environmental Management, 38(2), 197–217. doi:10.1007/s00267-005-0007-7.

    Article  Google Scholar 

  8. Chang, N. B. & Tseng, C. C. (2001). Assessing relocation strategy of urban air quality monitoring network by compromise programming. Environment International, 26, 524–541.

    Google Scholar 

  9. Chang, N. B., Wen, C. G., Chen, Y. L., & Yong, Y. C. (1996). Optimal planning of the reservoir watershed by grey fuzzy multi-objective programming (I): Theory. Water Research, 30(10), 2329–2334. doi:10.1016/0043-1354(96)00124-8.

    Article  CAS  Google Scholar 

  10. Chang, N. B., Wen, C. G., Chen, Y. L., & Yong, Y. C. (1996). Optimal planning of the reservoir watershed by grey fuzzy multi-objective programming (II): Application. Water Research, 30(10), 2335–2340. doi:10.1016/0043-1354(96)00125-X.

    Article  CAS  Google Scholar 

  11. Christensen, E. R., Phoomiphakdeephan, W., & Razak, A. (1997). Water quality in Milwaukee, Wisconsin versus intake crib location. Journal of Environmental Engineering, 123(5), 492–498. doi:10.1061/(ASCE)0733-9372(1997)123:5(492).

    Article  CAS  Google Scholar 

  12. City of Corpus Christi (not dated). Lake of Corpus Christi & Choke Canyon Reservoir. http://cctexas.com/?fuseaction=main.view&page=1020. accessed October 2003.

  13. Dixon, W., Smyth, G. K., & Chiswell, B. (1999). Optimized selection of river sampling sites. Water Research, 33(4), 971–978. doi:10.1016/S0043-1354(98)00289-9.

    Article  CAS  Google Scholar 

  14. Dubois, P. C., van Zyl, J., & Engman, T. (1995). Measuring soil moisture with imaging radars. IEEE Transactions on Geoscience and Remote Sensing, 33(4), 915–926. doi:10.1109/36.406677.

    Article  Google Scholar 

  15. Dyer, J. S. (1990). A clarification of remarks on the analytic hierarchy process. Management Science, 36, 274–275. doi:10.1287/mnsc.36.3.274.

    Article  Google Scholar 

  16. Earth System Science Center (ESSC).(2002). Soil Information for Environmental Modeling and Ecosystem Management, Penn State University. http://www.essc.psu.edu/soil_info/index.cgi?index.html. Accessed September 2003.

  17. Egmond, N. D. V. & Onderdelinden, D. (1981). Objective analysis of air pollution monitoring network data: Spatial interpolation and network density. Atmospheric Environment, 15(6), 1035–1045. doi:10.1016/0004-6981(81)90104-9.

    Article  Google Scholar 

  18. EROS Data Center.(2003). Band Designations. http://edc.usgs.gov/products/ satellite/band.html. Accessed November, 2003.

  19. Eynon, B. P. (1988). Statistical analysis of precipitation chemistry measurements over the Eastern United States. Part II: Kriging analysis of regional patterns and trends. Journal of Applied Meteorology, 27(12), 1334–1343. doi:10.1175/1520-0450(1988)027<1334:SAOPCM>2.0.CO;2.

    Article  Google Scholar 

  20. Glenn, N. F. & Carr, J. R. (2003). The use of geostatistics in relating soil moisture to RADASAT-1 SAR data obtained over the Great Bason, Nevada, USA. Computers & Geosciences, 29, 577–586. doi:10.1016/S0098-3004(03)00050-5.

    Article  Google Scholar 

  21. Glenn, N. F. & Carr, J. R. (2004). The effects of soil moisture on synthetic aperture radar delineation of geomorphic surfaces in the Great Basin, Nevada, USA. Journal of Arid Environments, 56(4), 643–657. doi:10.1016/S0140-1963(03)00085-5.

    Article  Google Scholar 

  22. Global Land Biosphere Data and Resources.(1999). Normalized Difference Vegetation Index. http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/LAND_BIO/ndvi.html. Accessed November 2003.

  23. Grayson, R. B., Western, A. W., Chiew, F. H. S., & Blöschl, G. (1997). Preferred states in spatial soil moisture patterns, local and non local controls. Water Resources Research, 33(12), 2897–2908. doi:10.1029/97WR02174.

    Article  Google Scholar 

  24. Grayson, R. B., Western, A. W., Wilson, D. J., Young, R. I., McMahon, T. A., Woods, R. A., et al. (1999). Measurement and interpretation of soil moisture for hydrological applications, Proceedings of water 99 joint congress 25th hydrology and water resources symposium and second international conference on water resources and environment research, vol. 1 (pp. 5–9). Brisbane: Institution of Engineers Australia.

    Google Scholar 

  25. Guswa, D. J., Celia, A., & Rodriguez-Iturbe, I. (2002). Models of soil moisture dynamics in ecohydrology: Comparative study. Water Resources Research, 38(9), 1166. doi:10.1029/2001WR000826.

    Article  Google Scholar 

  26. Harmancioglu, N. B. & Alpaslan, N. (1992). Water quality monitoring network design: A problem of multi-objective decision marking. Water Resources Bulletin, 28(1), 179–192.

    CAS  Google Scholar 

  27. Harmancioglu, N. B., Fistikoglu, O., Ozkul, S. D., Singh, V. P., & Alpaslan, M. N. (1999). Water quality monitoring network design. Dordrecht: Kluwer Academic.

    Google Scholar 

  28. Homer, C., Huang, C., Yang, L., Wylie, B., Coan, M (not dated). Development of a 2001 National Landcover Database for the United States, report. SAIC Corporation, USGS/EROS Data Center, Sioux Falls, SD 57198, USA.

  29. Huang, G. H., Baetz, B. W., & Patry, G. G. (1992). A grey linear programming approach for municipal solid waste management planning under uncertainty. Civil Engineering Systems, 9, 319–335. doi:10.1080/02630259208970657.

    Article  CAS  Google Scholar 

  30. Huang, G. H., Baetz, B. W., & Patry, G. G. (1995). Grey integer programming: an application to waste management planning under uncertainty. European Journal of Operational Research, 83(3), 594–620. doi:10.1016/0377-2217(94)00093-R.

    Article  Google Scholar 

  31. Huang, G. H. & Moore, R. D. (1993). Grey linear programming, its solving approach, and its application to water pollution control. International Journal of Systems Science, 24(1), 159–172. doi:10.1080/00207729308949477.

    Article  Google Scholar 

  32. Hudak, P. F., Loaiciga, H. A., & Marino, M. A. (1995). Regional-scale ground water quality monitoring via integer programming. Journal of Hydrology (Amsterdam), 164, 153–170. doi:10.1016/0022-1694(94)02559-T.

    Article  CAS  Google Scholar 

  33. Huete, A., Justice, C., & Leeuwen, W. V. (1999). MODIS vegetation index (MOD 13): Algorithm theoretical basis document, version 3. Tucson: University of Arizona. 129.

    Google Scholar 

  34. Hughes, J. P. & Lettenmaier, D. P. (1981). Data requirements for Kriging—Estimation and network design. Water Resources Research, 17(6), 1641–1650. doi:10.1029/WR017i006p01641.

    Article  Google Scholar 

  35. Jackson, R. D., Slater, P. N., & Pinter, P. J. (1983). Discrimination of growth and water stress in wheat by various vegetation indices through a clear and a turbid atmosphere. Remote Sensing of Environment, 13, 187–208. doi:10.1016/0034-4257(83)90039-1.

    Article  Google Scholar 

  36. Jager, H. I., Sale, M. J., & Schmoyer, R. L. (1990). Co-kriging to assess regional stream quality in the southern blue ridge province. Water Resources Research, 26(7), 1401–1412.

    Article  CAS  Google Scholar 

  37. Knyazikhin, Y., Glassy, J., Privette, J. L., Tian, Y., Lotsch, A., Zhang, Y., et al. (1999). MODIS Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Product (MOD15) Algorithm Theoretical Basis Document. http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf. Accessed December 2006.

  38. Kwok, R., Cunningham, G. F. (2000). RADASAT Geophysical Processor System Data User’s Handbook. NASA, JPL. JPL D-19149.

  39. Lacoul, M., Honda, K., Yokoyama, R., Saito, G. (not dated). Monitoring and Assessing Rice Crop with Multi-temporal RADASAT Fine beam mode data in Pathumthani and Ayuatthaya Province of Thailand. Asian Center for Research on Remote Sensing (ACRoRS), School of Advanced Technonlogies, Asian Institute of Technology, Pathumthani, Thailand.

  40. Land Processes Distributed Active Archive Center (LP DAAC).(2006). MODIS Data Products. USGS–NASA Distributed Active Archive Center. http://LPDAAC.usgs.gov/modis/dataproducts.asp. Accessed 16 February 2006.

  41. LINDO Systems Inc.(2006). http://lindo.com. Accessed January 2006.

  42. Lo, S. L., Kuo, J. T., & Wang, S. M. (1996). Water quality monitoring network design of Keelung River, northern Taiwan. Water Science and Technology, 34(12), 49–57. doi:10.1016/S0273-1223(96)00853-0.

    Article  CAS  Google Scholar 

  43. Makkeasorn, A. & Chang, N. B. (2009). Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed. Journal of Environmental Management, 90(2), 1069–1080. doi:10.1016/j.jenvman.2008.04.004.

    Article  Google Scholar 

  44. Makkeasorn, A., Chang, N. B., Beaman, M., Wyatt, C., & Slater, C. (2006). Soil moisture estimation in a semi-arid watershed using RADARSAT-1 satellite imagery and genetic programming. Water Resources Research, 42, 1–15. doi:10.1029/2005WR004033.

    Article  Google Scholar 

  45. McElroy, J. L., Behar, J. V., Meyers, T. C., & Liu, M. K. (1986). Methodology for designing air quality monitoring networks II: Application to Las Vegas, Nevada, for carbon monoxide. Environmental Monitoring and Assessment, 6(1), 13–34. doi:10.1007/BF00394285.

    Article  CAS  Google Scholar 

  46. Modak, P. M. & Lohani, B. N. (1985). Optimization of ambient air quality monitoring networks: Part I. Environmental Monitoring and Assessment, 5, 1–19. doi:10.1007/BF00396391.

    Article  Google Scholar 

  47. Modak, P. M. & Lohani, B. N. (1985). Optimization of ambient air quality monitoring networks: Part II. Environmental Monitoring and Assessment, 5, 21–38. doi:10.1007/BF00396392.

    Article  Google Scholar 

  48. Modak, P. M. & Lohani, B. N. (1985). Optimization of ambient air quality monitoring networks: Part III. Environmental Monitoring and Assessment, 5, 39–53. doi:10.1007/BF00396393.

    Article  Google Scholar 

  49. Moran, M. S., Hymer, D. C., Qi, J., & Sano, E. E. (2000). Soil moisture evaluation using multi-temporal synthetic aperture radar (SAR) in semiarid rangeland. Agricultural and Forest Meteorology, 105, 69–80. doi:10.1016/S0168-1923(00)00189-1.

    Article  Google Scholar 

  50. Myneni, R., Knyazikhin, Y., Glassy, J., Votava, P., & Shabanov, N. (2003). User’s Guide: FPAR, LAI (ESDT: MOD15A2) 8-day Composite NASA MODIS Land Algrithm. USA: Boston University. 17.

    Google Scholar 

  51. National Climate Data Center (NCDC).(2006). Normals, Means, and Extremes, San Antonio, TX (SAT), report, NCDC Ashville, NC

  52. National Research Council (NRC). (2008). Integrating multiscale observations of U.S. waters. Washington: National Academic.

    Google Scholar 

  53. National Weather Service.(2007). RFC Use of PRISM Data. National Oceanic and Atmospheric Administration. http://www.cnrfc.noaa.gov/products/rfcprismuse.pdf. Accessed January 2007.

  54. Ning, S. K. & Chang, N. B. (2002). Multi-objective, decision-based assessment of a water quality monitoring network in a river system. Journal of Environmental Monitoring, 4, 121–126. doi:10.1039/b107041j.

    Article  CAS  Google Scholar 

  55. Ning, S. K. & Chang, N. B. (2005). Screening and sequencing analysis for the relocation of water quality monitoring network by stochastic compromise programming. Journal of the American Water Resources Association, 41(5), 1039–1052. doi:10.1111/j.1752-1688.2005.tb03784.x.

    Article  CAS  Google Scholar 

  56. Odem, K. R. (2004). Optimizing a sampling network using multivariate statistics in a simulated annealing algorithm. Ph.D. Dissertation, University of Tennessee, Knoxville, TN.

  57. Oeltjenbruns, H., Kolarik, W. J., & Kirschner, R. S. (1995). Strategic planning in manufacturing systems—AHP application to an equipment replacement decision. International Journal of Production Economics, 38, 189–197. doi:10.1016/0925-5273(94)00092-O.

    Article  Google Scholar 

  58. Olmsted, C. (1993). Alaska SAR Facility Scientific SAR User’s Guide. University of Alaska Fairbanks, Geophysical Institute. ASF-SD-003.

  59. Pardo-Iguzquiza, E. (1998). Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing. Journal of Hydrology, 210(1–4), 206–220.

    Article  Google Scholar 

  60. Pickett, E. E. & Whiting, R. G. (1981). The design of cost-effective air quality monitoring networks. Environmental Monitoring and Assessment, 1, 59–74. doi:10.1007/BF00836876.

    Article  Google Scholar 

  61. PRISM Group.(2006). PRISM products, Oregon State Service, Oregon State University. http://www.ocs.orst.edu/prism/products. Accessed September 2006.

  62. Ramanathan, R. & Ganesh, L. S. (1995). Using AHP for resource allocation problems. European Journal of Operational Research, 80, 410–417. doi:10.1016/0377-2217(93)E0240-X.

    Article  Google Scholar 

  63. Romshoo, S. A., Nakaekawa, T., Koike, M., Musaike, K. (1999). Soil Moisture Determination Under Different Field Conditions Using a Scatterometer and Space Borne SAR Systems. http://gisdevelopment.net/aars/acrs/1999/ps3/ps300b.shtml. Accessed September 2003.

  64. Roth, C. H., Malicki, M. A., & Plagge, R. (1992). Empirical evaluation of the relationship between soil dielectric constant and volumetric water content as the basis for calibrating soil moisture measurements by TDR. Journal of Soil Science, 43, 1–13. doi:10.1111/j.1365-2389.1992.tb00115.x.

    Article  Google Scholar 

  65. Salgado, H., Genova, L., Brisco, B., & Bernier, M. (2001). Surface soil moisture estimation in Argentina using RADASAT-1 imagery. Canadian Journal of Remote Sensing, 27(6), 685–690.

    Google Scholar 

  66. Satty, T. L. (1980). The analytic hierarchy process—Planning, priority setting, resource allocation. New York: McGraw-Hill.

    Google Scholar 

  67. Satty, T. L. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science, 23(7), 841–855. doi:10.1287/mnsc.32.7.841.

    Article  Google Scholar 

  68. Satty, T. L. (1990). Exposition of AHP in reply to Dyer. Management Science, 36, 259–268. doi:10.1287/mnsc.36.3.259.

    Article  Google Scholar 

  69. Satty, T. L. (1994). Fundamentals of decision making and priority theory with the analytical hierarchy process. Pittsburgh: RWS.

    Google Scholar 

  70. Song, J., Wesely, M. L., Lemone, M. A., & Grossman, R. L. (2000). Estimating watershed evapotranspiration with PASS. Part II: Moisture budgets during drydown periods. Journal of Hydrometeorology, 1(5), 462–473. doi:10.1175/1525-7541(2000)001<0462:EWEWPP>2.0.CO;2.

    Article  Google Scholar 

  71. Spectrum Technologies, Inc. (not dated). Field Scout™ TDR 300 Soil Moisture Meter, User’s Manual: catalog # 6430FS.

  72. Timmerman, J. G., Adriaanse, M., Breukel, R. M. A., van Oirschot, M. C. M., & Ottens, J. J. (1997). Guidelines for water quality monitoring and assessment of transboundary rivers. European Water Pollution Control, 7(5), 21–30.

    Google Scholar 

  73. Topp, G. C., Davis, J. L., & Annan, A. P. (1980). Electromagnetic determination of soil water content: Measurements in coaxial transmission lines. Water Resources Research, 16(3), 574–582. doi:10.1029/WR016i003p00574.

    Article  Google Scholar 

  74. Tseng, C. C. & Chang, N. B. (2001). Assessing relocation strategies of urban air quality monitoring stations by GA-based compromise programming. Environment International, 26, 523–541. doi:10.1016/S0160-4120(01)00036-8.

    Article  CAS  Google Scholar 

  75. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150. doi:10.1016/0034-4257(79)90013-0.

    Article  Google Scholar 

  76. Tucker, C. J., Newcomb, W. W., Los, S. O., & Prince, S. D. (1991). Mean and inter-year variation of growing season normalized difference vegetation index for the Sahel 1981–1989. International Journal of Remote Sensing, 12, 1133–1135. doi:10.1080/01431169108929717.

    Article  Google Scholar 

  77. Tummala Rao, V. M., Chin, K. S., & Ho, S. H. (1997). Assessing success factors for implementing CE: A case study in Hong Kong electronics industry by AHP. International Journal of Production Economics, 49, 265–283. doi:10.1016/S0925-5273(97)00016-9.

    Article  Google Scholar 

  78. U.S. Geological Survey (USGS).(2001). National Land Cover Data (NLCD). http://landcover.usgs.gov/classes.html. Accessed October 2003.

  79. U.S. Geological Survey (USGS).(2005). Real-time data for Texas. http://waterdata.usgs.gov/tx/nwis/rt. Accessed April 2005.

  80. Ulaby, F. (1974). Radar measurement of soil moisture content. IEEE Transactions on Antennas and Propagation, 22(2), 257–265. doi:10.1109/TAP.1974.1140761.

    Article  Google Scholar 

  81. Venkatram, A. (1988). On the use of Kriging in the spatial analysis of acid precipitation data. Atmospheric Environment, 22(9), 1963–1975. doi:10.1016/0004-6981(88)90086-8.

    Article  CAS  Google Scholar 

  82. Walker, J. P., Willgoose, G. R., & Kalma, J. D. (2001). One-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: A simplified soil moisture model and field application. Journal of Hydrometeorology, 2(4), 356–373. doi:10.1175/1525-7541(2001)002<0356:ODSMPR>2.0.CO;2.

    Article  Google Scholar 

  83. Wan, Z. (1999). MODIS land-surface temperature algorithm theoretical basis document, version 3.3, NAS5-31370, Institute for Computational Earth System Science (p. 77). Santa Barbara: University of California.

    Google Scholar 

  84. Western, A. W., Duncan, M. J., Olszak, C., Thompson, J., Anderson, T., Grayson, R. B., et al. (2001). Calibration of CS615 and TDR instruments for MARVEX, tarrawarra and point Nepean soils. In C. H. Dowding (Ed.), TDR 2001: The second international symposium and workshop on time domain reflectometry for innovative geotechnical applications (pp. 95–108). Evanston: Infrastructure Technology Institute at Northwestern University.

    Google Scholar 

  85. Western, A. W. & Grayson, R. B. (1998). The Tarrawarra data set: Soil moisture patterns, soil characteristics and hydrological flux measurements. Water Resources Research, 34(10), 2765–2768. doi:10.1029/98WR01833.

    Article  Google Scholar 

  86. Wilson, D., Western, A. W., Grayson, R. B., Berg, A. A., Lear, M. S., Rodell, M., et al. (2003). Spatial distribution of soil moisture over 6 and 30 cm depth, Machurangi river catchment, New Zealand. Journal of Hydrology (Amsterdam), 276, 254–274. doi:10.1016/S0022-1694(03)00060-X.

    Article  Google Scholar 

  87. Zadeh, L. I. (1965). Fuzzy Sets. Information and Control, 8, 338–353. doi:10.1016/S0019-9958(65)90241-X.

    Article  Google Scholar 

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    Ni-Bin Chang & Ammarin Makkeasorn

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Chang, NB., Makkeasorn, A. Optimal Site Selection of Watershed Hydrological Monitoring Stations Using Remote Sensing and Grey Integer Programming. Environ Model Assess 15, 469–486 (2010). https://doi.org/10.1007/s10666-009-9213-7

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