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Future Scenarios of Environmental Vulnerability Mapping Using Grey Analytic Hierarchy Process

  • Satiprasad SahooEmail author
  • Anirban Dhar
  • Anupam Debsarkar
  • Amlanjyoti Kar
Original Paper
  • 28 Downloads

Abstract

Any sustainable resource utilization plan requires evaluation of the present and future environmental impact. The present research focuses on future scenario generation of environmental vulnerability zones based on grey analytic hierarchy process (grey-AHP). Grey-AHP combines the advantages of grey clustering method and the classical analytic hierarchy process (AHP). Environmental vulnerability index (EVI) considers twenty-five natural, environmental and anthropogenic parameters, e.g. soil, geology, aspect, elevation, slope, rainfall, maximum and minimum temperature, normalized difference vegetation index, drainage density, groundwater recharge, groundwater level, groundwater potential, water yield, evapotranspiration, land use/land cover, soil moisture, sediment yield, water stress, water quality, storage capacity, land suitability, population density, road density and normalized difference built-up index. Nine futuristic parameters were used for EVI calculation from the Dynamic Conversion of Land-Use and its Effects, Model for Interdisciplinary Research on Climate 5 and Soil and Water Assessment Tool. The resulting maps were classified into three classes: “high”, “moderate” and “low”. The result shows that the upstream portion of the river basin comes under the high vulnerability zone for the years 2010 and 2030, 2050. The effectiveness of zonation approach was between “better” and “common” classes. Sensitivity analysis was performed for EVI. Field-based soil moisture point data were utilized for validation purpose. The resulting maps provide a guideline for planning of detailed hydrogeological studies.

Keywords

Environmental vulnerability Grey-AHP MIROC5 Dyna-CLUE SWAT GIS Remote sensing 

Notes

Acknowledgment

The authors thank Irrigation and Waterways Directorate, Government of West Bengal, India, for providing necessary support for this research work. The authors also would like to thank the anonymous reviewer for providing valuable comments and suggestions to improve the quality of the paper.

Supplementary material

11053_2019_9462_MOESM1_ESM.docx (3.1 mb)
Supplementary material 1 (DOCX 3133 kb)

References

  1. Alonso, J., & Lamata, T. (2006). Consistency in the analytic hierarchy process: A new approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14(4), 445–459.CrossRefGoogle Scholar
  2. Araújo, J. C. D., & Knight, D. W. (2005). A review of the measurement of sediment yield in different scales. Rem Revista Escola de Minas, 58(3), 257–265.CrossRefGoogle Scholar
  3. Botero-Acosta, A., Chu, M. L., Guzman, J. A., Starks, P. J., & Moriasi, D. N. (2017). Riparian erosion vulnerability model based on environmental features. Journal of Environmental Management, 203, 592–602.CrossRefGoogle Scholar
  4. Dhar, A., Sahoo, S., Dey, S., & Sahoo, M. (2014). Evaluation of recharge and groundwater dynamics of a shallow alluvial aquifer in central ganga basin, Kanpur (India). Natural Resources Research, 23(4), 409–422.CrossRefGoogle Scholar
  5. Dhar, A., Sahoo, S., & Sahoo, M. (2015). Identification of groundwater potential zones considering water quality aspect. Environmental Earth Sciences, 74(7), 5663–5675.CrossRefGoogle Scholar
  6. Fang, G. H., Yang, J., Chen, Y. N., & Zammit, C. (2015). Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrology and Earth System Sciences, 19(6), 2547.CrossRefGoogle Scholar
  7. Gottero, E. (2019). Identifying vulnerable farmland: An index to capture high urbanisation risk areas. Ecological Indicators, 98, 61–67.CrossRefGoogle Scholar
  8. He, L., Shen, J., & Zhang, Y. (2018). Ecological vulnerability assessment for ecological conservation and environmental management. Journal of Environmental Management, 206, 1115–1125.CrossRefGoogle Scholar
  9. Hong, W., Jiang, R., Yang, C., Zhang, F., Su, M., & Liao, Q. (2016). Establishing an ecological vulnerability assessment indicator system for spatial recognition and management of ecologically vulnerable areas in highly urbanized regions: A case study of Shenzhen, China. Ecological Indicators, 69, 540–547.CrossRefGoogle Scholar
  10. Liou, Y. A., Nguyen, A. K., & Li, M. H. (2017). Assessing spatiotemporal eco-environmental vulnerability by Landsat data. Ecological Indicators, 80, 52–65.CrossRefGoogle Scholar
  11. Mandal, U., Sahoo, S., Munusamy, S. B., Dhar, A., Panda, S. N., Kar, A., et al. (2016). Delineation of groundwater potential zones of coastal groundwater basin using multi-criteria decision making technique. Water Resources Management, 30(12), 4293–4310.CrossRefGoogle Scholar
  12. Mavromatidi, A., Briche, E., & Claeys, C. (2018). Mapping and analyzing socio-environmental vulnerability to coastal hazards induced by climate change: An application to coastal Mediterranean cities in France. Cities, 72, 189–200.CrossRefGoogle Scholar
  13. Mitsova, D., Esnard, A. M., Sapat, A., & Lai, B. S. (2018). Socioeconomic vulnerability and electric power restoration timelines in Florida: The case of Hurricane Irma. Natural Hazards, 94, 1–21.CrossRefGoogle Scholar
  14. Nandy, S., Singh, C., Das, K. K., Kingma, N. C., & Kushwaha, S. P. S. (2015). Environmental vulnerability assessment of eco-development zone of Great Himalayan National Park, Himachal Pradesh, India. Ecological Indicators, 57, 182–195.CrossRefGoogle Scholar
  15. Nguyen, A. K., Liou, Y. A., Li, M. H., & Tran, T. A. (2016). Zoning eco-environmental vulnerability for environmental management and protection. Ecological Indicators, 69, 100–117.CrossRefGoogle Scholar
  16. Nilsalab, P., Gheewala, S. H., & Silalertruksa, T. (2017). Methodology development for including environmental water requirement in the water stress index considering the case of Thailand. Journal of Cleaner Production, 167, 1002–1008.CrossRefGoogle Scholar
  17. Nowak, A., & Schneider, C. (2017). Environmental characteristics, agricultural land use, and vulnerability to degradation in Malopolska Province (Poland). Science of the Total Environment, 590, 620–632.CrossRefGoogle Scholar
  18. Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. New York: McGraw-Hill.Google Scholar
  19. Sahoo, S., Dhar, A., Debsarkar, A., & Kar, A. (2018a). Impact of water demand on hydrological regime under climate and LULC change scenarios. Environmental Earth Sciences, 77(9), 341.CrossRefGoogle Scholar
  20. Sahoo, S., Dhar, A., & Kar, A. (2016). Environmental vulnerability assessment using grey Analytic hierarchy process based model. Environmental Impact Assessment Review, 56, 145–154.CrossRefGoogle Scholar
  21. Sahoo, S., Dhar, A., Kar, A., & Ram, P. (2017). Grey analytic hierarchy process applied to effectiveness evaluation for groundwater potential zone delineation. Geocarto International, 32(11), 1188–1205.CrossRefGoogle Scholar
  22. Sahoo, S., Sil, I., Dhar, A., Debsarkar, A., Das, P., & Kar, A. (2018b). Future scenarios of land-use suitability modeling for agricultural sustainability in a river basin. Journal of Cleaner Production, 205, 313–328.CrossRefGoogle Scholar
  23. Shah, A. A., Ye, J., Abid, M., Khan, J., & Amir, S. M. (2018). Flood hazards: Household vulnerability and resilience in disaster-prone districts of Khyber Pakhtunkhwa province, Pakistan. Natural Hazards, 93(1), 1–19.CrossRefGoogle Scholar
  24. Shen, J., Lu, H., Zhang, Y., Song, X., & He, L. (2016). Vulnerability assessment of urban ecosystems driven by water resources, human health and atmospheric environment. Journal of Hydrology, 536, 457–470.CrossRefGoogle Scholar
  25. Smitha, P. S., Narasimhan, B., Sudheer, K. P., & Annamalai, H. (2018). An improved bias correction method of daily rainfall data using a sliding window technique for climate change impact assessment. Journal of Hydrology, 556, 100–118.CrossRefGoogle Scholar
  26. Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. (2002). Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental Management, 30(3), 391–405.CrossRefGoogle Scholar
  27. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.CrossRefGoogle Scholar
  28. Zhang, L., Nan, Z., Yu, W., & Ge, Y. (2015). Modeling land-use and land-cover change and hydrological responses under consistent climate change scenarios in the Heihe River Basin. China. Water Resources Management, 29(13), 4701–4717.CrossRefGoogle Scholar
  29. Zhang, Y., Shen, J., Ding, F., Li, Y., & He, L. (2016). Vulnerability assessment of atmospheric environment driven by human impacts. Science of the Total Environment, 571, 778–790.CrossRefGoogle Scholar
  30. Zhang, Y., Shen, J., & Li, Y. (2018). An atmospheric vulnerability assessment framework for environment management and protection based on CAMx. Journal of Environmental Management, 207, 341–354.CrossRefGoogle Scholar
  31. Zhao, J., Ji, G., Tian, Y., Chen, Y., & Wang, Z. (2018). Environmental vulnerability assessment for mainland China based on entropy method. Ecological Indicators, 91, 410–422.CrossRefGoogle Scholar
  32. Zou, T., & Yoshino, K. (2017). Environmental vulnerability evaluation using a spatial principal components approach in the Daxing’anling region, China. Ecological Indicators, 78, 405–415.CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  1. 1.Faculty of Interdisciplinary Studies, Law and ManagementJadavpur UniversityKolkataIndia
  2. 2.Department of Civil EngineeringIndian Institute of TechnologyKharagpurIndia
  3. 3.Department of Civil EngineeringJadavpur UniversityKolkataIndia
  4. 4.Central Ground Water BoardKolkataIndia

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