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Cited by 32Climate Change, Adaptation, MigrationTransboundary Water Resource ManagementHydropower, Displacement, Environmental Impact

Geospatial Assessment of Water-Migration Scenarios in the Context of Sustainable Development Goals (SDGs) 6, 11, and 16

Open access
Nagabhatla, Nidhi, Brahmbhatt, Rupal·2020·Remote Sensing
DOI 10.3390/rs12091376
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Abstract

Communities and countries around the world are gearing up efforts to implement the 2030 Agenda goals and targets. In this paper, the water and migration scenarios are explained with a focus on Sustainable Development Goals (SDGs) 6 (water-related), 11 (urbanization), and 16 (peace and political stability). The study has two phases. The first phase illustrates the application of geospatial data and tools to assess the water-migration interlinkages (nexus) by employing a case study approach. Three case studies, Lake Chad, the Aral Sea region, and the Nile Delta, representing various geographic and socio-political settings, were selected to perform the multitemporal analysis. For this analysis, a mixed toolset framework that combined algorithmic functions of digital image processing, the Landsat sensor data, and applied a geographic information system (GIS) platform was adopted. How water-related events directly or indirectly trigger human migration is described using spatial indicators such as water spread and the extent of urban sprawl. Additionally, the geospatial outputs were analyzed in tandem with the climate variables such as temperature, precipitation data, and socio-economic variables such as population trends and migration patterns. Overall, the three case studies examined how water and climate crisis scenarios influence migration at a local and regional scale. The second phase showcases global-scale analysis based on the Global Conflict Risk Index (GCRI). This indicator reflects on the risks and conflicts with environmental, social, and political aspects and comments on the connection of these dimensions with migration. Together, the two phases of this paper provide an understanding ofthe interplay of water-related events on migration by applying the geospatial assessment and a proxy global index. Additionally, the paper reiterates that such an understanding can serve to establish facts and create evidence to inform sustainable development planning and decision making, particularly with regard to SDGs 6, 11, and 16. Targets such as 6.4 (managing water stress), 6.5 (transboundary challenges) and, 11.B (adaptation and resilience planning) can benefit from the knowledge generated by this geospatial exercise. For example, the high GCRI values for the African region speak to SDG targets 11.B (integrated policies/plans) and 16.7 (decision support systems for peaceful societies). Two key highlights from the synthesis: (a) migration and urbanization are closely interconnected, and (b) the impact of water and climate crisis is comparatively high for rural-urban migration due to the considerable dependence of rural communities on nature-based livelihoods. In conclusion, geospatial analysis is an important tool to study the interlinkages between water and migration. The paper presents a novel perspective toward widening the scope of remote sensing data and GIS toward the implementation of the SDG Agenda.

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  • The economic push mechanism centers on livelihood disruption: water scarcity reduces crop yields, pasture productivity, and farm labor demand, lowering household income and increasing food insecurity.

    “The economic push mechanism centers on livelihood disruption: water scarcity reduces crop yields, pasture productivity, and farm labor demand, lowering household income and increasing food insecurity (Nagabhatla & Brahmbhatt, 2020).”

    From Climate Security, Water Governance, and Resilience How Climate-Induced Water Stress and Migration Challenge State Capacity (2026) · cites this paper as Nagabhatla & Brahmbhatt, 2020

    The Discussion (page not provided) links water crises in the Aral Sea region to food insecurity and loss of livelihood and income. The Aral Sea case study (page not provided) also describes lost employment opportunities. Neither passage specifies reduced crop yields, pasture productivity, or farm labor demand as the mechanism. (LLM, conf=0.87)

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Research works printed but not linked: 44

  1. Nagabhatla, N.; Avellan, T. The United Nations World Water Development Report-Leaving No One Behind; WWDR: UNESCO, Paris, 2019; Chapter 2.
  2. Berchin, I.I.; Baltazar, S.; Guerra, O. Climate change and forced migrations: An effort towards recognizing climate refugees. Geoforum 2017, 84, 147-150. [CrossRef] · DOI
  3. Lechner, E.; Renault, L. Migration experiences and narrative identities: Viewing alterity from biographical research. Crit. Hermeneut. 2018, 2, 1-25. [CrossRef] · DOI
  4. Parry, M.L.; Canziani, O.F.; Palutikof, J.P.; Van Der Linden, P.J.; Hanson, C.E. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2007; Available online: https://www.ipcc.ch/publications_and_data/ ar4/wg2/en/contents.html (accessed on 1 April 2020).
  5. Internal Displacement Monitoring Centre (IDMC). Disaster Displacement-A Global Review 2008-2018. 2019. Available online: https://www.internal-displacement.org/publications/disaster-displacement-a-global- review (accessed on 1 April 2020).
  6. Nagabhatla, N.; Metcalfe, C.M. (Eds.) Multifunctional Wetlands: Pollution Abatement and Other Ecological Services from Natural and Constructed Wetlands; Springer International Publishing: Berlin, Germany, 2018; ISBN 978-3-319-67416-2.
  7. Warner, K.; Hamza, M.; Oliver-Smith, A.; Renaud, F.; Julca, A. Climate change, environmental degradation and migration. Nat. Hazards. 2018, 55, 689-715. [CrossRef] · DOI
  8. Gray, C.; Bilsborrow, R. Environmental Influences on Human Migration in Rural Ecuador. Demography 2013, 50, 1217-1241. [CrossRef] [PubMed] · DOI
  9. Daash, A.; Sr ivastava, A.; Nagp at, B.N.; Saxena, R.; Kcipta, S. Geographical Information System (GIS) in decision support to control Malaria-A case study of Korapat district in Orissa. J. Vector Borne Dis. 2009, 41, 72-74.
  10. Geoghegan, J.L.; Pritchard, Y.; Ogneva-Himmelberger, R.R.; Chowdhury, S.; Sanderson Turner, B.L., II. Socializing the Pixel" and "Pixelizing the Social in Land-Use and Land Cover Change; National Academy Press: Washington, DC, USA, 1998.
  11. Nagabhatla, N.; Sahu, S.; Gaetaniello, A.; Wen, L.; Lee, W. Understanding Impacts of Climate Variation in Varied Socio-ecological Domains: A Prerequisite for Climate Change Adaptation and Management. In Handbook of Climate Change Adaptation; Springer International Publishing AG: Berlin, Germany, 2015; pp. 589-617.
  12. Turner, B.L., II. Frontiers of Exploration: Remote Sensing and Social Science Research. In Pecora 13 Symposium, Human interaction with the Environment: Perspectives from Space, Souix Falls, SD, USA, 20-22 August 1996; American Society for Photogrammetry and Remote Sensing: Bethesda, MD, USA, 1998; pp. 15-19.
  13. Lemoalle, J.; Bader, J.C.; Leblanc, M.; Sedick, A. Recent changes in Lake Chad: Observations, simulations and management options (1973-2011). Glob. Planet. Chang. 2012, 80, 247-254. [CrossRef] · DOI
  14. Birkett, C.M. Synergistic remote sensing of Lake Chad: Variability of basin inundation. Remote Sens. Environ. 2000, 72, 218-236. [CrossRef] · DOI
  15. Tilho, J. Variations et disparition possible du lac Tchad. Annales de Géographie. France 1928, 37, 238-260.
  16. Gao, H.; Bohn, T.; Podest, E.; McDonald, K. On the causes of the shrinking of Lake Chad. Environ. Res. Lett. 2011, 6, 034021. [CrossRef] · DOI
  17. Leblanc, M.; Favreau, G.; Tweed, S.; Leduc, C.; Razack, M.; Mofor, L. Remote sensing for ground water modelling in large semiarid areas: Lake Chad basin, Africa. Hydrol. J. 2007, 15, 97-100.
  18. Okpara, U.T.; Stringer, L.C.; Dougill, A.J. Lake drying and livelihood dynamics in Lake Chad: Unravelling the mechanisms, contexts and responses. Ambio 2016, 45, 781-795. [CrossRef] [PubMed] · DOI
  19. Buma, W.G.; Lee, S.; Seo, J.Y. Hydrological evaluation of Lake Chad Basin using space borne and hydrological model observations. Water 2016, 8, 205. [CrossRef] · DOI
  20. Zohry, A. The place of Egypt in the regional migration system as a receiving country. Rev. Eur. des Migr. Int. 2003, 19, 129-149. [CrossRef] · DOI
  21. Micklin, P. The Future Aral Sea: Hope and despair. Environ. Earth Sci. 2016, 75, 844. [CrossRef] · DOI
  22. Glantz, M.H. (Ed.) Sustainable development and creeping environmental problems in the Aral Sea Region. In Creeping Environmental Problems and Sustainable Development in the Aral Sea Basin; Cambridge University Press: Cambridge, UK, 1999; pp. 1-25.
  23. Brahmbhatt, R.M.; Bahuguna, I.M.; Rathore, B.P.; Kulkarni, A.V.; Nainwal, H.C.; Shah, R.D.; Ajai, P. A comparative study of deglaciation in two neighboring basins (Warwan and Bhut) of Western Himalaya. Curr. Sci. 2012, 103, 298-304.
  24. Brahmbhatt, R.M.; Bahuguna, I.M.; Rathore, B.P.; Kulkarni, A.V.; Shah, R.D.; Kargel, J.S.; Rajawat, A.S. Significance of glacio-morphological factors in the glacier retreat: A case study of part of Chenab basin, Himalaya. J. Mt. Sci. 2017, 14, 128-141. [CrossRef] · DOI
  25. McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425-1432. [CrossRef] · DOI
  26. Chowdary, V.M.; Vinu Chandran, R.; Neeti, N.; Bothale, R.V.; Srivastava, Y.K.; Ingle, P.; Ramakrishnan, D.; Dutta, D.; Jeyaram, A.; Sharma, J.R.; et al. Assessment of surface and sub-surface waterlogged areas in irrigation command areas of Bihar state using remote sensing and GIS. Agric. Water Manag. 2008, 95, 754-766.
  27. CrossRef] · DOI
  28. Murray, N.J.; Phinn, S.R.; Clemens, R.S.; Roelfsema, C.M.; Fuller, R.A. Continental scale mapping of tidal flats across East Asia using the Landsat archive. Remote Sens. 2012, 4, 3417-3426. [CrossRef] · DOI
  29. Panigrahy, S.; Murthy, T.V.R.; Patel, J.G.; Singh, T.S. Wetlands of India: Inventory and assessment at 1:50,000 scale using geospatial techniques. Curr. Sci. 2012, 102, 852-856.
  30. Cao, B.C. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257-266.
  31. Ceola, S.; Laio, F.; Montanari, A. Human-impacted waters: New perspectives from global high resolution monitoring. Water Resour. Res. 2015, 51, 7064-7079. [CrossRef] · DOI
  32. Huang, S.; Siegert, F. Land cover classification optimized to detect areas at risk of desertification in North China based on SPOT VEGETATION imagery. J. Arid Environ. 2006, 67, 308-327. [CrossRef] · DOI
  33. Ahl, D.E.; Gower, S.T.; Burrows, S.N.; Shabanov, N.V.; Myneni, R.B.; Knyazikhin, Y. Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens. Environ. 2006, 104, 88-95. [CrossRef] · DOI
  34. Zha, Y.; Gao, J.; Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 2003, 24, 583-594. [CrossRef] · DOI
  35. Zhu, W.; Jiabao, Y.; Shaofeng, J. Monitoring Recent Fluctuations of the Southern Pool of Lake Chad Using Multiple Remote Sensing Data: Implications for Water Balance Analysis. Remote Sens. 2017, 9, 1032. [CrossRef] · DOI
  36. Uzodike, N.O.; Maiangwa, B. Boko Haram terrorism in Nigeria: Causal factors and central problematic. Afr. Renaiss. 2012, 9, 91-118.
  37. Ahmed, E.; Oliver, C.; Negm, A. Nile Delta Challenges and Opportunities, A State Of The Art Review. In Proceedings of the Eighteenth International Water Technology Conference, IWTC18, Sharm El Sheikh, Egypt, 12-14 March 2015. [CrossRef] · DOI
  38. Abdrabo, M.A.; Hassaan, M.A. An integrated framework for urban resilience to climate change-Case study: Sea level rise impacts on the Nile Delta coastal urban areas. Urban Clim. 2015, 14, 554-565. [CrossRef] · DOI
  39. Abdullaev, I. Der südliche Aralsee-Heutige Situation und Zukunftsperspektiven. In Zentralasienanalysen Nr. 55-56, Der Aralsee-Vom Umgang mit einer Weltkatastrophe. Forschungsstelle Osteuropa; Universität Bremen Academy Press: Washington, DC, USA, 2012; pp. 164-188.
  40. White, K. Nature and Economy in the Aral Sea Basin; Springer: Berlin/Heidelberg, Germany, 2014. [CrossRef] · DOI
  41. IFAS-UNEP. International Fund for the Aral Sea and the UN Environment Programme. Environment State of the Aral Sea Basin-Regional Report of the Central Asian States 2000. J. Vector Borne Dis. 2001, 46, 72-74.
  42. Fuchs, A.; Müller, A. Democracy and Aid Donorship. SSRN Electron. J. 2018, 2113, 19-23. [CrossRef] · DOI
  43. Wang, X.; Yang, W. Water quality monitoring and evaluation using remote sensing techniques in China: A systematic review. Ecosyst. Health Sustain. 2019, 5, 47-56. [CrossRef] · DOI
  44. Glieck, P. Water Conflict Chronology; Island Press: Washington, DC, USA, 2000.

Cited by

Sorted by citations

Authors
Nagabhatla, N., & Brahmbhatt, R.
Affiliations
  • United Nations University Institute for Water, Environment, and Health (CA)
  • University of Northern British Columbia (CA)
Corresponding author
Nagabhatla, Nidhi
ORCID
Nagabhatla, Nidhi
Venue
Remote Sensing
Publisher
MDPI AG
Published
2020-04-27
Volume
12
Issue
9
Pages
1376
ISSN
2072-4292 (ISSN-L)
DOI
10.3390/rs12091376
OpenAlex
W3020026004
Cited by
32
References
2
Citations by year
2026: 2 · 2025: 11 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 4
FWCI
5.01
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Sociology and Political Science › Climate Change, Adaptation, Migration
  • Social Sciences › Sociology and Political Science › Transboundary Water Resource Management
  • Social Sciences › Sociology and Political Science › Hydropower, Displacement, Environmental Impact
Keywords
climate-induced migration, water stress, urbanization, Lake Chad, Aral Sea, Nile Delta, rural-urban migration, transboundary water management, climate change adaptation, Sustainable Development Goals, 2030 Agenda for Sustainable Development, Landsat imagery, multitemporal analysis, population trends

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