Advertisement

Social Indicators Research

, Volume 140, Issue 3, pp 1131–1157 | Cite as

Spatial Variation in Seasonal Water Poverty Index for Laos: An Application of Geographically Weighted Principal Component Analysis

  • Marko Kallio
  • Joseph H. A. Guillaume
  • Matti Kummu
  • Kirsi Virrantaus
Article

Abstract

Water poverty, defined as insufficient water of adequate quality to cover basic needs, is an issue that may manifest itself in multiple ways. Extreme seasonal variation in water availability, such as in Laos, located in Monsoon Asia, results in large differences in water poverty conditions between dry and wet seasons. In this study, seasonal Water Poverty Indices (WPI) are developed for 8215 villages in Laos. WPI is a multidimensional composite index integrating five dimensions of water: resource availability, access to safe water, capacity to manage the resource, its use and environmental requirements. Principal Component Analysis (PCA) and Geographically Weighted PCA (GWPCA) were used to examine drivers of water poverty and to derive different weighting schemes. Three major drivers were identified: poverty, commercial/subsistence agriculture and village location. The least water poor areas are located around the capital city and along the Mekong River Valley while the highest water poverty is found in sparsely populated mountainous areas. Wet season WPI is on average more than 12 index points higher than in the dry season, but in some villages monsoon rain does not improve the situation. The results indicate large spatial and temporal differences in WPI within Laos. In analysis of WPI components, a mean–variance scaled PCA is recommended due to its capacity for uncovering processes driving water poverty. Extending to GWPCA is recommended when information on local differences is of interest.

Keywords

Water Poverty Index Geographically weighted principal component analysis Monsoon Water poverty Spatio-temporal analysis Laos 

Notes

Acknowledgements

The work was financially supported by Maa- ja vesitekniikan tuki ry, Emil Aaltonen Foundation funded Project ‘eat-less-water’, and Academy of Finland funded project WASCO (grant no. 305471). Authors are grateful for the support of Mr. Jorma Koponen and Dr. Juha Sarkkula.

Supplementary material

11205_2017_1819_MOESM1_ESM.docx (30 kb)
Supplementary material 1 (DOCX 30 kb)
11205_2017_1819_MOESM2_ESM.docx (1.8 mb)
Supplementary material 2 (DOCX 1875 kb)
11205_2017_1819_MOESM3_ESM.xls (5.6 mb)
Supplementary material 3 (XLS 5699 kb)

References

  1. Babel, M., & Wahid, S. (2009). Freshwater under threat: Southeast Asia. Vulnerability assessment of freshwater resources to environmental change. Nairobi: Mekong River Basin.Google Scholar
  2. Beilfuss, R., & Triet, T. (2014). Climate change and hydropower in the Mekong River Basin: A synthesis of research. https://www.giz.de/en/downloads/giz2014-en-study-climate-change-hydropower-mekong.pdf.
  3. Brunsdon, C., Fotheringham, A. S., & Charlton, M. (2002). Geographically weighted summary statistics—A framework for localised exploratory data analysis. Computers, Environment and Urban Systems.  https://doi.org/10.1016/S0198-9715(01)00009-6.CrossRefGoogle Scholar
  4. Charlton, M., Brunsdon, C., Demšar, U., Harris, P., & Fotheringham, S. (2010). Principal components analysis: From global to local. In 13th AGILE International Conference on Geographic Information Science, pp. 1–10.Google Scholar
  5. Chatfield, C., & Collins, A. J. (1980). Introduction to multivariate analysis. London: Chapman and Hall.CrossRefGoogle Scholar
  6. Cho, D. I., Ogwang, T., & Opio, C. (2010). Simplifying the water poverty index. Social Indicators Research, 97(2), 257–267.  https://doi.org/10.1007/s11205-009-9501-2.CrossRefGoogle Scholar
  7. Coulombe, H., Epprecht, M., Pimhidzai, O., & Sisoulath, V. (2016). Where are the poor? Lao PDR 2015 census-based poverty map : province and district level results. Washington, D.C. http://documents.worldbank.org/curated/en/477381468415961977/Where-are-the-poor-Lao-PDR-2015-census-based-poverty-map-province-and-district-level-results.
  8. Darby, S. E., Hackney, C. R., Leyland, J., Kummu, M., Lauri, H., Parsons, D. R., et al. (2016). Fluvial sediment supply to a mega-delta reduced by shifting tropical-cyclone activity. Nature, 539(7628), 276–279.  https://doi.org/10.1038/nature19809.CrossRefGoogle Scholar
  9. Demšar, U., Harris, P., Brunsdon, C., Fotheringham, A. S., & McLoone, S. (2013). Principal component analysis on spatial data: An overview. Annals of the Association of American Geographers.  https://doi.org/10.1080/00045608.2012.689236.CrossRefGoogle Scholar
  10. Falkenmark, M., Lundqvist, J., & Widstrand, C. (1989). Macro-scale water scarcity requires micro-scale approaches. Natural Resources Forum, 13(4), 258–267.  https://doi.org/10.1111/j.1477-8947.1989.tb00348.x.CrossRefGoogle Scholar
  11. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. New York: Wiley.Google Scholar
  12. Garriga, R. G., & Foguet, A. P. (2010). Improved method to calculate a water poverty index at local scale. Journal of Environmental Engineering, 136, 1287–1298.  https://doi.org/10.1061/(ASCE)EE.1943-7870.0000255.CrossRefGoogle Scholar
  13. Getis, A. (2010). Spatial Autocorrelation. In M. Fischer & A. Getis (Eds.), Handbook of applied spatial analysis (pp. 255–278). Berlin: Springer.CrossRefGoogle Scholar
  14. Guppy, L. (2014). The water poverty index in rural Cambodia and Vietnam: A holistic snapshot to improve water management planning. Natural Resources Forum.  https://doi.org/10.1111/1477-8947.12051.CrossRefGoogle Scholar
  15. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Upper Saddle River: Pearson Prentice Hall.Google Scholar
  16. Hajkowicz, S. (2006). Multi-attributed environmental index construction. Ecological Economics, 57(1), 122–139.  https://doi.org/10.1016/j.ecolecon.2005.03.023.CrossRefGoogle Scholar
  17. Harris, P., Brunsdon, C., & Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10), 1717–1736.  https://doi.org/10.1080/13658816.2011.554838.CrossRefGoogle Scholar
  18. Harris, P., Clarke, A., Juggins, S., Brunsdon, C., & Charlton, M. (2014). Geographically weighted methods and their use in network re-designs for environmental monitoring. Stochastic Environmental Research and Risk Assessment, 28(7), 1869–1887.  https://doi.org/10.1007/s00477-014-0851-1.CrossRefGoogle Scholar
  19. Harris, P., Clarke, A., Juggins, S., Brunsdon, C., & Charlton, M. (2015). Enhancements to a geographically weighted principal component analysis in the context of an application to an environmental data set. Geographical Analysis, 47(2), 146–172.  https://doi.org/10.1111/gean.12048.CrossRefGoogle Scholar
  20. Heidecke, C. (2006). EPT Discussion Paper 145. Development and evaluation of a regional water poverty index for Benin. Washington, D.C.: International Food Policy Research Institute.Google Scholar
  21. Jemmali, H., & Matoussi, M. S. (2013). A multidimensional analysis of water poverty at local scale: Application of improved water poverty index for Tunisia. Water Policy, 15(1), 98–115.  https://doi.org/10.2166/wp.2012.043.CrossRefGoogle Scholar
  22. Jemmali, H., & Sullivan, C. A. (2014). Multidimensional analysis of water poverty in MENA region: An empirical comparison with physical indicators. Social Indicators Research, 115(1), 253–277.  https://doi.org/10.1007/s11205-012-0218-2.CrossRefGoogle Scholar
  23. Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). In Encyclopedia of statistics in behavioral science, 30(3), 487. doi:  https://doi.org/10.2307/1270093.CrossRefGoogle Scholar
  24. Komnenic, V., Ahlers, R., & van der Zaag, P. (2009). Assessing the usefulness of the water poverty index by applying it to a special case: Can one be water poor with high levels of access? Physics and Chemistry of the Earth, 34(4–5), 219–224.  https://doi.org/10.1016/j.pce.2008.03.005.CrossRefGoogle Scholar
  25. Koponen, J., Lauri, H., Veijalainen, N., & Sarkkula, J. (2010). HBV and IWRM Watershed Modelling User Guide. Phnom Penh.Google Scholar
  26. Lao Statistics Bureau. (2005). Census of population and housing 2005. Vientiane, Laos: Lao Statistics Bureau. http://www.decide.la/.
  27. Lao Statistics Bureau. (2011). Lao agriculture census 2010/2011. Vientiane, Laos: Lao Statistics Bureau. http://www.decide.la/.
  28. Lauri, H., Räsänen, T. A., Kummu, M., Lauri, H., Räsänen, T. A., & Kummu, M. (2014). Using reanalysis and remotely sensed temperature and precipitation data for hydrological modeling in monsoon climate: Mekong River case study. Journal of Hydrometeorology, 15(4), 1532–1545.  https://doi.org/10.1175/JHM-D-13-084.1.CrossRefGoogle Scholar
  29. Lawrence, P., Meigh, J., & Sullivan, C. (2002). The water poverty index: An international comparison. Keele Economics Research Papers, 19(October), 17.  https://doi.org/10.1111/1477-8947.00054.CrossRefGoogle Scholar
  30. Lloyd, C. D. (2010). Analysing population characteristics using geographically weighted principal components analysis: A case study of Northern Ireland in 2001. Computers, Environment and Urban Systems.  https://doi.org/10.1016/j.compenvurbsys.2010.02.005.CrossRefGoogle Scholar
  31. Mekong River Commission. (2007). Diagnostic study of water quality in lower mekong basin. MRC Technical Paper No. 15. Vientiane, Laos.Google Scholar
  32. Mekong River Commission. (2011). Planning Atlas of the lower Mekong river basin. Vientiane: Mekong River Commission.Google Scholar
  33. Molle, F., & Mollinga, P. (2003). Water poverty indicators: Conceptual problems and policy issues. Water Policy, 5(5–6), 529–544.CrossRefGoogle Scholar
  34. Najdov, E., & Phimmahasay, K. (2016). Lao economic monitorChallenges in promoting more inclusive growth and shared prosperity : Thematic sectiondrivers of poverty reduction in Lao PDR. Washington, D.C. http://documents.worldbank.org/curated/en/515521468197368035/Lao-economic-monitor-Challenges-in-promoting-more-inclusive-growth-and-shared-prosperity-thematic-section-drivers-of-poverty-reduction-in-Lao-PDR.
  35. Pérez-Foguet, A., & Garriga, R. G. (2011). Analyzing water poverty in basins. Water Resources Management, 25(14), 3595–3612.  https://doi.org/10.1007/s11269-011-9872-4.CrossRefGoogle Scholar
  36. Räsänen, T. A., Someth, P., Lauri, H., Koponen, J., Sarkkula, J., & Kummu, M. (2017). Observed river discharge changes due to hydropower operations in the Upper Mekong Basin. Journal of Hydrology, 545, 28–41.  https://doi.org/10.1016/j.jhydrol.2016.12.023.CrossRefGoogle Scholar
  37. Sullivan, C. (2002). Calculating a water poverty index. World Development, 30(7), 1195–1210.  https://doi.org/10.1016/S0305-750X(02)00035-9.CrossRefGoogle Scholar
  38. Sullivan, C., & Meigh, J. (2007). Integration of the biophysical and social sciences using an indicator approach: Addressing water problems at different scales. Integrated Assessment of Water Resources and Global Change: A.  https://doi.org/10.1007/978-1-4020-5591-1-8.CrossRefGoogle Scholar
  39. Sullivan, C., Meigh, J., Giacomello, M., Fediw, T., Lawrence, P., Samad, M., et al. (2003). The water poverty index: development and application at the community scale. Natural Resources Forum, 27, 189–199.  https://doi.org/10.1111/1477-8947.00054.CrossRefGoogle Scholar
  40. Tang, X., & Feng, Q. (2016). The temporal-spatial assessment of water scarcity with the Water Poverty Index: A study in the middle basin of the Heihe River, northwest China. Water Science and Technology-Water Supply, 16(5), 1266–1276.  https://doi.org/10.2166/ws.2016.053.CrossRefGoogle Scholar
  41. The United Nations in Lao PDR. (2015). Country analysis report : Lao PDR. Vientiane. http://www.la.undp.org/content/lao_pdr/en/home/library/mdg/country-analysis-report.html.
  42. Ty, T., Van Sunada, K., Ichikawa, Y., & Oishi, S. (2010). Evaluation of the state of water resources using modified water poverty index: A case study in the Srepok River basin, Vietnam—Cambodia. International Journal of River Basin Management, 8, 305–317.  https://doi.org/10.1080/15715124.2010.523004.CrossRefGoogle Scholar
  43. Wei, C., Cabrera-Barona, P., & Blaschke, T. (2016). Local geographic variation of public services inequality: Does the neighborhood scale matter? International Journal of Environmental Research and Public Health.  https://doi.org/10.3390/ijerph13100981.CrossRefGoogle Scholar
  44. Wildlife Conservation Society—WCS, & Center for International Earth Science Information Network—CIESIN—Columbia University. (2005). Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). Palisades, NY: ASA Socioeconomic Data and Applications Center (SEDAC).Google Scholar
  45. Zhang, Q., Liu, B., Zhang, W., Jin, G., & Li, Z. (2015). Assessing the regional spatio-temporal pattern of water stress: A case study in Zhangye City of China. Physics and Chemistry of the Earth, 79–82, 20–28.  https://doi.org/10.1016/j.pce.2014.10.007.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Research Group on Geoinformatics, School of Engineering, Department of Built EnvironmentAalto UniversityEspooFinland
  2. 2.Water and Development Research Group, School of Engineering, Department of Built EnvironmentAalto UniversityEspooFinland

Personalised recommendations