Assessing structural uncertainty caused by different weighting methods on the Standardized Drought Vulnerability Index (SDVI)

  • Demetrios E. TsesmelisEmail author
  • Panagiotis D. Oikonomou
  • Constantina G. Vasilakou
  • Nikolaos A. Skondras
  • Vassilia Fassouli
  • Stavros G. Alexandris
  • Neil S. Grigg
  • Christos A. Karavitis
Original Paper


Indices are used for representing complex phenomena; however, concerns usually arise regarding their objectivity and reliability, particularly dealing with their uncertainties during the development process. The current overarching objective is to reveal the significance of employing different weighting techniques in the application of the Standardized Drought Vulnerability Index (SDVI) and demarcate any pertinent implications that may emerge in drought decision making. Greece, as it is very often facing the catastrophic effects of droughts, presents an almost ideal case for the SDVI testing. SDVI outcomes were tested utilizing five weighting techniques deriving from four weighting methods. The analyses indicated that the use of complex weighting models may not be necessary in all cases and that the simple equal weighting method seems more effective to estimate drought vulnerability. It also seems more important to address the search for valid, reliable and relevant individual indicators forming the complex index as well as appropriate index development processes that would measure performance of water bodies, systems and schemes, monitor the process of equitable sharing, and provide mechanisms for monitoring the state and changes in interdependent water systems.


Drought Drought vulnerability Drought Vulnerability Index Indices Indicators Structural uncertainty Water resources management 



  1. Abdi H, Valentin D (2007) Multiple correspondence analysis. In: Salkind N (ed) Encyclopedia of measurement and statistics. SAGE Publications Inc, Thousand Oaks, pp 651–657Google Scholar
  2. Adair J (2010) Decision making and problem solving strategies, 1st edn. Kogan Page, LondonGoogle Scholar
  3. Adam F (2008) Encyclopedia of decision making and decision support technologies. Information Science Reference, HersheyCrossRefGoogle Scholar
  4. Alexandris S, Kerkides P, Liakatas A (2006) Daily reference evapotranspiration estimates by the “Copais” approach. Agric Water Manag 82:371–386. CrossRefGoogle Scholar
  5. Allen RG, Pereira LS, Raes D, Smith M (1999) Crop evapotranspiration. Guidelines for computing crop water requirements. FAO irrigation and drainage paper no. 56. Rome, Italy: United Nations—FAO, p 300Google Scholar
  6. Ambas VT, Baltas E (2012) Sensitivity analysis of different evapotranspiration methods using a new sensitivity coefficient. Glob Nest J 14:335–343Google Scholar
  7. Bandura R (2005) Measuring country performance and state behavior: a survey of composite indices. Prepared for the book project the new public finance: responding to global challenges. Office of Development Studies United Nations Development Programme, New YorkGoogle Scholar
  8. Bandura R (2008) A survey of composite indices measuring country performance: 2008 update. Office of Development Studies United Nations Development Programme, New YorkGoogle Scholar
  9. Barraqué B, Karavitis CA, Katsiardi P (2008) The range of existing circumstances in the water strategy man case studies. In: Koundouri P (ed) Coping with water deficiency. Springer, Amsterdam, pp 45–112CrossRefGoogle Scholar
  10. Becker W, Saisana M, Paruolo P, Vandecasteele I (2017) Weights and importance in composite indicators: closing the gap. Ecol Ind 80:12–22. CrossRefGoogle Scholar
  11. Bianco D (2006) Decision making. In: Helms MM (ed) Encyclopedia of management, 5th edn. Thomson/Gale, Detroit, pp 160–164Google Scholar
  12. Booysen F (2002) An overview and evaluation of composite indices of development. Soc Indic Res 59:115–151. CrossRefGoogle Scholar
  13. Breier GP, de Paula I, ten Caten C, et al (2012) A review of value tools used in sustainability assessment. In: 18th International conference on industrial engineering and operations management, July 9–11, 2012, Guimaraes, PortugalGoogle Scholar
  14. Briggs J, Peat FD (2000) Seven life lessons of chaos: spiritual wisdom from the science of change. Harper Perennial, New YorkGoogle Scholar
  15. Burgman M (2005) Risks and decisions for conservation and environmental management. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  16. Chatzithomas CD, Alexandris SG (2015) Solar radiation and relative humidity based, empirical method, to estimate hourly reference evapotranspiration. Agric Water Manag 152:188–197. CrossRefGoogle Scholar
  17. Cherchye L, Moesen W, Puyenbroeck TV (2004) Legitimately diverse, yet comparable: on synthesizing social inclusion performance in the EU. J Common Mark Stud 42:919–955. CrossRefGoogle Scholar
  18. Cherchye L, Moesen W, Rogge N et al (2007) Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index. J Oper Res Soc 59:239–251. CrossRefGoogle Scholar
  19. Core Team R (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  20. Corvalán C, Briggs D, Zielhuis G (2000) Decision-making in environmental health: from evidence to action. World Health Organization, New YorkCrossRefGoogle Scholar
  21. Daellenbach HG (1994) Systems and decision making: a management science approach, 1st edn. Wiley, ChichesterGoogle Scholar
  22. Dahl AL (2012) Achievements and gaps in indicators for sustainability. Ecol Ind 17:14–19. CrossRefGoogle Scholar
  23. Desarbo WS, Ramaswamy V, Cohen SH (1995) Market segmentation with choice-based conjoint analysis. Market Lett 6:137–147. CrossRefGoogle Scholar
  24. Dobbie MJ, Dail D (2013) Robustness and sensitivity of weighting and aggregation in constructing composite indices. Ecol Ind 29:270–277. CrossRefGoogle Scholar
  25. Doorenbos J, Pruitt WO (1977) Crop water requirements. FAO irrigation and drainage paper 24, FAO, Rome, p 144Google Scholar
  26. Dracup JA (1991) Drought monitoring. Stoch Hydrol Hydraul 5:261–266. CrossRefGoogle Scholar
  27. Droogers P, Allen RG (2002) Estimating reference evapotranspiration under inaccurate data conditions. Irrigat Drain Syst 16:33–45. CrossRefGoogle Scholar
  28. Eakin H, Bojórquez-Tapia LA (2008) Insights into the composition of household vulnerability from multicriteria decision analysis. Glob Environ Change 18:112–127. CrossRefGoogle Scholar
  29. European Environment Agency (1999) Environmental indicators: typology and overview. CopenhagenGoogle Scholar
  30. European Environment Agency (2005) EEA core set of indicators: guide. Publications Office, LuxembourgGoogle Scholar
  31. Fomby T (2008) The unobservable components model. Accessed 25 Mar 2018
  32. French S (1995) Uncertainty and imprecision: modelling and analysis. J Oper Res Soc 46:70–79. CrossRefGoogle Scholar
  33. Gan X, Fernandez IC, Guo J et al (2017) When to use what: methods for weighting and aggregating sustainability indicators. Ecol Ind 81:491–502. CrossRefGoogle Scholar
  34. Garriga RG, Foguet AP (2010) Improved method to calculate a water poverty index at local scale. J Environ Eng 136:1287–1298. CrossRefGoogle Scholar
  35. Grigg NS (1996) Water resources management: principles, regulations, and cases, 1st edn. McGraw-Hill Professional, New YorkGoogle Scholar
  36. Grigg N (2008) Total water management: practices for a sustainable future. American Waterworks Association, DenverGoogle Scholar
  37. Guttman NB (1998) Comparing the palmer drought index and the standardized precipitation index 1. J Am Water Resour Assoc 34:113–121. CrossRefGoogle Scholar
  38. Guttman NB (1999) Accepting the standardized precipitation index: a calculation algorithm 1. J Am Water Resour Assoc 35:311–322. CrossRefGoogle Scholar
  39. Haines-Young R, Potschin M, Kienast F (2012) Indicators of ecosystem service potential at European scales: mapping marginal changes and trade-offs. Ecol Ind 21:39–53. CrossRefGoogle Scholar
  40. Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99. CrossRefGoogle Scholar
  41. Harris P, Charlton M, Fotheringham AS (2010) Moving window kriging with geographically weighted variograms. Stoch Environ Res Risk Assess 24:1193–1209. CrossRefGoogle Scholar
  42. Hayes MJ, Svoboda MD, Wilhite DA, Vanyarkho OV (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteorol Soc 80:429–438.;2 CrossRefGoogle Scholar
  43. Hellenic Statistical Authority (HSA) (2011) 2011 Population-housing census. Hellenic Statistical AuthorityGoogle Scholar
  44. Hilborn R (1987) Living with uncertainty in resource management. North Am J Fish Manag 7:1–5.;2 CrossRefGoogle Scholar
  45. Huang B, Wu B, Barry M (2010) Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int J Geogr Inf Sci 24:383–401. CrossRefGoogle Scholar
  46. Jensen ME, Richard GA (eds) (2016) Evaporation, evapotranspiration, and irrigation water requirements, 2016. Task Committee on Revision of Manual 70Google Scholar
  47. Kaly U, Pratt C, Mitchell J (2004) The environmental vulnerability index 2004Google Scholar
  48. Karavitis CA (1992) Drought management strategies for urban water supplies: the case of metropolitan Athens. Ph.D. Dissertation, Colorado State UniversityGoogle Scholar
  49. Karavitis CA (1998) Drought and urban water supplies: the case of metropolitan Athens. Water Policy 1:505–524. CrossRefGoogle Scholar
  50. Karavitis CA (1999) Decision support systems for drought management strategies in metropolitan Athens. Water Int 24:10–21. CrossRefGoogle Scholar
  51. Karavitis C, Bosdogianni A, Vlachos E (2001) Environmental management approaches and water resources in the stressed region of Thriassion, Greece. Glob NEST J 3:131–144Google Scholar
  52. Karavitis CA, Alexandris S, Tsesmelis DE, Athanasopoulos G (2011a) Application of the standardized precipitation index (SPI) in Greece. Water 3:787–805. CrossRefGoogle Scholar
  53. Karavitis CA, Alexandris SG, Fassouli VP et al (2011b) Vulnerability assessment, task 4.2.5, DMCSEE project. In: 5th DMCSEE consortium meeting and training, 28 June–1 July 201, Lasko, SloveniaGoogle Scholar
  54. Karavitis CA, Chortaria C, Alexandris SG et al (2012a) Development of the standardised precipitation index for Greece. Urban Water J 9:401–417. CrossRefGoogle Scholar
  55. Karavitis CA, Skondras NA, Tsesmelis DE et al (2012b) Drought impacts archive and drought vulnerability index. In: Gregorič G (ed) Drought management centre for south-east Europe—DMCSEE. Summary of the result of the project, co-financed by the South east Europe transnational Cooperation programme (contract no. See/a/091/2.2/X). Slovenian Environmental Agency, pp 33–37Google Scholar
  56. Karavitis CA, Alexandris SG, Fassouli VP et al (2013) Assessing drought vulnerability under alternative water demand deficits scenarios in South-Eastern Europe. Porto, PortugalGoogle Scholar
  57. Karavitis CA, Tsesmelis DE, Skondras NA et al (2014) Linking drought characteristics to impacts on a spatial and temporal scale. Water Policy 16:1172–1197. CrossRefGoogle Scholar
  58. Karavitis CA, Oikonomou PD, Waskom RM, et al (2015a) Application of the Standardized Drought Vulnerability Index in the lower south Platte Basin, Colorado. In: 2015 AWRA annual water resources conference, 16–19 November 2015, DenverGoogle Scholar
  59. Karavitis CA, Vasilakou CG, Tsesmelis DE et al (2015b) Short-term drought forecasting combining stochastic and geo-statistical approaches. Eur Water 49:43–63Google Scholar
  60. Kasperczyk N, Knickel K (1996) Analytic Hierarchy Process (AHP). Accessed 15 June 2018  
  61. Kaufmann D, Kraay A, Zoido-Lobatón P (1999) Aggregating governance indicators. World Bank, New YorkGoogle Scholar
  62. Kaufmann D, Kraay A, Mastruzzi M (2003) Governance matters III: governance indicators for 1996–2002. World Bank, NewYorkCrossRefGoogle Scholar
  63. Kosmas C, Tsara M, Moustakas N et al (2006) Environmentally sensitive areas and indicators of desertification. In: Kepner WG, Rubio JL, Mouat DA, Pedrazzini F (eds) Desertification in the mediterranean region. A security issue. Springer, New York, pp 525–547CrossRefGoogle Scholar
  64. Kosmas C, Kairis O, Karavitis C, Ritsema C, Salvati L, Acikalin S, Alcala M, Alfama P, Atlhopheng J, Barrera J, Belgacem A, Sole Benet A, Brito J, Chaker M, Chanda R, Coelho C, Darkoh M, Diamantis I, Ermolaeva O, Fassouli V, Fei W, Feng J, Fernandez F, Ferreira A, Gokceoglu C, Gonzalez D, Gungor H, Hessel R, Juying J, Khatteli H, Khitrov N, Kounalaki A, Laouina A, Lollino P, Lopes M, Magole L, Medina L, Mendoza M, Morais P, Mulale K, Ocakoglu F, Ouessar M, Ovalle C, Perez C, Perkins J, Pliakas F, Polemio M, Pozo A, Prat C, Qinke Y, Ramos A, Ramos J, Riquelme J, Romanenkov V, Rui L, Santaloia F, Sebego R, Sghaier M, Silva N, Sizemskaya M, Soares J, Sonmez H, Taamallah H, Tezcan L, Torri D, Ungaro F, Valente S, de Vente J, Zagal E, Zeiliguer A, Zhonging W, Ziogas A (2014) Evaluation and selection of indicators for land degradation and desertification monitoring: methodological approach. Environ Manag 54:951–970. CrossRefGoogle Scholar
  65. Landerretche O, Leiva B, Vivanco D, López I (2017) Welcoming uncertainty: a probabilistic approach to measure sustainability. Ecol Ind 72:586–596. CrossRefGoogle Scholar
  66. Li Q, Li P, Li H, Yu M (2015) Drought assessment using a multivariate drought index in the Luanhe River basin of Northern China. Stoch Environ Res Risk Assess 29:1509–1520. CrossRefGoogle Scholar
  67. Loucks DP, Beek EV, Stedinger JR et al (2005) Water resources systems planning and management: an introduction to methods, models and applications. UNESCO, ParisGoogle Scholar
  68. Macharis C, Springael J, De Brucker K, Verbeke A (2004) PROMETHEE and AHP: the design of operational synergies in multicriteria analysis: strengthening PROMETHEE with ideas of AHP. Eur J Oper Res 153:307–317. CrossRefGoogle Scholar
  69. Margerum RD, Born SM (1995) Integrated environmental management: moving from theory to practice. J Environ Plan Manag 38:371–392. CrossRefGoogle Scholar
  70. Millet I, Wedley WC (2002) Modelling risk and uncertainty with the analytic hierarchy process. J Multi-Crit Decis Anal 11:97–107. CrossRefGoogle Scholar
  71. Morçӧl G (2006) Handbook of decision making. CRC Press, Boca RatonCrossRefGoogle Scholar
  72. Munda G (2005) Multiple criteria decision analysis and sustainable development. In: Multiple criteria decision analysis: state of the art surveys. International Series in Operations Research & Management Science, vol 78. Springer, New York, pp 953–986.
  73. Munda G (2007) Social multi-criteria evaluation (SMCE). Springer, New YorkGoogle Scholar
  74. Nardo M, Saisana M, Saltelli A et al (2005) Handbook on constructing composite indicators. OECD Statistics Working Paper. STD/DOC(2005)3Google Scholar
  75. National Meteorological Service of Greece (HNMS) (2016) Meteorological data of the Sterea Hellas Stations. Hellinikon, AthensGoogle Scholar
  76. National Research Council (1986) Drought management and its impact on public water systems: report on a colloquium sponsored by the water science and technology board. National Academy Press, WashingtonGoogle Scholar
  77. Nikam BR, Kumar P, Garg V et al (2014) Comparative evaluation of different potential evapotranspiration estimation approaches. Int J Res Eng Technol 3:544–552CrossRefGoogle Scholar
  78. Norton BG (2005) Sustainability. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  79. OECD, European Commission (ed) (2008) Handbook on constructing composite indicators: methodology and user guide. Organisation for Economic Co-operation and Development Publishing, ParisGoogle Scholar
  80. Oikonomou PD (2017) Methodologies for transforming data to information and advancing the understanding of water resources systems towards integrated water resources management. Ph.D. Dissertation, Colorado State University, Department of Civil & Environmental Engineering, Fort Collins, Colorado, USAGoogle Scholar
  81. Oikonomou PD, Waskom RM (2018) Assessing drought vulnerability in Northeast Colorado. 2018 Fall Meeting of the American Geophysical Union, Washington, D.C., 10–14 DecemberGoogle Scholar
  82. Oikonomou PD, Karavitis CA, Kolokytha E (2018) Multi-index drought assessment in Europe. Proceedings of 3rd International Electronic Conference on Water Sciences (ECWS-3).
  83. Oikonomou PD, Tsesmelis DE, Waskom RM, Grigg NS, Karavitis CA (2019) Enhancing the standardized drought vulnerability index by integrating spatiotemporal information from satellite and In Situ data. J Hydrol 569:265–277. CrossRefGoogle Scholar
  84. Orme BK (2009) Which conjoint method should i use? Sawtooth software research paper series, article originally published in sawtooth solutions, 1996. Sawtooth Software Inc., Sequim, WAGoogle Scholar
  85. Palmer WC (1965) Meteorological drought. US Department of Commerce, Weather Bureau Washington, WashingtonGoogle Scholar
  86. Peterson G, De Leo GA, Hellmann JJ et al (1997) Uncertainty, climate change, and adaptive management. Conserv Ecol 1:4CrossRefGoogle Scholar
  87. Peterson AT, Papeş M, Eaton M (2007) Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30:550–560. CrossRefGoogle Scholar
  88. Public Power Corporation (PPC S.A.) (2016) Precipitation and temperature data for the Sterea Hellas Stations, Athens, GreeceGoogle Scholar
  89. Qudrat-Ullah H, Spector JM, Davidsen PI (eds) (2007) Complex decision making. Springer, BerlinGoogle Scholar
  90. Ramanathan R (2001) A note on the use of the analytic hierarchy process for environmental impact assessment. J Environ Manag 63:27–35. CrossRefGoogle Scholar
  91. Rogge N (2012) Undesirable specialization in the construction of composite policy indicators: the environmental performance index. Ecol Ind 23:143–154. CrossRefGoogle Scholar
  92. Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, New YorkGoogle Scholar
  93. Saaty TL (1988) What is the analytic hierarchy process? In: Mitra G, Greenberg HJ, Lootsma FA et al (eds) Mathematical models for decision support. Springer, Berlin, pp 109–121CrossRefGoogle Scholar
  94. Sadiq R, Tesfamariam S (2009) Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP). Stoch Environ Res Risk Assess 23:75–91. CrossRefGoogle Scholar
  95. Sagar AD, Najam A (1998) The human development index: a critical review. Ecol Econ 25:249–264. CrossRefGoogle Scholar
  96. Sage A (2007) Decision theory. In: McGraw-Hill encyclopedia of science and technology, 10 edn. McGraw-Hill Education, pp 302–308.
  97. Saisana M, Saltelli A, Tarantola S (2005) Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. J R Stat Soc Ser A (Statistics in Society) 168:307–323. CrossRefGoogle Scholar
  98. Segnestam L (2002) Indicators of environment and sustainable development: theories and practical experience. World Bank, New YorkGoogle Scholar
  99. Singh RK, Murty HR, Gupta SK, Dikshit AK (2009) An overview of sustainability assessment methodologies. Ecol Ind 9:189–212. CrossRefGoogle Scholar
  100. Skondras NA, Karavitis CA, Gkotsis II et al (2011) Application and assessment of the Environmental Vulnerability Index in Greece. Ecol Ind 11:1699–1706. CrossRefGoogle Scholar
  101. Sodhi B, Prabhakar TV (2012) A simplified description of fuzzy TOPSIS. arXiv:12055098[cs]
  102. Special Secretariat for Water (SSW) (2013) Management plans of the River Basins in Greece. Ministry of Environment, Energy and Climate Change (MEECG), AthensGoogle Scholar
  103. Special Secretariat for Water (SSW) (2016) Precipitation and temperature data for the Sterea Hellas Stations, Hydroskopion. Ministry of Environment, Energy and Climate Change (MEECG), AthensGoogle Scholar
  104. Tsesmelis DE (2010) SPI Application in Greece for Integrated Drought Management. Master’s Thesis, Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Athens, Greece (in Greek)Google Scholar
  105. Tsesmelis DE (2017) Development, implementation and evaluation of drought and desertification risk indicators for the integrated management of water resources. Ph.D. Dissertation, Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Athens, Greece (in Greek)Google Scholar
  106. Tsesmelis DE, Karavitis CA, Oikonomou PD, Alexandris S, Kosmas C (2019) Assessment of the vulnerability to drought and desertification characteristics using the standardized drought vulnerability index (SDVI) and the environmentally sensitive areas index (ESAI). Resources 8:6. CrossRefGoogle Scholar
  107. Valipour M (2014) Temperature analysis of reference evapotranspiration models. Met Appl 22:385–394. CrossRefGoogle Scholar
  108. Valipour M, Eslamian S (2014) Analysis of potential evapotranspiration using 11 modified temperature-based models. Int J Hydrol Sci Technol 4:192–207. CrossRefGoogle Scholar
  109. Wang Q, Ni J, Tenhunen J (2005) Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Glob Ecol Biogeogr 14:379–393. CrossRefGoogle Scholar
  110. Welte DR, Feenstra T, Jager H, Leidl R (2004) A decision chart for assessing and improving the transferability of economic evaluation results between countries. Pharm Econ 22:857–876. CrossRefGoogle Scholar
  111. Wilhite DA, Hayes MJ, Svoboda MD (2000) Drought monitoring and assessment: status and trends in the United States. In: Vogt J.V., Somma F. (eds) Drought and drought mitigation in Europe. Advances in Natural and Technological Hazards Research, vol 14. Springer, Dordrecht, pp 149–160.
  112. Williams BK (2011) Adaptive management of natural resources—framework and issues. J Environ Manag 92:1346–1353. CrossRefGoogle Scholar
  113. Zahir S (1999) Clusters in a group: decision making in the vector space formulation of the analytic hierarchy process. Eur J Oper Res 112:620–634. CrossRefGoogle Scholar
  114. Zhou P, Ang BW, Zhou DQ (2010) Weighting and aggregation in composite indicator construction: a multiplicative optimization approach. Soc Indic Res 96:169–181. CrossRefGoogle Scholar

Copyright information

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Authors and Affiliations

  • Demetrios E. Tsesmelis
    • 1
    Email author
  • Panagiotis D. Oikonomou
    • 2
  • Constantina G. Vasilakou
    • 1
  • Nikolaos A. Skondras
    • 1
  • Vassilia Fassouli
    • 1
  • Stavros G. Alexandris
    • 1
  • Neil S. Grigg
    • 3
  • Christos A. Karavitis
    • 1
    • 4
  1. 1.Department of Natural Resources Development and Agricultural EngineeringAgricultural University of AthensAthensGreece
  2. 2.Colorado Water InstituteColorado State UniversityFort CollinsUSA
  3. 3.Department of Civil and Environmental EngineeringColorado State UniversityFort CollinsUSA
  4. 4.Department of Civil and Environmental Engineering, Faculty AffiliateColorado State UniversityFort CollinsUSA

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