JDR Vol.13 No.6 pp. 1007-1014
doi: 10.20965/jdr.2018.p1007


Comparison of Global Databases for Disaster Loss and Damage Data

Kana Moriyama, Daisuke Sasaki, and Yuichi Ono

International Research Institute of Disaster Science (IRIDeS), Tohoku University
468-1-S302 Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-0845, Japan

Corresponding author

May 10, 2018
August 20, 2018
November 1, 2018
Sendai Framework for Disaster Risk Reduction, disaster loss and damage data, global database, DesInventar

After the Sendai Framework for Disaster Risk Reduction is adopted, a global database as a tool to monitor disaster loss and damage databases is required. Several disaster loss and damage databases are in use globally. This paper aims to explore how the existing databases vary in three aspects of threshold, spatial resolution, and data quality control, as well as the limitations of the existing databases. We review previous studies comparing the existing global databases and extract the differences and limitations. The threshold of EM-DAT is clear, but its threshold results in ignoring small-scale disasters that DesInventar captures. The differences in disaster threshold create different pictures of disaster losses and/or risks. Regarding spatial resolution, only DesInventar provides disaster impact data at a municipal level, while others provide information at a country level. The limitations of the existing global database are categorized into four aspects, as follows: lack of disaggregated data, limited spatial coverage and resolution, insufficiency of completeness and reliability of data, and insufficient information on indirect loss. The implication from our findings is that, in order to complement the limitations of the existing disaster loss databases to use for decision making on disaster risk reduction, the following are required: cross-checking of data across different databases; complementary disaster loss data; and collection of an exhaustive and firsthand dataset with a transparent and internationally consistent methodology by policy makers.

Cite this article as:
K. Moriyama, D. Sasaki, and Y. Ono, “Comparison of Global Databases for Disaster Loss and Damage Data,” J. Disaster Res., Vol.13, No.6, pp. 1007-1014, 2018.
Data files:
  1. [1] United Nations Office for Disaster Risk Reduction (UNISDR), DesInventarSendai, [accessed March 16, 2018]
  2. [2] J. Calkins, “Moving Forward after Sendai: How Countries Want to Use Science, Evidence and Technology for Disaster Risk Reduction,” PLoS Currents Disasters, doi: 10.1371/currents.dis.22247d6293d4109d09794890bcda1878, 2015.
  3. [3] Y. Ono and M. Nagaishi, “National disaster databases: An essential foundation for disaster risk reduction policies and disaster-related sustainable development goals and targets,” I. Davis, K. Yanagisawa, and K. Georgieva (eds.), Disaster Risk Reduction for Economic Growth and Livelihood: Investing in Resilience and Development, pp. 241-258, Routledge, 2015.
  4. [4] A. Wirtz, W. Kron, P. Löw, and M. Steuer, “The need for data: natural disasters and the challenges of database management,” Natural Hazards, Vol.70, pp. 135-157, 2014.
  5. [5] United Nations Development Programme (UNDP), “A Comparative Review of Country-Level and Regional Disaster Loss and Damage Databases,” UNDP, 2013.
  6. [6] E. Osuteye, C. Johnson, and D. Brown, “The data gap: An analysis of data availability on disaster losses in sub-Saharan African cities,” Int. J. of Disaster Risk Reduction, Vol.26, pp. 24-33, 2017.
  7. [7] C. Huggel, A. Raissig, M. Rohrer, G. Romero, A. Diaz, and N. Salzmann, “How useful and reliable are disaster databases in the context of climate and global change? A comparative case study analysis in Peru,” Natural Hazards and Earth System Sciences, Vol.15, pp. 475-485, 2015.
  8. [8] Integrated Research on Disaster Risk (IRDR), “Peril Classification and Hazard Glossary,” DATA Project Report No.1, IRDR, 2014.
  9. [9] N. Labonnote, “Stormwater-Related Databases – Review and Recommendations,” Klima 2050 Report No.6, 2017.
  10. [10] Sigma, “Information and methodology of sigma explorer data,” [accessed March 15, 2018]
  11. [11] UNISDR, DesInventar SENDAI, [accessed March 16, 2018]
  12. [12] L. A. Bakkensen, X. Shi, and B. D. Zurita, “The Impact of Disaster Data on Estimating Damage Determinants and Climate Costs,” Economics of Disasters and Climate Change,” Vol.2, Issue 1, pp. 49-71, 2018.
  13. [13] A. Soto, “Deriving information on disasters caused by natural hazards from limited data: A Guatemalan case study,” Natural Hazards, Vol.75, pp. 71-94, 2015.
  14. [14] M. Johansson, “Experience of data collection in support of the assessment of global progress in the Sendai Framework for Disaster Risk Reduction 2015-2030 – A Swedish pilot study,” Int. J. of Disaster Risk Reduction, Vol.24, pp. 144-150, 2017.
  15. [15] M. Ladds, A. Keating, J. Handmer, and L. Magee, “How much do disasters cost? A comparison of disaster cost estimates in Australia,” Int. J. of Disaster Risk Reduction, Vol.21, pp. 419-429, 2017.
  16. [16] R. Z. Zaidi, “Beyond the Sendai indicators: Application of a cascading risk lens for the improvement of loss data indicators for slow-onset hazards and small-scale disasters,” Int. J. of Disaster Risk Reduction, Vol.30, Part B, pp. 306-314,, 2018.
  17. [17] EM-DAT, Centre for Research on the Epidemiology of Disasters (CRED), [accessed March 16, 2018]
  18. [18] G. Gaprindashvili and C. J. V. Westen, “Generation of a national landslide hazard and risk map for the country of Georgia,” Natural Hazards, Vol.80, pp. 69-101, 2016.
  19. [19] W. Kron, M. Steuer, P. Löw, and A. Wirtz, “How to deal properly with a natural catastrophe database – analysis of flood losses,” Natural Hazards and Earth System Sciences, Vol.12, pp. 535-550, 2012.
  20. [20] L. M. Stough and D. Kang, “The Sendai Framework for Disaster Risk Reduction and Persons with Disabilities,” Int. J. of Disaster Risk Science, Vol.6, Issue 2, pp. 140-149, 2015.
  21. [21] United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), “Incheon Strategy to “Make the Right Real,” for Persons with Disabilities in Asia and the Pacific,” November 23, 2012.
  22. [22] B. Damm and M. Klose, “The landslide database for Germany: Closing the gap at national level,” Geomorphology Vol.249, pp. 82-93, 2015.
  23. [23] L. Jacobs, O. Dewitte, J. Poesen, D. Delvaux, W. Thiery, and M. Kervyn, “The Rwenzori Mountains, a landslide-prone region?,” Landslides, Vol.13, pp. 519-536, 2015.
  24. [24] P. Valenzuela, M. J. Domínguez-Cuesta, M. A. O. García, and M. Jiménez-Sánchez, “A spatio-temporal landslide inventory for the NW of Spain: BAPA database,” Geomorphology, Vol.293, pp. 11-23, 2017.
  25. [25] UNISDR, “Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction,” 2018.
  26. [26] T. Ito, M. Miyamoto, and Y. Ono, “Strengthening Governance on Disaster Risk Reduction Through Improved Disaster Damage Statistics,” J. Disaster Res., Vol.11, No.3, pp. 470-475, 2016.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 20, 2018