Cited by 88Disaster Management and ResilienceEarthquake and Disaster Impact StudiesInfrastructure Resilience and Vulnerability Analysis

A composite inherent resilience index for Zimbabwe: An adaptation of the disaster resilience of place model

Open access
Mavhura, Emmanuel, Manyangadze, Tawanda, Aryal, Komal Raj·2021·International Journal of Disaster Risk Reduction

Abstract

Building resilience to disasters has become a strategic goal of many risk reduction programs across the globe. This is because resilience ensures that communities develop capacities which prevent or minimise loses to disasters. In view of this, there is need to develop a baseline that tracks changes in resilience through time. This study responded to this gap in Zimbabwe by developing composite resilience indices (CRI) using 26 variables that reflected 5 subdomains of resilience: community capital, economic, infrastructure, social and health. The CRI were then used to map the spatial variation of resilience across 91 districts. The results show that the majority of the districts with below moderate resilience are mainly rural and marginalised, while the most resilient districts emerged in urban areas where service provision and infrastructure are better developed. These findings were further subjected to factor analyses which deconstructed the overall CRI and identified six latent factors behind resilience: infrastructure, health, household head, and income, access to maize and fortified food. These factors were mapped in a GIS environment to show their geographic variation in the country. Furthermore, Moran's Index and the Getis Ord Gi* statistical tests were applied to determine clusters of resilience across space. Results confirmed the spatial clustering of CRI. The results are therefore, useful in planning mitigation, response and preparedness measures across the country.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ijdrr.2021.102152

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Water storage capacity, reservoir size relative to demand, groundwater recharge management, and redundancy in distribution networks function as physical buffers that reduce the immediate impact of drought.

    “Water storage capacity, reservoir size relative to demand, groundwater recharge management, and redundancy in distribution networks all function as physical buffers that reduce the immediate impact of drought (Mavhura et al., 2021).”

    From Climate Security, Water Governance, and Resilience How Climate-Induced Water Stress and Migration Challenge State Capacity (2026) · cites this paper as Mavhura et al., 2021

    Section 2.1 describes resilience as buffering systems against hazards, and the Discussion (section 5) identifies water infrastructure as a driver of disaster resilience and irrigation as relevant in drought-prone districts. Neither passage identifies water-storage capacity, reservoir size relative to demand, groundwater recharge management, or distribution-network redundancy as buffers that reduce drought’s immediate impact. Page numbers were not supplied in our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 58

  1. L. Jones, T. Tanner, 'Subjective resilience': using perceptions to quantify household resilience to climate extremes and disasters, Reg. Environ. Change 17 (2017) 229-243, https://doi.org/10.1007/s10113-016-0995-2. · DOI
  2. L. Jones, E. Samman, P. Vinck, Subjective measures of household resilience to climate variability and change: insights from a nationally representative survey of Tanzania, Ecol. Soc. 23 (1) (2018) 9, https://doi.org/10.5751/ES-09840-230109. · DOI
  3. W.S.A. Saunders, S. Kelly, S. Paisley, L.B. Clarke, Progress toward implementing the Sendai framework, the Paris agreement, and the sustainable development goals: policy from aotearoa New Zealand, Int. J. Disaster Risk Sci. 11 (2020) 190-205, https://doi.org/10.1007/s13753-020-00269-8. · DOI
  4. C. Wamsler, Å. Johannessen, Meeting at the crossroads? Developing national strategies for disaster risk reduction and resilience: relevance, scope for, and challenges to, integration, Int. J. Disaster Risk Reduct. 45 (2020) 101452, https:// doi.org/10.1016/j.ijdrr.2019.101452. · DOI
  5. E. Mavhura, Applying a systems-thinking approach to community resilience analysis using rural livelihoods: the case of Muzarabani district, Zimbabwe, Int. J. Disaster Risk Reduct. 25 (2017) 248-258, https://doi.org/10.1016/j. ijdrr.2017.09.008. · DOI
  6. A. Clark-Ginsberg, B. Mccaul, I. Bremaud, G. Caceres, D. Mpanje, S. Patel, R. Patel, Practitioner approaches to measuring community resilience: the analysis of the resilience of communities to disasters toolkit, Int. J. Disaster Risk Reduct. 50 (2020) 101714, https://doi.org/10.1016/j.ijdrr.2020.101714. · DOI
  7. T. Tanner, A. Bahadur, M. Moench, Challenges for Resilience Policy and Practice (No. 519), 2017, https://doi.org/10.13140/RG.2.2.30130.30402. London. · DOI
  8. J.J. Bogardi, A. Fekete, Disaster-related resilience as ability and process: a concept guiding the analysis of response behavior before , during and after extreme events, Am. J. Clim. Change 7 (2018) 54-78, https://doi.org/10.4236/ajcc.2018.71006. · DOI
  9. M. Abdulkareem, H. Elkadi, From engineering to evolutionary, an overarching approach in identifying the resilience of urban design to flood, Int. J. Disaster Risk Reduct. 28 (2018) 176-190, https://doi.org/10.1016/j.ijdrr.2018.02.009. · DOI
  10. S.B. Manyena, The concept of resilience revisited, Disasters 30 (4) (2006) 434-450, https://doi.org/10.1111/j.0361-3666.2006.00331.x. · DOI
  11. S.L. Cutter, Resilience to what? Resilience for whom? Geogr. J. 182 (2) (2016) 110-113, https://doi.org/10.1111/geoj.12174. · DOI
  12. Y. Lei, J. Wang, Y. Yue, H. Zhou, W. Yin, Rethinking the relationships of vulnerability, resilience, and adaptation from a disaster risk perspective, Nat. Hazards 70 (2014) 609-627, https://doi.org/10.1007/s11069-013-0831-7. · DOI
  13. T. Quinn, W.N. Adger, C. Butler, K. Walker-Springett, Community resilience and well-being: an exploration of relationality and belonging after disasters, Ann. Assoc. Am. Geogr. 111 (2) (2021) 577-590, https://doi.org/10.1080/ 24694452.2020.1782167. · DOI
  14. A. Keating, K. Campbell, M. Szoenyi, C. Mcquistan, D. Nash, M. Burer, Development and testing of a community flood resilience measurement tool, Nat. Hazards Earth Syst. Sci. 17 (2017) 77-101, https://doi.org/10.5194/nhess-17-77- 2017. · DOI
  15. X. Li, N. Lam, Y. Qiang, K. Li, L. Yin, S. Liu, W. Zheng, Measuring county resilience after the 2008 wenchuan earthquake, Int. J. Disaster Risk Sci. 7 (4) (2016) 393-412, https://doi.org/10.1007/s13753-016-0109-2. · DOI
  16. D.G. Angeler, C.R. Allen, A. Garmestani, K.L. Pope, D. Twidwell, M. Bundschuh, Resilience in environmental risk and impact assessment: concepts and measurement, Bull. Environ. Contam. Toxicol. 101 (5) (2018) 543-548, https:// doi.org/10.1007/s00128-018-2467-5. · DOI
  17. M. Welsh, Resilience and responsibility: governing uncertainty in a complex world, Geogr. J. 180 (1) (2014) 15-26, https://doi.org/10.1111/geoj.12012. · DOI
  18. W.C. Chuang, A. Garmestani, T.N. Eason, T.L. Spanbauer, H.B. Fried-Petersen, C. P. Roberts, C.R. Allen, Enhancing quantitative approaches for assessing community resilience, J. Environ. Manag. 213 (2018) 353-362, https://doi.org/10.1016/j. jenvman.2018.01.083. · DOI
  19. E. Carabine, E. Wilkinson, How can local governance systems strengthen community resilience? A social-ecological systems approach, Polit. Govern. 4 (4) (2016) 62-73, https://doi.org/10.17645/pag.v4i4.746. · DOI
  20. V. Proag, Assessing and measuring resilience, Procedia Econ. Finan. 18 (2014) 222-229, https://doi.org/10.1016/S2212-5671(14)00934-4. · DOI
  21. T. Heikkila, A.K. Gerlak, A.R. Bell, S. Schmeier, Adaptation in a transboundary river basin: linking stressors and adaptive capacity within the Mekong River Commission, Environ. Sci. Pol. 25 (2013) 73-82, https://doi.org/10.1016/j. envsci.2012.09.013. · DOI
  22. R. Berman, C. Quinn, J. Paavola, The role of institutions in the transformation of coping capacity to sustainable adaptive capacity, Environ. Develop. 2 (2012) 86-100, https://doi.org/10.1016/j.envdev.2012.03.017. · DOI
  23. E. Serfilippi, G. Ramnath, Resilience measurement and conceptual frameworks: a review of the literature, Ann. Publ. Cooper. Econ. 89 (4) (2018) 645-663, https:// doi.org/10.1111/apce.12202. · DOI
  24. S. Davoudi, Resilience: a bridging concept or a dead end? Plann. Theor. Pract. 13 (2) (2012) 299-307, https://doi.org/10.1080/14649357.2012.677124. · DOI
  25. K. Rus, V. Kilar, D. Koren, Resilience assessment of complex urban systems to natural disasters: a new literature review, Int. J. Disaster Risk Reduct. 31 (2018) 311-330, https://doi.org/10.1016/j.ijdrr.2018.05.015. · DOI
  26. A. Chacowry, L.J. Mcewen, K. Lynch, Recovery and resilience of communities in flood risk zones in a small island developing state: a case study from a suburban settlement of Port Louis, Mauritius, Int. J. Disaster Risk Reduct. 28 (2018) 826-838, https://doi.org/10.1016/j.ijdrr.2018.03.019. · DOI
  27. D.K. Yoon, J.E. Kang, S.D. Brody, A measurement of community disaster resilience in Korea, J. Environ. Plann. Manag. 59 (3) (2016) 436-460, https://doi.org/ 10.1080/09640568.2015.1016142. · DOI
  28. L. Jones, M. D'Errico, Whose resilience matters? Like-for-like comparison of objective and subjective evaluations of resilience, World Dev. 124 (2019) 104632, https://doi.org/10.1016/j.worlddev.2019.104632. · DOI
  29. S.L. Cutter, L. Barnes, M. Berry, C. Burton, E. Evans, E. Tate, J. Webb, A place-based model for understanding community resilience to natural disasters, Global Environ. Change 18 (4) (2008) 598-606, https://doi.org/10.1016/j. gloenvcha.2008.07.013. · DOI
  30. L. Siebeneck, S. Arlikatti, S.A. Andrew, Using provincial baseline indicators to model geographic variations of disaster resilience in Thailand, Nat. Hazards 79 (2) (2015) 955-975, https://doi.org/10.1007/s11069-015-1886-4. · DOI
  31. J. Mochizuki, A. Keating, W. Liu, S. Hochrainer-stigler, R. Mechler, An overdue alignment of risk and resilience? A conceptual contribution to community resilience, Disasters 42 (2) (2018) 361-391, https://doi.org/10.1111/disa.12239. · DOI
  32. J. Frischen, I. Meza, D. Rupp, K. Wietler, M. Hagenlocher, Drought risk to agricultural systems in Zimbabwe: a spatial analysis of hazard, exposure, and vulnerability, Sustainability 12 (2020) 752, https://doi.org/10.3390/su12030752. · DOI
  33. B. Mucherera, E. Mavhura, Flood survivors' perspectives on vulnerability reduction to floods in Mbire district, Zimbabwe, Jàmbá -J. Disaster Risk Stud. 12 (1) (2020) a663, https://doi.org/10.4102/jamba.v12i1.663. · DOI
  34. E. Mavhura, Learning from the tropical cyclones that ravaged Zimbabwe: policy implications for effective disaster preparedness, Nat. Hazards 104 (3) (2020) 2261-2275, https://doi.org/10.1007/s11069-020-04271-7. · DOI
  35. S.L. Cutter, C.G. Burton, C.T. Emrich, Disaster resilience indicators for benchmarking baseline conditions, J. Homel. Secur. Emerg. Manag. 7 (1) (2010) 1-22, https://doi.org/10.2202/1547-7355.1732. · DOI
  36. A. Altuzarra, C. Gálvez-Gálvez, A. González-Flores, Economic development and female labour force participation: the case of European Union countries, Sustainability 11 (2019) 1-18, https://doi.org/10.3390/su11071962. · DOI
  37. J.Y. Koyuncu, R. Tilmaz, M. Unver, The relationship between female labor force participation and divorce: panel data analysis, Int. Refereed Acad. Soc. Sci. J. 6 (18) (2015) 237-250, https://doi.org/10.17364/iib.2015189755. · DOI
  38. S. Sarker, A. Khan, M.M. Mannan, Urban population and economic growth: south Asia perspective, Eur. J. Govern. Econ. 5 (1) (2016) 64-75, https://doi.org/ 10.17979/ejge.2016.5.1.4316. · DOI
  39. L. Bhandari, K. Roychowdhury, Night Lights and Economic Activity in India: a study using DMSP-OLS night time images, Proceed. Asia-Pacific Adv. Netw. 32 (2011) 218-236, https://doi.org/10.7125/apan.32.24. · DOI
  40. J.K. Galimberti, Forecasting GDP growth from outer space, Oxf. Bull. Econ. Stat. 82 (4) (2020) 697-722, https://doi.org/10.1111/obes.12361. · DOI
  41. S.K. Aksha, L. Juran, L.M. Resler, Y. Zhang, An analysis of social vulnerability to natural hazards in Nepal using a modified social vulnerability index, Int. J. Disaster Risk Sci. 10 (2019) 103-116, https://doi.org/10.1007/s13753-018-0192-7. · DOI
  42. A. Fekete, Spatial disaster vulnerability and risk assessments: challenges in their quality and acceptance, Nat. Hazards 61 (2012) 1161-1178, https://doi.org/ 10.1007/s11069-011-9973-7. · DOI
  43. I. Armas, D. Toma-Danila, R. Ionescu, A. Gavris, Vulnerability to earthquake hazard: bucharest case study, Romania, Int. J. Disaster Risk Sci. 8 (2) (2017) 182-195, https://doi.org/10.1007/s13753-017-0132-y. · DOI
  44. V. Cerchiello, P. Ceresa, R. Monteiro, N. Komendantova, Assessment of social vulnerability to seismic hazard in Nablus, Palestine, Int. J. Disaster Risk Reduct. 28 (2018) 491-506, https://doi.org/10.1016/j.ijdrr.2017.12.012. · DOI
  45. J. Manjengwa, C. Matema, D. Tirivanhu, Understanding urban poverty in two high- density suburbs of Harare, Zimbabwe, Dev. South Afr. 33 (1) (2016) 23-38, https://doi.org/10.1080/0376835X.2015.1116376. · DOI
  46. D. Dodman, H. Leck, M. Rusca, S. Colenbrander, African urbanisation and urbanism: implications for risk accumulation and reduction, Int. J. Disaster Risk Reduct. 26 (2017) 7-15, https://doi.org/10.1016/j.ijdrr.2017.06.029. · DOI
  47. A. Fekete, Critical infrastructure cascading effects. Disaster resilience assessment for floods affecting city of Cologne and Rhein-Erft-Kreis, Int. J. Flood Risk Manag. 13 (2) (2020), e312600, https://doi.org/10.1111/jfr3.12600. · DOI
  48. C. Zhang, J. Kong, S.P. Simonovic, Restoration resource allocation model for enhancing resilience of interdependent infrastructure systems, Saf. Sci. 102 (2018) 169-177, https://doi.org/10.1016/j.ssci.2017.10.014. · DOI
  49. C.H. Sung, S.-C. Liaw, A GIS approach to analyzing the spatial pattern of baseline resilience indicators for community (BRIC), Water 12 (2020) 1401, https://doi. org/10.3390/w12051401. · DOI
  50. R. Maini, L. Clarke, K. Blanchard, V. Murray, The Sendai framework for disaster risk reduction and its indicators -where does health fit in? Int. J. Disaster Risk Sci. 8 (2017) 150-155, https://doi.org/10.1007/s13753-017-0120-2. · DOI
  51. T.W. Collins, A.M. Jimenez, S.E. Grineski, Hispanic health disparities after a flood disaster: results of a population-based survey of individuals experiencing home site damage in El Paso (Texas, USA), J. Immigrant Minor. Health/Center Minor. Public Health 15 (2) (2013) 415-426, https://doi.org/10.1007/s10903-012-9626-2. · DOI
  52. P. Thebe, G. Maviza, The effects of feminization of migration on family functions in Tsholotsho district, Zimbabwe, Adv. Soc. Sci. Res. J. 6 (5) (2019) 297-306, https:// doi.org/10.14738/assrj.65.6462. · DOI
  53. P. Brown, A.J. Daigneault, E. Tjernström, W. Zou, Natural disasters, social protection, and risk perceptions, World Dev. 104 (2018) 310-325, https://doi.org/ 10.1016/j.worlddev.2017.12.002. · DOI
  54. K.R. Lekobane, T.B. Seleka, Determinants of household welfare and poverty in Botswana, 2002/2003 and 2009/2010, J. Poverty 21 (1) (2017) 42-60, https:// doi.org/10.1080/10875549.2016.1141381. · DOI
  55. N. Keys, M. Bussey, D.C. Thomsen, T. Lynam, T.F. Smith, Building adaptive capacity in South East Queensland, Australia, Reg. Environ. Change 14 (2014) 501-512, https://doi.org/10.1007/s10113-012-0394-2. · DOI
  56. E. Mavhura, B. Manyena, Spatial quantification of community resilience in contexts where quantitative data are scarce: the case of Muzarabani district in Zimbabwe, Geo: Geogr. Environ. 5 (2) (2018), https://doi.org/10.1002/geo2.65 e00065. · DOI
  57. K. Campbell, F. Laurien, J. Czajkowski, A. Keating, S. Hochrainer-stigler, First insights from the Flood Resilience Measurement Tool: a large-scale community flood resilience analysis, Int. J. Disaster Risk Reduct. 40 (2019) 101257, https:// doi.org/10.1016/j.ijdrr. · DOI
  58. K. Jan, The challenges of the population census round of 2020. Outline of the methods of quality assessment of population census data, Statist. Trans. New Ser. 18 (1) (2017) 115-138.

Cited by

Sorted by citations

Authors
Mavhura, E., Manyangadze, T., & Aryal, K. R.
Affiliations
  • Bindura University of Science Education (ZW)
  • Rabdan Academy (AE)
Corresponding author
Mavhura, Emmanuel
Venue
International Journal of Disaster Risk Reduction
Publisher
Elsevier BV
Published
2021-04
Volume
57
Pages
102152
ISSN
2212-4209 (ISSN-L)
OpenAlex
W3135137390
Cited by
88
References
0
Citations by year
2026: 20 · 2025: 19 · 2024: 14 · 2023: 20 · 2022: 13 · 2021: 2
FWCI
15.57
Type
journal article
Language
en
Open access
Locations
  • International Journal of Disaster Risk Reduction (publishedVersion, cc-by-nc-nd)
  • Aston Publications Explorer (Aston University) (acceptedVersion)
  • Rabdan Academy Digital Repository (Rabdan Academy) (submittedVersion, cc-by-nc-nd)
Funders
  • Department for International Development, UK Government
  • European Commission
  • United Nations Development Programme
Topics
  • Social Sciences › Sociology and Political Science › Disaster Management and Resilience
  • Social Sciences › Transportation › Earthquake and Disaster Impact Studies
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Resilience and Vulnerability Analysis
Keywords
disaster resilience, composite resilience index, Zimbabwe, spatial clustering, community capital, infrastructure resilience, health resilience, household income, food security, urban-rural differences, disaster risk reduction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review