Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model

ABSTRACT

This study presents a methodology for conducting sensitivity and uncertainty analysis of a GIS-based multi-criteria model used to assess flood vulnerability in a case study in Brazil. The paper explores the robustness of model outcomes against slight changes in criteria weights. One criterion was varied at-a-time, while others were fixed to their baseline values. An algorithm was developed using Python and a geospatial data abstraction library to automate the variation of weights, implement the ANP (analytic network process) tool, reclassify the raster results, compute the class switches, and generate an uncertainty surface. Results helped to identify highly vulnerable areas that are burdened by high uncertainty and to investigate which criteria contribute to this uncertainty. Overall, the criteria ‘houses with improper building material’ and ‘evacuation drills and training’ are the most sensitive ones, thus, requiring more accurate measurements. The sensitivity of these criteria is explained by their weights in the base run, their spatial distribution, and the spatial resolution. These findings can support decision makers to characterize, report, and mitigate uncertainty in vulnerability assessment. The case study results demonstrate that the developed approach is simple, flexible, transparent, and may be applied to other complex spatial problems.

KEYWORDS:

• Sensitivity analysis
• uncertainty analysis
• spatial
• OAT
• ANP
• python
• GDAL

Acknowledgments

We are grateful to all experts who participated in the Delphi survey, focus groups and workshops. This work was supported by the Brazilian Coordination for the Improvement of Higher-Education Personnel (CAPES) through the grant 13669-13.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability

The developed codes and data used in the preparation of this study are available upon request.

Additional information

Funding

This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [13669-13].

Notes on contributors

Mariana Madruga de Brito

Mariana Madruga de Brito is a postdoctoral researcher at the University of Bonn, Germany. She holds a Ph.D. in Geography from the University of Bonn and master’s degree in Engineering from the Federal University of Rio Grande do Sul, Brazil. Her research interests include participatory decision-making (e.g. multi-criteria decision analysis), collaborative modelling, natural hazards vulnerability and risk assessment, citizen science, and co-creation of knowledge.

Adrian Almoradie

Adrian Almoradie is a scientific researcher at the University of Bonn, Germany. His teaching and research interests are hydroinformatics, hydrology, water resources management, decision support systems, and stakeholder participation. He holds a Ph.D. in Water Science and Engineering – specialisation Hydroinformatics and a master’s degree on the same field and specialisation from UNESCO-IHE Institute for Water Education, Delft, The Netherlands.

Mariele Evers

Mariele Evers is Geographer by training and holds a professor chair at the University of Bonn, Germany, where she leads the working group on eco-hydrology and water resources management. Her research focus is human-water-research, water resource and flood risk management, socio-technical tools and methods of inter-and transdisciplinary research.