Coastal cell vulnerability viewer: A community-based tool for rapid spatial vulnerability appraisal
Acknowledgements
1Chaire
Ursule
[email protected]
Introduction Vulnerability to coastal hazard is a concept prescribed by UNISDR, IPCC and governments. (IPCC CZCS, 1992; UNISDR et al., 2010; ADEME, 2013; MDDELCC, 2015). However the challenge is methodological : a "just" use of vulnerability assessment with erosion would include predictive model and only inductive arguments (Hinkel, 2013). Also, previous coastal assessments lacked the assessment of
adequacy and maladaptation of adaptation structures (Bernatchez et al., 2011; Cooper and Pilkey, 2012). Thus the objective was to design a tool at the coastal cell-scale to assess spatial vulnerability that would take into account those constraints and be operational for non-experts. This was part of a Ph.D. project that lead to develop the Integrated Coastal Vulnerabiltiy Diagnosis (Boyer-Villemaire, 2016).
Methods and hypothesis Field surveys •
Semi-quantitative coastal classification, at 15 m resolution Legend available on www. researchgate.net (Modified from Freisinger and Bernatchez, 2010)
Coastal evolution and projection
1 Boyer-Villemaire ,
Pascal
1 Bernatchez
The Coastal Cell-scale Spatial Vulnerability Viewer Tool 2) OUTPUT: 2-pages vulnerability report
1) SELECT INPUT CELL Scrolldown menu with toponyms
• The data is presented in an Excel spreadsheet for non-GIS users • Synthesizes the spatial impacts but also positive adaptation • Quantifies built assets at risk under 3 scenarios • Integrates intangible impacts • Spreadsheet available on ResearchGate.net
• • • •
B’
A
C
D
B Built assets vulnerability under 3 scenarios to better appreciate uncertainties
Projected coastline migration from most inland historical limit, at 4 time intervals (current, 2026, 2056, 2106) under 3 scenarios based on security margins A1: no acceleration B1: +50m/100yrs B2: +100m/100yrs Inclusion of an event buffer sensitive to type of coast
Results for a given community A
(Kilkeel, Northern Ireland, UK)
B
C
D
(Resnik 2003 ; Thieler et al., 2009; Bernatchez et al., 2012a ; Del Rio et al., 2013; Doody, 2004; Cooper and Pileky, 2012 )
Population and built assets vulnerability The vulnerability (V) for each cell (c) at time (t) was expressed as a function of population number by coastline length (lcell), given the sum of the current population/asset exposed (A2006) and the potentially exposed population/assets under the B2 scenario (A2106) proportioned by a factor (fB2) that expresses the assumption that potentially exposed assets under B2 scenarios accounts only for 1/3 compared to assets exposed within linear projections: V(t,c) = (A2006+fB2*A2106)/lcell
Interactive identification of intangible impacts
Example: Esthetical value, between Greencastle et Kilkeel
mean dot density km2 / participants
For representation, 5-levels scales were used, based on the distribution quintiles.
The data for intangible impacts was collected using participative cartography (modified from Brown and Reed, 2009) during field survey during summer 2010. The data treatment was a Kernel’s density calculated based on 100 m
Identification of hotspots and key factors by cell
pixel resolution at a radius of 1 km, average nb. points/km2 over each coastal cells counted within current coastal exposition conditions (2006: 0-5m). The representation is 5-classes natural breaks.
Social-economical assets at risk
ADEME - Agence de l'environnement et de la maîtrise de l'énergie de France, 2013. Indicateurs de vulnérabilité d’un territoire au changement climatique: Recueil de littérature internationale. ADEME, consulted 2015/03, http://www.ademe.fr/sites/default/files/assets/documents/indicateurs-vulnerabiliteterritoirechangement-climatique-7406.pdf Angers, France. (Bernatchez et al., 2011; Bernatchez, P., Fraser, C., Dugas, S., Drejza, S., 2012a. Marges de sécurité en érosion côtière : évolution historique et future du littoral de la MRC d’Avignon, Chaire de recherche en géoscience côtière, Laboratoire de dynamique et de gestion intégrée des zones côtières, Université du Québec à Rimouski. Rimouski, Report prepared for Quebec Public Security Dpt. Boyer-Villemaire (2016) Integrated vulnerability assessment of coastal communities to natural hazards in a climate change context: the cases of Avignon (Canada), Kilkeel (United Kingdom) and Chipiona (Spain). Ph.D. Thesis environmental sciences (Geography), Université du Québec à Rimouski (UQAR).
Brown, G., Reed, P., 2009. Public Participation GIS: A New Method for Use in National Forest Planning. Forest Science 55, 166-182. Cooper, J.A.G., Pilkey, O.H., (eds.) 2012. Pitfalls of Shoreline Stabilization: Selected Case Studies. Springer, Dordrecht. Del Río, L., Gracia, F.J., Benavente, J., 2013. Shoreline change patterns in sandy coasts. A case study in SW Spain. Geomorphology 196, 252-266. Doody, J.P., 2004. ‘Coastal squeeze’ – an historical perspective. Journal of Coastal Conservation 10, 129-138. Friesinger, S., Bernatchez, P., 2010. Perceptions of Gulf of St. Lawrence coastal communities confronting environmental change: Hazards and adaptation, Québec, Canada. Ocean & Coastal Management 53, 669-678. Hinkel, J., 2011. "Indicators of vulnerability and adaptive capacity": Towards a clarification of the science-policy interface. Global Environmental Change 21, 198-208. IPCC CZMS, 1992. A common methodology for assessing vulnerability to sea-level rise-second revision: Global Climate Change and the Rising Challenge of the Sea. 385 Ministry of Transport,
Consideration of positive adaptations and natural coastline resilience
Discussion
The tool proposes a multidisciplinary perspective, broader than many others, It is oriented towards sustainable decisions Main limits: • Small sampling size for interactive mapping, (n=36) but reached publishable standards. • The erosion projection under a precautionary approach is not as precise as hazard mapping; fundamental research on erosion prediction and the role of climate vs. anthropic variables is necessary. • Consultation/use by the communities - Automatic generation from ArcGIS
Conclusion
Cranfield Bay and Point • Highly exposed location (-0.15 m/y) • Sensitive type of coast • High level of assets concerned • Key hotspots of intangible landscape values: esthetical, recreational and economic • Weak accomodation space • Weak natural coastline Other vulnerable population nucleus Power station, sewage station Adaptation factors: • Wetland protection status • Low adequacy of protection structures • No soft engineering
References
Intangible impacts
Automatic comment and synthesis of key vulnerability factors
Precautionary approach: • Quantification of coastal evolution using DSAS 4.3 (USGS), 15m transects and 7points smooth. •
de recherche en géoscience côtière, Laboratoire de dynamique et de gestion intégrée des zones côtières, Université du Québec à Rimouski.
[email protected],
[email protected]
The major contribution is the development of an operational method to asses coastal vulnerability at the local and regional scale, that distinguishes by its scientific rigor (including a projection model of erosion and maladaptation) and its anchor in the communities. It also builds on accessible representations for non-experts.
Future work
Public Works and Water Management, Report of the Coastal Zone Management Subgroup, Response Strategies Working Group of the Intergovernmental Panel on Climate Change, The Hague, The Netherlands. MDDELCC - Ministère du Développement durable de l’Environnement et de la Lutte contre les changements climatiques du Québec, 2015. Guide de réalisation des analyses de la vulnérabilité des sources destinées à l’alimentation en eau potable au Québec. Gouvernement du Québec, last accessed 2015/05/15, http://www.mddelcc.gouv.qc.ca/eau/prelevements/guide-analyse-vulnerabilite-dessources.pdf Resnik, D.B., 2003. Is the precautionary principle unscientific? Studies in History and Philosophy of Biological and Biomedical Sciences 34, 329-344. Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., Ergul, A., 2009. Digital Shoreline Analysis System (DSAS) version 4.0— An ArcGIS extension for calculating shoreline change. U.S. Geological Survey, Open-File Report 2008-1278, 1 p. UNISDR, ITC, UNDP, 2010. Local Governments and Disaster Risk Reduction: Good Practices and Lessons Learned: A contribution to the “Making Cities Resilient” Campaign. United Nations, Geneva, p. 69.
• Seeking funding opportunities for building free ArcGIS toolbox aiming at the automatic generation of the spreadsheet. • Seeking funding opportunities for preparing guidance material and training. • Seeking partners and opportunities for improving erosion projections by analyzing erosion/migration rates relationship with climate reanalysis and anthropogenic factors, using bayesian statistics or numerical models. • Peer-reviewed publishing under work.
