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‘FLOCKTON BASIN’ BUILDING IMPACT AND LOSS ESTIMATES FOR THE MARCH 5TH 2014 CHRISTCHURCH FLOOD EVENT R. Paulik 1, G. Smart, J. Bind 1 National Institute.

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Presentation on theme: "‘FLOCKTON BASIN’ BUILDING IMPACT AND LOSS ESTIMATES FOR THE MARCH 5TH 2014 CHRISTCHURCH FLOOD EVENT R. Paulik 1, G. Smart, J. Bind 1 National Institute."— Presentation transcript:

1 ‘FLOCKTON BASIN’ BUILDING IMPACT AND LOSS ESTIMATES FOR THE MARCH 5TH 2014 CHRISTCHURCH FLOOD EVENT R. Paulik 1, G. Smart, J. Bind 1 National Institute of Water and Atmospheric Research, 301 Evans Bay Parade, Greta Point, Wellington 6021 ryan.paulik@niwa.co.nzryan.paulik@niwa.co.nz RiskScape Modules and Model Sequence Hazard, asset and fragility modules were used to estimate building impact and loss from the March 5th 2014 Christchurch flood event. Module application in RiskScape’s model sequence is presented in (1) to (3) below for flood impact and loss calculation (4). Conclusions The March 5th 2014 Christchurch flood event provided a valuable opportunity to collect building impact data and validate flood damage functions. Immediately following flood events, the development of flood inundation models and collection of structural and non-structural attributes from affected buildings can be used in models such as RiskScape to quickly estimate impacts and losses. This information can support the assessment or development of resource and emergency management initiatives to improve the resilience of people and property exposed to future flood hazard events. References Christchurch City Council, 2014: Mayoral Flood Taskforce Temporary Flood Defence Measures Final Report – Part C: Appendix B, Detailed Area Reports. Trim 14/893899. p82 RISKSCAPE Acknowledgements The authors thank the people of Christchurch who contributed to the flood inundation model development by sending their March 5th 2014 flood photos to NIWA, and those who assisted field staff with the collection of building impact data. Also, we would like to thank Sylvia Maclaren from Jacobs SKM for her assistance in identifying flood affected areas. The survey was funded by NIWA under its RiskScape Programme (2013/14 SCI). For more information RiskScape visit www.riskscape.org.nz.www.riskscape.org.nz (2) Asset Module: Canterbury Buildings RiskScape’s national building inventory was updated with building attributes recorded during the flood impact survey. The March 5th 2014 flood inundation model raster file was overlaid on a Canterbury building point shapefile in ArcGIS, then aerial photography was used to verify and update building locations. The updated shapefile was imported into RiskScape’s asset module builder tool to build a Canterbury building asset module. Overview On March 5th 2014 a significant rainfall event caused surface and riverine flooding throughout Christchurch City. When flood waters receded, NIWA staff from Wellington and Christchurch conducted a field survey of water levels and building damage for the purpose of improving RiskScape’s flood damage functions for buildings. The data collected also allowed for a RiskScape flood impact and loss mitigation scenario to be tested in Mariehau otherwise known as the ‘Flockton Basin’, whereby building floor levels were raised above flood waters. Flood Impact Survey 125 flood inundated residential and commercial buildings were surveyed over a three day period. At each building site water levels were measured and damage estimated for the structure, contents and in case of some commercial buildings, stock and equipment. Structural and non-structural building attributes (e.g. floor level height) were also assessed and used to update RiskScape’s national building inventory. Building Impact and Loss Model Results For the modelled March 5th 2014 Christchurch flood scenario, RiskScape estimated a NZD$3.4m building reinstatement cost (asset repair, contents and clean-up) for 72 flood damaged buildings in Mariehau. The number of damaged buildings is comparable to the Christchurch City Council (2014) estimate flood waters potentially exceeded floor levels for 65 buildings. Forty seven buildings flooded above level were residential, contributing NZD$3m (88%) of the total building reinstatement cost. Public interest in implementing flood mitigation measures to reduce residential building losses from future flooding in the ‘Flockton Basin’ raised the option of raising the floor levels of affected buildings. RiskScape’s asset mitigation tool was used to estimate potential loss reduction for a repeat March 5th 2014 flood event by raising floor levels of inundated buildings. If all floor levels were raised 0.1m, only 13 of the 47 damaged buildings will be inundated and the estimated losses halved. RiskScape indicates that raising all remaining floor levels by a further 0.3m results in no flood related residential building losses in Mairehau from the flood event. If an average cost of NZD$25,000 to raise and pile a residential building is assumed, to raise all 47 buildings accounted for in the RiskScape model above flood waters would cost approximately NZD$1.15m. Figure 1 (opposite): March 5 th 2014 Flood inundation exposure and building asset repair cost estimates for the ‘Flockton Basin’ area in Mairehau, Christchurch City. (1) Hazard Module: March 5 th 2014 Flood Inundation Model Following the 5 March 2014 flood event, water levels were derived at each building by adding floor height above ground (m) and water depth above floor level (m) to LiDAR building ground elevation. Flockton Basin’s relatively small area meant water level was assumed to be uniform therefore, a 2D flood inundation model was developed from mean water level and high resolution LiDAR topography (±0.15m). (4) Impact and Loss Calculation In RiskScape, building and content reinstatement and clean-up costs ($NZD) were calculated from the March 5th 2014 Christchurch flood inundation model. (3) Fragility Module: Flood Damage Functions Surveyed buildings were classed on use, structural material, wall cladding, storeys and age. RiskScape flood damage functions for each building class were updated by plotting estimated building damage ratio and flood inundation depths (m) against ‘damage curves’. Damage curves were re-fitted to account for the new data then applied to buildings based on their attributes in order estimate an average damage ratio for the modelled flood inundation depth at each building location.


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