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Editor's Notes

• #2: In previous presentations have heard about impacts of CC and steps being taken to measure and alleviate these. In this presentation I want to focus in on one major impact of climate change – flooding. We know that Climate Change is going to cause more ‘extreme’ weather events. What sort of scale? What kind of data do we have now and into the future? How can we as GI professionals assist and inform?
• #3: In terms of scale: Insurance payouts of 1.3B in 10 years, flooding is the vast majority of this, over 90% Storm damage, as with Ophelia, freeze events too. If extreme events are going to be more regular, is this number going to go on increasing? What can be done?
• #4: There has been a ramping up of capital investment in flood defences. Up from 250m in the previous plan to 430m. Indeed the amount spent in the 5 year period from 2016-21 period will be higher than in the 20 years from 1995-2015, which was 410m Choosing where that investment is made is a data driven exercise, heavily underpinned by GIS.
• #5: Ophelia last month was one example of these extreme events. Not much flooding reported, mainly due to low rainfall and low tides at the time of landfall
• #6: Of course there was plenty of reaction online, much of it funny
• #7: However mapping played a key role here
• #8: Dublin Fire Brigade’s map was particularly effective. It had over 300,000 views. Cork County Council also trialled their system, which proved very useful.
• #9: Coming back to flooding, n the Irish context, 3 main types of flooding. They’re not mutually exclusive, can be impacted by more than one.
• #10: Return rates, typically calculated in Return rates or Annual Probability. Return rates might be easy for the general public to understand, but can also be confusing. If I have a 75 year flood this year then I should be ok for the next 75 years – absolutely not true. Climate Change is having a major impact on these return rates, and the increasing severity of events is causing return rates to fall (or increase probablility of flooding in any given year) How many buildings impacted – generated from vector data of flood extents from a commercial provider (JBA) and the Eircode database Gives an indication of the scale of the problem.
• #11: Of all of the impacts of climate change Flooding is perhaps the one that GIS has the most to offer. This is because there is huge amount of data and modelling techniques available Looking at historical, temporal and predictive data, there has been continual advances. Data is growing deeper in terms of content Wider in terms of themes and coverage And Timely in terms of update frequency and accuracy Lets look at some of this data
• #12: Historical flood information can come from unlikely sources: Placenames – Turlough for example This area might eb prone to flooding because of what it is called
• #13: Historical Mapping shows areas marked prone to flooding. This was regarded as sufficiently important over 150 years ago that it is marked on maps
• #14: OPW collected data from over 50 bodies covering over 5,000 historical flood events, available on floodmaps,.ie Polygons for extents, where known, points for others Lots of ancillary information including old newspaper reports etc.
• #15: Alongside historical information is ‘near-live’ info from the likes of Corpernicus
• #16: Example from last year – flooding in Cavan – here’s Belturbet. Cyan coloured areas are flooded
• #17: Again, lots of ancillary info, placenames and other features from the likes of GeoNames and OpenStreetMap. STRM DEM (90m) Pre and Post event imaging details Land use Even Population Data
• #18: Vector data available – download directly into your GIS
• #19: Here’s the activation for Ophelia.
• #20: Cyan areas are flood extents
• #21: Luckily, no people impacted in this area by flooding. Other impacts, such as wind damage are not yet available from the likes of Corpernicus, but this may well change in future
• #22: Big advances in flood prediction. Here is an example from a commercial system developed by Gamma
• #23: New predictive models make extensive use of LIDAR data for modelling purposes. As this data becomes more widely available models themselves are improving.
• #24: This data has underpinned the next stage of the OPW Flod mapping process - CFRAM
• #25: Draft Maps available Detailing the three main flood types
• #26: Includes flood depth information This is critical for a range of use cases
• #27: Commercial solutions, not using OPW data, also available – Perilfinder is in use by practically all insurance companies in Ireland
• #28: Even with the best data, exceptions will still happen – this site was granted planning, despite being in a floodplain. List of additional works were to be carried out. Were they? Were the planners fully informed?
• #29: Key thing is getting the right data in front of the right people at the right time. GIS has a huge role to play. Whether it is on the Desktop
• #30: Or in the cloud as seen here. Making informed decisions is reliant the data. Make it accessible, open, easy to use and interpret.
• #31: Thank You