This Programme Package will develop a set of computer models for determining current and future extreme weather event probabilities. These events will include flooding, heatwaves, water resource drought, wind, lightning and subsidence. These models will be based on the development of existing ‘weather generator’ models for providing time series of potential temperature, evaporation and precipitation.
A mapping framework will be developed to identify the probability of local extreme weather events and risk ‘hotspots’ as well as the probability of combined weather events occurring in any one place.
By combining existing and emerging technologies to provide local-scale estimates of extreme weather event probabilities the work should significantly improve the ability to assess risk and explore the current and future impacts of climate change.
PI Hayley Fowler1; Co-Is: Chris Baker2, Stuart Barr1, FionaBerryman3, Ian Cotton4, Michael Davies5, SlobodanDjordjevic6, Chris Kilsby1, Jim Hall1, StephenHallett7, JohnThornes2, and GavinWood7
1Newcastle University (UNEW), 2Birmingham University (UBIR), 3University of Wolverhampton (UOW), 4University of Manchester (UMAN),5University College London (UCL), 6Exeter University (UEX), 7Cranfield University (UCRAN)
Many studies have examined the likely changes in EWEs resulting from global warming, but there has been little work examining the potential impacts of EWEs at the city or borough scale. However, the ability of local communities to cope with the immediate impact and aftermath of EWEs is dependent on exposure to information on the probability of EWE occurrence and likely impacts on their community and the inclusion of EWE risks in planning decisions. It is critical to identify ‘hotspots’; where individuals may be exposed to impacts from multiple EWEs, although not necessarily simultaneously. The research challenge will be to select, adapt and implement the models most appropriate for the analysis of combined risks and vulnerabilities arising from EWEs; models able to capture spatial and temporal weather variability, whilst still providing realistic estimates of risk. By combining existing and emerging technologies to provide local-scale estimates of EWE probabilities we will significantly improve our ability to assess risk from EWEs and explore the potential impacts of climate change; constituting an advancement of current best science.
This Programme Package (PP4) will develop a physically-based modelling framework to identify probabilities of local EWEs and risk ‘hotspots’ for combined probabilities of different EWEs for current and future climates at the community-scale as inputs to socio-economic modelling (PP3) and a GIS toolkit (PP5). This toolkit will be developed to support decision makers and increase community capacity for resilience to EWEs. Stakeholders will guide the development of PP4 (through PP1/2) to ensure that outputs are easy to use and provide the information they need to aid their decision making. Finally, with PP3/5, appropriate ‘what if?’ scenarios will examine the potential impacts of climate change. This PP will draw upon and be aligned with ongoing EPSRC and non-EPSRC funded projects such as UKCIP08, the Tyndall Centre phase 2 Cities programme (2006-09), SCORCHIO (EP/E017428/1, 2007-09), LUCID (EP/E016375/1, 2007-10), AquaTerra (EU/FP6/505428, 2004-09), and FRMRC (GR/S76304/01, 2004-08). Activities are broken down into 4 inter-related work packages (WPs) (see Gantt chart).
WP1: Weather generator (lead: Fowler; contributors: Blenkinsop, Burton, Kilsby) A weather generator (WG) downscaling method in UKCIP08 will provide hourly time series at the point scale. Building on experience from other projects (AquaTerra, EARWIG, UKCIP08) spatially consistent catchment-scale climate change scenarios will be developed. A stochastic rainfall model implementing a non-stationary spatial component1 will be calibrated using observed rain data (gridded UKMO 5km and gauge). Rainfall series output at 5km resolution will be used in a WG, EARWIG2, to provide spatially and temporally consistent hourly series of T/PET validated for current climate; as inputs for physically-based modelling (WP2). The WG will be perturbed with change factors of P/T from UKCIP08 or other climate modelling programmes (e.g. EU ENSEMBLES; PI Fowler is an associate partner) to produce probabilistic climate change scenarios. Higher resolution regions will be embedded in the WG to simulate borough-scale detail.
WP2: Physically-based modelling (lead: Fowler) WP2 will use climate time series from WP1 in existing physically-based models, producing end-user appropriate probabilities for six types of EWE. If the desired data sources prove unavailable, alternative sources have already been identified.
Task WP2.1: Flooding (lead: Djordjevic; contributors: Chen, Fowler, Hall, Harvey) Different approaches will be used depending on the predominant flooding mechanisms and spatial scale and data resolution needed for the city- and borough-scale case studies. These will include 1D model of river networks, 2D models of urban flooding and integrated 1D/2D models that incorporate interactions between river flows, sewer networks and surface flooding, e.g. SIPSON-UIM model3, sensitivity-based flood risk attribution4, or LISFLOOD-FP5. Models will be selected, initially tested and adapted and then run with data from the WG (WP1) to provide detailed flood risk estimates for current and future climates for the selected boroughs. The flood modelling techniques will then be integrated within the GIS tool in collaboration with PP5. WP2.1 will use modelling tools and case study data from FRMRC, and outcomes will feed back to FRMRC2 (if funded).
Task WP2.2: Heatwaves (lead: Barr; contributors: Ford, Fowler, Kilsby, Hall, Davies) SCORCHIO relationships between remotely-sensed intra-urban temperatures and a building-level land use classification of thermal characteristics (air conditioning/heating, building design etc.) will be combined with the Tyndall land use model (TLUM) for London to predict heatwave impacts that are sensitive to population/employment change. Thermal properties of land use types will be coupled to predictive model runs of urban land use change and future heatwaves to provide heatwave vulnerability information at the city-scale, using temperature outputs from WP1. Temperature scaling coefficients for intra-urban land use will provide finer spatial disaggregation for borough-scale impacts. The TLUM allows the investigation of potential vulnerability adaptations through land use development patterns (e.g. proportion of urban green space, density of residential development etc.). SCORCHIO and the TLUM are developed in a GIS framework and thus easily transferable to PP5. LUCID is developing, testing and applying state-of-the-art methods for calculating local temperature and air quality in the urban environment with a specific focus on London. Data and results from LUCID will feed into this Task.
Task WP2.3: Water Resource Drought (lead: Fowler; contributors: Walsh) Historical water resource drought will be related to deficits in rainfall and increases in temperature using a hydrological model of the Thames and Lea catchments developed under the EPSRC-funded CRANIUM and Tyndall Centre projects by PDRA Walsh. A number of external factors could potentially affect water resources: management policy options (e.g. pipe replacement schemes or a new reservoir), land use change (e.g. increase in urbanisation) and demand change (e.g. population growth, changes in building stock or water pricing). Some of these factors will be evaluated, e.g. in relation to water pricing a spatial index of vulnerability in times of water stress could be developed in collaboration with PP3.
Task WP2.4: Wind (lead: Thornes; contributors: Baker, Robertson) Using 5km outputs from WP1, wind velocity will be modelled within urban canyons and open spaces at individual locations within boroughs, identifying areas at risk from high winds. Historic high wind speeds (> 200 year return period) and return periods recalculated using climate change scenarios from WP1 will be used to identify risk ‘hotspots’. Existing models of urban canyons (street scale) will be enhanced by incorporating land use (roughness length) from existing GIS databases and site visits to estimate the likely impacts of severe winds on infrastructure.
Task WP2.5: Lightning (lead: Cotton) Through local ground flash density and techniques detailed in standards such as the IEC 62305 series, the need for lightning protection of buildings can be assessed. UKCIP have previously noted that there is a potential for substantial increases in lightning flashes in summer. This work will use future estimates of lightning activity that will result from climate change to provide a simple tool that can be used to determine the risk to people, buildings and infrastructure.
Task WP2.6: Subsidence (lead: Wood; contributors: Hallett, Truckell) Cranfield University’s Natural Perils Directory (or NPD) has been in use for nearly ten years across the finance sector with clients in the insurance and reinsurance community using these assessments. National Soil Maps6 soils are allocated to one of five shrink-swell potential classes using Boolean logic. By crossing the soil shrink-swell class with classes of Maximum Potential Soil Moisture Deficit7 (PSMD), nine potential subsidence classes can be determined. Extremes of PSMD with return periods of up to 125 years are currently modelled using 30 years of historical meteorological data. WP2.6 will take this approach and apply it to future scenarios: i.e. use outputs from WP1 to model PSMD for future scenarios, and examine the implications of probability-based WG outputs on the robustness of the methodology.
WP3: Mapping EWE probabilities (lead: Berryman; contributors: Blenkinsop, Fowler, Wood) WP3 will produce a probability mapping method for single and combined EWEs suitable for integration with GIS (PP5). Critical EWE physical thresholds will be identified using inputs from WP1/2 (i.e. water level for flood inundation), a literature review and thresholds from current policy guidelines8. These will be visually formatted for different stakeholders and assessed in questionnaires by PP2. Seasonal/monthly high resolution mapped probabilities of individual EWEs above critical thresholds for decadal scale changes to 2080 will be produced using WP1/2. The appropriateness of this will be assessed by action research in PP2 and 6-monthly by the Advisory Committee. Confidence limits for probabilities will be estimated using climate model variable likelihoods from the IPCC9 and comparison with observations.Probabilities of multiple EWE occurrences will then be generated using fuzzy probability10, probabilistic weighting methods for climate change projections11 or significance probability mapping12. Results will be presented to the Advisory Committee at staged points to validate the model and ensure acceptability of the user interface.
WP4: “What if?” Scenarios (lead: Fowler; contributors: Blenkinsop, Wood) WP4 will generate “what if” scenarios with PP3. Action research (PP2, Task 6) and Advisory Committee consultation will identify scenarios ‘wish-lists’ for individual stakeholders. Inconsistencies/infeasible requirements will then be removed and wish-lists integrated. A limited number of “what if?” scenarios will be demonstrated with the GIS toolkit (PP5) using inputs from PP4/3 and qualitative inputs from PP1 and PP2 on stakeholder needs.
The proposed research will set the foundations for the development and uptake of a modular, interactive and updateable EWE toolkit to aid decision makers in all parts of the community. The research is timely as (a) the IPCC stated in February 20078 that it is very likely that global warming is attributable to human activities and that it is likely that extreme weather events will increase; (b) it will draw on ongoing projects, thus providing more outputs than would be anticipated based on funding level. Coping measures (adaptive capacity) are needed to deal with current and future EWEs at scales at which adaptation decisions are made. This research provides the first attempt to: (a) examine EWE impacts at the local scale, providing a toolkit for decision makers, and (b) identify risk ‘hotspots’ within the community; crucial for short and long term planning. The Programme uniquely integrates social and physical modelling to examine the dynamics of change in both climate and socio-economic factors, thus contributing to improved adaptive decision making.
A 3 year Research Programme is proposed. Each WP will be managed by the WP leader with PI Fowler leading the overall Programme Package at UNEW. PP4 will be managed to ensure that: key outputs are delivered at the appropriate milestones to the required quality; EPSRC is informed of progress and is consulted should unforeseen deviations emerge; there is close liaison between the PP4 Investigators. This will be achieved by maintaining a process of internal review; ensuring staff have the appropriate knowledge, skills and experience for each activity. Specifically, there will be regular (6-monthly) meetings of the full PP4 team supplemented by more frequent meetings of Investigators working in individual WPs. PI Fowler will engage with the CWEWE Programme Manager and the PIs of the other PPs to ensure appropriate links and collaborative arrangements for the WPs; inter-PP meetings will also be arranged as appropriate (e.g. for WP4). PP4 will also have a large role in feeding data into and pulling data out of the central data repository in PP6 for the other PPs. Milestones and deliverables are divided into two groups: those whose primary purpose is to inform other WPs; and those intended for use by stakeholders (see Gantt chart for more detail).
|Outputs to other PPs||Outputs to stakeholder groups|
|Providing hypothetical EWE scenarios for the questionnaire surveys in PP2||Input to prototype interactive, modular software and modelling toolkits and supporting material for use by policy makers and community groups to assist the development of training workshops and best practice|
|Providing results from physically-based modelling and probability maps to GIS tool for integration into community surveys (3 stages of feedback)||Identification of ‘risk hotspots’ within the community and better identification of probabilities of EWE under current and future climates|
|Providing inputs on EWEs into socio-economic impact simulators for PP3||Guidance on best practice for the community under ‘what if?’ scenarios|
- 1. Burton A., Fowler H.J. & Kilsby C.G. (2007) ‘Investigation of intensity and spatial representations of rainfall within stochastic rainfall model’, AquaTerra Deliverable H1.8, EUFP6 Integrated Project AquaTerra? (no. 505428).
- 2. Kilsby C.G., Jones P.D., Burton A., Ford A.C., Fowler H.J., et al. ‘A daily weather generator for use in climate change studies’, Environmental Modelling and Software, in press.
- 3. Djordjevi%u0107 S., Chen A., et al. (2007) ‘Integrated sub-surface/surface 1D/1D and 1D/2D modelling of urban flooding’, Proc. Cost Session Aquaterra Conference 2007, Amsterdam, 197-208.
- 4. Hall J., Dawson R., Speight L., Djordjevic S., et al. (2007) ‘Sensitivity based attribution of flood risk’, NOVATECH2007, 6th Int. Conf. on Sustainable Techniques and Strategies in Urban Water Management, Lyon.
- 5. Hunter N.M., Horritt M.S., Bates P.D., Wilson M.D. & Werner M.G.F. (2005) ‘An adaptive time step solution for raster-based storage cell modelling of floodplain inundation’, Advances in Water Resources, 28(9), 975-991.
- 6. Hodge, C.A.H. et al (1983) Soil map of England and Wales, Sheet 4, Eastern England. Soil Surv. E&W, Harpenden.
- 7. Jones, R.J.A. and Thomasson, A.J. (1985). An agroclimatic databank for England and Wales. Soil Survey Technical Monograph No.16, pp45.
- 8. Solomon S., et al. (2007) ‘Technical Summary’. In: Climate Change 2007: The Physical Science Basis. [Solomon, S., et al. (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA.
- 9. ODPM (2006) Planning Policy Statement 25 – Development and Flood Risk
- 10. Baudrit C., Couso I. and Dubois D. (2007) ‘Joint propagation of probability and possibility in risk analysis: towards a formal framework’, International Journal of Approximate Reasoning, 45(1), 82-105.
- 11. Fowler H.J., Ekström M. Blenkinsop S. & Smith A.P. ‘Estimating change in extreme European precipitation using a multi-model ensemble’, Journal of Geophysical Research – Atmospheres, in press.
- 12. Hassainia F., Petit D. and Montplaisir J. (1994) ‘Significance probability mapping: the final touch in t-statistic mapping’, Brain Topography, 7(1), 3-8.
Latest posts by Rob Bexley (see all)
- Extreme Weather is No Match for Weatherproof Rattan Garden Furniture - September 13, 2018
- Welcome Update - April 12, 2018