Water and Me!

1. Flood clustering, insurance, and a bit of sediment mixedin! Emma RavenWillis Research Fellow in Hazard and Risk,IHRR, Durham UniversityFebruary 21st 2011

2. Water and Me! Emma Waterhouse BSc / MSc: fluvial studies / catchment dynamics PhD: sediment / fluvial geomorphology / flood risk Extra-curricular water interest Fellowship: insurance / extreme floods / rainfall / clustering JBA: reinsurance / floods / cat modelling

3. Seminar Overview • Part 1: River Flow Clustering for UK Insurance Risk Analysis • Reinsurance / Willis Research Network; • Background: flood-risk stats (stationary vs. trends vs. cycles); • Methodology: discharge rather than rainfall; • Characterising clusters: visual / statistical; • Potential atmospheric links / future research needs. • Part2: Interactions Between Sediment, Engineering and Flood Risk in Gravel-Bed Rivers • The importance of sediment for flood risk; • Background; • Results from fieldwork; • Overview of model; • Model scenarios.

4. Part 1: Characterising High River Flow Clustering in UK Rivers

5. Reinsurance Willis Reinsurance Group Reinsurers 20,000 associates 400 offices 100 countries USD 11 billion in premiums & USD 5 trillion of exposed global risk protected every year Insurers risk transfer Clients Willis are a global reinsurance intermediary – need to understand extreme risks

6. The WRN and Durham funding / steering Institute of Hazard, Risk and Resilience research applications Improve knowledge and understanding of extreme events Making a difference to how we live with hazard and risk • Core Flooding Questions • what factors drive floods (characteristics of rainfall patterns from summer 07)? • what problems do short-records cause for analysis? • do floods occur in decadal-length temporal clusters? • can we quantitatively characterise these clusters? methods to help clients identify and quantify their risk exposure academic outputs: journals / teaching

7. Stats and Short Term Trends Flood risk analysis / management require quantitative statistics: Return Intervals / Probabilities. DATA: UK river flow data is now widely available (National River Archive) - predominantly post 1960. River Severn at Bewdley (photo & data) stationary trending cyclic Long-historical data sets are essential.

8. Problematic Probabilities Return Interval = years/rank Probability = 1/RI

9. Problematic Probabilities • Return Interval = years/rank • Probability = 1/RI • 1990-2010: 19 years / 3 flows • RI = 6.3 years Prob. = 16% • 1980-2010: 29 years / 3 flows • RI = 9.6 years Prob. = 10.4% • 1900-2010: 109 years / 20 flows • RI = 5.5 years Prob. = 18% As record length increases, probability changes - influences decisions.

10. Discharge Data • 22 longest flow records in UK; • created a Peak Over Threshold series for each; • RI of 1 in 1year, 2years, 4years; • 7-days between peaks. Circle diameter = catchment size POT eg: 100 year record: RI of 1year = top 100 flows; RI of 4years = top 25 flows

11. 2007 What about Rainfall Records? • Length of record: UK monthly average rainfall series (Met Office): July data: 1960-2008 vs. 1766-2008 Type of record: July totals only vs. June & July totals average of 132 mm average of 145 mm

12. Linking Rain to River Flow Seasonal totals for Central England: 1920 - 2008 What measure of rain do you use – hourly intensity / weekly totals / seasonal totals? How do we account for runoff processes? Rain complex factors Flood-poor period Autumn 2000 floods 1947 Snowmelt floods River

13. Concerns Over Discharge Data Changes in catchment land-use? (1) we are more interested in the timing of peaks rather than their magnitude. (2) changes in land-use are likely to be manifest as trends rather than cycles. (3) with only ~20 catchments we can examine each for unusual changes (e.g. step change associated with regulation).

14. Clusters - Bubble Plots • south • north Methods of capturing clustering: flood frequency counts No. of peaks within a moving 5-year window. RI = 1 year Cycles not Trends Spatial Correlations FLOOD RICH FLOOD POOR

15. Changing the Return Interval

16. Time Between Peaks Quantitative clustering: individual event timing rather than associated year.

17. Clustering Statistics Dispersion Stat: a measure of the deviation of points in time from equi-dispersion. over- and under-dispersion Statistical measure of clustering

18. Applicability of Dispersion Stat. 10 synthetic POT series Dispersion stat. tells us that a series has clustering but not about its nature.

19. Box Plots Thames at Kingston Peak counts in moving window (1year – 40years); POT RI = 1 year; Red : twice as many floods as we would expect; Clustering is manifest over different time-scales and at different times-periods. Moving away from individual events.

20. Box Plots Peak counts in moving window (1year – 40years); POT RI = 1 year; Red : twice as many floods as we would expect; Clustering is manifest over different time-scales and at different times-periods.

21. Climatic Drivers of Clustering Summer 2007 Floods and the Jet Stream Atlantic Multidecadal Oscillation River Lee But are catchment processes going to filter out the climate signal?

22. Challenges to Address • Spatial correlations - catchment size and location; • Advantages and difficulties associated with rainfall analysis (pluvial flooding); • Climatic influences –teleconnections / climate change? • Trends on top of clusters; • Clustering of other phenomena – windstorm, rainfall, droughts, landslides, banking collapse? • Application to risk management and the insurance industry – is clustering too complex to provide a product?

23. But what about the sediment? Part 2: Interactions between sediment, engineering and flood risk in gravel-bed rivers.

24. Sediment Morphology Interactions Processes in natural, unmanaged, sinuous upland gravel-bed rivers.

25. Interactions Provoking Management • High coarse sediment supply • In-channel deposition • Bank erosion • Channel capacity is maintained Loss in capacity Increased flood-risk CHANNEL MANAGEMENT too high too rapid Loss of land See Raven et al, (2010), PiPG 34, P23 for broad discussion Processes leading to channel management: levees, bank protection, gravel traps.

26. The Importance of Sediment • Sediment agg/deg can change channel capacity > flood risk > changing RI; • Sediment moved in large floods can end up deposited on flood plains: costly clear up; • Sediment can create problems for infrastructure - bridges, weirs; • Sediment is important for river aesthetics and habitat.

27. Combined Methodology Fieldwork: monitor channel change; monitor driving processes; explore interactions. (2) Modelling: develop, apply and test a model of channel change. • DATA • repeat cross-sectional surveys • bank erosion monitoring • sediment impact sensors • pebble counts / bulk samples • field surveys • bend velocity paths

28. The Upper Wharfe Study Reach upland-rural coarse gravel annual floods flashy managed meanders 12-30m wide exposed bars single thread bank erosion sinuous Yorkshire Dales, Northern England

29. Sediment and Overbank Flows 4-years of sediment accumulation = flood frequency, 2.6 times greater and overbank flow time increased by 12.8 hours. channel capacity, 02 channel capacity, 06 Raven et al. (2009) “The spatial and temporal patterns of aggradation…” , ESPL, 34, p23-45.

30. Modelling Framework time step Initial and boundary conditions Coupling a SRM model with a lateral channel change component; Three sub-models; Iterative scheme; Novel approach – lateral change using a split channel approach. updates Flow hydraulics Sediment transport Lateral channel change Output / results Raven et al. (online, early view, Jan 11), Hydrological Processes.

31. Coupling SRM with Lateral Change Q SRM: width-averaged Right bed elevation Left bed elevation LEFT SIDE hydraulics, shear stress, sediment transport, bed level change. RIGHT SIDE hydraulics, shear stress, sediment transport, bed level change. Splitting the cross-sectional geometrical representation in the model

32. Curvature and Lateral Change Curvature: shifts average shear τ > critical erosion τ = bank erosion τ < critical narrowing τ = bank narrowing Curvature and deeper flow = higher τ Bank erosion feeds back to lower flow depth and reduce shear stress Excess shear stress drives bank erosion

33. Preventing Lateral Change Curvature: shifts average shear Curvature and deeper flow = higher τ Hard Engineering Low critical shear = erodible banks High critical shear = protected banks Excess shear stress drives bank erosion

34. Model Calibration Downstream fining Steeper channel slope Model performance vs. field data

35. Scenarios: Benchmark Comparison > 0 bend is right turning, high shear on left τ high τ low L Bank protection R > 0 for bank erosion < 0 for bank narrowing Cross-sectional node 2-Years of Simulating the Actual River Conditions

36. Scenario 1: width change with protection Max BE: 0.4 m Max BE: 1.45 m

37. Scenario 1: width change with NO protection Max BE: 0.4 m • Further implications • changes in flow depth; • changes in shear stress distribution; • changes in the locations of sedimentation. • wider channel promoting in-channel deposition Max BE: 1.45 m Raises caution to restoration schemes

38. Scenario 2: Implementing Engineering Model Scenarios2: engineering a problematic reach severe bank erosion sediment accumulation • straighten a 350m reach (loss of 75m); • removes high curvature; • increases slope; • narrow and fix banks.

39. Scenario 2: Engineering Normal reach Engineered reach Bank erosion (m) Engineering simply shifts the problems (and makes them worse) up and downstream.

40. Part 2: Summary Sediment is important for flood risk and river management and also insurance; In-channel deposition can change RI / probabilities of flood events; The model’s split-channel approach and lateral change componentwas effective and allowed asymmetrical channel adjustment; Limitations remain – simplified geometry, fixed curvature, data. Scenarios raise caution to river management schemes that interfere with the sediment transfer system; This research supports a recommendation that managing sediment sources may be better than managing the after affects.

41. Emma Raven e.k.raven@durham.ac.uk IHRR Room 256