Floods in Future Climates -

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1 Floods in Future Climates - Process and Statistical Issues Paul H. Whitfield Meteorological Service of Canada, Environment Canada, Vancouver Department of Earth Sciences, Simon Fraser University, Burnaby

2 Floods in Future 4AR views What are floods? Mechanisms What causes floods? Will that change in future Measurement Issues Modelling issues Equifinality Extrapolation beyond observations Scale and resolution

3 4AR 2007 IPCC Increased precipitation intensity and variability are projected to increase the risks of flooding and drought in many areas. The frequency of heavy precipitation events (or proportion of total rainfall from heavy falls) will be very likely to increase over most areas during the 21st century, with consequences for the risk of rain-generated floods.

4 Fig 10. 9

5 Uncertainties arise from the incorporation of climate model results into freshwater studies for two reasons: the different spatial scales of global climate models and hydrological models biases in the long-term mean precipitation as computed by global climate models for the current climate Timing biases from GCM patterns A number of methods have been used to address the scale differences: simple interpolation of climate model results dynamic or statistical downscaling methods all such methods introduce uncertainties into the projection Biases in simulated mean precipitation are often addressed by adding modelled anomalies to the observed precipitation in order to obtain the driving dataset for hydrological models. changes in interannual or day-to-day variability of climate parameters are not taken into account in most hydrological impact studies. Hydrology has both short-term and long-term memory Over/underestimation of future floods, droughts and irrigation water requirements.

6 3.39

7 Impacts on Europe Increasing risk of winter flood in northern Europe and of flash flood in all of Europe Risk of snowmelt flood shifts from spring to winter Today s 100-year floods are projected to occur more frequently northern and north-eastern Europe (Sweden, Finland, N. Russia) Ireland, Central and E. Europe (Poland, Alpine rivers), Atlantic parts of S. Europe (Spain, Portugal); less frequently in large parts of S. Europe Globally - wet areas/periods get wetter and dry areas/periods get drier

8 Uncertainties of Climate Change Science Increasing GHG Concentrations Significance of Future Climate Change Magnitude/Rate of Global Temperature, Precipitation and Sea Level Change Discernible human Influence Amplified Polar/Continental warming Change in Regional Rainfall Patterns/Extreme Event Detailed Characteristics of Local Change Local Impacts High Confidence Level Low

9 What are floods? a rising and overflowing of a body of water especially onto normally dry land ; also : a condition of overflowing <rivers in flood. Flooding is primarily but not exclusively caused by hydro-meteorological conditions, acting either individually or in combination

10 Studying Future Floods Landuse Assumptions Future Climates Extreme events Pattern dependencies Uncertainty Issues Measurements Statistical Distribution return periods Equifinality Process changes Scaling effects Coupling models affected by central limit it theorem Extrapolation beyond observations Examples

11 10.18

12 Arnell et al Global Environmental Change 14:3-20

13 What Causes a Flood? Combination of circumstances that t results in an excess of water In a stream channel On a surface Combinations of precipitation and antecedent conditions Combinations of temperature and antecedent conditions Combination of conditions and landuse

14 Precipitation Unresolved in GCMs Downscaling Changes in Intensity Variety of processes Extrapolation beyond the observed

15 Scope and nature of precipitation p processes Process Scope Scale Orographic Windward sides on elevation features Locally persistent local Convection Differential heating of atmosphere local Km s Tropical Cyclonic Convergence in cyclones Tropical regional Extra-tropical Pineapple Express mid-latitude regional Frontal regional Monsoon Heavy precipitation alternates with hot, dry conditions on an annual basis due to seasonal reversal of winds widespread continental Unlikely that observed patterns and frequencies in the past 100 years will continue

16 Pineapple Express

17 Flood Processes Timing Storm Duration Rainfall depths Catchment Runoff Spatial state response coherence Long -rain No seasonality >1day Substantial wet due to persistent rainfall Short-rain No seasonality Hours to 1 day Moderate to substantial Flash flood Periods of <90min Small to extreme rainfall moderate Rain on snow Cold to warm transitions Moderate rainfall events Snowmelt and rainfall Slow Extent of storm Wet for large Fast Local or regional flood events Any Flashy Limited <30km Wet, snow covered Snow-melt Spring to Unimportant Minor rainfall Wet, snow summer covered Glacial outburst Coastal flood Event Event Fast to slow Medium to slow Areas of snow cover Medium Hurricanes and tropical storms can produce heavy rains, or drive ocean water onto land. Urban flood Various Rain water can not be absorbed into the ground and becomes runoff, filling parking lots, making roads into rivers, and flooding basements and businesses. Ice-jam flooding Groundwater Rise Event

18 Figure 7. Change in extremes by 2050, under HADCM3. This panel shows change in the magnitude of the 10-year return period maximum monthly runoff. Arnell et al Global Environmental Change 9:S31-S49

19 GCM Prudhomme et al 2003 J Hydrology 277:1-23

20 Measuring Flood Flows

21 Norish Creek near Dewdney 08MH >3Q >2h Stage (m) Q(m3/s)

22 How are Floods Statistically Distributed? Standard Practice Log Pearson Type 3 GEV Annual series partial series Mixed populations Multiple mixtures

23 Alila and Mtiraoui 2002 Hydrological Processes 16:

24 Alila and Mtiraoui 2002 Hydrological Processes 16:

25 Equifinality Models with many parameters have equally likely solutions that fit observations equally well Within simple models parameters may lack clear physical meaning Within complex models the physical meaning is improve but not better resolved

26 Cameron et al Hydrology and Earth System Sciences 4:

27 Booji 2005 J Hydrology 303: Scaling Effects

28 Coupling Models Prudhomme et al. 2002; Hydrological Processes 16:

29 Fig. 4. Errors of the simulated peak flows for peaks of different magnitudes during the test period for the four catchments. Both the median and the 80-per cent range of the prediction errors obtained using the 50 best parameter sets are shown. The assemblies of best parameter set were determined with regard to the model efficiency (Reff, left column), the mean of efficiency and goodness of groundwater level simulations (Reff and r2, middle), as well as the mean of efficiency and goodness of peak flow simulations (Reff and Rpeak, right). The shaded area indicates where the model was extrapolated, i.e., events that were larger than those observed during the calibration period. Seibert 2003 Nordic Hydrology 34: HBV

30 Seibert 2003 Nordic Hydrology 34: HBV

31 Streamflow Processes e m3/sec Discharg 60 Englishman 600 Capilano Snowmelt Coquitlam Fall Rains 50 Cheakamus Elaho Lillooet Winter Rains Glacier melt Dry 10 Summer After Wade et al observations

32 ENSO - Streamflow Fleming et al Statistical bootstrapping

33 PDO - Streamflow Fleming et al Statistical bootstrapping

34 Future Changes Coquitlam River 18 Discharg ge (cms) p< p< p< Day Periods Whitfield et al 2002 UBC Watershed - snowmelt

35 Floods and Process Change in % Coastal Rainfall Snowmelt Upper Campbell River Illecillewaet River Episodes Flood Days Duration Flood Volume Mean Flood Flow Mean Flood Peak Annual Maximum Day of Flood Centroid Day of Occurence of Peak Loukas and Quick, 1999 UBC Watershed Model - snowmelt

36 Statistical Models Model validation- Process & scale effects Index of agreement- Decadal change Index of agreement- Mann- Whitney U Inde ex of agreeme ent (decadal ch hange) b c d Drainage Area (sq. km) ney U) Inde ex of agreemen nt (Mann-Whit b c d Drainage Area (sq. km) Cannon and Whitfield, 2002 Neural Network Models

37 Statistical Models Model validation- Process & scale effects Predicted mean/obs. mean Predicted variance/obs. variance b c d b c d Predicted mean/observed mea n Predicted variance/ /observed variance Drainage Area (sq. km) Drainage Area (sq. km) Cannon and Whitfield, 2002 Neural Network Models

38 Georgia Basin Model Bias 1.2 Magnitude of 10 year Flood Englis shman ilano Capi Coqu uitlam Chea akamus Ela aho Lillo ooet UBC Watershed - snowmelt 0 High Low Decreasing Rainfall Influence Whitfield et al, 2003 Relative to Mean Observed Magnitude of 10 year Flood Observed, Danard, NCEP, CGCM1

39 Flood Days Percent Generating Mechanism Frequency Location Magnitude Englishman Capilano Coquitlam Cheakamus Elaho Lillooet Modelled Percent of Events Greater than Q05 Whitfield et al, 2003 UBC Watershed - snowmelt

40 Location Flows above Q05 Modelledd d 2013 Englishman River Modelled 2013 Capilano River Jan 31-Jan 1-Mar 31-Mar 30-Apr 30-May 29-Jun 29-Jul 28-Aug 27-Sep 27-Oct 26-Nov 26-Dec 1-Jan 31-Jan 1-Mar 31-Mar 30-Apr 30-May 29-Jun 29-Jul 28-Aug 27-Sep 27-Oct 26-Nov 26-Dec UBC Watershed - snowme elt Coquitlam River Cheakamus River Modelled Modelled Jan 1-Jan 31-Jan 1Mar 1-Mar 31-Mar 30-Apr 30-May 29-Jun 29-Jul 28-Aug 27-Sep 27-Oct 26-Nov 26-Dec 1Jan 1-Jan 31-Jan 1Mar 1-Mar 31-Mar 30-Apr 30-May 29-Jun 29-Jul 28-Aug 27-Sep 27-Oct 26-Nov 26-Dec Elaho River Lillooet River Modelled Modelled Jan 31-Jan 1-Mar 31-Mar 30-Apr 30-May 29-Jun 29-Jul 28-Aug 27-Sep 27-Oct 26-Nov 26-Dec 1-Jan 31-Jan 1-Mar 31-Mar 30-Apr 30-May 29-Jun 29-Jul 28-Aug 27-Sep 27-Oct 26-Nov 26-Dec Whitfield et al, 2003

41 Year - Englishman River Year - Capilano River 10 Year Annual Series Gumbel; Log Pearson Type Year - Coquitlam River Year - Cheakamus River UBC Watershed - snowmelt Year - Elaho River Year - Lillooet River Whitfield et al,

42 10 Year - Englishman River - Partial Series 10 Year - Capilano River - Partial Series Year Partial Series Gumbel; Log Pearson Type Year - Coquitlam River - Partial Series Year - Cheakamus River - Partial Series UBC Watershed - snowmelt Year - Elaho River - Partial Series Year - Lillooet River - Partial Series Whitfield et al,

43 CGCM2 emissions A2 Englishman Riv er near Parksville - 08HB Mean - ENGLISH CGCM2A ( ) Mean - ENGLISH CGCM2A ( ) p<0.05 Mean - ENGLISH CGCM2A ( ) p<0.05 Mean - ENGLISH CGCM2A ( ) p<0.05 Disch harge (m3/sec) IHACRES Rainfall model

44 CGCM2 emissions B2 Englishman River near Parksville - 08HB002 Disc charge (m3/sec) Mean - ENGLISH CGCM2B ( ) Mean - ENGLISH CGCM2B ( ) p<0.05 Mean - ENGLISH CGCM2B ( ) p<0.05 Mean - ENGLISH CGCM2B ( ) p< IHACRES Rainfall model

45 Annual Series 10-Year Floods IHACRES Rainfall model

46 Partial Series 10-Year Floods IHACRES Rainfall model

47 10-, 100-, 200-Year Floods CGCM2B Annual series (annual maximum) IHACRES Rainfall model

48 10-, 100-, 200-Year Floods CGCM2B Partial series (all events above Q 05 )

49 Ice-induced Flood Frequency Liard River at Mouth Ice effect return period of ice-jam floods tend to be lower than for open-water events extremes can extend into flood levels not considered for open-water events rarely are ice events analyzed when determining flood risk numerous flood analyses over the last century of minimal value because analysis conducted on discharge not water levels (ignore ice)

50 TRENDS IN NORTHERN RIVER-ICE BREAKUP AND TIMING OF SPRING 0 C-ISOTHERMS A B C days/49 years ( ) 98) D -25 earlier in the west later in the east matches some W- E GCM forecasts western trends match even longer term historical breakup records Bonsal & Prowse 2003 Climatic i Change Bonsal et al., 2001

51 Summary Shorter duration of precipitation season coupled with overall increase in precipitation intensity Changing dominance of snow to rainfall Larger volumes of water in shorter time periods More runoff in winter Less spring runoff

52 Not all floods are equal Importance of precipitation process as generating g force Changes in mixture of driving forces Changes in generating processes Modelling issues Atmosphere Hydrological models Equifinality

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