Flood Hydroclimatology: Insights into Mixed Flood Populations

1. Flood Hydroclimatology: Insights into Mixed Flood Populations Katie Hirschboeck Laboratory of Tree-Ring Research University of Arizona April 24, 2009

2. Key Question: How do we transfer the growing body of knowledge about global and regional climate change and variabilityto individual watersheds to develop useful scenarios about hydrologic extremes? Key Need: to understand the processes that deliver precipitation (or the lack thereof) to individual watersheds, at relevant time and space scales

3. A “Story” in Four Chapters: 1. UNCERTAINTY: The Challenge of the “Upper Tails” 2. ASSUMPTIONS:The Standard iid Assumption for FFA 3. RE-THINKING:New Insights from “Flood Hydroclimatology” 4. ANTICIPATING THE FUTURE: Scenario building for a post-stationary world

4. 1. UNCERTAINTY

5. StandardizedMean The Challenge of the “Upper Tails” o = partial series  = annual series StandardizedMean Gaged Flood Record -- Histogram(Standardized Discharge Classes) SKEWED DISTRIBUTIONExtreme events  tails of distribution

6. Flow Time Series Flow Time Series The flood of October 1983!(WY 1984) A fairly long record with lots of variability . . . . The gage was shut downin 1980

7. The Challenge of the “Upper Tails” Santa Cruz River, Tucson Arizona Example Typical dry river bed or minor low flow vs. The record flood of October 1983!

8. Flood Frequency Analysis: Theoretical Dilemmas (SOURCE: modified from Jarrett, 1991 after Patton & Baker, 1977)

9. The Challenge of the “Upper Tails” . . . can fail when “outlier” floods occur ! Pecos River nr Comstock, TX Curves A & B indicate the range (uncertainty) of results obtained by using conventional analysis of outliers for 1954 & 1974 floods. SOURCE: modified from Jarrett, 1991, after Patton & Baker, 1977

10. 2. ASSUMPTIONS

11. http://acwi.gov/hydrology/Frequency/B17bFAQ.html#mixed“Flood magnitudes are determined by many factors, in unpredictable combinations. It is conceptually useful to think of the various factors as "populations" and to think of each year's flood as being the result of random selection of a "population”, followed by random drawing of a particular flood magnitude from the selected population.”

12. The Standard iid Assumption for FFA The standard approach to Flood Frequency Analysis (FFA) assumes stationarity in the time series & “iid” “ iid ” assumption: independently, identically distributed

13. 3. RE-THINKING

14. FLOOD-CAUSING MECHANISMS Meteorological & climatological flood-producing mechanisms operate at varying temporal and spatial scales

15. Storm type  hydrograph Summer monsoon convective event The type of storm influences the shape of the hydrograph and the magnitude & persistence of the flood peak This can vary with basin size (e.g. convective events are more important flood producers in small drainage basins in AZ) Synoptic-scale winter event Tropical storm or other extreme event

16. HYDROMETEOROLOGY Weather, short time scales Local / regional spatial scales  Forecasts, real-time warnings vs. HYDROCLIMATOLOGY  Seasonal / long-term perspective  Site-specific and regional synthesis of flood-causing weather scenarios  Regional linkages/differences identifiedEntire flood history context  benchmarks for future events

17. Re-Thinking the “iid” Assumption It all started with a newspaper ad . . . .

18. THE FFA“FLOOD PROCESSOR” With expanded feed tube – for entering all kinds of flood data including steel chopping, slicing & grating blades– for removing unique physical characteristics, climatic information, and outliersplus plastic mixing blade – to mix the populations together

19. Time-varying variances Both SOURCE: Hirschboeck, 1988 Alternative Conceptual Framework: Time-varying means • Mixed frequency distributions may arise from: • storm types • synoptic patterns • ENSO, etc. teleconnections • multi-decadal circulation regimes

20. FLOOD HYDROCLIMATOLOGYis the analysis of flood events within the context of their history of variation - in magnitude, frequency, seasonality - over a relatively long period of time - analyzed within the spatial framework of changing combinations of meteorological causative mechanisms SOURCE: Hirschboeck, 1988

21. This framework of analysis allows a flood time series to be combined with climatological information To arrive at a mechanistic understandingof long-term flooding variability and its probabilistic representation.

22. “ Bottom–Up ” Approach(surface-to-atmosphere) • Observed Gage Record • Meteorological / Mechanistic / Circulation-Linked • Flood Hydroclimatology Framework / Link to Probability Distribution APPROACH

23. Seasonality of Peak Flooding WINTER &

24. Flood Hydroclimatology Example • Peaks-above-base: 30+ gaging stations in Arizona • Synoptic charts + precipitation data  causal mechanisms

25. Synoptic charts + precip data + decision tree  assigned causal mechanism / flood type • Analyzed floods grouped by type -- spatially -- temporally / interannually ANALYSIS • Peaks-above-base -- 30+ gaging stations in Arizona

26. Flood Hydroclimatology Example Sample Distributions of Gila Basin Gaged Peak Flows: Are there climatically controlled mixed populations within?

27. All Peaks Tropical storm Winter Synoptic Sumer Convective Santa Cruz River at TucsonPeak flows separated into 3 hydroclimatic subgroups Hirschboeck et .al. 2000

28. Remember the Santa Cruz record? What does it look like when classified hydroclimatically? What kinds of storms produced the biggest floods?

29. Hydroclimatically classified time series . . .

30. Verde River below Tangle Creek Peak flows separated into 3 hydroclimatic subgroups Tropical storm Winter Synoptic All Peaks Sumer Convective Hirschboeck et .al. 2000

31. Historical Flood

32. Thinking Beyond the Standardiid Assumption for FFA . . . . Based on these results we can re-envision the underlying probability distribution function for Gila Basin floods to be not this . . . .

33. . . . but this: Alternative Model to Explain How Flood Magnitudes Vary over Time Schematic for Gila River Basin based on different storm types Varying mean and standard deviationsdue to different causal mechanisms

34. Tropical storm Octave Oct 1983 Hurricane Lili Oct 2002 IMPORTANT FLOOD-GENERATING TROPICAL STORMS TropicalStorm Flood Events

35. . . . or this: El Nino year Blocking Regime Zonal Regime La Nina year Conceptual Framework forCirculation Pattern Changes When the dominance of different types of flood-producing circulation patterns changes over time, the probability distributions of potential flooding at any given time (t) may be altered.

36. . . . or this: Conceptual Framework for Low-Frequency Variations and/or Regime Shifts: A shift in circulation or SST regime (or anomalous persistence of a given regime) will lead to different theoretical frequency / probability distributions over time. Hirschboeck 1988

37. ADVANTAGES OF INTEGRATING THE PALEORECORD To fully understand flood variability, the longest record possible is the ideal . . . especially to understand and evaluate the extremes of floods and droughts! By definition extreme events are rare . . . hence gaged streamflow records capture only a recent sample of the full range of extremes that have been experienced by a given watershed.

38. Using Paleo-stage Indicators & Paleoflood Deposits . . . • -- direct physical evidence of extreme hydrologic events • -- selectively preserve evidenceof only the largest floods . . . • . . . this is precisely the information that is lacking in the short gaged discharge records of the observational period

39. Curves A & B indicate range (uncertainty) of results obtained by using conventional analysis of outliers for 1954 & 1974 floods. Curve C is from analyses of paleoflood data. Q (discharge) uncertainty R.I. uncertainty (SOURCE: Jarrett, 1991 after Patton & Baker, 1977) Flood Frequency Analysis Pecos River nr Comstock, TX

40. Compilations of paleoflood records combined with gaged records suggest there is a natural, upper physical limitto the magnitude of floods in a given region --- will this change? Envelope curve for Arizona peak flows

41. 1993 Largest paleoflood(A.D. 1010 +- 95 radiocarbon date) FLOOD HYDROCLIMATOLOGY evaluate likely hydroclimatic causes of pre-historic floods Historical Flood

42. 4. ANTICIPATINGTHE FUTURE

43. Key Question: How do we transfer the growing body of knowledge about global and regional climate change and variabilityto individual watersheds to develop useful scenarios about hydrologic extremes? Key Need: to understand the processes that deliver precipitation (or the lack thereof) to individual watersheds, at relevant time and space scales

44. Web-based “course” by UA’s Roger Caldwell:“Anticipating the Future” http://cals.arizona.edu/futures/ • Represent Events by Simple Curves • Question Assumptions • Watch for Groupthink and Fixed Mindsets • Expect Both Surprises & ‘Expected Results’ • Several Solutions are Likely

45. Flood Hydroclimatology “in practice?” MIXED POPULATION FAQ Question:“Floods in my study area are caused by hurricanes, by ice-affected flows, and by snowmelt, as well as by rainfall from thunderstorms and frontal storms. How do I determine whether mixed-population analysis is necessary or desirable?”

46. Answer: “In practice, one determines whether the distribution is well-approximated by the LPIII by: -- comparing the fitted LPIII --- with the sample frequency curve defined by plotting observed flood magnitudes versus their empirical probability plotting positions . . .If the fit is good, andifthe flood record includes an adequate sampling of all relevant sources of flooding(all "populations"), then there is nothing to be gained bymixed-population analysis.”

48. ONE APPROACH: DOWNSCALING (Def): Interpolation of GCM results computed at large spatial scale fields to higher resolution, smaller spatial scale fields, and eventually to watershed processes at the surface. from Hirschboeck 2003 “Respecting the Drainage Divide” Water Resources Update UCOWR

49. PROPOSED COMPLEMENTARY APPROACH:

50. RATIONALE FOR PROCESS-SENSITIVE UPSCALING: Attention to climatic driving forces & causes: -- storm type seasonality -- atmospheric circulation patternswith respect to: -- basin size -- watershed boundary / drainage divide -- geographic setting (moisture sources, etc.) . . . can provide a basis for a cross-scale linkage of GLOBAL climate variability withLOCALhydrologic variations at the individual basin scale . . .