Hazards Planning and Policy Panel on the Use of Scenarios to Address Natural Hazards Issues UCOWR / NIWR Annual Conference July 25, 2007 Some Thoughts On Developing Climate-Based Scenarios for Extreme Hydrologic Events Katie Hirschboeck Laboratory of Tree-Ring Research The University of Arizona katie@ltrr.arizona.edu

OVERVIEW: Process-Sensitive Upscaling Flood & Drought Hydroclimatology (causative mechanisms) Integrating the Paleo-Record

Key Question: How do we transfer the growing body of knowledge about global and regional climate change and variability to 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

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 #126 UCOWR

PROPOSED COMPLEMENTARY APPROACH:

RATIONALE FOR PROCESS-SENSITIVE UPSCALING: Attention to climatic driving forces & causes: -- storm type seasonality -- atmospheric circulation patterns with 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 with LOCAL hydrologic variations at the individual basin scale . . .

including EXTREME EVENTS. in the “tails” of streamflow . . . . including EXTREME EVENTS in the “tails” of streamflow probability distributions, such as DROUGHTS & FLOODS.

PLAN FOR BUILDING SCENARIOS VIA PROCESS-SENSITIVE UPSCALING: Scenarios are based on a systematic compilation of watershed-specific information that is used to determine the CAUSES of spatially and temporally varying hydroclimatic extremes: “FLOOD HYDROCLIMATOLOGY” also “DROUGHT HYDROCLIMATOLOGY” (see Hirschboeck 1988)

The flood of October 1983 (WY 1984) Flow Time Series A fairly long record with lots of variability . . . . The long record made the gaging station a candidate for discontinuation in the early 1980s . . . Flow Time Series The flood of October 1983 (WY 1984) What does this time series of floods look like when each event is classified according to hydroclimatic cause?

Convective events are the most common, but the largest floods in the record were produced by other mechanisms Now that we know the underlying causes for flood magnitudes & frequencies, how do we apply this information to scenario building?

Represent Events by Simple Curves Question Assumptions Some hints on scenario-building from the web-based “course” of 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

Santa Cruz River at Tucson Peak flows separated into hydroclimatic subgroups: Tropical storm Sumer Convective Winter Synoptic All Peaks Schematic climate-based curves to represent PDFs Hirschboeck et .al. 2000

Question (or Re-Examine) Assumptions: The standard “iid” approach assumes stationarity and independent, identically distributed event probabilities Why does FFA have these limitations? Vit Klemes’ explanation is: “ . . . The empirical data representing a hydrologic event are treated as a collection of abstract numbers that could pertain to anything or to nothing at all. Their hydrologic flavor, the physical substance that makes, for instance, a precipitation record an entity entirely distinct from ,say, a record of stock market fluctuations, is not reflected in the analysis.” (Klemes 1974, p 2) More specifically, it is the assumptions that underlie statistical flood frequency analysis that cause some of the problems. These assumptions are that the flood times series is “a reliable and representative time sample of random homogeneous events. “ (Bulletin 17B). In addition, the time ordered set of data must consist of observations (floods) that are independent and identically distributed, i.e. emerging from a theoretical probability distribution with an idnetical shpae (constant mean and variance) over the entire time series – the “iid” assumption. Hirschboeck 1988 Events at each point in time emerge from independently, identically distributed probabilities

Alternative Process-Based Conceptual Framework for Hydrologic Time Series: Time-varying means Time-varying variances Mixed frequency distributions may arise from: storm types synoptic patterns ENSO, PNA, NAM etc., teleconnections multi-decadal circulation regimes Both Hirschboeck, 1988

Alternative Conceptual Frameworks to Explain How Flood Magnitudes Vary over Time: (Arizona watershed scenarios) Based on Different Storm Types . . . Varying mean and standard deviations due to different causal mechanisms

. . . Based on Varying Circulation Pattern Changes . . . When the dominance of different types of flood-producing mechanisms or circulation patterns changes over time, the probability distributions of potential flooding at any given time (t) may be altered. El Nino year La Nina year Blocking Regime Zonal Regime

. . . Based on Low-Frequency Variations and/or Regime Shifts: Temporal shifts in dominance of circulation regimes, e.g. PNA, NAM / NAO patterns, etc A low frequency shift in atmosphere or ocean circulation regime (e.g. PDO) OR the anomalous persistence of a given regime . . . . . . will lead to different theoretical frequency / probability distributions over time. Abrupt shift from one PERSISTENT regime to another

ADVANTAGES OF INTEGRATING THE PALEORECORD To build reliable scenarios, 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.

Paleo -information extracted from . . . TREE RINGS & STRATIGRAPHIC PALEO - STAGE INDICATORS can augment the gaged record of extreme events in watersheds in many locations.

1899-1904 dry “signature” pattern A TREE-RING CORE FROM THE SALT RIVER BASIN showing ring-width variations in the 1900s 1899-1904 dry “signature” pattern 1950’s DROUGHT 1905 -1908 1914 – 1920 two wet episodes 1950 &1951 1953 -1956 series of narrow rings 1952 (one wet year) 1900 & 1904 = missing rings 1899 &1902 = narrow rings Even in a single tree, the record of extreme wet and dry streamflow episodes is evident. Extreme Years of High & Low Streamflow in the Salt-Verde-Tonto River Basin

Tree-Ring core from the Salt River Basin: COMPARISON OF TWO BASINS: Extreme High & Low Flow Years occuring in BOTH Basins Together Upper Colorado Basin Salt – Verde Basin 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 = missing ring 1671 1672 wide rings shift to narrower rings Tree-Ring core from the Salt River Basin:

Atmospheric Circulation Anomalies Driving the Extremes (based on observed record) Widespread Wet Period HIGH FLOW WATER YEARS Widespread Drought: LOW FLOW WATER YEARS higher-than-normal pressure over both basins lower-than-normal pressure over both basins LOW PRESSURE HIGH PRESSURE 500 mb Geopotential Height (m) Composite Anomaly, Oct-Sep water year

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

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

Verde River, Arizona Peak flows separated into hydroclimatic subgroups: Tropical storm Sumer Convective Winter Synoptic All Peaks Verde R. below Tangle Creek From Hirschboeck et .al. 2000

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

SUMMARY By associating seasonal and long-term variations in an individual basin’s stream’s hydrograph . . . with the present mix of storm types and the synoptic atmospheric circulation patterns which deliver them . . . . . . a PROCESS-BASED “upscaling” approach provides a complementary way to bridge the gaps between local, regional, and global scales of climate information when building scenarios . . . .

. . . to construct climate-sensitive SCENARIOS . . . The circulation patterns associated with: -- hydroclimatically grouped flood or drought events / PDFs -- persistent paleodrought episodes -- extreme paleofloods (magnitude or frequency) . . . can then be linked to other features of the global-scale climate that are projected to change such as: -- storm track latitudinal shifts -- circulation/ sea level pressure changes (e.g. Hadley circulation expansion) -- teleconnections & atmosphere-ocean circulation regime shifts. . . . to construct climate-sensitive SCENARIOS

“Intensification” of the Hydrologic Cycle  Rising temperatures, changing water balances, seasonal shifts, etc. HYDROCLIMATIC CHANGE Seager et al. 2007 JJA Projected IPCC Modeled Precipitation Change: Hydrologic impact of earlier snowmelt DROUGHTS FOR ADDRESSING: HYDROLOGIC EXTREMES “Intensification” of the Hydrologic Cycle  Projected increases in droughts & floods HAZARDS! FLOODS

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 outliers plus plastic mixing blade – to mix the populations together