flood hydroclimatology: insights into mixed flood populations
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Flood Hydroclimatology: Insights into Mixed Flood Populations. Katie Hirschboeck Laboratory of Tree-Ring Research University of Arizona April 24, 2009. Key Question:. - PowerPoint PPT PresentationTRANSCRIPT
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Flood Hydroclimatology:
Insights into Mixed Flood Populations
Katie HirschboeckLaboratory of Tree-Ring Research
University of ArizonaApril 24, 2009
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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 Question:
Key Need: to understand the processes
that deliver precipitation (or the lack thereof)
to individual watersheds, at relevant
time and space scales
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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
A “Story” in Four Chapters:
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1. UNCERTAINTY
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SKEWED DISTRIBUTIONExtreme events tails of
distribution
Gaged Flood Record -- Histogram (Standardized Discharge Classes)
The Challenge of the “Upper Tails”
StandardizedMean Standardized
Mean
o = partial series
= annual series
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Flow Time Series
The gage was shut downin 1980
A fairly long record with lots of variability . . . .
Flow Time Series
The flood of October 1983!
(WY 1984)
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Santa Cruz River, Tucson Arizona Example
The record flood of October
1983!
Typical dry river bed or minor low flow
vs.
The Challenge of the “Upper Tails”
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Flood Frequency Analysis:
Theoretical Dilemmas
(SOURCE: modified from Jarrett, 1991 after Patton & Baker, 1977)
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. . . can fail when “outlier” floods occur !
SOURCE: modified from Jarrett, 1991, after Patton & Baker, 1977
Curves A & B indicate the range (uncertainty) of results obtained by using conventional analysis of outliers for 1954 & 1974 floods.
Pecos River nr Comstock,
TX
The Challenge of the “Upper Tails”
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2. ASSUMPTIONS
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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.”
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“ iid ” assumption: independently, identically
distributed
The standard approach to
Flood Frequency
Analysis (FFA) assumes
stationarity in the time series
& “iid”
The Standard iid Assumption for FFA
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3. RE-THINKING
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Meteorological & climatological flood-producing
mechanisms operate at
varying temporal and spatial scales
FLOOD-CAUSING MECHANISMS
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Summer monsoon convective event
Synoptic-scale winter event
Tropical storm or other extreme 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)
Storm type hydrograph
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HYDROCLIMATOLOGY
Weather, short time scales Local / regional spatial
scales Forecasts, real-time warnings
vs.
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
HYDROMETEOROLOGY
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It all started with a newspaper ad . . . .
Re-Thinking the “iid” Assumption
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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
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Alternative Conceptual Framework:
Time-varying means
Time-varying variances
Both
SOURCE: Hirschboeck, 1988
Mixed frequency distributions may arise from:
• storm types
• synoptic patterns
• ENSO, etc. teleconnections
• multi-decadal circulation regimes
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FLOOD HYDROCLIMATOLOGY
is 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
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This framework of analysis allows a flood time series to be combined with climatological information
To arrive at a mechanistic understanding of long-term flooding variability and its probabilistic representation.
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APPROACH
Meteorological / Mechanistic / Circulation-Linked
Flood Hydroclimatology Framework / Link to Probability Distribution
“ Bottom–Up ” Approach(surface-to-atmosphere)
Observed Gage Record
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WINTER &
Seasonality of Peak Flooding
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Flood Hydroclimatology Example
• Peaks-above-base: 30+ gaging stations in Arizona
• Synoptic charts + precipitation data causal mechanisms
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ANALYSIS
• Peaks-above-base -- 30+ gaging stations in Arizona
• Synoptic charts + precip data + decision tree
assigned causal mechanism / flood type
• Analyzed floods grouped by type
-- spatially-- temporally /
interannually
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Sample Distributions of Gila Basin Gaged Peak
Flows:
Flood Hydroclimatology Example
Are there climatically controlled mixed populations within?
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Santa Cruz River at TucsonPeak flows separated into 3 hydroclimatic subgroups
Hirschboeck et .al. 2000
Tropical storm Sumer
Convective
Winter Synoptic
All Peaks
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Remember the Santa Cruz record?
What does it look like when classified hydroclimatically?
What kinds of storms produced the biggest floods?
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Santa Cruz at Tucson
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000
Water Year
Dis
char
ge in
(cf
s)52700 (cfs)
Convective
Tropical Storm
Synoptic
Hydroclimatically classified time series . . .
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Hirschboeck et .al. 2000
Verde River below Tangle Creek
Peak flows separated into 3 hydroclimatic subgroups
Tropical storm
Sumer Convectiv
e
Winter SynopticAll Peaks
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Historical Flood
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Thinking Beyond the Standard iid Assumption for FFA . . . .
Based on these results we can re-envision the underlying probability distribution function for Gila Basin floods to be not this . . . .
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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
. . . but this:
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IMPORTANT FLOOD-GENERATING TROPICAL STORMS
Tropical storm Octave Oct 1983
Hurricane Lili Oct 2002
Tropical
Storm Flood Events
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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.
Conceptual Framework for Circulation Pattern Changes
El Nino year
La Nina year
Blocking Regime
Zonal Regime
. . . or this:
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Conceptual Framework for Low-Frequency Variations and/or Regime Shifts:. . . or this:
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
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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.
To fully understand flood variability, the longest record possible is the ideal . . .
especially to understand and evaluate the extremes of floods and droughts!
ADVANTAGES OF INTEGRATING THE PALEORECORD
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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
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Flood Frequency Analysis
(SOURCE: Jarrett, 1991 after Patton & Baker, 1977)
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
Pecos River nr Comstock,
TX
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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
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Historical FloodLargest paleoflood
(A.D. 1010 +- 95 radiocarbon date)
1993
FLOOD HYDROCLIMATOLOGY evaluate likely hydroclimatic causes of pre-
historic floods
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4. ANTICIPATINGTHE FUTURE
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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 Question:
Key Need: to understand the processes
that deliver precipitation (or the lack thereof)
to individual watersheds, at relevant
time and space scales
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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
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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?”
Flood Hydroclimatology “in practice?”
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“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, and if the flood record includes an adequate sampling of all relevant sources of flooding (all "populations"),
then there is nothing to be gained by mixed-population
analysis.”
Answer:
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from Hirschboeck 2003 “Respecting the Drainage Divide” Water Resources Update UCOWR
(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.
ONE APPROACH: DOWNSCALING
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PROPOSED COMPLEMENTARY APPROACH:
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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 . . .
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CONCLUSIONS
Insights on Flood Hydroclimatology & Mixed Populations
for AnticipatingFuture Floods
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Mixed Distributions 1. Implications for predicting the
tails of a distribution
The distributions of key subgroups may be better for estimating the probability and type of extremely rare floods than the overall frequency distribution of the entire flood series.
Suggestion: Separate out causes & linkages by stratifying by subgroup.
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Hydroclimatic Regions 2. Implications for spatial
homogeneity
-- Basins can be grouped according to how their floods respond to different types of mechanisms and circulation patterns
-- This grouping can change from season to season
-- This grouping is also basin-size dependent
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Non-Stationarity & iid Implications for time series
homogeneity, stationarity & the iid assumption
The conceptual framework of climate-driven time-shifting means, variances and/or mixed distributions provides a useful explanation for non-stationarity in flood times series and challenges the iid assumption.
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For floods, climatic changes can be conceptualized as time-varying atmospheric circulation regimes that generate a mix of shifting streamflow probability distributions over time.
This conceptual framework provides an opportunity to evaluate streamflow-based hydrologic extremes under climatic scenarios defined in terms of shifting modes or frequencies of known flood-producing synoptic patterns, ENSO, etc.
Climatic Variability Implications for evaluating how flood time series may vary under a changing
climate
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PROCESS SENSITIVE UPSCALING:
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