Transcript Flood Hydroclimatology and Its Applications in Western

Flood Hydroclimatology:
Insights into Mixed Flood
Populations
Katie Hirschboeck
Laboratory of Tree-Ring Research
University of Arizona
April 24, 2009
Key Question:
How do we transfer
the growing body of knowledge
about global and regional
climate change and variability
to individual watersheds
Key Need:
to develop useful scenarios
about hydrologic
extremes?
to understand the processes
that deliver precipitation
(or the lack thereof)
to individual watersheds,
at relevant
time and space scales
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
1. UNCERTAINTY
The Challenge of the “Upper Tails”
o = partial series

= annual series
Standardized
Standardized
Mean
Mean
Gaged Flood Record -- Histogram
(Standardized Discharge Classes)
SKEWED DISTRIBUTION
Extreme events  tails of distribution
Flow Time
Time Series
Series
Flow
The flood of
A fairly long record with lots of
October
variability
. . .1983!
.
(WY 1984)
The gage was
shut down
in 1980
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!
Flood Frequency Analysis:
Theoretical Dilemmas
(SOURCE: modified from Jarrett,
1991 after Patton & Baker, 1977)
The Challenge of the “Upper Tails”
. . . can fail
when “outlier”
floods occur !
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
SOURCE: modified from Jarrett,
1991, after Patton & Baker, 1977
2. ASSUMPTIONS
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.”
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
3. RE-THINKING
FLOOD-CAUSING MECHANISMS
Meteorological &
climatological
flood-producing
mechanisms
operate at
varying temporal
and spatial
scales
Storm type 
hydrograph
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)
Summer monsoon
convective event
Synopticscale winter
event
Tropical storm or
other extreme event
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
Re-Thinking the “iid” Assumption
It all started with a newspaper ad . . . .
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
Alternative Conceptual Framework:
Timevarying
means
Timevarying
variances
Mixed frequency
distributions
may arise from:
• storm types
• synoptic patterns
Both
• ENSO, etc.
teleconnections
• multi-decadal
circulation regimes
SOURCE: Hirschboeck, 1988
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
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.
APPROACH
 “ Bottom–Up ” Approach
(surface-to-atmosphere)
 Observed Gage Record
 Meteorological / Mechanistic /
Circulation-Linked
 Flood Hydroclimatology Framework /
Link to Probability
Distribution
Seasonality of Peak Flooding
Flood Hydroclimatology Example
• Peaks-above-base: 30+ gaging stations in Arizona
• Synoptic charts + precipitation data  causal mechanisms
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
Flood Hydroclimatology Example
Sample
Distributions of
Gila Basin
Gaged Peak
Flows:
Are there
climatically
controlled mixed
populations
within?
Santa Cruz River at Tucson
Peak flows separated into
3 hydroclimatic subgroups
All Peaks
Tropical
storm
Winter
Sumer
Synoptic
Convective
Hirschboeck et .al. 2000
Remember the Santa Cruz record?
What does it look like when
classified hydroclimatically?
What kinds of storms produced the
biggest floods?
Hydroclimatically classified time series . . .
Santa Cruz at Tucson
52700 (cfs)
50000
45000
C onv e ctiv e
Discharge in (cfs)
40000
Tropical S torm
35000
S y noptic
30000
25000
20000
15000
10000
5000
0
1915
1920
1925
1930
1935
1940
1945
1950
1955
1960
1965
W ater Year
1970
1975
1980
1985
1990
1995
2000
Verde River below Tangle Creek
Peak flows separated into
3 hydroclimatic subgroups
Tropical
storm
All Peaks
Sumer
Convective
Winter
Synoptic
Hirschboeck et .al. 2000
Historical Flood
Thinking Beyond the Standard iid
Assumption for FFA . . . .
Based on these
results we can reenvision the
underlying probability
distribution function
for Gila Basin floods
to be not this . . . .
. . . 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 deviations
due to different causal mechanisms
Tropical storm Octave
Oct 1983
Tropical
Storm
Flood
Events
IMPORTANT FLOODGENERATING TROPICAL
STORMS
Hurricane Lili
Oct 2002
. . . or this:
Conceptual Framework for
Circulation Pattern Changes
When the dominance of different types of floodproducing circulation patterns changes over time, the
probability distributions of potential flooding at any
given time (t) may be altered.
Blocking
Zonal
Regime
Regime
La Nina
year
El Nino year
. . . 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
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.
Using Paleo-stage
Indicators &
Paleoflood
-- direct physical evidence of
Deposits . . .
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
Flood Frequency
Analysis
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)
Pecos River nr
Comstock, TX
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
FLOOD HYDROCLIMATOLOGY evaluate likely
hydroclimatic causes of pre-historic floods
1993
Largest paleoflood
Historical
(A.D. 1010 +-Flood
95 radiocarbon
date)
4. ANTICIPATING
THE FUTURE
Key Question:
How do we transfer
the growing body of knowledge
about global and regional
climate change and variability
to individual watersheds
Key Need:
to develop useful scenarios
about hydrologic
extremes?
to understand the processes
that deliver precipitation
(or the lack thereof)
to individual watersheds,
at relevant
time and space scales
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
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 mixedpopulation analysis is necessary or desirable?”
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, 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.”
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
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 . . .
CONCLUSIONS
Insights on Flood
Hydroclimatology &
Mixed Populations
for Anticipating
Future Floods
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.
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
Non-Stationarity & iid
Implications for time series homogeneity,
stationarity & the iid assumption
The conceptual framework of climatedriven time-shifting means, variances
and/or mixed distributions provides a
useful explanation for non-stationarity
in flood times series and challenges the
iid assumption.
Climatic Variability
Implications for evaluating how flood time
series may vary under a changing climate
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 floodproducing synoptic patterns, ENSO, etc.
PROCESS SENSITIVE UPSCALING: