Some issues in flood hydrology in the climate context

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Some issues in flood hydrology in the climate
context

• Dennis P. Lettenmaier
• Department of Civil and Environmental Engineering
• University of Washington
• VAMOS VPM11
• Miami
• March 27, 2008

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Flood response is a function of

• Basin geometry and orientation
• Precipitation intensity and other storm
  characteristics
• Channel characteristics (drainage density,
  cross-section, velocity, etc)
• Geology and soil characteristics
• Antecedent conditions (soil moisture, snow if
  present)

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Role of basin shape and channel geometry on flood
generation (from Baker et al, 1988)
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Sensitivity of flood hydrographs to channel
network characteristics and flood wave velocity
RB bifurcation ratio RA area ratio RL
length ratio L1 mean length first order streams
normalized discharge
Time (hours)
From Rodriguez-Iturbe and Valdes, 1979
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Three aspects of flood hydrology

1. Extreme flood estimation (where failure would
   result in extreme property damage and/or loss of
   life)
2. Flood frequency estimation (for planning
   purposes, e.g., delineation of 100-year flood
   plain)
3. Flood forecasting (real-time)

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1. Extreme flood estimation

• Typical application spillway design
• Standard approach (in U.S.) is PMP (probable
  maximum precipitation)/PMF (probable maximum
  flood)
• PMP is the greatest amount of precipitation, for
  a given storm duration, that is theoretically
  possible for a particular area and geographic
  location.
•  The PMF is the flood that may be expected from
  the most severe combination of critical
  meteorological and hydrologic conditions that are
  reasonably possible in a particular drainage
  area.
• General approach is to maximize worst case
  conditions, sometimes hypothesized mechanism is
  one that has not, or only very rarely, has
  occurred (e.g., hurricanes in New England)
• Approach is in general deterministic typically
  the PMF is not assigned a return period, for
  instance

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Llyn Brian Dam spillway, Wales (visual courtesy
Wikepedia)
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• Development of the PMP
•  Scientists use both meteorological methods and
  historical records to determine the greatest
  amount of precipitation which is theoretically
  possible within a region. These rainfall data are
  subsequently maximized through "moisture
  maximization" and other numerical methods.
  Moisture maximization is a process in which the
  maximum possible atmospheric moisture for a
  region is applied to rainfall data from a
  historic storm. This process increases the
  rainfall depths, bringing them closer to their
  potential maximum. The PMP is determined for
  different storm periods, generally ranging from
  six to seventy two hours.
• Development of the PMF
• The Probable Maximum Flood is the flood which is
  a direct result of the Probable Maximum
  Precipitation. However, drainage areas with the
  same PMP may have different PMFs. For this
  reason, the PMF, not the PMP, must be used as a
  design criterion for a dam.

From State of Ohio dam safety guidelines
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(No Transcript)
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2. Flood frequency estimation
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Typical empirical flood frequency distribution
with 80 years of observations
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Fitted flood frequency distribution, Potomac
River at Pt of Rocks, MD
Visual courtesy Tim Cohn, USGS
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Problems with traditional frequency fitting
methods
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Problems with traditional fitting methods mixed
distributions
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Flood frequency distributions can be dependent on
climate conditions
Visual courtesy Alan Hamlet, University of
Washington
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Are extreme floods increasing (hence frequency
distributions shifting?
American River, CA
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Trends in U.S. Streamflow, 1940-1999
Source Updated from Lins and Slack, Geophys.
Res. Lett., 26, p. 227
Visual courtesy Tim Cohn, USGS
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Paradox Given increases in precipitation and
runoff, why are there so few significant trends
in floods?
Visual courtesy Tim Cohn, USGS
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Explanation (?) (a)
Lins and Cohn, 2002
Visual courtesy Tim Cohn, USGS
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Explanation (?) (b)
Lins and Cohn, 2002
Visual courtesy Tim Cohn, USGS
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However, the jury is still out

• e.g., We find that the frequency of great floods
  increased substantially during the twentieth
  century
• Milly et al Nature (2002) Increasing risk of
  great floods in a changing climate

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3. Flood forecasting
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Sources of flood predictability

• Precipitation predictability
• Hydrologic predictability
• Channel routing predictability

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U.S. real-time stream gauge network
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Illustration of data assimilation with a
spatially distributed hydrology model
Visual courtesy D-J Seo, NWS
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U.S. flood frequency skill has not improved over
last 40 years (Welles et al, BAMS, 2007), why
not?

• Hydrologic models have been essentially static
• Weather forecast data (QPF) not always used (this
  is changing)
• Degradation of in situ observation networks
• Weather forecasts have improved, but not
  necessarily QPF, which is the main hydrologic
  driver
• Lack of systematic approaches to updating
  forecast initial conditions (e.g., data
  assimilation)
• Lack of data documenting forecast performance