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
Jan 13, 2016
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
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)
From Rodriguez-Iturbe and Valdes, 1979
RB = bifurcation ratio
RA – area ratio
RL = length ratio
L1 = mean length first order streams
Time (hours)
norm
aliz
ed d
isch
arge
Sensitivity of flood hydrographs to channel network characteristics and flood wave velocity
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)
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
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
Fitted flood frequency distribution, Potomac River at Pt of Rocks, MD
Visual courtesy Tim Cohn, USGS
Snow-Dominant Basins
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
EwPw EnPw EcPw EwPc EnPc EcPc
Climate Category
Pro
ba
bili
ty o
f F
loo
d E
ve
nt
Ab
ov
e
Th
res
ho
ld
Flood frequency distributions can be dependent on climate conditions
Visual courtesy Alan Hamlet, University of Washington
Source: Updated from Lins and Slack, Geophys. Res. Lett., 26, p. 227
Trends in U.S. Streamflow, 1940-1999
0
20
40
60
80
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120
140
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200
Q M
in
Q 1
0
Q 2
0
Q 3
0
Q 4
0
Med
ian
Q 6
0
Q 7
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Q 8
0
Mea
n
Q 9
0
Q M
ax
Flow Quantile
No
. of
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tio
ns
Increasing Trend
Decreasing Trend
50%
40%
30%
20%
10%
0%
435 Stations; p ≤ 0.05
Visual courtesy Tim Cohn, USGS
Paradox: Given increases in precipitation and runoff, why are there so few significant trends in
floods?
Visual courtesy Tim Cohn, USGS
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”
Sources of flood predictability
• Precipitation predictability• Hydrologic predictability• Channel routing predictability
Visual courtesy D-J Seo, NWS
Illustration of data assimilation with a spatially distributed hydrology model
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