Transcript Hydrology of a Flash Flood

Hydrology of
Fast Response Basins
Baxter E. Vieux, Ph.D., P.E.
School of Civil Engineering and Environmental Science
University of Oklahoma
202 West Boyd Street, Room CEC334
Norman, OK 73019
[email protected]
Biosketch
Dr. Baxter E. Vieux, PhD, PE specializes in the integration of computational methods and
visualization with Geographic Information Systems (GIS). Applications include
simulation of water quality and flooding. He was recently named Director of the
International Center for Natural Hazards and Disaster Research, University of
Oklahoma. Efforts to reduce impacts on civil infrastructures due to severe weather are
being undertaken by this center with an initial focus on flooding. Prior to joining the
faculty at the University of Oklahoma, he was a Visiting Assistant Professor at Michigan
State University. He has performed consulting and collaborative research with agencies
and private enterprises in the US and abroad in Japan, France, Nicaragua, and Poland.
Over fifty publications appearing as book chapters (2), refereed journal articles (14, 3 in
press), and conference proceedings (35, 2 in press) have been authored including a
forthcoming text for Kluwer entitled: Distributed Hydrology Using GIS (expected
2000). He has been on the Editorial Board of Transactions in GIS since 1995, serves on
the American Society of Civil Engineers Council on Natural Hazards and Disasters, and
is Fellow and member of the Advisory Council of the Cooperative Institute for
Mesoscale Meteorological Studies at the University of Oklahoma. He is a member of
ASCE, NSPE, AGU, and AMS, Tau Beta Pi, Phi Kappa Phi, and ASEE. Prior to his
academic career, ten years were spent in Kansas and Michigan with the USDA-Natural
Resources Conservation Service (formerly, USDA-SCS) supervising design and
construction of drainage, irrigation, soil conservation, and flood control projects.
Recipe for a flood
Ingredients
Take a generous amount of rainfall
Presoak the soil so it is saturated
Add the rainfall to steeply sloping land
Look out!
Flood Statistics
Flooding is the most deadly and costly of all
natural disasters.
Read the document Summary of Natural
Hazard Statistics.
From this document what would you
conclude to be the single most important
factor that might cause death during a
flood?
What constitutes a flash flood
No firm criteria exist to discriminate
between fast response and river floods
Response time in the range of 1-6 hours
As opposed to river floods, flash floods
have a quick response to rainfall input
Upland basins are most likely killers
Read the document flash floods.
Flooding
Country
Mozambique
Venezuela
India (Orissa)
China
Bangladesh
Date
Mar-00
Dec-99
Nov-99
Aug-98
Sep-98
Deaths
400
30,000
10,000
3,600
4,750
People
Affected
2m
0.6m
12m
200m
23m
Economic
Cost
($bn)
NA
15
2.5
30
5
• Last year natural disasters killed an estimated 100,000
people.
• In a typical year, floods claim half the victims of the
world’s natural disasters.
--The Economist, 11March 2000
Enabling Technologies
Ingest, storage and processing of data streams
from radar, satellite and other mesonet sensor
systems
Radar, Mesonets, remote sensing platforms are
next generation technologies providing new data
and information for mitigating the impact of
flooding and drought
Improved modeling, warning and information
dissemination technologies
WSR-88D or NEXRAD
2.5°
1.5°
0.5°
• Weather Surveillance Radar-1988 Doppler
• Prototyped in Norman at NSSL
• Scans Every 5 or 6 minutes during
precipitation
• 150+ installed in US and abroad
Why does one basin flood and
another doesn’t
Efficient drainage network
Debris clogged main channel
Denuded or burned vegetation
Urbanization effects on time and volume
Steep topography
Heavy rain over large areas
Read the document Flash Flood Factors.
Basin Characteristics
Factors that affect the basin response are—
Drainage area
Drainage network
Slope
Channel geometry and roughness
Overland flow and roughness
Vegetative cover
Soil infiltration capacity
Storage capacity
Hydraulics
Hydraulics of overland and channel flow
Turbulent flow
Chezy or Manning
Conservation of momentum and mass
Discharge computations using conservation
equations is the basis for distributed
hydrologic modeling.
Hydraulics of Runoff
Two basic flow types can be recognized:
Overland flow
This is conceptualized as thin sheet flow
before the runoff concentrates in recognized
channels.
Channel flow
The channel has hydraulic characteristics
that govern flow depth and velocity.
Runoff Mechanisms
There are two runoff producing
mechanisms:
1.
2.
Infiltration excess
Saturation excess
Mountainous watersheds tend to be
dominated by saturation excess.
Infiltration excess dominates runoff in
flatter agricultural watersheds.
Saturation Excess
Rain
Saturation Excess
Phre
atic S
urfac
e
Infiltration Excess
Rain
Ru
n
off
R<I
Infiltration
R>I
Infiltration Excess
9
9
8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1
0
0
00 1 12 2 33 44 5 56
Time (hr)
Infiltration Rate (in/hr)
Rainfall Intensity (in/hr)
Horton Infiltration Equation
Rain
Infil
Probabilistic Concepts
Key concepts-Intense rainfall happens infrequently
The return period is inversely proportional
to the frequency of being equaled or
exceeded.
T  1/ f
Intensity-Duration-Area-Frequency
Regional Frequency Analysis
Using regression analysis applied to stream gauge
records, we can estimate the discharge associated
with a particular frequency.
Most states have developed regression equations
for ungauged basins.
For example in Oklahoma given the drainage area,
A, in sq.mi. and the 2-year 24-hour storm depth, I,
in inches we can calculate:
USGS Regression Equations for
Oklahoma
For Cherokee County, the 2-year 24-hour
rainfall is 3.5 inches. Calculate the
following for the Cottonwood Basin:
A= 49 sq mi
0.27 2.00
D2  0.18A I
I = 3.5 inches
D50  1.58A
0.20 1.14
D100  1.95A
I
0.19 1.06
I
Lumped Versus Distributed
Lumped modeling represents the basin and
precipitation characteristics using single
values of roughness, slope, and rainfall over
each sub-basin.
Distributed modeling represents the spatial
variability within each sub-basin or basin
using grid cells, TINS or other
computational element.
Cottonwood Creek
Storm Total Oct 30 - Nov 1, 1998
Cottonwood Watershed
Storm Total Contours
HEC-HMS Model
Cottonwood Basin, Alfalfa County Oklahoma
10/30/98 - 11/01/98
0.5
Rainfall (in)
0.4
0.3
0.2
0.1
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71
Time (h)
Hydrograph
HEC-HMS 50-Year Storm
SCS CN increased from 79 to 90
Rainfall increased by 20%
Runoff Simulation
Grid Cell Resolution
Finite Elements
Connectivity
Watershed
Runoff
Simulation
Rainfall
Runon
Runoff
Runon
Infiltration
* Rainfall excess
at each cell
- Soil infiltration rate
- Rainfall rate
- Runon from upslope
Flow
Characteristics
Channel Characteristics
Stream
- Cross-Section Geometry
- Slope
- Hydraulic Roughness
Overland
Direction
Model Equations
 h  s1/2 . h5/3  γ.R  α.I
 t βn  x
INPUT
Discharge Hydrograph
Radar Rainfall (R)
250
Runoff
Land surface

h

200
150
100
50
Time (hrs)
Hydraulic Roughness (n)
96
72
48
Soil Infiltration (I)
24
0
0

300
Discharge (cfs)

OUTPUT
Runoff Flow Rates
Depth h is measured perpendicular to the bed and
the velocity, V is parallel to the landsurface.
Continuity equation— q  V * h
c 0 .5 5 / 3
Manning Equation— q  S o h
n
n
So
c
=
=
=
hydraulic roughness
landsurface slope
1 for metric, 1.49 english
Blue River Basin
• The 1200 km2 Blue River basin was
delineated from a 3-arc second
digital elevation model
• Aggregated to grid cell size = 270 m
• Hydrographs simulated for each
sub-basin
• Runoff is computed for each grid cell
• Routed downslope through each cell
eventually reaching the stream
network and basin outlet
Sensitivity to Initial Conditions
600
0 %Init ial wat er cont ent
50 %
70 %
90 %
95%
10 0 %
400
3
Discharge (m / s)
50 0
30 0
20 0
Q
Δ32

Si Δ10%
10 0
0
113
114
115
19 9 6 Day of Year
116
Distribute Model Advantages
Distributed has advantages because the spatial
variability of precipitation input and controlling
parameters are represented in the model.
Incorporating spatial variability in a distributed
model reduces the prediction variance.
Physics-based models are generally more
responsive to radar input than lumped models.
River basin models based on 6-hour unit
hydrographs are not suitable for basins with
response times less than 6 hours.
Self Examination
Label the following with a + or – according to the
effect on flood levels at a given location—
Debris clogged main channel
Denuded or burned vegetation
Urbanized landsurface conditions and channels
Steep terrain
Clayey soil
Dry intial moisture conditions
Questions...
--Ganges River Distributary, Bangladesh