Hydrology of Flash Flood

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Hydrology of Flash Flood Powered By Docstoc
					           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
                    bvieux@ou.edu
     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           Date    Deaths    People        Economic
                                   Affected         Cost
                                                    ($bn)
Mozambique       Mar-00      400            2m       NA
Venezuela        Dec-99   30,000          0.6m        15
India (Orissa)   Nov-99   10,000          12m        2.5
China            Aug-98    3,600         200m         30
Bangladesh       Sep-98    4,750          23m         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.   Infiltration excess
 2.   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
                              no
                                ff


            R<I         R>I
     Infiltration   Infiltration Excess
Horton Infiltration Equation
                             9                          9
                             8                          8
Rainfall Intensity (in/hr)




                                                            Infiltration Rate (in/hr)
                             7                          7
                             6                          6
                             5                          5                               Rain
                             4                          4                               Infil
                             3                          3
                             2                          2
                             1                          1
                             0                          0
                                 00 1 12 2 33 44 5 56
                                       Time (hr)
  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
                      D2  0.18 A I
                                  0.27 2.00

I = 3.5 inches
                   D50  1.58 A    0.20 1.14
                                      I
                   D100  1.95 A   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
                0.5
                                                   10/30/98 - 11/01/98



                0.4




                0.3
Rainfall (in)




                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
                                                             Watershed
 Grid Cell Resolution         Finite Elements
                                                              Runoff
                               Connectivity
                                                              Simulation
               Rainfall
 Runon              Runon


  Runoff       Infiltration

* Rainfall excess
     at each cell
                                  Flow
                              Characteristics   Channel Characteristics
 - Soil infiltration rate
 - Rainfall rate                                - Cross-Section Geometry
                                  Stream        - Slope
 - Runon from upslope
                                  Overland      - Hydraulic Roughness
                                  Direction
         Model Equations
                         h  s1/2 . h5/3  γ.R  α.I
                         t βn  x
                  INPUT                                                   OUTPUT
                                                                          Discharge Hydrograph


          Radar Rainfall (R)
                                                                    300


                                                                    250


                                                                    200




                                                  Discharge (cfs)
                                      
                             Runoff
                                                                    150


                                                                    100



Land surface                     h                                  50




        Soil Infiltration (I)
                                                                     0




                                                                          24


                                                                                 48


                                                                                         72


                                                                                                 96
                                                                     0
 
                                                                                  Time (hrs)

         Hydraulic Roughness (n)
     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  So h
                            n

n      =   hydraulic roughness
So     =   landsurface slope
c      =   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


                    50 0
                                              0 %Init ial wat er cont ent
                                              50 %
Discharge (m / s)




                    400                       70 %
                                              90 %
3




                                              95%
                    30 0                       0
                                              1 0%

                    20 0
                                                               Q   Δ32
                                                                  
                                                               Si Δ10%
                     0
                    1 0


                      0
                            1
                           13    1
                                14                      1
                                                       15                    1
                                                                            16
                                      9
                                     1 9 6 Day of Year
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

				
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posted:7/26/2012
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