Hydrology of Fast Response Basins

Baxter E. Vieux, Ph.D., P.E.School of Civil Engineering and Environmental Science

University of Oklahoma202 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 PeopleAffected

EconomicCost($bn)

Mozambique Mar-00 400 2m NAVenezuela Dec-99 30,000 0.6m 15India (Orissa) Nov-99 10,000 12m 2.5China Aug-98 3,600 200m 30Bangladesh Sep-98 4,750 23m 5

--The Economist, 11March 2000

• 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.

Enabling Technologies

Ingest, storage and processing of data streams from radar, satellite and other mesonet sensor systemsRadar, Mesonets, remote sensing platforms are next generation technologies providing new data and information for mitigating the impact of flooding and droughtImproved modeling, warning and information dissemination technologies

WSR-88D or NEXRAD

• Weather Surveillance Radar-1988 Doppler

• Prototyped in Norman at NSSL

• Scans Every 5 or 6 minutes during precipitation

• 150+ installed in US and abroad

0.5°

1.5°2.5°

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 areaDrainage networkSlopeChannel geometry and roughnessOverland flow and roughnessVegetative cover Soil infiltration capacityStorage 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.

Phreatic Surface

Saturation Excess

Rain

Saturation Excess

Rain

Runoff

Infiltration Excess

R > IR < I

Infiltration

Infiltration Excess

Horton Infiltration Equation

0

1

2

3

4

5

6

7

8

9

0 0 1 1 2 2 3 3 4 4 5 5 6

Time (hr)

Ra

infa

ll In

ten

sit

y (

in/h

r)

0

1

2

3

4

5

6

7

8

9

Infi

ltra

tio

n R

ate

(in

/hr)

Rain

Infil

Probabilistic Concepts

Key concepts--

Intense rainfall happens infrequently

The return period is inversely proportional to the frequency of being equaled or exceeded.

Intensity-Duration-Area-Frequency

fT /1

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

I = 3.5 inches

06.119.0100

14.120.050

00.227.02

95.1

58.1

18.0

IAD

IAD

IAD

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 CreekStorm 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

0.1

0.2

0.3

0.4

0.5

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)

Rai

nfa

ll (i

n)

Hydrograph

HEC-HMS 50-Year Storm

SCS CN increased from 79 to 90

Rainfall increased by 20%

Rainfall

Infiltration

Runon Runon

Runoff

Stream

Overland

Direction

Flow Characteristics Channel Characteristics

- Cross-Section Geometry- Slope- Hydraulic Roughness

* Rainfall excess at each cell

- Soil infiltration rate - Rainfall rate - Runon from upslope

Grid Cell Resolution Finite ElementsConnectivity

Watershed Runoff Simulation

Runoff Simulation

OUTPUT

Discharge Hydrograph

0

50

100

150

200

250

300

Time (hrs)

Dis

charg

e (

cfs

)

Radar Rainfall (R)INPUT

Land surface

Soil Infiltration (I)

Hydraulic Roughness (n)

α.Iγ.Rx5/3h.

nβ1/2s

th

h

Runoff

Model Equations

Runoff Flow Rates

Depth h is measured perpendicular to the bed and the velocity, V is parallel to the landsurface.

Continuity equation—Manning Equation—

n = hydraulic roughness

So = landsurface slope

c = 1 for metric, 1.49 english

hVq *3/55.0 hS

n

cq o

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

0

100

200

300

400

500

600

113 114 115 1161996 Day of Year

Dis

char

ge (m

3/s

)

0% Initial water content50%70%90%95%100%

Δ10%

Δ32

S

Q

i

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

--Ganges River Distributary, Bangladesh

Questions...