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Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS- SA Software Jeff Smithers and Roland Schulze School of Bioresources Engineering and Environmental Hydrology University of KwaZulu-Natal Pietermartizburg South Africa Tel: 033-2605490 E-mail: [email protected]

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Page 1: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Design Flood Estimation for Small Catchments in Southern Africa

Using The Visual SCS-SA Software

Jeff Smithers and Roland Schulze School of Bioresources Engineering and

Environmental HydrologyUniversity of KwaZulu-Natal

PietermartizburgSouth Africa

Tel: 033-2605490E-mail: [email protected]

Page 2: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Introduction

Your name

Organisation

Background and expertise in design flood estimation

What you would like to learn this morning

Page 3: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Flood Drought

Water in South Africa

We have either too much or too little!

Page 4: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Regional Scale Floods

Page 5: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 6: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 7: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Localised FloodsPietermaritzburg: 25 December 1999

Page 8: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Floods in Pietermaritzburg: 1987

Floods of 1987 (Pietermaritzburg) RES2234

Page 9: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

FLOOD HYDROGRAPHS FOR A SMALL CATCHMENT

Time (h)Time (h)

Dis

char

ge (

mD

isch

arge

(m

33 .s.s

-1-1))

Different Peak DischargesDifferent Peak Discharges

Same VolumesSame Volumes

Page 10: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

FLOOD HYDROGRAPHS FOR A SMALL CATCHMENT

Time (h)Time (h)

Dis

char

ge (

mD

isch

arge

(m

33 .s.s

-1-1))

Same Peak DischargesSame Peak Discharges

Different VolumesDifferent Volumes

Page 11: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

FLOOD HYDROGRAPHS FOR A SMALL CATCHMENT

Time (h)Time (h)

Dis

char

ge (

mD

isch

arge

(m

33 .s.s

-1-1))

Different Peak DischargesDifferent Peak Discharges

Different VolumesDifferent Volumes

Page 12: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

FLOOD HYDROGRAPHS FOR A SMALL CATCHMENT

Significance of peakcapacities exceededflood damage (local)

Significance of volumefills damstransports sediments, nutrients etcflood damage (regional, inundation)

Peak = f (volume)Implication / Conclusion

need a model to simulate both stormflow volume and peak dischargeneed to be able to simulate the entire hydrograph

Page 13: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

What is a design flood?Magnitude of flood which has acceptable risk associated with the failure of the hydraulic structures

Risk = probability of exceedance (Pe)

Return Period: T = 1/Pe

Not an observed event

What are design floods used for?Design of hydraulic structures (e.g. waterways, culverts, bridges, dams etc)

How do we estimate design floods in South Africa?

Design Flood Estimation

Page 14: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Methods of Design Flood Determination in South Africa

StandardFlood

UnitHydrograph

SCS

Rational

Design EventModel

Deterministic/ProbabilisticDesign Rainfall

FrequencyAnalysis

Continuous Simulation

Historical/Stochastic

Rainfall

Rainfall Based Methods

FloodEnvelopes

Regional

SiteFlood Frequency Analysis

EmpiricalMethods

Analysis of Streamflow Data

Design Flood Estimation Methods

Page 15: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Design Rainfall Event Based Models

SCS, Rational, Unit hydrographWidely used

Lump complex, heterogeneous catchment processes into a single process

AdvantagesSimple to apply

Generally longer rainfall records at more sites, with better quality, than streamflow

Areal extrapolation of rainfall

Long flood series generally not available, often contain inconsistencies and are frequently non-homogeneous and non-stationary

Page 16: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Design Rainfall Event Based Models

DisadvantagesUncertainties in inputs (e.g. storm duration, spatial & temporal distribution of design rainfall, model inputs)

Probability of rainfall taken into account, probabilistic nature of other parameters ignored

Antecedent soil moisture conditions

Assume that the exceedance frequency of the estimated flood = frequency of input rainfall

Design Rainfall Event Based Models Widely used

Need to estimate design rainfall

Page 17: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

WHY SCS-BASED DESIGN PROCEDURES?

There is frequent need for hydrological information recordingplanningdesign of water resourcesmanagement systems

For most small catchments the design hydrograph needs to be modelled / estimated because direct measurements are usually not availableSCS techniques were originally developed as a hydrological design tool on agricultural land uses

to generate safe limits in hydraulic designto compare effectiveness of different agricultural/conservation systems

Reasonsequations are simplerelated to physical properties of catchment (soils, land use, wetness)provides uniform answersuses daily rainfall input

Page 18: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

WHY SCS-BASED DESIGN PROCEDURES?

Has become an accepted / established model on small catchmentsprocedure “used internationally . . . . several million times annually” (Hawkins, 1980)recommended institutionally and accepted in court judgements

Tested / used widely in USA, Germany, France, mid-East, Australia, AfricaNow being used increasingly for other purposes through modification / adaptations, e.g.

daily water yield modelsremote sensing inputsenvironmental impact studiesurban areassemi-arid areasagricultural management systemslarge catchments

South African adaptions Regional differences in antecedent moisture conditionsJoint association of rainfall and runoff

Page 19: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Assessment of Methods Available for Small Catchments

in SA (SRK, 1985)Rational method

easy to use

but, peak discharge only

grossly overestimated peaks under all conditions

Time area method (e.g. Illudas model) & Kinematic Method (e.g. Witwat model)

neither performed consistently well

nor gave improved simulations considering increased model complexity

SCS based methods (esp. SA adaptations)performed well enough on a number of land uses and catchment sizes to be recommended for design in South Africa

Page 20: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Background : Summary

There is frequent need for estimates of stormflow volumes (Q) and peak discharges (qp) from small catchments for making economic and safe design of hydraulic structures

Stormflow volumes and peak discharges are highly sensitive to a catchment’s “wetness” (i.e. antecedent soil moisture status, ASM) just prior to runoff producing rainfall events

The SCS technique has become a standard method for estimating Q and qp from small catchments (<30 km2)

Page 21: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

HISTORY OF SCS METHODS IN SOUTHERN AFRICA

Concepts developed in USA in 1950sReich proposed its application in SA in 1962

Schulze & Arnold produced manual in 1979

Accepted / recommended by NTC, NPA, consultants

Considerable research effort at U of N, PmbCousens (1976), Arnold (1980), Schulze (1982), Hope (1983), Schulze (1984), Schmidt & Schulze (1984), Dunsmore, Schulze & Schmidt (1986), Weddepohl (1988), Topping (1993), Chetty (2001)

Page 22: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

HISTORY OF SCS METHODS IN SOUTHERN AFRICA

Water Research Commission / University of Natal contract 1984 – 1987

Update and revise SCS manual, integrating research findings

Research into joint association of rainfall and catchment moisture status to provide design runoff for different regions in SA

Production of manuals, technology transfer (700 sold)

Courses at 12 venues, 230 participants

Page 23: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

HISTORY OF SCS METHODS IN SOUTHERN AFRICA

WRC and consultants requests led to development of PC version in 1992

400 sold; prescribed text; SA & IHE courses

Visual SCS-SA (2004)Windows based, GUI

Regional scale invariance design rainfall estimation option added

Course @ SAIAE CPD 2004

Course @ SAIAE CPD 2005

Recent developments An internationalised version, based on concepts developed in SA

Page 24: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

The SCS Curve Number Model

SCS is the Soil Conservation Service of the USA Department of Agriculture

It works like this…Rainfall occurs

Initial abstraction includes all losses to surface depressions, interception and initial infiltration

Then some water is infiltrated while some water is runoff

Page 25: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Source: http://www.fao.org/docrep/U8480E/U8480E3k.jpgSource: http://www.fao.org/docrep/U8480E/U8480E3k.jpg

Page 26: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Runoff

Two componentsStormflow (surface)

Baseflow

SCS Estimates stormflow only

Empirical equation with some physical basis

Page 27: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

What factors influence stormflow depth ?

Rainfall DepthIntensity

Initial abstractionInterceptionSurface storageInitial infiltration

Antecedent soil water Soil properties

InfiltrabilityPermeabilityStorage capacity

Land coverTypeTreatment, practice and condition

Page 28: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Rainfall Excess

SCS Curve Number ModelSCS Curve Number Model

Time

Rat

e, D

epth

per

Uni

t T

ime Constant Intensity Rainfall

Infiltrated Water

Constant Runoff

Initial Abstraction (accumulated losses before runoff commences)

EvaporationEvaporation

RunoffRunoff

Depressional Storage &

Interception

Page 29: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

STORMFLOW GENERATION WITH THE SCS : CONCEPTS

T

Ia

Q

P F

AccumulatedF + Ia

AccumulatedStormflow (Q)

F Sas T

S

AccumulatedRainfall (P)

TIME (T)

F = accumulated infiltration from time of stormflow commencement

Page 30: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

SCS Stormflow Volume

Water balance

Assume that ratio of actual infiltration (F) to maximum retention (S)

is equal to the ratio of runoff (Q) to potential maximum runoff (rainfall –

initial abstraction)

Solve Equation 1 and 2 to estimate Q

Q

P - I

F

S

a

(2 )

P – I = F + Q a (1 )T

Ia

Q

P F

AccumulatedF + Ia

AccumulatedStormflow (Q)

F Sas T

S

AccumulatedRainfall (P)

TIME (T)

F = accumulated infiltration from time of stormflow commencement

Page 31: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

The SCS Curve Number Model

Rainfall (P) measured or design amount

Initial abstraction (Ia) occurs from:

Surface depressions

Water intercepted by vegetation

Evaporation and infiltration

Potential maximum retention of soil (S)

S)IP(

)IP(Q

a

a

2

Page 32: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

The SCS Curve Number Model

ProblemP known

S & Ia unknown

Ia tends to be quite variable!

But after much experimentation:Ia = 0.2 x S in the USA

Ia = 0.1 x S in SA

So substitute it back into the runoff equation:

But what about S ?

S9.0P

)S1.0P(Q

2

S)IP(

)IP(Q

a

a

2

Page 33: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

The SCS Curve Number Model

How can we estimate S?

where CN is a curve number according to the land use (from 0 to 100)98 = Parking lot

39 = Grassed area on a very sandy soil

CN is an index of hydrological response

Use Table 5.1

25425400

CN

S

Page 34: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 35: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

SATURATION (Porosity)(0 kPa)

DRAINEDUPPER LIMIT (Field Capacity)(-5 TO -33 kPa)

LOWER LIMIT (Permanent Wilt ing Point)(-1500 kPa)

AIR DRY

"WET" SOIL

= HIGH SOILWATERCONTENT

= LOW "S"

"DRY" SOIL

= LOW SOILWATERCONTENT

= HIGH "S"

S = 25400 - 254 CN

High S = Low CN = "dry" soil moisture conditions Low S = High CN = "wet" soil moisture conditions

COLUMNOF

SOIL

CONCEPT OF "S"

2.2.1.3c

Page 36: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Curve Numbers

Index of catchment response

How were CNs determined?

From measurements for given land cover a soil typePlot of flood vs rainfall for annual maximum floods

Overlay of SCS stormflow equation for various values of CN

Median CN selected

CNs for “Wet” and “Dry” conditions determined and procedures for adjusting CNs for these conditions were developed

S.P

)S.P(Q

90

10 2

25425400

CN

S

Page 37: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

What Role do Soils Play?

INFILTRATION /INFILTRABILITY (entry into soil)

PERMEABILITY (redistribution through soil)

STORMFLOW (overland, nearsurface)

Page 38: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

What Role do Soils Play?

Soil absorbsRetains water

Releases water

Soil therefore a prime regulator of catchment response to rainfall

Evaluate soils fromagriculturalist

mechanical strength viewpoint

hydrological response

Page 39: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

RES6511

Page 40: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

RES6517

Page 41: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

RES6519

Page 42: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

RES6521

Page 43: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

RES6523

Page 44: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Soil Categorisation in the Original USA SCS Model

There are 4 basic hydrological soil groupsGroup A

Low runoff potential, high infiltration rates, sand, loamy sand and sandy loams

Group BModerate infiltration rates, loams, silt loams

Group CLow infiltration rates, sandy clays

Group DHigh runoff potential, very low infiltration rates, clay loams, clays, etc…

Page 45: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 46: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Hydrological Classification of Soils in SA

Wide spectrum of properties in South African soilsFour-fold grouping too course

Intermediate soil classification

A/B, B/C, C/D to give 7 groups

Page 47: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Soil Classification System in SA

Binomial System (Macvicar et al., 1977)Soil form and series

Taxonomic System (SCWG, 1991)Soil form, family and textural class

Page 48: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Hydrological Classification of Soils in SA

Classification procedure: Binomial SystemEach soil placed in one of seven groups based according to the soils properties

Series graded up or down dependent onTexture

Leaching

Water Table

Crusting

Classification procedure: Taxonomic SystemSimilar procedure

Page 49: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 50: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 51: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 52: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Taxonomic System (Table 5.2)LEGEND Soil Form Code Soil Family Typical SCS

A - low runoff potential Textural Grouping B - moderately low potential Class C - moderately high potential ADDO Ad 1221 Walkraal SaClLm B/C D - high runoff potential B Ad 1221 Walkraal SaCl C

Sa - sand Ad 1222 Sylvania LmSa B

Cl - clay Ad 1222 Sylvania SaLm B/C

Lm - loam Ad 1222 Sylvania SaClLm B/C

Ad 1222 Sylvania SaCl C

Ad 2111 Maurmond LmSa A/B

Soil Form Code Soil Family Typical SCS Ad 2111 Maurmond SaLm B

Textural Grouping Ad 2111 Maurmond SaClLm B

Class Ad 2111 Maurmond SaCl B/C

ADDO Ad 1111 Glenconnor LmSa A/B Ad 2112 Airedale LmSa A/B B Ad 1111 Glenconnor SaLm B Ad 2112 Airedale SaLm B

Ad 1111 Glenconnor SaClLm B Ad 2112 Airedale SaClLm B

Ad 1111 Glenconnor SaCl B/C Ad 2112 Airedale SaCl B/C

Ad 1112 Dalby LmSa A/B Ad 2121 Felsenheim LmSa B

Ad 1112 Dalby SaLm B Ad 2121 Felsenheim SaLm B/C

Ad 1112 Dalby SaClLm B Ad 2121 Felsenheim SaClLm B/C

Ad 1112 Dalby SaCl B/C Ad 2121 Felsenheim SaCl C

Ad 1121 Centlivres LmSa B Ad 2122 Longhill LmSa B

Ad 1121 Centlivres SaLm B/C Ad 2122 Longhill SaLm B/C

Ad 1121 Centlivres SaClLm B/C Ad 2122 Longhill SaClLm B/C

Ad 1121 Centlivres SaCl C Ad 2122 Longhill SaCl C

Ad 1122 Kentvale LmSa B Ad 2211 Mimosa LmSa A/B

Ad 1122 Kentvale SaLm B/C Ad 2211 Mimosa SaLm B

Ad 1122 Kentvale SaClLm B/C Ad 2211 Mimosa SaClLm B

Ad 1122 Kentvale SaCl C Ad 2211 Mimosa SaCl B/C

Ad 1211 Spekboom LmSa A/B Ad 2212 Peperboom LmSa A/B

Ad 1211 Spekboom SaLm B Ad 2212 Peperboom SaLm B

Ad 1211 Spekboom SaClLm B Ad 2212 Peperboom SaClLm B

Ad 1211 Spekboom SaCl B/C Ad 2212 Peperboom SaCl B/C

Ad 1212 Gorah LmSa A/B Ad 2221 Suttondale LmSa B

Ad 1212 Gorah SaLm B Ad 2221 Suttondale SaLm B/C

Ad 1212 Gorah SaClLm B Ad 2221 Suttondale SaClLm B/C

Ad 1212 Gorah SaCl B/C Ad 2221 Suttondale SaCl C

Ad 1221 Walkraal LmSa B Ad 2222 Tregaron LmSa B

Ad 1221 Walkraal SaLm B/C Ad 2222 Tregaron SaLm B/C

65

Page 53: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Binomial System (Table 5.3)LEGEND Soil Form Code Soil Series Typical SCS

A - low stormflow potential Textural Grouping B - moderately low potential Class

C - moderately high potential AVALON Av 32 Middelpos Sa B D - high stormflow potential B Av 31 Mooiveld LmSa B

Sa - sand Av 25 Newcastle SaLm A/B

Cl - clay Av 17 Normandien SaCl B

Lm - loam Av 22 Rossdale Sa A/B

Av 16 Ruston SaClLm B Av 36 Soetmelk SaClLm B/C

Av 21 Uithoek LmSa A/B

Av 30 Viljoenskroon LmSa B

Av 23 Villiers SaLm B

Soil Form Code Soil Series Typical SCS Av 11 Welverdien LmSa A Textural Grouping Av 35 Windmeul SaLm B

Class Av 15 Wolweberg SaLm A

ARCADIA Ar 40 Arcadia Cl C/D BAINSVLEI Bv 23 Ashkelon SaLm A/B C/D Ar 11 Bloukrans Cl C/D A/B Bv 36 Bainsvlei SaClLm B

Ar 21 Clerkness Cl C/D Bv 12 Camelot Sa A

Ar 41 Eenzaam Cl C/D Bv 20 Chelsea LmSa A

Ar 20 Gelykvlakte Cl C/D Bv 30 Delwery LmSa A/B

Ar 10 Mngazi Cl C/D Bv 13 Dunkeld SaLm A/B

Ar 32 Nagana Cl C/D Bv 16 Elysium SaClLm A/B

Ar 12 Noukloof Cl C/D Bv 10 Hlatini LmSa A

Ar 31 Rooidraai Cl C/D Bv 34 Kareekuil SaLm B

Ar 30 Rydalvale Cl C/D Bv 31 Kingston LmSa A/B

Ar 42 Wanstead Cl C/D Bv 26 Lonetree SaClLm A/B

Ar 22 Zwaarkrygen Cl C/D Bv 25 Maanhaar SaLm A

AVALON Av 13 Ashton SaLm A/B Bv 11 Makong LmSa A B Av 26 Avalon SaClLm B Bv 27 Metz SaCl B

Av 12 Banchory Sa A Bv 22 Oosterbeek Sa A

Av 27 Bergville SaCl B/C Bv 37 Ottosdal SaCl B/C

Av 37 Bezuidenhout SaCl C Bv 24 Redhill SaLm A/B

Av 33 Bleeksand SaLm B/C Bv 32 Trekboer Sa A/B

Av 34 Heidelberg SaLm B/C Bv 15 Tygerkloof SaLm A

Av 20 Hobeni LmSa A/B Bv 33 Vermaas SaLm B

Av 14 Kanhym SaLm A/B Bv 21 Vungama LmSa A

Av 24 Leksand SaLm B Bv 35 Wedgewood SaLm A/B

Av 10 Mastaba LmSa A Bv 17 Wilgenhof SaCl B

77

Page 54: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Sensitivity of Hydrological Response to Soil Properties

For a given catchment:Area = 2 km2

Mean slope = 8%

Hydraulic length = 1500 m

Rainfall = 50 mm

Land use: veld cover : fair, i.e. plant cover 50 -75%

Soil moisture status : initial

Clovelly Oatsdale (Cv16) : A/BStormflow depth : 1.73 mm Peak discharge : > 1 m3.s-1

Glenrosa Robmore (Gs18) : B/CStormflow depth : 9.22 mm Peak discharge : 4 m3.s-1

Estcourt Estcourt (Es36) : DStormflow depth : 19.39 mm Peak discharge : 7 m3.s-1

Page 55: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Land Cover and Treatment

Land cover also makes a differenceParking lots run off more than golf courses

Hydrologic condition makes a differenceGood or poor condition

Page 56: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 57: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 58: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 59: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 60: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Urban Stormflow

Page 61: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 62: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 63: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Ms10

Gf13 A/B

C

A/BC/D

Example of soil units within a catchment at soil form and series level

Assignment of hydrologicalsoil groups to soil units

2.2.4.51c

DETERMINATION OF CURVE NUMBERS ON HETEROGENEOUSCATCHMENTS . . . 1

2.2.4.51c

Page 64: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 65: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Sensitivity of Land Use on Hydrological Response

For a given catchment Area = 2 km2 Rainfall = 100 mmResponse time (lag) = 0.5 h Intensity Distribution Type = 3

A/B : Griffin Farmhill, Veld in good hydrological conditionCN-II = 51 Stormflow volume = 35760 m3 Peak Discharge = 6.7 m3.s-1

A/B : Griffin Farmhill, Veld in poor hydrological condition CN-II = 74 Stormflow volume = 92000 m3 Peak discharge = 19.2 m3.s-1

B : Clovelly Clydebank, Veld in good hydrological condition CN-II = 61 Stormflow volume = 57000m3 Peak discharge = 11.4 m3.s-1

B : Clovelly Clydebank, Veld in poor hydrological conditionCN-II = 79Stormflow volume = 108200 m3 Peak discharge = 22.8 m3.s-1

Page 66: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 67: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 68: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Adjustment of Initial Curve Numbers: Original Procedure

Stormflow is highly sensitive to a catchment's "wetness" (i.e. soil moisture status, SMS) just prior to the rainfall event

Page 69: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 70: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Adjustment of Initial Curve Numbers: Original Procedure

Stormflow is highly sensitive to a catchment's "wetness" (i.e. soil moisture status, SMS) just prior to the rainfall event "Classical" categorisation of SMS

This is an oversimplification ….ET considered only in gross termsDrainage ignoredDiscrete “jumps” in SMS vs CNAMC – 5 days?

SMS by water budgeting needed

SMS class Accumulated 5-day

Antecedent Rainfall

Dormant Season Growing Season

SMS-I (CN-I) < 12 mm < 36mm

SMS-II (CN-II) 12-28 mm 36-53 mm

SMS-III (CN-III) >28mm >5 3mm

Page 71: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Adjustment of Initial Curve Numbers: Hawkins Procedure

ΔS = P – E –Q – D

Adjustment of CN-II therefore requiresCN-II

consideration of soil depth, soil texture, vegetation cover

regional climatic conditions

C N(1+ c)1000

(1+ c)1000

C N II

P E Q D

25.4

f

S25400

C N25f

f

4

Q(P - c S )

P + (1 - c)Sf

f2

f

C N

(1+ c)1000(1+ c)1000

C N II

S

25.4

f

Page 72: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Adjustment of Curve Numbers to Account for Antecedent Soil

Moisture Conditions in SCS-SA

Median Condition Method

Joint Association Method

Page 73: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Method 1: Median ConditionBasic Premise

Final CN (CNf)needs to be determined from soil moisture budgeting considerations

Compute the soil moisture status expected (statistically) to occur most frequently at a location prior to a design event (50th percentile, median)

Use this SMS information in CNf calculations to determine design Q

Page 74: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Method 1: Median ConditionProcedure

For a combination of input of location (one of 712 hydrologically homogeneous zones in SA)

CN-II

soil depth category (one of 3)

soil texture category (one of 3)

vegetation category (one of 3)

Compute a change in soil moisture storage (ΔS)by the ACRU model

from an initial soil moisture storage

for a 30-day antecedent period

for the 5 highest rainfall events of a year

for each year on record

The median condition of ΔS is computed

Page 75: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 76: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 77: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Method 1: Median ConditionComputations

Use median ΔS to compute a final Curve Number, CNf

Use CNf to compute find potential maximum retention, Sf

Use Sf with design rainfall to compute final design stormflow

C N1100

1100

C N II

S

25.4

f

S25400

C N25f

f

4

Q(P - 0.1S )

(P + 0.9Sff

2

f

)

Page 78: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Method 2: Joint Association Method

Basic PremiseAssumption that T-year return period rainfall produces T-year return period stormflow is invalid

2nd, 3rd, 4th or 5th ranked daily rainfall may produce highest annual Q, depending on antecedent SMS

Conclusion : "Assumption inherent in current flood design methods of simulating the T-year return period flood from the T-year return period rainfall does not provide the engineer with a sound basis for analysis in small catchments" (Dunsmore, Schulze, Schmidt, 1986)

Compute the highest daily Q per year, with a model, and use the series of simulated Q to determine design Q

Page 79: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Method 2: Joint Association Method

Procedures & ComputationsFor a combination of input of

location (one of 712 homogeneous zones in SA)CN-IIsoil depth category (one of 3)soil texture category (one of 3)vegetation category (one of 3)

Compute a change in soil moisture storage (ΔS)by the ACRU modelfrom an initial soil moisture storagefor a 30-day antecedent periodfor the 5 highest rainfall events of a yearfor each year on record

Page 80: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Method 2: Joint Association Method

Procedures & Computations

For CN-II’s of 50, 60, 70, 80 and 90Use ΔS to compute CNf for each of 27 land use/soil combinations

Calculate Qf for all combinations

Frequency analysis of Qf

50, 80, 90 and 95 perentiles

2, 5, 10 and 20 year return periods

Page 81: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 82: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 83: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 84: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 85: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Estimation of Daily Design Rainfallin South Africa

Option 1Search database containing Adamson’s (1981) TR102 report which accesses a 2200+ rainfall station information base for southern Africa

5 closest stations reported

Use select most appropriate

station

Page 86: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Estimation of Daily Design Rainfallin South Africa

Option 2Design rainfalls up to 20 year return periods computed for the representative station chosen for each of 712 zones

Zone number determined from user input latitude and longitude

Option 3User input design rainfall depths

Page 87: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Estimation of Daily Design Rainfall in South Africa

Option 4 (recommended)Design rainfall estimated using a regional, scale invariance approach (Smithers and Schulze, 2003)

Methodology to estimate design rainfall at 1’ x 1’ lattitude/longitude grid in South Africa

durations 5 minutes to 7 days

2 to 200 year return periods

WRC reports

Visual SCS-SA: 1 day design rainfall

http://www.beeh.unp.ac.za/HydroRisk/

Page 88: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 89: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 90: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 91: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

What Factors Affect Peak Discharge?

Time (h)Time (h)

Dis

char

ge (

mD

isch

arge

(m

33 .s.s

-1-1))

Different Peak DischargesDifferent Peak Discharges

Different VolumesDifferent Volumes

Page 92: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 93: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Estimation of Peak Discharge Using SCS Procedures

Unit HydrographsThe T-hour Unit Hydrograph (TUH) is defined as the surface runoff hydrograph resulting from a unit depth of effective rain falling uniformly in T hours over a catchment

Characteristic response from a catchment

Response is invariable

qp = f (Q)

SCS ProceduresBased on dimensionless Unit Hydrograph developed from large number of natural UHs

Shape of UH idealised to be triangular

Page 94: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

SCS Triangular UH

3

8

TTbb

TTpp TTrr

qqpp

5

8

Page 95: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 96: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 97: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 98: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 99: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Time Distributions Of Design Rainfall Intensity

The timing and magnitude of peak discharge in relation to rainfall intensity

Small catchmentsshort catchment response timeshort design storm duration critical (Why?)high intensity storms are critical

Large catchmentslong catchment response timelong design storm duration critical (Why?)lower intensity storms

Regional design rainfall intensityf (regional rainfall producing mechanisms)f (regional synoptic conditions) result in synthetic time distribution curves

Page 100: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Synthetic Time Distributions Of Rainfall Intensity In SA

One - day rainfall is distributed over timeDistribution assumed symmetrical over time

Element of conservatism built into procedures

Distribution based on PD : P24 h ratios

Four general types of time distribution curves identified for SA

Page 101: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Synthetic Temporal Storm Distributions for South Africa

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 4 8 12 16 20 24

Time (h)

Ra

tio

of

P(D

) / P

(1

-da

y)

Type 1 Type 2 Type 3 Type 4

Page 102: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Regionalisation of Temporal Distribution of Rainfall in SA

Page 103: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Synthetic Time Distributions Of Rainfall Intensity

Using SCS outside South AfricaDetermine dominant design rainfall producing storms

Convective? : Type 3

General rains / frontal /

longer duration? : Type 2

Page 104: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Synthetic Time Distributions Of Rainfall Intensity In SA

Semi-stochastic rainfall disaggregation developed by Knoesen and Smithers (2005) not incorporated yet

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Elapsed Time (hrs)

Fra

ctio

n o

f D

ail

y R

ain

fall

Page 105: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 106: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 107: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 108: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 109: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

.1 .2 .3 .4 .5 .6 .7 .8 .9 1. 2. 3. 4. 5. 6. 7. 8. 9..5

1

2

3

45

10

20

304050

7090

Flow Velocity ( m.s-1)

Page 110: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 111: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 112: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources
Page 113: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Visual SCS-SA

Visual SCS-SA IS A computerised version of the 1988 SCS documentation for southern Africa

designed specifically for southern Africa,

but applicable (with limitations) universally

essentially a user manual

a "small" catchments design hydrograph technique

areas < 30km2

no ARF applied

where specific characteristics ofprecipitation

land use

soils

physiography

dominate the hydrograph size and shape

Page 114: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Visual SCS-SA

Visual SCS-SA IS NOTA comprehensive flood estimation package for

multiple hydrographs

flow routing

An estimator of the PMF

A comprehensive theory document on the SCS techniques

A "large" catchments design hydrograph technique

Page 115: Design Flood Estimation for Small Catchments in Southern Africa Using The Visual SCS-SA Software Jeff Smithers and Roland Schulze School of Bioresources

Hands On Exercises