DSD-INT 2024 Wadi Flash Flood Modelling using Delft3D FM Suite 1D2D - Dangudubiyyam

Wadi Flash Flood Modelling
using Delft3D FM Suite 1D2D
A case study of Wadi Qows in Jeddah City, Saudi Arabia
Srija Dangudubiyyam, UL
Amgad Omer, Deltares
Prof. Mitja Brilly, UL
Eng. Fahed Saleh Alamoudi, KAU Jeddah
Prof. Sameh Kantoush, Kyoto University

1 2 3 4 5 6 7
2
PRESENTATION
OUTLINE
1. INTRODUCTION
2. OBJECTIVES
3. STUDY AREA
4. APPROACH
5. RESULTS
6. CONCLUSIONS
7. RECOMMENDATIONS
2

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INTRODUCTION
Source : Ahmad Arouri
Middle Eastern and North Africa (MENA)
region
• Hot desert climates and Limited rainfall
• Dry streams known as Wadis, active only
during rainfall events
• Non-absorbent soils leading to rapid
runoff & sediment flow
3
MENA REGION

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4
The impact of Wadi Flash Floods (S. A. Kantoush et al. 2022)
Flash Flood Impacts
• 1900-2016: 39 flash flood events in Egypt,
Jordan, Saudi Arabia
• $1.8B in damages, >1500 deaths
• Jeddah, KSA—severe floods in 2009, 2011,
2015, 2017; six rainstorms in 2018 alone
Challenges
• Rising socio-economic losses due to climate change &
urbanization
• Limited data: Few monitoring stations, lack of
continuous data
• Inadequate risk identification & emergency planning
Global Data
Hazard Mapping

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OBJECTIVES
5
• To investigate the reliability of global precipitation data on account for data
scarcity and establish extreme rainfall conditions
• To model flash floods and develop hazard maps for different boundary conditions.
• To investigate mitigation measures to reduce flash flood hazard.
Performance
Analysis ?? ?? Hazard Factors ?? Measures

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STUDY AREA:
JEDDAH CITY
6
• Area : 748 km2
• Population : 4.1 million
• Topography variation : 500-0 m
• Mean annual rainfall : 50 mm
• Mean summer temperatures > 40o C
Wadis and Drainage Network of Jeddah city (AM. Youssef et. Al. 2016)

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7
CASE STUDY: WADI QOWS
Area: 89,87 km²
Average Annual Precipitation : 52.62 mm

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APPROACH
8
RAINFALL
ANALYSIS
FLASH FLOOD
MODEL SETUP
FLASH FLOOD
HAZARD
MAPPING
HAZARD
REDUCTION
ASSESSMENT

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9
RAINFALL
ANALYSIS

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Setup Calibration Validation
Model for
hazard
mapping
FLASH FLOOD MODEL :DELFT3D FM
10

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Rainfall
Boundary
conditions
Flash flood
Model
Max Flood
depth and
Max velocity
Hazard
classification
Flash flood
Hazard
Mapping
FLASH FLOOD HAZARD MAPPING
11

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Scenario: Rainfall event
corresponds to 100 y Return
Period
Intervention of measures in the
model
Hazard reduction assessment
corresponds to city area
FLASH FLOOD HAZARD REDUCTION ASSESSMENT
12

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13
Observed Data
Station Coordinates:
longitude : 39° 12’ 00"
latitude : 21° 30’ 00"
Time period : 1984-2019
Wet Season : October to
April
Rainfall Analysis
RESULTS

14
Specifications
Temporal
Resolution
Spatial
Resolution
Start
Date
End
Date
MSWEP
3h/daily/
monthly 0.1◦
01-02-
1979
31-12-
2020
TRMM 3B43
3h/daily/
monthly 0.25◦
01-01-
1998
01-01-
2020
ERA 5 Land
h/daily/
monthly 0.25◦
01-01-
1979
31-12-
2023
Precipitation estimations
based on satellite products

PERFORMANCE ANALYSIS
Dataset R2 RMSE BIAS
TRMM 0,269 3,657 -0,066
MWSEP 0,324 3,438 -0,066
ERA5
Land 0,264 3,300 -0,036
Time Period: 1998-2019
Wet season
15

BIAS CORRECTION:
Quantile Mapping: Berngamma function
16

EXTREME VALUE ANALYSIS: GEV DISTRIBUTION
17

RAINFALL BOUNDARY CONDITIONS
18

Jeddah: 𝐼 = 𝑎𝐷𝑏
a= 236.63 ln(T)+ 388.48
b= 0.0107 ln(T)- 0.7869.
Source: Hatem A. Ewea et.al 2016
0
50
100
150
200
250
300
10 100 1000
Intenisty
(mm/hr)
Duration (min)
IDF-Wadi Qows
T =5 T =10 T =25 T =50 T =100
0
0,02
0,04
0,06
0,08
Dimensionless
rainfall
Dimensionless Time
Source: Mazen M. Abu Abdullah et al. 2019
DISTRIBUTION OF EVENTS
19

FLASH FLOOD MODEL SETUP
Dataset Type Usage Resolution Source
2D Rectangular
Grid
Computational Domain with
Finite Volume Solver
30 m Delft 3D FM 1D 2D
software
DEM Topography 30 m Copernicus DEM (2021)
Precipitation Runoff Generation Hourly Extreme Value Analysis
Land cover Roughness and Infiltration maps 10 m ESA world cover
Soil type Infiltration Map 250 m SoilGrids
20

CALIBRATION
Event : 25 November 2009
Rainfall : 80 mm
Duration : 3hr
Flood Depths
Parameters
• Infiltration
• Roughness
Model Runs Maximum Infiltration Rate Map Minimum Infiltration
Map
manning's n value
change
R1 None None Global
R2 Global Global Global
R3 Global /2 Global Global
R4 None None urban-0.1,barren-0.015
R5 Global /4 Global Global
R6 Global /4 Global urban-0.1,barren-0.015
R7 Global /4 Global /2 urban-0.1,barren-0.015
R8 Global /4 Global /2 urban-0.1,barren-0.010
21

VALIDATION
Event based on simulations
Rainfall : 80 mm
Duration : 3hr
Source: Kuswantoro Marko et al 2013
0
50
100
150
200
250
300
0,0 10,0 20,0 30,0 40,0 50,0 60,0
Discharge
m³/s
Time (hrs)
Normal (CN= 85,1) Dry (CN= 70,7) Model Simulation
23

100-year return period: 97.27 mm rainfall in 6 hr. duration
24
FLASH FLOOD HAZARD MAPPING

Maximum Flood Depth and Velocity corresponding to 100 Year RP Rainfall Event
25

Hazard Index Hazard Classification Range
(D*V m²/s)
Description
1 ≤0,3 Safe for all
2 ≤0,6 Unsafe for small vehicles and children
3 ≤1 Unsafe for vehicles and People
4 >1 Unsafe for people, vehicles, and all types
of structures
D- Water depth, V- Velocity of flow
26
Rangari, V.A et.al (2021)

RP10: 76,32 mm RP25: 85,70 mm RP50: 91,83 mm RP100: 97,27 mm
27

Sources of flood in the catchment
HAZARD REDUCTION ASSESSMENT
28

MEASURES
• Heightening of Existing
Dam
• Infiltration Pond
• Hybrid Measure
29

Type
Runoff Volume
(Mm³) % Decrease
R100 4,80 0
Dam Heightening 4,37 -8,91
Infiltration Pond 3,61 -24,85
Hybrid 3,07 -36,10 30

R100:
Hazard
Index Area (km²)
1 15,81
2 0,94
3 0,42
4 0,27
32

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Conclusions
33
• Global Precipitation Data: TRMM data
offers some reliability in estimating the
extreme rainfall conditions.
• Flash Flood Modelling: Delft3D is
efficient for modelling wadi flash floods
and incorporating structural interventions
• Hazard Mapping : Maximum depth and
velocity consideration for Wadis

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RECOMMENDATIONS
34
Wadi Qows
Finer resolution of
Topography
Global Precipitation
Data
Multiple gauge data for
bias correction
Modeling Flash Floods
Urban drainage network
and infrastructure
Hazard Mapping
Flash flood hazard index
based on maximum depth
and velocity in Wadis

35
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