Upload
troy-parks
View
24
Download
0
Embed Size (px)
DESCRIPTION
Climate change and Urban Vulnerability in Africa. Assessing vulnerability of urban systems , population and goods in relation to natural and man-made disasters in Africa. Course on Hazard , Risk and ( Bayesian ) Multi-risk assessement Napoli 10/24/2011 – 11/ 11 /2011. - PowerPoint PPT Presentation
Citation preview
Climate change and Urban Vulnerability in AfricaAssessing vulnerability of urban systems,
population and goods in relation to natural and man-made disasters in Africa
19/04/2023 1
Course on Hazard, Risk and (Bayesian) Multi-risk assessement
Napoli 10/24/2011 – 11/11/2011
Module 5: Case study: from climatic data to flooding risk assessment - Application for informal settlements
Prof. F. De Paola, prof. F. Jalayer, ing. R. De Risi
19/04/2023 2F. De Paola
MOLFETTA
19/04/2023 F. De Paola 3
BASIN MAIN CHARACTERISTICS
Area S [Kmq]
Length of main channel [Km]
Medium Slope
Drainage density DD
Average height
ii
i
i i
m
P
l
l
S
lDD i i
A= 11.02 km2
L= 7.9 km
P= 3.05 %
Hm= 77.77 mslm
tot
iiim S
SHH
_
The runoff curve number CN is an empirical parameter used in hydrology for predicting direct runoff or infiltration from rainfall excess.The curve number method was developed by the USDA- Natural Resources Conservation Service, which was formerly called Soil Conservation Service or SCS. The number CN is still popularly known in the literature as a "SCS runoff curve number".
The runoff curve number is based on the drainage basin characteristics (hydrological soil group, land use, land management) and hydrological condition. References, such as from USDA, indicate the runoff curve number for characteristic land cover descriptions and a hydrologic soil group
CN METHOD
19/04/2023 4F. De Paola
CN fundamental relationship:
CN METHOD
19/04/2023 5Maurizio Giugni
SIP
IPP
a
anet
2
where:
- Pnet
: accumulated runoff depth (mm)
- P : accumulative rainfall depth (mm)
- Ia : initial abstraction (mm), or the amount of water before runoff, such as infiltration, and rainfall interception by vegetation
- S : potential maximum soil moisture retention after runoff begins (mm).
Assuming Ia = 0.2S
SP
SPPnet
8.0
2.0 2
19/04/2023 5F. De Paola
CN METHOD
19/04/2023 6Maurizio Giugni
CN has a range from 30 to 100.Lower numbers (permeable soils with high infiltration rates) indicate low runoff potential.Larger numbers are for increasing runoff potential.
The runoff curve number, CN, is then related to potential maximum soil moisture retention, S, by:
19/04/2023 6F. De Paola
Curve Number based on hydrological soil groups:
Group A is composed of soils with very high infiltration rate and low runoff potential
Group B soils with high infiltration rate when thoughly wetted and moderate runoff potential
Group C soils with low infiltration rate and moderately high runoff potential
Group D soils with low infiltration rate even when saturated and high runoff potential
HYDROLOGIC SOIL GROUP
7F. De Paola19/04/2023
A Very permeable Medium to high permeability
B Moderately permeable Medium to low permeability
C poorly permeable low-permeability
D raincoats
19/04/2023 F. De Paola 8
Class A B C D1 Non-irrigated arable
Non-irrigated trees Agricultural areas with
significant areas of natural
Non-irrigated arable land
Non-irrigated vineyards Temporary crops
associated with permanent crops
Minor non-irrigated fruits
62 71 78 81
2 Urban Urban Areaas 92 92 92 92
3 Wet Zone Residential Areas 77 85 90 92
4 Water bodies Basins 100 100 100 100
5 mixed farming trees irrigated irrigated arable watering lawns
Herbage from the open-loop spring summer
Summer vegetable crop cycle autumn / spring
Horticultural crops in spring-summer cycle
Minor irrigation orchards and orchards
irrigated olive groves Cropping systems and
particle complex irrigated vineyards Cropping systems and
particle complex
72 81 88 91
6 Non-irrigated meadows Non-irrigated meadows 30 58 71 787 Natural Wooded Areas 45 66 77 83
9F. De Paola19/04/2023
LAND USE MAP
19/04/2023 F. De Paola 10
STAGIRR IRRIGUO IRR Area (km2) CNII
NI arboreal NI non-irrigated 5.56 62 344.72
NA urban non-agricultural 1.45 92 133.4
NI arable NI non-irrigated 0.12 62 7.44
NI meadows NI non-irrigated 0.09 30 2.7
NA urban non-agricultural 0.15 92 13.8
NA urban non-agricultural 0.52 92 47.84
NI mixed farming non-irrigated 0.12 72 8.64
NI arboreal NI non-irrigated 0.08 62 4.96
NI arboreal NI non-irrigated 1.99 62 123.38
NI mixed farming non-irrigated 0.77 72 55.44
NI arboreal NI non-irrigated 0.08 62 4.96
NA urban non-agricultural 0.09 92 8.28
11.02 755.56
CNII 68.56261
Average on the area
11F. De Paola19/04/2023
CN
Peak hydrograph (Mockus)
7.0
5.0
8.0
91000
342.0
CNs
Lt l
lca ttt 5.0
tl: catchment lag time (time between the hydrograph
centroid and the net rainfall centroid)
L [km]; s[%]
ab tt 67.2
ap t
AVQ
208.0
V [mm]
A [km2]
ta [h]19/04/2023 12F. De Paola
19/04/2023 13F. De Paola
0
2
4
6
8
10
12
0.00 5.00 10.00 15.00
t (h)
T=10
T=50
T=100
15.3exp
5.3
aap t
t
t
tQQ
19/04/2023 F. De Paola 14
T Duration Volume Peak hydrograph
years (h) (m3) (m3/s)
10 15 60138 2.80
30 15 118072 5.50
50 15 173555 8.08
100 15 242078 11.27
200 15 314302 14.64
500 15 427252 19.90
Depth-averaged shallow waterequations on land surface
INUNDATION MODEL
15F. De Paola
19/04/2023
TWO - DIMENTIONAL MODEL
19/04/2023 F. De Paola 16
19/04/2023 F. De Paola 17
T = 30 YEARS
19/04/2023 F. De Paola 18
T = 200 YEARS
19/04/2023 F. De Paola 19
T = 500 YEARS