UNESCO Japanese Fund-In-Trust (JFIT) Project titled ... · Cairo Office بتكم ةرهاقلا 1....

Regional Bureau for Science and Technology in Arab States

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UNESCO Japanese Fund-In-Trust (JFIT) Project titled “Urgent Capacity Development for

Managing Natural Disaster Risks of Flash Floods in Egypt, Jordan, Sudan and Yemen”

Flash Flood Hazard Assessment report of the selected hot spot area in Yemen

By:

Dr. Abdulla A. Noaman

National country coordinator


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Table of contents

No. Contents pages

1- Background 6

2- Objectives of the assessment report 7

3- Physical setting of Sanaá city 8

4- Population 9

5- Disaster risk profile of Yemen 11

6- Historical Development of the Sanaá city 13

7- Selection of the Hotspot area 15

8- Metrology 17

9- Hydrological characteristics of hot spot area 20

10- Dams in the Study area 26

11- Soil Type 27

12- Vegetation Cover 28

13- Flash flood assessment of the hot spot area 29

14- NEXT STEPS 30

15- References: 48

16- 55


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List of tables

content Page

Table 1. Natural disasters reported from 1980-2008) 11

Table 2. Top 10 natural disasters reported in Yemen (1988-2008) 18

Table 4: catchment area of the hot spot area 20

Table 5 : Characteristic of the Catchments area of the hot spot area 27

Table 6: Soil Group characteristic according to SCS classification 29

Table 7: Meteor/Rainfall Monitoring Station in the Sana’a Basin 33

Table 8: Rainfall data record 35

Table 9: Rainfall Stations Merged for Frequency Analysis 36

Table 10: Fine Resolution 5-min Rainfall Data Available 37

Table 11: Interpolated Missing Rainfall Depth 39

List of figures

Content Pages

Figure 1: Location of the Sana'a city 8

Figure 2: Sana'a municipality map (statistic year book, 2010) 9

Figure 3: Population distribution in Sana'a basin by district based on the 2004 Census 10

Figure 4: Chart of Population Forecast for Sana’a City (WEC,2005) 11

Figure 5 : Historical development of the Sanaá city ( source: sanaá municipality,2010) 15

Figure 6: Existing Storm-water Network in the Sanaá city 16

Figure 7: boundary of the Hotspot area in the Sana’a city. 17

Figure 8: location of the Hot spot within the Sanaá basin 19

Figure 9: Monthly Temperature (NWRA-A, 1989-2010) 21

Figure 10 : Daily High and Low Temperature 22

Figure 11: location of the rainfall stations in the Sanaá basin and rainfall isohyet map 23

Figure 12 : relative humidity in the Sanaá city 24

Figure 13 : wind speed 25

Figure 14: Wind Directions Over the Entire Year 25

Figure 15: Flow direction in the Sanaá basin 26

Figure 16: Wadi Network for the hot spot area 26

Figure 17: location of Dams in the study area (NIP,2014) 28


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Figure 18 : Sana’a Basin Soil Map 30

Figure 19: Sana’a Basin Land use Map 30

Figure 20: Sana’a City and the Surrounding Valleys 31

Figure 21 : Meteor/Rainfall Monitoring Station in the hot spot area within Sana’a Basin 33

Figure 22: Statistical Distribution for Sana’a Airport Station 41

Figure 23: Mass Curves for Single Storm Events 43

Figure 24: Isohyet Contours for 2-year Return Period 46

Figure 25: Isohyet Contours for100-year Return Period 46

Figure 26: DAD Curve for 2-year Return Period 47

Figure 27: DAD Curve for 100-year Return Period 47

Figure 28 : The schematic of the hydrological model 48

Figure 29: overview of the HEC-HMS Model 52

Figure 30: suggested developed vulnerability and flood risk assessment approach 54

ABBREVIATION

ASCE American Society of Civil Engineering

BFE Base Flood Elevations

CAMA Civil Aviation and Meteorological Authority

CN Curve Number

CSO Central Statistics Office

CWMU Central Water Management Unit

DAD Depth-Area-Duration

DDF Depth-Duration-Frequency

DEM Digital Elevation Model

FHBM Flood Hazard Boundary Map

FIRM Flood Insurance Rate Map

GDP Gross Domestic Product

GFDDR Global Facility for Disaster Reduction and Reconstruction

GOY Government of Yemen

GIS Geographical Information System

HEC- HMS Hydrologic Engineering Center-Hydrologic Modeling System

HEC-GeoHMS Hydrologic Engineering Center-Geospatial Hydrologic Modeling Extension


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HEC-RAS Hydrologic Engineering Center-River Analysis System

IFD Intensity-Frequency-Duration

MDR Mean Depth Velocity

MENA Middle East and North Africa region

MWE Ministry of Water and Environment

NWRA National Water Resources Authority

ROY Republic of Yemen

SCS Soil Conservation Service

SWMM Storm Water Management Model

SFD Social Fund for Development of Yemen

TOR Terms of Reference

UNDP United Nations Development Program

UCO Unesco Cairo Ofiice

UNESCO United Nations Economic Social and Cultural Organization

WEC Water & Environment Center


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1. Background Yemen, in general, is vulnerable to flash floods, floods (coastal storm surge and tsunami), earthquakes,

landslides and rockslides, and volcanic eruptions. Recently, a tropical storm 03B made landfall in

Yemen between October 23 and 25, 2008. Because of the tropical storm, the Hadramout and Al-

Mahara governorates suffered substantial damage. The storm caused widespread flooding in several

locations in the Hadramout and Al-Mahara governorates, leading them to be the two most heavily

affected areas. Over 4,600 houses and another 2,000 huts in both Governorates were totally or

substantially damaged, leading to as many as 25,000 internally displaced persons (IDP).

Sana’a city suffers from a severe problem with storm-water drainage due to its location.

The city sits in a valley in an inter-mountainous plain that contains many wadis originating from

surrounding mountains draining toward the Great Wadi of Saylah. Due to rapid expansion of the city

during the last two decades, natural wadi courses have been built up and populated. The residential

areas and main streets are prone to flooding, resulting in property damage and traffic problems during

the annual rainy season. Also, all city storm-water drainage systems drain into the main Wadi Saylah,

causing major property damage and traffic disruptions as this wadi has been integrated into the city’s

major transport arteries.

The urban development of Sana’a has increased flood hazard for two reasons:

(1) modifications to existing land features and,

(2) increased population and buildings in flood-prone areas. Modifications to land features change

the runoff of watersheds, resulting in greater floods than would occur with undeveloped

conditions. The primary impact of urbanization is conversion of natural ground cover to

impervious surfaces, such as paved roads and building rooftops. Natural ground cover and soils

provide depression storage that absorbs initial rainfall before surface runoff occurs. Rainfall

absorbed into the ground migrates slowly as subsurface flow.

The consultant identified the government sector partners as well as the key sources of the data and

information as follow:

• Ministry of Water and Environment

• Minister of Public Works and Highways

• Minister of Local Government

• National Water Resources Authority- Sana’a Branch

• Local Council, Sana’a Municipality


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• Civil Defense Authority

• Environment Projection Authority (EPA)

• Civil Aviation and Meteorological Authority

2. Objectives of the assessment report The objectives of the assessment report as defined in the Terms of Reference (TOR) are:

Undertake the field survey and data collection

Review of literature on flood modelling and management

An analysis of major historical events in the context of the compiled inventory. This

analysis will include the probabilistic methodology implemented for this study.

3. Physical setting of the Sanaá city The municipality of Sana'a is located in the central highlands of Yemen at an elevation ranging from

2200 m to 2300m above mean sea level (MSL). According to the most recent satellite image for the

municipality of 2004 the capital Sana'a covers an area of 1050 square kilo-meters, while the

inhabited area occupies 140 square kilometers out of the total area. It has a population of 1.75

million according to 2004 census, which constitutes 8.0% of the country's total population. The

location of Sana’a city as well the Sana’a municipality within Yemen and its administrative districts

are shown in Figures 1 and 2. The Municipality is divided into 9 planning sectors, each sector is

divided into 9 areas and each area is divided into 9 neighborhoods.

The city of Sana'a occupies a distinguished status in respect to urban growth in comparison with

other Yemeni cities due to economic and social development and expansion over the last years.

Besides, the factor of internal annual immigration to the city of Sana'a that occupies the first rank

among the Yemeni cities where the proportion of urban populations reached 7.52% out of the total

of the city's populations.


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Figure 1: Location of the Sana'a city

The socio-economic conditions across the Sana’a Basin have changed considerably in recent years.

The rapid growth of the urban population in the national capital city (Sana’a) has resulted in this

change. The expansion of the urban center into the rural areas as well as the modernization of life

style and improvement of infrastructure has increased the interaction between the city and its

surroundings. Figure 2 shows the current sanaá municipality map.


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Figure 2: Sana'a municipality map (statistic year book, 2010)

4. Population

Based on the 2004 Census, consultants have worked out the total population of Sana'a basin for

that year as 2.0 million - 1.75 million in the city of Sana'a with a 5.55% annual growth rate and 0.25

million in the rural area with a 3.2% annual growth rate. This estimate comes very close to the

estimated figure of Sana'a University WEC Socio-economic Study Report (October, 2001) for 2005 as

1.83 million. This study has also projected that by the year 2025, the Sana'a basin population will


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rise to 5.85 million. The other parameters of human resources for Sana'a Basin is assumed to be

the same as that of the Sana'a governorate, which indicate that the gender ratio is 103.2, and the

average number of persons per household is 7.8 (Statistical Year Book 2004, CSO, ROY). Figure 3

and 4 show the population distribution in Sana'a basin including sanaá city.

Figure 3: Population distribution in Sana'a basin by district based on the 2004 Census (in

thousands) (source: WEC,2005)

Population forecast for Sana’a City has been done by WEC (2005), adopting three growth scenarios

reflecting high, moderate and limited growth. The assumed rate under the high growth scenario

was 6.1% in 1997 and decease to 4.2% in 2020. Assumed rates under the moderate and limited

growth scenarios were 5.6% and 5.1% respectively in 1997 and decrease to 3.3% and 2.4%

respectively in 2020.

Population forecast for Sana’a City is shown in Figure 4. According to the results of population

forecast, the population of Sana’a City under the moderate growth rate which was adopted for

project planning purpose, for the year of 2006, the base year of this study, is 1.9 million inhabitants

and for 2020, 3.4 million inhabitants is estimated.

35635

19362

11057

1747311

62436

71601

45259

64759

19069

61255

Sana'a

Hamdan

Bany Hushaish

Bany Matar

Bany Al Hareth

Bany Bahlowl

Sanhan

Arhab

Khawlan

Nehm


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Figure 4: Chart of Population Forecast for Sana’a City (WEC,2005)

5. Disaster risk profile of Yemen

Floods are the most recurrent natural disaster in Yemen, followed by landslides and earthquakes. The

most recent major floods occurred in 1996, 2000, and 2008. While regular flooding has traditionally

been beneficial for agricultural practices in Yemen, when flooding occurs in areas that are densely

populated, there are significant economic damages that occur due to loss of lives, damage to

livelihoods, property and infrastructure. With an estimated per capita GDP of US $870 and therefore

limited financial resources, Yemen can ill afford the losses it currently sustains from recurrent disasters.

Table 1 provides an overview of the natural disasters reported in Yemen over the last 28 years, while

Table 2 provides estimates of loss from the ten most major disasters over the last twenty years.

Table 1. Natural disasters reported from 1980-2008

No of events 27

No of people killed 908

No of people affected 1,064,592

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

1990 1995 2000 2005 2010 2015 2020 2025

Po

pu

latio

n

Year

High Growth Rate

Moderate Growth Rate

Limited Grouwth Rate


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Economic Damage (US$ X 1,000) 2,849,500

Economic Damage per year (US$ X 1,000) 101,767

Source: Prevention Web and WB DLNA: October 2008 Tropical Storms and Floods, Republic of Yemen

2009.

Table 2. Top 10 natural disasters reported in Yemen (1988-2008)

Disaster Date Affected Killed

Cost (US$ X 1000)

Flood 2008 700,000 73 1,638,000

Earthquake 1991 40,039 70 10,000

Flood 1991 30,000 65 65 1,500

Flood 1993 21,500 50 NA

Flood 1999 19,750 36 NA

Flood 1996 5,000 33 NA

Flood 1998 3,000 32 NA

Flood 2006 2,000 31 NA

Flood 2007 2,000 28 NA

5.1. Problem of the Flash flood in the Sanaá city

Floods, especially flash floods, have killed many people in Sana'a in the past years. The urban

development of Sana'a, the capital of Yemen, has led to an increase in flood hazards due to rapid

changes in existing land use to impervious surfaces and the presence of increased population and

buildings in flood prone areas. The urban development of Sana’a has led to an increase in flood hazard

for two reasons: (1) modifications to existing land features and (2) presence of increased population


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and buildings in flood prone areas. Surface water runoff characteristics change when natural ground

cover is modified. Conversion of natural ground conditions to impervious surfaces throughout urban

areas reduces the amount of rainfall that can infiltrate into the ground. This results in a greater volume

of rainfall becoming surface runoff and causes greater floods. In addition, surface runoff across

impervious surfaces occurs faster than over natural ground conditions. The combination of greater

volumes of surface runoff and faster rates of runoff leads to fast-rising flood peaks of greater

magnitude than would occur for undeveloped natural ground cover conditions.

6. Historical Development of the Sanaá city

6.1. Sana'a after the 1962 Revolution:

The city of Sana'a witnessed a real start for its urban development by the emergence of Revolution of

26th September 1962 where the rhythm of life in the city began to change, and Yemen opened its

doors for international foreign relations. The country paid more attention for establishment of urban

infrastructure. The city of Sana'a attracted thousands of migrants from rural areas when the main

attractive factors were realized including educational and health services, job opportunities, prosperity

of commercial activity and other factors. The population growth of the city over the last 10 years is

8.7% according to the central organization of statistics. The number of populations of Sana'a has

jumped from 50,000 persons in 1962, to more than 1.9 million persons in 2007. This population

growth led to a continuous pressure on the area of the city that pushed its borders to peripheries to

incorporate with its surrounding areas. For example the areas of Dar Selm, Heziz, and Rmadah were

considered as pre-urban areas on the peripheries of the city in the beginning of 1980s while now

became inner city areas within Sana'a. Accordingly the city that covered an area not more than two

square kilometers in 1962, has reached 37 square kilometers at the beginning of 1980s.

6.2. Establishment of Municipality of the Capital Sana'a:

The urban growth, which the capital Sana'a witnessed, has led to the necessity of establishing

competent administrative body to supervise and monitor the implementation of laws and general

policy of the State in administering the affairs of the city in all fields, in addition to develop its resources

and to keep the general order. The Republican Decree No.13 of 1983 was issued to establish the

municipality of the Capital Sana'a (Amanet Al-A'asemah)and identify its functions. The decree included

determination of its borders by Beit Haroon in the north, Brash Mountain in the west, Wael Qaren

(Horn) in the south and Al-Sabaha in the west. Consequently, its area was increased to reach

approx¬imately 850 square kilometers. The Republican Decree No.2 of 2001 was issued to divide the

municipality of the capital Sana'a into nine adminis¬trative areas, and also it stated to include Bani


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Hareth Directorate to be 10 areas. Therefore, the total area of municipality of the capital Sana'a is

approximately 1,050 square kilometers. The period from 2000 to 2003 is considered as one of the most

important periods that witnessed the prosperity of the city' of Sana'a due to realization of vast

achievements, and speedy and tangible improve¬ment in the level of provision of different services and

infrastructure, in addition to organization, beautification and cleanli¬ness of the city where most

services achieved rapid growth, and the number of beneficiaries of sanitary drainage service were

doubled during a short period of time.

The other services also achieved huge growth during the same period such as water and tele-phone

services, paving, expansion and illumination of streets, organization, cleanliness and beautification of

the city and spread of green areas, beside imple¬mentation of mega projects. Therefore, this phase is

considered as beginning of modern renaissance of the city of Sana'a. The Features of this Phase are:

1. The phase is characterized by issuing of a number of laws and decrees that organized the affairs

of the city, its administrative divisions and local authority.

2. Carry out the first elections for the members of local councils at the level of municipality of the

capital and its subsidiary areas.

3. Provide areas, executive bureaus, and administrative boards of local councils with powers to

hold the responsibility of administering the affairs of the area, setup plans and projects at the b-el of

each area.

4. Reorganize the administrative and organizational structure at the level of municipality of the

capital Sana'a and areas.

5. Implement several infrastructure projects as well as vital utilities and improve its standard.

6. Setup a strategy to solve the problem of main intersections, mitigate the traffic congestion by

implementing a number of bridges on the main intersections.

In endeavor to meet the vast urban expansion that the city witnesses, the municipality of the capital

Sana'a dedicated its efforts and capabilities to implement several projects in the following fields:

1. Road projects.

2. storm-water drainage.

3. Sanitary drainage.

4. Improvement of traffic and transportation.


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Figure 5 : Historical development of the Sanaá city ( source: sanaá municipality,2010)

7. Existing Storm water network in the Sanaá city

The major storm water channel (Wadi Alsailah) with about 30 km long, 15 m to 16 m width and 1.4 m

to3.6 m height runs through the middle of the city and next to the old city of Sana’a also acts as a major

transportation route. However, sudden flooding of the channel during the rains leads to vehicles being

swept away and endangers human lives as well (see Figure 6). No mechanism currently exists to

prevent vehicles or people from using the channel during a flood event.


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Figure 6: Existing Storm-water Network in the Sanaá city

8. Selection of the Hotspot area

A criterion for the selection of the pilot area has been identified by the National country coordinator

(NPC) through consultation with relevant stakeholders as well as in cooperation with the project

manager. Three of case study areas have been proposed, the consultant has select one of them which

match to the following agreed criteria of the selection:

Security condition, the hot spot area should be safe and far from the conflicts zones.

Availability of previous studies and statistical data for local circumstances ,

Level of institutional capacity and governmental entities;


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The selected pilot study areas are considered as the most affected areas during the

floods,

After reviewed the available data, the consultant came to conclusion that city of Sana’a (urban area)

meet to the criteria of the selection. Figures 7 and 8 show the boundary of the hotspot area including

their catchment while table 3 shows the names of districts including the area of each district. Table 4

shows the names of the sub-catchment and their characteristics.

Figure 7: boundary of the Hotspot area in the Sana’a city.


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Table 3: Names, population and areas of Sanaá city districts

The location of the Hot spot area and its catchment area within the sanaá sub-basin is presented on

the figure 8, the names of the sub-basins, area and location are listed in the table 4.

No Districts

name

Population Area (km2)

1 Old city 91293.12 2 2 Shoub 301747.7 151

3 Azal 164953.4 22

4 Al-Safia 147761.3 20

5 Al-Sabeen 436403.5 242

6 Al-Wahdah 140716.8 45 7 Al-Tahreer 92044.8 3 8 Maeen 377435.5 269

9 Al-Thawrah 238386.2 21

Total 1990742.32 775


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Figure 8: location of the Hot spot within the Sanaá basin


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Table 4: catchment area of the hot spot area

No. of

sub-basin

Sub-basin name Area (sq.km) Location

18 Wadi Shahik 238.7 Eastern Catchment

19 Wadi Ghayman 143.3

17 Wadi Sa'wan 96

20 Wadi al Mulaikhy 69.6 Southern catchment

21 Wadi Hizyaz 81.9

22 Wadi Akhwar 125.6

16 Wadi al Mawrid 179.1 Central

All 934.2

9. METEOLORGY

9.1. Temperature

The average monthly temperature recorded at the station of NWRA-A is graphed in Figure 9. Though

obtained records are very limited, general tendency in the Sana’a City is observed. The hottest season

is from June to August, and the coldest season is around January and February. The average monthly

temperature ranges between about 15 and 25 C.


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Figure 9: Monthly Temperature (NWRA-A, 1989-2010)

9.2. Daily High and Low Temperature

Over the course of a year, the temperature typically varies from 5°C to 29°C and is rarely below 2°Cor above 31°C ( see Figure 10)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

1 2 3 4 5 6 7 8 9 10 11 12

Month

Tem

pera

ture

(C

)

Average Average Minimum Average Maximum


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Figure 10 : Daily High and Low Temperature

9.3. Precipitation

Rainfall is the source of runoff in the Wadis and recharge of ground water in Sana’a basin. Sana’a Basin

is characterized by convective rainfalls that are localized and having intensive precipitations of short

durations. A location of meteolorical station as well as the summary of the rainfall stations including

the data available for the Sana’a Basin are given in figure 11.

The spatial pattern of annual rainfall varies from year to another. It ranges between 250 mm at the

plain areas to more than 300 mm at the south-western mountains as shown in Figure 2-2, the Sana'a

Basin Isohyets Map presented in WEC (2002) well inventory report.

Rainfall in the Yemen is caused by three main meteorological mechanisms:

The Red Sea Convergence Zone effect (RSCZ);

A monsoonal Intertropical Convergence Zone effect (ITCZ) and;

The Mediterranean effect consisting of an influx of polar air following a depression that causes

occasional light rainfall events in December/January.


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Data having these characteristics is impossible to be statistically analyzed. These data are shown in the

Figures 7 shows the annual rainfall for the Sana'a basin for the period from 1989 to 2010. The annual

rainfall which is recorded at NWRA-A from 1989 to 2010 ranges from around 110 mm to 300 mm or

more as shown in Table 2.1. The maximum annual rainfall was recorded at 341 mm in 1998. The figure

indicates that rainy or wet seasons are generally from March to May and July to September, although

there were some exceptional years.

NWRA-A

ARHAB-A

ASTAN-A

MADHBAH-A

BIT ASSYID-A

MEND-A

MAQUALAH-A

DAR SALM-A

THAWRAN-A

P-8O-5

P-17P-15

P-21

F-783-AA-2069F-2356

F-2357

F-1446

F-2131F-2143

F-1445

F-1947-A

F-2003

C-1849

C-1564

D-25

C-1146-A

U-358-A

U-1146-A

B-665-A

B-683-A

E-2366

E-2377

E-1749

U-427-A

U-502-AA-878

A-1038

U-574-A

A-848-A

A-691-A

NWRA-SB-A

AL KHERBH-A

Monitoring Network in Sana'a Basin

Figure 11: location of the rainfall stations in the Sanaá basin and rainfall isohyet map


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9.4. Humidity

The relative humidity typically ranges from 12% (very dry) to 75% (humid) over the course of the year, rarely dropping below 6% (very dry), or exceeding 94% (very humid). The air is driest around June 13, at which time the relative humidity drops below 14% (very dry) three days out of four; it is most humid around August 8, exceeding 65% (mildly humid) three days out of four ( see figure 12).

Figure 12 : relative humidity in the Sanaá city

9.5. Wind speed and direction

Over the course of the year typical wind speeds vary from 0 m/s to 8 m/s (calm to moderate breeze), rarely exceeding 10 m/s (fresh breeze). The highest average wind speed of 3 m/s (light breeze) occurs around September 3, at which time the average daily maximum wind speed is 7 m/s (moderate breeze). The lowest average wind speed of 2 m/s (light breeze) occurs around December 30, at which time the average daily maximum wind speed is 6 m/s (moderate breeze) see figure 13.


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Figure 13 : wind speed The average daily minimum (red), maximum (green), and average (black) wind

speed with percentile bands (inner band from 25th to 75th percentile, outer band from 10th to 90th percentile).

The wind is most often out of the north east (16% of the time) and east (11% of the time). The wind is least often out of the south east (2% of the time) and north west (4% of the time) see figure 14.

Figure 14: Wind Directions Over the Entire Year


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10. Hydrological characteristics of hot spot area

The rainfall within Sana’a city and the surrounding valleys has been intensively studied following the

previous theoretical approach. The study started by collecting all of the historical data for rainfall

within the entire Sana’a city and the following valleys that are:

These sub-basins are presented in Figure 15. It is clear that there are six sub-basins that are decanting

water into Sana’a city boundary while the seventh basin is covering the entire city. There is no

information of the flood flow of the wadi Al-Asaila. Table 5 shows the hydrological characteristics of

catchment area in the study area (wadi Al-sayela). The catchment area of the wadi Al-sayela till the

hotspot area is 934.2 km2. Figure 16 shows the surrounding wadis of the hot spot area including the

catchment area.

Figure 15: Flow direction in the Sanaá basin Figure 16: Wadi Network for the hot spot area


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Table 5 : Characteristic of the Catchments area of the hot spot area

Sub-

basin

No.

Sub-basin

name

All Area

(sq.km)

Hydrogical characteristics

Rainfall

mm/year

Runoff

MCM/year

Runoff

coef. %

18 Wadi Shahik 238.7 200 1.7 0.1

15 Wadi Hamdan 63.5 228 0.4 0.15

16 Wadi al Mawrid 179.1 202 17.7 0.7

20 Wadi al

Mulaikhy 69.6 248

5.1 0.3

19 Wadi Ghayman 143.3 173 0.7 0.1

21 Wadi Hizyaz 81.9 248 2.0 0.1

22 Wadi Akhwar 125.6 248 2.9 0.1

17 Wadi Sa'wan 96 245 3.3 0.15

All 997.7 33.8

11. Dams in the Study area

Most of dams in the Sana’a basin are constructed to recharge groundwater as well as for flood

protection. 15 dams of them are also used for irrigation and only three dams are used for domestic

purpose. 15 dams which may be small-scale reservoirs constructed by rural people, are mainly used for

irrigation purpose. Total volume of the annual flow or yield of dam sites is calculated to be 24 MCM.

The location of the dams in the Sanaá basin in the study area is show in the figure 17.


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Figure 17: location of Dams in the study area (NIP,2014)

12. Soil Type

Soil Conservation Services (SCS) and Bureau of Reclamation of United States (USBR) has done

classification of soils according to their texture and infiltration capability. Four major soil groups are

recognized for the primary classification of watershed soils, which are as shown in the table 6 and


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figure 18.

Table 6: Soil Group characteristic according to SCS classification

Soil Group Soil characteristic

Group-A Includes soils having high infiltration rates even when thoroughly wetted e.g. sands or gravel that are deep and well to excessively drained. Soils belonging to Group-A generate less runoff.

Group-B Soils have moderate infiltration rates with moderately fine to moderately coarse texture.

Group-C Soils belonging to this group have slow infiltration rates and have moderately fine to fine texture.

Group-D Comprises very fine textured soils, which generate maximum runoff.

13. Vegetation Cover

Vegetation covering the soil provides protection from the impact of rainfall. Generally, more dense the

cover, lower the runoff and vice-versa. Vegetation cover conditions is estimated by the land use

classification such as fallow, row crops, pasture or range, woodlands etc. Cropping pattern of project

area plays an important role for the estimation of vegetation cover conditions. The vegetation cover

and land use is presented in the Figure 19.


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Figure 18: Sana’a Basin Soil Map Figure 19: Sana’a Basin Land use Map

14. Flash flood assessment of the hot spot area

The rainfall within Sana’a city and the surrounding valleys has been intensively studied following the

previous theoretical approach. The study started by collecting all of the historical data for rainfall

within the entire Sana’a city and the following valleys that are:

Wadi Shahik

Wadi Ghayman

Wadi Hizyaz

Wadi Akhwar


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Wadi al Mulaikhy

Wadi al Mawrid where the main part of the city is located.

These sub-basins are presented in Figure 20. It is clear that there are six sub-basins that are decanting

water into Sana’a city boundary while the seventh basin is covering the entire city. There is no

information of the flood flow of the wadi Al-Asaila. Table 6-1 shows the hydrological characteristics of

the effective catchment area in the wadi AlSayela.

Figure 20: Sana’a City and the Surrounding Valleys, the arrows shows the direction of water from these valleys to the direction of Sana’a water storm drain


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14.1. Flood Probabilistic Analysis

The Flood hazard analysis quantifies the extent and depth of flooding throughout the flood prone area for a range of flood frequencies. The subsequent vulnerability and risk assessment phases describe the impact of these floods on the community. In order to complete a flood analysis, the following steps need to be undertaken:

Rainfall analysis,

Hydrologic analysis, and

Hydraulic analysis.

14.2. Rainfall Analysis

Rainfall in Yemen depends in two mechanisms, the Red Sea and the Monsoonal Intertropical Convergence Zones. These mechanisms produce rainfall in convective storms. Rainfall in the Sana’a Basin has a variable distribution in time and space as a result of convective storms that bring precipitation with high intensity, short duration and limited extent. For instance, annual rainfall data has been recorded at the Airport Station ranging from 55 mm in 1990 to 531 mm in 1963. In addition, the mountain regions in the southeast and southwest areas of the basin receive more rainfall than the plains in the central area. The following sections discuss the available rainfall records, the temporal and spatial distribution of rainfall in Sana’a Basin, and the statistical frequency analysis conducted as part of the flood hazard analysis.

14.3. Available Rainfall Records

Daily rainfall data were obtained in digital format from three sources: (1) National Water Resource Authority (Sana'a Office), (2) National Water Resource Authority (Head Office), and (3) Civil Aviation and Meteorology Authority (CAMA). The location and the spatial distribution of the rain gages are shown in Figure 1. The daily rainfall data span from 1972 to 2012 and was available for 32 gage stations located in and around the Sana’a basin. Figure 21 presents the location of the rainfall stations in the Sanaá basin while the table 7 shows the coordinates of the stations with available rainfall data. Available rainfall data for Sana’a was received in several files of variable length and in different electronic formats like excel, text and access. Each file contained information identifying the station, year, month and recorded elements along with hour and date of the precipitation occurrence. In many cases, the data was repeated for the same interval of time or was missing for few days to several months in a give year. A rainfall database was generated for each station by consolidating data from various files. Available and missing rainfall record for the period from 2003 to 2007 is presented in the table 8.


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NWRA-A

ARHAB-A

ASTAN-A

MADHBAH-A

BIT ASSYID-A

MEND-A

MAQUALAH-A

DAR SALM-A

THAWRAN-A

P-8O-5

P-17P-15

P-21

F-783-AA-2069F-2356

F-2357

F-1446

F-2131F-2143

F-1445

F-1947-A

F-2003

C-1849

C-1564

D-25

C-1146-A

U-358-A

U-1146-A

B-665-A

B-683-A

E-2366

E-2377

E-1749

U-427-A

U-502-AA-878

A-1038

U-574-A

A-848-A

A-691-A

NWRA-SB-A

AL KHERBH-A

Monitoring Network in Sana'a Basin

Figure 21 : Meteor/Rainfall Monitoring Station in the hot spot area within Sana’a Basin

Table 7: Meteor/Rainfall Monitoring Station in the Sana’a Basin

No. Station Name Coordinates

E/Longitude N/Latitude

1 ADDAB'AT 44.36874 15.36413

2 ALARAQAH 44.48369 15.41144

3 ALIRRA 44.17737 15.46024

4 ARHAB-A 44.20039 15.67818

5 ASR 44.15501 15.34128

6 ASTAN-A 44.32217 15.76810


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7 BIRBAS'L-A 44.47519 15.64150

8 BIT ASSYID-A 44.40971 15.50342

9 CAMA 44.20901 15.34916

10 DAR SALM-A 44.25384 15.28428

11 DARSALM 44.25863 15.28153

12 DARWAN 44.07675 15.55385

13 DARWAN-A 44.08729 15.54425

14 DUTRAT 44.46438 15.67176

15 GRATEL 44.16800 15.39900

16 JIRAF 44.20788 15.40249

17 KHERBAH-A 44.20000 15.39000

18 MA'ADI-A 44.46100 15.71740

19 MAJHIZ 44.07602 15.40334

20 MAKARIB 44.22093 15.64978

21 MEND-A 44.06443 15.28446

22 MIND 44.06536 15.28668

23 MAQUALAH-A 44.34739 15.15345

24 NWRA-A 44.20401 15.39285

25 NWRA-S-BRANCH-A 44.23029 15.38946

26 SAMNAH-A 44.31516 15.64874

27 SANA'A AIRPORT 44.22345 15.47622

28 SHAHIK 44.44000 15.39000

29 SHERATON 44.22524 15.36955

30 SHU'UB 44.23124 15.38449

31 SUNINAH-A 44.12000 15.33000

32 WADIZHAR 44.125246 15.441504


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Table 8: Rainfall data record

For the period, 1972-2008, the available rainfall records are for 32 stations with varying periods. Out of these, daily rainfall data is available for all stations, hourly data is recorded at one station for period 2006-2007, and 5-minute rainfall data is available for 8 stations recorded during period 2006-2007. For this study, the rainfall data collected from all the above sources and durations were analyzed. The density of the stations that record daily rainfall is not uniform throughout the basin. Density is highest in the city and very poor over the northern and eastern parts of Sana’a basin. Multi-stage quality control of the observed data was performed before performing any analysis of the data. The station information was verified, when the details were available. The precipitation data were checked for available years and missing data. The data for several of the gage locations is incomplete. However, it was analyzed and incorporated into the statistical frequency analysis, if considered adequate. Based on the review of available rainfall data, three pairs of stations seemed to be duplicated, replaced or shown with a different name. Each pair of stations were located within 2 km or less and had rainfall data for different years. For the purpose of the statistical frequency analysis, rainfall data for each pair were merged as shown in Table 8. Each pair of stations was treated as one individual station, reducing the number of stations from 6 to 3.

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08

1 ADDAB'AT 72-76,78-79 8 Shu'ub, Cama

2 ALARAQAH 83-85 3

3 ALIRRA 86-89,91,92 X 4

4 ARHAB-A 06-08 3

5 ASR 75-77 3

6 ASTAN-A 91-97,99-04,07,08 X X 13 Samnah, Ma'adi, Birbas'l

7 BIRBAS'L-A 91-97,99-01 10 Astan, Samnah, Ma'adi

8* BIT ASSYID-A 06,07 -

9 CAMA 77-79,83,86 5

10 DAR SALM-A 83-85,03-08 9

Sana'a airport, Shu'ub, Cama,

Nwra, Nwra-s-branch

11 DARWAN-A 72-76,78,79,03-07 12 Sana'a airport

12 DUTRAT 83-85 3

13 GRATEL 07-08 X X -

14 JIRAF 73-76,78,79 6

15 KHERBAH-A 03-08 6 Nwra-s-branch, Sana'a airport

16 MA'ADI-A 91-97 7 Astan, Birbas'l, Samhna

17 MAJHIZ 83-85 3

18 MAKARIB 83-85 3

19 MEND-A 72-76,78-79,03-08 13

20 MAQUALAH-A 03-08 X 3

21 NWRA-A 89,90-93,97-05 14

Kherbah, Sana'a airport, Nwra-s-

branch

22 NWRA-S-BRANCH-A 02-06 5 Kherbah, Sana'a airport, Nwra

23 SAMNAH-A 91-97,99-00 9 Astan, Ma'adi, Birbas'l

24 SANA'A AIRPORT 74-79,83-07 31

25* SHAHIK 06,07 -

26 SHERATON 83-85 3

27 SHU'UB 75-83, 87 10 Sheraton, Cama

28 SUNINAH-A 06-08 3

29 WADIZHAR 76,77,80-82 X 4

Notes: Data available in a particular year

X Significant data missing in a particular year. Didnot use the year for statistical analysis

2 Station included in statistical analysis for small storm frequencies (less than 5 year return period)

8* Did not use station for statistical analysis because only 2 data points available. Average maximum value used for small storm frequencies (less than 2 year return period)

13 Did not use station for statistical analysis becuase significant missing data for short records

Stations used for missing dataYear

No Station Total Years

Years for

Model


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In addition, rainfall data from four other stations were merged into 2 stations because they were located within less than 1 km apart from each other. These stations are KHERBAH-A and NWRA-A; NWRA-S-BRANCH-A and SHU’UB. For the purpose of the statistical frequency analysis, rainfall data for each pair were merged as shown in Table 9. These stations, however, are considered individual stations for the purpose of reporting rainfall data. Table 9: Rainfall Stations Merged for Frequency Analysis

No. Station Name Station Name After Merge

10 DAR SALM-A DAR SALM-A

11 DARSALM

12 DARWAN DARWAN-A

13 DARWAN-A

21 MEND-A MEND-A

22 MIND

17 KHERBAH-A KHERBAH/NWRA

24 NWRA-A

25 NWRA-S-BRANCH-A NWRA-S-BRANCH/SHU’UB 30 SHU’UB

Table 8 shows that only Sana’a airport station has daily rainfall data recorded for more than 30 years. Several stations have data recorded for 2 or 3 years and others have major data gaps. However, there are several stations that have data recorded between 5 through 11 years. In additions, the reliability of the rainfall data has been reported as partly doubtful due to lack of financial resources, vandalism and some technical issues. Table 8 shows the summary of daily rainfall data with total amount of years available, years with significant missing data, and stations that were not used in the statistical frequency analysis for temporal distribution. A total of 29 stations, as shown in Table 9, were considered for the rainfall analysis. To analyze the rainfall temporal distribution, stations with more than 5 completed years were included in the frequency analysis because they provide enough information to determine probability of exceedance for small and large storm frequencies.


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To analyze the spatial distribution of rainfall, however, all stations with complete rainfall records were used in the analysis. Stations with more than 5 completed years were included in the frequency analysis and used for all storm frequencies. In addition, stations with 3 to 5 years were also included in the frequency analysis and were used for the spatial distribution of smaller storm frequencies (less than 5 year return period). For stations with 2 years of record, the annual maximum average values were used in the analysis of spatial distribution. Stations with less than 5 years of records were included in the analysis of spatial distribution to account for rainfall in areas with low density network. A statistical analysis Availability of fine temporal resolution of rainfall was very limited. Eight stations had a few months of 5-min rainfall data. However, most of the time series recorded zero values and only few storm events were identified in four of the eight stations. Table 10 summarizes the fine resolution rainfall data available. Table 10: Fine Resolution 5-min Rainfall Data Available

Location ID Starting

Date Ending

date

Astan-A 1/8/2007 1/12/2008

B-Al Saed-A 1/01/2007 8/12/2007

Darsalm - A 1/01/2007 8/24/2007

Dharwan-A 1/01/2007 9/22/2007

Mend-A 1/14/2007 9/18/2007

Suninah-A 6/05/2007 12/31/2007

Kherbah-A 12/28/2005 9/22/2007

Maqwalah-A 1/01/2007 12/31/2007

14.4. Statistical Frequency Analysis

Statistical frequency analysis of rainfall was performed using daily rainfall data to determine the rainfall depth for a storm event with a specified duration and exceedance probability. Frequency-based analyses are commonly used to define precipitation in a hydrologic model. After merging the 10 stations indicated in Table 2, a total of 26 statistical frequency analyses were conducted using daily rainfall data. All statistical analyses were performed using 24-hr duration rainfall. In addition, frequency analyses with smaller durations were performed on stations with 5 years or more, as explained in the following sections.


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The U.S. Army Corps of Engineers Statistical Software Package (HEC-SSP) was used to perform the statistical frequency analysis. HEC-SSP is an integrated software system with interactive statistical capabilities to perform flood and rainfall frequency analysis. The analysis was based on annual duration series by selecting the maximum daily rainfall depth for each year of record. Time-series with maximum daily values were created for each frequency analysis. Rainfall data pre-processing included checking for data availability and interpolating missing data.

14.5. Interpolating missing data

A review of monthly rainfall records for different stations shows that monthly rainfall is seasonal and the majority occurs in the months of March to May and July to September for most years. While checking the recorded data for each station, data availability was checked for the above months for each year. A year with significant missing data in the above months was not used in the analysis in order to minimize the risk of generating temporal in-homogeneities. Table 3 shows the stations and years that had significant missing data and were not used in the analysis. Some rainfall stations with years of recorded data used in the analysis had missing data. The missing days were identified from the daily time-series for each station. It is common to derive these data from secondary data sources. Missing data on dates that fall between recorded data at nearby stations were considered for rainfall interpolation. An inverse distance weighing method was used for estimating missing rainfall values at a particular target station based on the available rainfall values recorded in the neighbor stations. For a missing value for station i, up to four closest nodes containing data for that time were identified. The distance was computed between station i and its closest neighbors with non-missing values (denoted by d1, d2, d3, d4). Weights were calculated for each of the closest neighbors with non-missing data, and were assigned in inverse proportion to the square from the node i. Hence, a station closer to the node i will have greater weight in the calculation. The equation for the weight term using four closest stations is as follows: Wj = (1/d1

2)___________________ [(1/d1

2) + (1/d22) + (1/d3

2) + (1/d42)]

where wj is the weight assigned to the neighbor j in relation to station i. Similarly, weights are calculated for all other closest nodes. The missing value at node i for a time t is estimated using the following formulae: Pi (t) = ∑ wj pj (t)


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where j represents each neighbor node around the station i, and p is the rainfall depth at node j. P is the interpolated data value at the target station i. The interpolated data value is the missing value which is added to the daily time series. Table 11 shows the stations with missing values that were interpolated based on nearby rainfall stations. Table 11: Interpolated Missing Rainfall Depth

No. Station with missing data

Date Interpolated Rainfall (mm)

Station used for interpolation

1 Astan-A

8/6/1997 14.20 Samnah-A,Ma'adi-A, Birbas'l-A 10/23/1997 5.85

3/26/2001 6.22

2 DarSalm

3/11/1983 2.89 Sana'a Airport, Cama, NWRA-A, NWRA-A-

Branch-A 3/12/1983 21.42

3/27/2003 0.65

4/23/2003 5.81

4/25/2003 2.88

4/28/2003 2.89

3 Darwan

7/10/1978 81 Sana'a Airport

3/27/2003 41.4

4/2/2006 4

4 Maadi-A

7/13/1991 1.36 Astan-A, Birbas'l-A, Samnah-A 7/24/1991 8.19

10/23/1997 21.23

12/31/1997 8.85

5 Kherbah-A

8/7/2003 43.82 NWRA-S-Branch-A, Sana'a Airport 3/12/2006 2.28

7/6/2007 1.90

6 NWRA

10/2/2004 2.61 Kherbah-A, Sana'a Airport, NWRA-S-

Branch-A 4/18/2005 0.08

5/19/2005 1.41

7 NWRA_S_Branch 8/8/2006 1.65 Kherbah-A, Sana'a

Airport, NWRA 9/7/2006 1.54

8 Shu’ub 8/10/1983 16.49 Cama, Sheraton


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The dates shown in Table 11 are the dates with missing values that were interpolated for 8 stations. Missing values interpolated from stations Darwan, Maadi-A, Kherbah-A, and Shu’ub were added to the annual duration time series.

14.6. Statistical frequency analysis of rainfall

The rainfall stations used for the statistical frequency analysis in HEC-SSP are shown in Table 11. The statistical analysis is done using the generalized frequency analysis tool available in HEC-SSP. This tool provides various choices on analytical distributions and plotting positions to perform a graphical fit to the data. A unique HEC-SSP study file is created for each station modeled. For each study file, data import and analysis using general frequency analysis tool was performed. Following are the steps that were followed under the general frequency analysis tool to perform the statistical frequency analysis of rainfall data: a) Primary input data. HEC- SSP stores and manages data in HEC Data Storage System (HEC - DSS)

files. The data editor of the HEC- SSP study file is used to import Excel data into HEC- DSS file format. For each station modeled, different HEC- DSS files were generated corresponding to different durations.

b) Determination of best fitted curve. The annual duration series with 24-hr duration were used to determine the best fit from the available statistical distributions. Once the time series were brought into the HEC- SSP file format, the raw data were plotted without any transformations for visual inspection and to determine which distribution would fit best. If the data looked like a straight line, the normal or log-normal distributions were used. If the data had a curve, a Pearson or log-Pearson distributions were used.

c) Identify and select parameters. The parameters selected within HEC-SSP include:

Transformation – Both logarithmic and normal transformation were used Plotting Position - different plotting methods are available however the plotting position

method selected does not have any impact on the computed curve and therefore the Weibull plotting position was used for all fitted curves

Confidence limit – a confidence limits of 90% confidence interval was used Distribution – The statistical distributions used in the analysis to fit the curves included

Normal, Pearson III, Log Normal, and Log Pearson III d) Computed outputs of this analysis are 1) frequency curves plots that display the analytical

computed curve, confidence limits, and the raw data points plotted based on selected plotting position methods and 2) report file that includes a summary of input data and results.


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Statistical tests like high and low outliers, incomplete records, and regional skewness are also computed in the analysis.

The analytical plots computed in HEC-SSP show the relationships between rainfall depth and return period for unique storm durations. Using the selected parameters for a particular station, the best fitted distribution for the 24-hr duration was applied. The statistical frequency analysis was also performed on other durations (10-min, 15-min, 30-min, 1-hr, 2-hr, 3-hr, 6-hr, 12-hr, and 18-hr) for selected stations to develop depth-duration-frequency curves. See Appendix D for summary of input data, results and graphs from HEC-SSP for each station and duration. Figure 22 shows the best fitted curve for Sana’a airport station. The time series for this rainfall station had 31 points (years) and a Pearson III distribution was found to be the best fitted curve.

Figure 12: Statistical Distribution for Sana’a Airport Station

14.7. Rainfall Temporal Distribution

Rainfall in the Sana’a Basin has a variable distribution in time and space, as a result of the convective storms that bring precipitation with high intensities and short durations. Because of the sparse and

Probability

0.9999 0.999 0.99 0.9 0.5 0.2 0.1 0.02 0.005 0.001 0.0001

Return Period

1.0 1.1 2 5 10 50 100 500

Ra

infa

ll i

n m

m

-20

0

20

40

60

80

100

120

General Frequency Analy tical Plot f or Sanaa_24hr_Pearson

Computed Curve 5 Percent Confidence Limit

95 Percent Confidence Limit Observed Events (Weibull plotting positions)

High Outlier


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short term nature of the rainfall in Sana’a Basin, it is necessary to have fine temporal resolution rainfall. The temporal distribution of storm events can be defined by mass curves developed from hyetographs or depth-duration-frequency curves (DDF) developed from frequency analyses. Due to the insufficient fine resolution rainfall available, alternative analysis methods were used to develop the frequency-based analysis needed for the flood risk assessment. The temporal distribution of rain within a storm was analyzed based on the limited 5-min time resolution rainfall data available. The frequency-base analysis was performed using daily rainfall data to determine the rainfall depth for a storm event with a specified duration and exceedance probability. These methods are described in detail below.

14.8. Storm Event Hyetograph – Mass Curves

Temporal distribution of rainfall for a storm event is presented in hyetographs showing the relationship between time and rainfall depth. Hyetographs can be constructed from real time rainfall records or via synthetic methods able to capture the variability of rainfall. Fine temporal resolution of rainfall was very limited. Eight stations had a few months of 5-min rainfall data, mostly for year 2007. However, most of the time series recorded zero values and only few storm events were identified in five of the eight stations. Table 4 summarizes the fine resolution rainfall data available. From the historical rainfall data, 11 storm events were identified with a total depth of 9 mm or higher. Each storm event hyetograph was plotted and total storm duration and depth were calculated. The hyetographs are included in Appendix E. Most of the storm durations lasted from 25 minutes to approximately 4.7 hours. The cumulative rainfall depth for the storms varied from 9 mm to 42.25 mm. The hyetographs were converted into mass curves by plotting the cumulative rainfall versus cumulative time as a percentage. Figure 23 shows the mass curves for the identified storm events.


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Figure 23: Mass Curves for Single Storm Events

Based on the mass curves for the 11 storms identified, an average mass curve was developed. The average mass curve includes the low envelop curve at the beginning of the storm, the high envelop curve at the end of the storm and a quick rising peak in the middle of the storm. This mass curve was used for the temporal distribution of rainfall in the frequency storm event simulations.

14.9. Depth Duration Frequency (DDF) Curves

Depth-duration-frequency curves (DDF) provide the relationship between rainfall amount (depth or intensity), duration and frequency. DDF curves are constructed using rainfall depths developed from a probabilistic analysis for selected return periods and storm durations. Rainfall data for statistical analysis was available only with a 24-hr duration. Assuming that there are no major changes in the general precipitation climate in the basin, 24-hr rainfall can be desegregated into smaller durations using average ratios of x-hour/24 hour rainfall. Based on this assumption, DDF curves were developed using the results of the statistical frequency analysis explained above.

Rainfall Mass Curve

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1.10

0.00 0.20 0.40 0.60 0.80 1.00 1.20

Cumulative Time (%)

Cu

mu

lati

ve D

ep

th (

%)

Darsalm 1:15hr

Darsalm 0:55hr

Darsalm 0:25hr

Kherbah 4:40hr

Kherbah 2:25hr

Kherbah 2:00hr

Kherbah 0:35hr

BaitAssaed 4:25hr

BaitAssaed 3:00hr

Dharwan 0:45hr

Suninah 2:55hr

Average Mass Curve


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Rainfall duration in the Sana’a Basin last just a few hours or less. Even though the rainfall data is collected daily, it is a fact that storms occur in short durations of a few hours or less. Disaggregating daily rainfall into hourly rainfall using averages ratios (developed for rainfall in the United States) considerably reduces the maximum depth expected for storms in Sana’a. Therefore, the applicability of the DDF curves for Sana’a using daily rainfall was found not suitable. The results of the statistical frequency analysis performed with the 24-hr duration rainfall were used in the following studies. However, the results for other durations were not utilized in the modeling process.

14.10. Rainfall Spatial Distribution

Spatial distribution of precipitation provides an understanding of hydrological process and also helps to understand the role of complex topography of mountainous basins and plain areas of Sana’a basin. Depth-Area-Duration (DAD) curves were used in this analysis to represent the spatial distribution of rainfall. In several regions of Sana’a Basin, the rainfall stations are sparsely located. Historically, there have been several stations that have operated in Sana’a Basin, but for few stations, the available rainfall data is for less than 5 years. For the spatial distribution of rainfall, stations with 3 to 5 years were added to the frequency analysis previously performed to account for rainfall in areas with low rain density network. These additional stations were used for the shorter return periods (1.1 Year, 2 Year and 5 Year). In addition to the 13 statistical frequency analyses already conducted, 13 additional statistical analyses were conducted with the stations that had less than 5 years of data. These analyses were performed only for rainfall with 24-hour duration. To estimate the spatial distribution, the maximum daily rainfall (computed in HEC-SSP) for each station and return period were plotted in a map. The maps with short return periods (1.1 Year, 2 Year and 5 Year) have more data points because more stations were used in the analysis, as explained above. There are two common methods to analyze the spatial distribution of rain and develop DAD curves. The two methods include 1) Isohyets contours and 2) Thiessen polygons. Maximum rainfall depth for each return period and surfacing techniques in GIS were used to develop maps for both methods. A detail description of each method follows.

14.11. Thiessen Polygons

Thiessen polygons identify individual region of influence around a set of points. They are defined by the perpendicular bisectors of the lines connecting all points.


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Map surfacing techniques in ArcView 3.1 were used to generate Thiessen polygons. The final datasets were defined using Geographic Coordinate System, NGN Yemen 36N. ArcView extension – Create Thiessen Polygons uses the computed daily maximum rainfall data and Sana’a Basin boundary to generate the polygons. Thiessen Polygons are constructed by drawing lines between rainfall stations, and then making perpendicular bisectors of those lines form the polygons. The basin boundary layer was modified to include the Astan station (located outside of Sana’a Basin) in the interpolation process. The Thiessen polygon divides the watershed into polygons with the rainfall stations in the middle of each polygon. The value at the station is assumed to be representative for the rainfall on the area of land included in its polygon. Appendix G includes the Thiessen polygon maps for each return period. The method of estimating rainfall depth for a specified area using Thiessen polygons was not used to create DAD curves for Sana’a as this method does not account for topographic influences as those occurring in Sana’a Basin.

14.12. Isohyetal Contours

Isohyetal maps show lines or isohyets joining points that receive equal amounts of rainfall. These maps help to recognize the relative rainfall depth gradient in the basin and estimate rainfall depth at specific places. The Isohyets were created using the daily maximum rainfall depth from the statistical frequency analysis. Map surfacing techniques from Arc GIS 9.2 were used to generate the Isohyets contours. The final datasets were defined using Geographic Coordinate System, NGN Yemen 36N. Arc GIS’s Spatial Analyst’s tool for inverse distance weight was used to convert the computed daily maximum rain into a temporary raster dataset that was clipped to Sana’a Basin boundaries. This raster data set is then converted into feature lines to display the isohyetal contours. This method illustrates spatial distribution of rainfall in the form of isohyets based on rainfall depth for a particular storm event. Figure 24 shows the isohyetal map for rainfall with short return period (2 Year) and Figure 25 shows the isohyetal map for rainfall with long return period (100 Year).


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Figure 24: Isohyet Contours for Figure 25: Isohyet Contours for

2-year Return Period 100-year Return Period In general for all isohyetal maps, the mountainous areas on the east and west part of the basin have higher rainfall depths whereas the plain areas running in the center of the basin have fairly constant and lower rainfall depths. Also, for storms with short return periods, the rainfall seems to be more uniformly distributed in the entire basin except for a few areas in the mountains. Appendix F contains the isohyetal maps with maximum daily rainfall for all return periods in the analysis. The method of estimating rainfall depth for a specified area using Isohyetal contours was used to create the DAD curves as Isohyets are considered the most accurate method for computing rainfall over mountainous terrain. This method accounts for topographic influences as those occurring in Sana’a Basin.

14.13. Depth Area Duration (DAD) Curves

In any given storm event, the rainfall distribution over the basin is not uniform and varies from event to event. The purpose of this analysis is to account for the spatial variability of rainfall and determine the rainfall depth for a specific drainage area. The procedure is based on studying the rainfall spatial


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distribution over the basin for the worst historical storms and calculating the corresponding rainfall over the basin. The development of DAD curves in this study was made using the Isohyet maps. The DAD curves were constructed by calculating the area between isohyet contours and multiplying it by the average rainfall depth between isohyet contours to calculate the volume of rainfall. The cumulative volume is then divided by the cumulative area to calculate the areal rainfall. Cumulative areas and areal rainfall depths are plotted for each return period. The analysis was based on 24-hr duration rainfall. Figures 26 and 27 show the DAD curves for the 2-year and 100-year, respectively. Appendix I contains the tables and DAD curves for each return period.

Figure 26: DAD Curve for Figure 27: DAD Curve for 2-year Return Period 100-year Return Period The DAD curves were used for hydrologic modelling by applying the rainfall depth to each sub-basin based on the area. The Hydrologic Analysis section describes in more detail how the DAD curves were applied to the model.

14.14. Probabilistic Return Period

The return period determine the frequency of a storm event or the percentage exceedance of a particular storm event. In theory, from the statistical distribution, rainfall amounts are estimated for return periods longer than the historical data. However, only one station in Sana’a basin has more than 30 years of record. In the absence of longer recorded data, it was assumed that rainfall depth for higher return periods could be extrapolated from the distribution of recorded rainfall. A statistical frequency analysis of the maximum annual series was performed in HEC-SSP to determine the rainfall depth for every return period. Return periods for the 1.1, 2, 5, 10, 25, 50, 100, and 500 years were analyzed.

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15. NEXT STEPS

After getting approval of the assessment report from the UCO, the consultant will continue complete

the study according to the ToR with the following steps:

15.1. Hydrologic Analysis and modelling

The concept of watershed modelling is embedded in the interrelationships of soil, water, climate, and landuse and is represented by means of mathematical abstractions. The behaviour of each process is different and controlled by its own characteristics and its interaction with other processes within a given catchment. Rainfall is one of the predominant hydrologic processes, in addition to interception, evapotranspiration, infiltration, surface runoff, percolation, and subsurface flow. Various mathematical models have been formulated during the last four decades. The developed models vary from empirical models for the evaluation of flood events to simple ones containing a certain degree of physicality, to stochastic models of different kinds and finally to the distributed models (Gosain and Mani, 2009). The schematic of the hydrological model is shows in figure 28.

Figure 28 : The schematic of the hydrological model


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15.1.1. Classifications of hydrological models

From the historical development of the hydrological models, modelling approaches can be classified as black-box models, physically based models, and conceptual models.

a. Black-box models Black-box models describe mathematically the relation between input (precipitation) and output (runoff) without describing the physical process by which they are related, and establish a statistical correspondence between input and output. These models are often successful within the range of data being available/collected and analysed from a region. The reason is that the mathematical structure carries with it an implicit representation of the underlying physical system. Beyond the range of analysed data, the prediction depends only on mathematics, since the physical significance is lost (Gosain and Mani, 2009).

b. Physically based models Physically based models are based on the best available understanding of the physics of hydrological processes. These models are thus characterised by parameters that are, in principle, measurable and have a direct physical significance (Wheater, 2005). They require intensive data in addition to the extensive computational time. Hence, such models are very costly to develop and operate (Liddament et al., 1981). They are based on a continuum representation of catchment processes. These models are distributed because of the non-linear partial differential equations that describe the hydrologic processes. It has been noted that analytical solutions are generally not available to solve the equations, thus the equations of motion of the constituent processes are solved numerically using finite-difference methods (Freeze, 1971; Freeze, 1972a; Freeze, 1972b; Freeze and Harlan, 1969) and finite-element methods (Beven, 1977; Ross et al., 1979). These models offer the ability to simulate the complete runoff cycle and the effect of catchment changes. They offer the internal view of the process, which enables an improved understanding of the hydrologic system. The European Hydrological System (SHE) (Abbott et al., 1986) and the Institute of Hydrology Distributed Model IHDM (Beven et al., 1987) are the most well known distributed models of this category. Spatial distribution of catchment parameters has been achieved in the horizontal and vertical directions. Each process of the hydrological cycle has been modelled either by the finite-difference representations or by empirical equations derived from independent experimental research (Gosain and Mani, 2009). Physically based models can be applied to ungauged catchments, and the effects of catchment change can be explicitly represented. Two fundamental problems arise with such models; the underlying physics has been derived from small-scale (mainly laboratory-based) laws and process observations. Hence, the processes may not apply under field conditions and at field scales of interest. Although the


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parameters may be measurable at a small scale, they may not be at the scale of interest for application. Most of the complexity of physically based models arises from the representation of subsurface-flows and the inherent lack of their observability. The situation often met in arid areas is that overland flow is the dominant runoff mechanism and surface properties are, in principle, much more readily obtained (Wheater, 2005).

c. Conceptual models Conceptual models are the most common class of the hydrological models in general application. These models incorporate the hydrological processes in the form of a conceptual representation (Wheater, 2005). They serve as a trade-off between the physically based approach and black-box approach. They are formulated by a number of conceptual elements, each of which is a simplified representation of one process element of the system being modelled. Each element of the model is generally described by a (non-linear) reservoir. The basic advantage of this form of modelling is that it reflects the thresholds present in hydrological systems, which otherwise cannot be adequately incorporated in a linear model (Gosain and Mani, 2009). Conceptual models are characterised by parameters that usually have no direct, physically measurable identity and thus need to be optimized. Parameters are non-identifiable, which leads to the problem of equifinality (Beven, 1993) : for a given model, many combinations of parameter values may give a similar performance, This has given rise to the following limitation: .if the parameters cannot be uniquely identified, then they cannot be linked to catchment characteristics. On the other hand, conceptual models cannot be applied for to ungauged catchments, as there are no data to calibrate the models. Finally, it is difficult to represent catchment changes if the physical significance of parameters is uncertain (Wheater, 2005). The functioning of conceptual models is controlled by the parameters of different processes. Hence, assigning proper values to these parameters is very essential for obtaining accurate model results for the specific area being modelled (Gosain and Mani, 2009). These models can be characterised into: a) Event models that only represent single runoff event occurring over a period ranging from an hour or less to several days, depending on the size of the catchment. The initial conditions for each event must be given as input. These models cannot keep the record of soil moisture conditions of the basin in a continuous manner; and b) Continuous models that operate over an extended period determining flows irrespective of their magnitudes.

15.1.2. Application the hydrological model in the hot spot area

The HEC-HMS model developed by the US Army Corps of Engineers will be used for hydrologic modelling to identify the basin and sub-basin characteristics and determine flow in each sub-basin


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given a rainfall amount. HEC-HMS is widely used and regarded as a sophisticated and reliable tool (USACE 2008a). The steps will include in the hydrologic analysis are detailed below:

Model development Model calibration Model results

a. HEC-HMS Model Development

The Hydrologic Modeling System (HEC-HMS) software will be used to analyze the precipitation-runoff processes in the Sana’a Basin. A number of hydrologic modeling inputs will be developed using the Geospatial Hydrologic Modeling Extension (HEC- GeoHMS) tool (USACE 2003). HEC-GeoHMS works within a Geographic Information System (GIS) interface. HEC-GeoHMS transforms digital terrain information like drainage paths and watershed boundaries into a hydrologic data structure that represents the watershed response to precipitation.

b. Data Pre-processing using HEC-GeoHMS

HEC-GeoHMS is used to perform the terrain pre-processing that utilizes input data like raw DEM (Digital Elevation Model) and a River shape file for the study area in the Sana’a Basin. Following reconditioning of the raw DEM, several preprocessing steps will be conducted in sequential order to produce a series of grid and vector data. First, various grid data on flow direction, flow accumulation, stream, stream segments, and catchments are processed followed by generation of series of vector data like catchments polygon, adjoint catchments (aggregated catchments) and drainage points. A watershed layer is processed using all the grid and vector layers generated above by terrain pre-processing.

c. Watershed delineation

Topographic maps, IKONOS satellite photographs, and DEMs will be used to refine the watershed delineation from the existing hydrologic reports described in the hydrological studies of the Sanaá basin. As a result of these reviews and the independently collected data and maps, a wadi network and associated catchment areas were developed. Sub-basins will be delineated to maintain the approximate spatial homogeneity of their hydrologic characteristics and also will be delineated based on the locations of structures and stormwater network system. The SCS curve numbers will be assigned based on the various types of soil and landuse data for the basin. The soil and land use information will be merged using Arc GIS Spatial Analyst and HEC-GeoHMS. A CN (Curve number) grid that includes soil and land use information for the basin will be developed. The CN is an index that combines hydrologic soil group and landuse factors to estimate the amount of rainfall that becomes runoff.


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15.1.3. HEC-HMS Model Setup

Using the terrain pre-processed data and sub-basin data generated in the previous steps, a HEC-HMS model of the study area in the Sana’a Basin will be created. A total of 7 sub-basins will be generated. The HEC-HMS model consists of several components including basin data, meteorological data, and elevation-storage-discharge data. The model was setup using hydrologic parameters for each sub-basin. Most hydrologic parameters were developed using HEC-GeoHMS. Meteorological data included rainfall data for historical events and rainfall distribution and depth for the frequency storm events. The mass curve developed in the rainfall analysis will be used for the rainfall distribution of the storms. The overview of the HEC-HMS Model is present in figure 29.

Figure 29: overview of the HEC-HMS Model

15.2. Flood Risk assessment and mapping

The risk assessment for Sana’a will be carried out by identifying all possible water-related hazards,

including how they are likely to develop in the future as a consequence of urbanization or other

development. The distribution of the floodwater within Sanaá city is mainly determined by the

existence, location and behavior of critical points during a specific discharge.

The risk modeling will identify losses from natural disasters and develop scenarios to incorporate

estimates of urban growth and build-up by 2030.

The hydrology and hydraulic analysis will be conducted to identify the study area and sub-basin

characteristics and determine flow in each sub-basin given a rainfall amount. These analyses provide

information about the probability of a hazard’s occurrence and the respective loss potential. Different

probabilistic events will be modeled to quantify the risk to buildings by their occupancy types

residential, commercial, industrial, and squatters. The model will produces risk maps that provide


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information about the expected flood frequencies and magnitudes (extent, depth, duration, and flow

velocities).

15.2.1. Flood depth generation

The steps will include in the flood depth generation are detailed below

1- Generate the flood depth maps using contour interpolation and point interpolation.

From segment maps that already given, create the raster maps using contour interpolation and slicing

the result map in order to obtain different classes of flood depth. In term of point interpolation, create

attribute map of polygon maps using the certain column that correlated with the every return period

which is the table already given. Convert the polygon information to point information, and using the

point interpolation generates the flood depth for every return period. In order to obtain the class map

of flood depth, classify the result maps using slicing operation.

2- Generate the flood depth maps for maximum flood event using attribute map operation from

the table with the attribute, which contain maximum flood information.

3- Generate the lateral erosion hazard map by rasterizing the segment maps of the main river. Use

the distance formula to create the distance maps and followed by slicing operation to create

classes, In order to obtain the relation between the distance and the lateral erosion.

15.2.2. Generation of flood hazard maps

To generate the flood hazard maps, ILWIS or other suitable software will be used. From the segment

map for each return period, create raster maps that contain the flood depth information. From those

maps, converted to class maps using slicing operation in ILWIS or other suitable software. Contour

interpolation was chosen to create flood depth raster map than point interpolation, because the result

is better and more detailed.

The suggested developed vulnerability and flood risk assessment approach for the study area is shows

in the figure 30.


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Figure 30: suggested developed vulnerability and flood risk assessment approach for the study area.


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16. References:

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Appendix: 1- Pictures


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