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Megumi MUTOResearch Fellow JICA Research InstituteMuto.Megumi@jica.go.jp

ADB-JICA-WB Joint Study: Climate Change Impact and Adaptation in Asian Coastal Cities　～ Case of Metro Manila～

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Downscale IPCC climate models for temperature increase @2050 for B1 and A1FI scenarios

Assess local effects on precipitation and combine with sea level rise/ storm intensification

Simulate different types of hydraulic effects: 1) through river systems, 2) through accumulation of water at lake, and 3) through sea level rise and storm surge at the coast (combination depends on city)

Based on the flood maps produced for 12 cases (3 climate scenarios x 2 infrastructure scenarios x 2 return periods), estimate socio-economic impact (both direct and indirect) with available data, thus deriving the benefit side of adaptation.

Consider investment mix and their costs necessary for adaptation (focusing on flood control infrastructure)

Conduct Net Present Value (economic, not financial) and EIRR calculations

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Flood-prone areas in Manila

In addition:

・ Firm and urban poor household surveys to understand the details of vulnerabilities.

・ Health impact analysis　

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Flood Prone Areas in Metro-Manila

West Mangahan Area

Pasig-Marikina BasinKAMANAVA Area

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West Mangahan area

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West Mangahan area

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West Mangahan area

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KAMANAVA area

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Pasig-Marikina area

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1Downscale IPCC climate models for temperature increase @2050 for B1 and A1FI scenarios (University of Tokyo IR3S for all city case studies)

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2Assess local effects on precipitation and combine with sea level rise/ storm intensification

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Simulation Cases (Case of Metro Manila)Simulation Case Temperature

Rise (oC)(downscaled)

Sea Level Rise (cm)(global)

Increase Rate of

Rainfall (%)

Storm Surge Height (m)

1 Status quo climate 0 0 0 0.91

2 B1 with storm level at status quo

1.17 19 9.4 0.91

3 B1 with strengthened storm level

1.17 19 9.4 1.00

4 A1FI with storm level at status quo

1.80 29 14.4 0.91

5 A1FI with strengthened storm level

1.80 29 14.4 1.00

*Note: ground subsidence considered to arrive at local sea level rise

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3Simulate different types of hydraulic effects: 1) through river systems, 2) through accumulation of water at lake, and 3) through sea level rise and storm surge at the coast (combination depends on city)

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B1/30-year Flood/Existing flood control structures

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B1/30-year Flood/Continue 1990 Master Plan

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A1FI/100-year Flood/Existing flood control structures

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A1FI/100-year Flood/Continue 1990 Master Plan

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Based on the flood maps produced for 12 cases (3 climate scenarios x 2 infrastructure scenarios x 2 return periods), estimate socio-economic impact (both direct and indirect) with available data, thus deriving the benefit side of adaptation.

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Summary of Inundation Areain the Pasig-Marikina Basin

Simulation Case

30-year Flood 100-year Flood

Existing Structures

Implementing Current

Master Plan

Existing Structures

Implementing Current

Master Plan 1 Status quo

climate34.6 km2 14.7 km2 53.7 km2 29.1 km2

2 B1 42.5 km2 20.8 km2 63.2 km2 40.1 km2

3 A1FI 47.0 km2 22.8 km2 68.0 km2 44.1 km2

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2020

2Classification of Flood Water Depths 007

CategoryWater Depths (m)

RemarksMinimum Maximum

1 0.0000010 0.080No adverse effect to all buildings,

infrastructures, utilities and transportation

2 0.081 0.200Will affect transportation but no buildings,

infrastructures and utilities3 0.201 0.500 Definitely affects transportation, some

buildings, infrastructures and utilities4 0.501 1.000

5 1.001 3.000Adverse effects on transportation,

infrastructures, utilities and one floor level of buildings

6 3.001 6.000Same as category 5 but will affect two floor

levels of buildings

7 6.001 9.000Same as category 5 but will affect three

floor levels of buildings

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Socio-economic assessment

Source: Adapted with revisions from Southeastern Wisconsin Regional Planning Commission (1976) and Green et. al., (1983)

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Direct Impact Assessment Flowchart

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2020

Data from Direct Impact Secondary (Indirect) Impacts Analysis

Flood Affected Buildings

Flood Affected Area and Roads

Traffic Zones

Firms, residential

Income Loss of Income

Trips Generated/ Attracted (Public Mode)

Trips Generated/ Attracted (Private Mode)

Travel Time Delay Cost

Unit rate from Firm, household surveys

Time Value: Public Users“To work” &

“Business” Trips Time Value:

Private Users

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Added up benefits (savings) Cost of buildingsCost of buildings Vehicle operation cost savingsVehicle operation cost savings Travel time savings through existing/future Travel time savings through existing/future

road investmentsroad investments Avoided income loss (firms, formal/informal Avoided income loss (firms, formal/informal

households)households)- Use future predicted values as much as possible. Use

growth rate to arrive from these future values up to 2050.- Need more work on shadow prices, etc.- Should savings of power/rail operator be included?

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DATA:Future Land Use of Metro Manila, 2020

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DATA:Transport Infrastructurefor Metro Manila, 2015

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Trip dataTrip data

2003 20202007 1999

Sources: JICA-MMUTIS and JICA-PPP/MUEN

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Informal settlers data (present)

20202007

Sources: LGUs of MM and HLURB

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Affected area and population

P100 Affected Area (%)EX SQ 11.87EX B1 12.95EX A1F1 13.64

BAU SQ 5.25BAU B1 6.97BAU A1F1 7.62

P100 Affected Pop (%)EX SQ 20.08EX B1 22.13EX A1F1 23.27

BAU SQ 7.08BAU B1 7.08BAU A1F1 11.74

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Buildings’ Base Cost (present cost)Residential Buildings

Type* Median

Construction Cost

Finishing (25%)

Household Effects (35%)

1A9,200 2,300 4,0251B

1CIIA

6,150 1,538 2,691IIBIICIIIA 2,550 638 1,116

Commercial Buildings

Type1

Median Construction

Cost

Durable Assets2

Stocks2

1A11,100 27,750 333,0001B

1CIIA

7,750 19,375 232,500IIBIICIIIA 5,700 14,250 171,000

Institutional Buildings

Type1

Median Construction

Cost

Durable Assets2

Stocks2

1A11,000 2,970 1,1001B

1CIIA

7,500 2,025 750IIBIICIIIA 4,100 1,107 410

Industrial Buildings

Type1

Median Construction

Cost

Durable Assets2 Stocks2

1A6,050 26,620 22,9901B

1CIIA

3,550 15,620 13,490IIBIICIIIA 1,900 8,360 7,220

Source: LGU Assessor’s Office

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Building Damage Rates2020

Building Use Cost Item - 50 cm 100-200 cm

200-300 cm -

Above 300 cm -

Residential

Finishings 0.0920 0.119 0.580 0.834

Household Effects 0.1450 0.326 0.928 0.991

Business Entities1

(Commercial, Institutional,

and Industrial)

Assets 0.2320 0.453 0.9661 0.966

Stocks 0.1280 0.267 0.8971 0.8971

Source: Adapted from the Manual for Economic Study on Flood Control, May 2000, Ministry of Construction (presently the Ministry of Land, Infrastructure and Transport), Japan1/ maximum rate given is for depth of 200-299 cm. The same rates are likewise applied to Institutional and Industrial.

Flood Damage Rates by Building Use

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2020Cost of Damages to Buildings

Condition of Metro Manila

Land Use

Cost of Damage by Land Use by Water Depth (in 000 Php)

Present

20 - 50 cm Above 50

cm - 3 m Above 3 m - 6 m

Above 6 m - 9 m

Above 9 m Total

Existing Infra

Status Quo 22,523,23 113,559,497 3,919,805 190,832 121,958 140,315,328

B1 18,478,510 155,321,466 5,021,023 288,125 121,958 179,231,083

A1F1 17,871,154 177,466,638 5,504,319 422,788 123,012 201,387,913

Business-as-Usual

Status Quo 14,122,799 34,825,827 3,395,851 7,145,807 87,437 59,577,722

B1 19,535,278 54,809,224 2,717,361 7,173,132 84,651 84,319,648

A1F1 23,360,031 65,140,293 2,822,537 7,184,067 9,473 98,519,007

Building Damage Costs (present cost)

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Flood Scenario (Existing

Infra)

Road Length by Inundation Depth (kms)8-20 cm 21-50 cm Above 50 cm

Total

Major Minor Major Minor Major MinorStatus Quo 4.5 3.9 22.1 23.8 31.9 39.8 125.9 B1 5.4 9.7 13.6 15.1 47.9 55.6 147.3 A1FI 5.3 6.9 14.6 18.2 53.6 60.3 158.9

Flood Scenario

(Business-as-Usual)

Road Length by Inundation Depth (kms)8-20 cm 21-50 cm Above 50 cm

TotalMajor Minor Major Minor Major Minor

Status Quo 3.78 4.33 6.40 10.45 7.45 13.42 45.82B1 7.24 8.15 9.54 15.73 12.07 20.82 73.55A1FI 9.45 9.05 12.62 16.28 14.97 25.63 87.99

Affected Roads

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Affected VehiclesAffected Vehicles

2020

1 23

45

6

7

8 9

10

11

1213

14

15

16

1718

19

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2122

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2526

27

28

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Daily Traffic Volumes from Roadside Traffic Count Stations in Metro Manila

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2020

Road ConditionPublic Mode Private Mode

Peso/km Peso/kmGood / Fair 9.614 11.795Inundated (bad) 14.316 16.962

Flood Incremental Cost 4.702 5.167

Vehicle Operating Cost for Vehicles in MM

Traffic Count Stations in

Metro ManilaDirection

Total Public Total Private Total

VehiclesInunda-tion Cost (Php/km)

VehiclesInunda-tion Cost (P

hp/km)Vehicles

Inunda-tion Cost (Php/km)

Total Both Directions

146,313 687,965 796,606 3,745,642 918,271 4,433,607

Source: Department of Public Works and Highways, 2006 Price Levels

Note: Computation for all flood scenarios.

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2020

Mode Type

Time Value of Trip Makers

(Pesos/hour)20021/ 2050

Private 81.30 148.00Public 45.45 83.00

Flood ScenarioNo. of Trips

Cumulative Travel Time Delay Cost (Php/hr)

Public Private Public PrivateEXISTING INFRA: 2002

Status Quo 855,935 217,645 38,517,064 17,629,240B1 1,031,706 280,046 46,426,792 22,683,717

A1F1 1,058,941 295,015 47,652,359 23,896,185EXISTING INFRA: 2050

Status Quo 1,741,191 999,355 144,518,892 147,904,489B1 1,903,258 1,223,565 157,970,385 177,993,032

A1F1 1,924,578 1,212,727 159,739,956 179,483,552BUSINESS-AS-USUAL 2002

Status Quo 335,728 121,971 15,107,760 9,879,688B1 496,336 174,004 22,263,697 14,058,719

A1F1 613,269 229,933 27,597,116 18,624,607BUSINESS-AS-USUAL 2050

Status Quo 396,998 208,314 32,950,807 30,830,421 B1 622,938 341,766 51,703,832 50,581,344

A1F1 824,697 429,490 68,449,818 63,564,534

Travel Time Value

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2020LGU

Flood-affected Generated/Attracted Trips by PurposeSchool Recreation Medical Religious

City of Manila 2,002,254 41,034 164,033 135,976Kalookan City 92,195 284 3,339 5,507Makati City 169,085 2,464 5,445 18,075Malabon City 197,922 1,538 2,764 11,520Mandaluyong City 143,333 1,275 4,052 6,639Marikina City 173,042 2,820 4,878 16,313Navotas 115,391 1,943 1,758 6,840Pasay City 30,505 662 2,199 1,158Pasig City 280,050 4,897 13,777 22,249Quezon City 460,404 4,080 47,833 33,372San Juan City 58,564 499 2,725 3,408Taguig City 96,033 4,665 2,415 5,971Pateros 52,406 600 522 3929

Total MM 3,871,184 66,161 255,218 267,028Source: JICA-MMUTIS

With a trip rate of 2.2, approximately 1.8 M students affected

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Water facilities

2020

No damage of flood assumed since:

• Pipes are positively charged;

• No record of flood–related damage incidences; and

• Facilities are above flood levels.

Source: Maynilad Water Services and Manila Waters, Inc.

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Power sector2020

Flood-affected TRANSCO Substations in Metro Manila

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2020Rise in Flood Water Level

(m)Substations Elevation (m)1 Affected Areas

1CBP 1-A(at Mall of Asia, Pasay City)

0.61 Pasay City Paranaque City

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North Port(at Antipolo St., Tondo, Manila)

4.00 ManilaSouth KalookanNavotas

Capasco(at Napindan Road, Taguig)

4.88 CAPASCOTaguig

Taguig(at Elisco Road, Taguig)

4.88 Taguig CityPaterosMakati CityPasig CityMandaluyong City

Paco(at Quirino Hiway, Sta. Ana, Manila

5.79 Malate, ManilaSta. Ana, ManilaSan Andres, ManilaPaco, ManilaErmita, ManilaMakati City

Pasay(at EDSA near Tramo, Pasay City

6.10 Pasay CityParanaque CityMakati City

Affected Power Distribution Infrastructure

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2020

RSS No. & Location (LRT Line 1) Minimum Flood Depth

3 - Buendia Station 0.45 m

5 - Central Station * 0.42 m

6 - D. Jose Station * 0.55 m

7 - Blumentritt Station 0.26 m

Flood-affected RSS of Urban Rail Transport

* Any impact on these substations would paralyze entire line.Source: Light Rail Transit Authority

= income loss of P 4.7M per day for the line

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5Consider investment mix and their costs necessary for adaptation (focusing on flood control infrastructure)

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Adaptation Measures to Climate Change in Metro Manila

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6 Conduct Net Present Value (economic, not financial) and EIRR calculations

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Framework for EIRR & NPV P100

Benefits

P30 Benefits

Benefit side

•Savings on Buildings•Savings on Travel Time•Avoid Loss of Income •VOC savings, etc.

Investment Options and

Mixes for Climate Change

Adaptation Projects

Assumptions

•Project Life of 50 years•3 – 5 years Construction•2010 start construction•Flood probabilities of 1/100 and 1/30

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Results of EIRR & NPV (tentative numbers only)Results of EIRR & NPV (tentative numbers only)

Description Investment EIRR NPV@15% NPV@3%(%) (Php Million) (Php Million)

NO DAM, combining incremental flood control investments P100 A1FI Level

Php 479 MYen 2,971M

32.83 670 12,700

WITH DAMP100 A1FILevel

Php 3,974 MYen 24,639 M

8.12 1,833 9,912

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In addition

Firm and urban poor household surveys to understand the details of vulnerabilities.

Health impact analysis.

- both using present data

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Geographic distribution of householdsRiver Basin Community/Barangay Number of

HouseholdsTotal

San Agustin, Malabon 15Longos, Malabon 10Bangkulasi, Navotas 15Bagumbayan South, 30West Navotas, Navotas 15Barangay 28, Caloocan 15

Rosario, Pasig City 25Bagong Ilog, Pasig City 25Ugong, Pasig City 25Tumana, Marikina City 25

Ibayo Tipas, Taguig City 25Calzada, Taguig City 25Napindan, Taguig City 25San Joaquin, Pasig City 25

Total 300

West-Mangahan Area

100

KAMANAVA Area

100

Marikina-Pasig River Basin

100

Household Level

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Urban poor households affected by multiple-forms of disasters

Tidal surgeTyphoon

Flood

35 (11.67%)

9(3%) 1

(0.33%)

118(39.33%)

6(2%)

53(17.67%)

20(6.67%) Not affected

58(19.33%)

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Absent days from school of affected households

Days Floods Tidal Surge Typhoons

0 44 21 49(15%) (7%) (16%)

1--5 66 44 65(22%) (15%) (22%)

6--10 20 20 28(7%) (7%) (9%)

11--15 2 2 3(1%) (1%) (1%)

16--20 1 3 2(0%) (1%) (1%)

21--25 2 0 0(1%) (0%) (0%)

26--30 0 1 0(0%) (0%) (0%)

>30 4 3 4(1%) (1%) (1%)

Number of affectedhouseholds

139 94 151

Mean absent days 3.95 5.19 4.09Note:% of sample households is presented in the parenthesis

Type of disaster

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Distribution of sick days caused by the past disasters

Days Flood Tidal Surge Typhoon0 42 35 53 (14%) (12%) (18%)1--7 54 30 36 (18%) (10%) (12%)8--14 4 1 2 (1%) (0%) (1%)>=15 1 0 0 (0%) (0%) (0%)Number of affectedhouseholds

100 66 91

Mean absent days 5.13 2.53 2.33Note:% of sample households is presented in the parenthesis

Type of disaster

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Distribution of sickness caused by the past disaster

Sickness Flood Tidal Surge Typhoon

N/A 207 244 219 (69%) (81%) (73%)Primary Infections 4 0 2 (1%) (0%) (1%)Fever 10 3 7 (3%) (1%) (2%)Flu 55 39 60 (18%) (13%) (20%)Skin Diseases 8 5 3 (3%) (2%) (1%)Digestive Disorders 13 8 9 (4%) (3%) (3%)Dengue 2 0 0 (1%) (0%) (0%)Colds 1 0 0 (0%) (0%) (0%)Kidney Disorder 0 1 0 (0%) (0%) (0%)Total 300 300 300Note:% within column is presented in the parenthesis

Type of disaster

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Distribution of medications fee, caused by the past disasters

Medications Fee(Php) Flood Tidal Surge Typhoon

0 233 257 238 (78%) (86%) (79%)1--1000 60 41 58 (20%) (14%) (19%)1001--2000 0 0 1 (0%) (0%) (0%)2001--3000 1 1 1 (0%) (0%) (0%)3001--4000 0 1 0 (0%) (0%) (0%)4001--5000 2 0 1 (1%) (0%) (0%)>5000 4 0 1 (1%) (0%) (0%)Number ofhousholds getting ill

100 67 91

Mean medications fee 1063 301 318Note:% of sample households is presented in the parenthesis

Type of disaster

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Distribution of absent days from work caused by the past disasters

Days Floods Tidal Surge Typhoons0 244 272 243 (81%) (91%) (81%)1--5 39 21 45 (13%) (7%) (15%)6--10 13 5 10 (4%) (2%) (3%)11--15 3 0 1 (1%) (0%) (0%)>15 1 2 1 (0%) (1%) (0%)Total 300 300 300Note: % within column is presented in the parenthesis

Type of disaster

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Distribution of income loss caused by the past disasters

Value (Php) Floods Tidal Surge Typhoons0 229 267 233 (76%) (89%) (78%)1--500 36 19 38 (12%) (6%) (13%)501--1000 17 7 18 (6%) (2%) (6%)1001--1500 7 3 6 (2%) (1%) (2%)1501--2000 9 2 3 (3%) (1%) (1%)2001--2500 1 0 0 (0%) (0%) (0%)>2500 1 2 2 (0%) (1%) (1%)Number of the householdsabsent from work 92 52 86

Mean income loss 699 649 629

Type of disaster

Note: % of sample households is presented in the parenthesis

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Number of affected firms & the temporarily stopped days

No. % Mean Min Max

Pasig - Marikina Bgys 86 28 32.6 1.0 0.5 2

Kamanava Bgys 66 37 56.1 2.3 0.5 9

West Manggahan Bgys 58 29 50.0 1.8 0.1 14

Other Barangays 76 40 52.6 1.8 0.5 7

All Establishments 286 134 46.9 1.8 0.1 14

Stopped daysArea Obs.

Affected firms(over the past 3 years)

Firm Level

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Affected reasons and cause in typhoon Milenyo

Floodingwithin premise

Floodingwithin 50 km

Meters

Floodingwithin 5 km

RadiusStrong wind Others

Products delivering problem 97 11 13 44 24 1Low sales 109 17 26 31 20 5Raw materials receiving 65 5 16 25 13Employee shortage 154 15 27 53 47 1Machine/Equipments 24 13 1 5 2Inventory damage 38 13 1 4 4 5Order cancelled 25 3 3 9 5 2Electricity /Power outage 172 10 5 8 137 4Damages to 53 1 1 3 32 1Water shortage 9 2 1 3 1Others 11 3 1 3 2

Affected reasons No. of reasonscited by firms

Cause of damage

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Reasons of employees’ absence from work

Transportunavailability

Healthproblem

Foodssecuring

Houserepairing Others

Pasig - Marikina Bgys 148 93 6 6 66 30

Kamanava Bgys 55 46 2 3 30 8

West Manggahan Bgys 43 33 4 4 13 13

Other Barangays 60 46 2 1 13 1Note: One firm may cite more than one reason, so the number of affected firms does not equal the number of thereasons of work absence

Area Obs.Reason

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Income AssetsPasig - Marikina Bgys 86 480,194 254,582

(47) (75)Kamanava Bgys 66 364,359 268,461

(39) (55)West Manggahan Bgys 58 245,317 816,147

(30) (43)Other Barangays 76 229,238 113,247

(40) (60)All Establishments 286 341,719 325,099

(156) (233)Note: Number of affected firms is presented in the parenthesis

Area Obs. Loss in

Income & Assets losses caused by typhoon Milenyo (by Area)

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Income & Assets losses caused by typhoon Milenyo (by Sector)

Income Assets

Manufacturing 168 402,589 509,969

(97) (139)

Construction 12 218,750 31,982

(8) (11)

Wholesale and Retail Trade 43 227,429 57,891

(21) (33)

Hotels and Restaurants 23 102,773 22,310

(11) (21)Transport,Storage andCommunications 16 598,050 141,083

(10) (12)

Financial Intermediation 5 50,000 0

(3) (4)

Health and Social Work 19 78,333 33,744

(6) (13)All Establishments 286 341,719 325,099

(156) (233)Note: Number of affected firms is presented in the parenthesis

Area Obs. Loss in

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Health impact analysis

Estimated daily risk of infection of City of Manila(Data of 2003)

Estimated daily risk of infection via incidental ingestion of flood water in Manila City