Enhancing Risk Analysis Capacities for Flood, Tropical ... · 1.1 Background of the Study The...
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area
Component 3 – Flood Risk Analysis
PHILIPPINE ATMOSPHERIC GEOPHYSICAL AND ASTRONOMICAL SERVICES
ADMINISTRATION
GEOSCIENCE AUSTRALIA
Badilla, R. A.1, Barde, R. M.
2, Davies, G.
3, Duran, A. C
1, Felizardo, J. C.
4, Hernandez, E. C.
5, Ordonez,
M. G.6, Umali, R. S.
6
1. Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA) 2. Metropolitan Manila Development Authority (MMDA) 3. Geoscience Australia (GA) 4. Department of Public Works and Highways (DPWH) 5. Laguna Lake Development Authority (LLDA) 6. Mines and Geosciences Bureau (MGB)
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ii Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the iii Greater Metro Manila Area – Flood Risk Analysis
Contents
Executive Summary .................................................................................................................................. v
1 Introduction ............................................................................................................................................ 1
1.1 Background of the Study.................................................................................................................. 1
1.2 Area of the Study ............................................................................................................................. 2
1.2.1 Topography and Location ........................................................................................................... 2
1.2.2 Climate ....................................................................................................................................... 4
1.2.3 Population................................................................................................................................... 5
2 Literature Review ................................................................................................................................... 8
2.1 Hydraulic Studies ............................................................................................................................. 8
2.2 Mines and Geosciences Bureau Flood Susceptibility Mapping .....................................................10
2.3 General Approach to Flood Risk Analysis .....................................................................................11
2.3.1 Flood Hazard Information .........................................................................................................11
2.3.2 Exposure Information ...............................................................................................................12
2.3.3 Vulnerability Information ...........................................................................................................13
2.4 Introduction to flood inundation models .........................................................................................13
2.4.1 Rainfall Runoff Models .............................................................................................................13
2.4.2 Hydraulic Models ......................................................................................................................14
3 Methods ...............................................................................................................................................18
3.1 Data ................................................................................................................................................18
3.1.1 Elevation Data ..........................................................................................................................18
3.1.2 Hydrological data ......................................................................................................................19
3.1.3 Exposure Data and Vulnerability Curves .................................................................................23
3.2 Software Selection .........................................................................................................................23
3.2.1 Data Processing and Analyses ................................................................................................23
3.2.2 Flood inundation modelling ......................................................................................................24
3.3 Flood Inundation Model Development and Calibration ..................................................................25
3.3.1 Rainfall Runoff Model ...............................................................................................................25
3.3.2 Hydraulic Model ........................................................................................................................27
3.4 Design Flood Estimation ................................................................................................................36
3.4.1 Synthetic Storm Time Pattern ..................................................................................................36
3.4.2 Spatial Variation in Extreme Rainfalls in the Pasig-Marikina Catchment. ................................39
3.4.3 Catchment-Averaged Extreme Rainfall Frequency Analysis ...................................................41
3.4.4 Frequency analysis of high water levels in Laguna Lake .........................................................42
3.4.5 Relation to extreme rainfalls .....................................................................................................43
3.4.6 Design Flood Boundary Conditions ..........................................................................................46
3.5 Damage Calculation .......................................................................................................................47
3.5.1 Computation of ‘damaged floor area equivalent’ in a single exposure polygon, for a single building-type and a single depth .............................................................................................49
3.5.2 Computation of the inundated floor area in each exposure polygon. ......................................50
4 Methods ...............................................................................................................................................52
4.1 Hydrology .......................................................................................................................................52
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iv Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
4.1.1 Regional Extreme Rainfall Frequency Analysis .......................................................................52
4.1.2 Catchment Averaged Extreme Rainfall Frequency Analysis ...................................................55
4.1.3 Design Storm Temporal Pattern ...............................................................................................55
4.1.4 Laguna Lake Water Level AEP Curve .....................................................................................56
4.2 Hydraulics ......................................................................................................................................57
4.2.1 Model Calibration .....................................................................................................................57
4.2.2 Design Flood Scenarios ...........................................................................................................66
4.3 Damage Estimation ........................................................................................................................67
4.4 Patterns of Flood Hazard and Risk ................................................................................................73
4.4.1 Limitations of the Analysis ........................................................................................................74
5 Conclusion ...........................................................................................................................................77
6 Recommendations ...............................................................................................................................78
References .............................................................................................................................................79
Appendix A - Parameters used in the Rainfall Runoff Model Sub catchments ......................................82
Appendix B - Estimated building costs per m² (‘000s of Peso), for different combinations of Building type, L4_USE and L5_USE. .....................................................................................................84
Appendix C - Floor heights for different building categories, based on field survey data collected by PHIVOLCS .........................................................................................................................................92
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the v Greater Metro Manila Area – Flood Risk Analysis
Executive Summary
Metro Manila is highly vulnerable to flooding, as demonstrated by the events of Tropical Storm Ondoy
(2009), and more recently the enhanced monsoon rainfall (Habagat) events of 2012 and 2013. This
report details a flood risk analysis for the Pasig-Marikina Basin (which is the major river system in
Metro Manila), developed collaboratively by technical specialists from the Governments of the
Philippines and Australia, as part of the Greater Metro Manila Risk Analysis Project. The study
includes development of flood hazard maps for scenarios with annual exceedance probabilities of
between 20% and 0.5% (corresponding to average recurrence intervals of about 1/5-1/200 years). For
each of these scenarios, maps describing the building damages (damaged floor area and damage
cost) and number of people with inundated homes are also developed.
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vi Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 1 Greater Metro Manila Area – Flood Risk Analysis
1 Introduction
1.1 Background of the Study
The Philippines is one of the most flood-prone countries in the world. For the last ten years, there have
been over 60 reported major floods in the Philippines. Nearly 14 million people have been affected
and the death toll has reached more than 700 people with damages estimating over $400 million (EM-
DAT International Disaster Database).
Metropolitan Manila, the economic centre of the Philippines, is considered the most susceptible city to
flooding. Owing to its geographical location, low elevations, high density of population and
infrastructure, Metropolitan Manila has greater exposure to flooding impacts than most other parts of
the Philippines. This was illustrated during the passage of Tropical Storm Ondoy (Ketsana) in Greater
Metro Manila Area on 26 September 2009 which brought 455 mm of rainfall for 24-hr to its
catchments. This event caused severe flooding, resulting in many casualties (464 dead, 529 injured
and 37 missing), with direct impacts on around 5 million people; and damages to infrastructure and
agriculture of 11 billion Pesos (NDRRMC, 2009).
Given the continued growth of population and infrastructure that is expected in Metropolitan Manila, it
seems likely that events with even more severe impacts could occur in future. To enhance the
capacity of the Government of the Philippines Technical Agencies to understand and manage future
natural hazard risks in this area, a new disaster risk reduction initiative was developed collaboratively
between the Governments of the Philippines and Australia in 2010. The $30 million dollar BRACE
Program (Building the Resilience and Awareness of Metro Manila Communities to Natural Disasters
and Climate Change Impacts) aimed to reduce the vulnerability and enhance the resilience of Metro
Manila and neighbouring areas to the impacts of natural disasters. The program included four
components related to disaster risk reduction. One of these components was dedicated to “Enhancing
Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind, and Earthquakes for Greater Metro
Manila Area”, which became known as the Greater Metro Manila Area Risk Analysis Project (GMMA-
RAP). The GMMA-RAP involved Government of Philippines Agencies from the ‘Collective
Strengthening of Community Awareness of Natural Disasters’ (CSCAND) collaborating with scientists
from Geoscience Australia (GA), as well as other agencies from the Government and University
sectors in the Philippines, in the development of natural hazard risk information for the Greater Metro
Manila Area (GMMA).
The anticipated outcomes of the project were:
Basic datasets fundamental to natural hazard risk analysis (including a high resolution digital
elevation model) are available in GMMA for the analysis of natural hazard risks and climate
change impacts.
Technical specialists from the Government of Philippines technical agencies are better able to
assess the risk and impacts from floods, tropical cyclone severe wind, and earthquakes in the
Pasig-Marikina River Basin, and have an improved understanding of these risks.
Local Government units in GMMA are better informed about the risks of floods, earthquakes and
tropical cyclone severe wind.
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2 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
The enhancement of relationships between the Government of the Philippines technical agencies,
AusAID and Geoscience Australia.
This report details the results of the flood risk analysis that was undertaken within the GMMA-RAP.
The flood risk analysis technical working group included representatives from the Philippines
Atmospheric, Geophysical and Astronomical Services Administration (PAGASA), the Mines and
Geosciences Bureau (MGB), the Metropolitan Manila Development Authority (MMDA), the Laguna
Lake Development Authority (LLDA), the Department of Public Works and Highways (DPWH), and
Geoscience Australia (GA). It focusses on the flood risks in the Pasig Marikina River Basin, which
covers much if not all of the Greater Metro Manila Area, including the relatively large Marikina, Pasig
and San Juan River systems.
1.2 Area of the Study
1.2.1 Topography and Location
The Philippines is a tropical archipelago, consisting of 7,107 islands situated southeast of mainland
Asia. It covers a land area of around 300,000 square kilometres, with a north-south extent of
approximately 1,850 km, and an east-west extent of approximately 1,000 km. The Philippines is
typically mountainous (Figure 1.1), a result of high seismic and volcanic activity associated with its
location on the ‘ring-of fire’. The mountains often contain deeply incised river valleys, and extensive
tropical forest cover. The larger islands can contain larger rivers, and therefore they also feature some
significant areas of alluvial plains (e.g. the Pampanga River Delta in Luzon). In contrast, the smaller
islands tend to exhibit high relief in their centre, with a narrow, discontinuous rim of lowlands along the
coast.
Figure 1.1. Topography and location of the Philippines.
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 3 Greater Metro Manila Area – Flood Risk Analysis
Figure 1.2. Regional setting of Manila. The left pane shows Landsat imagery. The right pane shows SRTM elevation data.
Manila is the capital of the Philippines. It is situated on the west coast of Luzon (Figure 1.2), bounded
by Manila Bay to the west, and Laguna Lake to the east. Northeast of the city are the Sierra Madre
Mountains, while to the Northwest is the Pampanga river delta. To the south is the Taal Volcano.
Figure 1.3. Key rivers around Metro Manila. Left pane is LANDSAT image, right uses SRTM elevation data.
Much of Manila is built on floodplains and deltas associated with the Marikina and Pasig Rivers, and
on coastal plains around the edges of Manila Bay (Figure 1.3). The Marikina River flows south-
southwest through the Marikina valley, and drains the mountainous upper catchment. It is connected
to Laguna Lake via the man-made Mangahan Floodway, and the Napindan River. The Marikina River
may be divided into the ‘Upper Marikina’ upstream of the junction with the Mangahan Floodway, and
the ‘Lower Marikina’ downstream of this. At the junction of the Mangahan and Marikina Rivers is the
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4 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Rosario weir, a flood-control structure with 8 sluice gates which is generally closed during the dry
season (forcing all water from the Upper Marikina through the Lower Marikina), but opened during
floods to allow water from the Marikina River to pass through the Mangahan Floodway to Laguna
Lake. The low-lying land in the vicinity of the Mangahan Floodway and Napindan River rivers
represents the old Marikina river-delta system, formed where sediments from the Marikina River were
deposited as floodwaters flowed to Laguna Lake.
The Pasig River is connected with both the Napindan and Lower Marikina Rivers, and provides a
hydraulic connection between Manila Bay and Laguna Lake (Figure 1.3). It flows from the
Napindan/Marikina/Pasig junction through a topographic constriction, then a low-gradient, highly
urbanised coastal plain. The San Juan River system is a highly urbanised tributary to the Pasig River,
which drains much of Quezon City.
1.2.2 Climate
The climate of the Philippines is tropical and maritime. It is characterized by relatively high
temperature, high humidity and abundant rainfall. Fluctuations in rainfall are mainly due to the
disturbances in the monsoon flow, the easterly wave, the Intertropical Convergence Zone (ITCZ),
tropical cyclones and local weather systems. The intensity of rainfall is influenced by latitude,
topography, exposure and the season. The spatial variation of rainfall differs from one region to
another, depending upon the direction of the moisture-bearing winds and the location of the mountains
(Estoque, 1956; Kintanar, 1984; Patvivatsiri, 1972; Asuncion and Jose, 1980). The mean annual
rainfall of the Philippines varies from 965 to 4,064 mm annually.
Figure 1.4. Climate map of the Philippines based on the Modified Coronas Classification.
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 5 Greater Metro Manila Area – Flood Risk Analysis
Based on the distribution of rainfall shown in Error! Reference source not found., four climate types
are recognized.
Table 1.1. Types of Climate in the Philippines (Source: PAGASA, Climate Map of the Philippines).
Type Description
I Two pronounced seasons: dry, from November to April, and wet during the rest of the year. Maximum rain period is from June to September.
II No dry season with a very pronounced maximum rain period from December to February. Minimum rainfall occurs during the period from March to May.
III No very pronounced maximum rain period with dry season lasting only from one to three months, either from the period from December to February or from May to March.
IV Rainfall is more or less evenly distributed throughout the year.
Metro Manila in general experiences two pronounced seasons (Type I Climate Type), i.e., dry from
November to April, and wet during the rest of the year. The maximum rain period is from June to
September. In Science Garden (Diliman, Quezon City), the mean annual rainfall is 2,574.4 mm (1981-
2010). Historical records of climatological extremes of rainfall (1961-2010) in Science Garden show
that the greatest 24-hr rainfall occurred on 26 September 2009 during the passage of Tropical Storm
Ondoy in Metro Manila with 455.0 mm of rainfall. This record exceeded the normal monthly values for
September (1981-2010) in Science Garden which is 451.2 mm.
1.2.3 Population
Metro Manila comprises 16 cities and 1 municipality, with populations described in Table 1.2. The
population comprises 13% of the national population, which is 11,855,975 against 92,337,852, based
on the National Statistics Office, 2010 Population Census. It has a density of 18,925 per km², although
this varies spatially (Figure 1.5). The growth rate (based on the of 2010 census) is 2.02%. Taguig,
Parañaque, Las Piñas, and Pasay are considered to have relatively higher growth rates from 1990 to
2010. San Juan has negative growth rate due to migration or relocation of informal settlers to
municipalities in Rizal. Among the cities of Metro Manila, Manila is considered the most densely
populated, followed by Pateros, Mandaluyong, Caloocan, Navotas, Malabon, and San Juan. The
population sprawls from western central part towards the north, south and east. The most eastern
parts where population is relatively sparse are portions of Montalban, San Mateo, and Antipolo, mostly
in the upstream of Marikina River and its tributaries. These municipalities are outside Metro Manila.
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6 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Table 1.2: Population over time in cities around Metro Manila (Source: 2010 Census, National Statistics Office).
Cities
Total Population Population
Density (per km
2)
Growth Rate
1990 2000 2010 Area km2
1990-2000
2000-2010
1990-2010
Las Piñas 297,102 472,780 552,573 41.54 13,302.19 4.75 1.57 3.15
Makati 453,170 471,379 529,039 27.736 19,074.09 0.39 1.16 0.78
Malabon 280,027 338,855 353,337 15.76 22,419.86 1.92 0.42 1.17
Mandaluyong 248,143 278,474 328,699 11.26 29,191.74 1.16 1.67 1.41
Manila 1,601,234 1,581,082 1,652,171 38.55 42,857.87 -0.13 0.44 0.16
Marikina 310,227 391,170 424,150 21.5 19,727.91 2.34 0.81 1.58
Muntinlupa 278,411 379,310 459,941 46.7 9,848.84 3.14 1.95 2.54
Navotas 187,479 230,403 249,131 10.77 23,131.94 2.08 0.78 1.43
Parañaque 308,236 449,811 588,126 47.69 12,332.27 3.85 2.72 3.28
Pasig 397,679 505,058 669,773 31 21,605.58 2.42 2.86 2.64
San Juan 126,854 117,680 121,430 5.94 20,442.76 -0.75 0.31 -0.22
Valenzuela 340,227 485,433 575,356 44.58 12,906.15 3.62 1.71 2.66
Caloocan 763,415 1,177,604 1,489,040 53.33 27,921.25 4.43 2.37 3.39
Pasay 368,366 354,908 392,869 19 20,677.32 -0.37 1.02 0.32
Pateros 51,409 57,407 64,147 2.1 30,546.19 1.11 1.12 1.11
Quezon 1,669,776 2,173,831 2,761,720 161.12 17,140.76 2.67 2.42 2.55
Taguig 266,637 467,375 644,473 47.88 13,460.17 5.77 3.26 4.51
Total 7,948,392 9,932,560 11,855,975 626.456 18,925.47 2.25 1.78 2.02
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 7 Greater Metro Manila Area – Flood Risk Analysis
Figure 1.5. The spatial distribution of population density around Metro Manila (based on the Exposure Database, as detailed in the report on Exposure Information Development).
As early as 1942, the first recorded flood instance seriously affected the lives of the residents in
Manila. Major floods subsequently occurred in 1948, 1966, 1967, 1970, 1972, 1977, 1986, 1988,
1995, 1996, 1997 (Bankof, 2003). The degree of seriousness of drainage issues was illustrated in
August to September 1999 when Manila was stricken by knee-deep to neck-deep floods which
rendered most of the roads in Metropolis unpassable leaving thousands of people stranded in the
commercial and business centres of Makati and Manila (DICAMM, 2005). Typhoon Winnie came in
2004, leading to at that time the highest observed water elevations in Sto. Nino in the Marikina Valley
(EFCOS Staff, Personal Communication). TS Ondoy came in 2009, leading to the worst flooding in
recent memory. Typhoon Pedring came in 2011, causing moderate flooding in the Marikina Valley,
and a storm surge that lead to widespread coastal inundation. Since then, large floods also occurred
in 2012 and 2013 due to enhanced monsoonal rains (‘Habagat’ events).
Over the years, this problem has been enhanced in Metropolitan Manila and suburbs by rapid urban
expansion, inadequate river channel capacity and disappearance of waterways due to increase of
colonies of informal settlers. And in the core of Manila, inadequate drainage capacity was also one of
the key factors that contribute to the perennial flooding. Land Subsidence has also been identified as
important, given that much of Metro Manila is already near mean sea level (Rodolfo and Siringan,
2006).
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8 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
2 Literature Review
2.1 Hydraulic Studies
As early as 1952, local engineers from the then Ministry of Public Works, Transportation and
Communication formulated a Plan for the Drainage of Manila and Suburbs (MPWTC, 1952). Methods
used to compute the capacities of the drainage and pumping stations included the rational formula,
uniform flow formula and the mass runoff computation. This was still the era of slide rule. From this
report, the mean tide values of Manila were described. The report mentioned the Bureau of Public
Work’s Datum from a series of observation over a period of 19 years (1901-1919). This datum has
been a source of confusion amongst hydraulic modellers ever since, and the datum has not been
updated up to now.
In the 1970s and 1980s, Japanese consultants employed storage function models and other models
simulated through FORTRAN. As part of planning of flood control projects in Metro Manila in the early
1980’s, the National Hydraulics Research Center (NHRC) was tapped by the Japanese consultants to
probe the hydraulic conditions in the Pasig–Marikina River and Laguna Lake Complex through a
physical model. A downscaled physical model was laid out in the NHRC laboratory to examine the
effects of Mangahan Floodway and the Hydraulic Weir on flooding in the downstream Pasig River.
This simulation served as one of the bases for the planning and design of flood control structures at
different hydraulic conditions.
In 1983 and 1986, the Napindan Hydraulic Control Structures and Mangahan Floodway were
completed, respectively. To synchronize their operation along with other flood control structures, a
telemetered monitoring system, the Effective Flood Control Operation System (EFCOS), was
established in 1993. The system monitored the rainfall and water levels in the Pasig-Marikina River
and Laguna Lake Basins, while simulations gave forecasts for the operation purposes. The Japanese
storage function model was used in the hydrological modeling. River cross-sectional data of 1986
were inputs in the non-uniform and non-steady flow simulation using FORTRAN language. Rainfall
and water level data were inputted using the text editor (MIFES) and read by the executable
programs. Then graphical presentation of the results were shown in Lotus 123 and disseminated to
the officials of DPWH.
Parallel to this activity, the Pasig River Rehabilitation Secretariat, under the Department of
Environment and Natural Resources, acquired the EFCOS data for its clean-up project of Pasig River.
They simulated the flushing of high water level of Laguna Lake via Napindan Channel to Pasig River
using Mike 11. Seeing the need for more dense hydrological data, the EFCOS was enhanced with
more rain and water level gauges in 2001. Mike 11 and Arc View 3.2 simulated the rainfall and water
level forecasts in 2D animation. A related model was developed for flood forecasting, including data
assimilation capabilities based on Kalman Filtering (Madsen and Skotner, 2005). Unfortunately the
Mike 11 forecast model at EFCOS required an annual renewal of license, and due to budgetary
constraint and agency’s priorities it was not sustained.
In 2005, the Japan International Cooperation Agency (JICA) completed an update of drainage plans in
the Core Area of Metro Manila (DICAMM, 2005). The focus of simulation was on the drainage
discharges only, where the DHI Mouse was suitable for this purpose, and was used in combination
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 9 Greater Metro Manila Area – Flood Risk Analysis
with ESRI GIS software. For the Japanese consultants, the Storage Function Model, Flowca, etc.,
were very widely used especially in the crafting of different Master Plans and Feasibility Studies in
Metro Manila. A related study around this time was by CTI Engineering (2005), which assessed
drainage in the Mangahan/Napindan/Taguig area, using rational-method rainfall-runoff type models
combined with models of the performance of pumping stations. The focus was the development of
operation rules for pumping stations.
Badilla (2008) developed a simulation of river flows in the Upper Marikina, based on linking the HBV
rainfall-runoff model with the DUFLOW 1D unsteady hydraulic model. Emphasis was placed on
predicting water levels and the associated uncertainty. The model was calibrated to match river gauge
levels at Sto Nino, and showed reasonable capacity to predict flood peaks measured during the 2003-
2004 wet seasons.
Tropical Storm Ondoy came in 2009, and caused extreme flooding in Metro Manila. The event
triggered the World Bank to finance the updating of Master Plan in Metro Manila and Surrounding
Areas (WBCTI, 2012). In laying out the flood extent of Ondoy and the countermeasures for this study,
Mike Flood and HEC-RAS were used for the Pasig Marikina River Basins. Many cross sectional data
came from the Pasig River Rehabilitation Commission, the DPWH, and others. However, there was a
problem related to the reference datum because of varied references used by different elevation
datasets, which created confusion. Discrepancies in some datasets lead the consultant to use the
2002 survey data from DPWH. Since Mike 21 requires extended cross sectional points from the bank,
the one meter interval contour was the most likely source for modelling. For the surrounding areas
without the cross-sectional data, the consultant relied mostly on the hydrologic analysis and past
studies.
Soon after Tropical Storm Ondoy, Abon et al. (2010) developed a hydraulic model of flow in the
Marikina River. Their semi-distributed model combined the SCS Curve-Number loss model with the
SCS Unit Hydrograph, using the HEC-HMS modelling environment. With this they could well simulate
the timing of Ondoy’s flood peaks in the upper portions of the Marikina River, as compared with the
observed timing of the flood peak based on interviews of affected residents. However, the simulation
of the time-to-peak in the lower-gradient parts of the Marikina was less accurate, which the authors
attributed to other sources of floodwaters and backwater effects that were not included in their model.
Muto et al. (2011) considered the impact of climate change on flooding in Metro Manila, using a
mixture of storage function models, 1D and 2D flood modelling. They included extensive analyses of
economic damages predicted under various scenarios related to climate change and the development
of structural measures to reduce flooding. This study concluded that flood damages in Metro Manila
would continue to be highly significant under any scenario, but could be greatly reduced by continuing
implementation of structural measures proposed in the Master Plan.
Santillan et al (2012) developed a near-real-time forecast model for the Upper Marikina River, based
on the integration of HEC-HMS and HEC-RAS. Remotely-sensed data was classified and used to
parameterize the roughness and runoff coefficients in the model. They also developed methods to
automatically run the model and broadcast the predicted flood extents on the web. The model showed
good performance in simulating a flood peak during June 2012.
As of 2013, there are also several projects disseminating model-based online flood hazard maps for
the Philippines (including Manila). Key sites include Project NOAH (www.noah.dost.ph), and
Nababaha (www.nababaha.com). The description at the Nababaha site suggests these maps are
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10 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
based on direct-rainfall 2D hydraulic modelling, combined with the use of remote sensing information
to estimate relief and terrain roughness.
2.2 Mines and Geosciences Bureau Flood Susceptibility Mapping
The Mines and Geosciences Bureau has been conducting geohazard mapping and assessment since
the 1960's. But it was in 1999 when the Cherry Hills landslide incident emphasized the need for
hazard assessment as a subcomponent of the Environmental Compliance Certificate (ECC), which is
a requirement for land development projects. The subcomponent is called the Engineering Geological
and Geohazard Assessment Report (EGGAR). Following the implementation of these requirements,
the Guinsaugon landslide incident in February 2006 leads the government to take further action to
address the geohazards problem in the Philippines.
Through a directive from the President of the Philippines, the Geohazard Assessment and mapping
Program was immediately implemented by MGB with the main objective of identifying areas
susceptible or vulnerable to various types of flood and landslide hazards and to increase public
awareness of such hazards in order to mitigate the negative impacts.
The major activities are the Geohazard Mapping at 1:50,000 and 1:10,000 scale, conduct of
province/municipal-wide Information and Education Campaign (IEC), provision of hazard maps and
threat advisories and identification and assessment of relocation sites, assessment of evacuation
centers and establishment of community-based early warning system.
Prior to the field survey, a desk study of the area which includes the interpretation of available aerial
photographs and land satellite imageries is done. The data are then validated in the field by a team of
geologists. An example map for Metro Manila is in Figure 2.1.
In the field, a field data sheet is completed to standardize the procedure of the assessment. A hand-
held Global Positioning System or GPS is used to get the location of the observation point. For flood
assessment, the susceptibility ratings are based on the topographic location, land cover, flood type,
depth and duration of inundation, flood frequency, direction of flood water and cause of flooding
among others. These are verified in the field through anecdotal accounts of residents living in the
area. Photos of the affected areas, the recorded flood height, as well as the source of flood water are
also taken as these are important in the preparation of geohazard assessment report.
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Figure 2.1. Flood and landslide susceptibility map of Metro Manila from the Mines and Geosciences Bureau.
2.3 General Approach to Flood Risk Analysis
Flood risk analysis involves the combination of: 1) flood hazard information, which describes the
likelihood and intensity of a flood event; 2) exposure information, which describes the distribution of
people or elements ‘at-risk’ to a flood event; and 3) vulnerability information, which describes how the
exposed elements would be affected if subject to a given intensity of flooding. The typical form of each
of these inputs is described in the next three sections.
2.3.1 Flood Hazard Information
Flood hazard information consists of one or more flood scenarios, each with an annual exceedance
probability (AEP) which describes the likelihood a flood event larger than the scenario occurring in any
given year. For example, if a flood scenario is assigned an AEP of 1/20 (=0.05), then ideally 95% of all
years should experience floods smaller than the scenario, while 5% of all years will have floods larger
than the scenario.
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12 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
The flood scenarios typically provide an estimation of the inundated area (‘flood extent’), peak flood
depths and/or peak flood discharge, based on a flood inundation model. Depending on the model
complexity, other information may also be provided, such as the flood duration, the peak flow velocity
or momentum over the floodplains. All such information should be considered an estimate only, which
is susceptible to errors both because of model simplifications and uncertainties in the required input
data. Typical input datasets include topography, historical hydrological information, and estimates of
the ‘flow roughness’ of the terrain.
The AEP for the flood scenario is usually assigned based on a statistical analysis of hydrological
records at the site, such as the peak river discharge, the rainfall intensity and duration, and/or the
peak water levels somewhere within the flooded region. Exactly what sort of information is needed
depends on the geography of the study site. However, most sites will at least need information on
either river discharge or rainfall (the latter may be converted to river discharge using a rainfall-runoff
model). As an example of when other information might be required, consider a flood scenario for a
river which flows into a lake. If the lake level has a large ‘backwater’ effect on water levels in the river,
then it may be necessary to account for the likelihood of high lake levels, as well as the rainfall and/or
discharge inputs, to develop the flood scenario.
The flood scenario would then be created by inputting the hydrological information into a flood
inundation model. As a simple example, a 1/100 AEP flood scenario may be developed by estimating
the river discharge which is exceeded in only 1% of all years (based on statistical analysis of historical
discharge records), and then running a hydraulic model with this discharge to estimate the resulting
flood extents. Often hazard analyses include flood scenarios with several AEPs (e.g. 1/10, 1/25, 1/50,
1/100).
Even for a single AEP, generally more than one flood scenario is possible. For example, even if the
average rainfall intensity is fixed by the AEP, the spatial and temporal distribution of the rainfall time
series would have some effect on the flood behaviour, leading to an infinite number of flood events
with the same AEP. Other factors may also affect the results, such as assumptions regarding the
failure of levees, or blockages of drains by debris.
It is usually not possible to treat the full range of flood scenarios associated with a single AEP. Most
often a single scenario is taken to be broadly representative of the flooding for a given AEP (FEMA,
2003; Scawthorn et al., 2006). Alternatively, some studies treat this issue using a ‘Monte-Carlo’ type
approach where models for the variation in hydrological inputs are developed, and a large number of
scenarios are modelled to attempt to cover the range of flood behaviour with the given AEP (Aronica
et al., 2011; Christian et al., 2012).
2.3.2 Exposure Information
Exposure information describes the spatial distribution of ‘elements at risk’ from a flood, such as
people, buildings, and critical infrastructure. Examples might be spatial information on population
densities, the distributions of buildings of different types, and the locations of critical infrastructure
such as hospitals and schools. See the report on Exposure Information Development for more
information.
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2.3.3 Vulnerability Information
Vulnerability information describes how the ‘elements at risk’ as susceptible to damage or loss when
affected by a flood with particular properties. For example, a common type of vulnerability information
is a ‘depth-damage curve’ or ‘stage-damage curve’, which describes the damage to a building as a
function of the depth of flooding above the building floor. See “Development of vulnerability curves of
key building types in the Greater Metro Manila Area, Philippines” (Pacheco et al., 2013) for more
information.
For a single hazard scenario (e.g. AEP=1/100), the flood, exposure and vulnerability inputs can be
combined to compute a number ‘damage’ metrics associated with the flood event. For example, the
cost of building damage could be computed by 1) Calculating the depth of inundation at each building
based on the flood scenario; 2) Estimating the associated damage to the building, using a depth-
damage curve; 3) Aggregating the results to compute the total damage cost, or map the spatial
distribution of damage. Similarly, one might compute the number of people whose homes were
inundated, or the damaged building floor area.
Flood risk analyses explore the results of one or more flood scenarios and damage metrics at the site
of interest, to develop an overall picture of the flood risk. The scenarios and damage metrics that are
analysed can vary widely, depending on the purpose and resources of the study. Often the results will
be aggregated over politically useful areas, such as local government administrative boundaries. For
example, Middelman (2002) estimated the building damage cost associated with a single hazard
scenario (a widespread 1%AEP flood event) in South East Queensland, Australia, and mapped the
results aggregated to census districts. This demonstrated the relatively high building damages
associated with such an event (~1% of the total cost of the building stock), and their highly non-
uniform spatial distribution. Where a range of hazard scenarios are considered, they may be
associated with a range of AEPs (Poretti and Amicis, 2011), levee breaches (Castellarin et al., 2011),
or climate change (Dumas et al., 2013). A suite of hazard and damage maps and aggregated damage
estimates may be produced. If a very large number of events can be simulated, it may be possible to
directly compute the probability of damage levels, or the average annual damages (Apel et al., 2004;
Bouwer et al., 2009). Because the latter approaches require the simulation of many scenarios, they
require large scale computing resources except when based on very simple and computationally
efficient flood models.
2.4 Introduction to flood inundation models
Flood inundation models are one of the fundamental tools of flood risk analysis. They are very diverse,
and so a brief introduction to them is included here. Flood inundation models most often consist of a
combination of two separate models: 1) a rainfall-runoff model and; 2) a hydraulic model. The rainfall
runoff model is used to simulate the transformation of rainfall through the catchment to the river
system, while the hydraulic model simulates flows within the river system and floodplains.
2.4.1 Rainfall Runoff Models
Given an input rainfall time series, a rainfall runoff model is used to predict the time series of discharge
(water outflow) from one or more catchments (Beven, 2001; Pechlivani-dis et al., 2011). This outflow is
then fed into a hydraulic model to perform flood inundation computations.
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14 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Broadly, rainfall runoff models may be classified based on their spatial and temporal complexity.
Spatially, they are classified as one of:
1. Lumped models: These assign a homogenous description to the entire catchment. As a result,
they have the lowest spatial complexity and input data requirements of any rainfall runoff
model. Despite their simplicity, in some situations they can perform well, while enabling a
more rapid assessment of a site’s runoff characteristics than more sophisticated methods
(Beven, 2001).
2. Semi-distributed: Semi-distributed models describe the catchment with an interconnected set
of lumped models. They thus allow some treatment of the spatial variation of catchment
properties and rainfall inputs at a site, while retaining much of the simplicity of lumped models.
They are commonly used in applications (e.g. Moramarco et al., 2005; Abon et al., 2010).
3. Distributed: Distributed models represent the limiting case of a semi-distributed model, where
the catchment is approximated as a continuous region (although in practice, it will split into
small ‘cells’ or ‘pixels’). They have the strongest data input requirements, and also the most
flexibility in terms of treating variations in rainfall and catchment properties, which can be
advantageous in some situations (Pechlivanidis et al., 2011). However, uncertainties in the
definition of input parameters and process descriptions mean that distributed models do not
consistently outperform simpler lumped or semi-distributed rainfall runoff models, although this
is an ongoing area of research (Beven, 2001; Ghavidelfar et al., 2011; Pechlivanidis et al.,
2011).
From a temporal perspective, rainfall runoff models may be classified as either:
1. ‘Event Based’ models: Event based models are suitable for simulating a single flood event
(e.g. of 48 hours duration). Because of their limited temporal extent, they focus on modelling
the ‘direct runoff’ or ‘quick runoff’ which forms the bulk of the flood peak, and are not
concerned with detailed modelling of the ‘baseflow’ which is affected by water stored in the
catchment over longer periods. They are most often used for flood hazard assessment when
individual rainfall events are the major cause of flooding.
2. ‘Continuous’ models. Continuous models are designed to be applied to longer-term rainfall
series. While these also simulate the ‘quick flow’ described above, they also focus more on
accurate simulation of the baseflow component of runoff than do event based models.
Typically this is done by accounting for the time evolution of soil moisture conditions and
longer-term catchment storage. As such, continuous models are useful for simulating runoff
over longer time periods. This can be important for flood forecasting where the antecedent soil
moisture conditions can be estimated to improve flood prediction (Berthet et al., 2009). Such
information is not usually available in hazard applications, because they refer to hypothetical
future events. However, continuous models are sometimes used in flood hazard work to
create a synthetic time-series of flood events over long time periods (e.g. Winsemius et al.,
2012). Flood statistics are then estimated directly from the simulated time series.
2.4.2 Hydraulic Models
Hydraulic models are used to simulate the movement of water through channels and over floodplains.
As with rainfall runoff models, a wide variety of hydraulic models exist. The following classification is
based partly on Woodhead et al., 2007:
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1. 0D models: These use GIS type methods to interpolate the water surface elevation between
known sites, using topographic data. They are typically used for a ‘quick’ estimate of likely
flood extents, or in situations where other flood modelling methods would be too
computationally demanding (e.g. Global Flood Modelling, Winsemius et al., 2012). Typical
computational time is seconds.
2. 1D channel network models: These simulate the water surface elevation and discharge along
‘channels’ (i.e. user-defined flow paths). Floodplain inundation may be simulated using
‘extended cross-sections’ which account for the flow and storage of water over floodplains, or
using a ‘quasi-2D’ network of channels to represent the floodplain (e.g. Dung et al., 2013). The
flow direction is restricted to the along-channel direction, and so must be pre-defined when the
model geometry is set up. They are widely used for flood modelling, but are most suited to
situations in which the flow directions are well known and flows occur dominantly in channels.
Typical computational time is minutes
3. 1D+ models: These extend 1D models with storage areas, which are conceptually ponds
represented with a volume-water level relation. Storage areas provide another mechanism to
represent floodplain storage and flow, which allow flow in multiple directions (as opposed to
just up-or-down channels). Flow between storage areas and other storage areas or channels
is modelled using simplified momentum relations, such as a weir relation or diffusive flow
relation. They are widely used in flood modelling applications over large areas, or where
computational time is limited (e.g. Castellarin et al., 2011). Typical computation time is minutes
to hours, depending on the geometric detail.
4. 2D- models: These simulate the motion of flood waters in 2 horizontal dimensions, using mass
conservation and simplified 2D flow momentum conservation equations. Two dimensional
models are more computationally demanding than 1D models, but have the potential for
greater accuracy in situations in which flow paths cannot be well schematised using the 1D
approach. 2D- models are widely used in research, and in applications (e.g. Neal et al., 2012).
Typical computational time is hours to days or longer, depending on the geometric detail.
5. 2D models: These solve the 2D shallow water equations or some variant, and simulate the
motion of flood waters in 2 horizontal dimensions (e.g. Horrit and Bates, 2002). The theory
underlying 2D models requires less approximation than does the theory of 1D or 2D- models.
This has the potential to support greater model accuracy, so long as the model is run with a
‘sufficiently fine’ mesh resolution to: 1) well describe the flow topography and; 2) avoid large
discretization errors in the numerical method. In practice, meeting these criteria can be
computationally demanding if the model needs to be run over large areas of complex terrain,
or resolve fine topographic features (e.g. small channels or streets). If overly coarse mesh
resolutions are used, then 2D models may be either more or less accurate than simpler but
more computationally efficient approaches (e.g. 1D or 1D+), and performance will depend
heavily on the nature of the site and details of the model setup. 2D models are widely used in
flood modelling applications. Typical computational time is hours to days or longer, depending
on the geometric detail. Parallel computation is often used to help manage the computational
demands.
6. 1D-2D models: These combine 1D and 2D flow models for efficient representation of flow both
in channels and over floodplains. They are widely used in flood modelling applications, and
are often easier to apply with acceptable accuracy over large areas than 2D models (Syme et
al., 2004). This is because many computations which are demanding with a fully 2D approach
(e.g. accurate modelling of channel flow) can be performed efficiently in 1D. Typical
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16 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
computational time is hours to days or longer, depending on the geometric detail. Parallel
computation is often used to help manage the computational demands.
7. 3D models: These simulate fully 3-dimensional flow using e.g. the Reynolds Averaged Navier
Stokes Equations. These models are rarely used in flood hazard applications at present
because they are so computationally intensive, although are often used in ocean and lake type
applications where the model resolution does not have to be as detailed as generally required
for flood inundation modelling. Typical computational time is hours to days or more, depending
on the geometric detail. Parallel computation is often used to help manage the computational
demands.
The suitability of each type of model for a given application depends on the site characteristics, the
needs of the user, and the available computational power. Usually both the model accuracy and the
computational demands increase as the model resolution is refined (i.e. mesh size or cross-sectional
spacing is reduced, more topographic detail is added). For an equivalent level of topographic detail,
3D models are typically the most computationally intensive, followed by 2D and 2D-, 1D+, 1D, and 0D
models. Similarly, 3D models are based on the least restrictive physical assumptions, followed by 2D
and 2D-, 1D+, 1D, and 0D.
In all cases, model results will depend on the decisions made when setting up the model. For all
model types (except 0D), results will typically improve with calibration against observed data. This is
usually achieved by ‘tuning’ the model friction coefficients to obtain acceptable agreement with
observations of flood depths or flood extents, while keeping them within physically acceptable ranges.
In 3D and 2D models, the model resolution (and hence accuracy) will often be limited by the available
computational power. Long model run times are an option, although this may make the iterative
process of model calibration difficult. For 1D and 1D+ models, computational time is less of a problem:
however, the modeller must ‘schematize’ the flow paths into channels (and storage areas in the 1D+
approach). To get a good solution in a complex situation, the schematization process may be very
time consuming with many iterations, and requires good judgement on the part of the modeller. Even if
well designed, the assumptions underlying this approach are not appropriate for all flow problems (e.g.
complex floodplain flow paths which change strongly during a flood event).
If estimates of velocities over the floodplains are required, then 3D or 2D/2D- models should be used.
In urban areas, accurate velocity simulation typically requires a very fine mesh resolution (e.g. 1-2m),
and hence a long computational time over large areas (Smith and Wasko, 2012). If coarser resolutions
are used, then velocity estimates are unlikely to be accurate.
On the other hand, if only water depth estimates are required, then coarser resolution 2D or 1D
modelling may be appropriate. Horritt and Bates (2002) compared a 2D, 2D- and 1D model for
simulating flow routing and inundation over a 60km reach in the River Severn, and found that all
models performed quite well, given the uncertainties in inflows, topography and validation data.
However, the predictive power of the 1D model was best, followed closely by the 2D model, while the
2D- model performed less well. Aureli et al. (2006) compared a fully 2D and 1D+ model of levee-break
flooding in the Po River, Italy. They found that the flooded areas, maximum depths and the discharge
flowing through the breach were quite comparable between the two approaches, noting that these
were the most important variables for hazard assessment. However, the detail of the dynamics
differed just after the initial breach, reflecting limitations underlying the 1D+ model.
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Another issue relates to the importance of accurately simulating channel flow, and other small flow
paths. Some 2D models require a very high resolution (and hence long computational times) to do this
accurately, whereas it is usually straightforward in 1D or 1D/2D models. In some sites, this can have
an important impact on modelled flood extent, since small flow paths may be the main conveyer of
floodwaters. For example, Syme et al. (2004) compared the peak depth predictions of four hydraulic
models in Bristol, England, which variously employed 2D, 1D/2D, 1D+ and 2D- approaches. All
models showed differences in simulated flood depth time series at point locations, reflecting the
different assumptions on which they are based. However, simulated inundation extents were most
similar for the 1D/2D and 1D+ models, whereas the flood extents for the 2D and 2D- were quite
different from these and from each other, suggesting that the ability to resolve fine flow paths was
important at that site.
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18 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
3 Methods
3.1 Data
Among the data used in this study are:
1. Elevation data;
2. Hydrologic data (rainfall and water level)
3. Exposure data; and
4. Vulnerability data
3.1.1 Elevation Data
The key elevation data sources used in this study are:
1. The LIDAR Digital Elevation Model (DEM) for Metro Manila: This is a LIDAR-derived ‘bare-
earth’ elevation raster dataset, with a pixel size of 1m2, which was produced by FUGRO from
the raw LIDAR point data. The latter was flown in March 2011, and has a vertical accuracy of
+/- 15cm (1 standard deviation) in bare earth areas, and a point density of approximately 4
points per m2. Spatially the LIDAR DEM covers all areas of the Pasig Marikina Catchment
included in the hydraulic model in this study. Although considered accurate in terrestrial areas,
the LIDAR DEM is not accurate in ‘water areas’ such as rivers, lakes and ponds, which were
underwater at the time of data capture. It was used as the main source of elevation data in this
study - chiefly for defining the hydraulic model geometry outside of the water areas, and for
the computation of catchment boundaries to guide the rainfall runoff and hydraulic model
schematization. The technical report for Component 1 – High Resolution Elevation and
Imagery contains further details about the acquisition and processing of LiDAR elevation data.
2. Shuttle Radar Topography Mission (SRTM) DEM: This is a globally available mid-resolution
elevation dataset, with a pixel size of around 90 m, and a nominal vertical accuracy of 15 m
(although this varies depending on the nature of the land surface). It was used to compute
catchment boundaries in parts of the upper Pasig-Marikina Catchment which are not covered
by the LIDAR DEM.
3. River Cross-Sectional Surveys: These were variously provided by DPWH, the World Bank
/CTI flood modelling team, MMDA, and the ‘Study on Drainage Improvement the Core Area of
Metropolitan Manila, Republic of the Philippines’ (DICAMM, 2005). The cross-sectional
surveys included the Pasig, San Juan, Marikina, Mangahan and Napindan Rivers, and various
creeks in the western part of Manila. The DPWH data included major bridges as of 2002.
Cross-sections were typically spaced at 50-200 m intervals along the rivers, and were used to
estimate river bed elevations for the hydraulic model, in ‘water areas’ where the LIDAR DEM
was not suitable.
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3.1.1.1 Vertical Datum Issues
Around Manila, most topographic and water level data is reported with respect to a local vertical datum
(termed ‘DPWH datum’). In DPWH datum, mean sea level is often reported as being at 10.47m (e.g.
DICAMM, 2005), although other reports suggest 10.6m (RFCOSMM, 2002). The variation in MSL with
respect to DPWH datum may reflect subsidence around Manila (e.g. DICAMM, 2005). In the LIDAR
DEM, vertical elevations are reported with respect to MSL.
While the study team is not in a position to resolve the ambiguities in vertical data around Manila, a
method was required to translate between the DPHW vertical datum and the LIDAR DEM MSL. To
support this, the team compared cross-sectional survey data and LIDAR DEM data in multiple sites
throughout the basin, and also compared the LIDAR DEM with observed high water levels in Bay
Breeze, Taguig in November 2011 (where the water elevation could be simultaneously measured with
respect to the location of the wet-dry boundary on the LIDAR DEM, and with local water level gauges
in DPWH datum). Based on these comparisons, the team found a constant offset of 10.5 m was
reasonable for translating between the LIDAR DEM elevations and other measurements in DPWH
datum. This is close to the reported values of MSL in DPWH datum, as expected. However, it is worth
noting that if differential subsidence is occurring over Manila, it may be more appropriate to apply a
spatially varying adjustment to integrate old survey data with the newer data. This would require a
good understanding of the spatial distribution of subsidence around Manila, and has not been pursued
in the present study.
3.1.2 Hydrological data
3.1.2.1 Rainfall
The hourly rainfall data was provided by the EFCOS project in MMDA (stations shown in 3.1). The
data covered the period 2002-2011, although coverage at individual stations was often less because
of instrument malfunction. In addition, daily rainfall data was provided by PAGASA (stations shown in
3.1), with record lengths varying from less than 10 up to 50 years.
Figure 3.1. Location of Stage and Rain Gauge measurements at key sites around Manila.
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20 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
3.1.2.2 Water Levels
Hourly water level data at gauges in key rivers around Manila was provided by the EFCOS project in
MMDA (stations shown in Figure 3.1). The data covered the period 2002-2010, although coverage at
individual stations was often less because of instrument malfunction. This was supplemented with
daily water elevation data for Laguna Lake covering the period 1919-2010 (provided by LLDA), and
tide gauge measurements from Manila South Harbour (Port Area) in 2008-2009 (provided by
NAMRIA), which were used to cross-check results from some of the EFCOS stations. In addition,
hourly water level observations in September-October 2009 were obtained from Labasan Pumping
Station, both inside and outside the flood defences that protect Taguig and Pateros from high water
levels in Laguna Lake. Hourly water level observations during part of Tropical Storm Ondoy were also
obtained from MMDA, based on manual gauge recordings.
In addition, depths on the floodplains of Manila during Tropical Storm Ondoy were estimated using
data provided by the Philippines Flood Hazard Maps project (www.nababaha.com). This is collated
from citizens reports of flood depths, reported categorically as ‘No flooding’, ‘Ankle deep’, ‘Knee
Deep’, ‘Waist Deep’, ‘Neck deep’, ‘Top of head deep’, ‘1-storey high’, ‘1.5-storeys high’, ‘2-storeys or
higher’. To compare these with flood model depths, they were assumed to correspond to depth ranges
of (0-0.1), (0.1-0.25), (0.25 – 0.7), (0.7-1.2), (1.2-1.6), (1.6-2.0), (2.0-3.0), (3.0-4.5), (4.5+) metres
respectively. The boundaries between these categories are necessarily subjective, and given the
nature of the underlying data, not all records are expected to be accurate. Despite these limitations,
the data gives a useful picture of inundation over the floodplains and is complementary to the gauged
water levels in the rivers.
3.1.2.3 Vertical Datum Issues
While the EFCOS water level data was nominally in DPWH datum, some of the gauging stations
appear to have a ‘drift’ in their vertical datum over time. EFCOS staff indicated that funding has not
always been available to maintain the stations, which has compromised the quality of the data in some
instances.
The present study is most affected by data quality during the flood model calibration event (Tropical
Storm Ondoy, September 2009). To check this, the EFCOS water level data at Fort Santiago was
compared with independent water level measurements at Manila South Harbour (Port Area) in 2008
and 2009. This data exhibits has a stable mean sea level in these years (difference of 2mm in 2008
and 2009). Hydraulically, low water surface gradients are expected in Manila bay and the mouth of the
Pasig River except during high river discharge events, and thus most of the time Manila South
Harbour is expected to have fairly similar water levels to the Fort Santiago gauge. The 2008 data was
used to provide a dry-season (low river discharge) comparison of the two gauges, as the Fort
Santiago gauge was not functioning during the dry season in 2009. MSL for the Manila South Harbour
data was computed from the data itself in 2008 and 2009, so the comparison does not depend on the
assumed datum at Manila South Harbour.
Figure 3.2 shows that if the Fort Santiago gauge is assumed to be in DPWH datum (MSL~=10.5) in
2008 and 2009, then the water levels at Fort Santiago are unrealistically elevated about 0.5m above
those in Manila South Harbour in both the wet and dry seasons. They also regularly exceed commonly
used design high water levels for Manila bay in both the wet and dry seasons (~0.9m above MSL,
DICAMM, 2005; WBCTI 2012). This is unrealistic given the close proximity of the Fort Santiago gauge
to Manila South Harbour (Figure 3.1). For example, during periods of low river flows there is a tidally
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 21 Greater Metro Manila Area – Flood Risk Analysis
induced reverse flow from Manila Bay into the Pasig River, which must require the Manila Bay water
levels to be slightly above the Fort Santiago water levels at some time during the tidal cycle.
If instead the Fort Santiago data is assumed to have a datum of ‘DPWH less 0.5m’ (MSL ~=11.0m) in
2008 - 2009, there is good agreement with the Manila Bay gauge during periods of low river flows (the
dry season), and in wet season periods without strong floods (i.e. prior to Tropical Storm Ondoy, 26
September 2009) as shown in Figure 3.2. The observed truncation of low water levels at the Fort
Santiago Gauge in 2008 is assumed to reflect problems with gauge maintenance. Physically, the
‘DPWH less 0.5m’ datum is more reasonable than the DPWH datum. Thus, the ‘DPWH less 0.5m’
datum (MSL=11.0) is used in this study as a first approximation to convert the raw Fort Santiago
gauge data a MSL datum for the Tropical Storm Ondoy Calibration event. Future work to accurately
establish the vertical datum for stage gauges in Manila should be undertaken, and gauges should be
monitored for any apparent datum shifts which may indicate instrument malfunction. The adjustment
proposed in this study should be considered to be a ‘rough approximation’ only, and cannot replace
such detailed work.
Figure 3.2. Comparison of measured water elevations at Manila South Harbour and Fort Santiago, during a month the wet season (top), and dry season (bottom), 2008-2009.
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22 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
An equivalent comparison of raw water level data at the Fort Santiago gauge with Pandacan and San
Juan was undertaken. All these gauges show similar raw water levels during the dry season (Figure
3.3). During the wet season the low tide level is truncated upstream, while high water levels are more
similar at all stations except when there are significant river flow events. This behaviour is consistent
with our general expectations of tidal dampening in estuaries with and without river flow (Savenije,
2005), and suggests that there are no large datum differences between these stations (in 2008-2009).
Thus we conclude that Fort Santiago, Pandacan and San Juan are probably all in the ‘DPWH less
0.5m’ datum during the 2008-2009 period. When reported later on, water levels at the San Juan and
Pandacan stations are provided for the ‘DPWH less 0.5m datum’, and the ‘DPWH’ datum values also
reported in parentheses.
Figure 3.3: Comparison of water levels recorded at Fort Santiago, Pandacan and San Juan gauges in 2008 and 2009, during the dry season (top) and wet season (bottom).
We also assessed gauges in Laguna Lake and the Marikina River. The station at Napindan was not
functional during Tropical Storm Ondoy. Comparison of the EFCOS gauges at Angono and Rosario
weir suggest that the latter are in the same datum as each other. Further, the Angono record agrees
with independent Laguna Lake level measurements taken by LLDA, and manual measurements at
Labasan Pumping station, which are both reported as relative to DPWH datum. Thus, these stations
are presumed to be in DPWH datum. We do not have independent measurements at the gauges
upstream of Rosario, but they are believed to be in DPWH datum also (EFCOS, personal
communication).
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 23 Greater Metro Manila Area – Flood Risk Analysis
It is worth summarising the effect of the datum issues on the present study:
1. For the flood model calibration against Tropical Storm Ondoy, the Pasig River mouth boundary
condition is taken from the Fort Santiago Data, with the adjusted vertical datum ‘DPWH less
0.5m’ which matches with the Manila South Harbour data in 2008/2009.
2. The Laguna Lake boundary condition is based on data in DPWH datum. The Manila Bay
boundary conditions (except for the Pasig River Mouth) are based on the data for the Manila
South Harbour Station, where MSL is computed from the data itself.
3. When comparing the model against data, observed water levels at Pandacan and San Juan
are reported in both the ‘DPWH less 0.5m’ datum as well as the ‘DPWH’ datum. The modelled
results turn out to fall between these values.
4. If the model of the Ondoy flood developed in this study is run with the ‘DPWH datum’ Fort
Santiago data as a boundary condition, then the flood extent in most areas is only slightly
affected, as the upstream river discharge and rainfall is of greater significance. However, early
on in the event, there appears to be too much flooding in the downstream reaches of the Pasig
River and surrounds.
If would be very useful for further work to tie water level gauges around Manila into a consistent
vertical datum, and to ensure the availability of funding so that this can be maintained over time.
3.1.3 Exposure Data and Vulnerability Curves
Damage calculations in this study make use of the Exposure Database (described in the report for
Exposure Information Development). This provides information on the distribution of building types
and population densities in Manila. We also use the Flood Vulnerability Curves, which model the
damage to a building as a function of the local flow depth. These are described in “Development of
vulnerability curves of key building types in the Greater Metro Manila Area, Philippines” (Pacheco et
al., 2013). In addition, estimates of the replacement cost of buildings were used, based on data
reported in Appendix A.
3.2 Software Selection
Decisions on software to use for this project were made collaboratively by the study team. An
emphasis was placed on the use of free and/or open source software, to ensure its availability to all
participants both during the project and into the future.
3.2.1 Data Processing and Analyses
The open source geographic information system “Quantum GIS” (QGIS) was used to visualise and
process spatial data in this project (www.qgis.org). The study team found this software to be intuitive
for new users with some existing GIS background. Bugs were sometimes a problem, particularly early
in the project, but many were fixed as updated versions of the software were released. For batch
processing of vector and raster data, the team made direct use of the GDAL and OGR command line
tools (www.gdal.org), which are distributed with QGIS and were found to work very reliably.
In addition, the team made heavy use of the open source programming language R (www.r-
project.org) for general computation, statistical analyses and data processing. The R language is
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24 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
supplemented with several thousand freely available packages, of which the study team made heavy
use ‘lmomRFA’ and ‘lmomco’ for hydrological frequency analyses, and ‘raster’, ‘sp’, ‘rgdal’ and ‘rgeos’
for working with spatial data. Most of the study team had limited experience in this sort of
programming prior to the project, but over time became more familiar with the language and its
capabilities, and with using and modifying scripts to run analyses on different data sets or with
alternative processing options.
3.2.2 Flood inundation modelling
3.2.2.1 Rainfall Runoff Model
In selecting a rainfall-runoff modelling approach for the project, the team noted that:
1. Floods in the Pasig Marikina Basin are typically generated by storms with a relatively short
duration (from < 1 to a few days of intense rain). The time-delay between rainfall and flooding
throughout the basin has been estimated as ranging from 0-10hrs (Abon et al. 2010).
2. The land-uses in the Pasig Marikina basin are quite diverse (ranging from urban areas to
natural forested areas and grasslands), and as a result are expected to have strong variation
in their hydrological response to rainfall. Considering also the focus of the study on flood
hazards, the study team reasoned that an event based, semi-distributed rainfall runoff model
would be appropriate. This approach has been taken in several previous studies in the basin
(e.g. Abon et al., 2010; Muto et al., 2011).
The team chose to use HEC-HMS as a rainfall-runoff modelling tool in the present study. This was
because it can be used to implement a wide range of event-based semi-distributed rainfall runoff
models; is freely available, and extremely widely used. As such, it was considered a useful tool for the
team to learn to use, both in the present study, and potentially for future work.
3.2.2.2 Hydraulic Model
As discussed above, the suitability of a hydraulic model for a particular application depends on the
purpose of the study, the computational resources available to conduct the study, and the nature of
the site. The team assessed these factors as follows:
1. The purpose of flood modelling in the present study is to predict the peak flood depths
associated with relatively large flood events in the Pasig Marikina Basin, to support the flood
risk analysis. Flood velocity information was not considered essential.
The computational resources were limited to laptop computers which were available to the
study team. In terms of software, the team had a strong preference to use free or open source
software, as it would sustainably allow the project participants to use the software into the
future. Most commercial flood software requires ongoing annual licence fees in addition to the
upfront cost of the software, and this has created software access problems in the past for
the project participants.
The study site, Metro Manila in the Pasig Marikina Basin, is moderately large (~ 300-400 square
kilometres). It consists of an interconnected network of rivers, creeks, and minor drains, which
are partly affected by hydraulic structures such as the Mangahan Floodway, pumping stations,
and river walls (levees). Additionally, it contains large areas of urbanised floodplain,
which are subject to inundation during floods.
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 25 Greater Metro Manila Area – Flood Risk Analysis
Hence, the study team reasoned that a 1D/2D approach would be optimal for the study area, and that
if this was not possible, it would also be appropriate to adopt a 1D+ approach supplemented by
detailed 2D modelling at sites of particular interest. During the initial stages of the study, the team
could not identify any free or open source 1D/2D modelling packages (although we note that towards
the end of this study, a 1D/2D- model ‘Flo2D’ became free). However, several widely used and freely
available 1D+ models were identified, including SWMM and HEC-RAS. In addition, the open source
2D models ANUGA and Delft3D were known to individual members of the study team, and were
considered as options for detailed 2D work.
Ultimately the team chose to use the HEC-RAS 1D+ hydraulic model, because it is widely used for
flood inundation studies (including previous studies in Metro Manila); is not too computationally
demanding in an area the size of Metro Manila; can interact well with HEC-HMS for rainfall runoff
work; supports a wide variety of hydraulic structures; includes storage-areas for increased flexibility in
floodplain modelling; is freely available; and has a good graphical interface. While some preliminary
exploration of both ANUGA and Delft3D for detailed 2D modelling of small areas was undertaken,
there were technical challenges in applying both, and ultimately insufficient time to develop these
models in addition to the HEC-RAS model. Further modelling of this type could be considered in
future. However, evidence presented later in this report suggests that the 1D+ model provides
reasonable estimates of peak flood depths in the channels and floodplains, and is thus appropriate for
risk estimation in the present project.
3.3 Flood Inundation Model Development and Calibration
3.3.1 Rainfall Runoff Model
To support a semi-distributed modelling approach, catchment areas in the Pasig-Marikina basin were
divided into separate sub-catchments with different hydrologic properties. The sub-catchment
delineation was based on catchment boundaries computed from both SRTM data, and the LIDAR
DEM raster data (with the latter down-sampled to 10mx10m to make the computation tractable).
Catchment delineation calculations used the GRASS GIS module r.watersheds. The computed
catchment boundaries were combined with user judgement as to the appropriate locations for
boundary inflows into the hydraulic model, in order to produce the final sub-catchments.
For reasons explained later in the report, the final hydraulic model had to be split into two (a ‘Marikina’
model and a ‘Pasig-San Juan’ model). Hence, the rainfall-runoff model was also set up to support two
different hydraulic model configurations (Figure 3.4). Inflows from the sub-catchments were fed into
the hydraulic model in the form of either: a) discharge boundary conditions, b) uniform inflow sources
into channels, or c) uniform inflow sources into storage areas (Appendix A).
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26 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Figure 3.4. The sub catchment structure in the rainfall-runoff models used to support the Marikina (left) and Pasig-San Juan (right) HEC-RAS models.
The hydrology of the sub-catchments was modelled using the SCS Curve Number loss model
combined with the SCS unit hydrograph. This is a widely used event-based method for modelling flood
scenarios (HEC-HMS TRM, 2000; Abon et al., 2010, WBCTI, 2012). One advantage of using this
approach is the existence of heuristic methods to estimate the model parameters. This is required in
the absence of calibration data.
The SCS Curve Number loss model is used to estimate how much incoming precipitation is
transformed into runoff within each sub catchment. The ‘excess rainfall’ (total precipitation less
storage) of a rainfall event is modelled as a function of the precipitation and potential storage capacity
of the catchment:
(1)
Here Pe is the time-integrated ‘excess rainfall’ since the start of the event (mm), P is the time-
integrated precipitation since the start of the event (mm), and S (mm) is the ‘potential maximum
retention’ of the watershed, which describes the capacity of the watershed to store water.
The parameter S is related to the watershed characteristic via the ‘Curve Number’ CN as:
(2)
CN theoretically varies from 0 – 100, with higher CN values occurring in catchments with limited
storage (e.g. heavily urbanised areas), and lower CN values occurring in more pervious areas.
Importantly, CN may be estimated by calibration, or alternatively as a function of land-use and soil
type if calibration data is unavailable (HEC-HMS TRM, 2000, Appendix A). In the present study data
on soil types and land use in the Pasig Marikina basin was used to assign a CN value to each sub-
catchment in the model (Appendix A).
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To compute the outflow hydrograph for the catchment, the cumulative excess rainfall is routed through
the SCS unit hydrograph. Conceptually, this models the time-delay between the arrival of excess
rainfall in the catchment, and the catchment outflow. It depends on a single parameter t_lag as well as
the catchment area (HEC- HMS TRM, 2000). The t_lag parameter controls the lag-time or time to
peak of the watershed. Large values of t-lag are likely in larger catchments or those where the
transport of water through the catchment is delayed by e.g. low drainage slopes, hydraulically rough
surfaces, and tortuous channel paths.
In general, t_lag can be set by calibration against observation data, or using heuristic procedures
which relate it to the sub-catchment geometry and land-use. In the present study data is not available
to support calibration in most sub-catchments, and thus heuristic methods are used, as described in
the HEC-HMS manual. This is based on estimating the length and slope of channels and overland
flow paths in the catchment, and was done interactively in GIS using the LIDAR and SRTM DEMs,
and imagery.
As the rainfall runoff model parameters were set with a heuristic procedure, they were not directly
‘calibrated’ to match data. However, they were used to provide input to the hydraulic model, which was
calibrated to match flooding observed Tropical Storm Ondoy in September 2009. For these calibration
runs, the rainfall in each sub-catchment was taken from a nearby rain gauge, as described in
Appendix A. For design flood simulations, a design rainfall time series was applied, as described in the
section on design flood estimation.
3.3.2 Hydraulic Model
3.3.2.1 Model Theory
HEC-RAS can be used to simulate flow in a linked network of one-dimensional channels and storage
areas. Channel flows are modelled using a variant of the 1D St. Venant Equations (HEC-RAS TRM,
2010). The model can account for cross-channel variations in the flow velocity and bed roughness,
provided that the water surface elevation across the channel is constant. Several options are available
for modelling channel junctions, and the energy method is used herein. The energy method is also
applied in modelling flow momentum losses associated with bridges.
Within the 1D+ framework available in HEC-RAS, floodplain inundation may be simulated by either:
1. Extending river cross-sections to cover parts of the floodplain. A higher hydraulic roughness is
then typically assigned to the floodplain portion of the cross-section. This is a reasonable
approach when the water elevation over the floodplain is expected to be the same as the
water elevation in the channel.
2. Storage areas: These consist of a ‘pond’ type region with a water level-volume relation, in
which the water surface elevation is assumed to be constant. They can be connected to
channels or other storage areas as described below.
3. Using a network of channels over the floodplain to simulate flow paths.
Lateral weirs are used to connect storage areas to channels and/or other storage areas. These
simulate the exchange of flow based on the water levels in the two connected elements, according to
a broad-crested weir equation:
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28 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
(3)
where Q is the discharge (m³/s), C is a user-defined weir drag coefficient, L is the length of the weir,
and H is the upstream hydraulic head above the weir. For weirs with complex geometries, the weir is
broken into segments and Equation 3 is applied separately to each. When the weir is submerged,
HEC-RAS modifies Equation 3 to include backwater effects, which cause reductions in the flow over
the weir (HEC-RAS TRM, 2010). This ensures that no flow occurs between connected elements with
the same water elevation, which physically is reasonable. For a standard broad-crested weir,
recommended values of C are around 2.6- 3.1 (HEC-RAS TRM, 2010).
For linkages between storage areas on low-gradient floodplains, flows are generally more quiescent
(lower Froude number) than implied by the typical broad-crested weir model, and a lower weir drag
coefficient may be appropriate. In such cases H will be well approximated by the depth of flow over the
weir and L*H approximates the cross-sectional area of the weir-flow. In the absence of backwater
effects, the weir equation is then equivalent to setting the mean flow velocity to C*H(0.5)
, i.e. fixing a
constant flow Froude number. The form of this relation is identical to the Chezy friction model for
uniform flow, which also states that velocity scales with the square root of the flow depth (Chaudhry,
2008). The latter is a reasonable model for floodplain flows in the absence of backwater effects, and
can be used to provide a first-estimate of an appropriate weir drag coefficient, which may be refined by
calibration. With this method, the coefficient C is equal to the square root of the bed slope divided by
the Chezy bed roughness.
HEC-RAS solves the flow equations approximately, using an implicit finite difference numerical
method. In theory, the difference between the approximate and ‘true’ solution to the underlying flow
equations becomes vanishingly small as the model resolution (cross-sectional spacing) becomes finer,
and the model time-step is decreased. In practice, model accuracy is also affected by data errors, and
can be influenced by numerical instabilities (i.e. erroneous oscillations in the flow behaviour which are
an artefact of the numerical solution method). The latter can usually be controlled by 1) reducing the
model time-step or cross-sectional spacing, 2) adjusting some numerical method control parameters,
or 3) smoothing over rapid changes in the model geometry or boundary conditions, so long as the
changes still provide a good description of the flow situation.
The numerical method used by HEC-RAS is most widely applicable and stable for modelling sub-
critical flows (where the flow velocity is less than about 3.1 times the square root of the water depth).
HEC-RAS can also model super-critical flows (using the ‘mixed-flow’ option); however, it does so by
supressing the inertial terms in the flow equations (HEC-RAS TRM, 2010). This is reasonable for a
wide class of super-critical flows that vary sufficiently slowly in space and time, and is expected to be
reasonable in the present study when super-critical flows occur (which is relatively rare compared with
subcritical flows). Theoretically, it is less appropriate for extreme flow events such as dam-breaks,
where inertial effects are more significant. However, the latter situation does not occur in the present
study.
3.3.2.2 Construction of the model geometry
The main river systems are modelled as a linked network of one-dimensional channels and storage
areas (Figures 3.5 - 3.6). The channel cross-sections often extend well beyond the main river channel,
and are assigned a higher hydraulic roughness than the main channel regions. In addition, broad
channels are often placed in overland flow paths where there is no river, as these regions can have
defined flows during floods. Storage areas are also used to represent floodplain storage, and flows
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between these are modelled using lateral weirs, with calibrated drag coefficients (see below). These
approaches to model schematization allow flood inundation to be simulated over both channels and
the floodplains (e.g. Dung et al., 2013).
Figure 3.5: Example of model schematization in the Upper Marikina River, showing channels (white cross sections) and storage areas (polygons). Note that some of the channels at the bottom centre of the image are not associated with real rivers, but represent possible overland flow paths.
The cross-sectional elevation was taken from the LIDAR data in all areas, except the water areas
where the LIDAR cannot penetrate. In these regions, cross-sectional surveys were used. Aside from
the MMDA surveys, the data are not recent, and errors may be introduced due to post-survey changes
in the cross-sectional geometry. However, the data was the best available to the project at the time of
model development.
Data entry proceeded by inputting an initial cross-sectional profile (making use of survey data in the
channel), and then replacing this with LIDAR outside of the water areas. It was then necessary to
manually check and correct the connection of the two datasets, as in some instances, unphysical
‘topographic steps’ would occur at the boundary of the two datasets. This could be due to changes in
the topography over time, or to errors in either dataset. In some places, survey data was not available,
and the cross-sectional data was estimated by interpolation from neighbouring cross-sections, using
HEC-RAS’s automated interpolation algorithm. Even if survey data is lacking, this technique can be
useful to improve the accuracy and stability of the model’s finite difference approximation of the
underlying flow equations. In overland flow channels which would otherwise go dry outside of the
main flood event, small ‘pilot’ channels were cut into the model geometry. This prevents complete
channel drying, which cannot be simulated with HEC-RAS (drying tends to cause strong instabilities
and a termination of the program).
Storage areas were initially defined with boundaries which corresponded to local high-points in the
topography. The latter were identified from catchment boundaries computed from the LIDAR data
using the GRASS GIS algorithm r.watershed. This ensured that topographic barriers between parts of
the floodplain were respected by the storage area routing. In some instances, storage areas were
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30 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
further subdivided to allow for a more gradual change in the water surface elevation over the
floodplains, based on the results of initial simulations. Connections were made between storage areas
and other storage areas or channels using lateral weirs. The elevations of storage areas and lateral
weirs were taken from the LIDAR DEM. A procedure was developed whereby the user could define
the location of the storage areas in GIS, save the result to a shapefile, and then use an R script to
compute the associated elevation-volume relation, and the elevations of the connections between the
storage area and other storage areas or the main channel. The data was then automatically inserted
into the HEC-RAS geometry file. In many cases the resulting lateral weir elevations were manually
corrected to reflect e.g. the elevation of flood parapet walls, which are too small to be resolved by the
LIDAR DEM data.
Bridges were added at known locations, with the geometry defined based on data from DPWH.
Pumping stations were added at known locations, based on site visits to Taguig, and information in
DICAMM (2005) and WBCTI (2012).
In low-lying areas to the north and south of the Pasig River, pilot channels were used to prevent
channels from drying, to prevent numerical ‘blow-up’ in the HEC-RAS model (as the HEC-RAS solver
cannot treat wetting and drying). Underground drains were not included, as there is evidence that
these have often blocked by garbage and silt (DICAMM, 2005), and their present status is unknown to
the study team.
3.3.2.3 Splitting the model into the Marikina and Pasig /San Juan regions.
HEC-RAS was found to crash when running models with a large number of storage areas (~ 1000),
due to memory overflow errors. In the present study, it was found that using large numbers of storage
areas gave a better representation of floodplain flows, as it allowed a more gradual change in the
water surface elevation. To circumvent the limitation on the number of storage areas, the model was
split into two separate regions: A ‘Marikina’ model which includes a simple representation of the Pasig
and San Juan river systems, and a detailed Pasig / San Juan model. The model was then ran by 1)
Running the Marikina model using the original boundary conditions, and accounting for inflows from
the San Juan River, then 2) Running the Pasig / San Juan model, using already computed flows from
the lower Marikina River as a boundary condition. Results from the Pasig / San Juan model were used
for all mapping in that region, while results from the Marikina model were used elsewhere. The
schematization of channels and storage areas in each model is shown in Figure 3.6.
Figure 3.6. Schematization of channel cross-sections (orange) and storage areas (blue) for the Marikina (left) and Pasig / San Juan (right) models.
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3.3.2.4 Roughness and weir drag coefficients
The hydraulic roughness values were determined partially by calibration, while keeping them within
physically reasonable ranges. Unless otherwise stated, channels were given a roughness of 0.03, with
their floodplain regions (typically urbanised) given a value of 0.2. The exceptions are:
1. In the Upper Marikina River, between Wawa Dam and the junction of the tributary from Mount
Oro, the channel was given a value of 0.04, consistent with the enhanced bed-roughness here
(boulders on the channel bed).
2. Throughout the Upper Marikina River (upstream of the Rosario Weir, but most especially
upstream of Tumana), there are considerable areas of non-urbanised floodplain (e.g.
agricultural land). These were given a roughness of 0.05, except where upstream flow
blockages were observed which would prevent them from conveying much flow, in which case
a value of 0.2 was used.
3. The tributaries of the Upper Marikina River (except for the Nangka River) were given uniform
values of 0.05 for both the channels and the floodplains, reflecting the less-densely urbanised
nature of their floodplains, and the fact that their channels are not so cleanly defined and are
steep in parts. The Nangka river has more densely urbanised floodplains, so it was given a
channel value of 0.05 and a floodplain value of 0.2. The higher roughness in the tributaries
was also useful to enhance the model stability.
4. Tributaries to the San Juan River had their channel roughness set to 0.05, while retaining the
value of 0.2 for their urbanised floodplains.
For lateral weirs connecting channels with storage areas, the weir drag coefficient was set to 3.0. A
uniform value of 0.2 was used as a weir drag coefficient for flow between two storage areas on the
floodplains. This value is supported by analogy with the uniform flow Chezy equation for areas with a
bed slope ~ 0.0016, and Chezy coefficient of ~ 5, where the latter estimate is equivalent to a Manning
roughness of 0.2 for urban floodplains with a depth of 1m. These values were estimated to match a
key overland flow path in the HEC-RAS model around East Marikina and Cainta, and were found to
give reasonable agreement with observed floodplain depths during Tropical Storm Ondoy.
3.3.2.5 Boundary Conditions for the Tropical Storm Ondoy Calibration
For the Tropical Storm Ondoy simulation, inflows from the rainfall runoff model were imposed as
described in Appendix A. At the lower boundary of the Napindan River and the Mangahan Floodway, a
water level boundary condition was imposed based on water level observations from the Angono
gauge. At minor rivers flowing into Manila Bay, a downstream water level boundary condition was
imposed based on observations at the Manila Harbour South gauge. At the downstream end of the
Pasig River, a boundary condition was imposed using observed water elevations at Fort Santiago,
with the vertical datum estimated as described previously. In the Pasig / San Juan HEC-RAS model,
the water elevation and discharge at the downstream end of the lower Marikina was forced using the
stage and flow as computed from the Marikina model.
3.3.2.6 Model Setup and Calibration Approach
The model was set up iteratively, by gradually inputting more channels along with a crude storage
area representation. Often different parts of the model were worked on by different members of the
study team, and then integrated. As new channels and storage areas were added to the model, it was
re-run with idealised boundary conditions, to help detect any gross instabilities or errors introduced by
the new geometry (e.g. errors which could cause the model run to terminate). These could normally be
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32 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
fixed by making corrections or small adjustments to the new geometry, or the cross-sectional spacing.
This iterative strategy made it possible to gradually develop a complex model schematization, and
eliminate the main sources of model ‘blow-up’. Following the initial input of geometric and roughness
data, the model was run and iteratively adjusted to remove large instabilities or obvious errors in the
flow behaviour. These were typically related to input errors (or very rapid variations) in the models
geometry or roughness; insufficient cross-sectional spacing; or the use of overly large storage areas.
A short model time step (3s) was used to increase the model stability, while the non-linear solver was
allowed a maximum of 10 iterations to converge in each time-step. As super-critical flows occurred in
some reaches (typically in steep tributaries of the main river system), the model was run using the
‘mixed-flow’ option, with a Froude number threshold for the elimination of inertial terms set to 0.95.
The model was calibrated to predict peak depths during Tropical Storm Ondoy, both in the channel
and over the floodplains. Once the model was running with reasonable stability, the general strategy
for model improvement was to 1) note discrepancies between the model and observations; 2) consider
physically why these might be occurring, and look for any model input errors that might be
responsible. If this didn’t fix the problem, consider; 3) adjusting friction coefficients in broad areas of
the model to improve the agreement with data, while keeping them within physically reasonable
ranges. This led to the definition of Manning’s n and drag coefficients as described in the previous
section.
3.3.2.7 Taguig-Pateros-Lakeshore Region ‘bathtub’ models.
In the area south of the Napindan River and along the Laguna Lakeshore, flooding occurs largely due
to the ponding of standing water associated with flood events and high lake levels (CTI, 2005; Muto et
al., 2011). Regions in this area modelled for this study are shown in Figure 3.7.
Part of the area around Taguig and Pateros includes a large number of flood defence structures
(Figure 3.7). This includes pumping stations (total capacity of 27m³/s), floodgates on rivers which
connect to the Napindan River and Laguna Lake, a Parapet wall (elevation 3.6 m above MSL) along
the Napindan River, and a dyke along the Laguna Lakeshore (elevation 4.5 m above MSL) (CTI,
2005).
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 33 Greater Metro Manila Area – Flood Risk Analysis
Figure 3.7. Regions around Laguna Lake modelled using the 'bathtub' approach. Red polygon: Areas inside flood defence structures. Yellow polygon: Areas flooded by high Laguna Lake levels.
If functioning perfectly, these defences will hydraulically isolate the area inside the flood defences from
the Lake and the Pasig-Marikina River system, at least while the water elevation in Napindan Channel
remains below 3.6 m above MSL. In that situation, flooding is caused by the rain-induced inflow being
larger than the outgoing flux due to pumping.
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34 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
At the other extreme, if the flood defences completely fail, or in areas that are not protected by them,
the water elevation will equilibrate with Laguna Lake. As the coverage of the flood defences is
incomplete, and their historical performance has been mixed, two sets of models are developed for
this study. In one model, the flood defences function perfectly, and hydraulic isolate the area inside the
red polygon in Figure 3.8 from water in Laguna Lake and Napindan channel. In the other, the flood
level is determined by the Laguna Lake water elevation.
3.3.2.8 Model 1: Flood defences function perfectly
The model here applies to the red-polygon region in Figure 3.8, and represents the situation in which
flooding is caused by rainfall onto the catchment, offset by the operation of pumping stations. Inflows
from Laguna Lake and the Napindan River are treated as negligible. Previous studies have
constructed similar models for this region (CTI 2005; Muto et al., 2011). The main advantage for the
present study is the availability of better quality elevation data (LIDAR DEM), which improves the
accuracy of the stage-volume relation (Figure 3.8), and hence the computed inundation.
Figure 3.8. Stage-Volume and Stage-Area relations for regions in Figure 3.7, computed from the LIDAR data.
In the model, the volume of water inside the flood defences is computed as:
where V is the volume of water inside the flood defences (m³), t is time (s), I is the rate of inflow
(caused by rain into the catchment, routed through a rainfall runoff model), and PS is the rate of water
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 35 Greater Metro Manila Area – Flood Risk Analysis
removal by pumping stations. The stage was related to the volume via a stage-volume curve
computed from the LIDAR data (Figure 3.8).
The inflow hydrograph at the pumping stations was computed using the same methods as described
in the Rainfall Runoff Section. One exception is that in the t_lag computation, the channel velocity
scale was set to 0.9 m/s (following Muto et al, 2011) instead of begin estimated, because the slope-
based approach is not valid for channel slopes approaching zero as occur in low-land parts of Taguig.
For simulating the Tropical Storm Ondoy event, the input rainfall was taken as the observed Science
Garden hourly rainfall, scaled by a factor so that the total rainfall was equal to the catchment average
rainfall (i.e. 0.789 for Tropical Storm Ondoy). For the design flood scenarios, the same rainfall series
as employed in the HEC-RAS model was used. The pumping station operation was assumed to begin
when the stage exceeds 1.0 m above MSL, and end when the stage falls below 0.3m above MSL,
which is consistent with observations of pumping station behaviour in the lead up to Ondoy (Figure
3.9). For simplicity, it is assumed that the pumping stations begin pumping at full capacity (27m³/s).
The initial water elevation was set to 1m above MSL. Note that this model has no calibration
parameters to ‘tune’ its performance to the data.
A time series of stage observations both inside the flood defences and in Laguna Lake was obtained
from Labasan pumping station (Figure 3.9). Occasional spikes in the stage data are assumed to
reflect data-entry errors. Water levels inside the flood defences remained significantly below those in
Laguna Lake prior to and during Tropical Storm Ondoy, highlighting the impact of the flood defences.
Also of interest are the regular oscillations in the water level in the range 0-1m. These demonstrate
that even with the floodgates closed, water drains into the flood defences from some combination of
catchment baseflow and leakage from Laguna Lake / Napindan River.
Figure 3.9. Observed water levels in Laguna Lake and behind the flood defences during September -December 2009. Measurements provided by Labasan Pumping Station.
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36 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
3.3.2.9 Model 2: Flood Defences Fail
The previous model assumes that the flood defences (pumping stations, dykes and gates) work to
keep water levels in Taguig-Pateros hydraulically disconnected from water levels in Laguna Lake.
However, this is not necessarily realistic: for example, during the 2012 Habagat event, the pumping
stations ran out of fuel. Maps of the inundation extents provided by Taguig City Council suggest that
the peak water level inside the flood defences was similar to the peak Laguna Lake level, presumably
due to a combination of rainfall and leakage. More generally, for lake levels above 3.6m, water will
overtop the parapet walls along the Napindan River, eventually causing water elevations inside the
dyke to equilibrate with the Lake level. In general, the water levels inside the flood defences will not
rise above the lake level, because if this were to happen, the floodgates would be opened to allow the
water to flow out to Laguna Lake. Thus, the scenario where the flood defences fail and the water level
equilibrates with the lake level is considered a ‘worst-case’ scenario, which is nonetheless realistic. In
this case, annual exceedance probabilities can be estimated from the corresponding water level return
periods in Laguna Lake, described later in this report.
3.4 Design Flood Estimation
To provide suitable input to the flood inundation model in the present study, the design flood events
need to specify 1) Rainfall time series for each sub-catchment in the rainfall-runoff model, and 2)
Water level time series for Laguna Lake and Manila Bay. These should vary appropriately to reflect
the AEP of the design flood.
This section describes methods of statistical analysis to support design flood estimation. It includes
methods to define design-storm temporal patterns, investigate spatial variations in extreme rainfall,
estimate the magnitude of catchment-averaged extreme rainfalls and lake-levels, and an approach to
parameterising the relationship between the latter two variables.
As with any statistical methods, the approaches used here are most reliable for estimating events
which are within the range of the observed data (‘interpolation’), rather than for estimating events
outside of the range of observations (‘extrapolation’). This applies to both estimates of the extreme
values of quantities, and to their confidence intervals. For example, it is likely that the ‘true’ magnitude
of the 1/30 AEP 2-day catchment-averaged rainfall total is within the confidence limits that we
calculate. However, the estimate for the 1/200 AEP 2-day rainfall depth (and its confidence interval)
will inevitably be less reliable, because it requires extrapolating the statistical model well outside the
range of the observed data.
3.4.1 Synthetic Storm Time Pattern
In this section, a synthetic storm pattern is developed based on an existing Rainfall Intensity Duration
Frequency (RIDF) analysis at the Science Garden. The latter was developed by PAGASA, and is
available on their website. It describes the AEPs of rainfall depth, for storms with durations ranging
from 10 minutes to 24 hours.
The RIDF curves are shown in Figure 3.10, along with straight-line fits for each return period. These
are useful for design-storm construction, at least in the vicinity of the Science Garden, because for any
given storm duration and AEP, they allow the corresponding rainfall depth (or equivalently, average
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 37 Greater Metro Manila Area – Flood Risk Analysis
rainfall intensity) to be estimated. They can also be used to develop design-storm temporal patterns,
as outlined below.
Figure 3.10. Rainfall Depth, Duration Frequency Curves for the Science Garden Rain Gauge, developed in a previous PAGASA study.
Design storms require a rainfall temporal pattern. There are many approaches to developing these
(e.g. Ellouze et al., 2009). A common approach is to artificially construct a pattern which, for a given
AEP (e.g. 1/50), contains a range of shorter duration sub-storms (e.g. duration 1, 2, 3, 6, 12 and 24
hours) which all independently have a rainfall depth equal to the design rainfall depth for their duration
(e.g. peak 1 hour intensity equal to the 1 hour RIDF curve at AEP=1/50 on Figure 3.10; peak 2 hour
intensity equal to the 2 hour RIDF curve at AEP=1/50 on Figure 3.10; and so on). This approach is
taken in the present study, using the analysis at the Science Garden.
A single dimensionless design storm temporal pattern is developed for all AEP’s less than or equal to
1/5. The use of a single dimensionless temporal pattern is justified by the observation that, for each
AEP below 1/5, the ratio of the rainfall depth to the corresponding 24 hour rainfall depth is nearly
constant (Figure 3.11). This implies e.g. that the 1 hour design rainfall depth is always approximately
30% of the 24 hour design rainfall depth, independent of the AEP. Similarly, the 2 hour design rainfall
depth is always approximately 45% of the 24 hour design rainfall depth, and so on.
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38 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Figure 3.11. Ratio of rainfall depth to 24hr rainfall depth against duration, for each AEP <=1/5
Figure 3.12 shows the dimensionless design storm pattern constructed using the mean dimensionless
rainfall depth curve in Figure 3.11. It uses hourly time increments, and thus only applies for durations
of 1 hour and above. The dimensionless design storm pattern is used to construct design storms for
the catchment as a whole, by rescaling the total rainfall to agree with the 1-day rainfall depth with the
desired AEP. By construction, the design storm then contains sub-storms with the same AEP for
durations from 1, 2, 3,6,12 and 24 hours. Later, this design storm pattern is also extended to 2-day
rainfalls, by appending 24 hours of constant rain, with intensity chosen so that the 2-day rainfall depth
AEP (computed independently as described later) is also satisfied by the design storm. Methods to
estimate the 1-day and 2-day extreme rainfalls throughout the catchment are described subsequently.
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 39 Greater Metro Manila Area – Flood Risk Analysis
Figure 3.12. Dimensionless design storm pattern developed from the Science Garden RIDF curves
3.4.2 Spatial Variation in Extreme Rainfalls in the Pasig-Marikina Catchment.
An analysis of spatial variations in extreme rainfall intensities over the Pasig-Marikina catchment was
undertaken. The aim was to determine whether variations were so significant that they needed to be
accounted for in the design storms, or alternatively, whether a simpler ‘spatially constant design storm
intensity’ could be used over the entire catchment.
The analysis was restricted to maximum 1-day and 2-day rainfalls at rain-gauges in the Pasig-Marikina
Catchment. The temporal restriction was necessary because data recorded at shorter intervals was
not as widely available in space and time. Previous studies suggest that the 2-day rainfall total is an
appropriate guide for expressing the magnitude of a rainfall event in the Pasig Marikina basin, as
flooding rains most often occur over less than 2 days duration, and the typical time-delay between
peak rainfall and peak flooding throughout the basin is much less than 2 days (WBCTI 2012). The
spatial restriction was chosen because of our focus on the Pasig Marikina Basin, and because the use
of a larger region can reduce the likelihood that the assumptions underlying the statistical analysis will
hold.
To investigate spatial variations in extreme rainfall, annual exceedance probabilities of the 1-day and
2-day peak rainfall depth were estimated at each station with sufficient data, using an index-flood
procedure (Hosking and Wallis 1997). Similar techniques are widely applied to estimate extreme
rainfalls and river discharges as input to flood studies (e.g. Fowler and Killsby 2003; Kjeldsen et al.,
2008; Perica et al., 2011).
In the index-flood procedure, the data are assumed to be drawn from a ‘homogeneous region’, in
which the frequency distributions at each station are identical apart from a scale factor, which varies
from site to site according to the model:
(4)
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40 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Here is the intensity exceeded with frequency F at station ‘i’; q(F) is a probability distribution (the
‘regional growth curve’) which is identical within the homogeneous region; and is the scale factor
(termed the ‘index flood’) for Station i.
To see the implications of the index-flood assumption, consider a homogenous region containing
Station A and Station B. If the 10% AEP 1-day rainfall depth is 100mm at Station A and 150mm at
Station B, then the index-flood assumption implies that for any AEP, the 1-day rainfall depth at Station
B will be 1.5 (=150/100) times that at Station A. If this is not true, then Station A and Station B are not
from the same homogenous region, and alternative methods of analysis must be applied.
Hosking and Wallis (1997) describe the computational methods used in this analysis. This includes
methods for estimating the values of q(F) and , methods for checking the validity of the
‘homogeneous region’ assumption, and methods for computing uncertainties in the associated
extreme rainfall estimates. These computations are implemented in the open source software ‘R’
within the package ‘lmomRFA’, which was used for the present analysis.
The data was first converted into annual maximum rainfall series at each station. The index-flood
method used here requires that the data are statistically independent within each station, and this is
easiest to achieve by only selecting the maximum rainfall event for each calendar year (as the
calendar year contains one entire wet season, which does not usually overlap with the next year). For
each station, the maximum 1-day and 2-day rainfall was computed for every year in the record, so
long as that year contained > 90 days of wet season rainfall. Here the ‘wet season’ is defined as being
from May-October inclusive (184 days in total). Years for which the record covered less than 90 days
of wet-season rainfall (e.g. because of instrument malfunction) were excluded from the analysis,
because of the high chance that they did not record the peak rainfall event in that year. Finally, only
stations with > 10 years remaining were included in the analyses. The final datasets contained seven
stations with a total of 222 acceptable data-years (Table 1.1).
Table 1.1. Stations used in the Regional Frequency Analysis, and number of years of acceptably complete data.
STATION Port Area
NAIA Tipas Pasig Mt Oro Science Garden
Sitio Tabak
Number of accepted data years
47 36 20 34 16 49 20
Next, the L-moments for each station were computed, and the homogeneity of the region was checked
by computing the ‘discordancy statistic’ D_station for each station, and the ‘homogeneity statistic’ H for
the region (Hosking and Wallis, 1997). Hosking and Wallis (1997) suggest that stations may be
considered sufficiently consistent with the region as a whole if D_station<Dcrit, while the region may
be considered as acceptably homogeneous if H<1, possibly heterogeneous if 1<H<2, and definitely
heterogeneous if H> 2. More recently, Wallis et al. (2007) suggested that for rainfall data, H<2 should
be considered acceptably homogeneous, noting that the H<1 guideline does not account for non-
statistical sources of variability which often exist in rainfall data.
Even in homogeneous regions, it is possible for the discordancy and homogeneity statistics to suggest
non-homogeneity due to the presence of isolated extreme observations. This could be detected by re-
computing the homogeneity and discordancy statistics with the isolated extreme observation removed.
If the latter appear to be acceptably homogenous, it is recommended that the homogeneity
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 41 Greater Metro Manila Area – Flood Risk Analysis
assumption is accepted, and that the analysis proceeds including the extreme observation, which will
contain useful information on the behaviour of extreme events in the region (Hosking and Wallis 1997,
p 70; Fowler and Killsby 2003).
The best-fitting probability distribution was chosen from a selection of 5 different distributions, by using
an L-moment based goodness-of-fit test, and viewing the data-set on an L-moment ratio diagram.
Return period curves were computed for every station, with confidence limits computed using a
parametric boot-strap from a synthetic region with the same number of stations as included in the
analysis (see function ‘regsimq’ in the R package ‘lmomRFA’ version 2.4). The quantile function at
each site in the synthetic region was of the same type as the fitted regional distribution, but with
parameter values taken from the parameter estimates for each individual station. The bootstrap
calculations account for a constant correlation between the extreme values in the same year at each
pair of stations, which is estimated as the average correlation among stations in the data. Results
were checked to assess the sensitivity to this value. The fitted return period curves and observations
were plotted graphically to provide a further visual check on the quality of the fit.
3.4.3 Catchment-Averaged Extreme Rainfall Frequency Analysis
The AEPs of extreme rainfall at individual stations will generally differ from the AEPs for the catchment
as a whole (Allen and DeGaetano, 2005). This is because rainfall is spatially variable. For example, in
a large catchment, a heavy rainstorm may have a small ‘patch’ of particularly intense rain which
exceeds the 1-day 1% AEP intensity, while in other areas, the storm has a 1-day 5% AEP intensity.
While individual rainfall gauges do measure the effects of an intense rainfall patch, the catchment
averaged rainfall will ‘average-out’ these extreme patches and thus not be as significantly affected,
especially for larger catchments. Hence, spatially-averaged extreme rainfall depths tend to be less
than at-a-station extreme rainfall depths.
For design flood scenarios, the ratio of the catchment averaged rainfall depth to the point rainfall depth
is termed the ‘Areal Reduction Factor’. It depends on the chosen catchment, the chosen storm
duration, and the storm AEP. This can be used to construct extreme rainfall intensities for the
catchment as a whole, by multiplying the at-a-point extreme rainfall intensities with the areal reduction
factor. Such catchment-averaged extreme rainfall intensities are generally considered to be more
appropriate than at-a-point extreme rainfall intensities for design flood estimation, especially for larger
catchments.
To estimate the return periods of 1-day and 2-day catchment averaged rainfall, a grid of 1kmx1km was
generated within the Pasig-Marikina Catchment, including Taguig and sites North / South of the Pasig
River, which are treated in this study (Figure 3.13). The 1-day and 2-day rainfall data was interpolated
from the rain-gauges to each cell using inverse-distance-weighted interpolation, so long as at least 3
stations had non-missing data for that day. Otherwise the data was treated as missing. The same rain-
gauges as included in the regional frequency analysis were used. Irrespective of their distance to a
cell, stations were given a zero weight on days they were missing data (so they do not affect the
calculation). For each day, the catchment-averaged rainfall was then computed as the average of the
interpolated values. Finally, the annual maximum 1-day and 2-day rainfall was computed for the entire
catchment.
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42 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Figure 3.13. Pasig-Marikina Catchment including all areas treated in HEC-RAS model (polygon). Red + are rain gauges with > 10 years of extremes recorded. Blue dots are the 1kmx1km grid at which rainfall was interpolated to compute the catchment averaged rainfall.
The catchment-averaged data was then analysed using the same methods as applied for the regional
frequency analysis (as these also work for a single station). For consistency with that analysis, a
Generalised Normal distribution was fit to the 1-day data while a Pearson Type 3 distribution was fit to
the 2-day data. In both cases these distributions were ‘good-fits’ to the data based on the Z-statistic
(Hosking and Wallis, 1997).
The analysis was also repeated with the catchments broken into separate regions for the San Juan
Catchment, the Marikina Catchment, the Taguig-Pateros region catchment, and an area to the North
and South of the Pasig River. This turned out to have little effect on the results, and so for simplicity
the whole-of-catchment analysis was used for the design rainfall construction.
3.4.4 Frequency analysis of high water levels in Laguna Lake
The annual exceedance probabilities (AEP) for Laguna Lake water levels were computed using daily
lake level data, for which incomplete records are available since 1919. The dataset includes water
level observations at Angono, Kalayaan, Los Banos, and Looc. These were checked visually to ensure
consistency among stations, and exclude any obvious errors.
A single ‘annual maximum lake level’ data series was constructed from the maximum of all the station
observations in each year. Because of gaps in the recorded data, coverage was restricted to the
years 1919-1922, 1946-1956, 1958-1963, 1965-1967, 1972-1978, 1985-1986, 1988-2007 and 2009.
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 43 Greater Metro Manila Area – Flood Risk Analysis
To estimate AEPs of peak lake level, an extreme value analysis was conducted. Probability
distributions were fit to the annual maximum lake level dataset using the method of L-moments
(Hosking and Wallis, 1997). The analysis was conducted using the software R, with distribution fitting
done by the package ‘lmomco’. Where required, the empirical AEP’s of data points were estimated
using Cunane’s plotting position formula. Ninety-percent confidence intervals for the fitted quantiles
were computed via a parametric bootstrap (Kysely, 2008), using one hundred thousand bootstrap
samples. For the final fits, the convergence of the confidence interval estimates was confirmed by re-
running the fit with different numbers of bootstrap samples (50000, 25000, 10000), and checking that
changes in the confidence intervals were negligible.
Probability distributions tested on the dataset include the two parameter ‘Gumbel’ distributions, and
the three parameter ‘Generalised Extreme Value’, ‘Log Pearson Type 3’, and ‘Pearson Type 3’
distributions. In each case, the goodness-of-fit of the statistical model to the data was checked by
examining the linearity of quantile-quantile and probability plots, and also by plotting the data L-
moments on a L-moment ratio diagram (Hosking and Wallis, 1997), which can be used to select
among candidate distributions. On this basis, the Generalised Extreme Value distribution was selected
for the final analysis, although all of the tested distributions were found to give quite similar predictions
for the quantiles of interest.
3.4.5 Relation to extreme rainfalls
For the design flood simulations, an artificial water level time series is required at Laguna Lake. This
has to be supplied as a boundary condition to our hydraulic model, because our rainfall runoff model
does not simulate 80% of Laguna Lake’s catchment (which is outside of the Pasig-Marikina Basin).
The design water level time series is constructed to reflect the relations between the lake water
elevation and the design rainfall. For example, visual investigation of Figure 3.14 suggests that often
high lake levels may be preceded by high rainfall in the Pasig-Marikina Catchment. However, this is
not always true, as most of the Laguna Lake catchment lies outside the Pasig-Marikina catchment.
Figure 3.14. Catchment-averaged 2-day rainfall (black) with Laguna lake water levels (red). Green lines denote days with the annual maximum rainfall.
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44 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
To simplify the problem of constructing the lake level time series for the 2-day design flood events, it is
assumed that the lake level increases with the same time pattern as observed during Tropical Storm
Ondoy at Angono station from midday 25/09/2009 to midday 27/09/2009. However, the latter series is
translated and scaled to set the initial and final lake levels for the simulation. The initial and final lake
levels are related to the observed annual maximum catchment-averaged 2-day extreme rainfall as
described below. This enables the computation of the lake levels time series once the catchment
averaged rainfall for the design event is known.
Figure 3.15 compares annual 2-day maximum catchment-averaged rainfall against the initial Laguna
Lake levels (1-day prior to that rainfall event). There is no strong relation between these variables,
which might be expected as the rainfall event does not directly affect lake levels the day before.
However, the associated change in lake level from 1-day before to 1-day after the storm is positively
related to the annual maximum catchment-averaged 2-day rainfall (Figure 3.16). Physically, this is to
be expected, both because high rainfall events in the Pasig-Marikina Catchment contribute inflow to
Laguna Lake, and also because these rainfall events are likely to be correlated with high rainfall
elsewhere in the Laguna Lake catchment.
Figure 3.15. Annual maximum 2-day catchment averaged rainfall vs. Laguna Lake level the day before the storm.
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 45 Greater Metro Manila Area – Flood Risk Analysis
Figure 3.16: Annual maximum 2-day catchment averaged rainfall vs. the change in Laguna lake level from 1-day before the storm to 1-day after.
To construct the design flood lake level time series, the starting lake level is taken from the upper 90%
prediction interval in Figure 3.15, while the rise in the water level is taken as the upper 90% prediction
interval in Figure 3.16. This approach provides a conservative but realistic scenario, where both the
initial lake level and the rise in the lake level are higher than average, although not unrealistically so.
For example, during Tropical Storm Ondoy, the Lake level rose from ~ 2.3 to ~ 3.3 m. For the same
catchment-averaged 2-day rain (432 mm), the present method predicts a change in water level from
2.3 to 3.47, which is only slightly conservative as compared with observations during Tropical Storm
Ondoy (Figure 3.17).
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46 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Figure 3.17. Comparison of the changes in Laguna Lake during Tropical Storm Ondoy, and the design storm boundary condition with the same 2-day catchment averaged rainfall.
It is worth noting in that for the largest few observed rainfall events, both the initial lake level and the
change in lake level fall above the regression line, although within the prediction intervals (Figures
3.15 and 3.16). This may be purely by chance, or it may indicate that the relation between rainfall and
the lake levels becomes stronger for more extreme rainfall events. The latter seems physically
reasonable, as extreme events may be spatially larger on average, and thus be associated with more
rain throughout the Laguna Lake catchment. If correct, the method used herein will be less
conservative for large events than for small ones. Regardless, within the range of the data the current
method is reasonable and conservative.
3.4.6 Design Flood Boundary Conditions
3.4.6.1 Manila Bay Water Levels
For all design flood scenarios, Manila Bay Water Levels are set to 0.9m MSL, which is approximately
equal to the highest astronomical tide in Manila Bay, and previously used design high water level for
Manila Bay (DICAMM, 2005; WBCTI, 2012). This water level is assumed to be independent of the
AEP. Future work may consider the relations between high water levels in Manila Bay and extreme
rainfalls in the Pasig Marikina Catchment, which could allow for e.g. accounting for Storm Surge. This
would require long time series data from Manila Bay, which is not available for the present study.
3.4.6.2 Laguna Lake Water Levels
The Laguna Lake water level time series is imposed for each design flood based on the rainfall AEP,
as described in the section on the relation between high lake-levels and extreme rainfall.
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 47 Greater Metro Manila Area – Flood Risk Analysis
3.4.6.3 Design Flood Rainfall
For the design flood events, the catchment averaged rainfall was imposed on each sub catchment in
the rainfall-runoff model, with the rain time variation based on the design storm pattern described
previously. The rainfall intensity was scaled so that the 24 hour rainfall total was equal to the 1-day
rainfall total from the catchment-averaged frequency analysis, and a constant rate of rainfall was
appended for another 24hrs, with intensity chosen so that the 48 hour rainfall was equal to the 2-day
catchment-averaged rainfall for that return period.
The use of a single catchment-averaged rainfall throughout the Pasig Marikina Basin is justified by the
results of the Regional Frequency Analysis (see Results). It was found that the confidence intervals of
extreme rainfalls throughout the basin were generally overlapping, and that differences between
nearby stations were often as large as differences between far-apart stations. Thus it is reasonable to
neglect these differences as a first approximation, considering that they may be dominated by random
variation.
3.5 Damage Calculation
The damage calculations involve integrating outputs from the flood inundation model with the
exposure information and vulnerability models. The vulnerability models take the form of stage-
damage curves (Figure 3.18). These describe the damage fraction (i.e. ratio of damage to building
replacement value) as a function of the ‘peak flood depth minus the floor height’, for a range of
different building types.
Figure 3.18. Depth-damage curves developed for different building types by UPD-ICE for the present study. See “Development of vulnerability curves of key building types in the Greater Metro Manila Area, Philippines” (Pacheco et al., 2013) for further information.
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48 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
The full distribution of peak flood depths in each exposure polygon is used in the analysis. The peak
flood depth information is stored on a raster with a 10m pixel size, which typically leads to a range of
flood depths occurring in each exposure polygon (Figure 3.19). These peak depths are binned into
10cm classes (0m, 0.1m, 0.2m, …), with all non-inundated areas given zero depth. The exposure
polygons are then rasterised to 10m pixel size, with pixel values taking the ID value for the associated
polygon. Finally, a cell count of each depth-class in each exposure polygon is computed (via cross-
tabulation), resulting in a histogram of depth values for each exposure polygon.
Figure 3.19. Exposure polygons with the peak flood depths overlayed. Clearly most exposure polygons have a range of depths.
Damages are computed in several ways, all of which account for the multiple building-types that occur
in each exposure polygon. Initially in each exposure polygon, for each building-type / peak-depth
combination, the ‘damaged floor area equivalent’ in the floodable storeys is computed. For example, if
100ha of a given building type experiences a depth of 2m which causes 20% damage, then the
associated ‘damaged floor area equivalent’ would be 20 ha ( = 20% x 100 ha). In practice the depths
in each exposure polygon are variable, and so the total damaged floor area for each building-type is
computed as a sum of the damages in each depth-class. The ‘inundated floor area’ for each building
type is also computed. The details associated with computing these quantities are described in the
following sections.
Given these inputs, several useful measures of flood impact may be calculated for each exposure
polygon:
1) Damaged floor area equivalent: This is taken as the sum of the damaged floor area
equivalents for each building type.
2) Building damage cost: This is the sum of the damaged floor area equivalent per building
type multiplied by the associated building replacement cost per m².
3) Population with Inundated Homes: This is taken as the sum of the inundated floor area
multiplied by the population density per m² of floor area.
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When mapping the above quantities, it is useful to transform them via division by the exposure
polygon ground area. Visually, this removes the confounding effect of exposure polygon size on the
results. For illustration, consider two exposure polygons with the same flood depths, building types,
and building densities. Suppose one is large (100 ha) and one is small (1ha). Then the large polygon
will have damage measures being 100 times the small polygon. However, if we divide by polygon
area, then they will both show up as having the same intensity of damage. Typically, the latter gives a
visually clearer indication of the spatial distribution of impact.
3.5.1 Computation of ‘damaged floor area equivalent’ in a single exposure polygon, for a single building-type and a single depth
The computations are described in a step-by-step fashion.
1. For each building-type in the exposure polygon, estimate the footprint-area (i.e. ground floor
area) covered by each building storey-category (1-storey, 2-storey, 3-7 storeys, 8-15 storeys,
16 – 25 storeys, 26 – 35 storeys, 36+ storeys):
Footprint-area vs. storey-category and floor-area vs. storey-category information is available in the
exposure database. The latter is also provided per building type. In each storey category, the
distribution of building-type by footprint-area is assumed to be the same as the distribution of
building-type by floor-area.
For each building-type / storey-category combination, compute:
Replacement cost per m^2, using building costs estimates reported in Appendix A.
‘Total floor area in storeys 1, 2 and 3’: Assume that only the lower 3-storeys of any building are
vulnerable to flooding, as it is very rare for flooding to exceed this level.
i) For 1-storey and 2-storey buildings, this is equal to the total floor area.
ii) For storey-categories ‘midrise’ or larger, this is calculated as 3x (footprint-area for
the associated category).
Nominal Floor height:
i) Localised building survey data suggest most buildings have a floor height
categorised as either 0 or 0 – 0.25 m (Appendix C). Thus we assume a uniform
floor height of 0.125m for every building type. This is subtracted from the flood
depth when computing the depth for the vulnerability curves.
Floor area associated with the given depth class:
i) Assume that all buildings are uniformly distributed over the polygon. Then the floor
area associated with this depth class = (Total floor area in storeys 1, 2 and 3) x
(Proportion of the exposure polygon in this depth class).
ii) Note that often, not all of this floor area will actually be inundated. When computing
damages, this is accounted for in the vulnerability curves. When computing
inundated floor area, this is accounted for using the inter-storey height.
Select a vulnerability curve for this ‘building-category’ / ‘storey-category’ combination, and compute
the associated damage fraction given the depth and floor height.
iii) Vulnerability Curves for different building-types / storey categories are noted in 3.2.
The methods used to develop these are described in “Development of vulnerability
curves of key building types in the Greater Metro Manila Area, Philippines”
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50 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
(Pacheco et al., 2013). The mid-rise curves are taken as applying to the floodable
fraction of buildings with 3 or more storeys.
Compute the damaged floor area equivalent for this depth as the sum of the (Flooded floor area for
this depth) x (damage fraction for this depth) for each storey category.
Table 3.2: Vulnerability Curves used for each building type / storey height combination. Missing values did not
feature in the exposure database. See Figure 3.18 for a plot of the vulnerability curves.
Building Type Vulnerability Curve
Type
Vulnerability Class
(1-Storey)
Vulnerability Class
(2-Storey)
Vulnerability Class
(3+-Storey)
W1 H W1-L-1 W1-L-2
W2 H W1-L-1 W1-L-2
W3 H W3-L W3-L
N H N-L-1 N-L-2
CHB C CHB-L-1 CHB-L-2
URA C MWS-L MWS-L
URM C MWS-L MWS-L
RM1 C MWS-L MWS-L
RM2 C MWS-L MWS-L
MWS C MWS-L
CWS C CWS-L
C1 C C1-L-1 C1-L-2 C1-M
C2 C C1-M
C4 C C1-M
PC1 C C1-L-1 C1-L-2
PC2 C C1-L-1 C1-L-2 C1-M
S1 C S1-L-1 S1-L-2 S1-M
S2 C S1-L-1 S1-L-2 S1-M
S3 C S1-L-1 S1-L-2
S4 C S1-M
3.5.2 Computation of the inundated floor area in each exposure polygon.
1. Assume that all buildings are uniformly distributed over the polygon, and thus the distribution
of depth experienced by the buildings is the same as that of the polygon as a whole.
2. Get the floor height and inter-storey height for this polygon from the exposure database. Floor
heights are assumed to be 0.125m, as discussed above. Inter-storey heights give the typical
height of each building storey.
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3. Compute the floor area in the 1st storey (ground floor) as the sum of the building footprints.
The flooded fraction is equal to the fraction of depths in the polygon which are above the floor
height.
Compute the floor area in the 2nd
storey as the sum of the building footprints in buildings that are 2-
storeys and above
The flooded fraction is equal to the fraction of depths in the polygon which are above the floor
height plus the inter-storey height.
Compute the floor area in the 3rd storey as the sum of the building footprints in buildings that are 3-
storeys and above
The flooded fraction is equal to the fraction of depths in the polygon which are above the floor
height plus the twice the inter-storey height.
The inundated floor area is taken as the sum of all the (floor-areas x flooded-fractions) on storeys
1, 2 and 3.
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52 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
4 Methods
4.1 Hydrology
4.1.1 Regional Extreme Rainfall Frequency Analysis
The station annual maximum data were initially checked for homogeneity and discordancy using the H
and Dcrit statistics. This gave H=1.21 and 1.14 for the 1-day and 2-day rainfalls respectively,
suggesting the region is ‘possibly heterogeneous’ (or ‘acceptably homogenous’ according to the
weaker criterion of Wallis et al. 2007). All stations had D<Dcrit except for the NAIA station in the 2-
year rainfall analysis. Further investigation of the NAIA station was thus undertaken. This revealed the
presence of a single extreme observation in 1972 (472 and 291 mm of rain over consecutive,
total=763mm). Such high values are reasonable in Manila; for example, during Tropical Storm Ondoy,
the Science Garden station recorded 455mm of rain in 24 hours.
As noted in the Methods, it is possible for isolated extreme observations to give the appearance of
non-homogeneity in a region, and in this instance the analysis should proceed while including the
extreme observation. To check the significance of the NAIA outlier, the analysis was re-ran with 1972
data excluded from the NAIA station. This led to all stations having D_station<Dcrit, and H = 0.94/0.63
for the 1-day and 2-day rainfalls respectively, indicating that the region is acceptably homogeneous.
Thus, the analysis proceeded including the extreme observation at the NAIA station.
For the 1-day rainfall a Generalised Normal distribution was fit to the data, while a Pearson type-3
distribution was used for the 2-day rainfalls. These had the best fit to the data based on the Z-statistic,
although the results were not strongly sensitive to the use of other good-fitting distributions.
Confidence intervals were calculated using an inter-station correlation of 0.65, as estimated from the
data itself.
Table 4.1 and Figure 4.1 show an example of the AEP curve for a single station (Science Garden).
Figures 4.2 and 4.3 show the estimated 2-day 1/10 and 1/100 AEP peak rainfall events for stations
included in the analysis, along with their 90% confidence intervals.
For low AEP extreme rainfall events the 90% confidence intervals vary around +-30-40% of the point
estimate, due to the relatively short period of the data used in the analysis. This range is equivalent to
large changes in the estimated return periods. For example, at the Science Garden the upper
confidence limit of the 2-day 1/10 AEP rainfall is approximately equal to the lower confidence limit of
the 2-day 1/100 AEP rainfall (Table 4.1). There is also considerable overlap in extreme rainfall
estimates among stations for a given AEP, and it is difficult to distinguish any patterns (Figures 4.2
and 4.3). While extreme rainfalls are higher at stations north of the Pasig River, the differences are not
large compared with the variations between neighbouring stations, and could be an artefact of the
short nature of the records. Pairwise t-tests on the station data did not suggest any differences in the
mean annual maximum rainfall at each station, which is the ‘index flood’ value used to scale the AEP
curves. Similarly, a Kruskal-Wallis test did not suggest significant differences in the median annual
peak rainfalls between sites. In sum, differences in the observed extreme rainfall patterns do not seem
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 53 Greater Metro Manila Area – Flood Risk Analysis
inconsistent with statistical randomness, and so for simplicity the spatial distribution of extreme rainfall
in the design storms may be approximated as constant.
Table 4.1. Extreme values of 2-day rainfall at the Science Garden (with 90% confidence intervals) based on the
Regional Frequency Analysis.
AEP 1/5 1/10 1/25 1/50 1/100 1/200
Rainfall Depth (mm) 319 387 475 534 594 654
Lower 90% CI 283 334 390 425 459 490
Upper 90% CI 360 454 592 688 791 898
Figure 4.1. AEP curve (and 90% confidence intervals) for annual maximum 2-day rainfall at the Science Garden, based on the Regional Frequency Analysis
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54 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Figure 4.2. AEP 1/10 2-day rainfall depths (and 90% confidence limits) at stations used in the rainfall frequency analysis.
Figure 4.3. AEP 1/100 2-day rainfall depths (and 90% confidence limits) at stations used in the rainfall frequency analysis.
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4.1.2 Catchment Averaged Extreme Rainfall Frequency Analysis
The AEP curve for 2-day catchment-averaged rainfall is shown in Table 4.2 and Figure 4.4. For a
given AEP, the catchment-averaged extreme rainfall values are generally smaller than the at-a-station
values, as expected on physical grounds. For example, they are about 82-93% of extreme rainfall
values estimated at the Science Garden. The magnitude of this ratio is consistent with results reported
elsewhere for catchments of similar size (Allen and DeGaetano, 2005).
Table 4.2. Extreme Values of Catchment Averaged Rainfall, and 90% confidence limits
AEP 1/5 1/10 1/25 1/50 1/100 1/200
2-day total
Catchment Averaged Rainfall (mm)
284 339 408 454 500 545
Lower 90% CI 258 302 352 383 411 444
Upper 90% CI 316 384 476 541 606 679
Figure 4.4. AEP curve and 90% confidence limits for 2-day catchment averaged rainfall totals
4.1.3 Design Storm Temporal Pattern
Figure 4.5 gives an example of the design storm temporal pattern used in this study, for the case of a
1/100 AEP event. It consists of the 24 hour temporal pattern described previously, scaled to match the
1-day 1/100 AEP rainfall total, followed by 24 hours of constant rain, with the rate determined so that
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56 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
the total storm depth matches the 2-day 1/100 AEP rainfall total. The most intense rainfall occurs
around 6-18 hours into the storm event. There is a slight increase in the rainfall rate in the final 24
hours (as compared with the rate at hour 24), which can be attributed to differences in the statistical
methods used in the previous RIDF analysis and in the present study. However, the peak flood depths
are largely determined by the most intense rainfall which occurs in the first 24 hours of the design
storm.
Figure 4.5. Design storm temporal pattern for an AEP 1/100 event
4.1.4 Laguna Lake Water Level AEP Curve
The Generalised Extreme Value distribution was selected for the final analysis, although all of the
tested distributions were found to give quite similar predictions for the quantiles of interest. The AEP
curve for extreme water levels in Laguna Lake is shown in Table 4.3 and Figure 4.6.
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Table 4.3. Selected extreme values and 90% uncertainty limits for water levels in Laguna Lake (above Mean Sea
Level)
AEP 1/5 1/10 1/25 1/50 1/100 1/200
Lake Level 2.45 2.83 3.35 3.75 4.18 4.62
Lower 90% Confidence Limit
2.26 2.55 2.89 3.12 3.32 3.50
Upper 90% Confidence Limit
2.66 3.13 3.86 4.54 5.33 6.27
Figure 4.6. AEP curve for water levels in Laguna Lake
4.2 Hydraulics
4.2.1 Model Calibration
During calibration the HEC-RAS models were compared with peak depths during Tropical Storm
Ondoy, both in the channel where comparisons were made against EFCOS gauge data, and over the
floodplains.
The modelled and gauged water level peaks are reported in Table 4.4, with time series shown in
Figure 4.7. Results at Fort Santiago and Angono are not presented, as they are essentially forced to
agree with the data by the model boundary conditions. Several gauges broke during Tropical Storm
Ondoy, and in that instance the model is compared with the peak gauge value that was recorded prior
to breakage. In most instances, the model peak agrees reasonably well with the observations, with
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58 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
differences of 10-30cm. The modelled stages tend to rise and fall earlier than in the data. This
probably reflects the fact that the rainfall runoff model was not calibrated, does not include baseflow,
and is forced with rainfall data based on a small number of gauging stations. At Montalban the flood
peak was not recorded by the gauges, but independent evidence from a YouTube Video suggests the
modelled peak is reasonable (Table 4.4). The only large difference occurs at Nangka; however, this is
not of great concern because the gauge broke well before the flood peak, as explained in the notes of
Table 4.4.
Table 4.4. Comparison of measured and modelled peak water elevations (metres above mean sea level) in the
Pasig-Marikina Rivers during Tropical Storm Ondoy. * The gauge broke or otherwise failed to record the maxima.
In that case the model result at the time of the peak is reported, and the modelled peak is reported in the
parentheses. At bridges, the peak is reported as the mean of the upstream and downstream water level peaks.
SITE Data Model Notes
Pandacan 2.6 (3.1) 2.9 Data value in parentheses explained in ‘vertical datum issues’ section
San Juan 4.9 (5.4) 5.1 Data value in parentheses explained in ‘vertical datum issues’ section
Napindan 3.2* (4pm) 3.4 (4pm) (3.6 max)
Manual Observations (MMDA) which ceased at 4pm.
Rosario 7.4 7.7
Sto. Nino 11.7* (6pm) 11.6 (6pm) (11.7 max)
Nangka 12.0*
(12 noon)
12.8 (12 noon)
(15.4 max)
The modelled peak occurs at 3.30pm, 3.5 hours after the gauge broke (and > 3m above the last gauge value).
Hence the gauged value is not representative of the flood peak.
At the time of gauge breakage, the model is rising rapidly, and a small timing error in the input hydrology could be
responsible for the discrepancy. An equivalent gauge error could also be responsible.
Montalban 19.2* (4pm) 19.1 (4pm) (19.8 max)
Modelled peak occurs at 2pm. Gauge fails to record a value at 2pm or 3pm.
A peak of around ~ 20.0m can be independently estimated from a YouTube video of Ondoy Flooding @ Rodriguez Bridge between 2-4pm on 26 Sept 2009, by locating the
wet-dry boundary on the LIDAR data. See
https://www.youtube.com/watch?v=IT6kKqjDtok
A peak of around ~ 20.0m can be independently estimated from a YouTube video of Ondoy Flooding @ Rodriguez Bridge between 2-4pm on 26 Sept 2009, by locating the
wet-dry boundary on the LIDAR data. See
https://www.youtube.com/watch?v=IT6kKqjDtok
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Figure 4.7. Comparison of modelled and measured water levels at key gauging stations during Tropical Storm Ondoy.
In the dyke regions of Taguig, the storage model was used to simulate flooding. Figure 4.8 compares
the simulated and observed peak depths at Labasan Pumping Station. The rate of rise and the peak
are reasonably well simulated; however, the model predicts a more rapid rate of drawdown than
evident in the data. This is likely to be caused by the model overestimating the real pumping station
capacity behind the dykes in Taguig, which is rated at 27m³/s but will be less because of pump failure
during Tropical Storm Ondoy. Another factor is the leakage of water through the flood defences at
Taguig, which was a problem during Tropical Storm Ondoy because of incomplete construction of
dykes. Regardless, the peak is well simulated, and this is of greatest importance for hazard
estimation.
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60 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Figure 4.8. Comparison of modelled and observed water elevations behind the flood defences in the Taguig-Pateros area, during Tropical Storm Ondoy. Data was collected at Labasan Pumping Station.
Figures 4.9-4.13 compare the modelled peak depths with point observations of inundation depths on
the floodplains. The peak depth maps combine the two HEC-RAS models, the ‘flood defences
functioning’ model for the area behind the dykes in Taguig-Pateros, and the Laguna Lake water levels
to model lakeshore areas. Areas outside the modelled region are shown semi-transparently for
reference. There is broad agreement between with the simulated and observed patterns of inundation
during Tropical Storm Ondoy. Deep flooding is predicted and observed along the Marikina and San
Juan Rivers, to the east of the Mangahan Floodway, and along parts of the Laguna Lakeshore. Broad
areas of moderate to deep flooding also occur in western Manila to the North and South of the Pasig
River, and in the Marikina – Cainta Region and Taguig-Pateros Region. Similar inundation patterns
have been modelled in other recent studies of the area (WBCTI, 2012; www.nababaha.com).
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Figure 4.9. Modelled and observed (points) depths during Tropical Storm Ondoy. Areas outside the model region are shaded semi-transparently. The subsequent figures provide a close-up comparison.
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62 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Figure 4.10. Comparison of modelled and observed depths during Tropical Storm Ondoy around the San Juan and Pasig Rivers
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Figure 4.11. Comparison of modelled and observed peak depths Tropical Storm Ondoy in the Lower Marikina / Mangahan / Napindan Area
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64 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Figure 4.12.Comparison of modelled and observed depths during Tropical Storm Ondoy in the Upper
Marikina Area
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Figure 4.13. Comparison of modelled and observed peak depths during Tropical Storm Ondoy along the Laguna Lakeshore area.
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66 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
4.2.2 Design Flood Scenarios
Inundation maps for design floods with AEPs of 1/5, 1/10, 1/25, 1/50, 1/100 and 1/200 are reported in
the final risk map products. As an example, Figure 4.14 shows the simulated 1/200 AEP peak flood
depths. The MGB flood susceptibility zones are also overlaid, for comparison and reference outside
the extent of the hydraulic model.
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Figure 4.14. Modelled peak depths for an AEP 1/200 flood event.
4.3 Damage Estimation
For each AEP scenario, we computed the damaged floor area equivalent, building damage cost, and
number of people with inundated homes (Table 4.5 and Figure 4.14). For reference, computed values
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68 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
for Tropical Storm Ondoy are also shown. Example maps with this information are shown for the
design flood with an AEP of 1/200 (Figures 4.15-4.17). Similar maps for other AEPs are provided in
the set of final risk map products.
Table 4.5. Total damages estimated for each of the design flood scenarios, and Tropical Storm Ondoy.
AEP
Damage Metric
1/5 1/10 1/25 1/50 1/100 1/200 Tropical Storm Ondoy
Building damaged floor area equivalent (ha)
125 193 303 411 538 651 446
Building damage cost (million Pesos)
10682 16299 26431 36713 48596 59064 41097
Number of people with inundated homes (thousand
people)
705 967 1349 1665 1958 2164 1756
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Figure 4.14. Damage estimates for each AEP flood scenario.
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70 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Figure 4.15. Building damage intensity computed for the 1/200 AEP flood scenario.
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Figure 4.16. Building damage cost estimated for the 1/200 AEP flood scenario.
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72 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Figure 4.17. Number of people with inundated homes, estimated for the 1/200 AEP flood scenario.
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4.4 Patterns of Flood Hazard and Risk
Large areas of Metro Manila are vulnerable to severe flood inundation (Figures 4.9-4.14), with depths
of one to several metres being widespread during large events. Fundamentally this is because much
of Manila is built on naturally flood prone lands, including floodplains along the Marikina, Pasig and
San Juan Rivers, tidal flats along Manila Bay, and various lakeshore and deltaic landforms around
Laguna Lake. The land surface in these areas was originally built by sediments deposited during
flooding, and would always have been flood prone. Significant efforts to reduce this flooding have
been made, such as the construction of the Mangahan Floodway, numerous pumping stations, flood
gates, drains and dykes. However, Manila remains very flood prone, and urban development has also
contributed to flooding by constricting or obstructing overland and river drainage pathways, reducing
soil infiltration capacity, and accelerating land subsidence in some areas.
In the hypothetical 1/200 AEP scenario, the patterns of flooding are qualitatively similar to those
observed during Tropical Storm Ondoy, but with greater flood depths and extents (Figure 4.14). The
deepest inundation (3+m) occurs along the Upper Marikina and San Juan Rivers. Widespread
inundation of ~ 0.5-2 m depth also occurs east of the Marikina River and Mangahan Floodway. This is
caused by a mixture of inflows from the local catchment, overflow from the Marikina River, and high
river levels in the Mangahan Floodway which inhibit drainage due to backwater effects. Flooding
occurs in the Lakeshore and Taguig-Pateros regions, due to high water levels in Laguna Lake. In the
areas west of the San Juan River, north and south of the Pasig River, flooding is widespread but
typically shallower than in other regions ( ~0.2 – 1.2m), and is driven by a mixture of local rainfall,
inflow from rivers and Manila Bay, and the flat topography which promotes relatively slow drainage.
Regarding flood damages, there is a clear difference in the spatial patterns when measured in terms
of the damaged floor area equivalent, the cost of building damages, and the population with inundated
homes (Figures 4.15-4.17). For large flood events (e.g. AEP 1/200), the damaged floor area
equivalent shows patches of particularly intense damage around the Marikina River near Tumana,
along the banks of the Mangahan Floodway and the San Juan River, and at various locations along
the Lakeshore and Taguig-Pateros regions. Highly damaged areas are characterised by the
simultaneous occurrence of deep flooding, dense settlement, and a large proportion of ‘Makeshift’ and
‘Wooden’ buildings. The latter are more intensely damaged by deep flooding than are other building
types (Figure 4.16). However, they are also less expensive to replace, and so the building damage
costs are comparatively more evenly spread out within zones that experience deep flooding. In terms
of the number of people with inundated homes, large parts of the city have around 10-50 thousand
people per square kilometre, with the most intense patches occurring at sites of with high population
density in predominantly low rise housing.
In less extreme events (e.g. 1/10 AEP), deep flooding is concentrated along the margins of the Upper
Marikina and San Juan Rivers, and floodplain flows are much less extensive. Moderate flooding
occurs along lakeshore areas and low-lying parts of Taguig, and along drainage paths east of the
Upper Marikina and Mangahan rivers. The damaged floor area is still intense around Tumana, and
remains significant in many areas bordering the Marikina and San Juan Rivers, and the Mangahan
Floodway. Many of these areas also have dense populations with inundated homes. The damaged
building costs are relatively more evenly distributed, due to the lower replacement cost of the most
vulnerable building types.
All damages increase strongly with increasing AEP (Table 4.5, Figure 4.14). Tropical Storm Ondoy
falls between the 1/50 and 1/100 AEP for every damage measure used herein. For the 1/200 AEP
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74 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
scenario, the building damages are around 40% greater than Tropical Storm Ondoy, and the
population with inundated homes is around 20% greater. While these damages are substantially larger
than those experienced during Ondoy, latter can serve as a reasonable ‘mental picture’ for the
patterns of inundation and damages expected from large flood events in the Pasig Marikina Basin.
4.4.1 Limitations of the Analysis
The flood hazard and risk information developed herein can assist in understanding the large scale
patterns in flood hazard and risk in the Pasig-Marikina Basin. However, this should be done with an
appreciation of the limitations in the underlying datasets, methods and models used.
Broadly, it is suggested that basin-scale work such as presented above can be usefully supplemented
with smaller scale flood studies to support local flood management decisions. Smaller scale studies
have a greater capacity to ground-truth input data, and to develop and test detailed hazard and
vulnerability models, whilst still drawing on inputs from larger scale work such as the present. Such
studies are the most appropriate to support robust local-scale flood management decisions, especially
if discrepancies are found between observational data and the results of larger scale studies, or
between multiple large scale studies.
Several other recent large-scale flood studies exist in the Pasig Marikina Basin, and they provide
inundation maps and/or damage estimates covering some of the same areas treated in this study.
Because they use different data and methodologies to the present study (and to each other), the
results of all these studies vary to some extent, although the general patterns of flooding in the basin
are broadly consistent between recent studies (WBCTI, 2012) and the present work. It is suggested
that decisions about large-scale flood management issues should draw on the results of multiple
studies where applicable (e.g. DICAMM, 2005; CTI, 2005; WBCTI, 2010; www.nababaha.com; Muto et
al., 2011). This will assist in making flood management decisions robust to the limitations of any single
study, and highlight areas which may need more investigation before consensus can be reached.
The key limitations to the present study are now outlined.
4.4.1.1 Hydrology and Hazard Scenarios
To define rainfall intensities in the hazard scenarios, the present study develops estimates of extreme
rainfall frequencies based on a statistical analysis of data from a limited number of rain gauges around
the Pasig Marikina basin. The statistical uncertainties in these estimates can be quite large (Table 4.2,
Figure 4.4), but are unavoidable without the existence of longer data records. They do not account for
possible future climate changes, which could alter the likelihoods of extreme rainfall events. For rare
events, the statistical uncertainties probably underestimate the true uncertainty, because they assume
the correctness of the fitted statistical model even when extrapolating to rare events. The underlying
rain gauge data has incomplete spatial coverage, which can lead to significant errors in the estimation
of basin-wide rainfall during any single event (Heistermann et al., 2013). This is not explicitly
accounted for in the computed confidence intervals. In the future, the combination of longer term rain
gauge records with calibrated radar data will probably help to better constrain extreme rainfalls in the
Pasig Marikina basin.
Similarly, the analysis of extreme lake levels in the present study is based on a limited dataset, and so
inevitably has significant statistical uncertainties (Table 4.3, Figure 4.6). It also does not account for
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the uncertain impact of future climate changes or modifications to the Pasig River on annual maximum
lake levels.
The hazard scenarios developed in this study are based on a hypothetical combination of an input
rainfall time series, high Laguna Lake water levels, and a high water level in Manila Bay. A single
scenario is examined for each AEP, although in reality, an infinite number of possible scenarios could
be constructed, which would lead to differences in the pattern of flooding and damage. The spatially
constant design rainfall pattern is best suited to modelling areas flooded by a large fraction of the
catchment, and will tend to underestimate extreme localised rainfalls, and hence the AEP of flooding
in areas affected by small upstream catchments. In the Taguig-Pateros region, the degree of flooding
is significantly influenced by assumptions about the performance of flood defence structures, which in
practice have shown mixed performance. More detailed analysis based on Monte-Carlo type
modelling could be used to partially account for these factors, however, this process would still rely on
making assumptions about the probabilities of various hazard scenarios.
4.4.1.2 Flood modelling
The hazard scenarios are transformed into flood inundation maps using rainfall runoff and hydraulic
models. These approximate the actual flow processes with a linked set of simpler models (lumped sub
catchments with SCS runoff and routing models, networks of one-dimensional channels and storage
areas). The way these are defined depends partly on the subjective judgement of the modelling team,
and will have some effect on the results. In all cases, alternative models of these processes exist, and
it is unclear to what extent the results of the present study would be changed by using other models
(although this can partly be assessed by comparison with other studies). The models used in this
study are theoretically most appropriate for modelling channel and near-channel flows, and the results
might be further refined by the development and calibration of high-resolution 2D or linked 1D/2D
methods over the floodplains.
The models rely on input topographic data, and information on river structures. While the LIDAR DEM
provides relatively good elevation data for the present study, it cannot resolve submerged regions
(e.g. riverbeds), or fine topographic structures such as parapet walls. The information on the channel
geometry and flow structures available to the present study was incomplete, often around 10 years
old, and may not always provide a good description of the present-day geometry of the basin. Further,
in future the topography of Metro Manila will evolve, and this will affect flooding in ways that can’t be
anticipated in the present work.
The flood inundation model was calibrated to peak depths during the Tropical Storm Ondoy event, and
shows reasonable performance compared with data. However, it has not been tested in modelling
other events. Even in the calibration event, differences between the observations and the model do
occur (Figure 4.9-4.13), although the observations themselves may not always be accurate. In
addition, the model extent is limited to the Pasig Marikina basin, and excludes ‘upland’ regions such
as higher elevation parts of Quezon City, Taguig, and Cainta, which were included in the rainfall runoff
model in the present study. Flooding has been reported in these areas, but is not simulated in the
present model, which is most appropriate for simulating riverine flooding, driven by flows transported
from the main river systems.
4.4.1.3 Exposure and Vulnerability Inputs
The Exposure data is developed through a combination of subjective user judgement, and the
downscaling of other datasets to the exposure polygons. All these steps have limitations, which are
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outlined in the report for Exposure Information Development. For example, the distribution of building
types in each exposure polygon is estimated partly from 10 year old building information, which is not
thought to be highly suitable for present day Manila. In general, limited independent data exists with
which to check the properties in the exposure database, and it is expected that some inaccuracies
must remain.
The estimates of building replacement cost used in this study (Appendix A) are based on estimates
from two other sources, which were combined based on engineering judgement. In some instances
the cost estimates from each source would substantially disagree with each other.
The stage-damage vulnerability curves used in this study were developed with computational and
heuristic procedures, the limitations of which are described in “Development of vulnerability curves of
key building types in the Greater Metro Manila Area, Philippines” (Pacheco et al., 2013). In some
instances, large differences could occur between the computational and heuristic curves, and so
engineering judgement has been used to select the most appropriate curve. To our knowledge these
have not been tested against independent damage data.
4.4.1.4 Risk Analysis
As with the hazard scenarios developed for this study, the risk analyses are based on a suite of
scenarios. This approach doesn’t fully account for the diversity of storm spatial and temporal patterns
which contribute to the hazard. A more advanced approach could use Monte-Carlo analysis with a
suite of design storms. This would require work on defining the structure of the ‘random’ inputs to
define the hazard events, and further automation of the process of running models.
The damage metrics used in the present study are limited to estimates of the building damaged floor
area equivalent, damage cost, and number of people with inundated homes. Other damages not
treated in this study include indirect economic damages, the physical impacts of flood hazard on
people, or the increased spread of disease due to flooding. These may all be quite significant.
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 77 Greater Metro Manila Area – Flood Risk Analysis
5 Conclusion
The results of this study include the statistical estimates of: a) Extreme rainfall frequencies at a
number of sites in the Pasig-Marikina River Basin based on a regional frequency analysis; b)
Catchment-averaged extreme rainfall frequencies; and c) Frequency of high lake levels in Laguna
Lake.
These statistical estimates were used to simulate design flood scenarios in the Pasig-Marikina River
Basin using the combined capabilities of HEC-HMS and HEC-RAS models. The model was shown to
perform reasonably well in simulating the flood depths associated with Tropical Storm Ondoy
(Ketsana). The calibrated model was then used to simulate the range of design flood events with
AEPs of 1/5 to 1/200 years. The damages associated with events were estimated using the recently
developed exposure database for Metropolitan Manila (refer to report for Component 2 - Exposure
Information Development) and the building vulnerability models (refer to report for Development of
vulnerability curves of key building types in the Greater Metro Manila Area). Damage estimates were
based on the ‘damaged floor area equivalent’, the ‘building damage cost’ and the ‘number of people
with inundated homes’.
The calibrated model was compared with the observed data both in the river and from the floodplain.
The result of the model calibration agrees well with the observed water level data from EFCOS. The
model results also typically agreed with the reported spot-depth data collected by nababaha.com
except for areas outside the model domain.
Aside from the observed data from EFCOS and nababaha.com, the result of this study is broadly
consistent with the results of other hydrological studies in the area such as the results from DICAMM
2005, CTI 2005, WBCTI 2010 and Muto et al. 2011. With these observations, it can be concluded that
this study was able to simulate the TS Ondoy event and the resulting hazard and risk maps with
different AEPs can provide useful inputs to support contingency planning of the local government units
and other applications relating to flood risk mitigation.
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78 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
6 Recommendations
Here we suggest several avenues for future work which could enhance our understanding of flood
hazards and impacts in Metro Manila:
1. Re-measurement of the vertical datum of river gauges in Manila and maintenance of this over
time.
2. Developing methods to estimate damages beyond building cost and number of people
inundated.
3. Investigation of Monte-Carlo methods for treating uncertainty in the flood risk analysis,
allowing for a more thorough exploration of the flood scenarios that are possible for a given
AEP.
4. Extending the risk analysis to other parts of Metro Manila which are covered by exposure data.
This would either require the development of more flood inundation models and extension of
the hydrological analyses, or alternatively, the use of other existing flood models.
5. Development of a capability for 2D and/or linked 1D/2D flood modelling in the CSCAND
agencies, to support more advanced hydraulic analyses.
6. Investigate methods for simulating flash flood hazards in the Pasig Marikina River Basin.
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82 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Appendix A - Parameters used in the Rainfall Runoff Model Sub catchments
Sub catchment X* Y* Area
(km^2) CN
T_lag (min)
Boundary type*
Rain Gauge
(Calibration)*
wawa 528647.5654
1628279.332 287 74 168 Discharge Boso Boso
mt_oro_1 519975.7758
1632800.436 34.98 77 60 Discharge Mt Oro
Nangka 517089.2535
1618391.966 51.585 79 133 Discharge Aries
lower_marikina 507888.4813
1612425.73 8.175 79 28 Uniform lateral
Aries
north_west_of_nangka
511570.2903
1628053.333 24 81 73 Uniform lateral
Aries
east_of_montalban 519972.3158
1624260.754 25.5 74 43 Discharge Aries
North_of_nangka 517289.4862
1622747.779 24.8 77 44 Discharge Aries
Mangahan_napindan_area
510152.9448
1610971.532 16.683 92 10 Storage Aries
Pasig_01 504857.6887
1610519.985 15.6 93 52 Uniform Lateral
Science Garden
Pasig_02 499264.7122
1613715.972 73.148 93 10 Storage Science Garden
Sanjuan_1 503570.8552
1613682.93 3.45 92 33 Discharge Science Garden
Sanjuan_2 502109.1387
1614740.969 2.705 92 4 Uniform lateral
Science Garden
Sanjuan_3 506119.879 1613797.266 4.7 92 31 Discharge Science Garden
Sanjuan_4 503863.8376
1615787.891 8.7 92 33 Uniform lateral
Science Garden
Sanjuan_5 501144.3831
1617137.218 3.4 92 36 Uniform lateral
Science Garden
Sanjuan_t6 505525.39 1617675.493 15.2 92 33 Uniform lateral
Science Garden
Sanjuan_S7 503966.0222
1620631.795 10.22 92 53 Discharge Science Garden
Sanjuan_8 501397.7127
1620064.582 2.113 92 4 Uniform lateral
Science Garden
Sanjuan_9 501017.718 1622414.917 2.4 92 51 Discharge Science
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3 Garden
Sanjuan_10 505239.8782
1623174.906 18.6 92 98 Discharge Science Garden
Sanjuan_11 502565.8436
1621401.599 2.866 92 5 Uniform lateral
Science Garden
Sanjuan_12 504184.3382
1624652.662 5.367 92 44 Discharge Science Garden
Sanjuan_13 501960.6673
1623160.832 3.26 92 18.6 Uniform lateral
Science Garden
Sanjuan_5b 502692.5084
1618319.422 1.1 92 17 Uniform lateral
Science Garden
Sanjuan_5c 500102.917 1619360.888 7.5 92 113 Discharge Science Garden
Mt_oro_2 516295.592 1633859.883 45.5 77 60 Uniform lateral
Science Garden
Dilian_crk 504822.9894
1608317.274 2.5 80 15 Discharge Science Garden
Maricaban 504653.0092
1606121.874 5 80 17 Discharge Science Garden
Calatagan 503463.1475
1608820.98 1.72 80 9 Discharge Science Garden
Casili_Crk 497819.8038
1621015.634 1.76 92 16 Discharge Science Garden
Sunog_Apog 498686.0266
1620432.303 1.456 92 16 Discharge Science Garden
Marik_tumana 510666.498 1624254.092 27.6 78 28 Uniform lateral
Science Garden
SANJUAN_ALL 505775 1620271 82.992 92 192 Uniform lateral
Science Garden
East_mangahan 517101.4099
1614057.54 103.7 90 48 Storage Aries
*’X’, ‘Y’ are the x and y coordinates of a point in the sub catchment polygon;
*‘Boundary type’ describes how the outflow hydrograph from the sub catchment was applied as a
boundary condition in HEC-RAS. ‘Discharge’ refers to a point inflow discharge boundary condition;
‘Uniform Lateral’ refers to uniform lateral inflow into a channel; ‘Storage’ refers to the use of uniform
lateral inflow in all storage areas within the catchment polygon, at a rate proportional to area of each
storage area.
* Rain gauge (calibration) refers to the rain gauge that was used to assign rainfall to the catchment
during the Tropical Storm Ondoy Calibration Run. Not all gauges in the monitoring network were used,
because of instrument malfunction.
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84 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Appendix B - Estimated building costs per m² (‘000s of Peso), for different combinations of Building type, L4_USE and L5_USE.
The latter land-use classification variables are provided in the exposure database. Estimates are based on DL&SI. (2010), and Muto et al. (2011)
L4_USE L5_USE W1 W2 W3 N CHB URA URM RM1 RM2 MWS CWS C1 C2 C4 PC1 PC2 S1 S2 S3 S4
Formal Settlements
Residential 4.0 1.2 5.9 6.5 6.5 5.2 7.8 12.2 16.0
18.0 20.0 30.0 35.0 15.0 32.0
Formal Settlements
Mixed Residential and Small Commercial
5.6 5.6 2.0 1.2 6.5 7.2 7.2 8.0 8.0 6.0 9.0 13.2 18.0
20.0 20.0 20.0 30.0 35.0 15.0 32.0
Formal Settlements
Small Commercial
5.6 1.2 7.4 8.1 8.1 6.6 9.9 11.0 20.0
22.0 30.0 37.0 15.0 32.0
Informal Settlements
Mixed Informal Settlements
4.0 1.2 3.0 3.0 3.0 5.2 7.8 12.2 16.0
18.0 30.0 35.0 15.0 32.0
Education Schools 4.3 4.3 13.2 22.0 25.0
27.0 27.0 32.0
Education Universities 6.5 7.8 23.0 27.0 27.0
Education Vocational Colleges
6.5 22.0 27.0
Education Day Care Centers
13.2 22.0
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L4_USE L5_USE W1 W2 W3 N CHB URA URM RM1 RM2 MWS CWS C1 C2 C4 PC1 PC2 S1 S2 S3 S4
Health and Welfare
Hospitals 7.5 25.0 36.0 38.0
Health and Welfare
Health Centers
30.0
Health and Welfare
Aged Care Centers
4.3 20.0
Health and Welfare
Rehabilitation Centers
4.3 20.0
Health and Welfare
Orphanages 4.3 7.3 12.2 18.0
20.0 35.0 35.0 35.0
Government Administration
15.0 25.0 30.0
30.0 30.0 35.0
Government Services 6.5 1.2 7.0 7.0 7.0 7.0 10.0 25.0 30.0
30.0 35.0 35.0 15.0 35.0
Government Accommodation
7.0 25.0
Government Operations 25.0 30.0
30.0 35.0 35.0 35.0
Emergency and Defense
Police 25.0 30.0
35.0
Emergency and Defense
Fire and Rescue
25.0 30.0
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86 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
L4_USE L5_USE W1 W2 W3 N CHB URA URM RM1 RM2 MWS CWS C1 C2 C4 PC1 PC2 S1 S2 S3 S4
Emergency and Defense
Ambulance 25.0
Emergency and Defense
Armed Forces
15.0 25.0 30.0
30.0 35.0 35.0 35.0
Cultural Places of Worship
6.3 12.0 15.0
Cultural Places of Assembly
12.0 15.0 35.0
Cultural Cemeteries 4.3 12.0 15.0 30.0
30.0 35.0 35.0
Leisure Exhibitions 40.0 40.0
40.0 50.0 50.0 50.0
Leisure Indoor Sports 25.0 42.0 45.0
Leisure Outdoor Sports and Playgrounds
25.0 42.0 45.0 45.0 15.0
Energy Production
Electricity 15.0 20.0 15.0
Energy Production
Gas 4.3 10.0 15.0 20.0
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L4_USE L5_USE W1 W2 W3 N CHB URA URM RM1 RM2 MWS CWS C1 C2 C4 PC1 PC2 S1 S2 S3 S4
Energy Production
Liquid Fuels 4.3 10.0 15.0 18.0
20.0 20.0 22.0 32.0
Water Supply Potable Water Storage
15.0 18.0
20.0
Water Supply Treatment 15.0
Water Supply Transmission 15.0 18.0
20.0
Water Supply Urban Supply 20.0 25.0
Communications
Telecommunications
20.0
Communications
Broadcasting 20.0 23.0
Communications
Postal Services
20.0 23.0
Waste Management
Solid Waste 12.0 15.0 20.0
Waste Management
Liquid Waste 12.0 15.0 20.0
Waste Management
Hazardous Waste
12.0 20.0
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88 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
L4_USE L5_USE W1 W2 W3 N CHB URA URM RM1 RM2 MWS CWS C1 C2 C4 PC1 PC2 S1 S2 S3 S4
Transportation Road Transport
4.3 1.2 15.0 20.0 20.0
Transportation Rail Transport
15.0 20.0
Transportation Air Transport 4.3 4.3 25.0 35.0 20.0
Transportation Marine Transport
5.9 6.5 25.0 30.0
Transportation Cargo and Storage
4.3 4.3 12.0 15.0 20.0
Heavy Industry Manufacturing
4.3 4.3 13.0 17.0 18.0
20.0 20.0 22.0 25.0
Heavy Industry Processing 4.3 4.3 13.0 17.0 18.0
20.0 20.0 22.0 25.0
Heavy Industry Mining 17.0 18.0
20.0 20.0 22.0 25.0
Heavy Industry Construction 17.0 20.0
Major Commercial
Retail 5.4 20.0 30.0 35.0 30.0
Major Commercial
Wholesale 5.4 17.0 25.0 30.0 15.0
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 89 Greater Metro Manila Area – Flood Risk Analysis
L4_USE L5_USE W1 W2 W3 N CHB URA URM RM1 RM2 MWS CWS C1 C2 C4 PC1 PC2 S1 S2 S3 S4
Major Commercial
Office 5.4 5.4 18.0 28.0 32.0
Major Commercial
Accommodation
18.0 28.0 32.0
Major Commercial
Mixed Major Commercial
5.4 5.4 18.0 28.0 32.0
Major Commercial
Markets 5.4 5.4 18.0 28.0 30.0
Major Commercial
Tourism Facilities
5.4 3.9 5.9 7.0 28.0 30.0 30.0
Food Security Government Grain Storage
20.0
Food Security Private Storage
10.0 14.0
Flood Control Flood Gates 10.0 14.0
Flood Control Flood Monitoring Stations
10.0 15.0
Flood Control Flood Pumping Stations
15.0
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90 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
L4_USE L5_USE W1 W2 W3 N CHB URA URM RM1 RM2 MWS CWS C1 C2 C4 PC1 PC2 S1 S2 S3 S4
Rural Residential
Yet to be defined
4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 18.0
18.0 20.0 22.0 15.0 25.0
Agriculture Yet to be defined
4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 18.0
18.0 20.0 22.0 15.0 25.0
Aquaculture Yet to be defined
4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 18.0
18.0 20.0 22.0 15.0 25.0
Horticulture Yet to be defined
4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 20.0 15.0
Forestry Yet to be defined
4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 20.0 15.0
Livestock Yet to be defined
4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 20.0 15.0
Livestock Feedlots 4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 20.0 15.0
Market Gardening
Yet to be defined
4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 20.0 22.0 15.0
Mixed Farming Yet to be defined
4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 18.0
18.0 20.0 22.0 15.0 25.0
Poultry Yet to be defined
4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 20.0 15.0
Vacant Areas Yet to be defined
4.3 6.5 10.0 15.0 20.0
Natural Areas National Parks
4.3 4.3 6.5 10.0 15.0 18.0
18.0 25.0 20.0
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Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 91 Greater Metro Manila Area – Flood Risk Analysis
L4_USE L5_USE W1 W2 W3 N CHB URA URM RM1 RM2 MWS CWS C1 C2 C4 PC1 PC2 S1 S2 S3 S4
Reserved Areas Yet to be defined
4.3 1.2 5.9 6.5 6.5 5.2 10.0 15.0 20.0 15.0 25.0
Reserved Areas Urban Parks 4.3 6.5 10.0 15.0 18.0
18.0 20.0 22.0 25.0
Reserved Areas Greenbelts 4.3 6.5 10.0 15.0 20.0
Reserved Areas Buffer Zones 4.3 6.5 10.0 15.0 20.0
Reclamations
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92 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Appendix C - Floor heights for different building categories, based on field survey data collected by PHIVOLCS