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)
Transcript
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)
ii Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
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
2 Literature Review ................................................................................................................................... 8
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
3.5.1 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
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
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.
vi Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
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.
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.
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
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.
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.
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
Total 7,948,392 9,932,560 11,855,975 626.456 18,925.47 2.25 1.78 2.02
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).
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
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
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.
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).
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
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.
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).
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:
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
Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 29 Greater Metro Manila Area – Flood Risk Analysis
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
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.
Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 31 Greater Metro Manila Area – Flood Risk Analysis
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
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).
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.
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
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.
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
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.
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.
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)
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
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.
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.
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.
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.
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).
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.
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.
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”
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.
Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 51 Greater Metro Manila Area – Flood Risk Analysis
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.
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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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
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
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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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
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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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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.
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
Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 63 Greater Metro Manila Area – Flood Risk Analysis
Figure 4.11. Comparison of modelled and observed peak depths Tropical Storm Ondoy in the Lower Marikina / Mangahan / Napindan Area
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.
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
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.
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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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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.
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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References
Abon, C.C., David, C.P.C. and Pellejera, N.E.B., 2010. Reconstructing the tropical storm Ketsana flood event in Marikina River, Philippines. Hydrology and Earth System Science Discussions 7:6081-6087.
Allen, R. J. and DeGaetano, A.T. 2005. Areal Reduction Factors for Two Eastern United States Regions with High Rain Gauge Density. Journal of Hydrologic Engineering 10(4): 327-335
Apel, H., Thieken, A.H., Merz, B., Bloschl, G. 2004. Flood risk assessment and associated uncertainty. Natural Hazards and Earth System Sciences 4:295-308
Aronica, G. T., Candela, A., Fabio, P. and Santoro, M., 2011. Estimation of flood inundation probabilities using global hazard indexes based on hydrodynamic variables. Physics and Chemistry of the Earth, doi:10.1016/j.pce.2011.04.00
Asuncion, J. F. and A. M. Jose, 1980: A study of the characteristic of the northeast and southwest monsoons in the Philippines. NRCP Assisted Project I-B-12.
Aureli, F., Mignosa, P. and Ziveri, C. 2006. Fully-2D and quasi-2D modelling of flooding scenarios due to embankment failure. Proceedings of River Flow 2006.
Badilla, R.A., 2008. Flood modelling in Pasig-Marikina River Basin. MSc Thesis, International Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands.
Bankoff, G. 2003. Vulnerability and Flooding in Metro Manila. IIAS Newsletter 31.
Berthet, L., Andreassian, V., Perrin, C. and Javelle, P. 2009. How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments. Hydrology and Earth System Science 13:819-831.
Beven., K., 2001. Rainfall Runoff Modelling – The Primer. Wiley
Bouwer, L. M., Bubeck, P., Wagtendonk, A. J., Aerts, J. C. J. H., 2009. Inundation scenarios for flood damage evaluation in polder areas. Natural Hazards and Earth System Science. 9:1995-2007
Castellarin, A., Domeneghetti, A., Brath, A. 2011. Identifying robust large-scale flood risk mitigation strategies: A quasi-2D hydraulic model as a tool for the Po river. Physics and Chemistry of the Earth 36:299-308
Chaudhry, M. 2008. Open Channel Flow. Springer, New York.
Christian, J., Duenas-Osorio, L., Teague, A., Fang, Z. and Bedient, P., 2012. Uncertainty in floodplain delineation: expression of flood hazard and risk in a Gulf Coast watershed. Hydrological Processes. doi: 10.1002/hyp.9360
CTI, 2005. Study on Drainage System Operation for Metro Manila Flood Control Project: West of Mangahan Floodway.
DICAMM, 2005 The study on drainage improvement in the core area of Metropolitan Manila, Republic of the Philippines. Japan International Cooperation Agency, Metropolitan Manila Development Authority, Department of Public Works and Highways, The Republic of the Philippines.
DL&SI., 2010. Spon’s Asia-Pacific Construction Costs Handbook, Fourth Edition. Davis Langdon and Seah International.
Dumas, P., Hallegatte, S., Quintana-Segui, P., Martin, E., 2013. The influence of climate change on flood risks in France – first estimates and uncertainty analysis. Natural Hazards and Earth System Science 13:809-821.
Dung, N.V., Merz, B., Bardossy, A. and Apel, H. 2013. Flood hazard in the Mekong Delta – a probabilistic, bivariate, and non-stationary analysis with a short termed future perspective. Natural Hazards and Earth System Science Discussions 1:275-322.
Ellouze, M., Abida, A. and Safi, R., 2009. A triangular model for the generation of synthetic hyetographs. Hydrological Sciences Journal 54:287-299
80 Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the Greater Metro Manila Area – Flood Risk Analysis
Estoque, M. A., 1956: Circulation and convergence over the tropical northwestern Pacific from May to December. Proceedings of the Eight Congress, 1953. Volume II Geology and Geophysics Meteorology. Quezon City, University of the Philippines, National Research Council of the Philippines.
Federal Emergency Management Agency (FEMA), 2003. Guidelines and specifications for flood hazard mapping partners. FEMA’s flood hazard mapping program, Washington DC.
Fowler, H.J. and Kilsby, C.G. 2003. A regional frequency analysis of United Kingdom extreme rainfall from 1961 to 2000. International Journal of Climatology 23:1313-1334.
Ghavidelfar, S.. Alvankar, S.R. and Razmkhah, A. 2011. Comparison of the Lumped and Quasi-distributed Clark Runoff Models in Simulating Flood Hydrographs on a Semi-arid Watershed. Water Resource Management 25: 1775-1790
HEC-HMS TRM, 2000. HEC-HMS Technical Reference Manual. US Army Corps of Engineers Hydraulic Engineering Center.
HEC-RAS TRM, 2010. HEC-RAS Technical Reference Manual. US Army Corps of Engineers Hydraulic Engineering Center.
Heistermann, M., Crisologo, I., Abon, C.C., Racoma, B.A., Jacobi, S., Servando, N.T., David, C.P.C. and Bronstert, A. 2013. Using the new Philippine radar network to reconstruct the Habagat of August 2012 Monsoon event around Metro Manila. Natural Hazards and Earth System Science 13:653-657
Horritt, M.S. and Bates, P.D. 2002. Evaluation of 1D and 2D numerical models for predicting river flood inundation. Journal of Hydrology 268 87-99.
Hosking, J.R.M. and Wallis, J.R. 1997. Regional Frequency Analysis: An approach based on L-moments. Cambridge University Press.
Kintanar, R.L., 1984: Climate of the Philippines, PAGASA Technical Report
Kysley, J. 2008. A cautionary note on the use of nonparametric bootstrap for estimating uncertainties in extreme value models. Journal of Applied Meteorology and Climatology 47:3236-3251
Madsen, H. and Skotner, C., (2005). Adaptive state updating in real-time river flow forecasting—a combined filtering and error forecasting procedure. Journal of Hydrology 308:302-312
Middelman, M., (2002). Flood Risk in South East Queensland, Australia. 27 Hydrology and Water Resources Symposium, Institute of Engineers Australia, 20-23 May 2002, Melbourne.
Moramarco, T., Melone, F., and Singh, V.P. 2005. Assessment of flooding in urbanized ungauged basins: a case study of the Upper Tiber area, Italy. Hydrological Processes 19:1909-1924
MPWTC, 1952. Plan for the Drainage of Manila and Suburbs. Report of the Ministry of Public Works, Transportation and Communication
Muto, M., Morishita, K. and Syson, L. 2011 Impacts of Climate Change upon Asian Coastal Areas: The case of Metro Manila. Japan International Cooperation Agency
NDRMMC., 2009. Final Report on Tropical Storm Ondoy (Ketsana) and Typhoon Pepeng (Parma). National Disaster Coordinating Council.
Neal, J., Keef, C., Bates, P., Beven. K. and Leedal, D. 2012. Probabilistic flood risk mapping including spatial dependence. Hydrological Processes doi: 10.1002/hyp.9572
NSO, 2010. 2010 Census of Population and Housing, National Capital Region. National Statistics Office.
Pacheco, B.M et al., 2013. Development of vulnerability curves of key building types in the Greater
Metro Manila Area, Philippines. Institute of Civil Engineering, University of the Philippines Diliman,
Quezon City.
Patvivatsiri, P., 1972: The rainfall distribution associated with the intensified SW monsoon of August 31st to September 4th, 1970. PAGASA Technical Series No. 16
Pechlivanidis, I.G., Jackson, B.M., McIntyre, N.R. and Wheater, H.S. 2011. Catchment Scale Hydrological Modelling: A review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications. Global NEST Journal 13(3): 193-214
Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake for the 81 Greater Metro Manila Area – Flood Risk Analysis
Perica, S. et al., 2011. NOAA Atlas 14 Precipitation-Frequency Atlas of the United States. Volume 6, Version 2.0, California.
Poretti, I. and Amicis, M. D., 2011. An approach for flood hazard modelling and mapping in the medium Valtellina. Natural Hazards and Earth System Science 11:1141-1151
RFCOSMM, 2002. Report on Technical Guidance for the Project for the Rehabilitation of the Flood Control, Operation and Warning System in Metro Manila.
Savenije, H. H. G. 2005. Salinity and Tides in Alluvial Estuaries. Delft, The Netherlands.
Scawthorn, C., Blais, N., Seligson, H., Tate, E., Mifflin, E., Thomas, W., Murphy, J. and Jones, C., 2006. HAZUS-MH Flood loss estimation methodology 1: Overview and Flood Hazard Characterization. Natural Hazards Review, 7(2):60-71
Smith, G. and Wasko, C. 2012. Two Dimensional Simulations In Urban Areas – Representation of Buildings in 2D Numerical Flood Models. Australian Rainfall and Runoff Project 15.
Syme., W.J., Pinnel. M.G. and Wicks., J.M. 2004. Modelling Flood inundation of Urban areas in the UK using 2D/1D hydraulic models. Proceedings of the 8
th National Conference on Hydraulics in Water
Engineering.
Kjeldsen, T.R., Jones, D.A. and Bayliss, A.C. 2008. Improving the FEH statistical procedures for flood frequency estimation. Joint Defra/Environment Agency Flood and Coastal Erosion Risk Management R&D Program.
Wallis, J.R., Schaefer, M.G., Barker, B.L. and Taylor, G.H. 2007. Regional Precipitation frequency analysis and spatial mapping for 24 hour and 2 hour durations for Washington State. Hydrology and Earth System Science 11:415-442
WBCTI, 2012. Master Plan for Flood Management in Metro Manila and Surrounding Areas. Final Draft Report. The World Bank; CTI Engineering International Co., Ltd; Woodfields Consultants, Inc.
Winsemius, H.C., Van Beek, L.P.H., Jongman, B., Ward, P.J. and Bouwman, A. 2012. A framework for global river flood risk assessments. Hydrology and Earth Systems Science Discussions 9:9611-9659
Woodhead, S., Asselman, N., Zech, Y., Soares-Frazao, S., Bates, P. and Kortenhaus, A. 2007. Evaluation of Inundation Models. Limits and Capabilities of Models. Floodsite Report T08-07-01
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Appendix A - Parameters used in the Rainfall Runoff Model Sub catchments
