Flooding in the Philippines
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The University of Texas at Austin Department of Civil, Architectural and Environmental Engineering CE 394K.3: Geographic Information Systems in Water Resources Engineering Fall 2015 Term Project Flooding in the Philippines Jonathan David D. Lasco Submitted to: Professor David Maidment Submitted on: December 4, 2015 1 | P a g e I. Introduction Motivation While blessed with water resources, the Philippines is vulnerable to typhoons. Annually, about 10 typhoons make landfall in the country and most of these typhoons occur during the rainy season. There are many ways to describe a storm event – it may be described as the number of deaths, the damage, or the amount of precipitation that it caused. Shown in Table 1 are the deadliest cyclones in the Philippines. While there is not much information on Haiphong 1881, there is much that can be implied about the causes of these deaths based on personal experience (the author lived in the Philippines from 1991 to 2015). These causes may be a combination by one or a combination of the following: under-design of buildings, inadequate preparation, resistance of the people to evacuate their homes, underestimation of the magnitude of the cyclones, or simply people being at the wrong place at the wrong time. Under-design of the building may be intentional or unintentional. Intentional under-design may be due to the contractors not following the National Building Code of the Philippines or clients that don’t have money to pay for a more resilient home. In fact, a large number of Filipinos only live in nipa huts and shanties making them even more vulnerable to flooding. Unintentional under-design may be due to basing the design on past events. The problem with basing the design on past events is the assumption of hydrologic stationarity (Marriott, 2009) wherein the trend of rainfall events do not change from a cause (e.g. climate change). Inadequate preparation includes poor evacuation plan and negligence of hoarding of the basic necessities, particularly, food. People become resistant to evacuation possibly because of emotional attachment to their belongings – they just don’t want to leave their possessions. Moreover, the magnitude of the flood may be underestimate because of the scales of measurement that are used. For example, in 2009, Typhoon Ketsana was measured as a signal no. 1 storm which means that winds ranging from 30-60 kph are expected to occur within 36 hours. Yet despite the slow winds, precipitation was huge and the magnitude of precipitation is what caused the deaths. Aside from measuring the storm in terms of the number of deaths that it caused, a storm may also be measured by the damage that it caused (Table 2). It may be noticed that a catastrophic death toll does not necessarily make a storm event more destructive (e.g. Haiphong 1881, Haiyan 2013). This is because the amount of damage depends on the area affected by the storm – if it is more developed then it is more likely to suffer more damages compared to, say, a farmland. It may also be noticed that the destructive cyclones are concentrated on the 21st century. This is because there is more development in the 21st century than the 20th century. A storm may also be described in terms of the amount of precipitation it effects (Table 3). A high amount of precipitation leads to flooding. The most serious case of flooding is when it occurs to an area which is densely populated and highly developed because many people may die and many buildings may collapse leading to the loss of millions of dollars. 2 | P a g e Table 1. Deadliest cyclones in the Philippines. (Source: Typhoons in the Philippines, Wikipedia) Rank Storm Deaths 1 Haiphong 1881 > 20,000 2 Haiyan/Yolanda 2013 6,241 3 Thelma/Uring 1991 6,101 4 Bopha/Pablo 2012 1,901 5 Angela 1867 1,800 6 Winnie 2004 1,593 7 October 1897 Typhoon 1,500 8 Ike/Nitang 1984 1,492 9 Fengshen/Frank 2008 1,410 10 Durian/Reming 2006 1,399 Table 2. Most destructive cyclones in the Philippines. (Source: Typhoons in the Philippines, Wikipedia) Rank Storm Damage (million USD) 1 Haiyan/Yolanda 2013 2,020 2 Bopha/Pablo 2012 1,040 3 Rammasun/Glenda 2014 871 4 Parma/Pepeng 2009 608 5 Nesat/Pedring 2011 333 6 Fengshen/ Frank 2008 301 7 Megi/ Juan 2010 255 8 Ketsana/ Ondoy 2009 244 9 Mike/Ruping 1990 241 10 Angela/ Rosing 1995 241 Table 3. Wettest recorded cyclones in the Philippines. (Source: Typhoons in the Philippines, Wikipedia) Rank Storm Precipitation Millimeters Inches 1 July 1911 cyclone 2210.0 87.01 2 Parma/Pepeng 2009 1854.3 73.00 3 Carla/Trining 1967 1216.0 47.86 4 Zeb/Iliang 1998 1116.0 43.94 5 Utor/Feria 2001 1085.8 42.74 6 Koppu/Lando 2015 1077.8 42.43 7 Mindulle/Igme 2004 1012.7 39.87 8 Kujira/Dante 2009 902.0 35.51 9 September 1929 typhoon 879.9 34.64 10 Dinah/Openg 1977 869.6 34.24 3 | P a g e Aside from a quantitative description of storm events, they may also be described qualitatively. Shown in Figure 1 is a mosaic of pictures derived from Yahoo! images that depict the seriousness of typhoons in the Philippines – with a particular theme of flooding. From the quantitative and qualitative measures of typhoons, it may be concluded that flooding – an effect of typhoons – is a serious issue in the Philippines. This term project seeks to address flooding in the Philippines. First, the current flood forecasting system in the Philippines is discussed; second, the land cover variation of the Philippines is mapped and discussed; third, a terrain of analysis of Luzon is conducted; third, attention is focused one river basin in the Philippines – Pampanga River Basin – and hydrologic modeling is performed and compared with actual gage data; finally, recommendations are given of what can be improved to the flood forecasting system in the Philippines. Objectives Initially, the author aimed to construct a flood forecasting system for Luzon. However, upon the course of research and data sourcing, the objective was deemed as unreachable due to lack of data sources and lack of access to modeling software. Nevertheless, the ultimate aim of the author is still to create a flood forecasting system for the Philippines similar to the NFIE of the United States of America. This project is a step towards that goal. Henceforth, the final objective of the project is to apply GIS principles in discussing flood forecasting in the Philippines. Specifically, it aims to: 1. Discuss the current flood forecasting system of the Philippines 2. Map the land cover variation of the Philippines 3. Conduct terrain analysis of Luzon 4. Perform watershed delineation of a selected river basin in Luzon 5. Prepare a HEC-HMS model for the selected river basin 6. Validate the HEC-HMS model using historical streamflow data 7. Construct a rating curve for the selected river basin 8. Compare the simulation model of a US agency with the historical streamflow data 9. Recommend future work in the Philippines In accomplishing these objectives, data was sourced from various sources on the web and from the expertise of other people. 4 | P a g e Figure 1. Effects of flooding in the Philippines. 5 | P a g e II. Results and Discussion Flood forecasting in the Philippines (Objective 1) The Department of Science and Technology (DOST) is the main governmental body that handles typhoons and flooding issues of the country. One of its agencies is the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). As shown in its website, the main mandate of PAGASA is to “provide protection against natural calamities and utilize scientific knowledge as an effective instrument to insure the safety, well-being and economic security of all the people, and for the promotion of national progress” (Section 2, Statement of Policy, Presidential Decree No. 78; December 1972 as amended by Presidential Decree No. 1149; August 1977). One of the DOST’s project is the Nationwide Operational Assessment of Hazards (Project NOAH), which aims to prevents and mitigate disasters. The project makes use of Geographic Information Systems to disseminate critical information to the audience. This project aims to provide local government units a 6-hour lead time warning to vulnerable communities against imminent floods (Lagmay, 2012). Figure 1 is the user interface of Project NOAH where the left panel are links to flood maps and other natural disaster events. There is also a Twitter news feed about the weather conditions. Figure 2. Project NOAH. 6 | P a g e Land cover variation of the Philippines (Objective 2) Determination of the land cover variation is essential in hydrologic modeling because different land covers have different coefficient of permeability. More pervious surfaces are more desirable from the standpoint of flood alleviation because instead of water just rising, a part of it can infiltrate in the ground. Shown in Figure 3 is the land cover variation of the Philippines with the legend defined in Table 4. The dominant land covers are grassland (6), forests (5), and crops (1 and 10). It may also be seen that the developed area only constitutes 2.3% of the total land cover. It may therefore be concluded that the Philippines is still an agricultural country. Figure 3. Land use of the Philippines. (Source: NAMRIA) 7 | P a g e Table 4. Land use of the Philippines. Code Description Total area (hectares) %Total 1 Annual crops 6217767.123 21.0% 2 Barren 95694.37854 0.3% 3 Built-up area 694019.4828 2.3% 4 Fishpond 247348.6021 0.8% 5 Forest 6530137.254 22.1% 6 Grassland 8675408.056 29.4% 7 Inland water 482646.1804 1.6% 8 Mangrove 307879.5215 1.0% 9 Marshland 131439.595 0.4% 10 Perennial crops 6167900.41 20.9% 29550240.6 100.0% Another important area of research, which is not anymore part of the objective of this project, is determining the land cover change through time.