CALIBRATION OF WATERSHED LAG TIME EQUATION FOR PHILIPPINE HYDROLOGY USING RADARSAT DIGITAL ELEVATION MODEL

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1 CALIBRATION OF WATERSHED LAG TIME EQUATION FOR PHILIPPINE HYDROLOGY USING RADARSAT DIGITAL ELEVATION MODEL Fatima Cipriano 1, Alfredo Mahar Francisco Lagmay 1,2, Matt Horritt 3, Enrico Paringit 1,2, Christopher Uichanco 1, Jerico Mendoza 1, Glenn Sabio 1, Kenneth Nino Punay 1, Herbert James Taniza 1, and Mary Rose Oquindo 2 1 Nationwide Operational Assessment of Hazards, P. Velasquez Street, UP Diliman, Quezon City, 1101, Philippines mafcipriano@noah.dost.gov.ph 2 University of the Philippines, Diliman, Quezon City, 1101, Philippines, amfal2@yahoo.com 3 Horritt Consulting, 1 Tredunnock Barn, Llangarron, Ross on Wye, HR9 6PG, United Kingdom matt@horrittconsulting.co.uk KEY WORDS: Hydrology, RADARSAT, digital elevation model, Philippines ABSTRACT: Widespread flooding is a major problem in the Philippines. The country experiences heavy amount of rainfall throughout the year and several areas are prone to flood hazards because of its unique topography. Human casualties and destruction of infrastructure are some of the damages caused by flooding and the Philippine government has undertaken various efforts to mitigate these hazards. One of the solutions was to create flood hazard maps of different floodplains and use them to predict the possible catastrophic results of different rain scenarios. To produce these maps with accurate output, different input parameters were needed and one of those is calculating hydrological components from topographical data. This paper presents how a calibrated lag time equation was obtained using measurable catchment parameters. Lag time is an essential input in flood mapping and is defined as the duration between the peak rainfall and peak discharge of the watershed. The lag time equation involves measurable parameters, namely, watershed length and watershed slope, which were both available from RADARSAT Digital Elevation Models (DEM). The equation also involves the maximum potential retention of the soil derived from its curve number which was processed from the Philippine Curve Number Map. This approach was based on a similar method developed by CH2M Hill and Horritt for Taiwan, which has a similar set of meteorological and hydrological parameters with the Philippines. Rainfall data from fourteen water level sensors covering 67 storms from all the regions in the country were used to measure actual lag time values. The measured lag time values were plotted against the calculated values obtained from the Natural Resource Conservation Management handbook lag time equation. Regression analysis was used to obtain the final calibrated equation that would be used to calculate the lag time specifically for rivers in the Philippines. The calculated lag time values could then be used as a parameter for modeling different flood scenarios in the country. 1. INTRODUCTION The Philippines is one of the most hazard-prone countries in the world when it comes to water-related disasters like floods and landslides. The country has been among the top five most frequently hit by natural disasters, along with China, India, Indonesia, and the United States. According recent studies, the Southeast Asian region, where the country is part of, will more likely experience higher frequency of flooding events in the upcoming years (The World Bank, 2010). In 2009, typhoon Ketsana, locally known as Ondoy, one of the worst typhoons since the 1960's, struck the country (CDRC, 2013) and caused heavy flooding in the country's capital. This calamity resulted in 464 deaths (Rappler, 2014). Similarly, in 2013, typhoon Haiyan, locally known as Yolanda, was the biggest disaster for the country that year (CDRC, 2013) and struck areas in the Visayas region. The region was severely affected because of flooding and other related hazards. Six months after Yolanda, around 24,000 individuals were affected and 6,300 individuals were reported to be dead (Locsin, 2014). Inundation of both urban and rural areas caused by heavy rainfall greatly affect the lives of families and communities. It is essential to understand different hydrological parameters that affect the movement of water in different areas in order to analyze flooding in the country. When it comes to the study on floods, hydrology is one of the important factors that needs to be understood. For this study, a key hydrological parameter, the lag time (T L) is determined using a calibrated equation specific for Philippine setting. The equation will be determined using measurable parameters namely the watershed length, watershed slope, and maximum potential retention derived from the curve number, all of which are available from RADARSAT Digital Elevation Models (DEM). The value of the lag time is essential when it comes to determining river discharge values for flood inundation modeling.

2 2. THE PHILIPPINE SETTING 2.1 Geographical Location The geographical location of the Philippines makes it susceptible to flood-induced disasters such as typhoons. Being located at the typhoon belt, around 17 tropical cyclones enter the Philippine Area of Responsibility annually, 6 to 9 among which are making landfall in the country (PAGASA, 2009). As a tropical cyclone makes its landfall, it could generate significant amount of rainfall that will cause flooding to low-lying areas. About 47% of the average annual rainfall in the country is attributed to the occurrence of tropical cyclones, 14% to the monsoons, while 39% are due to the effects of the other weather disturbances (Kintanar, 1984). 2.2 Topographical Data The topography of the country also affects the climate of the locality. Topographic variations characterize the Philippines due to its archipelagic nature, but dominantly the country consists of narrow mountain ranges. Among them are the Sierra Madre Ranges that run along the eastern portion of Luzon, the Ilocos Ranges along the western coast of the Northern Luzon, and the Cordillera Ranges situated between the Ilocos and Sierra Madre Ranges. With these vast mountain ranges, the country experiences orographic precipitation where the windward side of the mountain receives more precipitation compared to its leeward side. In order to have a good representation of the topography of the different areas in country, RADARSAT Digital Elevation Models (DEM) were used as data. RADARSAT satellites are equipped with Synthetic Aperture Radar (SAR) that acquires advance geospatial information over given areas (Van Westen, 2013). These DEMs were processed in the form of rasters and layers that can be read and analyzed in Geographic Information System (GIS) softwares like ArcMap. 2.3 Hydrological Parameters Surface runoff, resulting from rainfall-runoff transformation process plays a significant role in the hydrological processes in tropical countries like the Philippines. Runoff significantly influences the amount of water available in the rivers, streams, and ponds, and determines the size and shape of the flood peaks (Troch, et. al., 1994). As long as the rainfall intensity exceeds the actual infiltration capacity of the soil, the runoff continues to generate which commonly the cause of flooding in low lying areas or floodplains. River flooding in the country is illustrated when runoff from high elevation areas, called the upper watershed, travel to floodplains through streams and natural channels. This happens usually during typhoons and other occurrences with intense rainfall. Flooding problems resulting from runoff of surface water generally increase as areas become more urbanized (FEMA, n.d.). Greater population density generally increases the amount of impervious area such as pavements and buildings which causes the reduction of the amount of natural ground that can absorb rainfall. As a result, the amount of surface runoff generated increases. For this study, runoff estimation is done using the Soil Conservation Service (SCS) curve number method. The method shows that runoff will result when the capacity of the soil to hold water is exceeded with the presence of continuous rainfall. The initial storage of water in millimeters is estimated using Equation 1, with the initial storage (I) as a function of the curve number (CN). I = 0.2 x ( ) x 25.4 CN Equation 1 Otherwise, when the rainfall exceeds the initial storage that the soil can hold, the value for runoff is determined by Equation 2, with the direct runoff (Q) as a function of the total rainfall (P) and the maximum potential retention (S). Q = (P 0.2S)2 (P + 0.8S) Equation 2 The value of the maximum potential retention (S) as a function of the curve number is shown in Equation 3. Equation 3

3 3. ESTIMATING WATERSHED LAG TIME S = 1000 CN 10 Watershed lag time is defined as the time difference from the centroid of the excess rainfall to the centroid of the direct runoff (Granato, 2012) in a hydrograph. According to the US Natural Resources Conservation Service (NCRS) hydrology handbook, the general watershed lag time equation is shown in Equation 4. T L = L0.8 (S + 1) Y 0.5 Equation 4 The parameters in the equation were identified as the watershed length (L), watershed slope (Y), and maximum potential retention (S). The watershed length is the longest drainage path within the catchment or upper watershed. It is measured from the top of the watershed to the point of the outlet in meters. It is also defined as the stream of which has greatest drainage area. The watershed slope is the average slope of the whole catchment. The maximum potential retention is defined as the capacity of the soil to store water. It is a function of the curve number which was shown in Equation 3. A higher curve number results to more runoff generated for a given rainfall scenario. Generally, the NRCS lag equation is applicable mainly for rural areas. Using the lag time value for urban areas would be an overestimate and flood discharge values would be inaccurate. This is due to having increased impervious areas and the changes in topography and natural conditions of streams in cities. 4. METHODOLOGY 4.1 Data Collection This study was developed by collecting data from 67 storms and 14 water level sensors, with their corresponding rain gauges summarized in Table 1. A map of the location of the sensors is shown in Figure 1. This allowed for the estimation of the lag time for storms that occurred in with records taken from the Philippine E-Science Grid web repository. The rain gauges that were identified and chosen had to satisfy the following criteria: (1) placed more than 10km from the sea to reduce the tidal effects; (2) with data of at least 100 days; (3) must cover a variety of catchment sizes, and; (4) must cover most regions in the country. Figure 1 Location of sensors chosen for the study Table 1. Water level sensors and rain gauges used for data acquisition

4 No. Catchment Gauge Location Area (sqkm) Number of Storms 1 Iponan San Simon Tagoloan Tagoloan Marikina Santo Nino Cagayan Buntun Naga Padre Garcia Jalaur Jalaur, Pototan Agusan del Sur Ihaoan Rio Grande de Mindanao Busco Cagayan de Oro Kabula Rio Grande de Mindanao Manuto Panay Maayon Panay Sigma Jalaur Ulian Cagayan Sangbay Total Data Processing Measuring Actual Lag Time Monthly rainfall and water level records were plotted to pinpoint short-duration storms that have clearly defined peak values of water level. The water level and rainfall data for each storm were used to calculate the runoff and time between peak runoff and peak water level. The equation from the SCS curve number method was used to get the values. Some data have water recorded water levels below the gauge and were therefore inverted. Spikes in the water levels due to measurement errors were also removed manually Data from RADARSAT DEM The coordinates of the gauge locations were identified using a software called Google Earth. The coordinates were then plotted in ArcMap to help process the parameters needed as described in Section 3. Using delineated catchments based on mean elevation from RADARSAT DEM data, the upper watershed for each gauge was determined. Then the watershed length and average slope for each upper watershed was computed using the data from the DEM and the analytical tools of ArcMap Data from the Philippine Curve Number (CN) Map The Philippine CN Map was developed based on two data: (1) the Digital Soil Map of the Wordl (DSMW) from the Food and Agriculture Organization of the United Nations (FAO-UN); and (2) GlobCover 2009 from the European Space Agency (ESA). Both organizations provided free rasters and shapefiles that were combined to generate 300msized grids of different curve numbers in the country. Similar with obtaining the average slope, an analytical tool in ArcMap was utilized to determine the mean curve number of the upper watershed. 4.3 Calibrating the Equation After calculating the 67 measured values of T L, a graph was made by plotting the measured lag time values against an uncalibrated equation shown in Equation 5. x = L0.8 (S + 1) 0.7 Y 0.5 Equation 5 Linear regression analysis was then applied by extracting the best-fit line of the graph and using it to calibrate the watershed lag time equation.

5 5. RESULTS The resulting graph made by plotting T L values against L 0.8 (S+1) 0.7 /Y 0.5 is shown in Figure 2. Based on the figure, the best-fit line indicates that the calibrated equation is shown in Equation 6. T L = L0.8 (S + 1) Y 0.5 Equation 6 It was also observed that there were values from three rain gauges, encircled in red, that were outliers in the graph. These gauges are located in Jalaur, Sigma and Ihaoan. The R 2 value of the best-fit line is low (0.483). It was concluded that the calibrated equation is acceptable. Nevertheless, further improvement of the equation must be done if more data are available. Figure 2 Graph of linear regression to fit lag time data 6. CONCLUSIONS AND RECOMMENDATIONS An equation has been developed to determine the lag time of a watersheds in the Philippines. Sixty-seven storms from fourteen catchments were analyzed to determine the actual value of the lag time. These values were plotted on a graph against the values calculated from L 0.8 (S+1) 0.7 /Y 0.5 and a final equation was derived. The equation can then be used to in simulations and studies for flood inundation in the country. It is possible for the equation to be further calibrated if additional data from the repository is available. The more data that can be used, the more accurate the equation is for Philippine setting. Fine tuning the equation for specific use can also be done with the same method. For example, a more specific equation just for urban areas can be developed by using the same methodology but with data from rain gauges found in urban areas like in Metro Manila. ACKNOWLEDEMENTS This research is the result of the hard work of the Flood Modeling Component of the Nationwide Operational Assessment of Hazards (Project NOAH)/Phil-LiDAR 1 and Dr. Matt Horritt of Voluntary Service Overseas (VSO). The researchers would also like to thank the Department of Science and Technology (DOST) for giving them the opportunity to gain more knowledge and work in the fields of remote sensing, hydrology, and natural hazards. REFERENCES Citizen's Disaster Response Center (CDRC), Philippine Disaster Report. Retrieved October 24, 2014, from Citizen's Disaster Response Center (CDRC), Philippine Disaster Report. Retrieved October 22, 2014, from Federal Emergency Management Agency, n.d. Unit 1: Floods and Floodplain Management. Retrieved October 27, 2014, from Granato, G.E., Estimating basin lagtime and hydrograph-timing indexes used to characterize stormflows for runoff-quality analysis: U.S. Geological Survey Scientific Investigations Report , 47 p. U.S. Geological Survey, Virginia. Horritt, M., DREAM recommended hydrology method. Kintanar, R. L., Climate of the Philippines. Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), Quezon City, Philippines.

6 Lidenburg, M. R., Environmental Engineering Reference Manual for the PE Exam. Professional Publications, Inc., Belmont. Linsley, K., and Paulhus, J., Hydrology for Engineers. McGraw-Hill, USA. Locsin, J., NDRRMC: Yolanda death toll hits 6,300 mark nearly 6 months after typhoon. Retrieved July 30, 2014, from mark-nearly-6-months-after-typhoon. Mirwansyah, P., Spatial Multi-criteria Analysis (SMCA) for Basin-wide Flood Risk Assessment as a Tool in Improving Spatial Planning and Urban Resilience Policy Making: A Case Study of Marikina River Basin, Metro Manila Philippines. Procedia - Social and Behavioral Sciences, pp Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), Member Report to the ESCAP/WMO Typhoon Committee 41st Session. Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), Quezon City, Philippines. Rappler, BY THE NUMBERS: Ondoy, Habagat 2012, Habagat Rappler, Philippines. The World Bank, Climate Risks and Adaptation in Asian Coastal Megacities: A Synthesis Report. In: The International Bank for Reconstruction and Development/The World Bank, Washington DC, USA. Troch, P. A., De Troch, F. P., and Brutsaert, W., Effective water table depth to describe initial conditions prior to storm rainfall in humid regions. Van Westen, C.J., Remote sensing and GIS for natural hazards assessment and disaster risk management. Treatise on Geomorphology, pp

7 APPENDIX A. Summary of Storms for Analysis No. Station Area (sqkm) Flow Length (m) Slope (%) CN S Uncalibrated Eqn (Equation 5) TL from Measured Average TL from Calibrated Eqn (Equation 6) 1 San Simon Tagoloan Santo Nino Buntun Padre Garcia Jalaur, Pototan Ihaoan Busco Kabula Manuto Maayon Sigma Ulian Sangbay Error Factor FSE

8 APPENDIX B. Summary of Measured Lag Time Values from Sensors No Gauge Location San Simon Tagoloan Santo Nino Buntun Padre Garcia Jalaur, Pototan 7 Ihaoan 8 Busco Date(s) Total Rainfall (mm) Total Runoff (mm) Measured Lag Time (hr) 1/1/ /19/ /13/ /20/ /7/ /5/ /15/2014-7/16/ /8/2013-8/9/ /20/ /20/ /29/ /30/ /15/ /17/ /28/ /22/ /23/ /8/ /9/ /20/ /12/2013-1/13/ /11/2013-8/13/ /10/ /14/ /8/ /10/ /10/ /13/ /12/2013-8/14/ /26/ /27/ /9/ /10/ /6/ /12/2013-8/13/ /8/2013-9/9/ /30/ /1/ /8/ /10/ /7/ /9/ /16/2013-9/18/ /27/2013-9/28/ /8/ /9/ /14/ /15/ /12/ /13/ /6/2013-8/23/ /12/2013-8/13/ Mean Lag Time (hr)

9 8/21/2013-8/23/ /12/ /13/ /5/2013-1/6/ Kabula 3/24/2013-3/25/ /4/2013-4/6/ /1/2013-5/2/ /16/ /17/ /19/ Manuto 7/27/ /28/ /1/ /23/2013-8/27/ Maayon 9/10/2013-9/12/ /6/ /7/ /27/ /28/ Sigma 10/ /28/ /7/ /28/ Ulian 7/19/ /25/2013-7/26/ /8/ Sangbay 8/10/ /15/ /12/