H51B-1369
Calibration of Watershed Lag Time Equation for Philippine Hydrology using RADARSAT Digital Elevation Models

Friday, 18 December 2015
Poster Hall (Moscone South)
Fatima Real Cipriano1, Alfredo mahar Amante Lagmay1,2, Matt Horritt3, Jerico Mendoza1, Glenn Sabio1, Kenneth NiƱo Punay4, Herbert James Taniza1 and Christopher Uichanco1, (1)Nationwide Operational Assessment of Hazards (Project NOAH), Department of Science and Technology, Quezon City, Philippines, (2)University of the Philippines, Quezon City, Philippines, (3)Horritt Consulting, Ross-on-Wye, United Kingdom, (4)Nationwide Operational Assessment of Hazards, Quezon City, 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 just 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 (TL) 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 three measurable parameters, namely, watershed length (L), maximum potential retention (S) derived from the curve number, and watershed slope (Y), all of which were available from RADARSAT Digital Elevation Models (DEM). 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 estimate the actual lag time. These sensors were chosen by using a screening process that considers the distance of the sensors from the sea, the availability of recorded data, and the catchment size. The actual lag time values were plotted against the 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 Philippine setting. The calculated lag time values could then be used as a parameter for modeling different flood scenarios in the country.