**5.1 LiDAR data processing and validation**

For the said projects, floodplains that did not have LiDAR data yet, manned airborne LiDAR data acquisition was conducted. LiDAR data collection and preprocessing is divided into three parts: 1) Data acquisition, 2) Point cloud generation, and 3) Data classification. Data acquisition included flight planning, system installation, data collection and data download. After data acquisition, pre-processing was done. Point cloud generation included the accuracy determination that will need the hardware and the software that came with the LiDAR system. For the data classification, Terrasolid was used for classifying infrastructures from the bare earth. The end-products of the preprocessing stage were unedited digital elevation models or DEMs.

Pre-processed LiDAR data were then subjected to processing. The processing of the LiDAR data began with manually editing some features of the unedited DEMs so that water will flow in a realistic manner. In the DEM, the bridges were removed, pits were filled and important features were removed in the last return digital

#### **Figure 3.**

*A LiDAR DEM processing output for Labo River basin in Camarines Norte Province, Philippines. It features a burned bathymetric data on LiDAR digital terrain model (DTM) processed by the Mapua-Phil LiDAR 1 Project of Mapua University through the Department of Science and Technology.*

#### *Flooding and Flood Modeling in a Typhoon Belt Environment: The Case of the Philippines DOI: http://dx.doi.org/10.5772/intechopen.98738*

terrain model (DTM) retrieved from the secondary DTM. DTM, together with digital surface model (DSM) is a type of DEM. When DEMs were already edited, they were mosaicked into a bigger DEM and integrated with the bathymetric data gathered from the field. A minimum of ArcGIS 10.2 (or more recent version) was used in the project for the processing data since the scripts of Phil-LiDAR 1 Project was used. Simultaneously, feature extraction or digitizing features on LiDAR DEM were done. This also used ArcGIS for processing.

Validation was done by getting the difference in elevation of each check point and its corresponding LIDAR data point where the checkpoint locations were based on the guidelines provided by the American Society for Photogrammetry and Remote Sensing (ASPRS). Reference points and benchmarks established by National Mapping and Resource Information Authority (NAMRIA), Land Management Bureau (LMB) and other agencies were researched and checked on the field. The elevation of the geographic location labeled (X,Y) of these bench marks were determined through static observation using dual frequency GNSS (*Global Navigation Satellite System)* receivers. For water surfaces, detailed survey of the cross-sections and bathymetry were done.

### **5.2 Topographic and hydrographic surveys**

Information on river topography and geometry and lake bathymetry is important in watershed and flood modeling. These features, nevertheless, are poorly represented in the LIDAR data due to low penetration of light signals in water. River profiling, cross-section and bathymetric surveying for lakes must be collected and integrated into the LIDAR data. GNSS kinematic surveys and topographic surveys using total station or digital level, and bathymetric mapping using an echo sounder equipped with GNSS positioning were done to obtain the above data needed. The collected data were analyzed and filtered to remove the outliers and then interpolated to come up with a continuous surface with the same spatial resolution as the LIDAR-derived elevation dataset. This surface data was subjected to accuracy assessment using an independent dataset and was ensured to have the same vertical accuracy as the validated LIDAR data or better. This was embedded into the LIDARdata and used in modeling of the watershed and flood hazards.

Modeling and simulations were done using a combination of software that included GIS software, hydrologic modeling software and river and floodplain hydraulic software to generate flood hazard maps at different rainfall scenarios of 5, 10, 25, 50, and 100-year return periods. **Figure 4** shows sample two (2) dimensional flood hazard maps produced from flood hazard computer models simulated in 5, 25, and 100-yr return periods.

#### **5.3 Hydrological assessment**

In the development of the watershed, GIS proved to be useful in the derivation of the the catchment and subcatchment boundaries and properties. These are then used to create the hydrologic model with additional input of the rainfall data at different return periods. The hydrologic models were calibrated using actual or observed data taken during a rainfall event caused by storms or typhoons to capture the direct runoff that exhibits the response of the watershed to excess rainfall.

The hydrologic model simulations generate river flow or discharge that are input to the river hydraulics model which simulates the depth and velocity of flow in the river channel and the floodplain.

For the projects, hydrological measurements such as for rainfall, water level and discharge were done in the sub-basins for use in the flood model calibration and

#### **Figure 4.**

*Flood hazard map generated using LiDAR digital elevation model in Maling River in Atimonan, Quezon Province, Philippines produced by the FRAMER Project of Mapua University and Department of Science and Technology (DOST).*

*Flooding and Flood Modeling in a Typhoon Belt Environment: The Case of the Philippines DOI: http://dx.doi.org/10.5772/intechopen.98738*

validation. Existing data from different government agencies e.g. PAGASA were also collected and assessed. For ungauged watersheds, rain gauges, water level sensors, and velocity meters were used for at least a year to capture seasonal changes. At least one rain gauge was ensured to be present near the center of each river basin. On the other hand, water level and velocity sensors were used at a selected point in the river in the usually with the presence of a bridge for ease and safety in data gathering. At that location, called project point, cross-section measurements were done so that the rate of discharge can be computed. The water level and the computed discharge were needed in generating rating curves to be used in forecasting water stage.

#### **5.4 Watershed and flood modeling and hazard mapping**

It is in the hydraulic model that the LiDAR DEM plays an important part of giving accurate flood depths. The flood depths are represented geographically in a map to generate the flood hazard map of an area.

With the validated LiDAR-DTM, the sub-basins were delineated using watershed delineation algorithm in GIS. HEC-HMS (Hydrologic Modeling System) and HEC-RAS (River Analysis System) programs of the Hydrologic Engineering Center (HEC) of the US Corps of Engineers were utilized [4]. The hydrological model for the upstream watersheds were developed using HEC HMS where soil type and land-cover related parameters needed in program were derived through analysis satellite images, and from the Department of Agriculture's Bureau of Soil and Water Management (DA-BSWM) soil maps. Other parameters of the model were set to initial values and were adjusted during calibration. After calibration, the model were validated with an independent set of rainfall and discharge data [5].

The hydraulic model for the floodplain were developed using HEC-RAS. Crosssection data extracted from the validated LiDAR DTM was used in this program to create the geometry of the river system in each sub-basin. The land-cover related parameter which is the Manning's roughness coefficient were determined through analysis satellite images.

The model generated from the combined HEC-HMS and HEC-RAS programs were used to run the actual flood events for each sub-basin using historical flood data. Flooding due to hypothetical extreme rainfall events of various return periods obtained from PAGASA were simulated to see the effects of such events in the watershed. The flood simulations were then used to generate water surface elevation grids which were overlain into the high resolution LiDAR-DTM to generate flood hazard maps. To enhance the flood hazard maps, a 2D hydrodynamic model was utilized as well [6]. This 2D model in HEC-RAS utilized the discharge hydrographs generated by HEC HMS.

### **6. Flood modeling results**

The use of a more sophisticated equipment to measure elevation at higher resolution is necessary when it comes to modeling flood inundation. Elevation is one of the most important inputs as this will tells us the depth of water which is a serious concern to people and for properties.

In **Figure 4**, sample flood hazard maps for one of our study areas in Quezon Province show the different flood inundation levels at different rainfall return periods, i.e. 5, 25, and 100 years. The flood hazard maps serve as an important tool for use by the Local Government Units (LGUs) planners and responders, and by the public in general. The maps produced show the flood inundation that can be related to the forecast of rainfall event the weather agency releases. Across the Philippines,

PAGASA has synoptic weather stations, each has corresponding rainfall intensity, duration, frequency curves (RIDF). A station nearest to the area of study adopts this specific RIDF.

During a storm or a typhoon, forecast of rainfall depth in time or intensity in an area i.e. with respect to a watershed, is checked with this curves for equivalent return period. The determined return period will tell us which flood hazard map should be used. From the specific inundation map, we can clearly see the extent of the flooding as well as the relative depth of flood. The geographic location of flooding can be easily visualized when it is overlain on the elevation DEM. When data of exposure is included, the number of households and establishments affected can be determined.

As shown in **Figure 4**, flood hazard maps are an important tools that can be used to see spatially the occurrence of flood and the potential impact it may cause to the area. Moreover, a risk assessment of the flood hazard that incorporates the aspects of exposure and vulnerability of elements such as the population and economic activities in the area has to be done in order to identify flood prioritization response for cost efficiency on responses [7]. Flood hazard and flood risk assessments are two different but related procedures whose outputs are an important decision-making tools for identifying flood prone areas needed for the design of flood control and drainage projects, and for the prioritization of responses for saving lives and properties, respectively.

Rainfall data such as in the RIDF should be continuously updated to account for new entries that reflect the effects of extreme climate variabilities such as climate change. Although there are still uncertainties in the projections of climate scenario due to limited knowledge, it has been observed that there are widespread increases in heavy precipitation events which are associated with the increase in water vapor in the atmosphere consistent with the observed warming [1]. Aside from rainfall, behavior of storms and tropical cyclones that brings with them rain are becoming stronger as manifested in the introduction of a higher category of Tropical Cyclones in the Philippines by its weather agency, PAGASA. In May 1, 2015, a new category Super Typhoon with speeds more than 220kph is added in the Tropical Cyclone Classification as a consequence of the strongest typhoon TC Haiyan (Typhoon Yolanda as it is locally known). Changing precipitation volumes and intensities increase the risk of flash flooding and urban flooding in many areas in the region and even the world.
