Technical ReportPDF Available

PHIL-LIDAR 1.B.14 LIDAR DATA PROCESSING AND VALIDATION IN MINDANAO: CARAGA REGION (13) - TERMINAL REPORT

Authors:

Abstract and Figures

The Phil-LiDAR 1.B.14 Project, or simply known as the "CSU Phil-LiDAR 1 Project" is a three-year project implemented in April 1, 2014 by the Caraga State University through the Caraga Center for Geo-informatics of the College of Engineering & Information Technology, in Ampayon, Butuan City, Mindanao, Philippines. It is one of several projects under the “Phil-LiDAR-1. Hazard Mapping of the Philippines using LiDAR” program funded by the Department of Science and Technology (DOST). The Phil-LiDAR 1 Program aims to produce detailed flood hazard maps for the 2/3 of the Philippine river systems using LiDAR Technology. CSU Phil-LiDAR 1 in particular was implemented to generate flood hazard maps of twelve (12) river basins of Caraga Region (excluding the Agusan River Basin which has been covered by the UP DREAM Program). The project was able to generate a total of 12 sets of detailed flood hazard maps of the 12 RBs of Caraga Region; each set comprised of two (2) historical flood events and six (6) hypothetical extreme rainfall scenarios flood hazard maps (2, 5, 10, 25, 50 and 100-return periods). From these maps, we were able to generate a total of 41 sets of city/municipal level detailed flood hazard maps for 41 cities/municipalities of Caraga Region. Other major outputs includes 7,749.46 km of validated and bathymetry integrated, 1-m spatial resolution LiDAR data products that include Digital Terrain Model (DTM) and Digital Surface Model (DSM), the development and deployment of three (3) web-based applications for localized flood disaster management; 41 research publications (of which 32 are indexed in SCOPUS); and various partnerships between the project and LGUs.
Content may be subject to copyright.
A preview of the PDF is not available
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
In this paper, we discuss how an academe-local government partnership can lead the way for the effective use of geospatial technologies for smarter and geospatially-informed decision making before, during, and after a flood disaster. In Jabonga municipality, in the province of Agusan del Norte, in Mindanao, Philippines, two significant flooding events occurred in the year 2014 which were caused by overflowing water bodies due to continuous heavy rains. These flood events inundated populated areas, caused massive evacuation, made roads un-passable, and greatly damaged sources of incomes such as croplands and other agricultural areas. The partnership between Caraga State University and the local government of Jabonga attempts to improve localized flood disaster management through the development of web-based Near-real Time Flood Event Visualization and Damage Estimations (Flood EViDEns) application. Flood EViDENs utilizes LiDAR-derived elevation and information products as well as other elevation datasets, water level records by monitoring stations, flood simulation models, flood hazard maps, and socio-economic datasets (population, household information, etc.), in order to visualize in near-real time the current and future extent of flooding, to disseminate early warnings, and to provide maps and statistics of areas and communities affected and to be affected by flooding. The development of Flood EViDEns as the main product of the partnership is an important application of geospatial technologies that will allow smarter and geospatially-informed decision making before, during, and after a flood disaster in Jabonga.
Article
Full-text available
An analysis of global statistics shows a substantial increase in flood damage over the past few decades. Moreover, it is expected that flood risk will continue to rise due to the combined effect of increasing numbers of people and economic assets in risk-prone areas and the effects of climate change. In order to mitigate the impact of natural hazards on European economies and societies, improved risk assessment, and management needs to be pursued. With the recent transition to a more risk-based approach in European flood management policy, flood analysis models have become an important part of flood risk management (FRM). In this context, free and open-source (FOSS) geospatial models provide better and more complete information to stakeholders regarding their compliance with the Flood Directive (2007/60/EC) for effective and collaborative FRM. A geospatial model is an essential tool to address the European challenge for comprehensive and sustainable FRM because it allows for the use of integrated social and economic quantitative risk outcomes in a spatio-temporal domain. Moreover, a FOSS model can support governance processes using an interactive, transparent and collaborative approach, providing a meaningful experience that both promotes learning and generates knowledge through a process of guided discovery regarding flood risk management. This article aims to organize the available knowledge and characteristics of the methods available to give operational recommendations and principles that can support authorities, local entities, and the stakeholders involved in decision-making with regard to flood risk management in their compliance with the Floods Directive (2007/60/EC).
Article
Full-text available
Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77% to 81%. This was supported with local expert knowledge which accurately classified 79% of communities deemed to be highly susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized.
Chapter
Full-text available
Abstract The frequency and intensity of flood disasters have become serious issues in the development process as flood disasters have caused serious environmental damage, loss of human lives and wanton destruction of economic assets globally. Loss of human lives and development assets, rising costs of reconstruction efforts and associated hardship are putting the issue of disaster reduction and risk management higher on the policy agenda of affected governments, multilateral agencies and NGOs. The starting point of concrete flood disaster mitigation efforts is to identify the areas with higher risk levels and fashion out appropriate preventive and response mechanisms. This paper proposes a GIS-based model for identifying flood-prone areas for the purpose of planning for disaster mitigation and preparedness, using a river basin as a unit of analysis. This model uses a number of physical, demographic and landuse data to identify areas and settlements that are vulnerable to flooding. Based on this multi-criteria model, areas, settlements and populations with varying degrees of vulnerability to flooding were identified and mapped. The model results showed that over 1,200 settlements harbouring over 13 million people are at grave risk of flooding. These vulnerable settlements and populations are mostly located within the coastal stretch, river valleys and urbanized parts of the study area. While the model proves to be usable for planning purposes, inclusion of population data at a finer level (Enumeration Areas) would improve the performance of this model by providing a near accurate estimation of population at risk as well as their spatial spread.
Article
Full-text available
This paper describes a case study of event and continuous hydrologic modeling in the Kelani River basin in Sri Lanka using the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS). An extremely high rainfall event in November 2005 was used to calibrate model parameters, and extremely high rainfall events in April-May 2008, May-June 2008, and May 2010 were used to validate the event model. The calibrated, direct runoff and base flow parameters were then used in the continuous hydrologic model. The Green and Ampt infiltration loss method was used to account for infiltration loss in event-based modeling and a five-layer soil moisture accounting loss method was employed in continuous modeling. The Clark unit hydrograph method and the recession base flow method were used to simulate direct runoff and base flow, respectively. The results depict the capability of HEC-HMS to reproduce streamflows in the basin to a high accuracy with averaged computed Nash-Sutcliffe efficiencies of 0.91 for event-based simulations and 0.88 for continuous simulations. Simulated river flows affirm that the event-based hydrologic modeling supported by intensive field data is useful to derive calibrated parameters for continuous hydrologic modeling. The study demonstrates potential HEC-HMS application in disaster mitigation, flood control, and water management in medium-size river basins in tropical countries.
Article
Full-text available
This study examined the utility of a high resolution ground-based (mobile and terrestrial) Light Detection and Ranging (LiDAR) dataset (0.2 m point-spacing) supplemented with a coarser resolution airborne LiDAR dataset (5 m point-spacing) for use in a flood inundation analysis. The techniques for combining multi-platform LiDAR data into a composite dataset in the form of a triangulated irregular network (TIN) are described, and quantitative comparisons were made to a TIN generated solely from the airborne LiDAR dataset. For example, a maximum land surface elevation difference of 1.677 m and a mean difference of 0.178 m were calculated between the datasets based on sample points. Utilizing the composite and airborne LiDAR-derived TINs, a flood inundation comparison was completed using a one-dimensional steady flow hydraulic modeling analysis. Quantitative comparisons of the water surface profiles and depth grids indicated an underestimation of flooding extent, volume, and maximum flood height using the airborne LiDAR data alone. A 35% increase in maximum flood height was observed using the composite LiDAR dataset. In addition, the extents of the water surface profiles generated from the two datasets were found to be statistically significantly different. The urban and mountainous characteristics of the study area as well as the density (file size) of the high resolution ground based LiDAR data presented both opportunities and challenges for flood modeling analyses.
Chapter
Full-text available
Flood is a natural disaster. However human activities in many circumstances change flood behavior. The objective of this study was to assess flood hazard and risk of Fogera woreda (district), which is one of the most severely flood affected areas in Ethiopia in general and Ribb–Gumara Catchment in particular, using Geographic information system (GIS) and remote sensing techniques. Land use/land cover change detection was done for the catchment using the 1973, 1985 and 1999 Landsat images and the general trend showed that vegetative and grassland areas were mainly changed to agricultural lands. Comparison between long year (1974–2006) annual maximum daily rainfall and annual maximum daily gauge levels (1971–2005) data of Ribb and Gumara Rivers showed that rainfall slightly decreases while gauge level increases, and this can be attributed to land cover conversion especially in the upper catchment. Flood frequency analysis was done using Ribb and Gumara Rivers annual maximum daily gauge levels by Gumbel’s, and the likely flood levels in different return periods were found. Digital elevation models (DEM) and the 100 year return period base-flood were combined in the GIS environment in order to produce flood inundation maps. More over, flood causative factors were developed in the GIS and remote sensing environment and weighted and overlaid in the principle of pair-wise comparison and Multicriteria Evaluation (MCE) technique in order to arrive at flood hazard and risk mapping. The major findings of the study from both the two methods revealed that most of the areas in the downstream part of the catchment and the different land uses in these areas were within high to very high flood hazard and risk level. The presence of risk assessment mapping will help the concerned authorities to formulate their development strategies according to the available risk to the area.
Article
Full-text available
This paper discusses the use of spatial data for risk and natural disaster management. The importance of remote-sensing (RS), Geographic Information System (GIS) and Global Navigation Satellite System (GNSS) data is stressed by comparing studies of the use of these technologies for natural disaster management. Spatial data sharing is discussed in the context of the establishment of Spatial Data Infrastructures (SDIs) for natural disasters. Some examples of SDI application in disaster management are analyzed, and the need for participation from organizations and governments to facilitate the exchange of information and to improve preventive and emergency plans is reinforced. Additionally, the potential involvement of citizens in the risk and disaster management process by providing voluntary data collected from volunteered geographic information (VGI) applications is explored. A model relating all of the spatial data-sharing aspects discussed in the article was suggested to elucidate the importance of the issues raised.
Article
Full-text available
The basic principles underlying the most commonly used physically-based models of the rainfall-runoff transformation process are reviewed. A thorough knowledge of these principles is a pre-requisite for flood hazard studies and, thus, this chapter reviews several physically-based methods to determine flood discharges, flow depths, and other flood characteristics. The chapter starts with a thorough review of linear system theory applied to the solution of hydrologic flood routing problems in a spatially aggregated manner -- Unit Hydrograph approaches. The chapter then proceeds to a review of distributed flood routing approaches, in particular the kinematic wave and dynamic wave approaches. The chapter concludes with a brief discussion about distributed watershed models, including single event models in which flow characteristics are estimated only during the flood, and continuous event models in which flow characteristics are determined continuously during wet periods and dry periods.
Article
At present, most river fl ood forecasts are conducted using a two-step procedure. First, fl ood routing is conducted, normally using hydrological models. The resulting fl ood peaks are then converted to water level forecasts using a steady fl ow hydraulic model, such as HEC-RAS. Recently, the HEC- RAS model has been extended to facilitate unsteady fl ow analyses, and while the numerical scheme is not robust enough to handle dynamic events (such as ice jam release fl oods) or supercritical fl ows, it does have the capability to route simple open water fl oods and produce water level forecasts at the same time. Here, the viability of the HEC-RAS unsteady fl ow routine for fl ood forecasting is examined through an application to the Peace River in Alberta and it is shown that accuracy comparable to more sophisticated hydraulic models can be achieved. Since many agencies already have HEC-RAS models established for fl oodplain delineation purposes, it would be a simple matter to extend them to the fl ood forecasting application. An ancillary advantage would be that fl ood forecasting accuracy could potentially be improved and simplifi ed into a one-step process, without necessitating a time-consuming transition to unfamiliar models.