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A WEB GIS-BASED VISUALIZATION AND ANALYTICAL PLATFORM FOR NEAR-REAL TIME FLOOD CHARACTERIZATION, FORECASTING AND IMPACT ASSESSMENT

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Access to near-real time information on the spatial distribution and detailed characteristics of the current and future (forecasted) flood scenarios is crucial for effective flood forecasting and early warning, especially when formulating decisions related to evacuation and response before, during, and after a flood scenario. In this paper, we present the development and application of a web GIS platform called "Near-real Time Flood Event Visualization and Damage Estimations (NRT-Flood EViDEns) that has the capability to show detailed maps of current and forecasted flood characteristics, including the capabilities to analyze and provide maps and statistics of the impacts of flooding to various infrastructures such as buildings, roads and bridges. The flood information reported by the platform are sourced from a two-dimensional flood model based on HEC RAS 5 that utilizes high-resolution LiDAR data, satellite-derived land-cover, and near-real time hydrological and meteorological data as among its vital inputs. The 2D flood model simulates historical (last 24 hours), current, and future (next 24 hours) flood scenarios at 30-minute interval. A combination of web mapping data storage, visualization and analysis tools that include OpenLayers, Geoserver, GeoDjango, Javascript, and PostgreSQL/PostGIS are utilized to enable the user to perform flood characteristics visualization and spatial overlay analysis for impact assessment. The accuracy of the flood depths and extents generated by the platform was determined to range from 52 to 71% overall accuracies and Root Mean Square Errors ranging from 0.30 to 0.58 m based on historical flood events that were simulated. The web platform is expected to be used for operational flood monitoring and forecasting, and is envisioned to be an important tool for geo-spatially informed decision making in Butuan City.
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A WEB GIS-BASED VISUALIZATION AND ANALYTICAL PLATFORM FOR
NEAR-REAL TIME FLOOD CHARACTERIZATION, FORECASTING AND
IMPACT ASSESSMENT
Jojene R. Santillan (1,2), Edsel Matt O. Morales (1), Meriam Makinano-Santillan (1,2),
Arthur M. Amora (1), Jennifer T. Marqueso (1), Amor L. Gingo (1)
1 Caraga Center for Geo-Informatics, Caraga State University, Butuan City, 8600, Philippines
2 Department of Geodetic Engineering, College of Engineering and Geosciences, Caraga State
University, Butuan City, 8600, Philippines
Email: jrsantillan@carsu.edu.ph; mmsantillan@carsu.edu.ph
KEY WORDS: Web GIS, Flood Forecasting, Impact Assessment, Butuan City, Philippines
ABSTRACT: Access to near-real time information on the spatial distribution and detailed
characteristics of the current and future (forecasted) flood scenarios is crucial for effective
flood forecasting and early warning, especially when formulating decisions related to
evacuation and response before, during, and after a flood scenario. In this paper, we present the
development and application of a web GIS platform called “Near-real Time Flood Event
Visualization and Damage Estimations (NRT-Flood EViDEns) that has the capability to show
detailed maps of current and forecasted flood characteristics, including the capabilities to
analyze and provide maps and statistics of the impacts of flooding to various infrastructures
such as buildings, roads and bridges. The flood information reported by the platform are
sourced from a two-dimensional flood model based on HEC RAS 5 that utilizes high-
resolution LiDAR data, satellite-derived land-cover, and near-real time hydrological and
meteorological data as among its vital inputs. The 2D flood model simulates historical (last 24
hours), current, and future (next 24 hours) flood scenarios at 30-minute interval. A
combination of web mapping data storage, visualization and analysis tools that include
OpenLayers, Geoserver, GeoDjango, Javascript, and PostgreSQL/PostGIS are utilized to
enable the user to perform flood characteristics visualization and spatial overlay analysis for
impact assessment. The accuracy of the flood depths and extents generated by the platform
was determined to range from 52 to 71% overall accuracies and Root Mean Square Errors
ranging from 0.30 to 0.58 m based on historical flood events that were simulated. The web
platform is expected to be used for operational flood monitoring and forecasting, and is
envisioned to be an important tool for geo-spatially informed decision making in Butuan City.
1. INTRODUCTION
Near-real time (NRT) information on the spatial distribution and detailed characteristics of the
current and future (forecasted) flood scenarios is crucial for effective flood forecasting and early
warning. Flood characteristics such as flood elevation, flood depth, flood velocities, arrival
times, flood duration, and flood recession times are very important to consider when
formulating decisions related to evacuation and response before, during, and after a flood
scenario. In comparison with static flood hazard maps which are generated using hypothetical
rainfall scenarios, NRT flood information can be considered advantageous as it depicts the flood
characteristics that can be expected based on the actual time pattern and intensity at which
actual rain is falling or occurring over a particular area.
The 40th Asian Conference on Remote Sensing (ACRS 2019)
October 14-18, 2019 / Daejeon Convention Center(DCC), Daejeon, Korea
ThF1-4
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Figure 1. Left: Location of Butuan City relative to the Agusan River Basin, Mindanao,
Philippines. Right: The city traversed by Agusan River, as viewed in Google Earth.
NRT flood information are usually generated by flood monitoring and forecasting systems
involving a network of water level and rainfall monitoring sensors that feeds information into a
suite of flood modeling software (Mioc et al., 2008; Merkuryeva et al., 2015; Loi et al., 2018).
In developing a flood forecasting system, precise and reliable simulation of hydrologic and
hydraulic processes by the flood models is very important (Loi et al., 2018). These flood models
are then used to forecast water levels in the next hours, and more importantly, to simulate
possible flooding scenarios. The forecasted flood maps can then be visualized using Web
Geographic Information System (GIS), a popular and effective information dissemination
platform, offering online analysis of model-forecasted flood scenarios (Cheng et al., 2004).
With Web GIS, the transfer of information and knowledge from the hydrological scientists and
managers to decision makers has been streamlined (Cheng et al., 2004).
In recent years, a number of flood forecasting systems for near-real time flood information
generation have been developed. Santillan et al (2012) developed and parameterized a near-real
time flood extent monitoring model for Marikina River, Philippines using the Hydrologic
Engineering Center - River Analysis System (HEC RAS). A model-based warning system was
developed by Desalegn et al. (2016) to forecast floodplain inundations for a watershed in
Ethiopia by integrating the Soil and Water Assessment Tool (SWAT) and LISFLOOD-FP
models. Loi et al. (2018) have developed a system which integrates a coupled hydrological-
hydraulic modeling system based on SWAT and HEC RAS, weather station network, and
stream gauges in a web-based visualization environment with computational efficiency of less
than 5 minutes processing time.
Agusan
Marsh
Butuan City
Butuan City
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The goal of this paper is to present the development and application of a web GIS platform
called “Near-real time Flood Event Visualization and Damage Estimations” (NRT-Flood
EViDEns) that has the capability to show detailed maps of current and forecasted flood
characteristics, including the capabilities to analyze and provide maps and statistics of the
impacts of flooding to various infrastructures such as buildings, roads and bridges. The paper is
an implementation of a proposed approach by Makinano-Santillan and Santillan (2016).
Butuan City, a highly-populated and urbanized city located in the downstream of the Agusan
River Basin in Agusan del Norte, Mindanao Island, Philippines (Figure 1) was chosen as pilot
site for the application. In recent years, the city has experienced major flooding incidents caused
by extreme rainfall occurring over the Agusan River Basin brought about by tropical storms,
low pressure systems, and tail end of a cold front that resulted to fatalities, and agricultural and
infrastructure damages (NDRRMC, 2014, NDRRMC, 2017).
2. METHODOLOGY
The flowchart of activities involved in the development of NRT-Flood EViDEns is shown in
Figure 2. Each activity has been described in Makinano-Santillan and Santillan (2016). For
reference, they are again discussed here with focus on the actual implementation.
Figure 2. Flowchart of developing the near-real time flood event visualization and damage
estimations (Source: Makinano-Santillan and Santillan, 2016).
2.1 Real-time Hydrologic Data Observation
Real-time hydrologic data of rainfall and river water level recorded by Automated Rain Gauges
(ARG) and Water Level Monitoring Stations (WLMS) was acquired in near-real time through
an Application Programming Interface (API) access to the data repository hosted by the
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Advance Science and Technology Institute of the Department of Science and Technology (ASTI
DOST). Real-time information is updated every 15 minutes for the rainfall and every 10 minutes
for the water level, and was accessed and downloaded in Comma Space Value (CSV) format
using automation scripts. The scripts also convert these files into specific format that is
compatible with the flood modelling software suites consisting of the Hydrologic Engineering
Center (HEC) Hydrologic Modeling System (HMS) and HEC River Analysis System (RAS).
2.2 Upstream Hydrologic Model Simulations using HEC HMS
A hydrologic model based on HEC HMS was developed for the Agusan River Basin using a 10-
m Digital Elevation Model derived from RADARSAT-2 (Ramirez, 2014) and provided by the
DREAM Program of the University of the Philippines-Diliman; land-cover data from the
analysis of 2017 Landsat 8 OLI images; and hydrologic data consisting of rainfall and discharge
measured at strategic locations with the basin. It was calibrated using discharge data measured at
Dankias Station (see Figure 1), together with rainfall data recorded by 15 stations distributed
within ARB. The calibration data period is 14:00, 16 November 2017 17:20, 17 November
2017. The Nash-Sutcliffe Coefficient of Model Efficiency (NSE), percentage bias (PBIAS), and
the RMSE-observations Standard Deviation Ratio (RSR) were used to evaluate the performance
of the hydrologic model based on the guidelines of Moriasi et al (2007). The overall
performances of the hydrologic model after the calibration were as follows: NSE = 0.93, PBIAS
= -0.53, and RSR = 0.27) indicating “Very Good” model performance. The difference between
the simulated and measured peak flow was minimal (24.80 m3/s). Details about the HEC HMS
model development are reported in Santillan et al. (2019). The calibrated HEC HMS model was
automated using Python, Batch Script, and HMS utilities to generate discharge hydrographs that
will be used as inputs into the HEC RAS model for near-real time flood forecasting.
2.3 Near-real time Hydraulic Modeling and Flood Mapping Using HEC RAS
The floodplain hydraulic model was developed using HEC RAS 5.0.7. This version of HECRAS
can perform 2-dimensional hydraulic calculations for a full network of natural channels
(USACE 2016). 2D modelling will be performed by creating a 2D flow area representing the
entire floodplain of the river basin. The primary source of elevation data for the 2D flow area
was the 1-m calibrated and bathymetry-integrated LiDAR-derived Digital Terrain Model, and
was parameterized with Manning’s roughness coefficients extracted from land-cover
information derived through the analysis of 2017 Landsat 8 OLI images (Santillan et al., 2019).
Flow hydrographs from the HEC HMS hydrologic simulation, observed hydrologic data
(rainfall, water level), and tidal time-series data were combined as inputs into HEC RAS 5’s 2D
unsteady flow simulation module to simulate the different flooding characteristics (arrival time,
depths, velocities, duration, recession time, and percent time inundated) for the current and
forecasted events. HEC RAS was automated using AutoIt software. The automation includes
updating the unsteady flow data to reflect recent hydrological conditions, updating of initial
conditions of the model, performing unsteady flow analysis, computation of flood characteristics
such as depth, velocity, duration, etc. using HEC RAS’ built-in RAS Mapper, and conversion of
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flood depth into a shapefile. The result of the simulation represents the current and forecasted
flood characteristics which are generated in near-real time. These results are then exported into
web GIS-compatible formats using GIS software for visualization and analysis in the web-based
platform.
2.4 Web-based Near-real Time Flood Forecast Visualization and Analysis
The various flood layers representing current and forecasted flood events, together with other
spatial layers (infrastructures such as buildings, roads, bridges, etc.; administrative boundaries)
are then be made available in near-real time through a web GIS platform. This platform is
similar to the “Flood Event Visualization and Damage Estimations” or Flood EViDEns
(http://evidens.csulidar1.info)” (Santillan et al., 2015). The platform was developed using a
combination of web mapping data storage, visualization and analysis tools like OpenLayers,
Geoserver, GeoDjango, Javascript, and PostgreSQL/PostGIS. An overview of the system is
depicted in Figure 3.
Figure 3. Overview of the NRT-Flood EViDEns.
3. RESULTS AND DISCUSSION
3.1 NRT-Flood EViDEns Web Platform
The system is currently hosted at the Caraga Center for Geo-Informatics, Caraga State
University, Butuan City. For the meantime, its web platform not yet accessible via the world-
wide web but there are already plans for it to be publicly available.
Figures 4 show the main web interfaces that can be viewed by a user accessing the system. When
accessing the system, the user has to option to choose either the current condition or the
forecasted conditions (e.g., in the next hours). For each option selected, the user can visualize
various flood characteristics (Figures 5-6). The user also has the option to view statistics for the
impacts of forecasted flood to structures (Figures 7-8).
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Figure 4. The main interface of NRT-Flood EViDEns.
Figure 5. Visualizing flood depth and flood recession time.
Figure 6. Visualizing flood velocity and flood arrival time.
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Figure 7. Example of statistics generated for estimating the impacts of a forecasted flooding to
structures.
Figure 8. Point-and-click assessment of impacts of a forecasted flooding to a structure.
3.2 Accuracy of Simulated Water Level, Flood Depths and Extents
To determine the level of accuracy of the flood information reported by NRT-Flood EViDens,
simulations of non-flood and flood conditions in Butuan City were conducted. Water levels
simulated by the model were compared with recorded data by automated water level sensors
(AWLS) (Figures 9-10). The accuracy of the flood depths and extents generated by the
platform was determined to range from 52 to 79% overall accuracies and Root Mean Square
Errors ranging from 0.30 to 0.58 m based on three historical flood events that were simulated.
Figures 11-13 provide graphical summaries of the accuracy assessments that were undertaken.
4. SUMMMARY AND CONCLUDING REMARKS
In this paper, the development and application NRT-Flood EViDEns was presented. The
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application has the capability to show detailed maps of current and forecasted flood
characteristics, including the capabilities to analyze and provide maps and statistics of the
impacts of flooding to various infrastructures such as buildings, roads and bridges. The flood
information reported by the platform are sourced from a two-dimensional flood model based
on HEC RAS 5 that utilizes high-resolution LiDAR data, satellite-derived land-cover, and
near-real time hydrological and meteorological data as among its vital inputs. The 2D flood
model simulates historical (last 24 hours), current, and future (next 24 hours) flood scenarios at
30-minute interval. A combination of web mapping data storage, visualization and analysis
tools are utilized to enable the user to perform flood characteristics visualization and spatial
overlay analysis for impact assessment.
Figure 9. Example simulated water surface elevation/level (WL) for “normal” or non-flooding
condition. The simulated levels were compared with data recorded by a WL sensor.
Figure 10. Example simulated water surface elevation/level (WL) for flooding condition.
Figure 11. Graphical summary of the flood model accuracy assessment using the December 2014
“Seniang” flood event.
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Figure 12. Graphical summary of the flood model accuracy assessment using the January 2014
“Agaton” flood event.
Figure 13. Graphical summary of the flood model accuracy assessment using the January 2017
flood event.
5. ACKNOWLEDGEMENTS
This paper is an output of the Geo-SAFER Agusan Project, one of the component projects of the
Geo-SAFER Mindanao Program (Geo-informatics for the Systematic Assessment of Flood
Effects and Risks towards a Resilient Mindanao), a research program supported and funded by
the Philippine Council for Industry, Energy and Emerging Technology Research and
Development of the Department of Science and Technology (PCIEERD-DOST). We gratefully
acknowledge PCIEERD-DOST for the financial support. LiDAR datasets used in this work was
provided by the DREAM and Phil-LiDAR 1 Programs of the University of the Philippines-
Diliman. Near-real time rainfall and water level datasets were provided by DOST-Advanced
Science and Technology Institute (ASTI).
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