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Combining Geospatial Data and Numerical Models to Map and Differentiate Flooding Extents Caused by Two Tropical Storms in the Philippines

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In the year 2014, heavy rains associated with tropical storms Lingling (known as Agaton in the Philippines; January 10-20, 2014) and Jangmi (locally known as Seniang; December 27, 2014 – January 1, 2014) triggered massive flooding and caused fatalities in many localities, particularly in Caraga Region in northeastern Mindanao. Based on ground data, the 10-day Agaton event brought a total rainfall of 922 mm, of which 221 mm where recorded on January 19 alone, a day before it dissipated to the southeast of the Philippines. On the other hand, the 6-day Seniang event brought a total rainfall of 356 mm, of which 259 mm where recorded on December 29 alone. To better understand and differentiate the impacts of heavy rains brought by these tropical storms to the extent of flooding, we reconstructed the two flooding events by combining geospatial data from remote sensing and field surveys with numerical modelling. We focused on the Cabadabaran River Basin (CBR) and the nearby Pandanon River and Caasinan River Watersheds in Agusan del Norte, Caraga Region as our case study area. First, we developed a HEC HMS-based hydrological model of the river basin using a 10-m Synthetic Aperture Radar (SAR) Digital Elevation Model (DEM) for sub-basin delineations, land-cover maps from Landsat 8 OLI images for model parameterization, and rainfall and discharge datasets for model simulation and validation. The purpose of the HEC HMS model was to determine the volume of water coming from the various sub-basins that drains into the floodplains during the storms. The discharge hydrographs were then used as inputs into a 2-dimensional flood model to simulate the movement of flood water along the rivers and in the floodplains and to map the areas that are flooded. The 2D model was developed using a 1-m resolution LiDAR-derived Digital Terrain Model (DTM) and Landsat-derived landcover maps as its major parameters. From the numerical model simulations and output flood maps, we found that the Agaton event produced more discharge and caused wider extent of flooding than the Seniang event. This result is consistent with the fact that rainfall during Agaton was greater in volume than during Seniang. More areas were also in low and medium flood hazard levels (0.1 – 1.5 m depth) during Agaton. However, areas in high hazard levels (>1.5 m depth) appeared to be similar in both events. The results of this study showed the importance of combining geospatial data and techniques with numerical models to reconstruct and understand past flooding events. The flood simulations and maps derived from this study can be useful not only in flood hazard mapping of the project area, but also as visual aids to help people understand the differences of the impacts of different tropical storms in the occurrence of flooding.
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Combining Geospatial Data and Numerical Models to Map and
Differentiate Flooding Extents Caused by Two Tropical Storms in the
Philippines
Jojene R. SANTILLAN and Meriam M. MAKINANO-SANTILLAN, Philippines
Key words: geospatial data, remote sensing, numerical modeling, flood, tropical storms,
Philippines
SUMMARY
In the year 2014, heavy rains associated with tropical storms Lingling (known as Agaton in
the Philippines; January 10-20, 2014) and Jangmi (locally known as Seniang; December 27,
2014 January 1, 2014) triggered massive flooding and caused fatalities in many localities,
particularly in Caraga Region in northeastern Mindanao. Based on ground data, the 10-day
Agaton event brought a total rainfall of 922 mm, of which 221 mm where recorded on January
19 alone, a day before it dissipated to the southeast of the Philippines. On the other hand, the
6-day Seniang event brought a total rainfall of 356 mm, of which 259 mm where recorded on
December 29 alone. To better understand and differentiate the impacts of heavy rains brought
by these tropical storms to the extent of flooding, we reconstructed the two flooding events by
combining geospatial data from remote sensing and field surveys with numerical modelling.
We focused on the Cabadabaran River Basin (CBR) and the nearby Pandanon River and
Caasinan River Watersheds in Agusan del Norte, Caraga Region as our case study area. First,
we developed a HEC HMS-based hydrological model of the river basin using a 10-m
Synthetic Aperture Radar (SAR) Digital Elevation Model (DEM) for sub-basin delineations,
land-cover maps from Landsat 8 OLI images for model parameterization, and rainfall and
discharge datasets for model simulation and validation. The purpose of the HEC HMS model
was to determine the volume of water coming from the various sub-basins that drains into the
floodplains during the storms. The discharge hydrographs were then used as inputs into a 2-
dimensional flood model to simulate the movement of flood water along the rivers and in the
floodplains and to map the areas that are flooded. The 2D model was developed using a 1-m
resolution LiDAR-derived Digital Terrain Model (DTM) and Landsat-derived landcover maps
as its major parameters. From the numerical model simulations and output flood maps, we
found that the Agaton event produced more discharge and caused wider extent of flooding
than the Seniang event. This result is consistent with the fact that rainfall during Agaton was
greater in volume than during Seniang. More areas were also in low and medium flood
hazard levels (0.1 1.5 m depth) during Agaton. However, areas in high hazard levels (>1.5
m depth) appeared to be similar in both events. The results of this study showed the
importance of combining geospatial data and techniques with numerical models to reconstruct
and understand past flooding events. The flood simulations and maps derived from this study
can be useful not only in flood hazard mapping of the project area, but also as visual aids to
help people understand the differences of the impacts of different tropical storms in the
occurrence of flooding.
13th South East Asian Survey Congress
Expanding the Geospatial Future
28th 31st July 2015
Marina Bay Sands, Singapore
2/12
Combining Geospatial Data and Numerical Models to Map and
Differentiate Flooding Extents Caused by Two Tropical Storms in the
Philippines
Jojene R. SANTILLAN and Meriam M. MAKINANO-SANTILLAN, Philippines
1. INTRODUCTION
In the year 2014, tropical
storms Lingling (known as
Agaton in the Philippines;
January 10-20) and Jangmi
(locally known as Seniang;
December 27 January 1,
2015) affected Mindanao in
Southern Philippines (Figure
1). Agaton brought
considerable rainfall over
several days to southern
Mindanao that caused flooding
and landslide incidents despite
not making landfall as a
tropical cyclone on the
Philippines (NDRRMC,
2014). Seniang, on the other
hand, made landfall over the
town of Hinatuan in the
province of Surigao del Sur.
Like Agaton, heavy rains
associated with Seniang
triggered massive flooding and
caused fatalities in many
localities, particularly in
Caraga Region in northeastern
Mindanao (NDRRMC, 2015).
Rainfall data recorded by one
of the rain gauges in the
region (Dugyaman-Anticala
Station in Agusan del Norte
province) showed that the 10-
day Agaton event brought a
total rainfall of 922 mm, of
which 221 mm where
recorded on January 19 alone,
Figure 1. Series of maps showing the study area which
consists of Cabadabaran River Basin and the watersheds
of Pandanon and Caasinan Rivers in Agusan del Norte,
Caraga Region, Mindanao, Philippines. The tracks of TS
Agaton and Seniang are also shown.
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a day before it dissipated to the southeast of the Philippines. On the other hand, the 6-day
Seniang event brought a total rainfall of 356 mm, of which 259 mm where recorded on
December 29 alone (PREDICT, 2014).
To better understand and differentiate the effects of heavy rains brought about by these
tropical storms to the extent of flooding, we reconstructed how flooding may have occurred
by combining geospatial data from remote sensing and field surveys with numerical
modelling. We focused on the Cabadabaran River Basin (CBR) and the nearby Pandanon
River and Caasinan River Watersheds in Agusan del Norte, Caraga Region as our case study
area (Figure 1). These areas (total of 238 km2) were reported to be one of those affected by
flooding during the onslaught of Agaton and Seniang.
2. METHODS
2.1 Overview of the Flood Reconstruction Process
The flood reconstruction process consisted of developing a hydrologic model of the river
basin, and a two-dimensional (2D) hydraulic model of the main river and its floodplain. The
purpose of the hydrologic model was to determine the volume of water coming from the
various sub-basins (also called watersheds) due to rainfall brought about by the tropical
storms. Rainfall depths recorded by rain gauges within and in the vicinity of the river basin
were used as input into the hydrologic model to compute discharge hydrographs for specific
locations in the river basin, specifically at those locations where the upstream watersheds ends
and the floodplain portions begin. The discharge hydrographs depict the volume of water per
unit time (in m3/s) that drains into the main river at these locations. These hydrographs,
together with rainfall data, were then used as inputs into the 2D hydraulic model to simulate
various processes such as the movement of water from the upstream watersheds into the main
river, as well as how its overflows from the rivers and travels into the flood plains. Storage
and movement of excess rainfall in the floodplains were also simulated by the 2D model. The
outputs of the 2D model were flood depth and inundation maps showing the depth and
progression of flooding through time.
The discharge hydrographs and the flood depth maps were analyzed and compared to see the
differences in flood characteristics and extent during Agaton and Seniang.
2.2 Geospatial Datasets Used
Various geospatial datasets were utilized in the development of these models (Figure 2). In
hydrologic model development, a 10-m Synthetic Aperture Radar (SAR) Digital Elevation
Model (DEM) was used for sub-basin delineations and for derivation of topography-related
parameters of the model such slope and elevation. Landsat 8 OLI images were also utilized to
derive a landcover map using Maximum Likelihood classification. The landcover map is
necessary for the derivation of land-cover-related model parameters such as Manning’s
surface roughness coefficient, and Curve Number for runoff volume computations. River
width and cross-section data obtained from field surveys as well as those extracted from 1-m
resolution LiDAR-derived Digital Terrain Model (DTM) were also used to estimate the
channel routing parameters of the model.
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For the 2D hydraulic
model, river bed
topography (obtained
from bathymetric
surveys), sea bed
topography (obtained
from a NAMRIA
topographic map),
LiDAR DTM, building
footprints (with top
elevation) extracted
from LiDAR Digital
Surface Model (DSM),
and the same land-cover
map derived from
Landsat 8 OLI image
were used as major
inputs. The river and sea
bed topographic datasets
and building footprints
were integrated into the
LiDAR DTM. These
integrations ensure that
the 2D model can
account for the effects of
river and sea bed
topography as well as
for the presence of
buildings in flow
simulation (e.g., in
computing depth, speed
and direction of flow).
In the absence of
observed tidal data,
predicted tidal data at
Butuan Bay was also
used as boundary
condition input into the
2D model to account for
the effects of tide.
Rainfall data corresponding to the Agaton and Seniang events were downloaded from the
ASTI DOST PREDICT server (http://repo.pscigrid.gov.ph/predict; PREDICT, 2014) and
utilized in hydrologic and 2D hydraulic model simulations. These data were recorded by two
rainfall stations (Figure 1), one in the upstream (Dugyaman-Anticala Station) and another one
in the downstream/floodplain (Cabadbaran City Hall Station).
Figure 2. Some of the geospatial datasets used in hydrologic and
hydraulic modelling to reconstruct flooding during Agaton and
Seniang.
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2.3 Hydrologic Model Development
The hydrologic model was developed using HEC HMS Version 3.5. It has four sub-models to
simulate the basic hydrologic processes of runoff generation from rainfall, its transformation
and combination with baseflow, and its routing towards the outlet (USACE, 2000). These four
components are (i) infiltration loss using the SCS CN method, (ii) direct runoff using the SCS
Unit Hydrograph method, (iii) baseflow using the Exponential Baseflow Recession method,
and (iv) channel routing using the Muskingum-Cunge method. Modeling in HEC-HMS relies
in three specific components: a basin model, a meteorological model, and a set of control
specifications. The basin model is the physical representation of watersheds (termed as “sub-
basin” in HEC HMS) and river systems into hydrological elements, each one configured with
its proper method for the simulation of hydrologic processes. A meteorological model consists
of a time series data of rainfall used for the simulation. The set of control specifications
determines the simulation time step and period or duration. HEC-HMS’s preprocessor, HEC-
GeoHMS (version 1.1), was used to prepare the basin model file using the geospatial datasets
mentioned in the previous section. HEC-GeoHMS is an extension of ArcView GIS software
that allows users to visualize spatial information, document watershed characteristics, perform
spatial analysis, delineate watershed boundaries, and construct inputs to hydrologic models
(USACE, 2003). The basin model file was imported into HEC HMS 3.5 for further model
development (e.g., for the setup of a meteorological model, and control specifications), and
for hydrologic simulations.
Three simulations were implemented: January 9-14, 2014 (for hydrologic model validation),
January 19 20, 2014 (Agaton event), and December 29-30, 2014 (Seniang event). Discharge
hydrographs computed by the model for Cabadabaran Bridge, Cabadbaran Upstream, and
Pandanon Upstream ((see Figure 2 for their locations) were then used in the analysis and as
inputs into the 2D hydraulic model.
Figure 3. The interface of HEC HMS hydrologic model of the study area.
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Before using the HEC HMS hydrologic
model in simulating discharge
hydrographs during Agaton and Seniang
events, a preliminary simulation was
first conducted to verify the accuracy of
the simulated hydrograph by comparing
it with actual discharge hydrograph
measured at Cabadbaran Bridge. As
shown in Figure 4, it can be said that the
hydrologic model performed
satisfactorily, although the simulated
hydrograph overestimated peak
discharge and the time of peak is
simulated earlier than the observed time
of peak. The comparison between the
observed and simulated hydrographs
revealed a Nash-Sutcliffe Model of
Efficiency of 0.72 indicating that the
hydrologic model can simulate discharge hydrographs within acceptable levels of accuracy
(Moriasi et al., 2007).
2.4 2D Hydraulic Model Development
We used a trial version of Flood Modeller Pro (CH2M Hill, 2015) in the 2D simulation of
flooding during Agaton and Seniang. The 2D model utilized all applicable geospatial datasets
mentioned in sub-section 2.2. The model computational domain (~45.53 km2) focused on the
floodplains of Cabadbaran, Pandanon and Caasinan Rivers (e.g., all areas downstream of
CBR Upstream and Pandanon Upstream as shown in Figure 2) including a portion of Butuan
Bay. The purpose of the 2D model was to route the discharge hydrograph from the upstream
watersheds into the main rivers and floodplains. Rain falling in the floodplain was likewise
routed. The model was used to determine whether the volume of water coming from the
upstream watersheds will cause overflowing of the main rivers, and if it overflows, where it
will go and for how long. In the case of rain falling in the floodplain, the model was also used
to determine whether rainwater will get infiltrated, or get stagnant (e.g., in a depression), or
continue to flow toward areas of lower elevation. For the simulations, rainfall recorded at the
Cabadbaran City Hall Station was used. Technical details on how Flood Modeller Pro does
the 2D simulation is available in the software’s website (CH2M Hill, 2015). The 2D model
was used to generate hourly flood depth and extent for the following periods: January 19 20,
2014 (Agaton event), and December 29-30, 2014 (Seniang event). Based on the hourly
outputs, maximum flood depth and extent grids were generated at 1-m spatial resolution.
2.5 Analyzing the Differences in Simulated Flooding Characteristics during Agaton and
Seniang
The maximum flood depth grids were further analyzed to categorize depths into flood hazard
levels (low, medium and high). Low flood hazards are those areas with maximum flood
depths ranging from 0.10 0.50 m; medium: 0.5 < depth ≤ 1.50; and high: depth > 1.50 m).
Figure 4. Graph showing the observed and HEC
HMS hydrologic model simulated discharge
hydrographs at Cabadbaran Bridge for the January
9-14, 2014 simulation period.
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The differences in the flooded areas according to hazard levels were then determined for
Agaton and Seniang events. The bay/sea portion of the 2D model domain was not included in
the computation of the flooded area statistics (i.e., the area considered in the computation is
only 34.61 km2 out of the 45.53 km2).
3. RESULTS AND DISCUSSION
3.1 Simulated Discharge Hydrographs during Agaton and Seniang
Shown in Figure 5 are the discharge hydrographs simulated by the HEC HMS model for
Cabadbaran Upstream and Pandanon Upstream for the Agaton and Seniang events. The model
simulation results are summarized in Table 1. The hydrologic simulation results suggest that
the Seniang event has a higher peak discharge compared to that of Agaton. However, the
Agaton event produced more discharge than the Seniang event. The total volume of discharge
entering the floodplains was computed by the model as 21,570 x 103 m3 during Agaton, which
is 1,849 x 103 m3 higher than that during Seniang.
Table 1. Summary of HEC HMS model simulation of discharge for Cabadbaran Upstream
and Pandanon Upstream for the Agaton and Seniang events.
Event
Peak Discharge (m3/s)
Discharge Volume, m3
Upstream
Cabadbaran
Upstream
Pandanon
Upstream
Cabadbaran
Upstream
Pandanon
Total
Volume
Agaton
433
40
19,758 x 103
1,812 x 103
21,570 x 103
Seniang
483
51
17,851 x 103
1,870 x 103
19,721 x 103
3.2 Simulated Maximum Flood Depths and Extents during Agaton and Seniang
Shown in Figure 6 are the maximum flood depth maps simulated by the 2D model. These
maps show the areas that have been flooded, including the maximum depth of flood within
the duration of the simulation periods. It can be observed that the flooding due to Agaton
event have a greater extent compared to that of Seniang. A common characteristic between
the two events is that flooding in the study area was not only due to the overflowing of the
Cabadbaran, Pandanon and Caasinan Rivers. Model results show that some of the flooded
areas were due to storage of rainfall. This is evident in the Cabadbaran City proper.
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Figure 6. Simulated maximum flood depth maps for the Agaton and Seniang events.
3.3 Differences in Flood Extent and Hazard Levels during Agaton and Seniang
The maximum flood depth maps categorized into low, medium and high hazard levels are
shown Figure 7. Analysis of these maps show that the Agaton event flooded 12.23 km2 of the
2d model domain (excluding the bay/sea portion), which is 1.27 km2 higher than the area
flooded during Seniang (Figure 8). Areas under low and medium hazard levels were also
higher during Agaton. However, there was a similarity in areas under high hazard levels, with
both events submerging approximately 1.4 km2 of land areas with depths greater than 1.5 m.
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Figure 7. Simulated maximum flood hazard maps for the Agaton and Seniang events.
Figure 8. Summary of flooded areas during Agaton and Seniang categorized into hazard
levels. The statistics does not include bay/sea portion of the 2D model domain.
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4. CONCLUDING COMMENTS
From the numerical model simulations and output flood maps, we found that the Agaton event
produced more discharge, and caused a wider extent of flooding than the Seniang event. This
result is consistent with the fact that rainfall during Agaton was greater in volume than during
Seniang. More areas were also in low and medium flood hazard levels during Agaton.
However, areas in high hazard levels appeared to be similar in both events.
The results of this study showed the importance of combining geospatial data and techniques
with numerical models to reconstruct and understand past flooding events. The flood
simulations and maps derived from this study can be useful not only in flood hazard mapping
of the study area, but also as visual aids to help people understand the differences of the
impacts of different tropical storms in the occurrence of flooding.
5. ACKNOWLEDGEMENTS
This work is an output of the “Phil-LiDAR 1.2.14: LiDAR Data Processing and Validation:
Caraga Region” or “CSU LiDAR 1” project under the “Phil-LiDAR 1. Hazard Mapping of the
Philippines using LiDAR” program funded by the Philippine Council for Industry, Energy
and Emerging Technology Research and Development of the Department of Science and
Technology (PCIEERD-DOST). We thank PCIEERD-DOST for the financial support. The
SAR DEM and the LiDAR DTM and DSM used in this work were provided by the University
of the Philippines Disaster Risk and Exposure for Mitigation (UP DREAM) Program. We
thank UP DREAM Program Leader, Dr. Enrico C. Paringit, for these datasets. We also thank
CH2M Hill for providing us a trial version of Flood Modeller Pro.
REFERENCES
CH2M Hill, 2015. Flood Modeller Pro. https://www.floodmodeller.com/
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., Veith, T. L.,
2007. Model evaluation guidelines for systematic quantification of accuracy in watershed
simulations. Transactions of the ASABE, vol. 50, pp. 885-900.
NDRRMC, 2014. NDRRMC Updates Sitrep No. 33 re: Effects of Tropical Depression
Agaton. National Disaster Risk Reduction and Management Council. February 1, 2014.
Available online at http://www.ndrrmc.gov.ph/.
NDRRMC, 2015. SitRep No. 22 re Effects of Tropical Storm SENIANG". National Disaster
Risk Reduction and Management Council. January 10, 2015. Available online at
http://www.ndrrmc.gov.ph/.
PREDICT, 2014. Rainfall Data of Dugyaman-Anticala and Cabadbaran City Hall Stations,
Agusan del Norte. Philippine Real-Time Environment Data Acquisition and Interpretation for
Climate-Related Tragedy Prevention and Mitigation (Predict), Advance Science and
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Technology Institute, Philippine Atmospheric, Geophysical, and Astronomical Services
Administration, Department of Science and Technology, Philippines. Available online at
http://repo.pscigrid.gov.ph/predict.
USACE, 2000. Hydrologic Modeling System HEC-HMS Technical Reference Manual,
United States Army Corps of Engineers, Hydrologic Engineering Center, Davis, California.
USACE, 2003. Geospatial Hydrologic Modelling Extension HEC-GeoHMS User’s Manual,
Version 1.1. , United States Army Corps of Engineers, Hydrologic Engineering Center, Davis,
California.
BIOGRAPHY
Engr. Jojene R. Santillan is Chief Science Research Specialist of the CSU LiDAR 1 Project of
the College of Engineering and Information Technology,Caraga State University (CSU) in
Butuan City, Agusan del Norte, Mindanao, Philippines since April 2014. He obtained his
Master of Science in Remote Sensing degree as well as a Bachelor’s degree in Geodetic
Engineering from the University of the Philippines in Diliman, Quezon City in 2008 and
2004, respectively. Before transferring to Caraga State University, Engr. Santillan worked as
a researcher at the Research Laboratory for Applied Geodesy and Space Technology of the
Department of Geodetic Engineering & Training Center for Applied Geodesy and
Photogrammetry, University of the Philippines-Diliman from 2005 to 2014. His research
interests include remote sensing (RS) and GIS-based environmental monitoring and modeling,
natural resources mapping using RS technology, and numerical modeling and simulation with
RS and GIS.
Engr. Meriam M. Makinano-Santillan is Associate Professor II in the College of Engineering
and Information Technology of the Caraga State University (CSU) in Butuan City, Agusan
del Norte, Mindanao, Philippines. In 2010, she obtained her Master of Science in Remote
Sensing degree from the University of the Philippines in Diliman, Quezon City. She
also has a Bachelor of Science degree in Geodetic Engineering that she earned in 2003 from
the former Northern Mindanao State Institute of Science of Technology (now CSU). Engr.
Makinano-Santillan heads the CSU LiDAR 1 Project, one of the component projects of the
“Phil-LiDAR 1 Hazard Mapping of the Philippines using LiDAR” program funded by
the Philippines’ Department of Science and Technology.
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CONTACTS
Engr. Jojene R. Santillan
CSU LiDAR 1 Chief Science Research Specialist
College of Engineering and Information Technology
Caraga State University
Ampayon
Butuan City
Philippines
Email: santillan.jr2@gmail.com
Engr. Meriam M. Santillan
CSU LiDAR 1 Project Leader
College of Engineering and Information Technology
Caraga State University
Ampayon
Butuan City
Philippines
Email: meriam.makinano@gmail.com
... The hydrologic model, based on the Hydrologic Engineering Center Hydrologic Modeling System (HEC HMS) Version 3.5, was used to compute how much volume of water is produced in the river basin during the occurrence of an extreme rainfall event; the hydraulic model (using a trial version of Flood Modeller Pro), was then used to simulate how these volume of water travels in rivers and in various locations within the river basin, including how it overflows from the rivers and floods nearby areas. The reader is referred to [4,6] for more details on how the flood simulations models were developed, including the various geospatial datasets used during the development process as well as its accuracy. For the flood scenario modeling, the hydrologic model was used to generate discharge hydrographs corresponding to 3 hypothetical, extreme rainfall events corresponding to return periods of 25, 50, and 100 years. ...
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LiDAR-derived Digital Terrain and Surface Models (DTM/DSM) and flood simulation models were used to determine exposure and vulnerability of building to various flood hazard scenarios caused by extreme rainfall events in a river basin in Mindanao, Philippines. The methodology consist of (i.) generating a database of buildings from the DTM and DSM; (ii.) generation of flood depth and hazard maps through the use of a flood simulation model; and (iii.) spatial overlay analysis utilizing the building database and flood maps to determine a building " s exposure and vulnerability. This study highlights the importance of combining high spatial information from LiDAR with simulation model to generate informative maps showing the exposure and vulnerability of buildings to flooding.
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Watershed models are powerful tools for simulating the effect of watershed processes and management on soil and water resources. However, no comprehensive guidance is available to facilitate model evaluation in terms of the accuracy of simulated data compared to measured flow and constituent values. Thus, the objectives of this research were to: (1) determine recommended model evaluation techniques (statistical and graphical), (2) review reported ranges of values and corresponding performance ratings for the recommended statistics, and (3) establish guidelines for model evaluation based on the review results and project-specific considerations; all of these objectives focus on simulation of streamflow and transport of sediment and nutrients. These objectives were achieved with a thorough review of relevant literature on model application and recommended model evaluation methods. Based on this analysis, we recommend that three quantitative statistics, Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and ratio of the root mean square error to the standard deviation of measured data (RSR), in addition to the graphical techniques, be used in model evaluation. The following model evaluation performance ratings were established for each recommended statistic. In general, model simulation can be judged as satisfactory if NSE > 0.50 and RSR < 0.70, and if PBIAS + 25% for streamflow, PBIAS + 55% for sediment, and PBIAS + 70% for N and P. For PBIAS, constituent-specific performance ratings were determined based on uncertainty of measured data. Additional considerations related to model evaluation guidelines are also discussed. These considerations include: single-event simulation, quality and quantity of measured data, model calibration procedure, evaluation time step, and project scope and magnitude. A case study illustrating the application of the model evaluation guidelines is also provided.
Geophysical, and Astronomical Services Administration, Department of Science and Technology, Philippines
  • Technology Institute
  • Philippine Atmospheric
Technology Institute, Philippine Atmospheric, Geophysical, and Astronomical Services Administration, Department of Science and Technology, Philippines. Available online at http://repo.pscigrid.gov.ph/predict.
He obtained his Master of Science in Remote Sensing degree as well as a Bachelor's degree in Geodetic Engineering from the University of the Philippines in Diliman His research interests include remote sensing (RS) and GIS-based environmental monitoring and modeling
  • Engr
  • R Jojene
Engr. Jojene R. Santillan is Chief Science Research Specialist of the CSU LiDAR 1 Project of the College of Engineering and Information Technology,Caraga State University (CSU) in Butuan City, Agusan del Norte, Mindanao, Philippines since April 2014. He obtained his Master of Science in Remote Sensing degree as well as a Bachelor's degree in Geodetic Engineering from the University of the Philippines in Diliman, Quezon City in 2008 and 2004, respectively. Before transferring to Caraga State University, Engr. Santillan worked as a researcher at the Research Laboratory for Applied Geodesy and Space Technology of the Department of Geodetic Engineering & Training Center for Applied Geodesy and Photogrammetry, University of the Philippines-Diliman from 2005 to 2014. His research interests include remote sensing (RS) and GIS-based environmental monitoring and modeling, natural resources mapping using RS technology, and numerical modeling and simulation with RS and GIS.
Makinano-Santillan is Associate Professor II in the College of Engineering and Information Technology of the Caraga State University (CSU) in Butuan City Agusan del Norte, Mindanao, Philippines
  • Engr
  • M Meriam
Engr. Meriam M. Makinano-Santillan is Associate Professor II in the College of Engineering and Information Technology of the Caraga State University (CSU) in Butuan City, Agusan del Norte, Mindanao, Philippines. In 2010, she obtained her Master of Science in Remote Sensing degree from the University of the Philippines in Diliman, Quezon City. She also has a Bachelor of Science degree in Geodetic Engineering that she earned in 2003 from the former Northern Mindanao State Institute of Science of Technology (now CSU). Engr.
NDRRMC Updates Sitrep No. 33 re: Effects of Tropical Depression Agaton. National Disaster Risk Reduction and Management Council
NDRRMC, 2014. NDRRMC Updates Sitrep No. 33 re: Effects of Tropical Depression Agaton. National Disaster Risk Reduction and Management Council. February 1, 2014. Available online at http://www.ndrrmc.gov.ph/.
Rainfall Data of Dugyaman-Anticala and Cabadbaran City Hall Stations Agusan del Norte. Philippine Real-Time Environment Data Acquisition and Interpretation for Climate-Related Tragedy Prevention and Mitigation (Predict) Advance Science
PREDICT, 2014. Rainfall Data of Dugyaman-Anticala and Cabadbaran City Hall Stations, Agusan del Norte. Philippine Real-Time Environment Data Acquisition and Interpretation for Climate-Related Tragedy Prevention and Mitigation (Predict), Advance Science and 11/12
Geospatial Hydrologic Modelling Extension HEC-GeoHMS User's Manual, Version 1.1. , United States Army Corps of Engineers
USACE, 2003. Geospatial Hydrologic Modelling Extension HEC-GeoHMS User's Manual, Version 1.1., United States Army Corps of Engineers, Hydrologic Engineering Center, Davis, California.
SitRep No. 22 re Effects of Tropical Storm SENIANG". National Disaster Risk Reduction and Management Council
NDRRMC, 2015. SitRep No. 22 re Effects of Tropical Storm SENIANG". National Disaster Risk Reduction and Management Council. January 10, 2015. Available online at http://www.ndrrmc.gov.ph/.
Hydrologic Modeling System HEC-HMS Technical Reference Manual, United States Army Corps of Engineers
USACE, 2000. Hydrologic Modeling System HEC-HMS Technical Reference Manual, United States Army Corps of Engineers, Hydrologic Engineering Center, Davis, California.
Santillan worked as a researcher at the Research Laboratory for Applied Geodesy and Space Technology of the Department of Geodetic Engineering & Training Center for Applied Geodesy and Photogrammetry, University of the Philippines-Diliman from
  • Engr
  • R Jojene
Engr. Jojene R. Santillan is Chief Science Research Specialist of the CSU LiDAR 1 Project of the College of Engineering and Information Technology,Caraga State University (CSU) in Butuan City, Agusan del Norte, Mindanao, Philippines since April 2014. He obtained his Master of Science in Remote Sensing degree as well as a Bachelor's degree in Geodetic Engineering from the University of the Philippines in Diliman, Quezon City in 2008 and 2004, respectively. Before transferring to Caraga State University, Engr. Santillan worked as a researcher at the Research Laboratory for Applied Geodesy and Space Technology of the Department of Geodetic Engineering & Training Center for Applied Geodesy and Photogrammetry, University of the Philippines-Diliman from 2005 to 2014. His research interests include remote sensing (RS) and GIS-based environmental monitoring and modeling, natural resources mapping using RS technology, and numerical modeling and simulation with RS and GIS.