Flood Modeling of Imus River Basin, Cavite, Philippines at Different Climate and Land Cover Change Scenarios

doi:10.21203/rs.3.rs-2584352/v1

The Imus River contributes to the flooding in the lowland areas of Cavite province. This study aimed to assess the flood risk of the Imus River Basin at different rainfall and land use change scenarios. The hydrologic model of the upstream basin was developed in Hydrologic Engineering Center (HEC) - Hydrologic Modeling System (HMS) and subjected to calibration and validation. The simulated hydrograph fitted very well with observed data as indicated by the NSE, Percent Bias (PBIAS), and Ratio of Root Mean Square Error (RSR) values of 0.903, -0.0376, and 0.3, respectively during calibration, and 0.818, -0.044, and 0.40, respectively after validation. Sensitivity analysis showed that the storage coefficient had the greatest effect while the initial abstraction had the least effect on the model results. With a constant increase of 2.08% in built-up areas from 2015 to 2025 based on the land cover surveys, the peak discharge increased by 2.33% and runoff volume by 1.86% on average. The downstream hydraulic model was done using in Hydrologic Engineering Center (HEC) – River Analysis System (RAS) and flood maps for normal and climate change-affected conditions were generated to show the extent of flooding. Areas affected by the flood were those situated in the cities of Imus and Bacoor and some portion of the municipality of Kawit.

flood

HEC-HMS

HEC-RAS

climate change

land use change Imus River Basin

Floods are one of the most destructive phenomena affecting human lives. Atmospheric processes are responsible for precipitation-driven flooding which occurs when rainfall exceeds the average infiltration capacity of dry land [1]. Many parts of the Philippines are characterized by well-defined wet seasons due to monsoons and other precipitation-producing phenomena making them more vulnerable to potentially disastrous flooding that destroys properties, livelihoods and even causes deaths [2]. The number of flood events in Asia was higher during the start of the 21st century compared to other natural disasters such as earthquakes, drought, volcano eruption, famine, landslides, and wildfires. The top ten most frequently flooded countries in Asia are Pakistan, Vietnam, Sri Lanka, Thailand, Iran, Bangladesh, Philippines, Indonesia, China, and India. China is the most frequently affected country by floods followed by India [3]. Developing countries like the Philippines are extremely vulnerable to these kinds of disasters because most of the river basins lack baseline data for flood risk assessment [4]. The accelerated change in the climate system adds concerns on the viability of our mitigation efforts for the next few decades. Local assessment is necessary to plan for strategic adaptation due to the varying degree of climate change in every region of the world [5]. The Intergovernmental Panel on Climate Change (IPCC) recognized that climate change has detectably influenced several water-related variables that contribute to floods, such as rainfall and snowmelt [6].

Floods are still not fully understood despite their great effect on economic and social sustainability. Identifying flood-prone areas is very important to prevent and mitigate the adverse effects of floods. Flood maps help in recording the locations of inundated areas and provide detailed information about the characteristics of historical flood events making it very important for risk management [7]. Hydrologic modeling is an indispensable tool in the study of hydrologic processes and the impact of natural variability and anthropogenic factors on the hydrologic systems [8]. Global and local climate models, on the other hand, are also very helpful tools to obtain relevant and robust climate projections for quantifying regional-scale changes and impacts [9]. Combining hydrologic modeling and climate projections will improve local baseline data for the assessment of potential flooding and will further improve spatial planning that provides reliable and relevant risk management. Improved spatial planning can also avoid or lessen the risk by influencing factors such as location, type, design, and function of development [10]. Flood models of a Quanali A Watershed in Albay, Philippines was done by analyzing the upstream watershed hydrology using a hydrologic model to estimate the respective runoff of a rainfall event, while the downstream watershed is analyzed through a hydraulic model to determine the river and floodplain response to runoff [11]. In the case of Po River in Northern Italy, the assessment of flood prone areas was done through integrated hydrological and hydraulic modeling approach. Runoff data obtained from the hydrologic model was used to developed Synthetic Design Hydrographs for different return period and subsequently processed using a hydraulic model to calculate the flow over the digital elevation model of the area and considering the channel geometry [12]. The flood maps developed using hydraulic modeling of Medjerda Basin in Norther Tunisia was validated using a Sentinel – 1 radar image showing the real flood extent in order to further evaluate performance of the model [13]. The main effect of urbanization is the drastic change in land use and land cover which increase the impervious surface area in terms of built-up areas such as building, roads, and other structures and affects the runoff process and increase in flood peaks at downstream of the rivers [14]

The main objective of this study was to assess the flood risk of the Imus River Basin at different rainfall and land cover change scenarios. It specifically aimed to (1) develop a hydrologic model for the Imus River Basin using HEC-HMS; (2) conduct parametric sensitivity analysis for the hydrologic model; (3) assess the response of the hydrologic model with respect to changing land cover; (4) develop a hydraulic model of the flood plain of Imus River using HEC-RAS, and; (5) generate flood inundation maps for 5-year, 10-year, 25-year, 50-year, and 100-year return periods under normal and climate change-affected conditions.

Data Gathering

The Imus River Basin is located in the province of Cavite having a total drainage area of 105 km² covering the municipalities of Silang, General Trias, Kawit, Noveleta, and the cities of Tagaytay, Dasmarinas, and Imus [15]. Its main stem has a total length of 38.4 km extending from Tagaytay and drains to Bacoor Bay. It is vulnerable to flooding due to its low ground elevation along the coastal area in the lower reaches and insufficient flow capacity of the river channel. Intensive industrialization in the area without adequate consideration about floods increases the potential risk [16]. The main problem in the Imus River Basin is the rapid increase in water level during typhoons and heavy rainfall [15].

Rainfall from an automatic rain gauge located at Dasmarinas, Cavite, and streamflow data from an automatic water level station at Daang Hari Bridge, Imus, Cavite were acquired from DOST-ASTI. The bridge divides the basin into the downstream and upstream of the river. Rainfall data readings (mm) are recorded at 15 min intervals, while water level data (m) are at 10 min intervals. A rating curve developed at the Daang Hari Bridge [15] was used to translate the water level readings observed into streamflow discharge. The Rainfall Intensity Duration Frequency (RIDF) data at Sangley Point, Cavite City obtained from PAGASA was used to develop a 24-h duration storm of different rainfall intensities at different return periods. Soil and land cover maps were acquired from the National Mapping and Resource Information Authority (NAMRIA) and processed using GIS.

Hydrologic Modeling

The hydrologic model of the upstream Imus River Basin was created in HEC-HMS using the 5-m resolution DEM and divided into subbasins to make the model more comprehensive, particularly in flood peak computation. The SCS Curve Number, Clark Unit Hydrograph, Exponential Recession, and Muskingum Routing were the models used for the loss, transform, base flow, and routing models, respectively [17–24]. Values of curve number, percent imperviousness, time of concentration, storage coefficient, recession constants, and Muskingum routing constants were initially estimated.

Rainfall data from a given storm event was entered into the HEC-HMS time series manager to simulate flow at the outlet, which was then compared with the actual observed flow data. Estimated parameters values for each model were then iteratively adjusted through manual and autocalibration process to attain the best fit between the simulated and observed hydrographs. The calibrated model was further validated using an independent data set from a different storm. Three statistical measures of accuracy, Nash-Sutcliffe efficiency (NSE), Percent bias (PBIAS), and RMSE-observations standard deviation ratio (RSR) [25], were used for watershed model evaluation. To further verify the accuracy of the model results, the flood inundation map developed from the simulated hydrograph of Storm Jolina and Storm Falcon were compared to the actual satellite image obtained from Sentinel-2: MultiSpectral Instrument, Level 2A.

A sensitivity analysis was done to determine which among the parameters had the greatest effect on the results of the model. Local sensitivity analysis was done in the study using the relative sensitivity coefficient, given by the formula [26–28]:

where Sr is the relative coefficient, ΔO is the difference between outputs, ΔP is the difference between parameter inputs, P is the average of initial and final parameter inputs, and O is the verage of initial and final outputs.

A relative sensitivity coefficient [26–28] was used to compare the sensitivity of each parameter on different hydrograph properties such as peak discharge (Q_p), time to peak (T_p), and runoff volume (V). The rainfall data of a 5-yr storm developed from the RIDF curve was used while the calibrated and validated parameters served as the nominal inputs. These nominal inputs were adjusted by ± 10%, ± 20%, and ± 30% in a series of simulation run to observe changes in the simulated hydrograph. The procedure was performed to subbasin 2 and reach 1 because it is located at the center of the upstream Imus River Basin and serve as a representative of the basin’s hydroclimatic regime [27].

Model Response to Land Cover Change

The effect of land cover change upstream on the extent of flooding downstream of Imus River Basin was also assessed by determining the change in the peak discharge and runoff volume. The curve number and percent impervious areas were adjusted using the projected rate of increase of built-up areas in the province of Cavite as presented in the Regional Physical Framework Plan (2017–2046) of CALABARZON based on 2003, 2010, and 2015 Land Cover maps [29]. These parameters were adjusted per subbasin while the other parameters remained constant during the process. The simulation run was done using the rainfall data at the normal condition for 5-, 10-, 25-, 50-, and 100-year rainfall scenarios as the baseline.

Hydraulic Modeling and Flood Inundation Mapping

Using the generated synthetic hydrographs for the different return periods at normal conditions and based on PAGASA climate projections [6, 30], hydraulic modeling for the river basin was done through 2D unsteady flow analysis in HEC-RAS. The flood model showed a simulated water level at each cross-section for every time step and flood simulation created [31, 32]. The resulting model was used in determining the flooded areas caused by the discharge of water from the upstream of the Imus River Basin. A raster file of the maximum depth of flood per return period was exported from HEC-RAS and processed as flood maps.

Hydrologic Model

The hydrologic model of the upstream Imus River Basin is composed of 10 subbasins, 7 reaches, and 7 junctions (Figure 1). It was calibrated using the rainfall data from Storm Jolina (Figure 2) that occurred on August 25, 2017. The simulated discharge from August 25, 2017, 15:00 to August 26, 2017, 15:00 was then generated using the estimated baseline parameters and compared with the actual hydrograph observed at the Daang Hari Bridge (Figure 3).

The baseline parameters were then adjusted both manually and by autocalibration using the Optimization Manager of HEC-HMS. The new simulation run of the calibrated model (Figure 4) was tested with NSE, PBIAS, and RSR values of 0.903, -0.0376, and 0.30, respectively, indicating a very good fit between the observed data and simulated results but with a slight overestimation bias [25].

The calibrated model was then subjected to validation using the rainfall data from Storm Falcon (Figure 5) that occurred on July 17, 2019. The simulated hydrograph was compared to the observed flow at the Daang Hari Bridge from July 17, 2019, 00:00 to July 18, 2019, 00:00 as shown in Figure 6. The model obtained NSE, PBIAS, and RSR scores of 0.818 (very good fit), -0.044 (near-optimal, overestimation biased), and 0.4 (better model simulation), respectively. Upon validation, the hydrologic model of the upstream Imus River Basin performed well and can be used for accurate simulation of streamflow in the river basin.

Generated flood maps for Storm Jolina and Storm Falcon were compared to satellite images of the actual flooding obtained from Sentinel-2: MultiSpectral Instrument, Level 2A to verify the accuracy of the model as shown in Figures 7 and 8. Both storms brought only low-intensity rainfall causing low runoff from the upstream basin. Storm waters traveled down the river and filled the wetlands near the coast and did not cause much flooding on the areas located adjacent to the river.

Parametric Sensitivity Results

The initial abstraction (I_a), time of concentration (T_c), storage coefficient (R), recession constant (R_c), and initial discharge (Q_i) were the parameters tested for their sensitivity to the results of the model. Based on the sensitivity test, changes in I_a did not affect both the peak discharge (Q_p) and the time to peak (T_p) and caused a very small variation to the runoff volume (V). Changes in R_c and Q_i did not also affect Q_p, T_p and V. Changes in the time of concentration did not affect peak discharges but affected both the time to peak and total runoff volume. The T_c showed a direct relationship with T_p and inverse relation to V. On average, a ±10% change on T_c caused a 30-min advance or delay on T_p, and 0.24 mm (12,204 m³) increase or decrease in V. Storage coefficient is inversely proportional to Q_p and V, and directly proportional to T_p. On average, a ±10% change in R caused a 1.2 m³/s increase or decrease on Q_p, a 40-min advance or delay on T_p, and a 2.90 mm (147,637 m³) increase or decrease in V. Figure 9 shows generated hydrographs indicating the model sensitivity to each parameter.

Summary of the relative sensitivity coefficient of each parameter to the hydrograph properties of the model output was shown in Table 1. The relative sensitivity coefficient was used to facilitate the comparison between the parameters. A positive value indicates a direct relationship between the parameter and model output, and vice versa. The higher the absolute value, the greater is the effect of the parameter on the model output. Based on the analysis, the storage coefficient (R) had the greatest effect for all the three model outputs.

Table 1. Computed relative sensitivity coefficient for each parameter in respect to the hydrograph properties.

Parameter	Relative sensitivity (s_r)
Parameter	Q_p	T_p	V
I_a	0.000	0.000	-0.006
T_c	-0.009	0.219	-0.016
R	-0.690	0.306	-0.198
R_c	0.000	0.000	0.000
B_i	0.000	0.000	0.000

Model Response to Land Cover Change

The projected change in land cover was demonstrated in terms of an increase in built-up areas only. Based on the 2010 and 2015 survey, the rate of built-up expansion was 2.08% annually. This modest rate of increase was assumed to continue for the next 10 years from the 2015 survey indicating a little over 20% increase from 2015 to 2025.

Results showed that the peak discharge may increase by 2.33% (Table 2) and runoff volume by 1.86% (Table 3) on average in the river basin. This increase was brought by the urbanization in the area that resulted in more impervious areas in the basin such as roofing, roads, sidewalks, pavements, and other artificial structures that caused less infiltration and hence greater flood peak and runoff [33 ,34].

Table 2. Change in peak discharge (Q_p) in m³/s due to the land cover change.

Return period	2015	2025	Percent change
5-year	91.00	93.80	3.08
10-year	111.70	114.70	2.69
25-year	138.50	141.50	2.17
50-year	157.70	160.80	1.97
100-year	177.10	180.20	1.75
Mean				2.33

Table 3. Change in runoff volume (V) in mm due to the land cover change.

Return period	2015	2025	Percent change
5-Year	154.17	157.98	2.47
10-Year	193.85	197.92	2.10
25-Year	246.09	250.42	1.76
50-Year	283.98	288.47	1.58
100-Year	322.61	327.33	1.46
Mean			1.87

Simulation of Hydrograph for Normal and Climate-affected Conditions

Based on the climate projections of PAGASA, there will be a 12.67% and 15.67% increase from the normal annual rainfall amount in the province for RCP 4.5 (moderate-level risk) and RCP 8.5 (high-level risk), respectively. Using the normal and projected climate change hyetographs, synthetic hydrograph for each storm return period were generated at the Daang Hari Bridge. The simulation period was three days with a 10-minute time interval. Figure 10 shows the synthetic hydrograph for each return period under normal conditions while Figure 11 and Figure 12 show the synthetic hydrographs at different return periods under RCP 4.5 and RCP 8.5, respectively.

Hydraulic Model and Flood Inundation Maps

Areas affected by the flood were those situated in the cities of Imus and Bacoor as well as some portion of the municipality of Kawit. The majority of the higher portion of the floodplain may experience maximum flood depth ranging from 0.21 m to 1.0 m. The lowest portions of the floodplain which extends to the coast are considered as the most susceptible with possible maximum flood depth ranging from 1.01 to 3.5 m. Intensified monsoons, tropical cyclones, and rising sea levels will enhance the destructive effect of floods in areas near the coast. As the stormwater increases due to the intensified rains and climate change, more flooded areas were observed in Imus City with depths of 0.2 to 1 m. As the return period increases, the amount of floodwaters coming upstream increases resulting in potentially wider and deeper flooding downstream of the basin.

Due to the low elevation, the floodwaters concentrated along the coastal areas. Maximum flood depths on the lower reaches were almost the same for the normal conditions, RCP 4.5 and RCP 8.5 under the same return period. The overflow of floodwater increases as the return period increases and due to the increase of total rainfall projected under RCP 4.5 and 8.5. The generated flood maps in HEC-RAS for each hypothetical rainfall scenario are shown in Figures 13 to 17.

The Imus River is one of the major rivers in Cavite that contributes to the occurrence of floods in the lowland areas of Cavite especially those adjacent to the coast. The hydrologic model developed in HEC-HMS accurately predicted the natural response of the Imus River Basin. Rainfall and streamflow data as well as proper basin and channel characterization were very critical in the calibration and validation of the model. Sensitivity analysis was very useful in identifying parameters with the greatest influence on the flood occurrence in the basin. Hydrologic modeling also enables further assessments of the watershed response to the changing natural features as represented by the different parameters of the model. In this study, an increase in built-up areas showed an increase in the percentage of impervious surfaces which will lead to lesser infiltration of rainfall and potentially increasing the runoff in the basin.

Hydraulic modeling with HEC-RAS enables the understanding and prediction of floods downstream of the Imus River Basin. The geometric features of the flood plain and the generated outflow from the upstream are important in identifying the extent and the depth of flooding. The generated flood maps showed that low-lying municipalities along the coast will be particularly affected by floods. The higher the return period, the larger the accumulated rainfall, and the extent and depth of flooding. With the PAGASA climate projections for Cavite predicting rainfall amounts exceeding 10% of the normal value per season, these flood maps will be very useful in disaster risk reduction and mitigation, and flood planning and management.

ACKNOWLEDGEMENTS

We would like to thank the Department of Science and Technology - Engineering Research and Development for Technology (DOST-ERDT) for funding this research, as well as the Advanced Science and Technology Institute (DOST-ASTI), National Mapping and Resource Information Authority (NAMRIA), and to the University of the Philippines Los Baños – SSIP project for providing the important data used in the study.

Ethical Approval

Not Applicable

Competing Interests

I declare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Author’s Contribution

Jose Carlo R. Dizon served as the main author, he wrote the main manuscript text and prepared all necessary figures and tables. Roger A. Luyun Jr. was the adviser of the main author, he thoroughly reviewed the paper and contributed to the improvement of the methodology and discussions. Rubenito M. Lampayan and Moises A. Dorado reviewed the manuscript and provided advice that improved the methods of the study.

Funding

Department of Science and Technology (DOST) of the Republic of the Philippines funded the conduct of this research under the Engineering Research and Development for Technology (ERDT) scholarship. There is no available funding that will be used for publication.

Availability of Data and Materials

Digital Elevation Models and other base maps can be requested from National Mapping and Resource Information Authority (NAMRIA): https://www.namria.gov.ph/

Satellite Images were downloaded from Google Earth Engine: https://earthengine.google.com/

Rainfall data were requested from Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA): https://www.pagasa.dost.gov.ph/

Water level data at Daang Hari Bridge can be requested from Advanced Science and Technology Institute (ASTI) of the Department of Science and Technology (DOST). Their data repository can be accessed through PhilSensors: https://philsensors.asti.dost.gov.ph/

Requests of data can be done through Electronic Freedom of Information (eFOI) of the Republic of the Philippines: https://www.foi.gov.ph/

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No competing interests reported.

Flood Modeling of Imus River Basin, Cavite, Philippines at Different Climate and Land Cover Change Scenarios

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Data Gathering

Hydrologic Modeling

Model Response to Land Cover Change

Hydraulic Modeling and Flood Inundation Mapping

Results And Discussion

Hydrologic Model

Parametric Sensitivity Results

Model Response to Land Cover Change

Simulation of Hydrograph for Normal and Climate-affected Conditions

Hydraulic Model and Flood Inundation Maps

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1