Floods are one of the extreme events and widespread natural disasters that significantly affect the civil infrastructure and livelihoods of people. Recently, climate change has significantly altered the rainfall pattern and increased flood events worldwide, especially in India. Therefore, it has become essential to map potential flood inundation regions for various future extreme events to develop appropriate flood mitigation and management strategies. This study aims to develop flood inundation maps for different return periods under climate change scenarios for the Chaliyar basin, Kerala. The Hydrologic Engineering Center-Hydrologic Modelling System model was used to simulate streamflow under SSP2-4.5 and SSP5-8.5 scenarios. Later, flood inundation maps were developed for different return periods using the Hydrologic Engineering Center-River Analysis System model. It was observed that for the near future (2031–2040) and far future (2071–2080), simulated streamflow is higher for SSP5-8.5. However, the mid-future (2051–2060) resulted in a higher streamflow for SSP2-4.5 than the SSP5-8.5 scenario. A maximum of 19.52 m of water surface elevation occurred at Kizhupparamba during mid-future for SSP2-4.5, followed by 18.38 m of water surface elevation at Cheekode during the near future for SSP5-8.5, for 100-year return period events. This study showed that hydrologic and hydraulic models could be effectively combined for mapping the flood inundation areas.

  • This study integrates the hydrological model, hydraulic model and climate change scenarios to develop flood inundation maps for various return periods.

  • SSP2-4.5 and SSP5-8.5 are considered for climate change scenarios to simulate the streamflow for the near future (2031–2040), mid-future (2051–2060) and far future (2071–2080).

  • Future streamflow in the Chaliyar basin will likely to increase for both SSP2-4.5 and SSP5-8.5 scenarios.

A flood is a high volume of water flow that rises and drowns the land which is otherwise not normally inundated. It is one of the most prevalent and widespread natural disasters in tropical nations like India, causing havoc on civil infrastructure and people's livelihoods (Mishra et al. 2018). It is widely reported that the climate is changing and that this change is already having an impact on every part of the world (Eum et al. 2011). As a result of climate change, the global average temperature is increasing, and the rainfall intensity and frequency have also increased. Consequently, the runoff and peak discharge have also increased, leading to flooding in many places. The impact of climate change on floods is severe in Indian river basins, especially in the low-lying floodplains, due to an increase in extreme rainfall events (Ramachandran et al. 2019). To identify the flood risk zones and to formulate effective flood mitigation and management strategies, it is crucial to map the possible flood inundation regions for a wide range of extreme rainfall events under climate change scenarios (Sahoo & Sreeja 2017; Hamdan et al. 2021; Kalra et al. 2021; Mohanty & Simonovic 2021).

The outputs of the general circulation models (GCMs) project future climate for different emission scenarios and are widely used in hydrological modelling to assess climate change's impact. Several studies have used the statistically downscaled GCM projections to simulate hydrological models and analyse the potential implications of climate change to a basin or sub-basin (Aich et al. 2016; Shrestha & Lohpaisankrit 2017; Nyaupane et al. 2018; Reshma & Arunkumar 2023). Similarly, flood inundation maps can also be developed by giving these GCM projections as input to an appropriate hydrological or hydraulic model. Hydrological models are simplified replications of the hydrologic cycle (Motovilov et al. 1999). Hydrological modelling at the river basin scale is essential to simulate the stream flows and predict floods under various climate change scenarios (Mohammed et al. 2018; Siddique & Palmer 2021; Wen et al. 2021). A few studies have been reported for mapping floodplains for future climate change scenarios (Shrestha & Lohpaisankrit 2017; Roy et al. 2021; Ukumo et al. 2022). El Alfy (2016) estimated the peak flows and abstraction losses by integrating the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) model with geographic information system (GIS) to assess the flash floods. A similar study was conducted by El-Naqa & Jaber (2018) integrating the Hydrologic Engineering Center-River Analysis System (HEC-RAS) model and GIS to map the inundated areas along the main Wadi of Al-Ghadaf watershed for various return periods. It was reported the water level in some places in the inundated areas reached as high as 5 m. Abdessamed & Abderrazak (2019) used both HEC-HMS and HEC-RAS to assess the flood risk for different return periods. It was reported that the presence of retaining walls reduced the area from flooding, however, extremely low-lying regions were at risk of flooding. Using the MIKE 11 model, Rahman et al. (2011) investigated the flood flows and associated stages for various return periods for the Teesta sub-catchment in Bangladesh. The model results were used to create a stage–discharge relationship that was used to calculate the flood stages for 25-, 50-, and 100-year return periods.

Climate change has significantly altered the rainfall pattern and increased flood events across the world, especially in India. Therefore, it has become essential to map the potential flood inundation regions for various future extreme rainfall events and identify the flood risk zones for developing appropriate flood mitigation and management strategies. Madhuri et al. (2021) studied the flood depth, building risk and the effectiveness of various flood adaption measures for urban floods under climate change scenarios. For the Sebeya catchment, Assoumpta & Aja (2021) used quantitative precipitation forecasts to assess flood predictions. Roy et al. (2021) used open-source mathematical models to map the flood inundation of the Arial Khan River for the projected climate change scenario RCP 8.5. Ukumo et al. (2022) prepared a flood hazard map for the Woybo River catchment under climate change scenarios. The floods for future periods under different climatic scenarios were simulated using the HEC-RAS model. It was reported that 25.68% of the Woybo River catchment fell into the very high-hazard category, while 28.56% fell into the high-hazard category. From these studies, it is evident that flood mapping is mostly done by integrating hydrological and hydraulic models. Streamflows are simulated using a hydrologic model, whereas flood inundation areas and inundation depth are estimated using a hydraulic model. During the 2018 monsoon, Kerala experienced unusually heavy rainfall. As a result, 13 out of 14 districts experienced severe floods. The Chaliyar basin is one of the severely affected basins in Kerala due to extreme rainfall events and floods. Therefore, the main objectives of the study are to simulate the future streamflow of the basin for different climate change scenarios using a hydrological model and to develop flood inundation maps for different return periods under climate change scenarios using a hydraulic model. This study effectively combines the hydrologic and hydraulic modelling for mapping the flood inundation areas.

The study area and data are explained in the next section, followed by the methodology adopted for hydrologic modelling, extraction of future precipitation and hydraulic model development. Then, the results of various methods regarding streamflow simulation, performance indices, and flood maps for various return periods are explained. Finally, the conclusions from the results are explained in the last section.

The present study is carried out in the Chaliyar river basin, Kerala, and its location is shown in Figure 1. The river Chaliyar is the fourth longest river in Kerala, with a total length of 169 km and flows from east to west through the districts of Malappuram and Kozhikode. It originates at an altitude of 2,066 m above sea level in the Elambalari Hills in Gudalur taluk of Nilgiris district in Tamil Nadu and drains into the Lakshadweep sea at Beypore, Kerala. The catchment area of the entire basin is 2,933 km2, of which 2,545 km2 are in Kerala and the remaining 388 km2 are in Tamil Nadu.
Figure 1

Location of Chaliyar basin.

Figure 1

Location of Chaliyar basin.

Close modal

The average annual rainfall in the basin is about 3,012 mm. The southwest (June–September) and northeast (October–November) monsoons are the two main rainy seasons. The main four soil types in the river basin are gravelly clay (60.73%), clay (24.56%), gravelly loam (9.85%), and loam (4.86%). Generally, the land use of the basin includes urban, rocky, and water bodies with agricultural land making up approximately 74.26% of the total area and 14.21% forest area. There is a hydrological observation station at Kuniyil and two meteorological stations in the basin located at Nilambur and Manjeri as shown in Figure 1.

The data collected for the study includes the observed daily precipitation for a period of 20 years from 1995 to 2014 from the Indian Meteorological Department (IMD). The digital elevation model (DEM) was obtained from the National Aeronautics and Space Administration (NASA) Earth data Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and observed daily streamflow (1995–2014) was obtained from Central Water Commission (CWC) through India-WRIS. The land use land cover (LULC) is obtained from United States Geological Survey (USGS) EarthExplorer. The precipitation and temperature projections for the future climate change scenarios were obtained from Mishra et al. (2020a). The statistical analysis of historical meteorological and streamflow data from 1995 to 2014 is given in Table 1. As seen from the table, a maximum of 571 mm was observed at Manjeri, whereas the difference in the mean of the two stations is less.

Table 1

Statistical analysis of historical rainfall and streamflow at Chaliyar basin

Statistic measuresNilambur rainguage station (mm)Manjeri rainguage station (mm)Kuniyil discharge station (m3/s)
Minimum 0.00 0.00 0.00 
Maximum 254.40 571.00 3,256.00 
Mean 6.90 7.30 137.00 
Skewness 4.40 8.40 4.10 
Kurtosis 27.80 168.70 24.30 
Statistic measuresNilambur rainguage station (mm)Manjeri rainguage station (mm)Kuniyil discharge station (m3/s)
Minimum 0.00 0.00 0.00 
Maximum 254.40 571.00 3,256.00 
Mean 6.90 7.30 137.00 
Skewness 4.40 8.40 4.10 
Kurtosis 27.80 168.70 24.30 

The methodology followed in this study is divided into three phases as shown in Figure 2. In phase 1, the HEC-HMS model was developed to simulate the streamflow in the basin for the baseline scenario using the observed historical data. HEC-HMS is a physically based semi-distributed model developed by the Hydrologic Engineering Centre of the US Army Corps of Engineers (Feldman 2021), and is widely used to simulate the streamflow. The main components of the HEC-HMS model are the basin model, meteorological model and control specifications. The inputs for the HEC-HMS model are delineated watershed, daily observed precipitation, sub-basin parameters and observed streamflow. The basin delineated from the ASTER-DEM was imported to the HEC-HMS and the other basin parameters were defined. The basic building blocks of the basin model are its hydrologic components, which include watershed, stream network, and outlet point and are to be represented by an element in HMS. The elements in HMS include sub-basin, reach, reservoir, junction, diversion, source, and sink. Each component is a part of the overall watershed response to a specified atmospheric forcing. In HMS, various methods are available to calculate canopy losses, surface losses, infiltration losses, baseflow, surface runoff estimation, and streamflow routing. The canopy losses were estimated using the simple canopy method, which assumes that all the precipitation is intercepted until the canopy storage capacity is filled. Once the storage is filled, all further precipitation falls to the surface. The simple surface method was used for computing surface losses. The infiltration losses were estimated using the deficit and constant approach and the recession method was used to determine the baseflow. Within the sub-basin, the surface runoff was estimated using a transform approach in which the excess precipitation was converted to runoff using a Clark unit hydrograph. To route the runoff from reaches to sink, the Muskingum method was applied. The model was calibrated with the observed streamflow once the model parameters and control specifications were specified. The performance measures such as the ratio of root mean square error to the standard deviation (RSR), Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and percentage bias (PBIAS) were used to assess the model. Moriasi et al. (2007) criteria were further used for rating the model performance.
Figure 2

Flowchart of the overall methodology.

Figure 2

Flowchart of the overall methodology.

Close modal

In the second phase, the bias-corrected climate projections developed by Mishra et al. (2020b) from Coupled Model Intercomparison Project-6 (CMIP6) were used in this study for future climate change scenarios. Among 13 GCMs, MPI-ESM-2-LR was selected based on the statistical analysis of the projections with the observed historical data. It has been reported that MPI-ESM-2-LR model precipitation gives good results for Indian climate conditions (Raju & Kumar 2020). To understand the variations in the streamflow, three future time periods, near future (2031–2040), mid-future (2051–2060), and far future (2071–2080) were considered. Flood frequency analysis was used to relate the magnitude of floods to their frequency of occurrence. The Gumbel extreme value distribution was used to develop the extreme flood events for 25-, 50-, and 100-year return periods (Ukumo et al. 2022).

In phase 3, flood inundation maps were developed for different return periods for SSP2-4.5 and SSP5-8.5 climate change scenarios using the HEC-RAS model. Coupled one-dimensional (1D) and two-dimensional (2D) HEC-RAS model (Brunner 2016) was used to simulate the water surface elevation and flood extent in the Chaliyar River and adjacent floodplain areas. Hydraulic model parameters of HEC-RAS include river cross-sections in each sub-basin, including left and right bank locations, roughness coefficients (Manning's n), and contraction and expansion coefficients. The flow hydrographs from HMS were given as the input to the hydraulic (HEC-RAS) model along with the land-use data. For the purpose of constructing the study area terrain model, the terrain's 1/3 arc-second DEM was used. Using RAS Mapper, the DEM of the study region was imported to generate the terrain model for the HEC-RAS 2D steady model. A 2D flow area around the floodplain was constructed using the geometry editor of HEC-RAS based on an estimate of the flood extent. The area was marked off with a polygon, and the flow area around the floodplain was created by taking into account the banks and the probability of flooding along the main channel. The mesh contains 11,280 cells. The cell size used for mesh was 100 m × 100 m square grids. The average width of the channel was 400 m. A floodplain of 1.5 km was considered for mesh generation on both sides of banks as shown in Figure 3. Therefore, the average width of the floodplain is 3 km. The hydraulic properties of each newly formed cell in the RAS Mapper were assigned by running the geometric pre-processor. Additionally, by establishing the land cover grid in GIS, the associated Manning's roughness coefficient n according to the kind of land use was assigned within the 2D flow area (Brunner 2016). The upstream and downstream boundary condition lines were drawn and boundary conditions were provided for the 2D steady flow simulation. HEC-RAS received the flow hydrograph for the upstream and normal depth at downstream as the hydrologic input. Making an accurate assessment of the mesh size, quantity, and computational time is crucial for modelling accuracy. Given this, a computation interval of 10 min was adopted as a moderate interval, and a small cell size was selected for good results even though it would require more time to run the simulation. The conservation of mass and energy equation is the formula utilized in the modelling (Equation (1)).
(1)
where Y1 and Y2 are the depths (m) of water at adjoining cross-section 1 and cross-section 2; Z1 and Z2 are the elevations (m) of the main channel; V1 and V2 are the average velocities (m/s) (total discharge/total flow area); α1 and α2 are the velocity weighting coefficients; g is the gravitational acceleration (m/s2); and he is the energy head loss (m).
Figure 3

RAS geometry and the cross-sections at various chainages of the Chaliyar basin.

Figure 3

RAS geometry and the cross-sections at various chainages of the Chaliyar basin.

Close modal
The diffusion wave technique was utilized for this 2D unsteady model without taking eddy viscosity and Coriolis effects into account (Equation (2)). The mass conservation or continuity equation is used in the unsteady computation.
(2)
where H is water surface elevation (m); q is the discharge term related to source or sink (m/s); h is the depth of water (m); u and v are the velocity components (m/s) in the X and Y directions.

In this study, the impact of climate change on streamflow and its subsequent flooding were assessed for various scenarios. The projected precipitation for different emission scenarios was used for the assessment. The HEC-HMS was used to simulate the streamflow for the near future (2031–2040), mid-future (2051–2060), and far future (2071–2080) under SSP2-4.5 and SSP5-8.5 scenarios. The simulated streamflow was then fed into the HEC-RAS model to create the flood maps for various return periods. The results of various models and assessments are discussed in the following sections.

Hydrological modelling

The values of the model parameter were modified until the observed streamflow and computed streamflow were relatively close to each other. Both manual and automatic calibrations were performed to fine-tune the simulations of the HEC-HMS model. For automatic calibration, simplex and univariate methods were used. The final calibrated values of the model parameters are presented in Table 2. It is observed that the initial storage in the canopy loss, time of concentration and Muskingum parameters are significant streamflow simulations.

Table 2

Calibrated model parameters of HMS model

ModelParameterInitial valueCalibrated value
Canopy Initial storage 8% 16% 
Maximum storage (mm) 5.2 
Crop coefficient 0.80 
Loss Initial deficit (mm) 11 4.73 
Maximum deficit (mm) 15 14 
Constant rate (mm/h) 1.14 0.46 
Transform Time of concentration (h) 21 16.33 
Storage coefficient (h) 56 56 
Baseflow Initial discharge (m3/s) 17 17 
Recession constant 0.90 0.90 
Ratio to peak 0.16 0.42 
Routing K (h) 0.39 0.32 
X 0.41 0.10 
ModelParameterInitial valueCalibrated value
Canopy Initial storage 8% 16% 
Maximum storage (mm) 5.2 
Crop coefficient 0.80 
Loss Initial deficit (mm) 11 4.73 
Maximum deficit (mm) 15 14 
Constant rate (mm/h) 1.14 0.46 
Transform Time of concentration (h) 21 16.33 
Storage coefficient (h) 56 56 
Baseflow Initial discharge (m3/s) 17 17 
Recession constant 0.90 0.90 
Ratio to peak 0.16 0.42 
Routing K (h) 0.39 0.32 
X 0.41 0.10 

Calibration of the HEC-HMS model for streamflow was performed using observed daily streamflow data for 8 years (1995–2002) and validated for 3 years (2011–2013). The RSR, NSE, R2, and PBIAS values of model simulations during calibration were evaluated; the estimated and observed streamflow are graphically assessed for agreement. The model performance during the calibration and validation are presented in Table 3. The value of R2 is 0.58 during calibration and 0.59 during validation. As these values are between 0.55 and 0.65, the performance was good as per Moriasi et al. (2007) criteria, especially at a daily time scale. Similarly, the value of NSE is 0.70 during calibration and 0.58 during validation. During calibration, the performance of the model was very good as it was more than 0.65 whereas during validation the performance was good as it was in the range of 0.55–0.65. Simulated streamflow is compared with the observed streamflow at the Kuniyil discharge station for 1995–2002 to check how well the simulated and observed streamflow are closely related, as shown in Figure 4. From Figure 4, it is clear that the day of simulated peak flow (i.e., 12 July 1997) closely matches the observed peak flow and the peak flow is 1740 m3/s. The model predicts 20.66% less than the observed value when the peak flow is compared with simulated peak flow.
Table 3

Performance indices of simulated and observed streamflow during calibration and validation

Performance measuresCalibration periodValidation period
RSR 0.54 0.66 
NSE 0.71 0.56 
R2 0.58 0.59 
PBIAS −5.92 2.76 
Performance measuresCalibration periodValidation period
RSR 0.54 0.66 
NSE 0.71 0.56 
R2 0.58 0.59 
PBIAS −5.92 2.76 
Figure 4

Comparison of simulated and observed daily streamflow.

Figure 4

Comparison of simulated and observed daily streamflow.

Close modal

Future projections of streamflow

Once the model was properly calibrated and validated using the observed historical data for the baseline scenario, the model was used to simulate the streamflow for future climate scenarios by giving the precipitation projections as input. For the near future, simulated streamflow is higher for SSP5-8.5 than SSP2-4.5 as shown in Figure 5. Compared with the historical period, SSP2-4.5 shows a decreasing trend and SSP5-8.5 shows an increase for the near future with a reduction of 26.97% and an increase of 17.82%, respectively.
Figure 5

Comparison of projected daily streamflow for near future under SSP2-4.5 and SSP5-8.5 scenarios.

Figure 5

Comparison of projected daily streamflow for near future under SSP2-4.5 and SSP5-8.5 scenarios.

Close modal
For mid-future, simulated streamflow is higher for SSP2-4.5 than SSP5-8.5 as shown in Figure 6. Compared with the historical period, SSP2-4.5 shows an increasing trend and SSP5-8.5 shows a decrease for mid-future with an increase of 56.76% and a reduction of 23.64%, respectively. For the far future, simulated streamflow is higher for SSP5-8.5 than SSP2-4.5 as shown in Figure 7. Compared with the historical period, SSP2-4.5 shows a decreasing trend and SSP5-8.5 shows an increase for the far future with a reduction of 47.40% and an increase of 29.18%, respectively.
Figure 6

Comparison of projected daily streamflow under SSP2-4.5 and SSP5-8.5 scenarios for mid-future.

Figure 6

Comparison of projected daily streamflow under SSP2-4.5 and SSP5-8.5 scenarios for mid-future.

Close modal
Figure 7

Comparison of projected daily streamflow under SSP2-4.5 and SSP5-8.5 scenarios for far future.

Figure 7

Comparison of projected daily streamflow under SSP2-4.5 and SSP5-8.5 scenarios for far future.

Close modal

Frequency analysis

The extreme flood frequency was calculated using the Gumbel distribution. The return periods of 25-, 50-, and 100-year of streamflow were taken into consideration for the flood inundation modelling under SSP2-4.5 and SSP5-8.5 scenarios during the near future (2031–2040), mid-future (2051–2060) and far future (2071–2080) as shown in Table 4. The maximum flood is recorded during mid-future, SSP2-4.5 for a 100-year return period. The 100-year maximum flood is increased by 41.2% for the mid-future for the SSP2-4.5 scenario. The maximum flood shows an increasing trend for the mid-future with respect to the baseline period. Similar results were also reported by Kim et al. (2023) for the Hangang and Geumgang basins in South Korea.

Table 4

Maximum flood (m3/s) for various return periods under SSP2-4.5 and SSP5-8.5 scenarios

Return periodBase line period (1995–2004)Near future (2031–2040)
Mid-future (2051–2060)
Far future (2071–2080)
SSP2-4.5SSP5-8.5SSP2-4.5SSP5-8.5SSP2-4.5SSP5-8.5
25 years 2943 2748 3116 3983 2179 1619 3166 
50 years 3325 3145 3623 4608 2484 1800 3595 
100 years 3703 3540 4126 5229 2786 1981 4020 
Return periodBase line period (1995–2004)Near future (2031–2040)
Mid-future (2051–2060)
Far future (2071–2080)
SSP2-4.5SSP5-8.5SSP2-4.5SSP5-8.5SSP2-4.5SSP5-8.5
25 years 2943 2748 3116 3983 2179 1619 3166 
50 years 3325 3145 3623 4608 2484 1800 3595 
100 years 3703 3540 4126 5229 2786 1981 4020 

Hydraulic modelling

In RAS Mapper, river banks, lines, and 2D mesh are first digitalized by using DEM. Cross-sections were created for every 1 km along the 32 km of the stretch. Following that, the geometric data were imported to HEC-RAS. Both the downstream and upstream boundary conditions are necessary for a mixed flow regime. By gradually changing Manning's coefficient from 0.03 to 0.08, the model was calibrated until the differences between the observed and the simulated values were within the acceptable ranges. When creating the flood inundation maps, the HEC-RAS model was set up to produce water levels and streamflow for 25-, 50-, and 100-year return periods. A total of 18 simulations were performed for different return periods and future periods. The output of the HEC-RAS model was viewed in the RAS Mapper before being exported to GIS to create flood inundation maps. The maximum water surface elevations (WSE) are tabulated in Table 5. SSP5-8.5 shows an increased WSE of 0.68 m than SSP2-4.5 during the near future for 100-year return period. SSP5-8.5 shows an increased WSE of 2.81 m than SSP2-4.5 during mid-future for a 100-year return period. Similarly, SSP5-8.5 shows an increased WSE of 2.77 m than SSP2-4.5 during the far future, 100-year return period.

Table 5

Maximum water surface elevation (m) for different return periods

Return periodNear future
Mid-future
Far future
SSP2-4.5SSP5-8.5SSP2-4.5SSP5-8.5SSP2-4.5SSP5-8.5
25 years 16.65 17.16 18.22 15.81 14.80 17.22 
50 years 17.19 17.80 18.90 16.27 15.14 17.77 
100 years 17.70 18.38 19.52 16.71 15.49 18.26 
Return periodNear future
Mid-future
Far future
SSP2-4.5SSP5-8.5SSP2-4.5SSP5-8.5SSP2-4.5SSP5-8.5
25 years 16.65 17.16 18.22 15.81 14.80 17.22 
50 years 17.19 17.80 18.90 16.27 15.14 17.77 
100 years 17.70 18.38 19.52 16.71 15.49 18.26 

Figure 8(a) represents the flood inundation boundary showing the places which are severely affected during mid-future for SSP2-4.5, 100-year return period which includes Vazhakkad, Kodiyathur, Kizhupparamba, Mavoor. A maximum of 19.52 m WSE occurred at Kizhupparamba. Figure 8(b) represents the flood inundation showing the places which are severely affected during the near future for SSP5-8.5 for 100-year return period which includes Vazhakkad, Kodiyathur, Kizhupparamba, Mavoor, Vilayil, and Cheekode. A maximum of 18.38 m WSE occurred at Cheekode. The inundated area is increased during SSP5-8.5 than SSP2-4.5 whereas during mid-future, 100 years, SSP2-4.5 shows maximum WSE than SSP5-8.5.
Figure 8

Flood inundation boundary during (a) mid-future for SSP2-4.5, 100-year return period (b) near future for SSP5-8.5, 100-year return period.

Figure 8

Flood inundation boundary during (a) mid-future for SSP2-4.5, 100-year return period (b) near future for SSP5-8.5, 100-year return period.

Close modal

In this study, the floodplains of the Chaliyar River basin are mapped using a hydrologic and hydraulic model for various climate change scenarios. Initially, a hydrologic model, HEC-HMS was developed to simulate the streamflow for various climate scenarios and time periods. Then, the frequency analyses were done to estimate the peak flows for different return periods. These peak flows were given as input to the HEC-RAS model to demarcate the floodplain regions. From the results, it is noticed that the HEC-HMS model has simulated the streamflow close to the observed values. The performance of the model is very good during calibration and good during validation. During the calibration of the HEC-HMS model, it is observed that time of concentration, initial deficit, maximum storage and constant rate are highly sensitive parameters as small change in the parameters affects the streamflow. For the near future and far future, simulated streamflow is higher for SSP5-8.5 than SSP2-4.5. But in the case of mid-future, simulated streamflow is higher for SSP2-4.5 than SSP5-8.5.HEC-RAS also has given good performance with R2 at 0.63 and NSE at 0.61 during calibration. Vazhakkad, Kodiyathur, Kizhupparamba, Mavoor, Vilayil, and Cheekode are vulnerable areas to flooding. A maximum of 19.52 m WSE occurred at Kizhupparamba during mid-future for SSP2-4.5 and 18.38 m occurred at Cheekode during the near future for SSP5-8.5 for a 100-year return period.

Authors thankfully acknowledge the U.S. Geological Survey, India's Geo platform of ISRO (Bhuvan), Central Water Commission (CWC) and Indian Meteorological Society (IMD) for providing the necessary data required for this study.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

Aich
V.
,
Liersch
S.
,
Vetter
T.
,
Fournet
S.
,
Andersson
J. C. M.
,
Calmanti
S.
,
van Weert
F. H. A.
,
Hattermann
F. F.
&
Paton
E. N.
2016
Flood projections within the Niger River Basin under future land use and climate change
.
Science of the Total Environment
562
,
666
677
.
https://doi.org/10.1016/j.scitotenv.2016.04.021
.
Assoumpta
M.
&
Aja
D.
2021
Flood forecasting using quantitative precipitation forecasts and hydrological modelling in the Sebeya catchment, Rwanda
.
H2Open Journal
4
(
1
),
182
203
.
https://doi.org/10.2166/h2oj.2021.094
.
Brunner
G. W.
2016
HEC-RAS 6.0 Users Manual
.
US Army Corps of Engineers, Institute for Water Resources, Hydrologic Engineering Center, Davis, CA, USA
.
El-Naqa
A.
&
Jaber
M.
2018
Floodplain analysis using ArcGIS, HEC-GeoRAS and HEC-RAS in Attarat Um Al-Ghudran Oil Shale Concession Area, Jordan
.
Journal of Civil & Environmental Engineering
08
(
05
).
https://doi.org/10.4172/2165-784X.1000323
Eum
H.-I.
,
Sredojevic
D.
&
Simonovic
S. P.
2011
Engineering procedure for the climate change flood risk assessment in the Upper Thames River Basin
.
Journal of Hydrologic Engineering
16
(
7
),
608
612
.
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000346
.
Feldman
A. D.
2021
Hydrologic Modeling System HEC-HMS User's Manual
.
US Army Corps of Engineers, Institute for Water Resources, Hydrologic Engineering Center
.
Hamdan
A. N. A.
,
Almuktar
S.
&
Scholz
M.
2021
Rainfall-runoff modeling using the HEC-HMS model for the Al-Adhaim River Catchment, Northern Iraq
.
Hydrology
8
(
2
),
58
.
https://doi.org/10.3390/hydrology8020058
.
Kalra
A.
,
Joshi
N.
,
Baral
S.
,
Nhuchhen Pradhan
S.
,
Mambepa
M.
,
Paudel
S.
,
Xia
C.
&
Gupta
R.
2021
Coupled 1D and 2D HEC-RAS floodplain modeling of Pecos River in New Mexico
.
World Environmental and Water Resources Congress
2021
,
165
178
.
https://doi.org/10.1061/9780784483466.016
.
Kim
S.
,
Kwon
J.-H.
,
Om
J.-S.
,
Lee
T.
,
Kim
G.
,
Kim
H.
&
Heo
J.-H.
2023
Increasing extreme flood risk under future climate change scenarios in South Korea
.
Weather and Climate Extremes
39
(
February
),
100552
.
Madhuri
R.
,
Raja
Y. S. L. S.
,
Raju
K. S.
,
Punith
B. S.
&
Manoj
K.
2021
Urban flood risk analysis of buildings using HEC-RAS 2D in climate change framework
.
H2Open Journal
4
(
1
),
262
275
.
https://doi.org/10.2166/h2oj.2021.111
.
Mishra
B. K.
,
Rafiei Emam
A.
,
Masago
Y.
,
Kumar
P.
,
Regmi
R. K.
&
Fukushi
K.
2018
Assessment of future flood inundations under climate and land use change scenarios in the Ciliwung River Basin, Jakarta
.
Journal of Flood Risk Management
11
,
S1105
S1115
.
https://doi.org/10.1111/jfr3.12311
.
Mishra
V.
,
Bhatia
U.
&
Tiwari
A. D.
2020a
Bias-corrected climate projections for South Asia from coupled model intercomparison project-6
.
Scientific Data
7
(
1
),
338
.
https://doi.org/10.1038/s41597-020-00681-1
.
Mishra
V.
,
Bhatia
U.
&
Tiwari
A. D.
2020b
Bias Corrected Climate Projections from CMIP6 Models for South Asia. Zenodo, (Data set). https://doi.org/10.5281/zenodo.3871316. Available from: https://zenodo.org/record/3987736 (accessed 2 August 2021)
.
Mohammed
K.
,
Islam
A. K. M. S.
,
Islam
G. M. T.
,
Alfieri
L.
,
Khan
M. J. U.
,
Bala
S. K.
&
Das
M. K.
2018
Future floods in Bangladesh under 1.5°C, 2°C, and 4°C global warming scenarios
.
Journal of Hydrologic Engineering
23
(
12
),
04018050
.
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001705
.
Mohanty
M. P.
&
Simonovic
S. P.
2021
Changes in floodplain regimes over Canada due to climate change impacts: observations from CMIP6 models
.
Science of the Total Environment
792
,
148323
.
https://doi.org/10.1016/j.scitotenv.2021.148323
.
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
50
(
3
),
885
900
.
https://doi.org/10.13031/2013.23153
.
Motovilov
Y. G.
,
Gottschalk
L.
,
Engeland
K.
&
Rodhe
A.
1999
Validation of a distributed hydrological model against spatial observations
.
Agricultural and Forest Meteorology
98
,
257
277
.
https://doi.org/10.1016/S0168-1923(99)00102-1
.
Nyaupane
N.
,
Thakur
B.
,
Kalra
A.
&
Ahmad
S.
2018
Evaluating future flood scenarios using CMIP5 climate projections
.
Water
10
(
12
),
1866
.
https://doi.org/10.3390/w10121866
.
Rahman
M. M.
,
Arya
D. S.
,
Goel
N. K.
&
Dhamy
A. P.
2011
Design flow and stage computations in the Teesta River, Bangladesh, using frequency analysis and MIKE 11 modeling
.
Journal of Hydrologic Engineering
16
(
2
),
176
186
.
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000299
.
Raju
K. S.
&
Kumar
D. N.
2020
Review of approaches for selection and ensembling of GCMs
.
Journal of Water and Climate Change
11
(
3
),
577
599
.
https://doi.org/10.2166/wcc.2020.128
.
Ramachandran
A.
,
Palanivelu
K.
,
Mudgal
B. V.
,
Jeganathan
A.
,
Guganesh
S.
,
Abinaya
B.
&
Elangovan
A.
2019
Climate change impact on fluvial flooding in the Indian Sub-Basin: a case study on the Adyar Sub-Basin
.
PLOS ONE
14
(
5
),
e0216461
.
https://doi.org/10.1371/journal.pone.0216461
.
Reshma
C.
&
Arunkumar
R.
2023
Assessment of impact of climate change on the streamflow of Idamalayar River Basin, Kerala
.
Journal of Water and Climate Change
14
(
7
),
2133
2149
.
https://doi.org/10.2166/wcc.2023.456
.
Roy
B.
,
Khan
M. S. M.
,
Islam
A. K. M. S.
,
Mohammed
K.
&
Khan
M. J. U.
2021
Climate-induced flood inundation for the Arial Khan River of Bangladesh using open-source SWAT and HEC-RAS model for RCP8.5-SSP5 scenario
.
SN Applied Sciences
3
(
6
),
648
.
https://doi.org/10.1007/s42452-021-04460-4
.
Sahoo
S. N.
&
Sreeja
P.
2017
Development of flood inundation maps and quantification of flood risk in an urban catchment of Brahmaputra River
.
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
3
(
1
).
https://doi.org/10.1061/AJRUA6.0000822
Shrestha
S.
&
Lohpaisankrit
W.
2017
Flood hazard assessment under climate change scenarios in the Yang River Basin, Thailand
.
International Journal of Sustainable Built Environment
6
(
2
),
285
298
.
https://doi.org/10.1016/j.ijsbe.2016.09.006
.
Siddique
R.
&
Palmer
R.
2021
Climate change impacts on local flood risks in the U.S. Northeast: a case study on the Connecticut and Merrimack River Basins
.
JAWRA Journal of the American Water Resources Association
57
(
1
),
75
95
.
https://doi.org/10.1111/1752-1688.12886
.
Ukumo
T. Y.
,
Abebe
A.
,
Lohani
T. K.
&
Edamo
M. L.
2022
Flood hazard mapping and analysis under climate change using hydro-dynamic model and RCPs emission scenario in Woybo River catchment of Ethiopia
.
World Journal of Engineering
.
https://doi.org/10.1108/WJE-07-2021-0410
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