The present study focused on the Kosasthalaiyar River basin in Chennai, Tamil Nadu, India. We analysed the 2015 peak flood and forecasted the feature data in the representative concentration pathways (RCP) 4.5 scenario for various years. We used the scientific data management system (SDSM) software to downscale the Geophysical Fluid Dynamics Laboratory (GFDL) 2.0 general circulation models (GCMs). According to hydrograph analysis, 142.7, 75.31, 461.73, and 248.22 mm runoff can occur in 2030, 2050, 2080, and 2100, respectively. The current study estimates probable peak flows by performing floodplain analysis on the Kosasthalaiyar River sub-basin using the Hydrologic Engineering Centre's Hydrologic Modelling System (HEC-HMS), the Hydrologic Engineering Centre's River Analysis System (HEC-RAS), and geographic information system (GIS) tools. It is possible to observe that the two major peak floods, measuring 581.6 and 110.7 m3/s, respectively, will occur on 28 November 2030 at 10:20 a.m. and 12 December 2050 at 9:20 a.m. Additionally, high floods of 997 and 1,438.4 m3/s can be recorded on 20 December 2080 at 9:50 a.m. and 29 November 2100 at 9:40 a.m., respectively.

  • Significant peak flow timing differences are projected for the years 2030, 2050, 2080, and 2100.

  • 2030 peak flow: 581.6 m3/s on 28 November 2050 and 110.7 m3/s on 12 December.

  • 2080 peak flow: 997 m3/s on 20 December 2100 and 1,438.4 m3/s on 29 November.

  • Peak flow timing shifts to late November and early December.

  • Sholavaram, Tamil Nadu faces infrastructure and agriculture impacts; urbanisation worsens flood risks, advocating rainwater harvesting and afforestation.

In today's fast-moving world, urbanisation is one of the global phenomena driven by factors such as industrialisation, economic opportunities, and social changes (Castro & Maidment 2020). It typically includes city growth, infrastructure expansion, and changes in land-use patterns (Srinivas et al. 2018a, b). Urbanisation, as one of the development factors, brings various benefits, but it also presents several changes and drawbacks, such as environmental degradation, public health challenges, and the loss of agricultural land (Khattak et al. 2016). One of the major consequences is drastic droughts and floods (Verma et al. 2022). Further, droughts are prolonged periods of abnormally low precipitation resulting from water shortages that can have significant social, economic, and environmental impacts (Mishra & Singh 2010; Miyan 2015). In addition, human activities such as deforestation and over-extraction of water resources primarily cause droughts (Miyan 2015). According to Şen (2018), floods are natural disasters characterised by the overflow of runoff water onto normally dry land. Ologunorisa et al. (2022) commented that, due to urbanisation, precipitated water is unable to infiltrate into the sub-surface of the earth, leading to water stagnation on the surface. Further, overtime runoff is increasing, leading to severe floods (Viglione & Rogger 2015; Şen 2018). Jain et al. (2007) and Prakash et al. (2023) commented that urbanised areas are well-developed locations, such as Mumbai, Bengaluru, Chennai, and so on; drastic floods may cause a huge amount of property loss. However, we cannot stop urbanisation as it is one of the economic factors contributing to a country's gross domestic product (GDP), but one can analyse the amount of flooding that will occur and do floodplain mapping, through which we can plot flood-prone areas of the particular basin (Chen et al. 2021). In addition, urban stormwater systems are crucial for transporting rainfall from these areas to lakes or rivers, which are the outlets for the water (Muis et al. 2015; Mazzoleni et al. 2021). However, managing stormwater in urban areas has become a big challenge since there is less space and land values are growing. Unfortunately, most stormwater drains in metropolitan areas are unable to handle excess runoff during high rainfall events due to inadequate maintenance and uncontrolled urbanisation (Prakash et al. 2023). These brief downpour events become more frequent and intense, and climate change alters the amount and peak of streamflow (Huntington 2010). Mujumdar et al. (2020) recommended that runoff measurement can solve numerous problems with watershed management. In conjunction with Mujumdar et al. (2020), Patz et al. (2014) commented that the primary cause of major and frequent flooding in a watershed is an excess of runoff volumes directed into the channels compared with the stream's carrying capacity.

Furthermore, as urbanisation and climate change raise the runoff peak, it is critical to evaluate the flood magnitude and intensity for flood control (Hettiarachchi et al. 2018). Climate change can significantly impact variations in peak flow (Patz et al. 2014; Guan et al. 2016; Hettiarachchi et al. 2018; Chen et al. 2021; Pasupuleti et al. 2023; Loganathan & Sathiyamoorthy 2024; Rajesh 2024). With early flood warnings, anticipating peak flows in urban areas can enhance flood preparedness. Kuller et al. (2021) conducted flow measurements with various instruments and a variety of sophisticated and conventional methods, particularly in flood areas. They found that the stage–discharge relationship, often known as a rating curve, is one of the oldest methods used by hydrographers worldwide (Stephens & Cloke 2014). However, a stream channel's floodplain and hydraulic characteristics are the primary determinants of its rating curve, which varies periodically at a specific stretch of the stream (Zhou et al. 2019). We use hydrologic modelling for a specific rainfall depth in the current study to assess how watersheds respond hydrologically, as Guan et al. (2016) used in the past. In addition, many studies used hydrological modelling to look at a wide range of topics, such as managing resources, figuring out the impacts of climate change and urbanisation, predicting and guessing the size of floods, and figuring out streamflow in an unmeasured basin (Huntington 2010; Patz et al. 2014; Demaria et al. 2016; Gangolu et al. 2023; Sharma et al. 2023; Al Khazaleh et al. 2024; Nandhini et al. 2024). In the current study, we simulated the HEC-HMS model for the 2008 flood event, collecting the no-service flood hydrograph at the basin's outlet from the Public Works Department of the Government of Tamil Nadu (Srinivas et al. 2018a, b). They demonstrate how to prepare using LIDAR digital elevation model (DEM)-based HEC-RAS and HEC-HMS models (Meresa 2019; Al-Areeq et al. 2023). The study indicated the usefulness of HEC-RAS software along with the Global Flood Monitoring System (GFMS) tool to identify the flood extension zone and return period of flood in the study area (Khattak et al. 2016). Kaya et al. (2022) used the HEC-RAS 5.0.7 open-source model and GFMS datasets to model floods and draw lines around different risk zones. However, research organisations, government agencies, and private agencies selected the HEC-RAS model due to its availability and widespread use. In addition, Malik & Pal (2020) used log-normal, Log-Pearson Type III (LPT-3), Gumbel's extreme value distribution (EV-I), and extreme value distribution-III (EV-III) models, as well as HEC-RAS software, to look at past and future flood frequencies and map out flood-prone areas. Subsequently, they collected historical gauge height data from the Dwarkeswar River near Arambag Town, Hooghly District, West Bengal, from 1978 to 2018 and computed the annual peak discharge for the same period using a rating curve. Derdour et al. (2018) developed an approach that utilised hydrological and hydraulic models, in conjunction with GIS techniques (Derdour et al. 2018), to examine the flooding patterns of the city of Ain Sefra during major events. This integrated methodology allows for a detailed assessment of flood risk, peak discharges, and floodplain inundation patterns, providing valuable insights for flood hazard assessment and urban planning in arid regions like Ain Sefra. Furthermore, Natarajan & Radhakrishnan (2020) noted that they demarcated the flood as a surplus runoff from a watercourse or river capable of submerging land areas. The medium-sized dendritic, ungauged urban catchment in Tiruchirappalli adopted an integrated modelling technique. HEC-HMS achieved a rainfall-runoff model, which is mandatory for flood risk assessment studies (Mandal & Chakrabarty 2016; Abdessamed & Abderrazak 2019; Guduru et al. 2023; Titus et al. 2024). In recent studies, Pradhan et al. (2022) studied the HEC-RAS model to find two flow variables, namely, water depth and flow velocity. They presented the flood inundation maps, which show that the depths of the 150-year return period are 14.01 m, respectively. Verma & Mirajkar (2024) combined HEC-RAS and GIS with the HEC-Geo-RAS companion for hydraulics modelling and flood inundation mapping. For the past few years, the application of remote sensing and GIS in terrain modelling and floodplain mapping has been relatively limited. Arc Map generated floodplain maps by importing HEC-RAS outputs and using the HEC Geo-RAS (Khattak et al. 2016). The aforementioned studies motivated the authors to study highly flood-prone regions, with Tamil Nadu being one of the most affected by North East monsoons annually (Srinivas et al. 2018a, b).

The study focusses on the Kosasthalaiyar River basin in Chennai, Tamil Nadu, India, and uses various tools such as the HEC-HMS, HEC-RAS, and GIS tools to perform floodplain analysis and forecast peak flood events for different years (2030, 2050, 2080, and 2100). These forecasts are significant for urban planning, flood mitigation, and climate adaptation, providing specific peak flow predictions that help in preparing for future extreme flooding scenarios.

The limited availability of a comprehensive study on floodplain analysis using HEC-HMS, HEC-RAS, and GIS on the Kosasthalaiyar river sub-basin motivated the authors to do the current study with the following objectives:

  • (a) to analyse floodplain characteristics along the Kosasthalaiyar River using HEC-HMS to understand precipitation-runoff processes and

  • (b) to simulate hydraulic behaviour and flood inundation using HEC-RAS to provide an accurate floodplain.

The Kosasthalaiyar River basin, located in the northern region of Tamil Nadu, includes parts of the districts of Kanchipuram and Tiruvallur (see Figure 1). This sub-basin is characterised by a monsoon-driven climate with significant agricultural activity and rapid urbanisation. It faces challenges related to water management, groundwater depletion, and pollution due to increasing demands from agriculture, industry, and urban growth. Effective management of both surface and groundwater resources, along with environmental conservation measures, is critical to ensuring the sustainability of water resources in the sub-basin. The climate characteristics namely, the sub-basin fall under the tropical wet and dry climate zones, influenced by both the Southwest and Northeast monsoons. The region experiences hot summers, with temperatures ranging from 25 to 42 °C during peak summer months (April–June). Winters are mild, with temperatures between 19 and 30 °C. The average annual rainfall in the basin is approximately 1,100 mm, with significant seasonal variations. The majority of the rainfall occurs during the Northeast monsoon (October–December), although some rainfall is also received from the Southwest monsoon (June–September). In addition to climate characteristics, the hydrological characteristics namely, the Kosasthalaiyar River is a non-perennial river, which means it has seasonal flows, with the riverbed often dry during summer months. However, it is active and flows strongly during the monsoon seasons. The river has several smaller tributaries that contribute to its flow during monsoon seasons. There are various reservoirs, tanks, and ponds used for irrigation and domestic purposes within the sub-basin. Major reservoirs include Poondi Reservoir (a primary water source for Chennai) and Red Hills Reservoir. Groundwater is an important resource in the basin, supporting agricultural and domestic water needs. However, over-extraction of groundwater for irrigation has led to depletion issues in some parts of the basin. During periods of heavy rainfall, the Kosasthalaiyar River and its tributaries can experience flooding, especially in low-lying areas. This flooding often impacts nearby settlements and infrastructure. After passing through Tamil Nadu and the Chennai Reservoir in Andhra Pradesh, the river empties into the Bay of Bengal. Approximately 1,400 square kilometres make up the basin. The river flows through lowlands and sporadically hilly areas, giving rise to the region's undulating topography. The main river in this basin is the Kosasthalaiyar River. It flows towards the southeast and is about 136 km long. It originates near Pallipattu in Tiruvallur district and drains into the Bay of Bengal. Along its course, the river receives water from numerous tributaries and tiny streams. For the areas it crosses, the river basin provides a vital supply of water for industrial, drinking, and agricultural uses. But like many other river systems in India, it has problems with over-extraction of water, pollution, and encroachment along its banks. The basin is located between 13018′46.74″ N and 15058′54.00″ N latitude, 79018′17.68″ E and 80017′58.86″ E longitude. The basin receives an average annual rainfall of 1,200 mm. The Kosasthalaiyar River sub-basin has experienced severe flooding in the past, particularly along the Adyar, Cooum, and Pallikaranai marshland waterways. The basin's Kosasthalaiyar River has a maximum flood discharge capacity of 1,10,000 cubic feet per second (cusecs). In November 2021, the river carried almost 45,000 cusecs of water, but sludge from villages caused reverse flow in some areas. The Kosasthalaiyar River in the basin is one of the major rivers that causes severe floods in Chennai metropolitan city.
Figure 1

Boundary map of the study area (Kosasthalaiyar sub-basin).

Figure 1

Boundary map of the study area (Kosasthalaiyar sub-basin).

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Data collection

We got information for this study area from a number of different places, such as India-WRIS rainfall data in csv format and the 30 m × 30 m resolution DEM map from the Shuttle Radar Topography Mission (SRTM) in dot-tif format (Goyal et al. 2018; Chakraborty 2019; Nagaveni et al. 2019; Kant et al. 2023). We obtained Landsat-8 maps for land use and land cover (LULC). We used the RCP 4.5 scenario to forecast the feature data for various years (Asadollah et al. 2022; Jahangir et al. 2022; Rahaman 2022; Zhang et al. 2022). We use the SDSM software to downscale the GFDL 2.0 GCM (Ahmed et al. 2013; Raju & Kumar 2014; Shakeri et al. 2021; Asadollah et al. 2022). Data gathered from various sources comes in two forms: spatial data, such as SRTM DEM, and non-spatial data, including details of the drainage network and rainfall data (Nagaveni et al. 2019; Verma & Mirajkar 2024). We use Arc Map 10.4 to convert the collected data into a GIS-compatible format. Obtained shape files of sub-catchments and drainage networks from Google Maps to construct a comprehensive watershed database. We extract elevation data from the SRTM DEM (Goyal et al. 2018), and we extract one of the major storm events (November–December 2015) from the India Meteorological Department (IMD) rainfall data used to run the model simulations (Singh et al. 2018). In November 2015, Chennai received 1,049.3 mm of rainfall, the highest amount since 1918. On 6 November 2015, Chennai received 246.5 mm of rainfall in a single day, the highest in the last 10 years for November at the time. On 1 December 2015, the city received 494 mm of rainfall in a 24-h period, causing severe flooding that killed more than 400 people and submerged lakhs of homes (Table 1).

Table 1

Data collection of the present study from various sources

S. NoData typeData sourceSources
DEM STRM https://earthexplorer.usgs.gov/ 
LULC Landsat 8 https://earthexplorer.usgs.gov/ 
Precipitation data IMD https://imdpune.gov.in/cmpg/Griddata/Rainfall_25_NetCDF.html 
Future precipitation data CMIP 5 (GFDL 2.0) https://esgf-node.ipsl.upmc.fr/projects/cmip5-ipsl/ 
Hydrological data CWC https://indiawris.gov.in/wris/ 
S. NoData typeData sourceSources
DEM STRM https://earthexplorer.usgs.gov/ 
LULC Landsat 8 https://earthexplorer.usgs.gov/ 
Precipitation data IMD https://imdpune.gov.in/cmpg/Griddata/Rainfall_25_NetCDF.html 
Future precipitation data CMIP 5 (GFDL 2.0) https://esgf-node.ipsl.upmc.fr/projects/cmip5-ipsl/ 
Hydrological data CWC https://indiawris.gov.in/wris/ 

Digital elevation model

The DEM is a digital representation of the topography of a terrain or surface in the form of a raster dataset (Figure 2), where each cell (or pixel) contains an elevation value (Goyal et al. 2018). Various fields such as geography, geology, hydrology, environmental science, and urban planning use DEMs (Goyal et al. 2018; Nagaveni et al. 2019). Initially, our study area was spread out into four footprints or plates. Processing the four footprints in Arc Map results in a clipped DEM image, as shown in Figure 3.
Figure 2

Methodology of the present study.

Figure 2

Methodology of the present study.

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Figure 3

DEM of the Kosasthalaiyar River sub-basin.

Figure 3

DEM of the Kosasthalaiyar River sub-basin.

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Land use and land cover

The classification and description of land use, along with the types of vegetation, terrains, and structures covering the earth's surface, is known as LULC. LULC data is crucial for understanding the spatial distribution of human activities, natural resources, and environmental conditions across landscapes. We collect the LULC data for our study area from the United States Geological Survey Landsat-8 satellite (refer to Figure 4) (Rahaman 2022; Zhang et al. 2022). We divide our study area into two footprints or plates. We collect the LULC data between the dates of 12/03/2024 and 19/03/2024. Zhang et al. (2022) used Arc Map software for the unsupervised classification.
Figure 4

LULC of the Kosasthalaiyar River sub-basin.

Figure 4

LULC of the Kosasthalaiyar River sub-basin.

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Stream networks

Stream networks, also known as river networks or drainage networks, are the interconnected systems of rivers, streams, and tributaries that drain water from a particular area into a larger body of water, such as a lake or an ocean (Figure 5; Meresa 2019). Stream networks exhibit a hierarchical structure, with smaller tributaries feeding into larger rivers and ultimately flowing into a main river or water body. At the smallest scale, first-order streams (also called headwaters or rills) are the smallest channels where water initially begins to flow. As streams merge and grow in size, they form higher-order streams, such as second-order, third-order, and so on, until they form major rivers and river basins. Precipitation, runoff, erosion, and channelisation combine to form stream networks. Factors such as geology, topography, climate, and vegetation influence the formation and pattern of stream networks. Arc Map represents stream networks as vector datasets. These datasets consist of line features that represent the courses of rivers, streams, and other water bodies. The lines typically contain attributes such as stream order, flow direction, flow accumulation, and stream slope. Streams within a network are classified using this key attribute. It represents a stream segment's hierarchical position within the drainage network.
Figure 5

Stream networks of the Kosasthalaiyar River sub-basin.

Figure 5

Stream networks of the Kosasthalaiyar River sub-basin.

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Arc Map commonly uses the Strahler stream order system, which merges two streams of the same order to form a stream one order higher. The process persists until it reaches the highest-order stream. Arc Map allows users to determine the flow direction and accumulation for each cell in a raster grid. This information is critical for delineating stream networks and analysing water flow patterns. Flow accumulation represents the number of cells that contribute flow to each cell in the raster grid, which helps identify stream channels. Arc Map provides tools for conducting network analysis on stream networks. This includes calculating distances along stream channels, identifying upstream and downstream locations, determining optimal routes for navigation or resource extraction, and performing network-based spatial analysis. Overall, stream networks in Arc Map serve as essential datasets for understanding and managing water resources, supporting a wide range of applications in environmental science, engineering, and spatial analysis. To make a flood model for the study area, we put together GIS, the HEC-HMS rainfall-runoff model, and the HEC-RAS hydraulic model, as shown in Figure 5 (Olayinka & Irivbogbe 2017; Meresa 2019; Nagarajan et al. 2022). The process of developing flood hazard cross-sections involves understanding model creation, input and output data, and interconnections. To prepare the input parameter, the dataset establishes a geodatabase in Arc Map (Kiran et al. 2022; Pradhan et al. 2022; Kant et al. 2023; Verma & Mirajkar 2024). We import and reproject the base data to the projected coordinate system WGS 1984/UTM zone 44 N datum before clipping it to the study region. We prepare the input dataset and then use HEC-HMS to create a rainfall-runoff model (Olayinka & Irivbogbe 2017). We use the results of the rainfall-runoff simulation to run the 1D flow routing model and create cross-sections for the specific strip (Kiran et al. 2022). We selected the 2015 flood, a major storm event in the Kosasthalaiyar basin's history, for model simulation. We use the RCP 4.5 scenario in SDSM software to downscale the GCM GFDL 2.0 data, obtaining future precipitation data for 2030, 2050, 2080, and 2100 years. We process the precipitation data using the same HEC-HMS rainfall-runoff model and the HEC-RAS hydraulic model.

Rainfall-runoff model: HEC-HMS

We employ the HEC-HMS hydrological model, integrated with GIS, to develop the basin model. HEC-HMS simulates the runoff response of the area under consideration to a given amount and distribution of precipitation over a defined time period. When high-precision datasets are unavailable, we extract land surface information from a DEM. In the current study, we present the 30 m × 30 m resolution DEM from the SRTM in Figure 3. Figure 5 represents the steam networks for the area, prepared by processing the DEM in Arc Map. Figure 1 segments the study area into a series of interconnected sub-basins. Inputs for the HEC-HMS model include land-use information, hydrological soil groups, and rainfall event data. Table 2 represents the calculation of percentage imperviousness for each sub-basin based on land-use type. We also extract other physical characteristics from the SRTM DEM, including the length and slope of the stream, the location of the sub-basin centroid and its elevation, the longest flow path for each sub-basin, and the length along the stream path. Figure 6 depicts the basin model that was developed using the HEC-HMS setup. The model integrates various hydrological components, including precipitation, runoff, and streamflow routing, to simulate the hydrological behaviour of the catchment areas in northeast Tamil Nadu.
Table 2

Percentage imperviousness based on LULC

Sub-basinImpervious (%)Sub-basinImpervious (%)
Sub-basin 1 Sub-basin 22 
Sub-basin 2 Sub-basin 23 
Sub-basin 3 Sub-basin 24 30 
Sub-basin 4 Sub-basin 25 
Sub-basin 5 15 Sub-basin 27 
Sub-basin 7 Sub-basin 28 
Sub-basin 8 Sub-basin 29 30 
Sub-basin 9 Sub-basin 30 
Sub-basin 10 65 Sub-basin 31 
Sub-basin 11 Sub-basin 32 
Sub-basin 12 30 Sub-basin 33 
Sub-basin 12 15 Sub-basin 35 30 
Sub-basin 14 Sub-basin 37 15 
Sub-basin 15 15 Sub-basin 38 100 
Sub-basin 16 Sub-basin 39 
Sub-basin 17 Sub-basin 42 30 
Sub-basin 18   
Sub-basin 19 30   
Sub-basin 20 30   
Sub-basinImpervious (%)Sub-basinImpervious (%)
Sub-basin 1 Sub-basin 22 
Sub-basin 2 Sub-basin 23 
Sub-basin 3 Sub-basin 24 30 
Sub-basin 4 Sub-basin 25 
Sub-basin 5 15 Sub-basin 27 
Sub-basin 7 Sub-basin 28 
Sub-basin 8 Sub-basin 29 30 
Sub-basin 9 Sub-basin 30 
Sub-basin 10 65 Sub-basin 31 
Sub-basin 11 Sub-basin 32 
Sub-basin 12 30 Sub-basin 33 
Sub-basin 12 15 Sub-basin 35 30 
Sub-basin 14 Sub-basin 37 15 
Sub-basin 15 15 Sub-basin 38 100 
Sub-basin 16 Sub-basin 39 
Sub-basin 17 Sub-basin 42 30 
Sub-basin 18   
Sub-basin 19 30   
Sub-basin 20 30   
Figure 6

HEC-HMS basin model for the Kosasthalaiyar basin.

Figure 6

HEC-HMS basin model for the Kosasthalaiyar basin.

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We employ the soil conservation service (SCS) unit hydrograph method as a transform model to convert rainfall into runoff. The lag time is the only input required for this transform model. According to the HEC-HMS technical reference manual, the lag time for ungauged watersheds should be 0.6 times the time of concentration. We calculate the time of concentration for each sub-basin using the Kirpich formula (Kamath et al. 2011; Jung et al. 2017), and then calculate the lag time accordingly. We chose the SCS method for analysis due to its suitability in all environmental conditions, its simplicity in calculations requiring only a few variables (lag time, land-use type, and slope), and its ability to produce results comparable to those of complex models. The HEC-HMS model provides rainfall input to the model, and control specifications collectively specify the model for event simulations. We simulate the rainfall events from November to December 2015 (Figure 7(a)) to examine the impact of historical rainfall on current land-use conditions. The model's control specifications define the time range for simulating the rainfall event. The Muskingum routing method manages water movement within the reach. The discharge hydrograph at each sub-basin outlet is the model's output. Each sub-basin's runoff response is unique due to differences in watershed properties, including geology and geomorphology.
Figure 7

(a) Daily rainfall of Kosasthalaiyar sub-basin (November–December 2015). (b) Daily rainfall of Kosasthalaiyar sub-basin (November–December 2030) from GFDL 2.0. (c) Daily rainfall of Kosasthalaiyar sub-basin (November–December 2050) from GFDL 2.0. (d) Daily rainfall of Kosasthalaiyar sub-basin (November–December 2080) from GFDL 2.0. (e) Daily rainfall of Kosasthalaiyar sub-basin (November–December 2100) from GFDL 2.0.

Figure 7

(a) Daily rainfall of Kosasthalaiyar sub-basin (November–December 2015). (b) Daily rainfall of Kosasthalaiyar sub-basin (November–December 2030) from GFDL 2.0. (c) Daily rainfall of Kosasthalaiyar sub-basin (November–December 2050) from GFDL 2.0. (d) Daily rainfall of Kosasthalaiyar sub-basin (November–December 2080) from GFDL 2.0. (e) Daily rainfall of Kosasthalaiyar sub-basin (November–December 2100) from GFDL 2.0.

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The model was calibrated by hand at Sholavaram. In November 2015, performance metrics like Nash–Sutcliffe efficiency (NSE) and r2 were used to evaluate the calibration. Parameters like curve number (CN) and time of concentration (Tc) were changed until the simulated results were very close to the observed data. In calibration, NSe and r2 values are 0.81 and 0.78, respectively. After calibration, we validated the model for December 2015 by comparing the simulated results with the observed data for validation (NSE), resulting in r2 values of 0.77 and 0.73, respectively.

Hydrographs from the rainfall-runoff model are saved as time series data and directly input into the hydraulic model we developed using HEC-RAS. We used GFDL 2.0, a general circulation model (GCM), to predict future climate data under the RCP 4.5 scenario for the years 2030, 2050, 2080, and 2100, as illustrated in Figure 7(b)–7(e), respectively. On November 28, in the year 2030, the maximum precipitation observed was 45.5 mm. Two days, the 11th and 23rd of December, saw the maximum precipitation in 2050. We observed the maximum precipitation on November 20 and November 29, respectively, for 2080 and 2100. We will collect this data for November and December using the statistical downscaling model. We used the statistical downscaling model to reduce the coarse-resolution outputs of the GFDL 2.0 model to a finer spatial resolution suitable for regional analysis of India's southeast catchments. This downscaling process captures the detailed climate variations and trends necessary for accurate hydrological modelling. Further, Figure 7(b)–7(e) demonstrates the projected changes in precipitation during the critical months of November and December for the years 2030, 2050, 2080, and 2100, which are pivotal for understanding the hydrological responses and flood risks in the region. These projections are integral to the HEC-HMS model simulations, providing essential inputs for predicting future flood events and aiding in the development of robust flood management and mitigation strategies. For accurate hydrological modelling in the HEC-HMS setup, the basin parameters for each sub-basin (Table 3) are very important. These include CN, longest flow path length, basin slope, potential maximum retention, and time of concentration. The CN denotes the soil's infiltration characteristics, which directly influence runoff generation. The higher value for sub-basin 17 is 97.5; on the other hand, the lower value for sub-basin 12 is 55.5. It depicts that lower numbers result in low runoff, while higher numbers indicate rising runoff potential. The longest flow path length determines the distance water travels within the sub-basin; for sub-basin 7, the maximum distance is 57.08 km, and for sub-basin 15, the least distance is 8.71 km. This will influence the time it takes for runoff to reach the outlet. We observed a maximum slope of 5.531% for sub-basin 11, and a minimum slope of 2.776% for sub-basin 20. The steeper slopes generally lead to faster runoff. Potential maximum retention (S): sub-basin 13 records a higher value of 8.05 inches, while sub-basin 17 records a lower value of 0.25 inches. This value indicates the maximum water retention capacity of the basin before generating runoff, and it inversely correlates with the CN. Tc is the time required for water to travel from the most distant point in the sub-basin to the outlet, impacting the timing and peak of the flood hydrograph. For sub-basin 7, the highest Tc is 10.928 h, and for sub-basin 11, the lowest Tc is 1.686 h. We integrate these parameters, derived from topographic and soil data, into the HEC-HMS model to accurately simulate hydrological processes and predict flood events under various climate scenarios.

Table 3

Basin parameters

Kosasthalaiyar River sub-basin parameters
Sub-basinCNLongest flow path length (km)Basin slope (Y)Potential maximum retention (S) = (1,000/CN) − 10 (in)Basin slope (Y%)Time of concentration (h)
Sub-basin 1 74.0 22.47 0.073 3.52 7.3 4.388 
Sub-basin 2 94.8 26.11 0.045 0.54 4.478 2.978 
Sub-basin 3 98.3 20.25 0.043 0.18 4.325 2.046 
Sub-basin 4 78.4 12.47 0.029 2.75 2.852 3.846 
Sub-basin 5 79.6 10.53 0.034 2.56 3.369 2.978 
Sub-basin 7 79.1 57.08 0.039 2.64 3.856 10.928 
Sub-basin 8 78.4 13.80 0.033 2.76 3.335 3.862 
Sub-basin 9 72.5 19.45 0.032 3.79 3.184 6.169 
Sub-basin 10 73.7 28.62 0.034 3.58 3.414 7.854 
Sub-basin 11 96.2 16.01 0.055 0.39 5.531 1.686 
Sub-basin 12 72.0 11.85 0.032 3.88 3.233 4.171 
Sub-basin 13 55.4 11.51 0.032 8.05 3.189 6.322 
Sub-basin 14 85.6 18.26 0.031 1.68 3.146 3.927 
Sub-basin 15 60.3 8.71 0.032 6.58 3.2 4.460 
Sub-basin 16 69.4 28.04 0.030 4.42 3.041 9.212 
Sub-basin 17 97.5 17.81 0.045 0.25 4.523 1.884 
Sub-basin 18 85.5 33.84 0.046 1.70 4.63 5.333 
Sub-basin 19 65.3 11.95 0.032 5.32 3.221 5.044 
Sub-basin 20 60.7 15.14 0.028 6.48 2.776 7.380 
Sub-basin 22 80.6 14.18 0.037 2.41 3.72 3.491 
Sub-basin 23 82.7 18.08 0.042 2.10 4.227 3.717 
Sub-basin 24 77.6 21.06 0.032 2.89 3.159 5.704 
Sub-basin 25 79.3 15.89 0.051 2.61 5.111 3.396 
Sub-basin 27 93.1 20.36 0.036 0.74 3.566 2.976 
Sub-basin 28 76.1 16.29 0.034 3.13 3.372 4.689 
Sub-basin 29 72.1 18.64 0.036 3.86 3.552 5.699 
Sub-basin 30 80.7 15.29 0.051 2.40 5.088 3.163 
Sub-basin 31 66.6 16.04 0.032 5.02 3.201 6.181 
Sub-basin 32 85.2 36.94 0.035 1.74 3.533 6.616 
Sub-basin 33 78.5 19.43 0.037 2.74 3.658 4.829 
Sub-basin 35 71.6 21.24 0.030 3.97 3.027 6.964 
Sub-basin 37 89.2 27.42 0.041 1.21 4.085 4.170 
Sub-basin 38 56.5 7.60 0.042 7.71 4.245 3.826 
Sub-basin 39 66.2 20.87 0.031 5.11 3.081 7.863 
Sub-basin 42 67.0 21.88 0.030 4.93 3.012 8.091 
Kosasthalaiyar River sub-basin parameters
Sub-basinCNLongest flow path length (km)Basin slope (Y)Potential maximum retention (S) = (1,000/CN) − 10 (in)Basin slope (Y%)Time of concentration (h)
Sub-basin 1 74.0 22.47 0.073 3.52 7.3 4.388 
Sub-basin 2 94.8 26.11 0.045 0.54 4.478 2.978 
Sub-basin 3 98.3 20.25 0.043 0.18 4.325 2.046 
Sub-basin 4 78.4 12.47 0.029 2.75 2.852 3.846 
Sub-basin 5 79.6 10.53 0.034 2.56 3.369 2.978 
Sub-basin 7 79.1 57.08 0.039 2.64 3.856 10.928 
Sub-basin 8 78.4 13.80 0.033 2.76 3.335 3.862 
Sub-basin 9 72.5 19.45 0.032 3.79 3.184 6.169 
Sub-basin 10 73.7 28.62 0.034 3.58 3.414 7.854 
Sub-basin 11 96.2 16.01 0.055 0.39 5.531 1.686 
Sub-basin 12 72.0 11.85 0.032 3.88 3.233 4.171 
Sub-basin 13 55.4 11.51 0.032 8.05 3.189 6.322 
Sub-basin 14 85.6 18.26 0.031 1.68 3.146 3.927 
Sub-basin 15 60.3 8.71 0.032 6.58 3.2 4.460 
Sub-basin 16 69.4 28.04 0.030 4.42 3.041 9.212 
Sub-basin 17 97.5 17.81 0.045 0.25 4.523 1.884 
Sub-basin 18 85.5 33.84 0.046 1.70 4.63 5.333 
Sub-basin 19 65.3 11.95 0.032 5.32 3.221 5.044 
Sub-basin 20 60.7 15.14 0.028 6.48 2.776 7.380 
Sub-basin 22 80.6 14.18 0.037 2.41 3.72 3.491 
Sub-basin 23 82.7 18.08 0.042 2.10 4.227 3.717 
Sub-basin 24 77.6 21.06 0.032 2.89 3.159 5.704 
Sub-basin 25 79.3 15.89 0.051 2.61 5.111 3.396 
Sub-basin 27 93.1 20.36 0.036 0.74 3.566 2.976 
Sub-basin 28 76.1 16.29 0.034 3.13 3.372 4.689 
Sub-basin 29 72.1 18.64 0.036 3.86 3.552 5.699 
Sub-basin 30 80.7 15.29 0.051 2.40 5.088 3.163 
Sub-basin 31 66.6 16.04 0.032 5.02 3.201 6.181 
Sub-basin 32 85.2 36.94 0.035 1.74 3.533 6.616 
Sub-basin 33 78.5 19.43 0.037 2.74 3.658 4.829 
Sub-basin 35 71.6 21.24 0.030 3.97 3.027 6.964 
Sub-basin 37 89.2 27.42 0.041 1.21 4.085 4.170 
Sub-basin 38 56.5 7.60 0.042 7.71 4.245 3.826 
Sub-basin 39 66.2 20.87 0.031 5.11 3.081 7.863 
Sub-basin 42 67.0 21.88 0.030 4.93 3.012 8.091 

We compute the peak discharges for our rainfall-runoff model from November to December 2015 using the rainfall-runoff model HEC-HMS. On December 2 at 9:30 a.m. in 2015, we observed the peak discharge of 4,057 m3/s at the outlet we selected for our basin, which had a volume of 1,159.32 mm (Figure 8(a)). From Figure 8(a), it can observe that the peak flow will occur twice within a 2-month span. Figure 8(b) shows that a detailed picture of the hydrograph and hyetograph for each sub-basin provides insights into the timing and intensity of rainfall events, as well as their corresponding impacts on streamflow. Hydrograph analysis for 2030, 2050, 2080, and 2100: The analysis of the hydrographs for the years 2030, 2050, 2080, and 2100 reveals significant insights into future peak flow events. Figure 8(c)–8(f) illustrates the projected peak flows for these years. We project the peak flow in 2030 to reach the basin's outlet at 581.6 m3 /s on 28 November 2030 at 10:20 a.m., with a runoff of 142.7 mm. We project the peak flow to occur at the basin's outlet at 110.7 m3/s on December 12 at 9:20 a.m. in 2050, with a runoff of 75.31 mm. We project the peak flow to reach 997 m3/s at the basin's outlet on December 20 at 9:50 a.m. in 2080, with a runoff of 461.73 mm. We project the peak flow in 2100 to occur at the basin's outlet at 1,438.4 m3/s on November 29 at 9:40 a.m., with a runoff of 248.22 mm. Climate change (precipitation pattern) caused the peak flow in 2050 to be smaller in scale compared with other years (2030, 2080, and 2100). These projections highlight a potential pattern of peak flow timing within the 2-month span of November–December.
Figure 8

(a) Hydrograph for 2015 precipitation data. (b) Hydrographs for each sub-basin 1 to sub-basin 35. (c) Hydrograph for 2030 precipitation data. (d) Hydrograph for 2050 precipitation data. (e) Hydrograph for 2080 precipitation data. (f) Hydrograph for 2100 precipitation data.

Figure 8

(a) Hydrograph for 2015 precipitation data. (b) Hydrographs for each sub-basin 1 to sub-basin 35. (c) Hydrograph for 2030 precipitation data. (d) Hydrograph for 2050 precipitation data. (e) Hydrograph for 2080 precipitation data. (f) Hydrograph for 2100 precipitation data.

Close modal

Hydraulic model: HEC-RAS

We calibrated the model by adjusting hydraulic parameter (Manning's n) to match simulated water levels with observed values collected from CWC (I-WRIS). Calibrate Manning's n using flow data from a specific storm in November 2015, and then validate the model using a different event in December 2015. We determined the average Manning's roughness coefficient to be 0.04524. We provide the peak discharge as input for HEC-RAS for the years 2030, 2050, 2080, and 2100. The observed cross-section is in Sholavaram, Tamil Nadu. The peak discharge of 2030 was 581.6 m3/s on 28 November 2030, at 10:20 a.m., the channel cross-section is shown in Supplementary Figure S1. On December 12 at 9:20 a.m., we will observe the peak discharge of 2050 at 10.7 m3/s and illustrate the channel's cross-section in Supplementary Figure S2. Similarly, Supplementary Figures S3 and S4 show that the peak discharges of 2080 and 2100 are 997 and 1,438.4 m3/s, on December 20 at 9:50 a.m. and November 29 at 9:40 a.m. respectively. We observe in the channel's cross-section that the water level in 2050 is lower than in previous years due to changes in precipitation patterns. These results underscore the critical role of HEC-RAS in understanding the hydraulic impacts of climate change and facilitating the development of effective flood management and mitigation strategies to safeguard communities and infrastructure against future flood risks. Further, in comparison with the present study, wherein, the use of HEC-HMS for modelling rainfall-runoff and HEC-RAS for modelling hydraulics, agrees with what other researchers have found about urban floods. For instance, Kamath et al. (2011) used the SCS unit hydrograph method for rainfall-runoff modelling, concluding that its simplicity and adaptability to diverse conditions make it ideal for flood prediction. This corresponds with the current study's use of the same process to calculate lag times and runoff patterns. Similarly, Olayinka & Irivbogbe (2017) highlighted the effectiveness of integrating GIS with HEC-HMS and HEC-RAS for detailed spatial analysis, particularly in urban flood modelling. Also, their research showed how important high-resolution DEMs and LULC data are for figuring out flow patterns and flood risks. This shows that adding GIS to models makes them much more accurate. Future climate scenarios, such as RCP 4.5, are critical components of flood prediction in climate change studies. In addition, Zhang et al. (2022) used future precipitation data to simulate flood events, revealing rising flood risks in urban areas under changing climate conditions. Our study's findings on the projected changes in peak flow for 2030, 2050, 2080, and 2100 echo these concerns, as the results indicate a shift in flood peak occurrences during November and December due to altered precipitation patterns. This is consistent with Nagarajan et al. (2022), who reported that changes in monsoon dynamics and urbanisation escalate flood hazards in rapidly developing regions, further validating the present study's conclusion on the influence of urbanisation and climate change. The comparison of percentage imperviousness across sub-basins in the Kosasthalaiyar River basin also finds parallels in other studies. Meresa (2019) and Rahaman (2022) both noted the significant role of impervious surfaces in increasing runoff and decreasing infiltration rates in urban basins. Our study, which showed higher imperviousness in sub-basins with increased urbanisation (such as sub-basin 10 with 65% imperviousness), aligns with their findings that urban development amplifies flood potential. These comparisons reinforce the broader consensus that urbanisation, when combined with changing climate patterns, requires advanced flood risk management and planning using reliable hydrological and hydraulic models.

Urban flooding is becoming increasingly frequent due to urbanisation and climate change. During monsoon seasons, drainage systems often surpass their capacity, leading to overland flooding. This study focused on the Kosasthalaiyar River basin in Chennai, Tamil Nadu, analysing peak floods from 2015 and forecasting future flood scenarios using climate models. The results indicate significant peak floods for 2030, 2050, 2080, and 2100, with projected flows increasing over time. Rainfall-runoff modelling and floodplain analysis highlight the growing flood risks, emphasising the need for better flood management in urban areas.

The present study conducts floodplain analysis on the Kosasthalaiyar River sub-basin using HEC-HMS, HEC-RAS, and Arc Map to estimate probable peak flows for the years 2030, 2050, 2080, and 2100. From the ongoing study, the following conclusions are drawn:

  • (a) From the SCS method, the significant peak floods, namely, 581.6 and 110.7 m3/s, can be observed on 28 November 2030 and 12 December 2050, respectively.

  • (b) Further, on 20 December 2080 and 29 November 2100, peak floods of 997 and 1,438.4 m3/s were observed, respectively.

  • (c) These peak floods are particularly visible in Sholavaram, Tamil Nadu, emphasising areas that may be more vulnerable in future flood scenarios.

  • (d) These floods may have a heavy impact on infrastructure, agriculture, and human lives. Apart from climate change, urbanisation significantly contributes to severe floods in urbanised areas by reducing the land's infiltration capacity and increasing the flow of precipitated water on the surface.

As urbanisation is one of the developing aspects of a country's growth, it provides various benefits but also has several drawbacks. Therefore, we need to follow various techniques to mitigate the flood risk, like roof-top rainwater harvesting and afforestation. The current study recommends the following actions to lessen the risk of flooding, particularly in metropolitan areas susceptible to urbanisation and climate change:

  • (a) Upgrade and expand drainage infrastructure: This will help to keep rainwater and sewage systems apart and lessen overflow during periods of intense precipitation. To avoid clogs, make sure your drains are cleaned and maintained on a regular basis.

  • (b) Green infrastructure: To improve water infiltration, lessen runoff, and ease the burden on drainage systems, install green infrastructure such as rain gardens, permeable pavements, green roofs, and urban wetlands.

  • (c) Restoration of floodplains: Preserve and restore natural wetlands and floodplains, which serve as buffers by soaking up extra water during periods of high precipitation, lowering the chance of flooding downstream.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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