Introduction

Globally, the occurrence of floods has been unprecedented resulting in huge economic and social losses (Simonovic et al. 2021). In India, a country with varying topography and climatic conditions, the frequency of floods recorded in cities is increasing drastically (Dhiman et al. 2018). In 1960, 18% of the country population was urban which increased to 28% in the year 2000 and 35% in the year 2019 with an average urbanization of around 2.5% per year. Urban population is expected to go beyond 50% by 2050, in search of employment opportunities and with the development of Smart Cities (Sukhwani et al. 2020). An increase in the urbanization results in uncontrolled increasing settlements, industrial growth and infrastructure development (Al Jarah et al. 2019). Several urban areas in the world are not functioning well because of the population growth, improper planning, lack of knowledge, canal encroachments, demolishing of water bodies, leading to the stress on the urban areas (Ferronato and Torretta 2019). Among important cities in India, the average annual rainfall varies from 2932 mm in Goa and 2401 mm in Mumbai on the higher side, to 669 mm in Jaipur on the lower side (Malik 2017). Increase in population and settlement results in overloading of existing drainage system in cities resulting in urban floods (Vorobevskii et al. 2020). Climate change also plays a major role in triggering floods by changing monsoon pattern, land use land cover, increase of greenhouse gases, demographic and socioeconomic changes (Loo et al. 2015). A change in climatic pattern also leads to the increase in sea level resulting as a threat to all coastal cities (Mimura 2013). To mitigate urban flood disasters, innovative approaches may be adopted to reduce the loss caused by the climate change on urban flooding, which may hinder the growth of city and associated economy (Miller and Hutchins 2017). To fulfill the challenges of climate change and its impacts on urban flooding, the problems need to be addressed (Huynh Thi Lan and Pathirana 2011). The present review article focuses on urban floods in India and the tools that can be adopted for the modeling of floods and help in disaster mitigation and management purposes.

Covering an area of 3,287,263 km2 (1,269,346 sq. mi), from the Himalayas in the north to the Indian Ocean in the south, India is one of the oldest and richest cultural heritage countries in the world with several major rivers such as Ganga (2525 km), Godavari (1465 km), Cauvery (800 km), Krishna (1401 km), Mahanadhi (851 km), Narmada (1312 km) and Yamuna (1370 km). With the population of around 1.36 billion population, India is severely affected by hazards such as monsoon floods, flash floods, earthquake, drought, landslides and urban floods recently (De et al. 2013). With the increase in the movement of population towards cities, based on the census report (2011), Mumbai has the highest population followed by Delhi (India). Table 1 lists the percent growth of population over a decade gap in the major metropolitan cities in India. Hyderabad population has increased 113.04%, which is more than twice the amount when compared to 2001 population (UNDP 2012). Bangalore, Chennai and Surat are the major cities, where the population increased about 96.30, 63.18 and 83.33% in 2011 compared to 2001 respectively. Population growth leads to the burden on metropolitan cities in terms of facility creation, infrastructure, roads, railway network, canals, rivers, etc. Improper design and planning result in economic loss and loss of human life, and one of the best examples for such scenario is Chennai floods in Tamil Nadu that occurred in the year 2015.

Table 1 Major cities and population of India
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History of floods in India

India is one of the fast developing urban systems and a country with a drastic increase in population over decades (Sun et al. 2020). With a vast number of flood events recorded in India, some of the major floods are listed in Table 2. Due to the migration of population towards cities, a massive amount of population is exposed to urban floods (Lyu et al. 2019). In 1943, around 5000 to 10,000 people died in Rajputna floods, and the second largest flood event recorded in India was in 2013 that lead to over 5700 causalities (UNDP 2012). Figure 1 shows the percentage of floods in South Asian context. Occurrence of floods over decades has a huge impact on Indian population and resulted in economic loss (Parida et al. 2021). Table 3 lists the yearly total area affected due to floods and its impact to the population since 1953.

Table 2 History of major floods in India
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Fig. 1
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Floods in India and nearby contributing countries

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Table 3 Year-wise flood damages from 1953 to 2016
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(*Source: EM- DAT and local, regional data)

(Source: CWC)

Urban flood occurs in cities, metropolitans and developed areas (Rahman et al. 2016). In India, urban floods are recently emerging disaster due to the development of urban settlement (Gupta 2020). Many people migrate towards cities in search of employment opportunities and to lead a comfortable life (Moses et al. 2017). Population and over-explosion is two of the major issues in India as it is the second most populated country in the world behind China (Suresh et al. 2018). Recently, frequency of urban flood occurrences is increasing in India, and it is identified that metropolitan cities such as Bangalore, Chennai, Hyderabad, Kolkata, Delhi, Ahmedabad, Surat, Guwahati and Mumbai are affected by urban floods at a larger magnitude (Surampudi and Yarrakula 2020).

In 2005, 26th of July, Mumbai faced 944 mm (37.17 inches) of rain causing huge floods that made lots of people stranded, losing their homes, livelihood, etc. Economic loss due to the flood event was estimated nearly 100 million dollars (Das et al. 2007). Figure 2 shows the images captured during the disaster occurrence in Mumbai city.

Fig. 2
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Mumbai floods 2005 (people following a single, safe pathway for evacuation from the flooded area)

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Jammu and Kashmir faced highest flood of the century between 2nd and 26th September of 2014 as shown in Fig. 3. Floods in Jammu and Kashmir were a result of high-intensity rainfall over a short period of time, effect of climate change and lack of capacity in the drainage system that failed to withstand the substantial quantity of water, resulting in overflow, which ultimately caused floods (Mishra 2015).

Fig. 3
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Jammu and Kashmir floods 2014 (view of J&K submerged in massive flood)

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Chennai city experienced one of the severe floods between 8th November and 14th December 2015, due to the heavy rainfall of 1049 mm (41.3 inch), three times its monthly rainfall (J. and Chandar 2015). The flooding in Chennai city was worse due to years of improper development and poor levels of flood preparedness (UNDP 2012; Sundaram and Yarrakula 2017). Nearly 500 deaths were recorded, and property loss was estimated about 3 billion US dollars (200 billion rupees). Most of the city was submerged in water due to urban floods (Seenirajan et al. 2017). Figure 4 shows the image captured during the disaster.

Fig. 4
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Chennai floods 2015 (view of Chennai submerged in storm water flood)

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With the advancement in technologies, several early warning systems are implemented, and experts are educating the general public about the seriousness of a disaster event. Fatalities are recorded at a higher rate due to the lack of awareness of people towards protective measures and emergency situations, improper planning of structures, encroachments in dried water bodies, occupying pavements, dumping garbage in drainage and pathways, etc.

Causes of urban floods

Global warming, urbanization and improper land use patterns are the major reasons that triggers urban flooding (Handayani et al. 2020). Global warming leads to climate change resulting in sudden and intense rainfall like cloud burst which causes floods. Improper settlement distribution, encroachment on river bed or lakes, improper planning and lack of draining network design maintenance, garbage dumping and siltation are some of the reasons for urban floods (Hasnat et al. 2018). Improper planning and maintenance of runoff water during heavy rainfall lead to the rise in the water level in rivers and lakes leading to flash floods in urban settlements (Ancona et al. 2014; Chung et al. 2015). Encroachment of dried-up areas of lakes, river bed and establishing settlements is the main reason for urban flooding (García-Pintado et al. 2015; Konrad 2016). An increase in urbanization leads to the variation in the catchment areas resulting in the development of impervious regions that reduces infiltration and increases the runoff leading to floods (Gebre SL 2015; Du et al. 2019). Ghimire (2013) studied the impacts of extreme climate rainfall and developed model rainfall profiles for representing rainfall under different conditions (Ghimire 2013). Flooding in cities is caused by slow accumulation of flood or runoff water and rapid inundation of water in low-lying areas (Jang 2015). Cities located near coastal region experience high tide from storms causing inflow of seawater causing floods (Lund 2012). Flash flood are triggered by sudden and intense rainfall; such floods can be predicted by using an effective process oriented urban flood model (Suarez et al. 2005; Tazyeen and Nyamathi 2015). Figure 5 illustrates the major factors that influence urban floods.

Fig. 5
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Major factors that influence urban floods

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Impacts of urban floods

Urban floods result in higher causalities and economic loss compared to any other type of floods, as they hit urban settlement directly (Rubinato et al. 2019). Urban floods disturb human’s socioeconomic activities at local, regional and even national level (Wang 2015). One of the major impact of flood event is loss of lives by drowning and transmission of diseases by water (Dewan 2015). Due to overpopulation and complex urban networks, relocation of people during the disaster event is a challenging task resulting in loss of livelihood (Satterthwaite et al. 2010). Restoration of flood hit location is time consuming and challenging in India due to lack of awareness, facilities, participation and group activities (Safiah Yusmah et al. 2020). During a flood event, evacuation proves to be a complex task because of population, locating the survivors, prioritizing emergency rescues, etc. (Rufat et al. 2015). Few major issues faced during an urban floods are listed below.

  • Transportation obstruction, submergence of roads under water during urban floods (Suarez et al. 2005).

  • Urban flood causes various waterborne diseases affecting water quality and chances of epidemics causing distress to the people (Ouyang et al. 2012).

  • Urban pluvial floods lead to severe damages and disruption in highly urbanized and populated areas (Simoes et al. 2015).

  • Flood also causes severe damage to crop and any vegetation (Baky et al. 2012; Kwak et al. 2015).

  • In India, major cities such as Chennai, Mumbai and Kolkata are near to coastal region making them vulnerable to coastal flooding (de Sande et al. 2012).

  • Higher level of precipitation in monsoon seasons causes flooding in low-lying regions especially poorly planned areas, where the economy of the people is affected directly (Ramlal and Baban 2008).

  • Urban floods cause heavy economic and property loss. Major metropolitans such as Chennai, Mumbai faced millions of dollars loss because of urban floods (Ramlal and Baban 2008).

  • Daily activities were obstructed. Evacuation of people was cumbersome due to high population

Factors influencing urban floods

Climate change

Climate change plays an important role in urban floods (Zhou 2014; Emilsson and Ode Sang 2017). The abrupt change in climate affects the season and monsoons of a particular area resulting in unexpected rainfall which results in flash floods (Mujumdar et al. 2020). Flash floods are considered to be dangerous because of their uncertainty (Lakshmi and Yarrakula 2018). A large amount of rain could cause damage to property, livestocks as well as loss of life of humans (Kanianska 2016). Flash floods over the urban region are critical than flood over river basin. One of the major challenges faced by the global countries across the world is climate change (Kundzewicz et al. 2014). The impact of climate change is inevitable in the present decade and has a direct effect on the urban population (Milesi and Churkina 2020). Several models and technologies are being developed for the prediction of climate change, yet few limitations are faced in implementation of the models at real time (Singh and Singh 2012). Combination of numerical and satellite-based models integrated with artificial intelligence is widely used in the prediction of the disaster event precisely and is successfully adopted in near real-time analysis (Sun and Scanlon 2019).

Climate resilience will be an essential factor in adaptation of the effects of climate change (Carter et al. 2015). Global cities, particularly Asian cities, should follow urban flood resilience schemes mainly aiming on land use and environment aspects (Albano et al. 2015; Qi et al. 2020). The ability of a city or urban region to withstand a series of shocks and stress is referred as urban resilience (Kim and Lim 2016). Urban climate resilience is withstanding and adapting the change in the climate system over a period of time and ensuring proper methods and ways to understand the conditions for survivability (Egerer et al. 2021). Ecological and economic resilience should be promoted through urban governance and institutions (Meyer and Auriacombe 2019). Urban resilience results in disaster risk and hazard reduction (Ferreira and Lourenco 2019). Factors such as heavy storm, lack of storm drainage systems, population explosion and urbanization are considered as the major contribution for urban flood risk, whereas climate change also proves to be an important factor in the event of flood occurrences which contributes heavier and frequent storms (Morita 2014). Due to global warming, meteorological research is exercised vastly for predicting the changes in the characteristics of rainfall/storm (Wu et al. 2016). Several methods are used for modeling the flood frequencies, such as considering global warming and rainfall intensities, vulnerability assessment of flood-prone urban areas using greenhouse flood data (Shrestha and Lohpaisankrit 2017). Double CO2 conditions indicate the possibility of increase in both the magnitude and frequency of flood events (Fowler and Hennessy 1995). Morita (2014) developed a damage potential curve using a simple return period shift method (RPS) from the present damage potential curve for studying the changes in the damage potential curve of post global climate change conditions (Brown and Saunders 2020). An increase in precipitation intensity and a decrease in snowpack (glaciers) are some of the adverse effects of climate change (Sivalingam et al. 2021). Rain-generated floods occur more at areas having an increase in frequency and intensity of heavy rainfall (Tabari 2020).

Land use and land cover on urban floods

Land cover data consists of regions covered by naturally formed features such as forest, mountains, wetlands (Barredo and Engelen 2010), whereas land use resembles the use of landscape for various human uses (Anderson et al. 1976). Adopting traditional methods, it is a time-consuming process to monitor large area and identify the land use land cover pattern (Reddy et al. 2019). Recent advancement in technologies leads to the use of images obtained from satellites for determining land use land cover pattern (Alam et al. 2020). In order to estimate the changes over a period of time, change detection analysis of the features is estimated using temporal analysis by analyzing land cover land use maps (Alawamy et al. 2020). Land use change affects the climate through activities like deforestation, urbanization (Arshad et al. 2020). Flood losses are estimated by various methods such as GIS tools and remote sensing imageries (Elkhrachy 2015). Remotely sensed data are used progressively for mapping land use and land cover; such land use and land cover information provides a detailed report of regions that are more prone to flood loss (Gómez et al. 2016). Reclassification of existing land use classes into desired groups results in better information on estimation of flood occurrences and damage assessment (Prütz and Månsson 2021). Temporal analysis of urban change by detecting the change in land use and land cover shows the status of surface water situation (Hua 2017). Based on the change in urbanization, a model is constructed to simulate the response of surface water environment (Mason et al. 2014). The model identifies the direct effect of urbanization in water surface quantity and quality. Primary and secondary losses caused by flood events can be prevented through better planning of land use, especially in urban areas (Loucks and van Beek 2017). Direct and indirect losses can also estimate and be prevented by using better flood emergency measures (Tanoue et al. 2020). Integration of flood models, urbanization, delineation of watershed (flood prone areas) zones and land use land cover information help in minimizing the flood damage (Abdrabo et al. 2020). Local governing authorities must ensure that the planning of urban infrastructures is in approved law and regulations (Ahluwalia 2019). Land use, climate condition and demographic data combined for modeling urban transport system, ensuring a reliable transport system during urban storm flood event (Revilla-Romero et al. 2015; Andimuthu et al. 2019). Future land use scenario is also computed for exploring impacts at the time of excess of expected flood event (Krause et al. 2019).

Importance of urban flood models

Urban flood models are designed and implemented for the prediction and estimation of impact of floods (Xing et al. 2019). Nowadays, mathematical, physical and numerical methods are applied for monitoring the effects and impacts of floods (Croci et al. 2014). Space technologies are widely used for estimating the influence of climate change and its impacts on future urban flooding (Ferreira 2020). Delineation of drainage pattern, watershed and water resource management are effectively carried out using GIS and remote sensing tools (Conesa-Garcia et al. 2010; Carbone et al. 2014; Devaraj and Yarrakula 2020). Huong et al. (2013) used land use simulation model (Dinamica EGO), atmospheric model (WRF), land surface model with vegetation (Noah LSM) and 1-D/2-D urban-drainage model SWMM-Brezo for estimation of flood inundation zones and hazard mapping (Huong and Pathirana 2013). Complex vegetation and water composition is troublesome for creating urban flood inundation models (Talbot et al. 2018). Malinowski et al. (2015) used high-resolution satellite image for overcoming this difficulty (Malinowski et al. 2015). Timbadiya et al. (2014) addressed the simulation of floods and the development of stage–discharge relationship along a river (Timbadiya et al. 2014). 1D hydrodynamic models using MIKE11 are widely used for calibration and validation using low- and high-flood data for forecasting floods (Singh et al. 2020). Tarekegn et al. (2010) conducted a study to integrate remote sensing, GIS with SOBEK 2D flood model (Tarekegn et al. 2010). Digital elevation model (DEM) from ASTER and a GIS procedure are developed to modify the terrain of the river and channel bathymetry and suggested to use ASTER 15-m high accuracy DEM for 2D hydrodynamic modeling (Ettritch et al. 2018). Wang et al. (2008) experimented several methods for developing a grid-based hydrological model for simulating storm water inundation (Wang et al. 2008). Grids of the city, land use and land cover, DEM from the 1:500 digital maps were used and concluded that remote sensing and hydrological models can be integrated to solve problems relating to hydrologic influences (Szypuła 2019). Zhang et al. (2015) investigated Nash–Sutcliffe efficiency (NSE) of the SWAT model and proposed that SWAT shows better model results of wet seasons on comparison with dry seasons (Vorobevskii et al. 2020). SWAT-SC models show significant performance of runoff simulation in the dry period (Budamala and Baburao Mahindrakar 2021). Kulkarni et al. (2013) modeled and designed web GIS-based flood tool, in which the flood impacts were monitored for coastal lying city floods (Kulkarni et al. 2013). Mason et al. (2013) used advanced technologies such as synthetic aperture radar (SAR) for mapping urban floods (Mason et al. 2014). Synthetic aperture radar (SAR) sensors are capable of mapping flood because of its advantage of all weather, day and night mapping capability (Surampudi and Yarrakula 2020). Continuous development of SAR sensors resulted in generation of high-resolution data for monitoring urban floods (Suresh and Yarrakula 2020). Li et al. (2014) used constrained Delaunay triangular irregular network (CD-TIN) data to model urban surfaces; such fine-constrained features provide information on accurate urban water depressions (Li et al. 2014). Gichamo et al. (2012) described about accurate river model, exact representation of the river stream, geometry of the floodplain and concluded that model parameters need to be accurate for predicting the possible river flow magnitude and water levels in the stream (Gichamo et al. 2012). Chen et al. (2009) used Green–Ampt model for infiltration calculation and GIS-based urban flood inundation model (GUFIM). These models replaced physical model, showing high performance and accurate results (Chen et al. 2009). Syme et al. (2004) investigated different models for modeling of urban floods, a quasi-2D model (1D network), 2D raster routing models, full 2D regular grid hydrodynamic models (finite difference), full 2D irregular grid hydrodynamic models (finite element) and finally combination of 1D hydrodynamic models with one of the models to achieve near complete solution (Syme et al. 2004). Audisio et al. (2011) examined the occurrences of flood from historical data and used the data from documents, maps, GIS techniques, field surveys of urban development. The author compared two main flood events: one from the present and the other from the past to display the resemblance and deviations that have changed over years (Audisio and Turconi 2011). Mark et al. (2001) combined physical-based model and GIS and used MOUSE for configuring urban drainages. Free surface flow network and sewer pipe system interaction is modeled in a simple way for representation of real-life situation of urban floods (Ole et al. 2021). Bamford et al. (2008) integrated modeling approach will be useful in effective understanding of flood events (Bamford et al. 2008). Chen et al. (2008) studied surface flood flow modeling, building coverage ratio (BCR), and conveyance factors (CRFs) are introduced to urban inundation model (UIM)(Chen et al. 2008). Turner et al. (2013) used light detection and ranging (LIDAR) technology for flood modeling. Multi-platform (mobile, terrestrial and airborne) LIDAR data is combined to form a composite dataset, and TIN (triangular irregular network) model is generated for modeling accurate flood events (Turner et al. 2013).

Research developments on urban floods

Advancements in field of urban flood modeling 1D/2D(Chen et al. 2008; Audisio and Turconi 2011; Kulkarni et al. 2013; Li et al. 2014; Supriya et al. 2015; Budamala and Baburao Mahindrakar 2021), GIS and remote sensing techniques and various methods are frequently used for urban flood modeling and estimation (Dey and Kamioka 2007; S.M.J.S.Samarasinghea et al. 2010; Ranger et al. 2011; Suroso et al. 2013; Zeng et al. 2015; Zhang et al. 2015). Table 4 shows the various studies and research works on urban floods, flood management, flood risk assessment, flood forecasting, mitigation and management, flood routing, flood modeling, magnitude of floods and simulations. Various researchers who have done different works on urban floods from 2001 to 2015 are listed. Around 35 research finding have been observed from various works. In most of the studies, HECRAS, LISFLOOD, Mike 11, SWMM, TUFLOW, TELEMAC and XP-SWMM are used to monitor urban floods (De et al. 2013; Tazyeen and Nyamathi 2015; Komi et al. 2017; Fleischmann et al. 2017, 2018; Abdessamed and Abderrazak 2019; Vercruysse et al. 2019; Dehghanian et al. 2020).

Table 4 Research work and the models used in flood studies
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Urban flood management and recommendations

Important guidelines are framed by NIDM (National Institute of Disaster Management) India for effectively managing urban floods. They also include some of the measures for urban floods such as early warning system and communication, design and management of urban drainage systems.

Urban flood management includes:

  • Watershed analysis for managing and estimating urban floods.

  • Vulnerability analysis and risk assessment.

  • Estimating flood inundation level for respective rainfall.

  • Designing spatial decision support systems.

  • National and state level flood disaster information systems.

  • Establishing urban flood cells.

  • Emergency flood response teams.

  • Awareness programs and training for both civilians and rescue teams.

Flood management decision support system describes about the category of floods based upon the flood impact and warnings issued for the types of flood. Table 5 shows the effect of flood and necessary action to be taken, provided by national disaster management authority.

Table 5 Flood management decision support system (NDM Guidelines, 2010)
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Management of floods in urban areas plays a vital role in safety of people and sustaining socioeconomic conditions (Notaro et al. 2014). Periodical maintenance and cleaning of drainage facility by removal of garbage increases water infiltration capacity and decreases the surface runoff. Such measures lead to minimizing human loss and economic damages (Haider et al. 2003). Reliable technologies, early warning systems and mitigation are lacking in many developing countries around the world (Hansson et al. 2008). Management of floods also includes effective and improved city planning (Shimokawa et al. 2016), modeling the flow of floods (Chen et al. 2008) and clearing the path for the flow of water without any obstruction into the sea ensuring minimal damage caused by the flood. Yan et al. (2011) proposed that urban flood and rain water can be utilized for better use by building water collection systems, water transportation system, efficient rain water harvesting systems, etc. (Yan et al. 2011). Fanghong et al. (2012) proposed that urban flood studies are key for management and use of rain water at times of water stress or drought conditions. Benefits of urban storm water resources are analyzed and can be used to improve sustainable development of the area (Fanghong et al. 2012). Sande et al. (2012) stated that, in recent technologies such as remote sensing and GIS, it is important to use high-resolution digital elevation models (DEM), which determines the flood risk area by referring the elevation (de Sande et al. 2012). In urban drainage systems, water detention storages are designed and developed to minimize the impact/effect of urban floods (Jang et al. 2007). Prawiranegara (2014) studied on basin wide flood risk assessment and suggested that proper spatial planning and urban resilience policies reduce the flood risk exposure (Prawiranegara 2014). Digital city concept shows managing urban floods by integrating urban storm water cycle with proper urban planning. Both structural and non-structural strategies are utilized for effective flood management. During the event of urban floods, local governments must provide shelter in public structures such as sports halls, schools, auditoriums and malls that are situated in high elevation where the flood cannot be reached (Melgarejo and Lakes 2014). Research effort was to get progress on data collection, analysis and development of models. Empirical and synthetic data collection provides consistent, reliable data. Lo et al. (2015) studied visual sensing for acquiring dynamic image information and used spatio-temporal information for automated remote analysis of urban flood monitoring. By identifying the root causes and characteristics of urban floods, suitable methods and models can be practiced for urban flood management (Lo et al. 2015).

Urban flood risk/hazard assessment

Urban regions exposed and vulnerable to hazard (urban floods) are called (urban flood) risk zone (Solaimani 2009). Many researchers, policy makers, government authorities explained on flood mitigation, management processes and flood risk zone assessment (McGuigan et al. 2015). Lhomme et al. (2013) introduced new concept like urban resilience for reducing urban flood risk (Lhomme et al. 2013). The possibility of flood occurrence over an area and the magnitude of damage or economy loss decides the flood risk over that particular region (de Sande et al. 2012). Assessment of risk and vulnerable zones is needed for effective implementation of flood prevention and mitigation (Marconi et al. 2016) and developing risk reduction strategies. Figure 6 shows the relationship between risk, hazard, exposure and vulnerability.

Fig. 6
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Relationship between risk, hazard, exposure and vulnerability

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Flood risk is essential for evacuation planning and can be done by mapping flood hazard areas (Paquier et al. 2015). Addo et al. (2011) identified the number of building exposed to floods, using aerial photographs for estimating the population at risk (Appeaning Addo et al. 2011). Damage assessment includes three major factors such as flood water velocity, maximum water level discharged and flood event duration. Flood preparedness, disaster response and management during large-scale floods require hazard mapping to improve services and recovery measures. Suitable planning and strengthening the policies result in reduction in disaster risk and maintaining considerable funds, and estimating vulnerability assessment towards disaster events such as urban floods minimizes the collateral damage caused by it.

Conclusion

Every year, India is facing several flood events, and the property as well as loss of lives is also increasing enormously. Due to rapid urbanization, the flood peaks increase 1.8 to 8 times, and volume increased by 6 times; as a result flash, floods are occurring in a matter of minutes. To manage the urban floods in an efficient manner, flood inundation mapping, vulnerable areas in terms of demographic data are to be identified properly. The challenging tasks can be achieved by modeling floods with the available data including high-resolution satellite data, good quality of digital elevation models, rainfall and drainage network. The present review article addresses the frequency of urban floods in India, impacts of urban floods, climate change impacts, urban floods in south Asia, importance of modeling. Apart from this, the government has to create awareness and encourage the people to acquire the knowledge in pre and post disaster events. Public involvement, education can effectively reduce the impact of urban floods.

Improvements in flood inundation modeling tools are developing over decades enabling the researchers and decision makers in prediction of disaster events. Introduction of space based datasets paved way for the development of hydrological models, aiming at monitoring and modeling flood events. Even though technological advances utilize various parameters as input, there is no “perfect model” derived which can be adopted for exact prediction of the climatic variation. Hydrological and hydraulic flood modeling are characterized by several parameters such as topography, flood depth, extent of inundation, time of inundation and velocity of water flow. Models existing require high-resolution input to offer a flood risk assessment information.

Development in space technology addresses the data challenge of providing high-resolution datasets, whereas limitations exist in the cost of operation and acquisition of the datasets. Existing SRTM and ASTER DEM are widely used as a topographical dataset for several researches across the world which does not provide results at good accuracy resulting in developing a realistic model at lesser accuracy. Development of empirical methodologies proved to be a significant method for flood modeling and post disaster assessment.

With several research work focusing on statistical and machine learning-based approaches for modeling floods at higher accuracies, models developed are improving and assisting in understanding the disaster event. However, researchers focusing on flood modeling are tempted towards developing a model with higher accuracies considering the identical parameters as input. Considering the research community being wide open for new ideas, new approaches have to be focused on selecting the input parameters, which might assist in developing an innovative model.