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Top1. Introduction
Floods are one of the most recurring and frequent natural catastrophes experienced by mankind since time immemorial. They pose serious hazards to the economy, population, and environment in many parts of the world (Jongman, 2018; Hu et. al., 2018). A recent statistic by CRED (2018) reports that floods constitute around 43.4% of all natural disasters that occurred worldwide during 1998-2017, affecting nearly 2 billion people (44.9%). The Asian region, with a share of one-third of the earth’s land surface, is the most prone to frequent floods according to the flood events statistics collected globally during 1900-2013. It accounts for 41% and 61% of the total number of flood events and total economic damages respectively, while the Americas account for 23% and 17%, Africa for 20% and 1%, Europe for 13% and 19% and Oceania (and Australia) for 3% and 2% respectively. At the same time, it is also reported that the flood disasters have increased in the last century, with a sharp increase beyond 1980’s. In developing countries, the trend of destruction due to flood is increasing significantly due to severe intrusion into the floodplains for urbanization and agriculture (Halgamuge & Nirmalathas, 2017). For instance, in India, the area affected from floods sums up to around 4.94 million hectares between 2000 and 2009. During this period, the monetary damage to the tune of Indian rupees (INR) 36,004.75 million was also observed (CWC, 2010). Intense rainfalls in monsoon season (June-September), drainage congestion leading to the water-logging problem and low carrying capacity of river channels trigger frequent flooding in the delta and coastal areas of the country (NDMA, 2008). Despite the fact that the river channels in these areas are protected through embankments, low carrying capacity of the channels leads to bank spillover and breaching (Dixit, 2009). Consequently, flooding in these areas is aggravated.
Under such situations, there is a dire need to follow a structured approach to minimize losses and keep fatalities to a minimum. In recent years, floodplain management has been extended beyond conventional (structural) measures involving scientific knowledge and practice to reduce risks and impacts (Burrel et al., 2007). Under this cover, flood risk mapping, flood hazard mapping, floodplain zoning and flood forecasting are playing important roles in assessing the current trend and future scenarios of flood risk (Salvati et al., 2018). In flood hazard assessment, the flood events are characterized by spatio-temporal characteristics (extent of areal inundation, inundation depth, inundation duration, flow velocity, etc.) and associated recurrence interval/return period (Scawthorn et al., 2006; Mosquera-Machado & Ahmad, 2007). Quantification of parameters such as flood depth, inundation extent, inundation duration and flow velocity which contribute to the damaging potential of a flood event, requires use of flood inundation models based on advanced algorithms and high-quality input data (Liu et al., 2015; Gobeyn et al., 2017).
Based on the numerical solving schemes, these models solve can be categorised into (a) one-dimensional (1D) model accounting for the channel flow; (b) two-dimensional (2D) model accounting for the overland flow; and (c) 1D river models coupled with 2D overland flow model (Horritt & Bates, 2001; Blade et al., 2012). The 1D and 2D models solve the Saint Venant equations of open channel hydraulics that consist of conservation of continuity and momentum. Most of the time, owing to computational limitations, 1D models are preferred. However, with a challenge as they fail to accurately model complex topography. On the other hand, recent advances in computational capacity have made 2D solvers more feasible (Vojinovic & Tutulic, 2009; Tyrna et al., 2018). Although the 2D models offer several advantages over 1D models, they are not as computationally efficient as a 1D model, and possess difficulty in modelling in-channel structures. Under such cases, the advantages of both types of models can be combined by coupling 1D and 2D models (Noh et al., 2018; Gori et al., 2019). Hence, a combination of 1D model of the channel with a 2D model of the overland can yield better results in terms of accuracy and computational efficiency in hydraulic modelling.