Probability of flooding and vulnerability assessment in the Ajay River, Eastern India: implications for mitigation

Estimation of flood intensity for a desired return period is of prime importance for flood management through flood plain zoning. Flood frequency analysis enables estimation of the probability of occurrence of a certain hydrological event of practical importance by fitting a probability distribution to one that is empirically obtained from recorded annual maximum discharge and/or stage data. This case study considers the use of four probability distributions, namely Gumbel's extreme value distribution (EV-I), extreme value distribution-III (EV-III), log-normal (LN) and Log-Pearson Type III (LPT-3) in flood modelling of monsoon-dominated Ajay River and illustrates the applicability of goodness of fit (GOF) and D-index tests procedures in identifying which distributional model is best for the specific data. Twenty-five years (1985-2009) of existing and estimated annual peak discharge (Q(max)) data have been used for analyzing the trend of flood occurrence. After identifying the best fit model, the peak gauge height data (h(max)) are then analysed combining with geographic information systems (GIS) for predicting flood affected area and preparing inundation map at a specific return period (T). Results of the study showed that the LPT-3 distribution is better suited for modelling flood data for Ajay at Nutanhat in West Bengal. The computed Q(max) for LPT-3 distribution are slightly higher as compared to the results obtained by EV-I, EV-III and LN which are used for vulnerability assessment. The analysis also predicts that the affected area will be ranging from 235 to 290 km 2 in near future (at 25- to 200-year T). These findings provide clear picture for the pattern of hydrological fluxes and aftermath in the next decades in lower Ajay River Basin (ARB). Sustainable planning and developmental measures that consider the modelled pattern of hydrological fluxes of the study area were recommended for decision making.