Modeling of peak discharges and frequency analysis of floods on the Jhelum river, North Western Himalayas
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ORIGINAL ARTICLE
Modeling of peak discharges and frequency analysis of floods on the Jhelum river, North Western Himalayas Sheikh Umar1 · M. A. Lone1 · N. K. Goel2 Received: 21 May 2020 / Accepted: 25 August 2020 © Springer Nature Switzerland AG 2020
Abstract The modeling of peak flood discharges and flood frequency analysis at various sites on a river is essential for planning, design, and management of hydraulic structures. The first and the foremost aim of this study is to choose the best-fit flood model among Log Pearson type 3 (LP3), Generalized Extreme Value (GEV), and Gumble (EV1) for each of the eight sites on the Jhelum River and for the same purpose goodness-of-fit tests like Anderson–Darling (A–D) and Kolmogorov–Smirnow (K–S) and distribution graphs (P–P plot and Probability difference graph) were used. The parameters of these models were determined by L-moments. The outcomes of the study reveal that the LP3 model is best-fit for Khanabal, Sangam, Awantipora, Padshahi Bagh, Ram Munshi Bagh, and Asham, and GEV is the best fit for Sopore, and Baramullah sites. Furthermore, peak discharges for 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year return periods were estimated and the analysis depicts that the discharge rate determined by distribution models at a return period of 5 years or more would surpass the safe carrying capacity (990.85 cumecs) of the Jhelum river. The study further shows that there exists a high positive correlation (R 2 = 0.99) between observed and predicted peak discharges of LP3 and GEV models. Thus, indicating LP3 and GEV as best-fit models for modeling and flood frequency analysis of annual peak discharges on the Jhelum River. Keywords River Jhelum · Flood frequency analysis · l-moments · Probability distributions · Goodness of fit · Return periods
Introduction Flooding is one of the most devastating natural catastrophes, and during the years 1998–2017, floods were estimated to be the most frequent type of disaster and constitute about 43% of all weather-related catastrophes (CRED 2018). In fact, floods are the foremost cause of natural catastrophe deaths worldwide and were accountable for 6.8 million deaths in the twentieth century (Doocy et al. 2013). Also, the frequency of torrential rainfall events and floods has increased globally (Lal et al. 2001; Kundzewicz et al. 2010) including the North-Western Himalayas (Valdiya 2011; Mishra and Srinivasan 2013). Therefore, the modeling and frequency analysis of extreme floods is consequential for the evaluation * Sheikh Umar [email protected] 1
Department of Civil Engineering, National Institute of Technology, Srinagar, India
Department of Hydrology, Indian Institute of Technology, Roorkee, India
2
of risk and hazards of flood and eventually mitigating its catastrophic consequences (Renard et al. 2013; Benameur et al. 2017). The generally accepted approach to mitigate the loss due to flood hazards is structural measures, but the design of hydraulic structures depends mostly on the conduct of the river flow (Chow et
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