Modeling of annual rainfall extremes in the Jhelum River basin, North Western Himalayas

• Published in: Sustainable water resources management Vol. 7; no. 4
• Main Authors: Umar, Sheikh, Lone, M. A., Goel, N. K.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2021; Springer Nature B.V
• Subjects: Analysis; Annual rainfall; Chi-square test; Development Economics; Distribution; Earth and Environmental Science; Earth Sciences; Extreme values; Extreme weather; Floods; Goodness of fit; Graphs; Hydrogeology; Hydrologic data; Hydrology/Water Resources; Mitigation; Original Article; Parameter estimation; Parameter identification; Precipitation; Probability distribution; Probability theory; Rain; Rainfall; Risk management; River basins; Root-mean-square errors; Sustainable Development; Water Policy/Water Governance/Water Management; Rainfall frequency analysis; Probability distributions; L-moments; Jhelum basin; Goodness of fit; Return periods
• ISSN: 2363-5037; 2363-5045
• DOI: 10.1007/s40899-021-00539-3

The probable extreme rainfall analysis can be of great importance for the development of efficient models of risk management and mitigation. This study involves analyzing the recurrence of yearly rainfall extreme using three important probability distribution models such as Generalized Extreme Value (GEV), Log Pearson-III (LP3), and Gumble (EV1). The parameters of the distribution model were identified using L-moments (LMOs). The maximum precipitation for 2, 5, 10, 25, 50, 100, 200, and 500 year recurrence periods was obtained by using annual maximum rainfall data. The available length of data was varying, due to which analysis was performed for four periods: 1969–2018 at Srinagar and Qazigund, 1980–2018 at Pahalgam and Kokernag and 1977–2018 at Kupwara and 1970–2018 at Gulmarg station. Goodness-of-Fit (GoF) such as Kolmogorov–Smirnov (K–S), Anderson darling (A–D), Chi-square ( χ 2 ) and Root Mean Square Error (RMSE) tests at 5% significance level i.e., α  = 0.05, and probability difference graphs such as P–P plot and probability difference graph were applied for identification of best-fit distribution model. The analysis divulges LP3 as the best fit for Qazigund, Kokernag, Pahalgam, Kupwara, and Gulmarg stations and the GEV is most suitable for Srinagar station.