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<p>Floods are natural phenomena with significant societal and environmental impacts. Understanding the frequency and magnitude of floods is crucial for effective water resource management, infrastructure planning, and risk mitigation. The Upper Krishna River Basin (UKRB) is prone to flooding, with major flood events occurring in the last three decades. This study was conducted in a UKRB sub-basin to analyze flood frequency. The log-normal, Gumbel Max, and Log Pearson Type III (LP3) probability distributions were used to predict future peak discharge scenarios using annual peak discharge data of 50&#xa0;years (1970–2019) at Warunji, Samdoli, Arjunwad, Kurundwad, and Sadalga gauging stations. The probability distribution functions were used for estimating discharge values for return periods (<i>T</i><sub>r</sub>) of 2&#xa0;years, 5&#xa0;years, 10&#xa0;years, 25&#xa0;years, 50&#xa0;years, 100&#xa0;years, and 200&#xa0;years. The results show that the estimated discharge for return periods greater than 5&#xa0;years exceeds the mean annual peak discharge (1758.94 m<sup>3</sup>/s, 1494.99 m<sup>3</sup>/s, 3674.38 m<sup>3</sup>/s, 4741.32 m<sup>3</sup>/s, and 1204.25 m<sup>3</sup>/s), and discharge greater than the 25&#xa0;years return period is likely to cross the river’s carrying capacity for all five sites. This study also shows that all three probability distribution methods employed can project the river discharge satisfactorily, but the log-normal was found best fitted for Warunji and Samdoli with maximum estimated discharge of 6840 m<sup>3</sup>/s and 3481 m<sup>3</sup>/s, whereas LP3 was best fitted for Kurundwad and Sadalga sites with maximum estimated discharge of 11,973 m<sup>3</sup>/s and 3430 m<sup>3</sup>/s, while for Arjunwad, Gumbel Max was found to be the better-suited probability distribution with maximum estimated discharge of 11,128 m<sup>3</sup>/s, as indicated by the goodness-of-fit test using Kolmogorov–Smirnov (K-S), Anderson–Darling (A-D), and chi-square tests. The predicted peak discharge also shows a good correlation (<i>R</i><sup>2</sup> = 0.98) with the actual discharge data computed with the Weibull method. Hence, the results of the study can be used for future infrastructure planning in the study area to avoid damage due to flash floods.</p>

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Estimating extreme flood magnitudes in the Upper Krishna River Basin using multiple probabilistic methods

  • 期刊论文

  • 发表日期：

    2025年8月18日

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  • Preetam Choudhary,
  • Adani Azhoni,
  • C. P. Devatha

摘要

Floods are natural phenomena with significant societal and environmental impacts. Understanding the frequency and magnitude of floods is crucial for effective water resource management, infrastructure planning, and risk mitigation. The Upper Krishna River Basin (UKRB) is prone to flooding, with major flood events occurring in the last three decades. This study was conducted in a UKRB sub-basin to analyze flood frequency. The log-normal, Gumbel Max, and Log Pearson Type III (LP3) probability distributions were used to predict future peak discharge scenarios using annual peak discharge data of 50 years (1970–2019) at Warunji, Samdoli, Arjunwad, Kurundwad, and Sadalga gauging stations. The probability distribution functions were used for estimating discharge values for return periods (Tr) of 2 years, 5 years, 10 years, 25 years, 50 years, 100 years, and 200 years. The results show that the estimated discharge for return periods greater than 5 years exceeds the mean annual peak discharge (1758.94 m3/s, 1494.99 m3/s, 3674.38 m3/s, 4741.32 m3/s, and 1204.25 m3/s), and discharge greater than the 25 years return period is likely to cross the river’s carrying capacity for all five sites. This study also shows that all three probability distribution methods employed can project the river discharge satisfactorily, but the log-normal was found best fitted for Warunji and Samdoli with maximum estimated discharge of 6840 m3/s and 3481 m3/s, whereas LP3 was best fitted for Kurundwad and Sadalga sites with maximum estimated discharge of 11,973 m3/s and 3430 m3/s, while for Arjunwad, Gumbel Max was found to be the better-suited probability distribution with maximum estimated discharge of 11,128 m3/s, as indicated by the goodness-of-fit test using Kolmogorov–Smirnov (K-S), Anderson–Darling (A-D), and chi-square tests. The predicted peak discharge also shows a good correlation (R2 = 0.98) with the actual discharge data computed with the Weibull method. Hence, the results of the study can be used for future infrastructure planning in the study area to avoid damage due to flash floods.