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Item Impact of recent floods on river morphology of Upper Krishna River: a decadal analysis using remote sensing approach(Springer Science and Business Media Deutschland GmbH, 2024) Choudhary, P.; Azhoni, A.; Devatha, C.P.Alluvial rivers are dynamic landscapes on the earth’s surface that evolve with time. While many studies have examined the immediate effects of floods on river channels, there is a lack of research that investigates the longer-term evolution of river morphology following such events. The present study was carried out on the Upper Krishna River which flows between the southern part of Maharashtra and the northern part of Karnataka states in India for 375 Km. The morphological parameters were analyzed for three decades (1991–2021) and the year 2019 with the highest flood level was also considered for change analysis. The assessment was done for change in active channel area, mean width, bank line migration, sinuosity index, and erosion-accretion. The land use classification was also analyzed for the study period to understand the exposure to future floods. The spatial data was retrieved from different satellite missions and analyzed with the help of Remote Sensing (RS) and Geographical Information System (GIS). The river was divided into seven segments (R1, R2, R3, R4, R5, R6, and R7) and bank lines were digitised manually to minimise possible errors. The results show that during the study period, the river channel has been modified in terms of active channel area expansion in the R1, R5, R6, and R7, and erosion was found the dominating process while the left bank was more erosive than the right bank of the river. The built-up area was seen going through a major expansion than any other land use class. The discharge and sediment data confirm the flood years (1994, 2005, 2006, and 2019) which accelerated the morphological activity in the river segment. The results of the study provide new insights related to short-term morphological changes in the Upper Krishna River and can be used by policymakers and managers to carry out future development plans and river training work at affected sites. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.Item Estimating extreme flood magnitudes in the Upper Krishna River Basin using multiple probabilistic methods(Springer, 2025) Choudhary, P.; Azhoni, A.; Devatha, C.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 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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.