Faculty Publications
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Item Geo-Morphological Assessment in the Upper Reaches of Krishna River (India) Using Multi-temporal Satellite Data During 1991–2021(Springer Science and Business Media Deutschland GmbH, 2024) Choudhary, P.; Azhoni, A.; Devatha, C.P.Rivers have historically served as the birthplace of human civilization. The study of channel planform and geo-morphological characteristics is of utmost importance in investigating the effects of climate change and alterations in land use on the overall well-being of rivers. This study examines the temporal variations in the geo-morphological characteristics of the upper reaches of Krishna River during three decades (1991–2021). Spatial data is obtained from several satellite missions and afterward processed using Remote Sensing and Geographic Information Systems (GIS) to assess alterations in the active river channel's extent, erosion, and accretion areas and the sinuosity index. The river channel has been segmented into seven sub-reaches, and it has been observed that some sections of the bank line require prompt attention from the relevant authorities to carry out bank protection measures. The findings indicate that the left bank exhibits a greater tendency towards erosion and shifting compared to the right bank. Additionally, the river has undergone geo-morphological alterations due to the construction of hydraulic structures and the occurrence of numerous flood events within the basin. The sinuosity index of the river provides evidence of its meandering nature. This study offers valuable insights into the dynamic behavior of the Upper Krishna River, hence providing useful information for authorities and decision-makers involved in river training initiatives and future development projects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.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.Item Integration of multi-layer perceptron neural network and cellular Automata-Markov chain approach for the prediction of land use land cover in land change modeler(Elsevier B.V., 2025) Choudhary, P.; Devatha, C.P.; Azhoni, A.Land use and land cover (LULC) significantly influence the hydrological cycle and various earth processes. Understanding these dynamics is essential for effectively managing environmental issues within river basins. The study focuses on a highly dynamic and flood-prone sub-basin of the Upper Krishna River, where major urban settlements and intensive agricultural activities are concentrated along the riverbanks. The uniqueness of this research comes from the selection of this hydrologically sensitive landscape, shaped by both natural processes and anthropogenic pressures, which presents a critical case for land use and land cover modeling. Utilizing high-resolution satellite data (10 m), combined with the advanced Multi-Layer Perceptron Neural Networks (MLPNN) and Cellular Automata-Markov Chain (CA-Markov) modeling techniques within TerrSet's Land Change Modeler (LCM), which is not only capable of generating spatial transitions and dynamic maps but also identifies the key contributors in gain and loss of various land use classes. We projected LULC scenarios for the mid-century (2049) and end-century (2099) using data from 2015 to 2020. Our model was validated against the actual LULC map from 2024 and showed a strong correlation (Kappa = 0.85). The results indicate significant urban growth along the riverbank and predict an increase in built-up area from 6.53 % in 2024 to 9.59 % in 2049 and further to 15 % by 2099 of the total geographical area. We observed consistent declines in forest cover, cropland, and barren land. These findings are valuable for future hydrological studies and provide important insights for policymakers to support sustainable urban planning and flood risk management. © 2025