Faculty Publications
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Item Trends in extreme rainfall over ecologically sensitive Western Ghats and coastal regions of Karnataka: an observational assessment(Springer Verlag service@springer.de, 2018) Chandrashekar, V.D.; Shetty, A.Rainfall is one of the pivotal climatic variables, which influence spatio-temporal patterns of water availability. In this study, we have attempted to understand the interannual long-term trend analysis of the daily rainfall events of ? 2.5 mm and rainfall events of extreme threshold, over the Western Ghats and coastal region of Karnataka. High spatial resolution (0.25° × 0.25°) daily gridded rainfall data set of Indian Meteorological Department was used for this study. Thirty-eight grid points in the study area was selected to analyze the daily precipitation for 113 years (1901–2013). Grid points were divided into two zones: low land (exposed to the sea and low elevated area/coastal region) and high land (interior from the sea and high elevated area/Western Ghats). The indices were selected from the list of climate change indices recommended by ETCCDI and are based on annual rainfall total (RR), yearly 1-day maximum rainfall, consecutive wet days (? 2.5 mm), Simple Daily Intensity Index (SDII), annual frequency of very heavy rainfall (? 100 mm), frequency of very heavy rainfall (? 65–100 mm), moderate rainfall (? 2.5–65 mm), frequency of medium rainfall (? 40–65 mm), and frequency of low rainfall (? 20–40 mm). Mann-Kendall test was applied to the nine rainfall indices, and Theil-Sen estimator perceived the nature and the magnitude of slope in rainfall indices. The results show contrasting trends in the extreme rainfall indices in low land and high land regions. The changes in daily rainfall events in the low land region primarily indicate statistically significant positive trends in the annual total rainfall, yearly 1-day maximum rainfall, SDII, frequency of very heavy rainfall, and heavy rainfall as well as medium rainfall events. Furthermore, the overall annual rainfall strongly correlated with all the rainfall indices in both regions, especially with indices that represent heavy rainfall events which is responsible for the total increase of rainfall. © 2018, Saudi Society for Geosciences.Item Long-term analysis of waves off Mangaluru coast(National Institute of Science Communication and Policy Research, 2020) Upadhyaya, S.K.; Rao, S.; Rao, M.A competent coastal engineer should have adequate knowledge with regard to wave analysis. Long-term analysis is being effectively used worldwide to predict wave heights based on extreme wave statistics. The objective here is to analyze the ocean waves off Mangaluru coast, India. Long-term analysis is initially performed based on probability distributions like Log-normal distribution, Gumbel distribution, Fretchet distribution, Exponential distribution, and Weibull distributions on the in-situ wave data recorded at coastal waters off Mangaluru coast. The Extreme wave analysis is performed based on Peak Over Threshold method from which the best-fit probability distribution is obtained. Further, the best performing distribution is applied on the simulated wave heights obtained from MIKE 21 numerical model forced with ERA-interim wind speed data of 38-years. From the analysis, amongst the five different distributions considered Weibull distribution with ? = 1.3 gave the best-fit for the wave dataset considered. Long-term analysis is then performed on 38-years hindcast data for 10, 50 and 100 year return periods for the Mangaluru coast region. © 2020 National Institute of Science Communication and Information Resources (NISCAIR). All rights reserved.Item Future transition in climate extremes over Western Ghats of India based on CMIP6 models(Springer Science and Business Media Deutschland GmbH, 2023) Shetty, S.; Umesh, P.; Shetty, A.The effect of climate change on the tropical river catchments in the Western Ghats of India is studied using the Coupled Model Intercomparison Project-6 data (CMIP-6). Multi-model ensembles of rainfall and temperature are constructed using the Random Forest ensemble technique for bias-corrected GCMs in the near future (2014–2050) and far future (2051–2100) horizons. For the two catchments each in the southern, central, and northern Ghats, the trend in minimum and maximum temperatures, precipitation, and other indices are calculated. By 2100, dry sub-humid and humid catchments will see a higher increase in mean annual temperature than per-humid central catchments. In future decades, the warm days and nights increase by 45–50% and 40–70%, respectively, with twofold warming in the winter season. Under a climate change scenario, annual rainfall increases in Vamanapuram, Ulhas, and Purna, while Chaliyar, Netravati, and Aghanashini catchments experience a decrease in rainfall in the far future with an increase in pre-monsoon rainfall. The southern catchments are anticipated to have contrasting variations in the rainfall extremes; northern catchments face a substantial increase in very wet to extremely wet days and medium to heavy rainfall. In all catchments (excluding Vamanapuram), cumulative wet days increase with a decrease in cumulative dry days. After the mid-twenty-first century, humid to per-humid catchments encompass an increase in cool nights, whereas it disappears in dry sub-humid catchments of the Ghat. Interestingly, warming tendencies begin to slow down after 2050. This investigation can assist in comprehending the regional climate extremes in the Western Ghats to formulate better climate risk planning and adaptation strategies. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Recent Changes in Hydrometeorological Extremes in the Bilate River Basin of Rift Valley, Ethiopia(American Society of Civil Engineers (ASCE), 2023) Lambe, B.T.; Kundapura, S.The hydroclimatic extremes such as floods and droughts have been causing damage and losses with rising frequency than ever before. The human-induced and internal climate variability create extreme events and local hydrometeorological changes influencing climate-sensitive sectors. This research is aimed at analyzing the recent changes in the hydrometeorological extremes using indices over the Bilate basin in Ethiopia. Mann-Kendall and Sen's slope estimator were used to examine changes in hydrometeorological extreme indices. The rainy days' rate of change falls between þ10.64 mm in the downstream to −10.67 mm in the upstream north. The wet day rainfall and heavy rainfall day indices were stronger in the basin's southwest, implying more likely flood events. The consecutive dry days show a rising tendency with more variability, while the consecutive wet days show no trend with less variability. The change point analysis revealed inconsistencies for the majority of the extreme indices. The stations' average warmest nights and days significantly increased at a rate of 0.0358°C and 0.0320°C per annum, respectively. The coldest nights in most of the stations show a significant and negligible rise in the basin while on the coldest days more than half of the stations declining. The peak flow in the annual and seasonal time series shows a rising trend and a dominant rise in most low flow indices, which possibly flashes downstream flooding. The global and local climate anomalies revealed a weak correlation, but with overlap of wet and drought years. Basin water resource plans may benefit from identified overlap cross of threshold years for improved flood control and drought monitoring. © 2018 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.Item Extreme hydroclimatic variability and impact of local and global climate system anomalies on extreme flow in the Upper Awash River basin(Springer, 2023) Tola, S.Y.; Shetty, A.Extreme hydroclimatic variability in a changing climate and the possible causes of extreme hydrological variability are essential for effectively mitigating floods. The study aims to investigate the variability of extreme hydroclimatic conditions and the relationship between anomalies in extreme local precipitation, ENSO indicators (Southern Oscillation index (SOI), Niño 3.4, and multivariate ENSO index (MEI)), and extreme flow indices in the Upper Awash River basin, Ethiopia. The analysis used standardized anomaly index and coefficient of variation statistics to examine variability, the modified Mann-Kendall and Pettitt tests for trend and change point analysis, and Spearman’s correlation test to explore relationships. The study revealed that the basin-wise extreme precipitation indices had less variability but higher variability spatially, while the extreme flow indices showed high variability. Furthermore, the basin experienced extreme wet to normal wet conditions in the 1990s compared to the 2000s. The maximum temperature increased significantly, while the minimum temperature decreased significantly (except at a few northwest stations), with a considerable shift in the 1990s and 2000s. Anomalies, extreme to normal wet conditions, and a decrease in extreme precipitation were consistent with the extreme flow at the basin outlet, Hombole station. However, the extreme flow indices at Melka Kunture increased significantly and shifted upward (2003/2005), and the anomalies in extremely wet and very wet precipitation in the northwest were possibly responsible for this change. The study also revealed that the annual wet and very wet days of precipitation strongly affected the extreme flow in the basin. The effect of annual wet day precipitation, annual maximum precipitation, and ENSO anomalies on extreme flow at the Hombole was significant. These findings enhance the understanding of extreme hydroclimatic variability and prospective flood predictability and aid flood risk management. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.Item Large-scale atmospheric teleconnections and spatiotemporal variability of extreme rainfall indices across India(Elsevier B.V., 2024) Vinod, D.; Mahesha, A.Identifying trends in hydrometeorological time series during extreme weather events and their interactions with large-scale atmospheric teleconnections is crucial for climate change research. This study evaluates 14 precipitation-based indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) across seven climatic zones of India using gridded daily rainfall data from the India Meteorological Department (IMD) for 120 years (1902–2021) utilised. Trend analysis was carried out using the Mann-Kendall (MK) test, Theil-slope Sen's estimator, Innovative Trend Analysis (ITA), and other statistical tools. Change point detection is established using the Pettitte test and Cumulative Sum algorithm. The relationships between large-scale atmospheric teleconnections and ETCCDI indices are also found, and Multiple Linear Regression (MLR) models are developed between them. The results show significant increasing trends in extreme rainfall indices in India's Ladakh region, located in the arid desert-cold climatic zone. The annual, Southwest Monsoon (SW-Monsoon), Northeast Monsoon (NE-Monsoon), and summer rainfall trends were positive, while winter rainfall had a negative trend across most climatic zones. Significant associations between large-scale atmospheric teleconnections, including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Global Temperature Anomaly (GTA), Southern Oscillation Index (SOI), SST of Niño 3.4 region, Oceanic Niño Index (ONI), and Dipole Mode Index (IOD) and ETCCDI indices were established across multiple climatic zones. Using MLR analysis, this study attempts to establish, for the first time, the relationship between teleconnections and ETCCDI indices across India. Extreme rainfall indices are influenced by climate change during the SW-Monsoon across most of the climatic zones of India. During the previous El Niño event (2014–2016), average annual rainfall decreased by 19.5%, SW-Monsoon rainfall decreased by 25.2%, and NE-Monsoon rainfall decreased by 64.1% in India. The findings may provide valuable insights into mitigation strategies to sustain the adverse effects of extreme weather conditions and enhance climate resilience. © 2023 Elsevier B.V.Item Spatial Dependence of Extreme Rainfall and Development of Intensity-Duration-Frequency Curves Using Max-Stable Process Models(American Society of Civil Engineers (ASCE), 2025) Vinod, D.; Mahesha, A.The effective management of flood risk and urban drainage design hinges on a comprehensive understanding and accurate modeling of extreme rainfall variations, particularly in vulnerable areas. The study proposes to model spatial extreme rainfall across various durations in the Ganga River basin of India using max-stable processes (MSP). Incorporating geographical covariates like longitude, latitude, and elevation, 28 surface response models were constructed for location and scale parameters, with linear variations in marginal parameters while keeping the shape parameter constant across space. Various max-stable characterizations were evaluated using the Takeuchi information criterion (TIC) value and likelihood ratio test statistics, including Brown-Resnick, Smith, Extremal-t, Schlatter, and Geometric-Gaussian models with different correlation functions. The findings showed that the Brown-Resnick model consistently simulated well for shorter extreme rainfall for 3, 4, and 6-h and 36-h durations. The extremal coefficients revealed higher dependency between closer locations for most durations. In comparison with classical univariate extreme value theory (UEVT), the MSP exhibits a minimal overestimation in extreme rainfall intensity at New Delhi (by 13.6 mm/h) and Diamond Harbor (by 10.2 mm/h) stations for shorter durations, i.e., 2-h to 6-h range. Its estimations align within the uncertainty bounds of the identical and independent distribution (I.I.D) for longer durations. This suggests the importance of considering the strengths and limitations of M.S.P. and UEVT approaches for accurate rainfall intensity estimation, especially in flood risk management and urban drainage design. In data-sparse region/ungauged basins, where traditional methods like univariate UEVT may be limited due to the absence of observed rainfall data. The fitted max-stable processes MSP can serve as a valuable tool when relevant geographical covariates are known. © 2024 American Society of Civil Engineers.Item Landslides and debris flow triggered by the July 2024 extreme rainstorm in the Chooralmala watershed in Wayanad, India(Springer Science and Business Media Deutschland GmbH, 2025) Kolathayar, S.; Menon, V.; Kundu, P.[No abstract available]Item Modeling non-stationary 1-hour extreme rainfall for Indian river basins under changing climate(Elsevier B.V., 2025) Vinod, D.; Mahesha, A.India's complex topography and the increasing influence of climate change have exacerbated the challenges of modeling 1-hour non-stationary extreme rainfall events. Prior studies have indicated rising intensities of such events, particularly in coastal and urban areas. This study addresses these issues by developing 155 basin-specific non-stationary surface response models, incorporating geographical, climatic, and temporal covariates. Using 13 Max-Stable Process (MSP) characterizations, extreme rainfall variability across 11 major river basins and three-time scales were effectively modeled. The Brown-Resnick, Geometric-Gaussian, and Extremal-t models demonstrated varying effectiveness across regions. The findings emphasize the critical role of region-specific analysis in water resource management and disaster preparedness, where the high temporal resolution datasets are limited for the point process-based models. The global processes and regional climate change are found to predominantly influence 1-hour extreme rainfall across the majority of river basins in India. © 2025 Elsevier B.V.Item Characterizing extreme rainfall using Max-Stable Processes under changing climate in India(Elsevier B.V., 2025) Vinod, D.; Mahesha, A.Climate change has markedly intensified the frequency and intensity of extreme rainfall events globally over recent decades. The present investigation introduces a novel approach to modeling Intensity-Duration-Frequency (IDF) curves for major river basins in India using max-stable processes (MSPs). In contrast to earlier studies that mainly dealt with univariate extreme value theory and point-based IDF curves, this work uses a variety of MSP characterizations, such as Brown-Resnick, Schlather, Geometric Gaussian, and Extremal-t, to capture the spatial dependencies and non-stationary characteristics of extreme rainfall. This comprehensive two-stage modeling approach incorporates geographical covariates to capture spatial variation in extreme rainfall, followed by additional climate-informed covariates. One hundred fifty-six surface response models are analyzed across nine hourly extreme rainfall durations over 11 river basins. The Brown-Resnick process effectively captured spatiotemporal dependencies across all durations in the annual timeframe, while the Geometric Gaussian process also demonstrated strong performance. During the Indian Monsoon season, distinct covariates such as the Southern Oscillation Index (SOI) and Global Temperature Anomaly (GTA) significantly influenced extreme rainfall patterns. The analysis reveals that the Brahmaputra basin consistently exhibits the highest short-duration extreme rainfall, while the Indus basin shows the lowest. Long-term projections indicate alarming trends, with potential short-duration extreme rainfall reaching 338.9 mm for a 100-year return period in the Godavari basin. The findings highlight the importance of updating IDF relationships in climate variability, providing insights that could lead to disaster preparedness and resilience planning for vulnerable communities across India. © 2025 Elsevier B.V.