Faculty Publications
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Publications by NITK Faculty
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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 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.