Faculty Publications
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Item Downscaling algorithms for CMIP6 GCM daily rainfall over India(Springer, 2024) Raj, R.; Vinod, D.; Mahesha, A.The global climate models (GCMs) are sophisticated tools for determining how the climate system will respond. However, the output of GCMs has a coarse resolution, which is unsuitable for basin-level modelling. Global climate models need to be downscaled at a local/basin scale to determine the impacts of climate change on hydrological responses. The present study attempted to evaluate how effectively various large-scale predictors could reproduce local-scale rain in 35 different locations in India using artificial neural networks (ANN), change-factors (CF), K-nearest neighbour (KNN), and multiple linear regression (MLR). The selection of predictors is made based on the correlation value. As potential predictors, air temperature, geo-potential height, wind velocity component, and relative humidity at specific mean sea-level pressure are selected. The comparison of four different downscaling methods concerning the reproduction of various statistics such as mean, standard deviation at chosen locations, quantile–quantile plots, cumulative distribution function, and kernel density estimation of the PDFs of daily rainfall for selected stations is examined. The CF approach outperforms the other methods at almost all sites (R2 = 0.92–0.99, RMSE = 1.37–28.88 mm, and NSE = –16.55–0.99). This also closely resembles the probability distribution pattern of IMD data. © Indian Academy of Sciences 2024.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 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.