B Jaya Bharat Reddy supervised by Dr. Rehana Shaik received his Master of Science – Dual Degree  in Computer Science and Engineering (CSD). Here’s a summary of his research work on Multivariate Risk Analysis of River Water Quality: A Copula Based Approach Across Diverse Indian River Basins:

River water quality is fundamentally influenced by hydrological factors such as discharge and water temperature, both of which are increasingly affected by climate variability and human-induced pressures. These variables, when considered independently, can provide a limited view of water quality risks. However, compound events, where low discharge coincides with high water temperatures, can pose significant ecological threats, particularly to sensitive aquatic species and overall river health. Existing studies often rely on univariate or deterministic models, which fail to capture the joint behaviour of these interconnected variables. There is also a noticeable gap in translating advanced statistical models into practical tools for river water quality risk management, especially in computing compound event probabilities and supporting basin-specific decision-making. This study addresses these gaps by developing a copula-based joint modelling framework to quantify compound low-flow and high-temperature risks across six diverse Indian rivers: Yamuna, Bhadra, Kaveri, Mahi, Sabarmati, and Vardha. The study begins by fitting appropriate univariate probability distributions to river discharge and water temperatures using diagnostic tools such as skewness, kurtosis, Q-Q plots, and information criteria to ensure statistically sound marginal modelling. Dependence structures between the variables are assessed using rank correlation metrics to identify rivers where joint modelling is both appropriate and necessary. An extensive set of 18 copula families, including symmetric, asymmetric, and tail-dependent models, is evaluated to capture complex interdependencies across river basins. The selected copulas are then applied to estimate joint probabilities, conditional probabilities, and return periods of compound events of discharge and water temperatures of various river gauging stations of India. The findings reveal clear differences in compound event frequencies and intensities across the studied rivers, underscoring the need for localized and river-specific water quality management strategies. The key objectives of this study include identifying the best-fit univariate distributions, selecting the most appropriate copula families for joint modelling, and constructing a flexible, transferable statistical framework that can support future applications, including multivariable risk assessments, climate change impact studies, and adaptive water resource management. By addressing the underutilization of joint probabilistic frameworks for Indian river systems and demonstrating their practical application, this study contributes to a more holistic understanding of compound water quality risks and provides valuable guidance for sustainable river management and policy development.

  November 2025