Understanding Interannual Variability of Brahmaputra River Basin Flows Through Bayesian Causal Networks

Abstract Bayesian Causal Networks (BCNs) are adapted to investigate causal factors of hydroclimate variability in the Brahmaputra River Basin (BRB) during the seasonal and sub‐seasonal periods. This is the longest monsoonal river system in India, prone to frequent severe flooding. Directed Acyclic Graphs are constructed to connect response variable (streamflow) with various covariates, which include indices of large‐scale climate drivers, Sea Surface Temperatures (SSTs), across tropical Indo‐Pacific and basin Precipitation, and Bayes Factors are computed for flow regimes to quantify the strength of causal connections. Results show that eastern precipitation exerts strong influence during Early and Peak monsoon (|BF| > 1), while western precipitation dominates during the late season. Niño4 index exhibits substantial influence ((|BF| > 0.5) during Early, Peak and Monsoon season. Bayesian Causal Network was applied to each grid point SSTs and Antecedent SSTs in Directed Acyclic Graph configuration to capture lagged ocean influences. Regions (“hotspots”) in Central Pacific in the Niño4 and Eastern Pacific regions exert strong causality in modulating streamflow variability during Early, Peak and Monsoon season. Composite analysis of Integrated Vapor Transport and pressure anomalies show concomitant patterns. In wet years, strong moisture transport from the Bay of Bengal during Early and Peak seasons is driven by anomalous high‐pressure region over Southeast Asia, and vice‐versa in dry years. SSTs from the hotspots in machine learning model show interesting nonlinear interactions at varying thresholds in enabling skillful probabilistic estimates of seasonal flow categories. The results provide new insights into streamflow variability in BRB and potential for use developing skillful forecasts of streamflow attributes.