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This article reports the findings of a recent study by Das and Chanda (2022), wherein a Bayesian Network (BN) approach was applied to analyze the influence of large-scale climate modes and local hydro-meteorological variables on streamflow and rainfall in four river basins in India. Bayesian Networks (BN) offers a thorough conditional independence structure that can improve comprehension and forecasting of hydroclimatic systems. This served as the main impetus for the work, which explored the relative contributions of large-scale climate modes and local hydro-meteorological variables for the prediction of rainfall and streamflow at the basin scale. Once the conditional independence structure is developed, variables possessing a ‘directed arc’ from the target variable were selected as the potential predictors for developing the prediction models. The results showed that the most important predictors for streamflow were rainfall, u-wind, and soil moisture, while the most important predictors for rainfall were u-wind, air temperature, geo-potential height, precipitable water, vertical velocity, and relative humidity. The analysis also revealed that the influence of large-scale climate modes on the target variables was generally insignificant, except for the Pacific Decadal Oscillation and El-Niño Southern Oscillation. Furthermore, the network structure showed that about 87 and 97% of the initial inputs are redundant. The accuracy of the prediction models are comparable across all of the basins and is higher for rainfall (Refined index of agreement (MD) ranging from 0.61 to 0.81) than for streamflow (MD ranging from 0.61 to 0.78). The study also found that dry, intermediate, and wet months can be satisfactorily classified using two drought indices, the Standardized Drought Index (SDI) for streamflow and the Standardized Precipitation Anomaly Index (SPAI) for rainfall.Keywords: Large-scale climate modes, local hydro-meteorological variables, Bayesian Networks (BN), Standardized Drought Index (SDI), Standardized Precipitation Anomaly Index (SPAI)Reference:Das P, Chanda K (2022) A Bayesian network approach for understanding the role of large-scale and local hydro-meteorological variables as drivers of basin-scale rainfall and streamflow. Stoch Environ Res Risk Assess 2:. https://doi.org/10.1007/s00477-022-02356-2 |
