A grid-wise approach for accurate computation of Standardized Runoff Index (SRI)

The Standardized Runoff Index (SRI) is a major indicator for evaluating hydrological drought conditions, accomplished by comparing the current runoff data with retrospective runoff conditions of an area for the same period. This hydrological drought indicator facilitates the characterisation of runoff variations across diverse regions. This study introduces a refined methodology for accurate computation of SRI by employing a grid-wise approach. Distinct probability distributions were fitted to each grid within the study area, diverging from the conventional practice of using a single probability distribution for the entire basin or sub-basin. The research endeavours to assess the efficacy of the grid-wise approach in improving the representation of drought characteristics when compared to the traditional areal approach. A comparative analysis between the performances of SRI computed through grid-wise fitting (where the probability distribution dynamically adapts to each grid) and the areal fitting approach (employing a uniform distribution across all grids) was conducted within the Godavari Basin, India. The findings in this study underscore that the misrepresentation of extreme events is inevitable for large heterogeneous basins like Godavari when the traditional areal approach was employed for SRI computation. Consequently, the grid-wise fitting emerges as a more accurate method for computing the SRI, particularly in characterising extreme dry or wet events. [Display omitted] •Traditional approach for SRI computation leads to misrepresentation due to single distribution used across diverse regions•The proposed grid-wise approach overcomes this limitation by fitting distinct probability distributions to each grid.•Grid-wise adaptability proves crucial for accurately characterising extreme dry or wet events.•SRI computed using novel grid-wise approach outperforms the conventional areal method.