Regional irrigation water quality index for the Old Brahmaputra River, Bangladesh: A multivariate and GIS-based spatiotemporal assessment

• Novel region-specific IWQI developed using 14 parameters for comprehensive assessment • Proposed IWQI revealed the river's suitability for year-round irrigation with minor risks • Multivariate analysis revealed distinct spatial clusters and key water quality drivers • Seasonal variations in hydrochemical interactions highlight need for adaptive management • Integration of GIS and statistics enhances understanding of irrigation water quality Ensuring sustainable irrigation water quality is vital for agricultural productivity and environmental health, particularly in regions with variable seasonal water quality. This study introduces an enhanced Irrigation Water Quality Index (IWQI) specifically developed for the Old Brahmaputra River in Bangladesh to address local hydrochemical complexities. Unlike conventional IWQI methods that primarily assess salinity and sodicity without statistical validation, this approach integrates a broader range of parameters with multivariate statistical validation, enabling a more precise, seasonally adaptive assessment. Through combined multivariate statistical analysis and geographic information systems, results indicate the river water generally remains in the "low restriction" category for irrigation across seasons (dry season: 73.42–83.85; wet season: 77.63–80.57). Seasonal fluctuations were observed in certain areas, with elevated ion concentrations during the dry season due to reduced flow and evaporation, while monsoon rains provided natural pollutant dilution in the wet season. This seasonal variability highlights the importance of continuous monitoring. Principal component analysis identified three primary components, accounting for 77.16% of the variance in the dry season and 66.30% in the wet season. Strong correlations were observed between IWQI and indices like Permeability Index (r = 0.94, p < 0.01) and Sodium Percentage (r = 0.91, p < 0.01) in the dry season. This enhanced IWQI offers a promising tool for seasonally adaptive water management, promoting sustainable agriculture and soil health amidst climate change. Future research should expand the temporal and spatial scope to capture long-term trends and adapt this model for broader regional water management applications.