Seasonal hydrological dynamics have profound socio-economic implications for communities in the Ganges-Brahmaputra-Meghna (GBM) River basin. Climate change and El Niño-Southern Oscillation (ENSO) phase are known to impact extreme flood magnitude in GBM River, however how they affect seasonal flooding pattern is not revealed. Utilizing large ensemble climate data (comprising 6000 years of non-warming and warming climate scenarios) and the global hydrodynamic model CaMa-Flood, we assess the influence of climate change and ENSO on seasonal hydrological patterns specially focusing on maximum river flow. The quantitative effects of La Niña and El Niño are calculated utilizing the Fractional Attribution Risk (FAR) method, separately for non-warming and historical climate scenarios. We assess climate change’s impact on flooding by contrasting historical and non-warming climate conditions using the FAR method. Climate change has substantially increased the maximum river flow for all seasons. In the monsoon season, climate change amplifies the likelihood of flooding with a 10-year return period of 34%, 46%, and 31% at the Hardinge Bridge, Bahadurabad, and Bhairab Bazar gauge stations of the Ganges, Brahmaputra, and Meghna Rivers, respectively. The influence of ENSO still remains significant even with the influence of climate change. ENSO influence presents a nuanced picture, exhibiting variations both between seasons and across different rivers within the GBM basin. The relationship between ENSO and seasonal flood occurrence in the GBM basin can be effectively elucidated by the upward movement of moisture through vertical wind velocity, which serves as a large-scale controlling factor for flood variation.

The state and dynamics of river chemistry are influenced by both anthropogenic and natural catchment characteristics. However, understanding key controls on catchment mean concentrations and export patterns comprehensively across a wide range of climate zones is still lacking, as most of this research is focused on temperate regions. In this study, we investigate the catchment controls on mean concentrations and export patterns (concentration–discharge relationship, C–Q slope) of river chemistry, using a long-term data set of up to 507 sites spanning five climate zones (i.e., arid, Mediterranean, temperate, subtropical, tropical) across the Australian continent. We use Bayesian model averaging (BMA) and hierarchical modelling (BHM) approaches to predict the mean concentrations and export patterns and compare the relative importance of 26 catchment characteristics (e.g., topography, climate, land use, land cover, soil properties and hydrology). Our results demonstrate that mean concentrations result from the interaction of catchment intrinsic and anthropogenic factors (i.e., land use, topography and soil), while export patterns are more influenced by catchment intrinsic characteristics only (i.e., topography). We also found that incorporating the effects of climate zones in a BHM framework improved the predictability of both mean concentrations and C–Q slopes, suggesting the importance of climatic controls on hydrological and biogeochemical processes. Our study provides insights into the contrasting effects of catchment controls across different climate zones. Investigating those controls can inform sustainable water quality management strategies that consider the potential changes in river chemistry state and export behaviour.

We need to understand spatial variability in the mean concentrations and dynamics of riverine water quality for effective water quality management. Using river chemistry data for up to 578 locations across the Australian continent, we assessed the impact of climate zones on (i) interannual mean concentration and (ii) river chemistry dynamics as represented by constituent export regimes (ratio of the coefficients of variation of concentration and discharge) and export patterns (slope of the concentration-discharge relationship). We found that interannual mean concentrations vary significantly by climate zones. However, export regimes and patterns are generally consistent across climate zones. This suggests that intrinsic properties of individual constituents rather than catchment properties determine export regimes and patterns. The spatially consistent river chemistry dynamics highlights the potential to predict riverine water quality across the Australian continent, which will support national riverine water quality management.