Machine Learning in Assessing the Impact of Climate Change on Water Resources

The water resources of Bangladesh are highly susceptible to climate change which is reflected in patterns of river flows, groundwater and water quality. This work uses a machine learning (ML) approach to quantify the changes induced in representative river basins under historical and future SSP2-4.5 and SSP5-8.5 scenarios. Historical climate monitoring also indicated significant regional warming, especially in the coastal region (+0.25 °C/decade) and growing spatial heterogeneities of rainfall. Future projections under SSP5-8.5 suggest dramatic changes, such as a +4.1 °C temperature increase and a 13.5% change in rainfall by the end of the century. Comparing different ML models for predicting streamflow, Long Short-Term Memory (LSTM) model proved to be the best, with an accuracy and the fewest errors. The increase in mean annual streamflow in the Ganges, Brahmaputra, and Meghna basins is substantial under the LSTM projections and the Meghna basin could witness as high as 21% increase under SSP5-8.5. These transformations align with a significant increase in the frequency of extreme floods, that might extend to yearly events before the century is over. Groundwater is estimated to decrease drastically, and water quality is likely to deteriorate, with coastal salinity increasing by almost 100%. Spatial vulnerability diagnosis indicates that the South-West and coastal zones are severely vulnerable due to both water scarcity and salinity encroachment. A region-specific adaptation plan for sustainable groundwater management needs to be developed, with an advanced plan for enhancing the resilience of Bangladesh’s groundwater resources under future climate scenarios.