Seasonal Variability in Soil Salinity and its Climatic Drivers in Khulna, Bangladesh

Bangladesh is one of the countries in the world most severely affected by soil salinity issues. This research focuses on the seasonal variation in soil salinity and the associated impact of climate change across different sites in the Batiaghata sub-district of Khulna, located in the southwestern coastal belt of Bangladesh. The study encompasses four meteorological seasons: pre-monsoon (March-April-May), monsoon (June-July-August-September), post-monsoon (October-November), and winter (December-January-February). Maximum and minimum electrical conductivity values are employed, collected from the Soil Research Development Institute (SRDI) in Khulna, and which show variations in the pre-monsoon and monsoon seasons. The Normalized Difference Salinity Index (NDSI) is used to detect soil salinity aspects using remote sensing techniques. Satellite-derived NDSI indices, visualised via the ArcGIS template, indicate that soil salinity peaks during the pre-monsoon season, which is consistent with the observed data. The minimum values were recorded in the monsoon season. The highest maximum value of the NDSI indices for the pre-monsoon season was 0.11580, while the lowest maximum value for the monsoon season was 0.06533. Rainfall is the main reason for lower soil salinity in the monsoon season. Conversely, soil salinity increases during the pre-monsoon season due to higher average air temperatures (2m above surface). The broader implication of the study is that it highlights how climate drivers influence soil salinity. It also supports the formulation of targeted climate adaptation and coastal resilience policies. The main focus of the study is on temperature, rainfall and cyclone data; however, this could be considered a limitation, as other elements that also affect soil salinity, such as wind patterns, evapotranspiration and tidal effects are not fully examined. Furthermore, understanding of long-term salinity trends and variability influenced by interannual climatic patterns may be limited due to the use of short-term data.