Soil and River-water Salinity Dynamics in Coastal Bangladesh

Soil and River-water Salinity Dynamics in Coastal Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Soil and River-water Salinity Dynamics in Coastal Bangladesh AHMED Z. RAHMAN, Mohammad Shamsudduha, Md. Izazul Haq, Md. Hanif, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6234327/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Changes in soil and water salinity pose critical challenges to agriculture, water management, and livelihoods in deltaic environments globally, and particularly in the densely populated Asian mega-deltas. Using observations from 24 stations over nearly two decades (2004–2022) in the Ganges-Brahmaputra-Meghna delta of Bangladesh, our scientific study examined the influences of local weather, tropical cyclones, and hydrology on the seasonal variability in soil and river-water salinity. We applied statistical analyses including cross-correlation, seasonal trends, and wavelet decomposition, to explore the spatiotemporal dynamics of soil and river-water salinity. We developed statistical models to assess how hydrological, meteorological, and climatic factors explain its variability. Pronounced seasonal fluctuations in soil and river-water salinity are observed, with levels rising during the dry season and declining sharply during the monsoon season. We also observed how tropical cyclones contribute to short-term spikes in salinity, with stronger impacts observed for those making a landfall during early monsoon period (April‒May). Statistical models reveal a significant positive association between soil and surface-water salinity and sea-surface salinity during the pre-to-early monsoon season. In contrast, the seasonal rise in sea levels during the monsoon coincides with reduced soil and river-water salinity due to monsoon rainfall and freshwater discharges to the sea. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Soil and water salinity seasonality cyclones climate adaptation Bangladesh Figures Figure 1 Figure 2 Figure 3 Introduction In low-lying deltaic environments around the world, soil and water salinity pose significant challenges to food security, public health, and environmental sustainability (Mukhopadhyay et al., 2021; Negacz et al., 2022). Increased salinisation of soil, surface water, and coastal groundwater has become a critical problem, affecting agricultural lands, food production, and the livelihoods of millions of farmers in deltaic environments including the densely populated Asian mega-deltas. High soil salinity and its associated adverse effects on the environment, ecosystems, food security, and livelihoods have been observed in the Asian mega-deltas (Fig. 1 a), including India (Kumar and Sharma, 2020), the Indus River Basin of Pakistan (Qureshi et al., 2010), the Mekong Delta of Vietnam (Morton et al., 2023), the Yellow River Delta of China (Yu et al., 2014), and the Ganges-Brahmaputra-Meghna Delta of Bangladesh (Shawkhatuzamman et al., 2023) and West Bengal, India (Sahana et al., 2020). In Bangladesh, soil and surface-water (i.e., river and pond waters) salinity is a growing concern, particularly in the coastal region (Salehin et al., 2018), which covers one-fifth of the country and is home to nearly 35 million people. Around 17 million of these people in the southwestern coastal areas are acutely affected by high water and soil salinity. Surface water in the southwestern Bangladesh is highly saline (Fig. 1 b) with substantial seasonal variability. Several factors exacerbate soil and surface water salinity, including reduced freshwater flows from the upstream rivers (Haq et al., 2024), geomorphological changes, storm surges, land subsidence (Feist et al., 2023), irrigation, brackish-water shrimp farming (Chowdhury et al., 2011; Clarke et al., 2015), and the construction of polders (Islam et al., 2019). Climate change further intensifies this problem through rising sea levels, increased frequency and intensity of tropical cyclones, and amplification of rainfall extremes. Robust evidence linking these climate-related changes to increased salinisation of soil and surface water is limited. Previous studies have explored the spatiotemporal dynamics of soil or river-water salinity in coastal Bangladesh, primarily through visual or statistical means (Bhuyan et al., 2023; Dasgupta et al., 2015; Haq et al., 2024; Salehin et al., 2018). Kawser et al. (2022) investigated long-term changes in soil salinity in southeastern Bangladesh whereas Rahman and Rahman (2022) and Jahan et al. (2022) focused on water salinity in the southwestern region. Globally, there is a lack of long-term monitoring of soil salinity. Due to this dearth in ground-based observations, Earth Observation data have been applied to explore changes in soil salinity. For example, satellite data (e.g., Landsat and Sentinel) were used to characterise soil salinity in Hungary (Sahbeni and Székely, 2022), salinity trends in the Bakhtegan Salt Lake region of Iran (Taghadosi and Hasanlou, 2017), the impact of drainage network on soil salinity in Northeast Iran in Bandak et al. (2024), and soil salinity dynamics in Kuwait (Bannari and Al-Ali, 2020). In coastal Bangladesh, Sarkar et al. (2023) and Bhuyan et al. (2023) used Landsat satellite data to map spatiotemporal variability in soil and water salinity. Despite recent advancements in remote sensing, machine learning, and electromagnetic methods for soil salinity monitoring (Eltarabily et al., 2024), there remains limited integration of locally relevant datasets and field-level time-series data to assess soil salinity dynamics and understand the complex interplay of factors contributing to soil and water salinity in coastal Bangladesh and other Asian mega-deltas. Research to date examines salinity in isolation, focusing on either soil salinity, surface water salinity, or groundwater salinity, without addressing the interconnected influences of hydrological, meteorological, climatic, and anthropogenic factors. No previous study in Bangladesh has simultaneously considered the roles of hydrological factors (e.g., river discharge, surface water levels), meteorological influences (e.g., tropical cyclones, rainfall, and temperature patterns), climate change impacts (e.g., sea level rise, long-term rainfall distribution, and temperature increases), and anthropogenic activities (e.g., polder construction, land-use changes, and agricultural practices) in driving/controlling changes in soil and water salinity. A key barrier to understanding these dynamics has been a lack of long-term monitoring data. In this study, we present rare time-series of combined monthly monitoring of soil and river-water salinity from three coastal districts in Bangladesh, covering the period from January 2004 to June 2022. We analyse these salinity data both visually and statistically, examining their relationship with hydrological, meteorological, climatic, and anthropogenic factors that influence the spatiotemporal dynamics of river-water and soil salinity. Our analysis addresses the following key questions: (1) What drives the seasonal variation in soil and river-water salinity? (2) How do tropical cyclones and local weather influence soil and river-water salinity? and (3) What are the impacts of rising sea levels, sea salinity, terrestrial hydrology, and climate variability on soil and river-water salinity? In Bangladesh and other Asian mega-deltas, where water and soil salinity are major concerns and infrastructure development, and climate adaptation efforts are underway to address the adverse impacts of salinisation. Effective climate adaptation strategies require a clear understanding of the mechanisms driving soil and water salinity to inform more directly climate resilience-building activities. The findings from this research seek to contribute to better-targeted and more effective climate adaptation efforts in salinity-affected regions. Results Pronounced seasonality in soil and river-water salinity We observe that soil and surface water (i.e. river channels) salinity (measured in terms of Electrical Conductivity or EC) values are highly seasonal in nature. Pronounced seasonal fluctuations occur in which mean minimum and maximum values of soil salinity ( n = 11) range from ~ 1,000 to 22,100 µS/cm and river-water salinity ( n = 13) range from ~ 370 to 30,400 µS/cm. At Krishnanagar station in Khulna (Fig. 2 ), soil salinity rises slowly from the end of monsoon season in September from ~ 2,000 µS/cm to 7,600 µS/cm in the month of May. Similarly, surface-water salinity rises slowly from the end of monsoon season during October from ~ 750 µS/cm in River Rupsha to a value of 25,500 µS/cm in the month of May. Similar time series of soil and surface-water salinity data for other monitoring stations are presented in the supplementary information (Figure S1 ). The Pearson correlation between soil and river water salinity time-series data is 0.75 ( p value < 0.001). Further, the Cross Correlation Function (CCF) analysis confirms no lag in the monthly time-series data in the soil and river-water salinity time-series records (January 2004 to June 2022). To explore the seasonal component in the time-series data, we applied the Seasonal and Trend decomposition using Loess (STL) method (Cleveland et al., 1990). STL decomposition reveals that the seasonal component represents nearly 60% and 78% of the variability observed in the soil and river-water salinity time-series, respectively. Wavelet analysis reveals periodicity in the salinity data as it decomposes the time series into components associated with different time scales or frequencies (Percival and Walden, 2000). Both soil and river-water salinity time-series records clearly show annual seasonality (12-month cycle) though a 6-month cycle is visible in some stations. Wavelet coherence analysis also shows correlations between soil and river-water salinity as well as with monthly rainfall data from the Bangladesh Meteorological Department (BMD). Effects of local weather (rainfall and temperature) on salinity Local temperature and rainfall have significant effects on the seasonal variability in the soil and river-water salinity observed in all 24 monthly time-series records from southwestern Bangladesh. Visually, the monthly climatology plots of rainfall and temperature with salinity show clear associations (Fig. 2 ). Rises in monthly soil and river-water salinity closely follow the temperature that peaks around April. Seasonal decreases in soil and river-water salinity also follow the declines in monthly temperature. Associations between soil and river-water salinity, and monthly rainfall are nearly inverse. Rapid declines in monthly soil and river-water salinity levels in June follow the onset of the summer monsoon season. The lowest levels of salinity are observed in September following the bulk of the seasonal rainfall from June to August. The rate of change in salinity varies substantially between the two consecutive months with the highest change (i.e., decrease in salinity) observed in June and July (Fig. 3 a and b). In addition, Cross Correlation Function (CCF) between mean soil salinity and temperature is 0.48 with a 2-month lag indicating a delayed temperature effect in the monthly salinity data. Inversely, a strong negative (‒0.79) correlation with rainfall with a lag of 2 months indicates flushing effects of rainfall on soil salinity. Similar associations are observed between mean surface-water salinity, and seasonal temperature and rainfall data. Long-term patterns in soil and water salinity We applied linear trend and non-parametric Sen’s slope (Hirsch et al., 1982), as well as STL decomposition (Cleveland et al., 1990) methods to characterise long-term patterns in soil and river-water salinity data (Figs. 2 and S4); wavelet analysis shows periodicity and coherence in records (Figures S5-S7). Overall, long-term patterns (Jan 2004 to Jun 2022) in both soil and river-water salinity show mean decreasing trends: -176 µS/cm/year in soil salinity stations ( n = 11) and − 93 µS/cm/year in river-water salinity stations ( n = 13). Sen’s slopes are − 129 µS/cm/year and − 35 µS/cm/year respectively. Seasonal Sen’s slopes are also negative (Tables S1 and S2). Interestingly, we observe that the trend in soil salinity at Krishnanagar (Khulna) is not monotonic; it shows a declining pattern in the first part of the time series (2004–2014) and a rising pattern in the latter part (2014–2022) that ultimately result in no consistent overall trend from 2004 to 2022 (Fig. 2 e). Critically, dry-season soil salinity levels have been increasing since 2014. The long-term trend in river-water salinity at Rupsha (Khulna) shows a similar decreasing pattern though the magnitude of changes is small compared to soil salinity. Application of the Seasonal-Trend decomposition using LOESS (STL) on Krishnanagar soil salinity time-series data (Figure S4) reveals a very small trend component represented by 15% variability of the total variance in the time-series data. Seasonal and irregular or residual components represent 47% and 37% variability at Krishnanagar site (Table S1 ). On average, the trend, seasonality and irregular components represent 15%, 60% and 25% variability in all soil salinity ( n = 11) time-series data. In comparison, on average, the trend, seasonality and irregular components represent 3%, 78% and 19% variability in all river-water salinity ( n = 13) time-series data. These analyses clearly demonstrate strong seasonal variations in soil and river-water salinity in southwest Bangladesh. Modelling soil and river-water salinity To explain seasonal and long-term variability in soil and river-water salinity data, we applied a multiple linear regression statistical model through mean monthly as well as mean annual time-series records of 11 soil and 13 river-water salinity stations. Twelve covariate datasets (meteorology: rainfall, temperature and cyclone; hydrology: evapotranspiration, surface runoff, soil moisture, surface water levels, river discharge; climatology: sea level anomaly and sea surface salinity; and anthropology: groundwater levels and Normalised Difference Vegetation Index or NDVI) are considered in the models to explain the variability observed in the salinity time-series data. A series of multiple regression models are developed to explain the seasonal and annual mean variability observed in soil and river-water salinity data. One of the main challenges we encounter is that covariates are highly seasonal (Figures S8) as well as highly correlated to each other (Figure S9). When predictors are highly correlated, multicollinearity can distort the interpretation of model coefficients and the direction of association. Further details on the exploratory analyses and statistical models are provided in the methods section. Results from statistical models reveal interesting associations between soil salinity and predictor variables, as well as between river-water salinity and the predictor variables. Since we use the natural logarithm of soil and river-water salinity data in the model, the interpretation of the model coefficients is that a one-unit increase in a predictor variable is associated with a change (depends on the sign) in the logarithm of salinity by the corresponding model coefficient of that predictor or explanatory variable. For example, a statistically significant ( p value < 0.001) positive association is modelled between temperature and soil salinity. This model result (temperature, β = 0.07022) suggests that a one-unit increase in temperature (seasonal or long-term change) leads to a percentage change (i.e., increase) in soil salinity, \(\:y={(e}^{0.07022}-1)\:x\:100\%\approx\:7.3\%\) (Table 1 ). Rainfall shows a strong correlation with surface runoff so that model results are affected by the multicollinearity effect. So, in the model we disregard surface runoff. NDVI, which is a measure of vegetation health (i.e., higher values typically indicate healthy, dense vegetation, and lower values indicate less healthy or sparse vegetation), has a negative association with soil salinity ( β =-2.11420, p value < 0.001) suggesting that a one-unit increase in NDVI leads to a decrease in soil salinity of about 88%. Groundwater level ( β =-0.27976, p value = 0.01) and river discharge ( β =-0.00008, p value = 0.015) have statistically significant negative associations with soil salinity suggesting an increase in groundwater levels (1 m) or river discharge (100 m 3 /sec) would lead to a decrease in soil salinity by 24% and 1%, respectively. The model also suggests that the occurrence of tropical cyclones has a positive ( β = 0.27851, p value = 0.012) influence on soil salinity (i.e. soil salinity can potentially increase by 32% per additional cyclone occurrence). Sea surface salinity has a positive statistically significant association ( β = 0.00005, p value < 0.001) with soil salinity whereas sea-level anomaly has a negative ( β =-0.00187, p value 0.70, p value < 0.001) the variability observed in river-water salinity data. Further, we model the association between river-water salinity and the twelve predictor covariates using the monthly as well as mean annual salinity time-series data. For modelling river-water salinity, we also consider lagged rainfall values by up to 2 months as additional model factors. Results are consistent with soil salinity models. Temperature, lagged rainfall, sea-level anomaly, sea surface salinity and groundwater levels are important predictors of river-water salinity. However, we find no statistically significant associations between river-water salinity and cyclones, surface water levels, and NDVI. Both monthly and mean annual models adequately explain ( R 2 ≥ 0.90, p value < 0.001) the variability observed in river-water salinity data. Table 1 Summary statistics of multiple linear regression models for soil and river-water salinity in southwestern coastal Bangladesh. Discussion Here, we discuss the observed seasonal, annual and decadal-scale dynamics of soil and water salinity and influences of hydrological, climatological and anthropogenic drivers in the southwestern coastal region of Bangladesh. One of the key features of our analyses is that it reveals substantial seasonal variations (i.e., up to 2 orders of magnitude) in monthly soil and river-water salinity. This outcome is consistent with the findings from previous, localised studies of soil salinity (Kawser et al., 2022; Salehin et al., 2018) and regional-scale analyses of surface-water salinity in coastal Bangladesh (Feist et al., 2023; Haq et al., 2024). The novelty in our study lies in the detailed characterisation of soil and river-water salinity from observations and their statistical associations with the local weather (i.e. temperature and rainfall) and climate change (i.e. rising sea levels). Seasonal soil and surface water salinity rises steadily as temperature increases from winter to summer months and then falls very quickly (i.e. within a couple of months) as soon as the monsoon season begins. This association clearly demonstrates the critical role of local weather in the seasonal dynamics of soil and river-water salinity in coastal Bangladesh. Exploratory analyses and visualisation of salinity time-series data and statistical models indicate positive associations between tropical cyclones and soil and water salinity. The impact of cyclones on soil salinity is stronger and slower than it is for river-water salinity as revealed in monthly monitoring data. The impacts of river-water salinity could be better observed in daily monitoring data that do not exist in coastal Bangladesh. We observe from the time-series records that river-water and soil salinities return to background levels within a month or so. The response could be, however, different in the soil and surface-water salinity in areas that are located within coastal embankments (locally known as polders). Recent studies (Tsai et al., 2024) report contamination of freshwater ponds due to storm surge inundation and breaching of protective earthen polders. We examined the timing of tropical cyclones and their impacts on soil and river-water salinity. Close inspection of the timing of cyclones and their signature on soil and river-water salinity data reveal short-term deflections in salinity values that are short-lived but sufficient to increase soil salinity. This is consistent with the regional-scale analysis of river-water salinity in coastal Bangladesh (Haq et al., 2024). Statistical models clearly suggest a positive association between cyclones and soil salinity. Observations suggest that cyclones making landfalls during the late monsoon season (September to November) when sea-surface salinity is at its lowest level (~ 25,000 µS/cm), have lower impacts on soil and river-water salinity than those making landfalls during the pre-monsoon (March to May) season when sea salinity is at its highest levels (~ 40,000 µS/cm). For example, Cyclone Aila (May 2009) is reported to have much greater impacts on surface water such as ponds (Tsai et al., 2024) and soil salinity compared to super-cyclone Sidr that made landfall in November 2007. The backwater effects of the sea and tropical cyclones clearly highlight the complex interplay between marine and terrestrial salinity dynamics in coastal Bangladesh. Our statistical models reveal significant associations between seasonal sea surface salinity and sea levels on soil and river-water salinity. Cross correlation between monthly sea levels and sea salinity shows a strongly negative (Pearson correlation, r =‒0.803, p value < 0.001) association. A positive association with seasonal sea surface salinity emphasises the argument above on the timing of tropical cyclones landfalls and their impacts on salinity. A negative association with seasonal sea-level anomaly suggests that seasonally rising sea levels (i.e. positive anomaly) during the monsoon season coincide with lower soil and river-water salinity. Due to the highly seasonal nature of salinity data and the large variations in salinity levels, the gradual rise in sea-level anomaly has minimal impact on seasonal salinity patterns. In fact, rising sea levels do not necessarily mean an increased sea-surface salinity (Cheng et al., 2020; Durack et al., 2014). In monsoon-dominated deltaic systems, freshwater discharge from runoff and direct rainfall dilutes salinity levels. For instance, in the Ganges-Brahmaputra-Meghna (GBM) delta, the monsoon season brings substantial freshwater input, which flushes salt in seawater and reduces sea-surface salinity (Bricheno et al., 2021). Our statistical models reveal significant, inverse associations between soil and river-water salinity and river discharge in the upstream (i.e. River Gorai – an offshoot of River Ganges) in the presence of an interaction with surface water levels which shows a positive relationship. These may suggest that a decrease in freshwater discharge in the upstream rivers can increase brackish-saline water through backflow effects within the tidal rivers in the coastal region of Bangladesh. The model reveals an interesting association between groundwater levels and salinity. A seasonal rise in groundwater levels (i.e., shallowing of the water table) in shallow, brackish- to saline-water aquifers can influence soil salinity through capillary rise (Jorenush and Sepaskhah, 2003). In contrast, an inverse association between deepening groundwater table and river-water salinity suggests that water salinity is at its higher levels when base-flow to rivers is small or even absent due to reversal in hydraulic gradients. These associations mainly explain the seasonal variability in soil and river-water salinity. Our models, however, are not able to explain the long-term dynamics in the soil or river-water salinity in relation to river discharge or groundwater-level variations. Overall, our data-driven analyses of long-term soil and river-water salinity time-series data reveal complex seasonal patterns influenced by various factors. Through data visualisation, statistical analyses, and modelling, we examined long-term time-series data alongside a dozen related variables. This approach uncovered significant associations between salinity levels and local weather, climate, hydrology, and anthropogenic factors. Our observations show that while cyclones can cause rapid changes in salinity levels, the impact is usually short-lived, and its intensity is largely influenced by seasonal factors. Impacts of storm-surge inundation from tropical cyclones are more pronounced in soil salinity than in river-water salinity. This is because storm-surge inundation causes occasional breaches of earthen polders (Islam et al., 2019) creating prolonged water-logging conditions. As a result, higher soil salinity can sustain for a long period of time (weeks to months) when polders are breached during tropical cyclones contaminating land and water inside them (Bhuyan et al., 2023). The land surface elevation within polders is often lower than the adjacent floodplains (Zaman and Mondal, 2020). A significant consequence of this elevation loss was the failure of polders during Cyclone Aila in 2009, causing tidal inundation of large areas for up to two years (Auerbach et al., 2015). In contrast, cyclone-induced rainfall and runoff negate the effects of saline-water intrusion within a very short period of time (i.e. within days to weeks) as observed in Pearl River Delta in China (Gao et al., 2024). Our analysis shows that early monsoon cyclones (April-May) cause more severe soil and water salinity increases than post-monsoon cyclones, due to peak sea salinity levels during this period. For example, cyclones Aila (May 2009) and Amphan (May 2020) had long-lasting impacts on southwestern coastal Bangladesh and notably disrupted the expected seasonal salinity in surface water and soil in the southwestern coast (Tsai et al., 2024). Tropical cyclones are becoming very common in May (e.g., Roanu in 2016, Fani in 2019, Amphan in 2020, Yaas in 2022, Remal in 2024) as we find in the International Best Track Archive for Climate Stewardship (IBTrACS) database (Gahtan, 2024). The occurrence of cyclones in the early monsoon season along with slowly rising sea-levels may contribute to an increasing trend in soil salinity observed in the last decade. Our analysis found no significant overall trends in soil or river-water salinity over the past 18 years (Jan 2004 to Jun 2022), as opposing trends in different periods offset each other. Steadily rising trends are observed primarily in the dry-season soil salinity since 2012-14, consistent with Salehin et al. (2018) and both rising summer temperatures (Figure S10) and lower rainfall (Figure S11) during the early monsoon season throughout the southwestern coastal Bangladesh. Substantial increases in peak monsoon rainfall, mainly in July, are aligned to the global amplification of precipitation extremes under global warming (Douville et al., 2021) and help to buffer seasonal soil and river-water salinity. Furthermore, rising trends in sea-level anomaly are inversely correlated to sea-surface salinity indicating the effect of an increased seasonal rainfalls from July to September diluting sea-water salinity near the coastline (Fig. 3 c, d and e). These meteorological and climatic changes shape the seasonal dynamics of soil and river-water salinity in southwestern coastal Bangladesh. Our findings from the analysis of soil and surface water salinity in Bangladesh provide valuable insights into water and soil salinisation in other Asian mega-deltas and coastal regions globally, particularly where long-term monitoring of salinity is limited or non-existent. By leveraging rare salinity time-series data, our data-driven visualisations and statistical analyses highlight critical dynamics essential for soil and water management and inform climate adaptation strategies in Bangladesh. It is crucial to evaluate the existing salinity adaptation measures such as the Managed Aquifer Recharge (Naus et al., 2020; Shammi et al., 2019), rainwater harvesting (Ashrafuzzaman et al., 2023; Islam et al., 2023), saline-resistant crops (Islam et al., 2020; Paul and Rashid, 2016), and improved coastal embankment (i.e., polder) management (Islam et al., 2020; Rahman et al., 2021) not only in Bangladesh but also across other Asian mega-deltas where climate adaptation measures are being planned and implemented. Methods Soil and river water salinity datasets We use long-term (January 2004 to December 2022) monthly time-series salinity data from 11 soil salinity and 13 river-water salinity stations in southwestern coastal Bangladesh. These monitoring stations are located within three coastal districts of Bangladesh namely Bagerhat, Khulna and Satkhira (Fig. 1 b). The monthly time-series data were collated from the local office of the Soil Resource Development Institute (SRDI) in Khulna, Bangladesh. SRDI is a government organisation operating under the Ministry of Agriculture. SRDI collects information on soil and water properties for sustainable agricultural production through improved management of soil as well as preservation of environment. To analyse seasonal variation in soil and water salinity, SRDI collects monthly soil samples from 11 stations and river-water samples from 13 stations in southwestern Bangladesh which is one of the most salinity-affected areas in the country. Seven of the 11 stations of soil salinity have monthly data from January 2004 to June 2022 and the remaining 4 stations have soil salinity data from January 2018 to June 2022, whereas all the 13 stations of water salinity have monthly data from January 2004 to June 2022.These rare time-series data of soil and water salinity have not previously been analysed or published. Monthly soil salinity at the 11 stations derives from soil samples from the respective locations. Samples are then tested for the Electrical Conductivity (EC) of a saturated soil Extract (ECe), a reliable method for measuring soil salinity (Jones, 2001). In this sampling, soils are collected from 0 to 15 cm depth of the soil profile. The collected soil samples are first air-dried and cleared from unwanted materials such as plant remains. Once the sample is properly dried, wooden hammer is used to break down the larger aggregates into smaller particles. The powdered soil sample is then filtered through a 2 mm sieve. This filtered soil is added to distilled water maintaining a ratio of 1:5 to fill the available pores and then is mixed to the consistency of a paste that glistens with water and if jarred flows slightly. After allowing sufficient time, ideally overnight for the soil to equilibrate and dissolve the salts thoroughly, the resulting water is extracted by suction filtration and Electrical Conductivity (EC) is determined by a portable EC meter. However, saturated extract (soil:water = 1:1) produces the best result for soil EC but preparation of saturated extract is cumbersome. Therefore, a ratio of 1:5 (soil:water) is used and the result is converted into saturated extract (ECe). In case of surface water salinity, river water was collected in plastic bottles after rinsing it with sample water for about two to three times. The samples were labelled with sample IDs and the information about the sampling points are recorded. After the collection of water samples, all bottles were sealed immediately to avoid exposure to air. After labelling, water samples were transported to the laboratory of SRDI following sampling protocols from sampling to transportation. All the samples were filtered through filter paper (Whatman No.1) to remove unwanted solid and suspended materials before analysis. The samples were then measured by an EC meter. Monthly rainfall and temperature time-series data We collated daily temperature and rainfall time-series data from the Bangladesh Meteorological Department (BMD) at three stations (Khulna, Satkhira and Mongla) located in the southwestern Bangladesh. BMD is a government meteorological agency that has been collecting data since 1950s. We collated the available daily time-series data (January 2004 to December 2021) at three stations and converted it to monthly data for statistical analysis and modelling in this study. Monthly rainfall and temperature data for 2022 at the three BMD station locations were downloaded from the TerraClimate product (Abatzoglou et al., 2018) available on an online data portal called Climate Engine ( https://www.climateengine.org ) (Huntington et al., 2017). We also collated monthly evapotranspiration, root-zone soil moisture, and surface runoff data at the selected BMD locations from the NASA's Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) via the Climate Engine data portal. Sea level anomaly and sea surface salinity data We use monthly sea level anomaly and sea surface salinity data from the Copernicus Marine data services through the data visualisation tool called My Ocean Pro portal ( https://marine.copernicus.eu/access-data/myocean-viewer ). Monthly gridded (0.25° × 0.25° spatial resolution) mean of Sea Level Anomalies (SLA) derived from altimeter satellite measurements computed with respect to a 20-year (1993–2012) mean. The sea-level anomalies (Product: SEALEVEL_GLO_PHY_L4_MY_008_047) were estimated by an Optimal Interpolation method that merged the Level 3 (L3) along-track measurement from different altimeter satellite missions (Pujol, 2023). The sea surface salinity gridded (0.083° × 0.083°) monthly mean data derived from numerical models under the Global Ocean Physics Reanalysis product (GLOBAL_MULTIYEAR_PHY_001_030; (Drévillon et al., 2022). Both SLA and sea salinity data were downloaded for the upper portion of the Bay of Bengal (north of 19° latitude and within 86° and 95° E longitudes) and aggregated monthly time-series data were generated using all the available grid points. Gridded data were processed in R programming language (R Core Team, 2022) using various packages. Data processing and exploratory analysis Soil and river water salinity time-series data were collated in MS Word format from SRDI, Khulna. Daily rainfall and temperature data were collated in MS Excel format from BMD. All time-series data are then converted to a standard MS Excel comma-separated values (CSV) file format, processed and analysed in R programming language. Both R GUI and RStudio software were used for data processing and statistical analyses. All time-series data are converted to mean monthly values for consistency. Missing values are imputed using the Random Forest algorithm implemented under the missForest R package (Stekhoven and Bühlmann, 2012). However, imputed data were used primarily for visualisation purposes. Geospatial data were processed and visualised using ESRI’s ArcGIS Desktop software. Trend analysis using parametric and non-parametric methods Descriptive statistics from the 24 time-series (Jan 2004 to Dec 2022) salinity data are calculated in R platform. We apply both parametric and non-parametric trend analyses and decomposed the monthly series into seasonal, trend and residual components using the Seasonal and Trend Decomposition using Loess (STL) method (Cleveland et al., 1990). For the trend analysis, we apply linear trend, Mann-Kendall (MK) trend test, seasonal MK trend, Sen’s slope and seasonal Sen’s slope (Hipel and McLeod, 1994; Hirsch et al., 1982). Cross-correlation and wavelet analysis As a part of our exploratory analysis, we examined the correlation and cross-correlation between soil and river-water salinity along with 12 covariates using the Pearson correlation and Cross Correlation Function (CCF) in R environment. We approximately grouped the covariate datasets into hydrological factors (i.e., evapotranspiration, river discharge, surface water levels, soil moisture, groundwater levels), meteorological (i.e., rainfall, temperature, occurrence of cyclones), climatological (i.e., sea level anomaly, sea surface salinity), and anthropological (i.e., normalized difference vegetation index or NDVI as an indicator of land-use changes). We apply wavelet analysis using the WaveletComp (Rösch and Schmidbauer, 2016), an R package for continuous wavelet-based analysis of soil and river water salinity and meteorological time series records. Wavelet analysis is a powerful tool that decomposes a time series into time-frequency space, allowing the examination of how periodic components of the data evolve over time (Jevrejeva et al., 2004). When applied to a single time series, wavelet analysis reveals the presence and strength of periodicities (cyclical patterns) at different scales (frequencies) and how these periodicities change over time which makes it particularly useful for identifying localised events or changes in cyclic behaviour (Percival and Walden, 2000). For coherence analysis between two time series, the cross-wavelet transforms, and wavelet coherence techniques are used (Jevrejeva et al., 2004). Statistical modelling In this study, we develop statistical models to explain the variability observed in the time-series data of soil and river-water salinity in coastal Bangladesh using fourteen covariates representing hydrological, meteorological, climatological and anthropogenic processes. We use multiple linear regression (MLR) to model the statistical variability in the response or dependant variables such as the soil salinity and river-water salinity. The MLR model take the following form in Eq. (1). \(\:\mathbf{log\:}\left(\varvec{y}\right)={\varvec{\beta\:}}_{0}+{\varvec{\beta\:}}_{1}{\varvec{x}}_{1}+{\varvec{\beta\:}}_{2}{\varvec{x}}_{2}+\dots\:{+\:\varvec{\beta\:}}_{\varvec{n}}{\varvec{x}}_{\varvec{n}}+\:\varvec{\epsilon\:}\) ………eq. (1) The response variable, \(\:\text{l}\text{o}\text{g}\left(y\right)\:\) (soil or river water salinity) is log-transformed (natural logarithm) due to the skewness in the original data distribution. In Eq. (1), \(\:{\beta\:}_{0},\:{\beta\:}_{1},\:{\beta\:}_{2},\:{\dots\:\beta\:}_{n\:}\) are model coefficients, \(\:{x}_{1\:},\:{x}_{2\:},\:{x}_{n\:}\) are predictor or covariate datasets ( \(\:n=14)\) , and \(\:ϵ\) represents the error term in the model. We use dynamic linear regression in dynlm R package for modelling soil and river-water salinity (Zeileis et al., 2019). Exploratory analysis shows that the time-series data of response variables and covariates have high seasonality, and some are highly correlated to each other raising the concern of multicollinearity. This occurs when two or more predictors in a regression model are highly correlated. Multicollinearity can cause the estimates of the regression coefficients to become unstable and highly sensitive to small changes in the model or data that can result in large standard errors and less reliable coefficient estimates (Kutner et al., 2004). We check correlation matrix and Variance Inflation Factor (VIF) in the fitted model to consider dropping some variables that can cause multicollinearity effects. We also consider adding interaction terms to the model to explore joint effects on the response variable. The fitted model is diagnosed using standard plots in R, and model summary statistics are derived from the final model for interpretation. Two separate MLR models are created using the same set of covariates to explain the variability observed in soil and river-water salinity datasets. Declarations Data availability Monthly time series of soil and surface-water salinity monitoring data (2004-2022) collated from the Soil Resource Development Institute (SRDI), Bangladesh along with the variables used in statistical models can be made available to researchers upon receiving a request to the corresponding author. We do not have the necessary permissions from the SRDI Bangladesh to make the monitoring data publicly available. Statistical analyses and modelling were conducted in R programming environments. Upon request to the corresponding author, all R codes can also be made available to researchers who might be interested in reproducing the results of this study. Acknowledgements Ahmed Z. Rahman acknowledges the funding support from the Government of Bangladesh for his doctoral research at UCL. However, the views expressed in the article do not necessarily represent the views of the Government of Bangladesh. M Shamsudduha acknowledges UCL’s Research England QR Policy Support Fund. The authors acknowledge the soil and surface-water salinity data from the Soil Resource Development Institute (SRDI) and the Bangladesh Meteorological Department (BMD). Author’s contributions A.Z.R. contributed to the conceptualisation of the study, collated monitoring data, analysed data, and drafted the manuscript; M.S. contributed to project supervision, data curation, visualisation, statistical modelling and writing the manuscript; M.I.H, M.H., and M.S.I. contributed to writing, commenting and reviewing the manuscript; S.S.I. and A.B. contributed to data collation and writing; and R.G.T. contributed to project supervision, writing, commenting and editing of the manuscript. Corresponding authors Correspondence to Ahmed Z. 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Scientific Reports 13, 17056. Shammi, M., Rahman, M.M., Bondad, S.E., Bodrud-Doza, M., 2019. Impacts of salinity intrusion in community health: a review of experiences on drinking water sodium from coastal areas of Bangladesh, Healthcare. MDPI, p. 50. Shawkhatuzamman, M., Roy, S.R., Alam, M.Z., Majumder, P., Anka, N.J., Hasan, A.K., 2023. Soil salinity management practices in coastal area of Bangladesh: a review. Research in Agriculture Livestock and Fisheries 10, 1–7. Stekhoven, D.J., Bühlmann, P., 2012. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118. Taghadosi, M.M., Hasanlou, M., 2017. Trend analysis of soil salinity in different land cover types using Landsat time series data (case study Bakhtegan Salt Lake). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 251–257. Tsai, C., Hoque, M.A., Vineis, P., Ahmed, K.M., Butler, A.P., 2024. Salinisation of drinking water ponds and groundwater in coastal Bangladesh linked to tropical cyclones. Scientific Reports 14, 5211. Yu, J., Li, Y., Han, G., Zhou, D., Fu, Y., Guan, B., Wang, G., Ning, K., Wu, H., Wang, J., 2014. The spatial distribution characteristics of soil salinity in coastal zone of the Yellow River Delta. Environmental Earth Sciences 72, 589–599. Zaman, S., Mondal, M.S., 2020. Risk-based determination of polder height against storm surge Hazard in the south-west coastal area of Bangladesh. Progress in Disaster Science 8, 100131. Zeileis, A., Fisher, J.C., Hornik, K., Ihaka, R., McWhite, C.D., Murrell, P., Stauffer, R., Wilke, C.O., 2019. colorspace: A toolbox for manipulating and assessing colors and palettes. arXiv preprint arXiv:1903.06490. Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Onlinefloatimage4.png Table 1. Summary statistics of multiple linear regression models for soil and river-water salinity in southwestern coastal Bangladesh. SupplementaryInformationSoilSWSalinity14March2025.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6234327","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444008908,"identity":"8995db01-b915-4f9b-9a4e-1915d28d63ff","order_by":0,"name":"AHMED Z. 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(b) Mean surface water salinity map of the southern coastal Bangladesh and the location of soil and surface-water (i.e., river water) salinity monitoring stations used in this study.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6234327/v1/0917da55c5b01e981967da37.png"},{"id":81390876,"identity":"7be5ca8d-e353-42a6-9a80-e2907f1690dc","added_by":"auto","created_at":"2025-04-25 14:40:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":266195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-series plots of soil and river-water salinity data in Bangladesh.\u003c/strong\u003e (a) Time-series (Jan 2004 to Jun 2022) of soil and river-water salinity and rainfall at selected stations (Krishnanagar, Rupsha) in the coastal Bangladesh. (b) Monthly seasonality of salinity and rainfall. (c) Time-series of soil and river-water salinity and temperature at the same stations. (d) Monthly seasonality of salinity and temperature. (e) Time-series plots of soil salinity at Krishnanagar plotted with the timing of tropical cyclones shown as vertical dash lines; the month of peak seasonal salinity in each year is marked with a red solid circle. (f) Monthly soil salinity at River Rupsha station with tropical cyclones. The orange vertical dash lines indicate cyclones making landfall in April and May, and the grey lines indicate cyclones making landfall during and after the monsoon season. The dashed red lines show the non-linear trend derived through the Seasonal-Trend decomposition using LOESS (STL) method. Similar plots for the other stations are presented in the supplementary information (see Figures S1 and S2).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6234327/v1/d665c17c4a57a14ccbc308f5.png"},{"id":81390877,"identity":"b2a2cc07-d08a-435b-ad65-e719099f6a8e","added_by":"auto","created_at":"2025-04-25 14:40:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-series plots of soil and river-water salinity data in Bangladesh.\u003c/strong\u003e (a) Time-series (Jan 2004 to Jun 2022) of soil and river-water salinity and rainfall at selected stations (Krishnanagar, Khulna). (b) Same as (a) but for river-water salinity. (c) Monthly time-series of sea-level anomaly in the Bay of Bengal. (d) Monthly time-series of sea surface salinity in the Bay of Bengal. (e) Monthly seasonality in sea-level anomaly and sea-surface salinity aggregated by month from the time-series data presented in (c) and (d).\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6234327/v1/022c89e4f10c7e3c578517ae.png"},{"id":89069978,"identity":"85b10865-9323-41cc-9cca-af3c128672db","added_by":"auto","created_at":"2025-08-14 10:54:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2029428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6234327/v1/c9c3e457-9f42-43d3-b982-2af1292a6af5.pdf"},{"id":81391184,"identity":"878f3b1a-7c24-4d4e-86cf-6e963b1b67f2","added_by":"auto","created_at":"2025-04-25 14:48:18","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":193049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1. \u003c/strong\u003eSummary statistics of multiple linear regression models for soil and river-water salinity in southwestern coastal Bangladesh.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6234327/v1/9e59b200df508ae2764e7c15.png"},{"id":81390885,"identity":"2b960fc6-f600-4673-85a1-1cb2e79dcc20","added_by":"auto","created_at":"2025-04-25 14:40:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8043772,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationSoilSWSalinity14March2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-6234327/v1/28cbe13a9812445d00af944f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Soil and River-water Salinity Dynamics in Coastal Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn low-lying deltaic environments around the world, soil and water salinity pose significant challenges to food security, public health, and environmental sustainability (Mukhopadhyay et al., 2021; Negacz et al., 2022). Increased salinisation of soil, surface water, and coastal groundwater has become a critical problem, affecting agricultural lands, food production, and the livelihoods of millions of farmers in deltaic environments including the densely populated Asian mega-deltas. High soil salinity and its associated adverse effects on the environment, ecosystems, food security, and livelihoods have been observed in the Asian mega-deltas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), including India (Kumar and Sharma, 2020), the Indus River Basin of Pakistan (Qureshi et al., 2010), the Mekong Delta of Vietnam (Morton et al., 2023), the Yellow River Delta of China (Yu et al., 2014), and the Ganges-Brahmaputra-Meghna Delta of Bangladesh (Shawkhatuzamman et al., 2023) and West Bengal, India (Sahana et al., 2020).\u003c/p\u003e \u003cp\u003eIn Bangladesh, soil and surface-water (i.e., river and pond waters) salinity is a growing concern, particularly in the coastal region (Salehin et al., 2018), which covers one-fifth of the country and is home to nearly 35\u0026nbsp;million people. Around 17\u0026nbsp;million of these people in the southwestern coastal areas are acutely affected by high water and soil salinity. Surface water in the southwestern Bangladesh is highly saline (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) with substantial seasonal variability. Several factors exacerbate soil and surface water salinity, including reduced freshwater flows from the upstream rivers (Haq et al., 2024), geomorphological changes, storm surges, land subsidence (Feist et al., 2023), irrigation, brackish-water shrimp farming (Chowdhury et al., 2011; Clarke et al., 2015), and the construction of polders (Islam et al., 2019). Climate change further intensifies this problem through rising sea levels, increased frequency and intensity of tropical cyclones, and amplification of rainfall extremes. Robust evidence linking these climate-related changes to increased salinisation of soil and surface water is limited.\u003c/p\u003e \u003cp\u003ePrevious studies have explored the spatiotemporal dynamics of soil or river-water salinity in coastal Bangladesh, primarily through visual or statistical means (Bhuyan et al., 2023; Dasgupta et al., 2015; Haq et al., 2024; Salehin et al., 2018). Kawser et al. (2022) investigated long-term changes in soil salinity in southeastern Bangladesh whereas Rahman and Rahman (2022) and Jahan et al. (2022) focused on water salinity in the southwestern region. Globally, there is a lack of long-term monitoring of soil salinity. Due to this dearth in ground-based observations, Earth Observation data have been applied to explore changes in soil salinity. For example, satellite data (e.g., Landsat and Sentinel) were used to characterise soil salinity in Hungary (Sahbeni and Sz\u0026eacute;kely, 2022), salinity trends in the Bakhtegan Salt Lake region of Iran (Taghadosi and Hasanlou, 2017), the impact of drainage network on soil salinity in Northeast Iran in Bandak et al. (2024), and soil salinity dynamics in Kuwait (Bannari and Al-Ali, 2020). In coastal Bangladesh, Sarkar et al. (2023) and Bhuyan et al. (2023) used Landsat satellite data to map spatiotemporal variability in soil and water salinity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite recent advancements in remote sensing, machine learning, and electromagnetic methods for soil salinity monitoring (Eltarabily et al., 2024), there remains limited integration of locally relevant datasets and field-level time-series data to assess soil salinity dynamics and understand the complex interplay of factors contributing to soil and water salinity in coastal Bangladesh and other Asian mega-deltas. Research to date examines salinity in isolation, focusing on either soil salinity, surface water salinity, or groundwater salinity, without addressing the interconnected influences of hydrological, meteorological, climatic, and anthropogenic factors. No previous study in Bangladesh has simultaneously considered the roles of hydrological factors (e.g., river discharge, surface water levels), meteorological influences (e.g., tropical cyclones, rainfall, and temperature patterns), climate change impacts (e.g., sea level rise, long-term rainfall distribution, and temperature increases), and anthropogenic activities (e.g., polder construction, land-use changes, and agricultural practices) in driving/controlling changes in soil and water salinity. A key barrier to understanding these dynamics has been a lack of long-term monitoring data.\u003c/p\u003e \u003cp\u003eIn this study, we present rare time-series of combined monthly monitoring of soil and river-water salinity from three coastal districts in Bangladesh, covering the period from January 2004 to June 2022. We analyse these salinity data both visually and statistically, examining their relationship with hydrological, meteorological, climatic, and anthropogenic factors that influence the spatiotemporal dynamics of river-water and soil salinity. Our analysis addresses the following key questions: (1) What drives the seasonal variation in soil and river-water salinity? (2) How do tropical cyclones and local weather influence soil and river-water salinity? and (3) What are the impacts of rising sea levels, sea salinity, terrestrial hydrology, and climate variability on soil and river-water salinity?\u003c/p\u003e \u003cp\u003eIn Bangladesh and other Asian mega-deltas, where water and soil salinity are major concerns and infrastructure development, and climate adaptation efforts are underway to address the adverse impacts of salinisation. Effective climate adaptation strategies require a clear understanding of the mechanisms driving soil and water salinity to inform more directly climate resilience-building activities. The findings from this research seek to contribute to better-targeted and more effective climate adaptation efforts in salinity-affected regions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePronounced seasonality in soil and river-water salinity\u003c/h2\u003e \u003cp\u003eWe observe that soil and surface water (i.e. river channels) salinity (measured in terms of Electrical Conductivity or EC) values are highly seasonal in nature. Pronounced seasonal fluctuations occur in which mean minimum and maximum values of soil salinity (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11) range from ~\u0026thinsp;1,000 to 22,100 \u0026micro;S/cm and river-water salinity (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13) range from ~\u0026thinsp;370 to 30,400 \u0026micro;S/cm. At Krishnanagar station in Khulna (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), soil salinity rises slowly from the end of monsoon season in September from ~\u0026thinsp;2,000 \u0026micro;S/cm to 7,600 \u0026micro;S/cm in the month of May. Similarly, surface-water salinity rises slowly from the end of monsoon season during October from ~\u0026thinsp;750 \u0026micro;S/cm in River Rupsha to a value of 25,500 \u0026micro;S/cm in the month of May. Similar time series of soil and surface-water salinity data for other monitoring stations are presented in the supplementary information (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Pearson correlation between soil and river water salinity time-series data is 0.75 (\u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further, the Cross Correlation Function (CCF) analysis confirms no lag in the monthly time-series data in the soil and river-water salinity time-series records (January 2004 to June 2022). To explore the seasonal component in the time-series data, we applied the Seasonal and Trend decomposition using Loess (STL) method (Cleveland et al., 1990). STL decomposition reveals that the seasonal component represents nearly 60% and 78% of the variability observed in the soil and river-water salinity time-series, respectively. Wavelet analysis reveals periodicity in the salinity data as it decomposes the time series into components associated with different time scales or frequencies (Percival and Walden, 2000). Both soil and river-water salinity time-series records clearly show annual seasonality (12-month cycle) though a 6-month cycle is visible in some stations. Wavelet coherence analysis also shows correlations between soil and river-water salinity as well as with monthly rainfall data from the Bangladesh Meteorological Department (BMD).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffects of local weather (rainfall and temperature) on salinity\u003c/h3\u003e\n\u003cp\u003eLocal temperature and rainfall have significant effects on the seasonal variability in the soil and river-water salinity observed in all 24 monthly time-series records from southwestern Bangladesh. Visually, the monthly climatology plots of rainfall and temperature with salinity show clear associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Rises in monthly soil and river-water salinity closely follow the temperature that peaks around April. Seasonal decreases in soil and river-water salinity also follow the declines in monthly temperature. Associations between soil and river-water salinity, and monthly rainfall are nearly inverse. Rapid declines in monthly soil and river-water salinity levels in June follow the onset of the summer monsoon season. The lowest levels of salinity are observed in September following the bulk of the seasonal rainfall from June to August. The rate of change in salinity varies substantially between the two consecutive months with the highest change (i.e., decrease in salinity) observed in June and July (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and b). In addition, Cross Correlation Function (CCF) between mean soil salinity and temperature is 0.48 with a 2-month lag indicating a delayed temperature effect in the monthly salinity data. Inversely, a strong negative (‒0.79) correlation with rainfall with a lag of 2 months indicates flushing effects of rainfall on soil salinity. Similar associations are observed between mean surface-water salinity, and seasonal temperature and rainfall data.\u003c/p\u003e \n\u003ch3\u003eLong-term patterns in soil and water salinity\u003c/h3\u003e\n\u003cp\u003eWe applied linear trend and non-parametric Sen\u0026rsquo;s slope (Hirsch et al., 1982), as well as STL decomposition (Cleveland et al., 1990) methods to characterise long-term patterns in soil and river-water salinity data (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and S4); wavelet analysis shows periodicity and coherence in records (Figures S5-S7). Overall, long-term patterns (Jan 2004 to Jun 2022) in both soil and river-water salinity show mean decreasing trends: -176 \u0026micro;S/cm/year in soil salinity stations (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11) and \u0026minus;\u0026thinsp;93 \u0026micro;S/cm/year in river-water salinity stations (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13). Sen\u0026rsquo;s slopes are \u0026minus;\u0026thinsp;129 \u0026micro;S/cm/year and \u0026minus;\u0026thinsp;35 \u0026micro;S/cm/year respectively. Seasonal Sen\u0026rsquo;s slopes are also negative (Tables S1 and S2).\u003c/p\u003e \u003cp\u003eInterestingly, we observe that the trend in soil salinity at Krishnanagar (Khulna) is not monotonic; it shows a declining pattern in the first part of the time series (2004\u0026ndash;2014) and a rising pattern in the latter part (2014\u0026ndash;2022) that ultimately result in no consistent overall trend from 2004 to 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Critically, dry-season soil salinity levels have been increasing since 2014. The long-term trend in river-water salinity at Rupsha (Khulna) shows a similar decreasing pattern though the magnitude of changes is small compared to soil salinity. Application of the Seasonal-Trend decomposition using LOESS (STL) on Krishnanagar soil salinity time-series data (Figure S4) reveals a very small trend component represented by 15% variability of the total variance in the time-series data. Seasonal and irregular or residual components represent 47% and 37% variability at Krishnanagar site (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). On average, the trend, seasonality and irregular components represent 15%, 60% and 25% variability in all soil salinity (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11) time-series data. In comparison, on average, the trend, seasonality and irregular components represent 3%, 78% and 19% variability in all river-water salinity (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13) time-series data. These analyses clearly demonstrate strong seasonal variations in soil and river-water salinity in southwest Bangladesh.\u003c/p\u003e\n\u003ch3\u003eModelling soil and river-water salinity\u003c/h3\u003e\n\u003cp\u003eTo explain seasonal and long-term variability in soil and river-water salinity data, we applied a multiple linear regression statistical model through mean monthly as well as mean annual time-series records of 11 soil and 13 river-water salinity stations. Twelve covariate datasets (meteorology: rainfall, temperature and cyclone; hydrology: evapotranspiration, surface runoff, soil moisture, surface water levels, river discharge; climatology: sea level anomaly and sea surface salinity; and anthropology: groundwater levels and Normalised Difference Vegetation Index or NDVI) are considered in the models to explain the variability observed in the salinity time-series data. A series of multiple regression models are developed to explain the seasonal and annual mean variability observed in soil and river-water salinity data. One of the main challenges we encounter is that covariates are highly seasonal (Figures S8) as well as highly correlated to each other (Figure S9). When predictors are highly correlated, multicollinearity can distort the interpretation of model coefficients and the direction of association. Further details on the exploratory analyses and statistical models are provided in the methods section.\u003c/p\u003e \u003cp\u003eResults from statistical models reveal interesting associations between soil salinity and predictor variables, as well as between river-water salinity and the predictor variables. Since we use the natural logarithm of soil and river-water salinity data in the model, the interpretation of the model coefficients is that a one-unit increase in a predictor variable is associated with a change (depends on the sign) in the logarithm of salinity by the corresponding model coefficient of that predictor or explanatory variable. For example, a statistically significant (\u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) positive association is modelled between temperature and soil salinity. This model result (temperature, \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07022) suggests that a one-unit increase in temperature (seasonal or long-term change) leads to a percentage change (i.e., increase) in soil salinity, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y={(e}^{0.07022}-1)\\:x\\:100\\%\\approx\\:7.3\\%\\)\u003c/span\u003e\u003c/span\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Rainfall shows a strong correlation with surface runoff so that model results are affected by the multicollinearity effect. So, in the model we disregard surface runoff. NDVI, which is a measure of vegetation health (i.e., higher values typically indicate healthy, dense vegetation, and lower values indicate less healthy or sparse vegetation), has a negative association with soil salinity (\u003cem\u003eβ\u003c/em\u003e=-2.11420, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) suggesting that a one-unit increase in NDVI leads to a decrease in soil salinity of about 88%. Groundwater level (\u003cem\u003eβ\u003c/em\u003e=-0.27976, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;0.01) and river discharge (\u003cem\u003eβ\u003c/em\u003e=-0.00008, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;0.015) have statistically significant negative associations with soil salinity suggesting an increase in groundwater levels (1 m) or river discharge (100 m\u003csup\u003e3\u003c/sup\u003e/sec) would lead to a decrease in soil salinity by 24% and 1%, respectively. The model also suggests that the occurrence of tropical cyclones has a positive (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27851, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;0.012) influence on soil salinity (i.e. soil salinity can potentially increase by 32% per additional cyclone occurrence). Sea surface salinity has a positive statistically significant association (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00005, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with soil salinity whereas sea-level anomaly has a negative (\u003cem\u003eβ\u003c/em\u003e=-0.00187, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) statistically significant associations with soil salinity. Model results are similar for the mean annual time-series of soil salinity data. Overall, the models adequately explain (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.70, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) the variability observed in river-water salinity data.\u003c/p\u003e \u003cp\u003eFurther, we model the association between river-water salinity and the twelve predictor covariates using the monthly as well as mean annual salinity time-series data. For modelling river-water salinity, we also consider lagged rainfall values by up to 2 months as additional model factors. Results are consistent with soil salinity models. Temperature, lagged rainfall, sea-level anomaly, sea surface salinity and groundwater levels are important predictors of river-water salinity. However, we find no statistically significant associations between river-water salinity and cyclones, surface water levels, and NDVI. Both monthly and mean annual models adequately explain (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.90, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) the variability observed in river-water salinity data.\u003c/p\u003e\u003cp\u003e Table 1 Summary statistics of multiple linear regression models for soil and river-water salinity in southwestern coastal Bangladesh.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere, we discuss the observed seasonal, annual and decadal-scale dynamics of soil and water salinity and influences of hydrological, climatological and anthropogenic drivers in the southwestern coastal region of Bangladesh. One of the key features of our analyses is that it reveals substantial seasonal variations (i.e., up to 2 orders of magnitude) in monthly soil and river-water salinity. This outcome is consistent with the findings from previous, localised studies of soil salinity (Kawser et al., 2022; Salehin et al., 2018) and regional-scale analyses of surface-water salinity in coastal Bangladesh (Feist et al., 2023; Haq et al., 2024). The novelty in our study lies in the detailed characterisation of soil and river-water salinity from observations and their statistical associations with the local weather (i.e. temperature and rainfall) and climate change (i.e. rising sea levels).\u003c/p\u003e \u003cp\u003eSeasonal soil and surface water salinity rises steadily as temperature increases from winter to summer months and then falls very quickly (i.e. within a couple of months) as soon as the monsoon season begins. This association clearly demonstrates the critical role of local weather in the seasonal dynamics of soil and river-water salinity in coastal Bangladesh. Exploratory analyses and visualisation of salinity time-series data and statistical models indicate positive associations between tropical cyclones and soil and water salinity. The impact of cyclones on soil salinity is stronger and slower than it is for river-water salinity as revealed in monthly monitoring data. The impacts of river-water salinity could be better observed in daily monitoring data that do not exist in coastal Bangladesh. We observe from the time-series records that river-water and soil salinities return to background levels within a month or so. The response could be, however, different in the soil and surface-water salinity in areas that are located within coastal embankments (locally known as polders). Recent studies (Tsai et al., 2024) report contamination of freshwater ponds due to storm surge inundation and breaching of protective earthen polders.\u003c/p\u003e \u003cp\u003eWe examined the timing of tropical cyclones and their impacts on soil and river-water salinity. Close inspection of the timing of cyclones and their signature on soil and river-water salinity data reveal short-term deflections in salinity values that are short-lived but sufficient to increase soil salinity. This is consistent with the regional-scale analysis of river-water salinity in coastal Bangladesh (Haq et al., 2024). Statistical models clearly suggest a positive association between cyclones and soil salinity. Observations suggest that cyclones making landfalls during the late monsoon season (September to November) when sea-surface salinity is at its lowest level (~ 25,000 µS/cm), have lower impacts on soil and river-water salinity than those making landfalls during the pre-monsoon (March to May) season when sea salinity is at its highest levels (~ 40,000 µS/cm). For example, Cyclone Aila (May 2009) is reported to have much greater impacts on surface water such as ponds (Tsai et al., 2024) and soil salinity compared to super-cyclone Sidr that made landfall in November 2007. The backwater effects of the sea and tropical cyclones clearly highlight the complex interplay between marine and terrestrial salinity dynamics in coastal Bangladesh.\u003c/p\u003e \u003cp\u003eOur statistical models reveal significant associations between seasonal sea surface salinity and sea levels on soil and river-water salinity. Cross correlation between monthly sea levels and sea salinity shows a strongly negative (Pearson correlation, \u003cem\u003er\u003c/em\u003e=‒0.803, p value \u0026lt; 0.001) association. A positive association with seasonal sea surface salinity emphasises the argument above on the timing of tropical cyclones landfalls and their impacts on salinity. A negative association with seasonal sea-level anomaly suggests that seasonally rising sea levels (i.e. positive anomaly) during the monsoon season coincide with lower soil and river-water salinity. Due to the highly seasonal nature of salinity data and the large variations in salinity levels, the gradual rise in sea-level anomaly has minimal impact on seasonal salinity patterns. In fact, rising sea levels do not necessarily mean an increased sea-surface salinity (Cheng et al., 2020; Durack et al., 2014). In monsoon-dominated deltaic systems, freshwater discharge from runoff and direct rainfall dilutes salinity levels. For instance, in the Ganges-Brahmaputra-Meghna (GBM) delta, the monsoon season brings substantial freshwater input, which flushes salt in seawater and reduces sea-surface salinity (Bricheno et al., 2021).\u003c/p\u003e \u003cp\u003eOur statistical models reveal significant, inverse associations between soil and river-water salinity and river discharge in the upstream (i.e. River Gorai – an offshoot of River Ganges) in the presence of an interaction with surface water levels which shows a positive relationship. These may suggest that a decrease in freshwater discharge in the upstream rivers can increase brackish-saline water through backflow effects within the tidal rivers in the coastal region of Bangladesh. The model reveals an interesting association between groundwater levels and salinity. A seasonal rise in groundwater levels (i.e., shallowing of the water table) in shallow, brackish- to saline-water aquifers can influence soil salinity through capillary rise (Jorenush and Sepaskhah, 2003). In contrast, an inverse association between deepening groundwater table and river-water salinity suggests that water salinity is at its higher levels when base-flow to rivers is small or even absent due to reversal in hydraulic gradients. These associations mainly explain the seasonal variability in soil and river-water salinity. Our models, however, are not able to explain the long-term dynamics in the soil or river-water salinity in relation to river discharge or groundwater-level variations.\u003c/p\u003e \u003cp\u003eOverall, our data-driven analyses of long-term soil and river-water salinity time-series data reveal complex seasonal patterns influenced by various factors. Through data visualisation, statistical analyses, and modelling, we examined long-term time-series data alongside a dozen related variables. This approach uncovered significant associations between salinity levels and local weather, climate, hydrology, and anthropogenic factors. Our observations show that while cyclones can cause rapid changes in salinity levels, the impact is usually short-lived, and its intensity is largely influenced by seasonal factors. Impacts of storm-surge inundation from tropical cyclones are more pronounced in soil salinity than in river-water salinity. This is because storm-surge inundation causes occasional breaches of earthen polders (Islam et al., 2019) creating prolonged water-logging conditions. As a result, higher soil salinity can sustain for a long period of time (weeks to months) when polders are breached during tropical cyclones contaminating land and water inside them (Bhuyan et al., 2023). The land surface elevation within polders is often lower than the adjacent floodplains (Zaman and Mondal, 2020). A significant consequence of this elevation loss was the failure of polders during Cyclone Aila in 2009, causing tidal inundation of large areas for up to two years (Auerbach et al., 2015).\u003c/p\u003e \u003cp\u003eIn contrast, cyclone-induced rainfall and runoff negate the effects of saline-water intrusion within a very short period of time (i.e. within days to weeks) as observed in Pearl River Delta in China (Gao et al., 2024). Our analysis shows that early monsoon cyclones (April-May) cause more severe soil and water salinity increases than post-monsoon cyclones, due to peak sea salinity levels during this period. For example, cyclones Aila (May 2009) and Amphan (May 2020) had long-lasting impacts on southwestern coastal Bangladesh and notably disrupted the expected seasonal salinity in surface water and soil in the southwestern coast (Tsai et al., 2024). Tropical cyclones are becoming very common in May (e.g., Roanu in 2016, Fani in 2019, Amphan in 2020, Yaas in 2022, Remal in 2024) as we find in the International Best Track Archive for Climate Stewardship (IBTrACS) database (Gahtan, 2024). The occurrence of cyclones in the early monsoon season along with slowly rising sea-levels may contribute to an increasing trend in soil salinity observed in the last decade.\u003c/p\u003e \u003cp\u003eOur analysis found no significant overall trends in soil or river-water salinity over the past 18 years (Jan 2004 to Jun 2022), as opposing trends in different periods offset each other. Steadily rising trends are observed primarily in the dry-season soil salinity since 2012-14, consistent with Salehin et al. (2018) and both rising summer temperatures (Figure S10) and lower rainfall (Figure S11) during the early monsoon season throughout the southwestern coastal Bangladesh. Substantial increases in peak monsoon rainfall, mainly in July, are aligned to the global amplification of precipitation extremes under global warming (Douville et al., 2021) and help to buffer seasonal soil and river-water salinity. Furthermore, rising trends in sea-level anomaly are inversely correlated to sea-surface salinity indicating the effect of an increased seasonal rainfalls from July to September diluting sea-water salinity near the coastline (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, d and e). These meteorological and climatic changes shape the seasonal dynamics of soil and river-water salinity in southwestern coastal Bangladesh.\u003c/p\u003e \u003cp\u003eOur findings from the analysis of soil and surface water salinity in Bangladesh provide valuable insights into water and soil salinisation in other Asian mega-deltas and coastal regions globally, particularly where long-term monitoring of salinity is limited or non-existent. By leveraging rare salinity time-series data, our data-driven visualisations and statistical analyses highlight critical dynamics essential for soil and water management and inform climate adaptation strategies in Bangladesh. It is crucial to evaluate the existing salinity adaptation measures such as the Managed Aquifer Recharge (Naus et al., 2020; Shammi et al., 2019), rainwater harvesting (Ashrafuzzaman et al., 2023; Islam et al., 2023), saline-resistant crops (Islam et al., 2020; Paul and Rashid, 2016), and improved coastal embankment (i.e., polder) management (Islam et al., 2020; Rahman et al., 2021) not only in Bangladesh but also across other Asian mega-deltas where climate adaptation measures are being planned and implemented.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eSoil and river water salinity datasets\u003c/h2\u003e\u003cp\u003eWe use long-term (January 2004 to December 2022) monthly time-series salinity data from 11 soil salinity and 13 river-water salinity stations in southwestern coastal Bangladesh. These monitoring stations are located within three coastal districts of Bangladesh namely Bagerhat, Khulna and Satkhira (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eThe monthly time-series data were collated from the local office of the Soil Resource Development Institute (SRDI) in Khulna, Bangladesh. SRDI is a government organisation operating under the Ministry of Agriculture. SRDI collects information on soil and water properties for sustainable agricultural production through improved management of soil as well as preservation of environment. To analyse seasonal variation in soil and water salinity, SRDI collects monthly soil samples from 11 stations and river-water samples from 13 stations in southwestern Bangladesh which is one of the most salinity-affected areas in the country. Seven of the 11 stations of soil salinity have monthly data from January 2004 to June 2022 and the remaining 4 stations have soil salinity data from January 2018 to June 2022, whereas all the 13 stations of water salinity have monthly data from January 2004 to June 2022.These rare time-series data of soil and water salinity have not previously been analysed or published.\u003c/p\u003e\u003cp\u003eMonthly soil salinity at the 11 stations derives from soil samples from the respective locations. Samples are then tested for the Electrical Conductivity (EC) of a saturated soil Extract (ECe), a reliable method for measuring soil salinity (Jones, 2001). In this sampling, soils are collected from 0 to 15 cm depth of the soil profile. The collected soil samples are first air-dried and cleared from unwanted materials such as plant remains. Once the sample is properly dried, wooden hammer is used to break down the larger aggregates into smaller particles. The powdered soil sample is then filtered through a 2 mm sieve. This filtered soil is added to distilled water maintaining a ratio of 1:5 to fill the available pores and then is mixed to the consistency of a paste that glistens with water and if jarred flows slightly. After allowing sufficient time, ideally overnight for the soil to equilibrate and dissolve the salts thoroughly, the resulting water is extracted by suction filtration and Electrical Conductivity (EC) is determined by a portable EC meter. However, saturated extract (soil:water = 1:1) produces the best result for soil EC but preparation of saturated extract is cumbersome. Therefore, a ratio of 1:5 (soil:water) is used and the result is converted into saturated extract (ECe).\u003c/p\u003e\u003cp\u003eIn case of surface water salinity, river water was collected in plastic bottles after rinsing it with sample water for about two to three times. The samples were labelled with sample IDs and the information about the sampling points are recorded. After the collection of water samples, all bottles were sealed immediately to avoid exposure to air. After labelling, water samples were transported to the laboratory of SRDI following sampling protocols from sampling to transportation. All the samples were filtered through filter paper (Whatman No.1) to remove unwanted solid and suspended materials before analysis. The samples were then measured by an EC meter.\u003c/p\u003e\u003ch3\u003eMonthly rainfall and temperature time-series data\u003c/h3\u003e\u003cp\u003eWe collated daily temperature and rainfall time-series data from the Bangladesh Meteorological Department (BMD) at three stations (Khulna, Satkhira and Mongla) located in the southwestern Bangladesh. BMD is a government meteorological agency that has been collecting data since 1950s. We collated the available daily time-series data (January 2004 to December 2021) at three stations and converted it to monthly data for statistical analysis and modelling in this study. Monthly rainfall and temperature data for 2022 at the three BMD station locations were downloaded from the TerraClimate product (Abatzoglou et al., 2018) available on an online data portal called Climate Engine (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.climateengine.org\u003c/span\u003e\u003cspan address=\"https://www.climateengine.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Huntington et al., 2017). We also collated monthly evapotranspiration, root-zone soil moisture, and surface runoff data at the selected BMD locations from the NASA's Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) via the Climate Engine data portal.\u003c/p\u003e\u003ch2\u003eSea level anomaly and sea surface salinity data\u003c/h2\u003e\u003cp\u003eWe use monthly sea level anomaly and sea surface salinity data from the Copernicus Marine data services through the data visualisation tool called My Ocean Pro portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://marine.copernicus.eu/access-data/myocean-viewer\u003c/span\u003e\u003cspan address=\"https://marine.copernicus.eu/access-data/myocean-viewer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Monthly gridded (0.25° × 0.25° spatial resolution) mean of Sea Level Anomalies (SLA) derived from altimeter satellite measurements computed with respect to a 20-year (1993–2012) mean. The sea-level anomalies (Product: SEALEVEL_GLO_PHY_L4_MY_008_047) were estimated by an Optimal Interpolation method that merged the Level 3 (L3) along-track measurement from different altimeter satellite missions (Pujol, 2023). The sea surface salinity gridded (0.083° × 0.083°) monthly mean data derived from numerical models under the Global Ocean Physics Reanalysis product (GLOBAL_MULTIYEAR_PHY_001_030; (Drévillon et al., 2022). Both SLA and sea salinity data were downloaded for the upper portion of the Bay of Bengal (north of 19° latitude and within 86° and 95° E longitudes) and aggregated monthly time-series data were generated using all the available grid points. Gridded data were processed in R programming language (R Core Team, 2022) using various packages.\u003c/p\u003e\u003ch2\u003eData processing and exploratory analysis\u003c/h2\u003e\u003cp\u003eSoil and river water salinity time-series data were collated in MS Word format from SRDI, Khulna. Daily rainfall and temperature data were collated in MS Excel format from BMD. All time-series data are then converted to a standard MS Excel comma-separated values (CSV) file format, processed and analysed in R programming language. Both R GUI and RStudio software were used for data processing and statistical analyses. All time-series data are converted to mean monthly values for consistency. Missing values are imputed using the Random Forest algorithm implemented under the missForest R package (Stekhoven and Bühlmann, 2012). However, imputed data were used primarily for visualisation purposes. Geospatial data were processed and visualised using ESRI’s ArcGIS Desktop software.\u003c/p\u003e\u003ch2\u003eTrend analysis using parametric and non-parametric methods\u003c/h2\u003e\u003cp\u003eDescriptive statistics from the 24 time-series (Jan 2004 to Dec 2022) salinity data are calculated in R platform. We apply both parametric and non-parametric trend analyses and decomposed the monthly series into seasonal, trend and residual components using the Seasonal and Trend Decomposition using Loess (STL) method (Cleveland et al., 1990). For the trend analysis, we apply linear trend, Mann-Kendall (MK) trend test, seasonal MK trend, Sen’s slope and seasonal Sen’s slope (Hipel and McLeod, 1994; Hirsch et al., 1982).\u003c/p\u003e\u003ch2\u003eCross-correlation and wavelet analysis\u003c/h2\u003e\u003cp\u003eAs a part of our exploratory analysis, we examined the correlation and cross-correlation between soil and river-water salinity along with 12 covariates using the Pearson correlation and Cross Correlation Function (CCF) in R environment. We approximately grouped the covariate datasets into hydrological factors (i.e., evapotranspiration, river discharge, surface water levels, soil moisture, groundwater levels), meteorological (i.e., rainfall, temperature, occurrence of cyclones), climatological (i.e., sea level anomaly, sea surface salinity), and anthropological (i.e., normalized difference vegetation index or NDVI as an indicator of land-use changes).\u003c/p\u003e\u003cp\u003eWe apply wavelet analysis using the WaveletComp (Rösch and Schmidbauer, 2016), an R package for continuous wavelet-based analysis of soil and river water salinity and meteorological time series records. Wavelet analysis is a powerful tool that decomposes a time series into time-frequency space, allowing the examination of how periodic components of the data evolve over time (Jevrejeva et al., 2004). When applied to a single time series, wavelet analysis reveals the presence and strength of periodicities (cyclical patterns) at different scales (frequencies) and how these periodicities change over time which makes it particularly useful for identifying localised events or changes in cyclic behaviour (Percival and Walden, 2000). For coherence analysis between two time series, the cross-wavelet transforms, and wavelet coherence techniques are used (Jevrejeva et al., 2004).\u003c/p\u003e\u003ch2\u003eStatistical modelling\u003c/h2\u003e \u003cp\u003eIn this study, we develop statistical models to explain the variability observed in the time-series data of soil and river-water salinity in coastal Bangladesh using fourteen covariates representing hydrological, meteorological, climatological and anthropogenic processes. We use multiple linear regression (MLR) to model the statistical variability in the response or dependant variables such as the soil salinity and river-water salinity. The MLR model take the following form in Eq.\u0026nbsp;(1).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{log\\:}\\left(\\varvec{y}\\right)={\\varvec{\\beta\\:}}_{0}+{\\varvec{\\beta\\:}}_{1}{\\varvec{x}}_{1}+{\\varvec{\\beta\\:}}_{2}{\\varvec{x}}_{2}+\\dots\\:{+\\:\\varvec{\\beta\\:}}_{\\varvec{n}}{\\varvec{x}}_{\\varvec{n}}+\\:\\varvec{\\epsilon\\:}\\)\u003c/span\u003e \u003c/span\u003e ………eq.\u0026nbsp;(1)\u003c/p\u003e \u003cp\u003eThe response variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{l}\\text{o}\\text{g}\\left(y\\right)\\:\\)\u003c/span\u003e\u003c/span\u003e(soil or river water salinity) is log-transformed (natural logarithm) due to the skewness in the original data distribution. In Eq.\u0026nbsp;(1), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0},\\:{\\beta\\:}_{1},\\:{\\beta\\:}_{2},\\:{\\dots\\:\\beta\\:}_{n\\:}\\)\u003c/span\u003e\u003c/span\u003eare model coefficients, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{1\\:},\\:{x}_{2\\:},\\:{x}_{n\\:}\\)\u003c/span\u003e\u003c/span\u003eare predictor or covariate datasets (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n=14)\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ϵ\\)\u003c/span\u003e\u003c/span\u003e represents the error term in the model. We use dynamic linear regression in dynlm R package for modelling soil and river-water salinity (Zeileis et al., 2019).\u003c/p\u003e\u003cp\u003eExploratory analysis shows that the time-series data of response variables and covariates have high seasonality, and some are highly correlated to each other raising the concern of multicollinearity. This occurs when two or more predictors in a regression model are highly correlated. Multicollinearity can cause the estimates of the regression coefficients to become unstable and highly sensitive to small changes in the model or data that can result in large standard errors and less reliable coefficient estimates (Kutner et al., 2004). We check correlation matrix and Variance Inflation Factor (VIF) in the fitted model to consider dropping some variables that can cause multicollinearity effects. We also consider adding interaction terms to the model to explore joint effects on the response variable. The fitted model is diagnosed using standard plots in R, and model summary statistics are derived from the final model for interpretation. Two separate MLR models are created using the same set of covariates to explain the variability observed in soil and river-water salinity datasets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMonthly time series of soil and surface-water salinity monitoring data (2004-2022) collated from the Soil Resource Development Institute (SRDI), Bangladesh along with the variables used in statistical models can be made available to researchers upon receiving a request to the corresponding author. We do not have the necessary permissions from the SRDI Bangladesh to make the monitoring data publicly available. Statistical analyses and modelling were conducted in R programming environments. Upon request to the corresponding author, all R codes can also be made available to researchers who might be interested in reproducing the results of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAhmed Z. Rahman acknowledges the funding support from the Government of Bangladesh for his doctoral research at UCL. However, the views expressed in the article do not necessarily represent the views of the Government of Bangladesh. M Shamsudduha acknowledges UCL\u0026rsquo;s Research England QR Policy Support Fund. The authors acknowledge the soil and surface-water salinity data from the Soil Resource Development Institute (SRDI) and the Bangladesh Meteorological Department (BMD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.Z.R. contributed to the conceptualisation of the study, collated monitoring data, analysed data, and drafted the manuscript; M.S. contributed to project supervision, data curation, visualisation, statistical modelling and writing the manuscript; M.I.H, M.H., and M.S.I. contributed to writing, commenting and reviewing the manuscript; S.S.I. and A.B. contributed to data collation and writing; and R.G.T. contributed to project supervision, writing, commenting and editing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Ahmed Z. Rahman: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received clearance by the UCL Research Ethics Committee (Ref no. 23072501).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbatzoglou, J.T., Dobrowski, S.Z., Parks, S.A., Hegewisch, K.C., 2018. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958\u0026ndash;2015. Scientific data 5, 1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshrafuzzaman, M., Gomes, C., Guerra, J., 2023. The Changing Climate Is Changing Safe Drinking Water, Impacting Health: A Case in the Southwestern Coastal Region of Bangladesh (SWCRB). 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Progress in Disaster Science 8, 100131.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeileis, A., Fisher, J.C., Hornik, K., Ihaka, R., McWhite, C.D., Murrell, P., Stauffer, R., Wilke, C.O., 2019. colorspace: A toolbox for manipulating and assessing colors and palettes. arXiv preprint arXiv:1903.06490.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil and water salinity, seasonality, cyclones, climate adaptation, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-6234327/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6234327/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChanges in soil and water salinity pose critical challenges to agriculture, water management, and livelihoods in deltaic environments globally, and particularly in the densely populated Asian mega-deltas. Using observations from 24 stations over nearly two decades (2004\u0026ndash;2022) in the Ganges-Brahmaputra-Meghna delta of Bangladesh, our scientific study examined the influences of local weather, tropical cyclones, and hydrology on the seasonal variability in soil and river-water salinity. We applied statistical analyses including cross-correlation, seasonal trends, and wavelet decomposition, to explore the spatiotemporal dynamics of soil and river-water salinity. We developed statistical models to assess how hydrological, meteorological, and climatic factors explain its variability. Pronounced seasonal fluctuations in soil and river-water salinity are observed, with levels rising during the dry season and declining sharply during the monsoon season. We also observed how tropical cyclones contribute to short-term spikes in salinity, with stronger impacts observed for those making a landfall during early monsoon period (April‒May). Statistical models reveal a significant positive association between soil and surface-water salinity and sea-surface salinity during the pre-to-early monsoon season. In contrast, the seasonal rise in sea levels during the monsoon coincides with reduced soil and river-water salinity due to monsoon rainfall and freshwater discharges to the sea.\u003c/p\u003e","manuscriptTitle":"Soil and River-water Salinity Dynamics in Coastal Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-25 14:40:13","doi":"10.21203/rs.3.rs-6234327/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"807c0879-2afb-4469-a1d3-f24e459ba2c1","owner":[],"postedDate":"April 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47274191,"name":"Earth and environmental sciences/Climate sciences"},{"id":47274192,"name":"Earth and environmental sciences/Environmental sciences"},{"id":47274193,"name":"Earth and environmental sciences/Hydrology"},{"id":47274194,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-08-14T10:54:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-25 14:40:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6234327","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6234327","identity":"rs-6234327","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}