When the River Moves, Lives Shift: Linking Hydro-Geomorphology to Climate-Induced Migration

When the River Moves, Lives Shift: Linking Hydro-Geomorphology to Climate-Induced Migration | 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 Research Article When the River Moves, Lives Shift: Linking Hydro-Geomorphology to Climate-Induced Migration Md Hasibul Hasan, Imran Hossain Newton, Md. Arif Chowdhury, Md. Monoar Hossain Munna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8332436/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 Riverbank erosion in South Asia’s deltaic regions presents a growing socio-environmental threat, intensifying displacement and livelihood disruption. This study investigates the erosion–migration nexus along a 44 km reach of the Ganges River in northwestern Bangladesh, integrating three decades of Landsat-based geomorphological analysis with household-level survey data. Between 1989 and 2020, the river’s sinuosity declined approximately by 19%, accompanied by intensified braiding and dynamic erosion–accretion cycles. These geomorphic transformations led to the permanent loss of over 2600 hectares of land and the formation of 1500 hectares of new floodplain terrain, heightening exposure for riverbank settlements. Survey data from 130 households reveal that erosion-induced housing damage and environmental proximity remain key predictors of migration decisions, but socio-economic characteristics shape divergent adaptive responses. Education is positively associated with anticipatory migration (Odds Ratio, OR = 1.86, p < 0.001), while income has a weak and statistically insignificant negative effect, suggesting that better-off households often invest in local adaptation instead of relocating. Institutional support shows weak correlations with migration outcomes, highlighting significant gaps in relief delivery and governance responsiveness. A logistic regression classifier based on five predictors achieved 72.2% sensitivity, demonstrating strong potential for proactive targeting of at-risk populations, though specificity was moderate. These findings underscore the need for integrated, data-driven adaptation policies that bridge geomorphological diagnostics with household vulnerability metrics. By linking physical river transformations with migration behavior, this study offers critical insights for climate mobility governance in erosion-prone deltas globally. Riverbank erosion Hydro-geomorphology Climate-induced migration Socio-ecological resilience Adaptive capacity Erosion-displacement nexus Bangladesh Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Riverbank erosion is an increasingly critical natural hazard shaped by the interaction of climate change, hydrological variability, and human modification of river systems. Intensified rainfall, upstream dam construction, deforestation, and sediment supply disruptions are accelerating erosion across major global river basins, including the Mekong, Mississippi, Amazon, and Ganges. These processes alter river morphology, destabilize land–water interfaces, and amplify flood risk in low-lying deltaic and alluvial regions (Zhang et al. 2016 ; Khare et al. 2017 ). The Intergovernmental Panel on Climate Change (IPCC) and the United Nations Office for Disaster Risk Reduction (UNDRR) highlight that these hydro-geomorphological processes are central drivers of climate-induced displacement, land loss, and livelihood disruption. As warming accelerates glacial melt, extreme precipitation, and sediment mobilization, riverine landscapes are undergoing rapid morphological change, positioning erosion as a key component of 21st-century Earth system hazards (Herman et al. 2021 ). The socio-economic consequences of riverbank erosion are profound. In South and Southeast Asia, erosion destroys homes, agricultural lands, and critical infrastructure, triggering large-scale population movements and intensifying poverty and marginalization (Das et al. 2017 ; Barua et al. 2019 ; Freihardt and Frey, 2023 ). Such displacement frequently occurs in the absence of institutional protection, within governance systems characterized by limited coordination, minimal early-warning capacity, and constrained adaptation support. Bangladesh represents one of the most severe global hotspots of erosion-related risk. Located at the confluence of the Ganges, Brahmaputra, and Meghna rivers, the country’s tectonically active and sediment-rich deltaic landscape is subject to continuous morphodynamic change (Misachi 2017 ; Islam 2021; Arefin et al. 2021 ). Riverbank erosion displaces an estimated 130,000 to 200,000 people annually across more than twenty districts (Hasnat et al. 2018 ; Freihardt and Frey 2023 ). These displacements exacerbate pre-existing socio-economic vulnerabilities, including agricultural disruption, housing destruction, infrastructure loss, and weakened social networks (Rahman and Gain 2020 ; Hossain et al. 2021 ). As a slow-onset hazard, erosion deepens inequality and heightens long-term insecurity for marginal households (Islam et al 2020 ). Extensive research has examined geomorphological change in Bangladesh using satellite imagery, toposheets, and GIS-based techniques, documenting channel migration, bankline shifts, and morphometric evolution across major rivers such as the Ganges, Jamuna, and Meghna, alongside highly dynamic smaller rivers such as the Manu (Islam and Rashid 2011 ; Thakur et al. 2012 ; Deb and Ferreira 2015 ; Arefin et al. 2021 ; Khatun et al. 2022 ). Long-term remote sensing analyses have proven central for erosion hazard assessment. For example, Deb and Ferreira ( 2015 ) quantified multi-decadal planform dynamics of the Manu River, generating predictive insights for erosion hotspots. Likewise, vulnerability frameworks such as the Pressure and Release (PAR) model have been used to characterize hazard exposure and risk accumulation (Biswas and Anwaruzzaman 2019 ). Despite these contributions, several critical gaps persist. First , most studies analyze either the physical evolution of river morphology or the socio-economic impacts of erosion, but rarely within an integrated framework. These limits understanding of how geomorphological processes shape household-level decisions related to migration, displacement, and (im)mobility. While displacement is widely recognized as a consequence of erosion (Islam and Rashid 2011 ; Majumdar 2018 ), few studies trace the mechanisms linking morphological change to different types of mobility, including temporary, permanent, labor-driven, and anticipatory migration. Second , institutional and governance dimensions remain under-examined. Critical factors such as early warning systems, relief mechanisms, embankment maintenance, NGO engagement, and local conflict are often addressed only superficially, despite their substantial influence on vulnerability and adaptation outcomes (Islam et al. 2017 ; Hossain et al. 2021 ). This limits the usefulness of existing research for designing effective policy interventions. Third , few studies integrate remote sensing diagnostics with predictive socio-economic modeling to identify households likely to be displaced before erosion impacts materialize, even though remote sensing studies are abundant and migration research is well documented (Islam and Rashid 2011 ; Thakur et al. 2012 ; Deb and Ferreira 2015 ; Majumdar 2018 ). In response to these gaps, this study develops an integrated socio-spatial framework linking long-term river morphological change to household vulnerability and migration outcomes in one of the world’s most erosion-prone regions. Using multi-decadal satellite imagery from 1989 to 2020, we quantify changes in channel pattern, sinuosity, and erosion–accretion dynamics along the Ganges River (Thakur et al. 2012 ; Khatun et al. 2022 ). These geomorphological insights are paired with a stratified household survey of erosion-exposed communities, capturing socio-economic variables such as income, education, housing condition, institutional support, and migration decisions (Islam et al. 2020 ; Rahman and Gain 2020 ). By explicitly linking physical river transformations with household-level responses, this study contributes new evidence on how environmental stress interacts with social vulnerability to shape mobility outcomes. The research advances hazard science by integrating geomorphological and socio-economic data, distinguishing environmental, socio-economic, and institutional determinants of migration, and offering predictive insights relevant to anticipatory adaptation, managed retreat, and resilience planning in deltaic regions where erosion, weak governance, and human displacement increasingly intersect. 2 Materials and Methods 2.1 Case Study This study examines a 44 km stretch of the Ganges River near the Rajshahi Division in northwestern Bangladesh, between 24°11′ N to 24°02′ N and 88°44′ E to 89°01′ E. The riverbanks span Bagha, Ishwardi and Lalpur Upazilas on the left and Bheramara Upazila on the right side (Fig. 1 ). Bagha Upazila, known for severe erosion, was selected for primary fieldwork (see Section 2.4 ). The river originates as the Ganges in the Indian Himalayas and enters Bangladesh as a sediment-heavy river before merging with the Brahmaputra and Meghna to form the dynamic Ganges delta. Recent shifts from a meandering to a braided morphology driven by sediment load, monsoonal flow variability, and upstream interventions have intensified bank instability and channel migration (Thakur et al. 2012 ; Khatun et al. 2022 ). The region’s subtropical monsoon climate brings heavy rainfall from June to October, heightening seasonal flooding and erosion (Akter and Tsuboki 2014 ; Misachi 2017 ). The area is predominantly agrarian, relying on rice cultivation, flood recession agriculture, and inland fisheries (Tania et al. 2023 ; Everard 2016). Socio-economic vulnerability is high due to dependence on the river and rising displacement risks from erosion and infrastructure loss (Islam et al. 2020 ; Rahman and Gain 2020 ). 2.2 Overview of Analytical Framework This study applies an integrated spatial and socio-economic analytical framework that links long-term river morphological change with household-level vulnerability and migration outcomes. The full methodological workflow is presented in Fig. 2 , which illustrates how geomorphological analysis, survey-based vulnerability assessment, and statistical modeling are connected within a single analytical sequence. The framework combines quantitative remote sensing analysis with structured household survey data, allowing a stepwise assessment of how physical landscape transformation shapes exposure, socio-economic sensitivity, and adaptive responses in erosion-prone communities. The analytical process consists of three interconnected components. First , multi-temporal satellite imagery is used to quantify channel migration, erosion and accretion dynamics, and planform change along the Ganges River. These geomorphological indicators establish the physical hazard context and are used to delineate high, moderate, and transitional erosion zones. Second , a stratified household survey is implemented across these erosion zones to capture socio-economic characteristics, housing conditions, environmental exposure, institutional support, and migration decisions. The sampling design is intentionally structured around spatial erosion categories to ensure alignment between geomorphological risk and household-level exposure. Third , the combined dataset is examined through a set of statistical analyses designed to identify correlates and predictors of displacement. Pairwise associations among socio-economic, environmental, and institutional variables are assessed through correlation matrices, while a predictive migration model is estimated to evaluate which household attributes most strongly influence the probability of relocation. These analytical steps trace a logical chain of causality from physical river change through household vulnerability to observed mobility outcomes. 2.3 River Morphology Analysis 2.3.1 Satellite Image Acquisition and Preprocessing To assess long-term river morphological change and channel dynamics, we used multi-temporal Landsat satellite imagery spanning more than three decades. Four cloud-free scenes were selected for the years 1989 (Landsat 5 TM), 2000 (Landsat 5 TM), 2010 (Landsat 5 TM), and 2020 (Landsat 8 OLI). These time points were chosen to represent consistent decadal intervals of river behaviour, while maintaining comparability across seasonal conditions. All images were collected during the low-flow period between November and January to minimize hydrological variability and ensure similar discharge conditions. Scenes with less than 10 percent cloud cover were selected to reduce atmospheric interference. All imagery was obtained from the United States Geological Survey (USGS) Earth Explorer platform ( https://earthexplorer.usgs.gov/ ) at a spatial resolution of 30 meters. The datasets share a common WGS 84 datum and UTM Zone 45N projection. Because the datasets were provided as Level-1 Terrain Corrected (L1T) products, only minimal geometric adjustments were required. Preprocessing included geometric verification, radiometric enhancement, and atmospheric normalization to ensure temporal and spatial consistency. These steps were completed using ArcGIS 10.6 and ERDAS IMAGINE 2015. False color composites were generated for visual interpretation and training data development. For Landsat 5 TM, bands 4-3-2 were used, and for Landsat 8 OLI, bands 7-6-4 were used. After preprocessing, each image was clipped to the defined area of interest along the 44 km study reach of the Ganges River within the Rajshahi Division. 2.3.2 Image Classification and Change Detection To delineate river morphology and quantify erosion and deposition patterns, all pre-processed Landsat images were classified using a supervised maximum likelihood algorithm in ArcGIS 10.6. Training signatures were developed from field-validated reference points collected using handheld GPS units and from high-resolution Google Earth imagery to ensure robust discrimination among spectral categories. Three land cover classes central to fluvial geomorphology were identified: open water, exposed sandy bars (char lands), and stable vegetated land. Classification accuracy was assessed using an error matrix generated from 120 independent reference points distributed across the three land cover categories. A confusion matrix was constructed to evaluate omission and commission errors, and classification performance was quantified using two standard metrics. Overall Accuracy (OA) was computed as the proportion of correctly classified samples, and the Kappa coefficient was used to account for agreement occurring by chance. The classification achieved an OA greater than 84 percent and a Kappa coefficient of 0.755 (see Supplementary Table 2), indicating reliable performance suitable for subsequent morphometric and change detection analyses. The Kappa statistic (K) was calculated following Congalton and Green ( 2019 ): \(\:K=\frac{n\sum\:_{i=1}^{k}nij-\sum\:_{i=1}^{k}ni+nj}{N2-\sum\:_{i=1}^{k}ni+nj}\) ( Eq. 1 ) Where, n is the total number of reference points, \(\:nij\) is the number of correctly classified samples in category i, and \(\:ni\) and \(\:nj\) represent row and column totals of the confusion matrix. The land cover types of each year are shown in Fig. 3 . Figure 3 presents the classified land cover maps for each study year. After classification, raster outputs were converted to vector polygons using the raster-to-polygon tool in ArcGIS to support more precise spatial overlay. Vector layers were cleaned to remove sliver polygons and resolve topological inconsistencies, ensuring comparability across time periods. Channel boundaries were then delineated from each classified image to extract the active river corridor for 1989, 2000, 2010, and 2020. Change detection was conducted by overlaying vector layers from successive time steps (for example, 1989 to 2000, 2000 to 2010, and 2010 to 2020). Spatial difference and erase operations were used to identify and quantify erosion zones where land converted to water or sandy bars, and accretion zones where water or sandy bars converted to vegetated land. 2.3.3 Morphological Metrics To capture the dynamics of river planform evolution, a suite of morphological indicators was derived from the classified and vectorized images, focusing on bankline migration, channel width variability, and changes in sinuosity and braiding index over time. These indicators collectively characterize the degree of geomorphological instability within the selected reach of the Ganges River. 2.3.4 Bankline Shifting and Channel Migration Channel migration and bankline displacement were analyzed using overlay techniques in ArcGIS 10.6, where river polygons from different years (1989, 2000, 2010, 2020) were spatially intersected. The extreme left and right outer channels were identified and digitized for each time point, and bankline shifts were calculated by measuring perpendicular distances between equivalent riverbank positions across time slices. Areas lost (erosion) and gained (accretion) were calculated using Erase and Symmetrical Difference tools in ArcGIS 10.6 platform. 2.3.5 Channel Width Measurement Channel width was measured as the distance between the extreme left and right banks, incorporating any mid-channel bars or islands. For each year, the river reach was segmented into 1 km transects, and the width was calculated at regular intervals. The mean channel width was determined for each time period, allowing a temporal comparison of width variability across decades. This process followed the methodology proposed by Winterbottom ( 2000 ), with additional spatial refinements using ERDAS Imagine 2015. 2.3.6 Sinuosity Index The sinuosity index (SI) ( Eq. 2 ), a measure of the meandering nature of a river, was calculated using the ratio of channel length ( \(\:\text{A}\text{C}\) ) to valley length (AV) (Mueller 1968 ; Pavelsky and Smith 2008 ). \(\:\text{S}\text{I}=\:\frac{\text{A}\text{C}}{\text{A}\text{V}}\) ( Eq. 2) Channel centrelines were extracted using Hydro-Width Automated Tool for Hydraulics (HWATH), an ArcGIS extension, and the shortest straight-line path between reach endpoints was used to determine \(\:\text{A}\text{V}\) . A higher SI indicates a more meandering channel, while values near 1 suggest a straighter course (Mueller 1968 ; Pavelsky and Smith 2008 ; Leopold et al. 2020 ). 2.3.7 Braiding Index To quantify planform complexity, the braiding index (BI) was calculated for each 1 km segment using the method of Friend and Sinha ( 1993 ) as follows ( Eq. 3 ): \(\:BI=2\sum\:\frac{\text{L}\text{i}}{Lr}\) ( Eq. 3) Where \(\:{\text{L}}_{\text{i}}\) is the total length of mid-channel bars and islands within the segment, and \(\:{\text{L}}_{\text{r}}\) is the reach length measured along the channel centreline. This index helps to distinguish the transition from meandering to braided morphology over time. 2.4 Socio-Economic Data Collection 2.4.1 Survey Design and Sampling Strategy To complement the spatial analysis of riverbank erosion and examine its socio-economic impacts at the household level, a structured field survey was conducted in Bagha Upazila, one of the most erosion-affected areas along the Ganges River in northwestern Bangladesh. The survey was designed to capture household exposure to erosion hazards, socio-economic vulnerability, access to institutional support, and behavioural responses with an emphasis on migration decisions. A stratified random sampling approach was applied to ensure systematic representation across varying erosion exposure levels. Erosion zones were delineated using a multi-temporal overlay of satellite-derived bankline change maps for the period 1989 to 2020. Based on these spatial classifications, households were randomly selected from high-risk zones characterized by frequent bankline retreat and from moderate-risk transitional zones where erosion pressure is variable. This stratification ensured that the sample reflected differing levels of hazard intensity and adaptive constraints. The final sample consisted of 130 households. The sample size was considered sufficient for multivariate statistical analysis, including correlation testing and predictive modeling, and met the commonly recommended minimum of 10 to 15 observations per predictor variable. To reduce potential sampling bias, field teams verified household locations within the stratified zones and confirmed that selected households had experienced at least some degree of erosion exposure or risk during the previous decade. All participants were informed about the objectives of the study prior to data collection. Verbal informed consent was obtained following the ethical guidelines of the Declaration of Helsinki (World Medical Association, 2013 ). No personally identifiable information was collected to maintain anonymity and confidentiality, and participation was entirely voluntary. Field data collection was conducted with support from the International Centre for Climate Change and Development (ICCCAD), funded by the Global Resilience Partnership. 2.4.2 Variable Construction and Coding Following data collection, all survey responses were cleaned, standardized, and transformed into analytically meaningful variables that capture household vulnerability, exposure to riverbank erosion, socio-economic sensitivity, and adaptive capacity. The coding strategy was designed to ensure conceptual clarity, internal consistency with the analytical framework, and statistical suitability for multivariate modeling. Socio-economic characteristics were coded using a combination of continuous, ordinal, and binary indicators. Monthly household income was treated as a continuous variable. Education was categorized on an ordinal scale where 0 = Illiterate, 1 = Primary, 2 = Secondary, 3 = Higher Secondary, and 4 = Graduate or above. Occupation was coded to reflect income stability, with 0 = Unemployed or Daily Laborer, 1 = Agriculture-based Occupation, 2 = Skilled Worker, and 3 = Business Owner or Service Holder. Housing quality was classified as 0 = Kutcha (temporary structure), 1 = Semi-pucca (mixed materials), and 2 = Pucca (permanent structure). Environmental exposure indicators were derived jointly from the survey dataset and spatial classifications based on satellite-derived erosion zones. River proximity was coded as 0 = Distant, 1 = Moderate, and 2 = Adjacent. These categories reflect relative exposure levels based on a combination of satellite-identified erosion zones and field verification of household locations, rather than precise GPS-measured distances. Higher values indicate closer proximity to the active river channel and therefore greater erosion exposure. Erosion-related housing damage was coded as 0 = No damage, 1 = Partial damage, and 2 = Complete loss. Embankment protection was coded as 0 = Embankment present and 1 = Embankment absent, with interviewers clarifying the distinction between permanent engineered structures and temporary local protective measures. Institutional support variables captured household access to formal and informal assistance. Government or NGO support was coded as 1 = Received support and 0 = Did not receive support. Perceived institutional alignment was coded as 1 = Coordinated response and 0 = Uncoordinated response, based on a structured question asking whether government agencies, NGOs, and community leaders were perceived to work together effectively during erosion events. Respondents assessed coordination based on their experiences with relief distribution, embankment repair, and local decision-making processes. Migration outcome was coded as a binary variable indicating whether a household relocated as a result of erosion. Migration status was recorded as 1 = Migrated (temporarily or permanently) and 0 = Not migrated. To minimize recall bias, respondents were asked to specify the timing of migration relative to major erosion episodes identified in the satellite analysis. When responses were unclear, current residential location and self-reported displacement history were cross-checked for verification. All variables were processed and analysed using Julia. Missing values accounted for fewer than five percent of observations and were handled through pairwise deletion, which was selected because the missingness was random across variables and the approach preserved the maximum number of usable observations without distorting multivariate relationships. 2.4.3 Analytical Strategy for Socio-Economic Vulnerability To evaluate how socio-economic, environmental, and institutional factors shape household vulnerability and migration decisions, the study applied a structured analytical strategy combining descriptive statistics, bivariate correlation tests, and exploratory pattern identification. The primary objective was to detect statistically meaningful associations among variables before developing the predictive migration model. Pearson’s correlation coefficient was used for continuous variables such as income and distance to the river, while point–biserial correlations were applied where binary variables were involved. Ordinal variables (e.g., education, housing type, damage severity) were treated as approximately continuous due to their ordered structure, a common practice in hazard and migration studies. A correlation matrix was generated to quantify the strength and direction of associations among key indicators including household income, education level, housing condition, proximity to erosion zones, severity of erosion-related damage, institutional support, and migration status. The correlation results highlighted several notable patterns. Erosion-related housing damage showed a strong positive correlation with migration (r = 0.81), indicating that damage severity is a primary driver of displacement. River proximity, however, showed a very weak and statistically negligible association with migration (r = − 0.01), indicating that distance alone did not meaningfully predict displacement in this sample. By contrast, institutional variables exhibited weak negative correlations with migration, such as government or NGO support (r = − 0.38) and perceived institutional coordination (r = − 0.29), suggesting that support interventions were either insufficient in scale or unable to offset displacement pressures. 3 Results 3.1 River Morphology Transformation and Erosion Hotspots 3.1.1 Spatiotemporal Channel Shifts (1989–2020) The mapped banklines for 1989, 2000, 2010, and 2020 show clear spatiotemporal variation in channel position across the study reach (Fig. 4 ). The 1989–2000 period exhibits noticeable eastward and southeast-ward lateral migration, with distinct separation between the two bankline positions. From 2000 to 2010, additional shifts occur, including localized narrowing and changes in meander alignment relative to earlier years. Between 2010 and 2020, upstream segments display reduced lateral movement, while downstream segments show continued channel widening and eastward displacement. The cumulative bankline positions across all four periods delineate multiple zones of pronounced channel mobility, marking the primary areas of erosion activity. 3.1.2 Transition from Meandering to Braided River System The sinuosity index (SI) shows a consistent decline across the study period, decreasing from 1.57 in 1989 to 1.46 in 2000, 1.41 in 2010, and 1.27 in 2020 (Table 1 ). These values indicate a shift from a strongly meandering planform in 1989 to a lower-sinuosity configuration by 2020. Table 1 Temporal decline in Sinuosity Index from 1989 to 2020 Year Curvature Length (km) Straight Distance (km) Sinuosity Index 1989 57.32 36.4 1.57 2000 52.92 36.1 1.46 2010 51.42 36.25 1.41 2020 46.28 36.51 1.27 Classification thresholds: SI < 1.05 (Straight River), 1.05 ≤ SI < 1.25 (Braided River), 1.25 ≤ SI < 1.50 (Twisty River), SI ≥ 1.50 (Meandering River). Channel width measurements also show temporal variation (Fig. 5 a). Maximum width increased from approximately 14 m in 2000 before declining to about 5.2 m in 2020. Average width rose from roughly 6 m in 1989 to 7.2 m in 2000, then decreased to about 3.3 m by 2020. Minimum width changed from about 0.65 m in 1989 to 1.6 m in 2020. Braiding index (BI) values exhibit additional changes across the four time points (Fig. 5 b). The BI was approximately 3.3 in 1989, increased to about 3.7 in 2000, declined to roughly 3.2 in 2010, and then rose sharply to 5.9 in 2020. 3.1.3 Quantified Erosion and Accretion Patterns Erosion and accretion values for the left and right banks show clear temporal variability across the three study periods (Fig. 6 ; Tables 2 ). During 1989–2000, the right bank recorded 59.90 km² of erosion, while the left bank recorded 3.10 km². Accretion measured 2.54 km² on the right bank and 1.53 km² on the left. Between 2000 and 2010, accretion increased on both banks, with 80.26 km² on the left and 50.09 km² on the right. Erosion during the same period measured 41.72 km² on the right bank and 8.25 km² on the left (Table 2 ). During 2010–2020, the left bank experienced 24.36 km² of erosion, and the right bank experienced 18.97 km². Accretion values were 4.15 km² on the left bank and 87.96 km² on the right. Table 2 Quantitative assessment of left and right bank erosion and accretion in the UP-river (1989–2020). Area changes (in km²) document the asymmetric morphological evolution of the river across three decades. Year Left Bank (area in km²) Right Bank (area in km²) Erosion Accretion Erosion Accretion 1989 to 2000 3.097 1.526 59.896 2.535 2000 to 2010 3.526 80.259 41.723 50.085 2010 to 2020 24.364 4.148 18.968 87.955 3.2 Displacement Dynamics and Migration Patterns 3.2.1 Migration Triggers and Strategies Households situated within the high- and moderate-erosion zones delineated through channel migration, sinuosity change, and erosion–accretion patterns (See section 3.1 ) reported multiple displacement responses following erosion events. Of the surveyed households, 49% migrated immediately after bankline retreat or homestead loss (Fig. 7 ). Among households that did not relocate permanently, 14% moved temporarily as tenants in nearby settlements, and 10% stayed temporarily with relatives. A further 10% reported staying on roadsides or other exposed areas after erosion-related damage. Formal shelter use was limited, with 2% relocating to designated emergency shelters. Additionally, 12% remained in their damaged dwellings, and 3% reported other temporary coping arrangements. Among households that migrated, the destinations were distributed as follows: 71% moved to nearby cities, 18% resettled on newly formed char lands, and 11% moved to neighbouring villages. These destinations correspond spatially with the erosion hotspots and accretion–erosion transitions (Fig. 6 and Tables 2 ). 3.2.2 Socioeconomic Determinants of Migration Probability: Divergent Effects of Education and Income Logistic regression analysis was conducted on households located within the erosion-affected zones mapped through the erosion–accretion assessment (Figs. 1 and 6 ), which identified the areas experiencing the greatest land loss and therefore the highest displacement pressure. The model evaluated how five variables e.g. education, income, erosion damage, river proximity, and institutional engagement influenced the likelihood of household migration (Fig. 8 ; Supplementary Table 1). Education showed a strong and statistically significant positive effect on migration probability (β = 0.62, p < 0.001). The corresponding odds ratio (OR = 1.86, 95% CI: 1.35–2.55) indicates that each one-level increase in educational attainment substantially increased the likelihood of migration. This pattern is reflected in the predicted probabilities, which rise steeply with higher education levels and exhibit narrow confidence intervals (Fig. 8 ). Income exhibited a negative but statistically insignificant association with migration (β = − 0.00005, p = 0.16). Predicted probabilities show a gradual decline in migration likelihood with increasing household income, accompanied by wider confidence intervals among upper-income groups. Erosion-related housing damage was positively associated with migration and represented one of the strongest correlates in the model (Supplementary Table 1). In contrast, river proximity demonstrated a very weak and statistically negligible relationship with migration (β ≈ 0; r = − 0.01), indicating that distance from the active river channel alone did not meaningfully discriminate between households that migrated and those that stayed. Institutional variables, including government or NGO support and perceived coordination, also showed weak and statistically insignificant associations with migration outcomes (Supplementary Table 1). 3.3 Risk Insights: From Exposure to Prediction The correlation matrix (Fig. 9 ) shows that migration outcomes are influenced by multiple socio-economic, environmental, and institutional variables rather than a single dominant factor. Among all predictors, erosion-related house damage displays the strongest positive correlation with migration (r = 0.45). This corresponds directly with the high-erosion zones identified in the geomorphological analysis, particularly in the right-bank erosion hotspots from 1989–2000 and the renewed erosion on both banks between 2010–2020 (Fig. 6 ; Tables 2 ). Education also shows a moderate positive relationship with migration (r = 0.35), consistent with its role as a socio-economic characteristic of households exposed to severe geomorphic change. Other variables exhibit weak or negligible correlations. Income shows a small negative association with migration (r = − 0.12). Proximity to the river has almost no linear relationship with migration (r = − 0.01), reflecting that non-migrant households were also present within the moderate-erosion zones where bank retreat was less severe (Sections 3.1.2 – 3.1.3 ). Institutional variables show similarly weak correlations: government/NGO support (r = − 0.09) and stakeholder coordination (r = − 0.08). These weak associations align with the limited institutional presence reported by households in erosion-affected tracts. To evaluate predictive capacity, a logistic regression classifier was trained using five variables representing socio-economic status (education, income), physical exposure (erosion damage, proximity to river), and institutional engagement (government/NGO involvement). The model’s performance is summarised in the confusion matrix (Fig. 10 ). The classifier correctly predicted 72.2% of households that migrated, indicating high sensitivity to displacement risk in the areas of greatest erosion pressure documented earlier (Section 3.1.3 ). However, only 47.6% of non-migrant households were correctly classified, while 52.4% were predicted as migrants despite remaining in place. 4 Discussions This study demonstrates how three decades of hydro geomorphological change along the Ganges River have shaped household vulnerability and migration outcomes in one of Bangladesh’s most erosion prone landscapes. Between 1989 and 2020, the river underwent substantial planform adjustments, including progressive eastward migration, declining sinuosity, narrowing channel width, and fluctuating braiding intensity. These findings are consistent with regional studies that document similar patterns of meander adjustment, sediment driven instability, and bar formation in the Tista, Manu, Jamuna, and Padma systems (Arefin et al. 2021 ; Deb and Ferreira 2015 ; Khatun et al. 2022 ; Pal and Pani 2016 ). The reduction in sinuosity from 1.57 to 1.27 reflects broader trends of channel straightening linked to sediment entrapment behind upstream barrages and flow regulation structures in the lower Gangetic basin (Das 2024 ). The strong fluctuations in the braiding index, including the rise to 5.9 by 2020, mirror documented cycles of bar emergence, channel splitting, and localized stabilization in comparable South Asian rivers (Hossain et al. 2024 ; Sultana et al. 2025 ). These planform transitions produced alternating periods of erosion and accretion, matching observations from other floodplains where erosion hotspots shift over time due to sediment dynamics and flow variability (Sarker and Rahman 2024 ). The socio-economic impacts of these geomorphological changes are immediate and severe. Nearly 49% of all surveyed households migrated after losing land or homes to erosion, while others relied on temporary coping strategies such as staying with relatives, renting temporary rooms, or living on roadsides. Formal shelter use remained extremely low at only 2%, which reflects similar findings from Rahman and Gain ( 2020 ) regarding limited institutional capacity and barriers to accessing emergency facilities. Most displaced families moved to nearby cities for informal employment opportunities, which is consistent with internal migration patterns reported among erosion affected communities elsewhere in Bangladesh (Barua et al. 2019 ). Respondents also reported psychological distress, including anxiety, disrupted sleep, and chronic stress, which aligns with existing evidence on the mental health impacts of erosion induced displacement (Arobi et al. 2020 ; Kaiser 2023 ). Analysis of socio-economic drivers reveals two distinct adaptive pathways. Education was one of the strongest predictors of migration, supporting research showing that education enhances anticipatory adaptation by improving awareness of hazards, access to information, and mobility options (Martin 2016 ). In contrast, income showed a weak and statistically insignificant negative association with migration, suggesting that higher income households often prefer to invest in in place adaptation, such as structural improvements or social network support, rather than relocating. Similar patterns have been documented by Islam and Filho ( 2023 ), who found that financially better off households in erosion prone settings often prioritize home strengthening and livelihood recovery instead of moving. Results from the correlation analysis show that erosion related housing damage is the most consistent predictor of migration, closely matching the spatial distribution of land loss identified in the geomorphological assessment. Other factors, including income, institutional support, and perceived coordination among stakeholders, show only weak relationships with migration. This pattern reflects earlier research by Barua et al. ( 2019 ), who argued that erosion related mobility emerges from the interaction of multiple socio environmental stressors rather than from any single dominant variable. Migration outcomes in such contexts are shaped by thresholds of damage, varying levels of resources, and uneven institutional response. The predictive model developed in this study further highlights the value of integrating geomorphological data with socio economic indicators. The logistic classifier correctly identified 72.2% of households that migrated, indicating high sensitivity to displacement in the areas of greatest erosion pressure. However, the lower specificity rate of 47.6% shows that some households exposed to similar levels of erosion remained in place. This reflects the heterogeneous and sometimes nonlinear nature of decision making in slow onset hazards, where families balance environmental pressure with social, financial, and cultural constraints. The findings show that erosion driven mobility is best understood as a combined outcome of physical landscape change and socio-economic differentiation. The close alignment between geomorphological hotspots and migration destinations points to the importance of hazard monitoring systems that integrate remote sensing diagnostics with household vulnerability assessments. For policymakers, the results emphasize the need for anticipatory relocation programs, stronger early warning systems, and better coordination among government and nongovernmental actors in unstable riverine regions. By linking multi decadal geomorphological trends with household mobility outcomes, this study offers a rigorous evidence base to guide targeted adaptation planning in Bangladesh and provides a transferable framework for managing erosion related displacement in other deltaic environments around the world 5 Limitations and Directions for Future Research This study integrates multi-decadal geomorphological analysis with household-level socio-economic data, yet several limitations should be acknowledged. First , the river morphology assessment relied on 30-meter Landsat imagery, which is appropriate for detecting long-term planform changes but cannot capture fine-scale bankline retreat, small embayments, or micro-erosion processes that critically affect individual homesteads (Deb and Ferreira 2015 ; Khatun et al. 2022 ). Higher-resolution platforms such as Sentinel-2, commercial satellites, or UAV-based mapping would allow more precise detection of rapidly evolving erosion features. Second , the socio-economic survey was conducted in a single erosion-prone Upazila. Although Bagha is representative of severe riverbank instability, the reliance on one field site limits generalizability across Bangladesh’s diverse river systems, where geomorphological processes and livelihood strategies differ across the Jamuna, Padma, Tista, and coastal basins (Arefin et al. 2021 ; Rahman and Gain, 2020 ). Third , several important social and institutional drivers of migration such as land tenure conflicts, gendered constraints, informal support networks, access to credit, and political dynamics could not be captured within the scope of the present dataset, even though such factors are known to shape environmental mobility outcomes (Barua et al. 2019 ). Additionally, the predictive model showed high sensitivity but moderate specificity, suggesting that factors explaining non-migration such as place attachment, social obligations, or resource-based immobility remain unobserved (Martin 2016 ; Islam and Filho, 2023 ). And last but not the least , the study design aligns cross-sectional household data with long-term geomorphological indicators. While this approach is analytically robust, future advances would benefit from longitudinal household tracking integrated with higher-resolution spatial monitoring to better capture the cumulative and evolving nature of erosion impacts. Addressing these limitations through multi-site comparative research, finer-resolution geomorphological mapping, and mixed-method approaches that incorporate social, institutional, and psychological dimensions would offer deeper insight into mobility pathways and enhance the predictive capacity of erosion-displacement models. Such developments would strengthen efforts to design anticipatory and socially responsive adaptation strategies for communities living within Bangladesh’s increasingly unstable riverine landscapes. 6 Conclusion This study provides compelling evidence that riverbank erosion in northwestern Bangladesh is not merely an environmental phenomenon but a profound driver of socio-economic disruption and displacement. By integrating multi-decadal remote sensing analysis of river geomorphology with detailed household-level data, our findings elucidate how physical landscape changes intricately link with social vulnerabilities, shaping migration decisions in complex and differentiated ways. Our analysis demonstrates that river morphology transitions marked by decreasing sinuosity, intensified braiding patterns, and dynamic erosion-accretion cycles directly correlate with heightened displacement pressures. Crucially, we identified that while environmental exposure remains a dominant trigger for migration, socio-economic factors like education significantly enhance households’ adaptive capacities, facilitating proactive and anticipatory migration decisions. In contrast, higher income, although providing material resources for localized adaptation, does not uniformly reduce migration risk, underscoring heterogeneity in adaptive responses. The weak associations observed between institutional support mechanisms and migration outcomes highlight substantial gaps in governance, revealing critical areas for policy intervention and institutional strengthening. These findings collectively emphasize the urgent need for policy strategies that are not only multidimensional but also context-sensitive, addressing environmental vulnerabilities, enhancing socio-economic resilience, and strengthening governance structures concurrently. Future efforts must prioritize equitable, data-driven resilience planning and comprehensive institutional engagement to mitigate displacement risks effectively in deltaic and similarly vulnerable regions worldwide. Such integrative approaches will be essential as climate variability intensifies, driving the interconnected dynamics of environmental change, socio-economic vulnerability, and human mobility. To support at-risk populations in erosion-prone riverine regions, we recommend designing anticipatory migration frameworks that integrate geomorphological risk mapping with household vulnerability assessments (Birkmann et al. 2013a , 2014 ; Schindler et al. 2023 ). This includes developing early warning systems, formalizing relocation support, and strengthening local governance mechanisms particularly those related to institutional responsiveness and land security (Moench 2010 ; Birkmann et al. 2013b ). Future research should build on this study by incorporating longitudinal panel data to capture household migration trajectories over time, and by modeling feedback between environmental degradation, social adaptation, and institutional trust (Adger et al. 2002 ; Warner et al. 2010 ; Hermans et al. 2023 ). Comparative studies across deltaic systems worldwide would also help refine global frameworks for climate mobility governance under increasing hydro-climatic stress (Kuenzer and Renaud 2012 ; Ndehedehe 2023 ). Declarations This study involved non-clinical social science survey interviews conducted with households affected by riverbank erosion in Bangladesh. The research was conducted under the coordination and oversight of the International Centre for Climate Change and Development (ICCCAD), which ensured that all research activities complied with recognized ethical standards. The study is a part of the research project thay was reviewed and approved by the ICCCAD Ethics Review Committee prior to the commencement of data collection. The research adhered to internationally recognized ethical standards for social science research, including the principles of the World Medical Association Declaration of Helsinki (2013). The study adhered to the ethical principles of the World Medical Association Declaration of Helsinki (2013). All participants were informed about the objectives of the study prior to data collection, verbal informed consent was obtained, participation was voluntary, and respondents were free to withdraw at any time. No personally identifiable information was collected in order to maintain anonymity and confidentiality. Acknowledgement We are deeply grateful to the people of Bagha Upazila for their cooperation and support during fieldwork. We also thank the Institute of Water and Flood Management (IWFM), Bangladesh University of Engineering and Technology (BUET), for their valuable guidance and technical assistance throughout the research process. This study was supported by the International Centre for Climate Change and Development (ICCCAD), funded by the Climate Justice Resilience Fund (CJRF) under grant number CLJI–ICCCAD Limited–NVF–013459–2021-01. We gratefully acknowledge this support, which made the fieldwork and data collection possible. Author contributions MHH: Conceptualization, Data curation, Methodology, Writing original draft, Supervision, Review & editing, Funding acquisition, Validation, Visualization; IHN: Writing original draft, Review & editing, Validation, Visualization. MAC: Writing original draft, Review & editing, Validation, Visualization; MMHM: Writing original draft, Review & editing, Validation, Visualization. Data Availability The datasets generated during and/or analysed during the current study are available from the authors upon request. Competing Interests The authors state that they have no recognized financial conflicts or personal interests. Ethics Approval and Clinical Trial Not applicable. This research does not involve any clinical trials. References Adger WN, Kelly PM, Winkels A, et al (2002) Migration, remittances, livelihood trajectories, and social resilience. AMBIO A J Hum Environ 31:358–366. https://doi.org/10.1579/0044-7447-31.4.358 Akter N, Tsuboki K (2014) Role of synoptic-scale forcing in cyclogenesis over the Bay of Bengal. Clim Dyn 43:2651–2662. https://doi.org/10.1007/s00382-014-2077-9 Arefin R, Meshram SG, Seker DZ (2021) River channel migration and land-use/land-cover change for Padma River at Bangladesh: a RS- and GIS-based approach. Int. J. Environ. Sci. 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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-8332436","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565395564,"identity":"9d196f9e-6330-4d2b-9d01-0a7815f870b3","order_by":0,"name":"Md Hasibul Hasan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYDACCTDJxsMP4TITrYVPTrKBRC1yxgYHiNWiO7v58YefO8wSNx8//kyCocI6sYGQFrM7x8wke8+kJW47k2MmwXAmnQgtNxLMGHjbjiVuO5DDJsHYdpgYLemfP/5t+5+4uf/5MwnGf0RpyTGQ5m1jMzaQSDCTYGwgTkuZtGwbm5zEjTfGFgnH0o2Jcdjmj2/bgFHZn/7wxocaa1mCWlBBAmnKR8EoGAWjYBTgAgD8nT/d/YpurwAAAABJRU5ErkJggg==","orcid":"","institution":"Arizona State University","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Hasibul","lastName":"Hasan","suffix":""},{"id":565395565,"identity":"1cf4db62-aa58-498f-ad3c-2a70f8fda094","order_by":1,"name":"Imran Hossain Newton","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Imran","middleName":"Hossain","lastName":"Newton","suffix":""},{"id":565395566,"identity":"56e7b8f5-b524-4e64-9a7f-aa71c162b707","order_by":2,"name":"Md. Arif Chowdhury","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Arif","lastName":"Chowdhury","suffix":""},{"id":565395567,"identity":"7aeaeedb-f599-4261-98a3-0ad00ad4b8b8","order_by":3,"name":"Md. Monoar Hossain Munna","email":"","orcid":"","institution":"Patuakhali Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Monoar Hossain","lastName":"Munna","suffix":""}],"badges":[],"createdAt":"2025-12-11 04:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8332436/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8332436/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99795302,"identity":"eeb333a2-80cd-4a8b-85f2-c9885632a32f","added_by":"auto","created_at":"2026-01-08 13:37:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":610422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudy area along the Ganges River in northwestern Bangladesh. Surveys were conducted in Bagha Upazila.\u003c/em\u003e \u003cem\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003c/em\u003e\u003cem\u003ea) Regional map showing the location of the study reach within the Ganges–Brahmaputra–Meghna delta system. (b) Satellite image highlighting a 44 km stretch of the Padma River between Bagha, Ishwardi, and Lalpur Upazilas on the left bank and Bheramara and Daulatpur Upazilas on the right bank. The area, adjacent to the Rajshahi Division, is characterized by active channel migration, seasonal flooding, and extensive riverbank erosion. This reach was selected for integrated geomorphological and socio-economic analysis due to its history of high erosion intensity and vulnerability to displacement.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/394860da9f984fabd86609af.jpeg"},{"id":99795182,"identity":"7c890fb9-5ed2-4d4c-8be6-69ba34aa6e5a","added_by":"auto","created_at":"2026-01-08 13:37:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMethodological framework linking hydro-geomorphological change to migration dynamics and policy pathways\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/c1bc75090d8d02f4de9e9506.png"},{"id":99797015,"identity":"00f59bc1-73db-4a69-8bdc-fb5bdd39bc25","added_by":"auto","created_at":"2026-01-08 13:44:23","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":411071,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatiotemporal classification of river morphology and land cover along the Ganges River (1989–2020)\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eSupervised classification of Landsat imagery depicts changes in river, sandy land, and stable land cover across four key years: (a) 1989, (b) 2000, (c) 2010, and (d) 2020. The dynamic redistribution of river channels and sandy bars reflects significant erosion–accretion cycles within the 44 km study reach. These patterns reveal the geomorphic instability of the Padma River and provide critical context for understanding household-level vulnerability and displacement.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/f1987665a00108e8ba590228.jpeg"},{"id":99795232,"identity":"cdbd907a-bdc6-4547-9c54-bcef6bd3de0b","added_by":"auto","created_at":"2026-01-08 13:37:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":237231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eProgressive lateral migration and morphological evolution of the river channel from 1989 to 2020. Four distinct time points including 1989, 2000, 2010, and 2020 are presented with black, red, purple, and blue lines, respectively. Historical river channel trajectories illustrate significant spatiotemporal shifts over three decades, highlighting dynamic erosion, accretion processes, and evolving exposure risks for riparian communities.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/43146499516bff68a1aa6a55.png"},{"id":99682486,"identity":"fac32eb4-c03e-4870-853f-f22b101547e2","added_by":"auto","created_at":"2026-01-07 09:01:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":130874,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTemporal trends in channel morphology along the Ganges River in northwestern Bangladesh, 1989–2020. (a) Maximum, average, and minimum channel widths; (b) Braiding Index (BI).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/521ebf867796be8076036fd1.png"},{"id":99796257,"identity":"80d213e1-3cbe-48bf-ba6c-73958ef4a9ce","added_by":"auto","created_at":"2026-01-08 13:40:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":295158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial-Temporal Dynamics of Riverbank Erosion and Accretion Along the UP-River. Maps illustrate patterns of erosion (red) and accretion (green) over three periods: (a) 1989–2000, (b) 2000–2010, and (c) 2008–2020. Black lines indicate the river channel position in 2010 for reference.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/7431e2e6f02cda854f82e956.png"},{"id":99796623,"identity":"59fc72f8-a5f4-4223-9814-5c65f5823491","added_by":"auto","created_at":"2026-01-08 13:42:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":49994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eProportional Distribution of Post-Disaster Residence Strategies by Relocation Destination. Each bar shows the percentage distribution of household relocation strategies across four key destination types (Char land, City, Neighboring Village, and Others). The results highlight city-bound migration as the dominant pattern across most coping strategies, while options like char land and shelter housing remain marginal.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/106ccb6df01d9c585159b0cb.png"},{"id":99796359,"identity":"8679094d-1e91-4281-b55f-84093055cab2","added_by":"auto","created_at":"2026-01-08 13:41:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":103280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDiverging Effects of Income and Education on Migration Probability. Left: A logistic regression model reveals a negative relationship between monthly household income and migration probability, indicating that wealthier households are less likely to migrate under erosion risk. Right: In contrast, migration probability increases with higher levels of education, suggesting that education enhances hazard awareness and adaptive decision-making. Shaded areas represent 95% confidence intervals.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/900579c58628132b8937c7ad.png"},{"id":99682495,"identity":"4dabe96e-84cb-48cf-8216-f41e06987ccf","added_by":"auto","created_at":"2026-01-07 09:01:24","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":91890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePearson Correlation Matrix of Socio-Economic, Environmental, and Institutional Drivers of Migration. The heatmap illustrates the strength and direction of linear associations among key variables influencing migration in erosion-prone contexts, revealing modest correlations and highlighting the multidimensional nature of displacement risk.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/921229c9548504db0bbe6572.jpeg"},{"id":99682492,"identity":"7589f144-b71f-4bb8-b26b-22999e1a78d8","added_by":"auto","created_at":"2026-01-07 09:01:24","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":85656,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eClassification Accuracy of Migration Prediction Model Based on Socio-Economic, Environmental, and Institutional Determinants. The confusion matrix illustrates the predictive performance of a logistic regression model trained on education, income, erosion damage, river proximity, and institutional support. The model achieved high sensitivity (72.2%) in correctly identifying migrated households and moderate specificity (47.6%) in detecting non-migrants, indicating a strong ability to flag displacement risk but with some overestimation of migration.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/135f69904beee735e18d2de3.png"},{"id":99805226,"identity":"af4874b6-a71f-4732-8062-67575ca2d028","added_by":"auto","created_at":"2026-01-08 14:16:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3060769,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/0695d315-db84-4b3c-9520-10c6daafa09a.pdf"},{"id":99682485,"identity":"9eb431fc-9ccc-4b49-ac22-59246ca9f62a","added_by":"auto","created_at":"2026-01-07 09:01:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17456,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8332436/v1/4d98d74b84aac6a20f0562ed.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"When the River Moves, Lives Shift: Linking Hydro-Geomorphology to Climate-Induced Migration","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eRiverbank erosion is an increasingly critical natural hazard shaped by the interaction of climate change, hydrological variability, and human modification of river systems. Intensified rainfall, upstream dam construction, deforestation, and sediment supply disruptions are accelerating erosion across major global river basins, including the Mekong, Mississippi, Amazon, and Ganges. These processes alter river morphology, destabilize land\u0026ndash;water interfaces, and amplify flood risk in low-lying deltaic and alluvial regions (Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Khare et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Intergovernmental Panel on Climate Change (IPCC) and the United Nations Office for Disaster Risk Reduction (UNDRR) highlight that these hydro-geomorphological processes are central drivers of climate-induced displacement, land loss, and livelihood disruption. As warming accelerates glacial melt, extreme precipitation, and sediment mobilization, riverine landscapes are undergoing rapid morphological change, positioning erosion as a key component of 21st-century Earth system hazards (Herman et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe socio-economic consequences of riverbank erosion are profound. In South and Southeast Asia, erosion destroys homes, agricultural lands, and critical infrastructure, triggering large-scale population movements and intensifying poverty and marginalization (Das et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Barua et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Freihardt and Frey, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such displacement frequently occurs in the absence of institutional protection, within governance systems characterized by limited coordination, minimal early-warning capacity, and constrained adaptation support.\u003c/p\u003e \u003cp\u003eBangladesh represents one of the most severe global hotspots of erosion-related risk. Located at the confluence of the Ganges, Brahmaputra, and Meghna rivers, the country\u0026rsquo;s tectonically active and sediment-rich deltaic landscape is subject to continuous morphodynamic change (Misachi \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Islam 2021; Arefin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Riverbank erosion displaces an estimated 130,000 to 200,000 people annually across more than twenty districts (Hasnat et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Freihardt and Frey \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These displacements exacerbate pre-existing socio-economic vulnerabilities, including agricultural disruption, housing destruction, infrastructure loss, and weakened social networks (Rahman and Gain \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hossain et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a slow-onset hazard, erosion deepens inequality and heightens long-term insecurity for marginal households (Islam et al \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExtensive research has examined geomorphological change in Bangladesh using satellite imagery, toposheets, and GIS-based techniques, documenting channel migration, bankline shifts, and morphometric evolution across major rivers such as the Ganges, Jamuna, and Meghna, alongside highly dynamic smaller rivers such as the Manu (Islam and Rashid \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Thakur et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Deb and Ferreira \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Arefin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khatun et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Long-term remote sensing analyses have proven central for erosion hazard assessment. For example, Deb and Ferreira (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) quantified multi-decadal planform dynamics of the Manu River, generating predictive insights for erosion hotspots. Likewise, vulnerability frameworks such as the Pressure and Release (PAR) model have been used to characterize hazard exposure and risk accumulation (Biswas and Anwaruzzaman \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these contributions, several critical gaps persist. \u003cem\u003eFirst\u003c/em\u003e, most studies analyze either the physical evolution of river morphology or the socio-economic impacts of erosion, but rarely within an integrated framework. These limits understanding of how geomorphological processes shape household-level decisions related to migration, displacement, and (im)mobility. While displacement is widely recognized as a consequence of erosion (Islam and Rashid \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Majumdar \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), few studies trace the mechanisms linking morphological change to different types of mobility, including temporary, permanent, labor-driven, and anticipatory migration. \u003cem\u003eSecond\u003c/em\u003e, institutional and governance dimensions remain under-examined. Critical factors such as early warning systems, relief mechanisms, embankment maintenance, NGO engagement, and local conflict are often addressed only superficially, despite their substantial influence on vulnerability and adaptation outcomes (Islam et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hossain et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This limits the usefulness of existing research for designing effective policy interventions. \u003cem\u003eThird\u003c/em\u003e, few studies integrate remote sensing diagnostics with predictive socio-economic modeling to identify households likely to be displaced before erosion impacts materialize, even though remote sensing studies are abundant and migration research is well documented (Islam and Rashid \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Thakur et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Deb and Ferreira \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Majumdar \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn response to these gaps, this study develops an integrated socio-spatial framework linking long-term river morphological change to household vulnerability and migration outcomes in one of the world\u0026rsquo;s most erosion-prone regions. Using multi-decadal satellite imagery from 1989 to 2020, we quantify changes in channel pattern, sinuosity, and erosion\u0026ndash;accretion dynamics along the Ganges River (Thakur et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Khatun et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These geomorphological insights are paired with a stratified household survey of erosion-exposed communities, capturing socio-economic variables such as income, education, housing condition, institutional support, and migration decisions (Islam et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rahman and Gain \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By explicitly linking physical river transformations with household-level responses, this study contributes new evidence on how environmental stress interacts with social vulnerability to shape mobility outcomes. The research advances hazard science by integrating geomorphological and socio-economic data, distinguishing environmental, socio-economic, and institutional determinants of migration, and offering predictive insights relevant to anticipatory adaptation, managed retreat, and resilience planning in deltaic regions where erosion, weak governance, and human displacement increasingly intersect.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Case Study\u003c/h2\u003e \u003cp\u003eThis study examines a 44 km stretch of the Ganges River near the Rajshahi Division in northwestern Bangladesh, between 24\u0026deg;11\u0026prime; N to 24\u0026deg;02\u0026prime; N and 88\u0026deg;44\u0026prime; E to 89\u0026deg;01\u0026prime; E. The riverbanks span Bagha, Ishwardi and Lalpur Upazilas on the left and Bheramara Upazila on the right side (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Bagha Upazila, known for severe erosion, was selected for primary fieldwork (see Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe river originates as the Ganges in the Indian Himalayas and enters Bangladesh as a sediment-heavy river before merging with the Brahmaputra and Meghna to form the dynamic Ganges delta. Recent shifts from a meandering to a braided morphology driven by sediment load, monsoonal flow variability, and upstream interventions have intensified bank instability and channel migration (Thakur et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Khatun et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The region\u0026rsquo;s subtropical monsoon climate brings heavy rainfall from June to October, heightening seasonal flooding and erosion (Akter and Tsuboki \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Misachi \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe area is predominantly agrarian, relying on rice cultivation, flood recession agriculture, and inland fisheries (Tania et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Everard 2016). Socio-economic vulnerability is high due to dependence on the river and rising displacement risks from erosion and infrastructure loss (Islam et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rahman and Gain \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Overview of Analytical Framework\u003c/h2\u003e \u003cp\u003eThis study applies an integrated spatial and socio-economic analytical framework that links long-term river morphological change with household-level vulnerability and migration outcomes. The full methodological workflow is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which illustrates how geomorphological analysis, survey-based vulnerability assessment, and statistical modeling are connected within a single analytical sequence. The framework combines quantitative remote sensing analysis with structured household survey data, allowing a stepwise assessment of how physical landscape transformation shapes exposure, socio-economic sensitivity, and adaptive responses in erosion-prone communities.\u003c/p\u003e \u003cp\u003eThe analytical process consists of three interconnected components. \u003cem\u003eFirst\u003c/em\u003e, multi-temporal satellite imagery is used to quantify channel migration, erosion and accretion dynamics, and planform change along the Ganges River. These geomorphological indicators establish the physical hazard context and are used to delineate high, moderate, and transitional erosion zones. \u003cem\u003eSecond\u003c/em\u003e, a stratified household survey is implemented across these erosion zones to capture socio-economic characteristics, housing conditions, environmental exposure, institutional support, and migration decisions. The sampling design is intentionally structured around spatial erosion categories to ensure alignment between geomorphological risk and household-level exposure. \u003cem\u003eThird\u003c/em\u003e, the combined dataset is examined through a set of statistical analyses designed to identify correlates and predictors of displacement. Pairwise associations among socio-economic, environmental, and institutional variables are assessed through correlation matrices, while a predictive migration model is estimated to evaluate which household attributes most strongly influence the probability of relocation. These analytical steps trace a logical chain of causality from physical river change through household vulnerability to observed mobility outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 River Morphology Analysis\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Satellite Image Acquisition and Preprocessing\u003c/h2\u003e \u003cp\u003eTo assess long-term river morphological change and channel dynamics, we used multi-temporal Landsat satellite imagery spanning more than three decades. Four cloud-free scenes were selected for the years 1989 (Landsat 5 TM), 2000 (Landsat 5 TM), 2010 (Landsat 5 TM), and 2020 (Landsat 8 OLI). These time points were chosen to represent consistent decadal intervals of river behaviour, while maintaining comparability across seasonal conditions. All images were collected during the low-flow period between November and January to minimize hydrological variability and ensure similar discharge conditions. Scenes with less than 10 percent cloud cover were selected to reduce atmospheric interference.\u003c/p\u003e \u003cp\u003eAll imagery was obtained from the United States Geological Survey (USGS) Earth Explorer platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) at a spatial resolution of 30 meters. The datasets share a common WGS 84 datum and UTM Zone 45N projection. Because the datasets were provided as Level-1 Terrain Corrected (L1T) products, only minimal geometric adjustments were required. Preprocessing included geometric verification, radiometric enhancement, and atmospheric normalization to ensure temporal and spatial consistency. These steps were completed using ArcGIS 10.6 and ERDAS IMAGINE 2015. False color composites were generated for visual interpretation and training data development. For Landsat 5 TM, bands 4-3-2 were used, and for Landsat 8 OLI, bands 7-6-4 were used. After preprocessing, each image was clipped to the defined area of interest along the 44 km study reach of the Ganges River within the Rajshahi Division.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Image Classification and Change Detection\u003c/h2\u003e \u003cp\u003eTo delineate river morphology and quantify erosion and deposition patterns, all pre-processed Landsat images were classified using a supervised maximum likelihood algorithm in ArcGIS 10.6. Training signatures were developed from field-validated reference points collected using handheld GPS units and from high-resolution Google Earth imagery to ensure robust discrimination among spectral categories. Three land cover classes central to fluvial geomorphology were identified: open water, exposed sandy bars (char lands), and stable vegetated land.\u003c/p\u003e \u003cp\u003eClassification accuracy was assessed using an error matrix generated from 120 independent reference points distributed across the three land cover categories. A confusion matrix was constructed to evaluate omission and commission errors, and classification performance was quantified using two standard metrics. Overall Accuracy (OA) was computed as the proportion of correctly classified samples, and the Kappa coefficient was used to account for agreement occurring by chance. The classification achieved an OA greater than 84 percent and a Kappa coefficient of 0.755 (see Supplementary Table\u0026nbsp;2), indicating reliable performance suitable for subsequent morphometric and change detection analyses. The Kappa statistic (K) was calculated following Congalton and Green (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:K=\\frac{n\\sum\\:_{i=1}^{k}nij-\\sum\\:_{i=1}^{k}ni+nj}{N2-\\sum\\:_{i=1}^{k}ni+nj}\\)\u003c/span\u003e \u003c/span\u003e (\u003cb\u003eEq.\u0026nbsp;1\u003c/b\u003e)\u003c/p\u003e \u003cp\u003eWhere, n is the total number of reference points, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:nij\\)\u003c/span\u003e\u003c/span\u003e is the number of correctly classified samples in category i, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ni\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:nj\\)\u003c/span\u003e\u003c/span\u003e represent row and column totals of the confusion matrix.\u003c/p\u003e \u003cp\u003eThe land cover types of each year are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the classified land cover maps for each study year. After classification, raster outputs were converted to vector polygons using the raster-to-polygon tool in ArcGIS to support more precise spatial overlay. Vector layers were cleaned to remove sliver polygons and resolve topological inconsistencies, ensuring comparability across time periods. Channel boundaries were then delineated from each classified image to extract the active river corridor for 1989, 2000, 2010, and 2020. Change detection was conducted by overlaying vector layers from successive time steps (for example, 1989 to 2000, 2000 to 2010, and 2010 to 2020). Spatial difference and erase operations were used to identify and quantify erosion zones where land converted to water or sandy bars, and accretion zones where water or sandy bars converted to vegetated land.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Morphological Metrics\u003c/h2\u003e \u003cp\u003eTo capture the dynamics of river planform evolution, a suite of morphological indicators was derived from the classified and vectorized images, focusing on bankline migration, channel width variability, and changes in sinuosity and braiding index over time. These indicators collectively characterize the degree of geomorphological instability within the selected reach of the Ganges River.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Bankline Shifting and Channel Migration\u003c/h2\u003e \u003cp\u003eChannel migration and bankline displacement were analyzed using overlay techniques in ArcGIS 10.6, where river polygons from different years (1989, 2000, 2010, 2020) were spatially intersected. The extreme left and right outer channels were identified and digitized for each time point, and bankline shifts were calculated by measuring perpendicular distances between equivalent riverbank positions across time slices. Areas lost (erosion) and gained (accretion) were calculated using Erase and Symmetrical Difference tools in ArcGIS 10.6 platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Channel Width Measurement\u003c/h2\u003e \u003cp\u003eChannel width was measured as the distance between the extreme left and right banks, incorporating any mid-channel bars or islands. For each year, the river reach was segmented into 1 km transects, and the width was calculated at regular intervals. The mean channel width was determined for each time period, allowing a temporal comparison of width variability across decades. This process followed the methodology proposed by Winterbottom (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), with additional spatial refinements using ERDAS Imagine 2015.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 Sinuosity Index\u003c/h2\u003e \u003cp\u003eThe sinuosity index (SI) (\u003cb\u003eEq.\u0026nbsp;2\u003c/b\u003e), a measure of the meandering nature of a river, was calculated using the ratio of channel length (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{A}\\text{C}\\)\u003c/span\u003e\u003c/span\u003e) to valley length (AV) (Mueller \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1968\u003c/span\u003e; Pavelsky and Smith \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}\\text{I}=\\:\\frac{\\text{A}\\text{C}}{\\text{A}\\text{V}}\\)\u003c/span\u003e \u003c/span\u003e (\u003cb\u003eEq.\u0026nbsp;2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eChannel centrelines were extracted using Hydro-Width Automated Tool for Hydraulics (HWATH), an ArcGIS extension, and the shortest straight-line path between reach endpoints was used to determine \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{A}\\text{V}\\)\u003c/span\u003e\u003c/span\u003e. A higher SI indicates a more meandering channel, while values near 1 suggest a straighter course (Mueller \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1968\u003c/span\u003e; Pavelsky and Smith \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Leopold et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.7 Braiding Index\u003c/h2\u003e \u003cp\u003eTo quantify planform complexity, the braiding index (BI) was calculated for each 1 km segment using the method of Friend and Sinha (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) as follows (\u003cb\u003eEq.\u0026nbsp;3\u003c/b\u003e):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:BI=2\\sum\\:\\frac{\\text{L}\\text{i}}{Lr}\\)\u003c/span\u003e \u003c/span\u003e (\u003cb\u003eEq.\u0026nbsp;3)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{L}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the total length of mid-channel bars and islands within the segment, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{L}}_{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e is the reach length measured along the channel centreline. This index helps to distinguish the transition from meandering to braided morphology over time.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Socio-Economic Data Collection\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Survey Design and Sampling Strategy\u003c/h2\u003e \u003cp\u003eTo complement the spatial analysis of riverbank erosion and examine its socio-economic impacts at the household level, a structured field survey was conducted in Bagha Upazila, one of the most erosion-affected areas along the Ganges River in northwestern Bangladesh. The survey was designed to capture household exposure to erosion hazards, socio-economic vulnerability, access to institutional support, and behavioural responses with an emphasis on migration decisions.\u003c/p\u003e \u003cp\u003eA stratified random sampling approach was applied to ensure systematic representation across varying erosion exposure levels. Erosion zones were delineated using a multi-temporal overlay of satellite-derived bankline change maps for the period 1989 to 2020. Based on these spatial classifications, households were randomly selected from high-risk zones characterized by frequent bankline retreat and from moderate-risk transitional zones where erosion pressure is variable. This stratification ensured that the sample reflected differing levels of hazard intensity and adaptive constraints. The final sample consisted of 130 households. The sample size was considered sufficient for multivariate statistical analysis, including correlation testing and predictive modeling, and met the commonly recommended minimum of 10 to 15 observations per predictor variable. To reduce potential sampling bias, field teams verified household locations within the stratified zones and confirmed that selected households had experienced at least some degree of erosion exposure or risk during the previous decade.\u003c/p\u003e \u003cp\u003eAll participants were informed about the objectives of the study prior to data collection. Verbal informed consent was obtained following the ethical guidelines of the Declaration of Helsinki (World Medical Association, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). No personally identifiable information was collected to maintain anonymity and confidentiality, and participation was entirely voluntary. Field data collection was conducted with support from the International Centre for Climate Change and Development (ICCCAD), funded by the Global Resilience Partnership.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Variable Construction and Coding\u003c/h2\u003e \u003cp\u003eFollowing data collection, all survey responses were cleaned, standardized, and transformed into analytically meaningful variables that capture household vulnerability, exposure to riverbank erosion, socio-economic sensitivity, and adaptive capacity. The coding strategy was designed to ensure conceptual clarity, internal consistency with the analytical framework, and statistical suitability for multivariate modeling.\u003c/p\u003e \u003cp\u003eSocio-economic characteristics were coded using a combination of continuous, ordinal, and binary indicators. Monthly household income was treated as a continuous variable. Education was categorized on an ordinal scale where 0\u0026thinsp;=\u0026thinsp;Illiterate, 1\u0026thinsp;=\u0026thinsp;Primary, 2\u0026thinsp;=\u0026thinsp;Secondary, 3\u0026thinsp;=\u0026thinsp;Higher Secondary, and 4\u0026thinsp;=\u0026thinsp;Graduate or above. Occupation was coded to reflect income stability, with 0\u0026thinsp;=\u0026thinsp;Unemployed or Daily Laborer, 1\u0026thinsp;=\u0026thinsp;Agriculture-based Occupation, 2\u0026thinsp;=\u0026thinsp;Skilled Worker, and 3\u0026thinsp;=\u0026thinsp;Business Owner or Service Holder. Housing quality was classified as 0\u0026thinsp;=\u0026thinsp;Kutcha (temporary structure), 1\u0026thinsp;=\u0026thinsp;Semi-pucca (mixed materials), and 2\u0026thinsp;=\u0026thinsp;Pucca (permanent structure). Environmental exposure indicators were derived jointly from the survey dataset and spatial classifications based on satellite-derived erosion zones. River proximity was coded as 0\u0026thinsp;=\u0026thinsp;Distant, 1\u0026thinsp;=\u0026thinsp;Moderate, and 2\u0026thinsp;=\u0026thinsp;Adjacent. These categories reflect relative exposure levels based on a combination of satellite-identified erosion zones and field verification of household locations, rather than precise GPS-measured distances. Higher values indicate closer proximity to the active river channel and therefore greater erosion exposure. Erosion-related housing damage was coded as 0\u0026thinsp;=\u0026thinsp;No damage, 1\u0026thinsp;=\u0026thinsp;Partial damage, and 2\u0026thinsp;=\u0026thinsp;Complete loss. Embankment protection was coded as 0\u0026thinsp;=\u0026thinsp;Embankment present and 1\u0026thinsp;=\u0026thinsp;Embankment absent, with interviewers clarifying the distinction between permanent engineered structures and temporary local protective measures. Institutional support variables captured household access to formal and informal assistance. Government or NGO support was coded as 1\u0026thinsp;=\u0026thinsp;Received support and 0\u0026thinsp;=\u0026thinsp;Did not receive support. Perceived institutional alignment was coded as 1\u0026thinsp;=\u0026thinsp;Coordinated response and 0\u0026thinsp;=\u0026thinsp;Uncoordinated response, based on a structured question asking whether government agencies, NGOs, and community leaders were perceived to work together effectively during erosion events. Respondents assessed coordination based on their experiences with relief distribution, embankment repair, and local decision-making processes.\u003c/p\u003e \u003cp\u003eMigration outcome was coded as a binary variable indicating whether a household relocated as a result of erosion. Migration status was recorded as 1\u0026thinsp;=\u0026thinsp;Migrated (temporarily or permanently) and 0\u0026thinsp;=\u0026thinsp;Not migrated. To minimize recall bias, respondents were asked to specify the timing of migration relative to major erosion episodes identified in the satellite analysis. When responses were unclear, current residential location and self-reported displacement history were cross-checked for verification.\u003c/p\u003e \u003cp\u003eAll variables were processed and analysed using Julia. Missing values accounted for fewer than five percent of observations and were handled through pairwise deletion, which was selected because the missingness was random across variables and the approach preserved the maximum number of usable observations without distorting multivariate relationships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Analytical Strategy for Socio-Economic Vulnerability\u003c/h2\u003e \u003cp\u003eTo evaluate how socio-economic, environmental, and institutional factors shape household vulnerability and migration decisions, the study applied a structured analytical strategy combining descriptive statistics, bivariate correlation tests, and exploratory pattern identification. The primary objective was to detect statistically meaningful associations among variables before developing the predictive migration model.\u003c/p\u003e \u003cp\u003ePearson\u0026rsquo;s correlation coefficient was used for continuous variables such as income and distance to the river, while point\u0026ndash;biserial correlations were applied where binary variables were involved. Ordinal variables (e.g., education, housing type, damage severity) were treated as approximately continuous due to their ordered structure, a common practice in hazard and migration studies. A correlation matrix was generated to quantify the strength and direction of associations among key indicators including household income, education level, housing condition, proximity to erosion zones, severity of erosion-related damage, institutional support, and migration status.\u003c/p\u003e \u003cp\u003eThe correlation results highlighted several notable patterns. Erosion-related housing damage showed a strong positive correlation with migration (r\u0026thinsp;=\u0026thinsp;0.81), indicating that damage severity is a primary driver of displacement. River proximity, however, showed a very weak and statistically negligible association with migration (r = \u0026minus;\u0026thinsp;0.01), indicating that distance alone did not meaningfully predict displacement in this sample. By contrast, institutional variables exhibited weak negative correlations with migration, such as government or NGO support (r = \u0026minus;\u0026thinsp;0.38) and perceived institutional coordination (r = \u0026minus;\u0026thinsp;0.29), suggesting that support interventions were either insufficient in scale or unable to offset displacement pressures.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 River Morphology Transformation and Erosion Hotspots\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Spatiotemporal Channel Shifts (1989\u0026ndash;2020)\u003c/h2\u003e \u003cp\u003eThe mapped banklines for 1989, 2000, 2010, and 2020 show clear spatiotemporal variation in channel position across the study reach (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The 1989\u0026ndash;2000 period exhibits noticeable eastward and southeast-ward lateral migration, with distinct separation between the two bankline positions. From 2000 to 2010, additional shifts occur, including localized narrowing and changes in meander alignment relative to earlier years. Between 2010 and 2020, upstream segments display reduced lateral movement, while downstream segments show continued channel widening and eastward displacement. The cumulative bankline positions across all four periods delineate multiple zones of pronounced channel mobility, marking the primary areas of erosion activity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Transition from Meandering to Braided River System\u003c/h2\u003e \u003cp\u003eThe sinuosity index (SI) shows a consistent decline across the study period, decreasing from 1.57 in 1989 to 1.46 in 2000, 1.41 in 2010, and 1.27 in 2020 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These values indicate a shift from a strongly meandering planform in 1989 to a lower-sinuosity configuration by 2020.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemporal decline in Sinuosity Index from 1989 to 2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurvature Length (km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStraight Distance (km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSinuosity Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eClassification thresholds: SI\u0026thinsp;\u0026lt;\u0026thinsp;1.05 (Straight River), 1.05\u0026thinsp;\u0026le;\u0026thinsp;SI\u0026thinsp;\u0026lt;\u0026thinsp;1.25 (Braided River), 1.25\u0026thinsp;\u0026le;\u0026thinsp;SI\u0026thinsp;\u0026lt;\u0026thinsp;1.50 (Twisty River), SI\u0026thinsp;\u0026ge;\u0026thinsp;1.50 (Meandering River).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eChannel width measurements also show temporal variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Maximum width increased from approximately 14 m in 2000 before declining to about 5.2 m in 2020. Average width rose from roughly 6 m in 1989 to 7.2 m in 2000, then decreased to about 3.3 m by 2020. Minimum width changed from about 0.65 m in 1989 to 1.6 m in 2020.\u003c/p\u003e \u003cp\u003eBraiding index (BI) values exhibit additional changes across the four time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The BI was approximately 3.3 in 1989, increased to about 3.7 in 2000, declined to roughly 3.2 in 2010, and then rose sharply to 5.9 in 2020.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Quantified Erosion and Accretion Patterns\u003c/h2\u003e \u003cp\u003eErosion and accretion values for the left and right banks show clear temporal variability across the three study periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During 1989\u0026ndash;2000, the right bank recorded 59.90 km\u0026sup2; of erosion, while the left bank recorded 3.10 km\u0026sup2;. Accretion measured 2.54 km\u0026sup2; on the right bank and 1.53 km\u0026sup2; on the left.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBetween 2000 and 2010, accretion increased on both banks, with 80.26 km\u0026sup2; on the left and 50.09 km\u0026sup2; on the right. Erosion during the same period measured 41.72 km\u0026sup2; on the right bank and 8.25 km\u0026sup2; on the left (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During 2010\u0026ndash;2020, the left bank experienced 24.36 km\u0026sup2; of erosion, and the right bank experienced 18.97 km\u0026sup2;. Accretion values were 4.15 km\u0026sup2; on the left bank and 87.96 km\u0026sup2; on the right.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eQuantitative assessment of left and right bank erosion and accretion in the UP-river (1989\u0026ndash;2020). Area changes (in km\u0026sup2;) document the asymmetric morphological evolution of the river across three decades.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLeft Bank (area in km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRight Bank (area in km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eErosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccretion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1989 to 2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000 to 2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010 to 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Displacement Dynamics and Migration Patterns\u003c/h2\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Migration Triggers and Strategies\u003c/h2\u003e \u003cp\u003eHouseholds situated within the high- and moderate-erosion zones delineated through channel migration, sinuosity change, and erosion\u0026ndash;accretion patterns (See section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e) reported multiple displacement responses following erosion events. Of the surveyed households, 49% migrated immediately after bankline retreat or homestead loss (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Among households that did not relocate permanently, 14% moved temporarily as tenants in nearby settlements, and 10% stayed temporarily with relatives.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA further 10% reported staying on roadsides or other exposed areas after erosion-related damage. Formal shelter use was limited, with 2% relocating to designated emergency shelters. Additionally, 12% remained in their damaged dwellings, and 3% reported other temporary coping arrangements. Among households that migrated, the destinations were distributed as follows: 71% moved to nearby cities, 18% resettled on newly formed char lands, and 11% moved to neighbouring villages. These destinations correspond spatially with the erosion hotspots and accretion\u0026ndash;erosion transitions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Socioeconomic Determinants of Migration Probability: Divergent Effects of Education and Income\u003c/h2\u003e \u003cp\u003eLogistic regression analysis was conducted on households located within the erosion-affected zones mapped through the erosion\u0026ndash;accretion assessment (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), which identified the areas experiencing the greatest land loss and therefore the highest displacement pressure. The model evaluated how five variables e.g. education, income, erosion damage, river proximity, and institutional engagement influenced the likelihood of household migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e; Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eEducation showed a strong and statistically significant positive effect on migration probability (β\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The corresponding odds ratio (OR\u0026thinsp;=\u0026thinsp;1.86, 95% CI: 1.35\u0026ndash;2.55) indicates that each one-level increase in educational attainment substantially increased the likelihood of migration. This pattern is reflected in the predicted probabilities, which rise steeply with higher education levels and exhibit narrow confidence intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Income exhibited a negative but statistically insignificant association with migration (β = \u0026minus;\u0026thinsp;0.00005, p\u0026thinsp;=\u0026thinsp;0.16). Predicted probabilities show a gradual decline in migration likelihood with increasing household income, accompanied by wider confidence intervals among upper-income groups.\u003c/p\u003e \u003cp\u003eErosion-related housing damage was positively associated with migration and represented one of the strongest correlates in the model (Supplementary Table\u0026nbsp;1). In contrast, river proximity demonstrated a very weak and statistically negligible relationship with migration (β\u0026thinsp;\u0026asymp;\u0026thinsp;0; r = \u0026minus;\u0026thinsp;0.01), indicating that distance from the active river channel alone did not meaningfully discriminate between households that migrated and those that stayed. Institutional variables, including government or NGO support and perceived coordination, also showed weak and statistically insignificant associations with migration outcomes (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Risk Insights: From Exposure to Prediction\u003c/h2\u003e \u003cp\u003eThe correlation matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) shows that migration outcomes are influenced by multiple socio-economic, environmental, and institutional variables rather than a single dominant factor. Among all predictors, erosion-related house damage displays the strongest positive correlation with migration (r\u0026thinsp;=\u0026thinsp;0.45). This corresponds directly with the high-erosion zones identified in the geomorphological analysis, particularly in the right-bank erosion hotspots from 1989\u0026ndash;2000 and the renewed erosion on both banks between 2010\u0026ndash;2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Education also shows a moderate positive relationship with migration (r\u0026thinsp;=\u0026thinsp;0.35), consistent with its role as a socio-economic characteristic of households exposed to severe geomorphic change.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOther variables exhibit weak or negligible correlations. Income shows a small negative association with migration (r = \u0026minus;\u0026thinsp;0.12). Proximity to the river has almost no linear relationship with migration (r = \u0026minus;\u0026thinsp;0.01), reflecting that non-migrant households were also present within the moderate-erosion zones where bank retreat was less severe (Sections \u003cspan refid=\"Sec20\" class=\"InternalRef\"\u003e3.1.2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003e3.1.3\u003c/span\u003e). Institutional variables show similarly weak correlations: government/NGO support (r = \u0026minus;\u0026thinsp;0.09) and stakeholder coordination (r = \u0026minus;\u0026thinsp;0.08). These weak associations align with the limited institutional presence reported by households in erosion-affected tracts.\u003c/p\u003e \u003cp\u003eTo evaluate predictive capacity, a logistic regression classifier was trained using five variables representing socio-economic status (education, income), physical exposure (erosion damage, proximity to river), and institutional engagement (government/NGO involvement). The model\u0026rsquo;s performance is summarised in the confusion matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The classifier correctly predicted 72.2% of households that migrated, indicating high sensitivity to displacement risk in the areas of greatest erosion pressure documented earlier (Section \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003e3.1.3\u003c/span\u003e). However, only 47.6% of non-migrant households were correctly classified, while 52.4% were predicted as migrants despite remaining in place.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussions","content":"\u003cp\u003eThis study demonstrates how three decades of hydro geomorphological change along the Ganges River have shaped household vulnerability and migration outcomes in one of Bangladesh\u0026rsquo;s most erosion prone landscapes. Between 1989 and 2020, the river underwent substantial planform adjustments, including progressive eastward migration, declining sinuosity, narrowing channel width, and fluctuating braiding intensity. These findings are consistent with regional studies that document similar patterns of meander adjustment, sediment driven instability, and bar formation in the Tista, Manu, Jamuna, and Padma systems (Arefin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Deb and Ferreira \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Khatun et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pal and Pani \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The reduction in sinuosity from 1.57 to 1.27 reflects broader trends of channel straightening linked to sediment entrapment behind upstream barrages and flow regulation structures in the lower Gangetic basin (Das \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The strong fluctuations in the braiding index, including the rise to 5.9 by 2020, mirror documented cycles of bar emergence, channel splitting, and localized stabilization in comparable South Asian rivers (Hossain et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sultana et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These planform transitions produced alternating periods of erosion and accretion, matching observations from other floodplains where erosion hotspots shift over time due to sediment dynamics and flow variability (Sarker and Rahman \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe socio-economic impacts of these geomorphological changes are immediate and severe. Nearly 49% of all surveyed households migrated after losing land or homes to erosion, while others relied on temporary coping strategies such as staying with relatives, renting temporary rooms, or living on roadsides. Formal shelter use remained extremely low at only 2%, which reflects similar findings from Rahman and Gain (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) regarding limited institutional capacity and barriers to accessing emergency facilities. Most displaced families moved to nearby cities for informal employment opportunities, which is consistent with internal migration patterns reported among erosion affected communities elsewhere in Bangladesh (Barua et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Respondents also reported psychological distress, including anxiety, disrupted sleep, and chronic stress, which aligns with existing evidence on the mental health impacts of erosion induced displacement (Arobi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kaiser \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalysis of socio-economic drivers reveals two distinct adaptive pathways. Education was one of the strongest predictors of migration, supporting research showing that education enhances anticipatory adaptation by improving awareness of hazards, access to information, and mobility options (Martin \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast, income showed a weak and statistically insignificant negative association with migration, suggesting that higher income households often prefer to invest in in place adaptation, such as structural improvements or social network support, rather than relocating. Similar patterns have been documented by Islam and Filho (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who found that financially better off households in erosion prone settings often prioritize home strengthening and livelihood recovery instead of moving.\u003c/p\u003e \u003cp\u003eResults from the correlation analysis show that erosion related housing damage is the most consistent predictor of migration, closely matching the spatial distribution of land loss identified in the geomorphological assessment. Other factors, including income, institutional support, and perceived coordination among stakeholders, show only weak relationships with migration. This pattern reflects earlier research by Barua et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who argued that erosion related mobility emerges from the interaction of multiple socio environmental stressors rather than from any single dominant variable. Migration outcomes in such contexts are shaped by thresholds of damage, varying levels of resources, and uneven institutional response.\u003c/p\u003e \u003cp\u003eThe predictive model developed in this study further highlights the value of integrating geomorphological data with socio economic indicators. The logistic classifier correctly identified 72.2% of households that migrated, indicating high sensitivity to displacement in the areas of greatest erosion pressure. However, the lower specificity rate of 47.6% shows that some households exposed to similar levels of erosion remained in place. This reflects the heterogeneous and sometimes nonlinear nature of decision making in slow onset hazards, where families balance environmental pressure with social, financial, and cultural constraints.\u003c/p\u003e \u003cp\u003eThe findings show that erosion driven mobility is best understood as a combined outcome of physical landscape change and socio-economic differentiation. The close alignment between geomorphological hotspots and migration destinations points to the importance of hazard monitoring systems that integrate remote sensing diagnostics with household vulnerability assessments. For policymakers, the results emphasize the need for anticipatory relocation programs, stronger early warning systems, and better coordination among government and nongovernmental actors in unstable riverine regions. By linking multi decadal geomorphological trends with household mobility outcomes, this study offers a rigorous evidence base to guide targeted adaptation planning in Bangladesh and provides a transferable framework for managing erosion related displacement in other deltaic environments around the world\u003c/p\u003e"},{"header":"5 Limitations and Directions for Future Research","content":"\u003cp\u003eThis study integrates multi-decadal geomorphological analysis with household-level socio-economic data, yet several limitations should be acknowledged. \u003cem\u003eFirst\u003c/em\u003e, the river morphology assessment relied on 30-meter Landsat imagery, which is appropriate for detecting long-term planform changes but cannot capture fine-scale bankline retreat, small embayments, or micro-erosion processes that critically affect individual homesteads (Deb and Ferreira \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Khatun et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Higher-resolution platforms such as Sentinel-2, commercial satellites, or UAV-based mapping would allow more precise detection of rapidly evolving erosion features. \u003cem\u003eSecond\u003c/em\u003e, the socio-economic survey was conducted in a single erosion-prone Upazila. Although Bagha is representative of severe riverbank instability, the reliance on one field site limits generalizability across Bangladesh\u0026rsquo;s diverse river systems, where geomorphological processes and livelihood strategies differ across the Jamuna, Padma, Tista, and coastal basins (Arefin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rahman and Gain, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eThird\u003c/em\u003e, several important social and institutional drivers of migration such as land tenure conflicts, gendered constraints, informal support networks, access to credit, and political dynamics could not be captured within the scope of the present dataset, even though such factors are known to shape environmental mobility outcomes (Barua et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, the predictive model showed high sensitivity but moderate specificity, suggesting that factors explaining non-migration such as place attachment, social obligations, or resource-based immobility remain unobserved (Martin \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Islam and Filho, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eAnd last but not the least\u003c/em\u003e, the study design aligns cross-sectional household data with long-term geomorphological indicators. While this approach is analytically robust, future advances would benefit from longitudinal household tracking integrated with higher-resolution spatial monitoring to better capture the cumulative and evolving nature of erosion impacts.\u003c/p\u003e \u003cp\u003eAddressing these limitations through multi-site comparative research, finer-resolution geomorphological mapping, and mixed-method approaches that incorporate social, institutional, and psychological dimensions would offer deeper insight into mobility pathways and enhance the predictive capacity of erosion-displacement models. Such developments would strengthen efforts to design anticipatory and socially responsive adaptation strategies for communities living within Bangladesh\u0026rsquo;s increasingly unstable riverine landscapes.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study provides compelling evidence that riverbank erosion in northwestern Bangladesh is not merely an environmental phenomenon but a profound driver of socio-economic disruption and displacement. By integrating multi-decadal remote sensing analysis of river geomorphology with detailed household-level data, our findings elucidate how physical landscape changes intricately link with social vulnerabilities, shaping migration decisions in complex and differentiated ways. Our analysis demonstrates that river morphology transitions marked by decreasing sinuosity, intensified braiding patterns, and dynamic erosion-accretion cycles directly correlate with heightened displacement pressures. Crucially, we identified that while environmental exposure remains a dominant trigger for migration, socio-economic factors like education significantly enhance households\u0026rsquo; adaptive capacities, facilitating proactive and anticipatory migration decisions. In contrast, higher income, although providing material resources for localized adaptation, does not uniformly reduce migration risk, underscoring heterogeneity in adaptive responses. The weak associations observed between institutional support mechanisms and migration outcomes highlight substantial gaps in governance, revealing critical areas for policy intervention and institutional strengthening.\u003c/p\u003e \u003cp\u003eThese findings collectively emphasize the urgent need for policy strategies that are not only multidimensional but also context-sensitive, addressing environmental vulnerabilities, enhancing socio-economic resilience, and strengthening governance structures concurrently. Future efforts must prioritize equitable, data-driven resilience planning and comprehensive institutional engagement to mitigate displacement risks effectively in deltaic and similarly vulnerable regions worldwide. Such integrative approaches will be essential as climate variability intensifies, driving the interconnected dynamics of environmental change, socio-economic vulnerability, and human mobility.\u003c/p\u003e \u003cp\u003eTo support at-risk populations in erosion-prone riverine regions, we recommend designing anticipatory migration frameworks that integrate geomorphological risk mapping with household vulnerability assessments (Birkmann et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schindler et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This includes developing early warning systems, formalizing relocation support, and strengthening local governance mechanisms particularly those related to institutional responsiveness and land security (Moench \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Birkmann et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e). Future research should build on this study by incorporating longitudinal panel data to capture household migration trajectories over time, and by modeling feedback between environmental degradation, social adaptation, and institutional trust (Adger et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Warner et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hermans et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Comparative studies across deltaic systems worldwide would also help refine global frameworks for climate mobility governance under increasing hydro-climatic stress (Kuenzer and Renaud \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ndehedehe \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eThis study involved non-clinical social science survey interviews conducted with households affected by riverbank erosion in Bangladesh. The research was conducted under the coordination and oversight of the International Centre for Climate Change and Development (ICCCAD), which ensured that all research activities complied with recognized ethical standards. The study is a part of the research project thay was reviewed and approved by the ICCCAD Ethics Review Committee prior to the commencement of data collection. The research adhered to internationally recognized ethical standards for social science research, including the principles of the World Medical Association Declaration of Helsinki (2013). The study adhered to the ethical principles of the World Medical Association Declaration of Helsinki (2013). All participants were informed about the objectives of the study prior to data collection, verbal informed consent was obtained, participation was voluntary, and respondents were free to withdraw at any time. No personally identifiable information was collected in order to maintain anonymity and confidentiality.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to the people of Bagha Upazila for their cooperation and support during fieldwork. We also thank the Institute of Water and Flood Management (IWFM), Bangladesh University of Engineering and Technology (BUET), for their valuable guidance and technical assistance throughout the research process. This study was supported by the International Centre for Climate Change and Development (ICCCAD), funded by the Climate Justice Resilience Fund (CJRF) under grant number CLJI\u0026ndash;ICCCAD Limited\u0026ndash;NVF\u0026ndash;013459\u0026ndash;2021-01. We gratefully acknowledge this support, which made the fieldwork and data collection possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMHH: Conceptualization, Data curation, Methodology, Writing original draft, Supervision, Review \u0026amp; editing, Funding acquisition, Validation, Visualization; IHN: Writing original draft, Review \u0026amp; editing, Validation, Visualization. MAC: Writing original draft, Review \u0026amp; editing, Validation, Visualization; MMHM: Writing original draft, Review \u0026amp; editing, Validation, Visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the authors upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that they have no recognized financial conflicts or personal interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Clinical Trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This research does not involve any clinical trials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdger WN, Kelly PM, Winkels A, et al (2002) Migration, remittances, livelihood trajectories, and social resilience. 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Hydrol Res 47:175\u0026ndash;186. https://doi.org/10.2166/nh.2016.092 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Riverbank erosion, Hydro-geomorphology, Climate-induced migration, Socio-ecological resilience, Adaptive capacity, Erosion-displacement nexus, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-8332436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8332436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRiverbank erosion in South Asia\u0026rsquo;s deltaic regions presents a growing socio-environmental threat, intensifying displacement and livelihood disruption. This study investigates the erosion\u0026ndash;migration nexus along a 44 km reach of the Ganges River in northwestern Bangladesh, integrating three decades of Landsat-based geomorphological analysis with household-level survey data. Between 1989 and 2020, the river\u0026rsquo;s sinuosity declined approximately by 19%, accompanied by intensified braiding and dynamic erosion\u0026ndash;accretion cycles. These geomorphic transformations led to the permanent loss of over 2600 hectares of land and the formation of 1500 hectares of new floodplain terrain, heightening exposure for riverbank settlements. Survey data from 130 households reveal that erosion-induced housing damage and environmental proximity remain key predictors of migration decisions, but socio-economic characteristics shape divergent adaptive responses. Education is positively associated with anticipatory migration (Odds Ratio, OR\u0026thinsp;=\u0026thinsp;1.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while income has a weak and statistically insignificant negative effect, suggesting that better-off households often invest in local adaptation instead of relocating. Institutional support shows weak correlations with migration outcomes, highlighting significant gaps in relief delivery and governance responsiveness. A logistic regression classifier based on five predictors achieved 72.2% sensitivity, demonstrating strong potential for proactive targeting of at-risk populations, though specificity was moderate. These findings underscore the need for integrated, data-driven adaptation policies that bridge geomorphological diagnostics with household vulnerability metrics. By linking physical river transformations with migration behavior, this study offers critical insights for climate mobility governance in erosion-prone deltas globally.\u003c/p\u003e","manuscriptTitle":"When the River Moves, Lives Shift: Linking Hydro-Geomorphology to Climate-Induced Migration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-07 09:01:19","doi":"10.21203/rs.3.rs-8332436/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":"e56835a4-d479-4d5f-ac17-e0229b71e51d","owner":[],"postedDate":"January 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T16:20:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-07 09:01:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8332436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8332436","identity":"rs-8332436","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}