Hydrological response to climate and land use and land cover change in the Teesta River basin

Water constitutes a fundamental element crucial for sustaining all forms of livelihood and represents the predominant natural resource distributed across the globe. Regions reliant on agriculture within a transboundary river basin encounter a multitude of challenges. Teesta, a crucial transboundary river shared by Bangladesh and India, holds significant importance in the bilateral relationship and sustains the livelihoods of over 30 million people in Bangladesh through subsistence agriculture (Momen & Rahman 2023). The basin, in particular, grapples with a multifaceted hydrological crisis characterized by the occurrence of recurring floods during the monsoon season and, conversely, droughts or acute water scarcity in the winter months (Goyal & Goswami 2018; Das et al. 2022; Tamang 2023). Additionally, the lower part of the basin has experienced numerous floods, particularly devastating ones occurring in 1988, 1998, 2008, and more recently in 2021 (Shampa et al. 2023). This complex situation is further exacerbated by extensive anthropogenic activities and the impact of climate change, leading to a concerning increase in the frequency and intensity of hydrological events on a daily basis (Knoesen 2012; Ficklin et al. 2022; Tamang 2023). Land use and land cover (LULC) change within river basins results from a complex interplay of factors such as population growth, technological advancements, socioeconomic structures, and ecological conditions (Leta et al. 2021; Sun et al. 2022). Human development activities in these areas have profound effects on hydrological cycles, water balance, radiation budget, and biodiversity, impacting climate, biogeochemical cycles, energy flow, and livelihoods (Mollel et al. 2023).

In the past, many studies exemplified the significant influence of LULC changes in basin flow patterns (Gaur & Singh 2023; Mohseni et al. 2023) while exploring the impact of climate change in the flow across diverse basins (Mohammed et al. 2017, 2018; Jiménez-Navarro et al. 2021; Sun et al. 2022; Mohseni et al. 2023; Mollel et al. 2023; Ullah et al. 2023; Yalcin 2023). Recent research that combines climate change and LULC effects reveals a notable impact on river basin flow, predicting decreased flow during the dry period and increased flow during the wet season flow (Bal et al. 2021; Fields et al. 2021; Mohseni et al. 2023). Although any specific studies for the Teesta basin are lacking, research in the broader Indian sub-continent, such as that conducted by Mohseni et al. (2023), underscores the substantial impact of LULC and climate change on future flow, indicating a significant increase in peak flow.

This study encompasses answering the following three research questions: (i) What are the changes in LULC and runoff for the Teesta basin during the baseline period? (ii) How will the LULC be changed in the near future (2035–2064) and the far future (2071–2100)?, and (iii) What is the combined effect of LULC and climate change scenarios (SSP245 and SSP585) on runoff? These findings will enhance the understanding of potential flow pattern changes, aiding water resource management and adaptation strategies in the Teesta basin. To address these research questions, we have applied the cellular automata-Markov (CA-Markov) model for LULC modeling and the Soil and Water Assessment Tool (SWAT) for basin-scale flow modeling. Climate model output from the Coupled Model Intercomparison Project Phase 6 (CMIP6) has been used here to derive the SWAT model. This dataset is widely used for robust simulations in advancing our understanding of complex interactions within river basins (Aksoy & Kaptan 2022; Girma et al. 2022; Hossein et al. 2023; Nath et al. 2023).

The transboundary river basin spans 12,159 km², with 10,155 km² in India and 2,004 km² in Bangladesh. Of the total, 4,108 km² lie in the plains, with West Bengal covering 2,104 km² and Bangladesh 2,004 km² (Momen & Rahman 2023). Around 9,667 km², encompassing 35 Upazilas and 5,427 villages, rely on the river for water. As per the 2011 census, approximately 9.15 million people depend on the Teesta River for freshwater.

Landsat satellite images from the 2000s, 2010s, and 2020s have been obtained from the official website of the US Geological Survey (USGS). The study areas are covered by Landsat Paths 138 and 139, and Rows 41 and 42 cover the study area. The collected satellite images are projected in the Universal Transverse Mercator (UTM) system within Zone 46 using the World Geodetic System (WGS84) Datum, and the pixel size is 30 m. The reference data were collected from Google Earth Pro and OpenStreetMap for calibration and validation. The future LULC controlling drivers' data – road network, rail network, and city center data – were collected from OpenStreetMap.

The discharge data for Dalia Point, located upstream of the Teesta Barrage, was obtained from the Bangladesh Water Development Board (BWDB) for the years 1997 to 2013. The station-based water level data are required to analyze the trend and to calibrate and validate the model. The model calculates the discharge based on precipitation and snowmelt, and its outputs must be checked with a set of observed discharge data. The details of the collected data are shown in Table 1.

The digital elevation model (DEM) was collected from the Shuttle Radar Topography Mission (SRTM) at 90 m × 90 m resolution. Besides topography, the DEM is used extensively to delineate and determine a given watershed's drainage pattern. The SWAT model is set up for a large-scale basin, with a size of 12,159 km² using a DEM of 90-m resolution. This resolution allows for the effective delineation of most rivers within the basin. As the selected DEM has a resolution of 90 m, all other spatial data were also resampled to a 90-m resolution to maintain consistency across datasets. The soil map data were collected from the FAO–UNESCO soil map.

LULC is the initial watershed behavior parameter in the SWAT model. Basin soil properties and characteristics are changed by LULC, such as forest, urban, or agricultural areas. There are several sources of land-use data, but this study used freely available satellite image analysis data and model-driven future LULC data.

The SWAT model necessitates diverse meteorological data, including maximum and minimum temperature, daily precipitation, relative humidity, solar radiation, and wind speed, for accurate hydrological simulations. Due to the unavailability of historical data for relative humidity, solar radiation, and wind speed in the base area, we use the WXGEN database to run the model (Sharpley & Williams 1990). In this study, rainfall and temperature data (2035–2065 for the 2050s and 2071–2100 for the 2080s) were obtained from freely available bias-corrected datasets by Mishra et al. (2020). They employed the empirical quantile mapping (EQM) technique to downscale daily temperatures and precipitation for South Asia and Indian sub-continental river basins. Bias correction was made using daily precipitation at 0.25°, which was obtained from the India Meteorological Department (IMD) for the Indian region. These gridded daily precipitation data for India, using station observations from more than 6,000 stations located across India, were developed by Pai et al. (2014). The datasets cover 13 CMIP6 general circulation models (GCMs) under five scenarios: historical, SSP126, SSP245, SSP370, and SSP585, with a spatial resolution of 0.25°. Data for different periods were accessed from the Zenodo Sarver platform (https://zenodo.org/record/3874046#.YWvmdRpBxnJ). The CMIP6 scenarios correspond to various shared socioeconomic pathways (SSPs) with target radioactive forcing levels for the 21st century. Eight out of the 13 models were used in this study, which are better performing and widely used (Table 2; Jiménez-Navarro et al. 2021; Sun et al. 2022).

The CA-Markov model, integrating CA and MC approaches, predicts spatiotemporal LULC changes. While it may have limitations in capturing the impact of individual cells on the entire space, CA-Markov excels in accurately forecasting transitions between LULC states based on prior conditions (Hossein et al. 2023; Nath et al. 2023). This integrated model offers advantages over traditional methods, providing more reasonable and accurate LULC transition predictions (Long et al. 2021; Matlhodi et al. 2021).

A two-step validation process was employed to ensure prediction accuracy. Initially, using LULC data from 2000 to 2010 as inputs, the model predicted the LULC map for 2020 and compared it to satellite-derived data. Kappa indices assessed prediction accuracy, with values exceeding 80% indicating excellent performance (Matlhodi et al. 2021). Following validation, the IDRISI software incorporated land-use maps for 2000 and 2010 to create a transition matrix. This matrix was then used to project land-use patterns for 2020, 2050, and 2080.

Several hydrological models are available, among them MIKE SHE and SWAT, which are widely used. MIKE SHE requires extensive physical parameters and simulates surface and groundwater movement. In contrast, the freely available, open-source SWAT model, developed by the USDA, is a semi-distributed physical model that assesses rainfall-runoff and flow prediction. Moreover, the SWAT model is widely used in the ungauged basin because of its flexibility of operation in the basin where there are data constraints (Arnold et al. 1998; Qi et al. 2020; Gwal et al. 2023). It is known for its computational efficiency, auto-calibration, and uncertainty analysis techniques, which facilitate high-quality water resource management.

The SWAT, a semi-distributed hydrological model developed in the 1990s, operates at watershed-to-river basin scales, simulating daily surface and groundwater dynamics. Utilizing physiographical data and meteorological information, it predicts environmental impacts from land use, management, and climate change. The model integrates key hydrological processes, such as evapotranspiration, runoff, infiltration, and percolation. Sub-basins, segmented into hydrological response units (HRUs) based on uniformity in land cover, soil, and climate, enable detailed analysis (Arnold & Allen 1996). SWAT's adaptability extends to GIS platforms like MapWindows (MWSWAT) and QGIS (QSWAT), facilitating efficient modeling and assessment.

The weather generator, WGNMaker 4.1 (http://swat.tamu.edu/), determined parameters using data from 13 evenly distributed weather stations across the basin. Daily maximum and minimum temperature and precipitation were used as input. The rest of the weather parameters (solar radiation, humidity, and wind speed) have been taken from the model base value from the WXGEN model. The model uses the Hargreaves method to estimate evapotranspiration (ET) (López-Ramírez et al. 2021). The SWAT model integrated land-use scenarios for the 2020s, 2050s, and 2080s. The model was calibrated and validated using observed discharge in Dalia Point in Bangladesh. The model evaluation employed R² and Nash–Sutcliffe efficiency (NSE) coefficients. Simulations were conducted with outputs from eight GCMs under SSP245 and SSP585 scenarios, considering 2020s LULC and projected LULC data for the 2050s and 2080s. These simulations assessed the impacts of LULC change on runoff dynamics.

The LULC layers for 2000, 2010, and 2020 were classified using the maximum likelihood method and refined through auxiliary data editing. Evaluation metrics, including producer and user accuracies, total accuracy, and kappa values, were employed, with results detailed in Table 3. User accuracy exceeded 80% for most LULC classes in 2000, 2010, and 2020, except for water/river due to the braided Teesta River characteristics. Overall classification accuracy was 87, 85, and 85% for 2000, 2010, and 2020, respectively. The kappa values, which measure the agreement between the classified map and the reference data, were calculated as 0.85, 0.84, and 0.82 for 2000, 2010, and 2020, respectively (Table 4).

The prediction of future land use is influenced by two fundamental factors: transition probability and transition area. The transition probability matrix shows the likelihood of transitioning to other classes for each LULC class within the given time frame. The matrix was derived by multiplying the likelihood matrix with the corresponding areas of the LULC classes during the designated period. The transition area and the transitional matrix were developed for 2020 using LULC layers from 2000 and 2010, and the predicted LULC of 2020 was then validated with an analyzed 2020 LULC image to ensure accuracy >80%.

The predicted LULC of 2020 was obtained from the LULC of 2000 and 2010 using the CA-Markov model. To assess the accuracy and reliability of the predictions, the transition areas, transition potential, and compatible and non-compatible areas were determined by comparing the predicted LULC with the reference layers of the actual LULC in 2020. The CA-Markov model exhibited an accuracy of 86.21% when considering the composition of the transition potential. The accuracy level indicates that the model can provide reliable projections for future land use dynamics (Aksoy & Kaptan 2022). Based on the satisfactory accuracy (Kappa > 80%), the CA-Markov model has been run for the years 2050s and 2080s.

Upon the completion of the calibration and validation process, the model output is depicted in Figure 3. This process involved conducting 400 simulations across four iterations. Notably, the NSE value of 0.81 obtained during the calibration and validation periods indicates a strong agreement between the observed and simulated discharges. Furthermore, the coefficient of determination (R²) values of 0.85 for calibration and 0.84 for validation signify a high level of goodness of fit between the observed and simulated data as shown in Table 6. The high R² values provide evidence of the model's reliability and robust performance. The PBIAS values of 18.0% for calibration and 16.9% for validation indicate a slight overestimation of the simulated discharges compared with the observed values. However, these values fall within an acceptable range, suggesting that the model adequately captures the overall discharge behavior. Based on these statistical measures, it can be concluded that the SWAT model performed exceptionally well in both the calibration and validation stages, utilizing historical data from the Dalia Point Teesta River basin. The model demonstrated a strong ability to reproduce the observed discharge patterns and capture the hydrological dynamics of the basin.

Diverse projections for future flow characteristics emerge from the considered GCMs. ACCESS-CM2 and MPI-ESM1-2-HR suggest fewer flood peaks, with flow dynamics more pronounced in the worst scenario (SSP585) in the near future period. While the simulation anticipates higher peak flow at Dalia Point for SSP585, the dry period between SSP245 and SSP585 scenarios exhibits no significant changes. Among the GCMs, EC-Earth3 predicts a substantial increase in flow exceeding 100% during the pre-monsoon period (MAM) for both scenarios, whereas ACCESS-CM2, BCC-CSM2-MR, and CanESM5 models show relatively unchanged discharges.

The Teesta River's future flow patterns, as projected by various models, exhibit disparities. While some models anticipate a decrease post-monsoon, the multimodel average flow shows an overall reduction in flow for the near future. CanESM5 predicts a significant increase, contrasting with INM-CM5-0 and MPI-ESM1-2-HR, which suggested a notable reduction. Pre-monsoon predictions vary, but the multimodel average indicates an overall increase. Comparing the baseline (1995–2014) to the near future, most models project drier winters and wetter wet seasons, with BCC-CSM2-MR and CanESM5 showing similar changes. SSP245 indicates a higher winter flow increase than SSP585, while in the pre-monsoon, SSP585 exhibits a higher rate of increase than SSP245.

Table 7 presents the multimodel mean of seasonal flow changes from the baseline to the near future, indicating wetter pre-monsoon and monsoon seasons and a drier winter. In the near future, about −46% (−72 to 21%) and −49% (−84 to 27%) flow reductions are projected under SSP245 and SSP585 scenarios in winter. In contrast, pre-monsoon flow is expected to increase by up to 15% (−68 to 194%) for SSP245 and 13% (−73 to 199%) for SSP585 scenarios. Monsoon flow is projected to rise by 26% (−40 to 120%) for SSP245 and 38% (−36 to 151%) for SSP585 scenarios. On the contrary, the post-monsoon flow will decrease by −9% (−50 to 69%) for SSP245 while increasing by 5% (−38 to 65%) for SSP585 scenarios.

In the far future, the average monthly flow projections for the Teesta basin under SSP245 and SSP585 scenarios are shown in Figure 4. The model outputs exhibit diverse trends, encompassing decreasing, increasing, and flat patterns with random fluctuations for both scenarios. BCC-CSM2-MR, MPI-ESM1-2-HR, MRI-ESM2-0, and NorESM-MM models indicate an increase of peak flow in the far future, surpassing the base period, while ACCESS-CM2, CanESM5, and EC-Earth3 models indicate higher peak flow for both base periods and in the far future.

The SSP585 scenario, associated with fossil-fuel-based development and high radioactive energy emissions, shows a more pronounced increase in peak flow compared with the SSP245 scenario. Except for BCC-CSM2-MR, most of the models project higher peak flow than the SSP245 scenario. Notable variations in peak flow, including rightward shifts (higher peaks) in NorESM-MM and leftward shifts (lower peaks) in INM-CM5-0, are observed.

Changes in the seasonal flow pattern are evident, with most models depicting a consistent flow pattern during the dry period, particularly from December to March. The model outputs indicate future flow increases in pre-monsoon and monsoon periods and a decrease in winter. Figure 5(a) illustrates projected changes in mean monthly flow for the pre-monsoon (MAM) and monsoon (JJAS) periods in the near future, while Figure 5(b) shows corresponding changes in the post-monsoon (ONDJ) period for the far future. Among the eight GCMs, INM-CM5-0 predicts a flow increase exceeding 100% during the pre-monsoon period (MAM) in the near future for both scenarios, while ACCESS-CM2, BCC-CSM2-MR, and CanESM5 models indicate relatively unchanged discharges for both scenarios.

The monsoon period (JJAS) shows that the flow will decrease from the base period, while BCC-CSM2-MR CanESM5, INM-CM5-0, MRI-ESM2-0, MPI-ESM1-2-HR, and NorESM-MM models indicate that flow will be significantly increasing for both scenarios, The EC-Earth3, MPI-ESM1-2-HR, and NorESM-MM models show significantly decreasing flow for the SSP585 scenario in the far future. In the post-monsoon season, mixed results have been found; some models predict that flow will decrease, and others indicate an increasing pattern. While the CanESM5 model predicts that flow will increase significantly in the far future for both scenarios, the flow will substantially decrease based on the INM-CM5-0 and MPI-ESM1-2-HR models. However, average data from multiple models show that the average flow will decline in the near future in the post-monsoon period.

Most models project a drier dry season and a wetter wet season. BCC-CSM2-MR and CanESM5 exhibit similar change patterns. The increased winter flow is more pronounced in the SSP245 scenario than SSP585, while, in the pre-monsoon season, SSP585 displays a higher rate of increase than SSP245.

Climate change's impact on the water balance in the Teesta basin is severe, as indicated in Table 7, showcasing multimodel seasonal variability for both scenarios in the far future compared with the baseline period (1997–2013). Winter flow is projected to reduce by −40% (−84 to 51%) for SSP245 and −38% (−86 to 48%) for SSP585. Contrastingly, pre-monsoon flows are expected to increase by 16% (−68 to 200%) and 40% (−65 to 235%), respectively, for SSP245 and SSP585. Similarly, monsoon flow will be increased by 30% (−42 to 144%) and 45% (−30 to 167%) for SSP245 and SSP585. However, post-monsoon flow is anticipated to increase by 1% (−45 to 60%) in the near future but will increase by 22% (−41 to 100%) in 2080.

Under developmental pressures, the Teesta basin is undergoing rapid LULC changes, dominating by agriculture, settlements, and forests. Simulations predict significant future transformations, projecting decreased agriculture and forest cover and increased settlements. Modeled Teesta River flow at the Dalia station, calibrated under predicted LULC and climate change scenarios, indicates substantial impacts in the near and far future. Anticipated changes lead to increased additional flow during the wet season and either reduced or unchanged flow in the dry period. The simulation suggests a 10% mean annual flow change under SSP245 and SSP585 scenarios across eight models, with BCC-CSM2-MR, CanESM5, and EC-Earth3 showing the most significant influence on flow patterns (Figure 5). LULC changes, especially an increase in agriculture and settlements by 76 and 42%, respectively, and a decrease in forest and water by 13 and 36%, are expected by the 2050s, influencing discharge patterns. This results in additional discharge increases of 4% (−8 to 5%) for SSP245 and 5% (−8 to 10%) for SSP585 scenarios during wet seasons, with marginal changes in winter and post-monsoon periods. Emphasizing afforestation, reforestation, and sustainable development, these measures could mitigate future flood risks in the lower Teesta basin.

Results using multimodal ensemble climate projection showed a decrease of −6% (−16 to 4%) in winter season flow for SSP245 and a −13% (−64 to 63%) decrease for SSP585 in the 2080s. Conversely, the monsoon will witness an increase in flow, and post-monsoon seasons will witness increased flow under SSP245. Far-future predictions indicate more significant discharge changes than in 2050, predominantly impacting dry to pre-monsoon periods.

Anticipated LULC shifts by the 2080s, including a 121.9% increase in agriculture and a 293.2% surge in settlements, in contrast with declines of −33.2 and −38.2% in forest and water areas, respectively. As a result, flow is projected to rise by 13% (0.4 to 23%) for SSP245 and 52% (−29 to 151%) for SSP585 in the 2080s. Monsoon seasons are expected to witness higher flows, while winters might experience a −6% (−16 to 4%) reduction under SSP245 and a −13% (−64 to 63%) reduction under SSP585 in the 2080s as shown in Table 8.

Changes in LULC can significantly impact hydrological processes, particularly ET, infiltration, and canopy interception. Using the LULC map in the 2020s as a baseline and the simulated LULC maps of 2050s and 2080s, we evaluated absolute changes in mean annual ET, water percolation (PERC), surface runoff (SURQ), and lateral flow contribution (LATQ) during the near future. Precipitation (PREC) patterns showed a varying impact, particularly in the monsoon period, reflecting afforestation or deforestation trends. Notably, the prominence of deforestation was observed, as indicated by rising PREC rates. Additionally, runoff components showed a 2 mm decrease in SURQ during the monsoon, countered by a 7 mm increase in LATQ due to LULC changes. Temporal variation in potential evapotranspiration (PET) revealed June's highest change (0.4 mm) and December's lowest (−0.1 mm). Significantly higher changes were observed in SSP585 scenarios compared with SSP245 scenarios.

This study encompasses eight GCMs and highlights considerable variability in hydrological parameters due to complex interactions between location-specific factors. Catchment slope, soil permeability, vegetation characteristics, and settlements contribute to unique watershed responses. Soil depth emerges as a critical factor shaping how watershed discharge responds to changes in LULC. The simulations at Dalia Point in the Teesta River reveal a significant decrease in discharge under evolving LULC scenarios, emphasizing the far-reaching impact of such changes.

Extreme floods are a common scenario in the Teesta floodplain area of Bangladesh. To understand the future peak flow scenario of the basin, we use the generalized extreme value (GEV) distribution to analyze the simulated discharge between the 2050s and 2080s. Figure 8 shows the multimodal average results for the eight GCM models while Table 9 displays the return periods of peak flow under two scenarios in the 2050s and 2080s. The result indicates that the peak flow will increase in the future. In the 2050s, the average peak flow will increase by 14, 5, and 6% for a 5-, 20-, and 100-year return period flood under the SSP245 scenario. However, the average peak flow will increase to 24, 13, and 13% of 5-, 20-, and 100-year return period flows under the SSP585 scenario. In the far future, peak flow will increase by 24, 13, and 12% under the SSP245 scenario and by 43, 32, and 33% under the SSP585 scenario for a 5-, 20-, and 100-year return period flood. The result indicates that the peak flood magnitude will be higher for high-emission scenarios.

The probability distribution function (PDF) of the peak flow in the Teesta River basin at Dali Point under two climate change scenarios and land-use change scenarios in the 2050s and 2080s is shown in Figure 8. The PDFs are obtained by applying a GEV to simulate peak flow values from eight GCMs and two emission scenarios. PDFs are shown for different combinations of the following scenarios: SSP245 in the 2050s, SSP245 in the 2080s, SSP585 in the 2050s, and SSP585 in the 2080s. Figure 8 shows model probability density plots where the x-axis denotes the discharge in cumec and the y-axis indicates the probability of occurrence. Most of the models showed a rightward shift of the extreme flood flows. Extreme flows will be increased for the SSP585 scenarios than SSP245 scenarios in both near and far future periods.

There are several limitations of this study. Teesta is a transboundary river, and most of the basin lies in India. Ground-based observational data in the Indian part of the catchment was limited. The satellite images are mostly high (>30%) cover around the year; for the lower cloud coverage area, this study had to compromise with the time of image collection. This study did not consider upstream interventions like dams and barrages and their impact on the flow of the Teesta.

This study attempted to quantitatively assess the impacts of LULC changes and climate change on the water balance of the Teesta River basin for the two future periods 2050s (2035–2064) and 2080s (2071–2100). The analysis involves an examination of LULC classification in the Teesta River basin during the baseline period of 2020, as well as predictions for two future periods: the near future 2050 and the far future 2080. For the classification, we use a supervised classification technique and the CA-Markov model used for the LULC prediction. The LULC changes and climate change impacts on the water balance are evaluated using the widely used semi-distributed hydrological model, SWAT. Additionally, SWAT-CUP has been used for calibration and validation purposes.

It has been found that the basin has experienced significant LULC changes from 2000 to 2010. During this decade, agriculture, settlement, and water/river areas increased, while forest and sand bar areas decreased. A similar trend was observed from 2010 to 2020. Using the CA-Markov model with 2020 LULC as the base year, the projection indicates that these trends will continue in 2050 and 2080. The prediction results show that agriculture and settlements will increase by 76.2 and 42.9% from 2020 to 2050 and by 121 and 293% by 2080. Conversely, forest, water, and snow cover will decrease by 13, 36, and 5.2% by 2050 and by 33, 38, and 0.8% by 2080.

To fit the model for the Teesta River basin, calibration and validation were performed for the periods 1997–2005 and 2006–2013, respectively. The sensitivity analysis, using the NSE, R², and PBIAS parameters, yielded values of 0.85, 0.81, and 18 for the calibration period, and 0.84, 0.81, and 16.9 for the validation period. These values indicate a good fit between the simulated and observed discharge. The prediction result illustrates that annual flow will be increasing for the near future period 2050s for all scenarios. The monsoon flow may increase by 26% for SSP245 and 38% for SSP585, while the dry season might see a 46% decrease for SSP245 and 49% for SSP585 in the near future. Similar patterns are expected in the far future, with monsoon flow increasing by 30% for SSP245 and by 45% for SSP585, while winter flow is projected to decrease by 40% for SSP245 and by 38% for SSP585. With climate change, LULC changes bring additional stress to the water balance of this region. As settlements increase, pre-monsoon flow may rise by 3% and monsoon flow by 4% under SSP245 in the near future. Under SSP585, pre-monsoon flow may increase by 4.5% and monsoon flow by 5%. However, post-monsoon flow will decrease by 0.04% for SSP245 and 0.3% for SSP585. In the far future, LULC changes could lead to a 13% increase in monsoon flow under SSP245 and a 52% increase under SSP585, while dry period flow might decrease by 6 and 13%, respectively. The peak flow frequency analysis indicates an increase in future peak flows. By the 2050s, peak flow is expected to rise by 14, 5, and 6% for 5-, 20-, and 100-year return periods under SSP245 and by 24, 13, and 13% under SSP585. In the far future, peak flow is projected to increase by 24, 13, and 12% under SSP245 and by 43, 32, and 33% under SSP585 for the same return periods.

The overall results indicate that the Teesta River basin will face a severe water crisis during dry periods and increased flooding during the monsoon season. Higher emission scenarios are expected to exacerbate these impacts. The return period analysis shows that future floods will be more intense, leading to more devastating effects on agriculture, land, infrastructure, households, and other properties. To mitigate these impacts, it is recommended to adopt a comprehensive and participatory approach, including implementing integrated watershed management practices to balance water resources and reduce flood risks and strengthen cooperation with countries sharing the Teesta River by negotiating equitable water-sharing agreements and joint management initiatives to ensure sustainable water use and disaster risk reduction. Additionally, integrating climate change adaptation into regional planning processes and enforcing sustainable land-use practices to prevent excessive deforestation and unplanned urbanization may ease the crisis in the future.

The work has been supported by the research project on ‘PROVATi3 (Promoting Resilience of Vulnerable through Access to Infrastructure, Improved Skills, and Information’ carried out by the Institute of Water and Flood Management (IWFM) of Bangladesh University of Engineering and Technology (BUET). It is funded by the Local Government Engineering Department (LGED) and sponsored by the International Fund for Agricultural Development (IFAD). The authors would like to acknowledge the access to observed water level, discharge, and bathymetric data from the Bangladesh Water Development Board (BWDB).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.