Assessment of reservoir capacity and sedimentation rate in Chohal Dam, Punjab, India, using multi-satellite data Open Access

Soil erosion and reservoir sedimentation represent intricately linked and complex processes (Sedlacek et al. 2023). The erosion of soil particles occurs due to the flow of runoff water down the slopes of a catchment area, primarily via streams, eventually depositing these sediments into reservoirs (Walia et al. 2020; Singh et al. 2021a, 2023a). Upon entry into a reservoir, the sediments settle due to the decrease in water flow velocity and the expansion of the cross-sectional area of the stream (Shaydulina & Gyrdymov 2019). The sediment accumulation reduces the original storage capacity of the reservoir and diminishes its overall useful lifespan (Pandey et al. 2016; Adongo et al. 2021; Singh et al. 2023c). Additionally, the downstream ecosystem in the area is adversely affected by the accumulation of sediments in the reservoir, leading to decreased water availability for various purposes, which, in turn, has direct environmental and economic consequences (Kummu & Varis 2007). It is estimated that reservoirs worldwide are losing capacity at an annual rate of 0.1–1.0% due to sedimentation (Walling 2006). In India, this rate is slightly higher, ranging from 0.1 to 1.5% per year (Vishwakarma et al. 2015). Furthermore, sedimentation alters the elevation–area–capacity curve of the reservoir, leading to inaccurate estimates of available water (Prasad et al. 2018).

Sedimentation in reservoirs is a significant concern because it impacts storage capacity, and this issue is prevalent in almost all reservoirs due to silt deposition (Liang et al. 2021). When establishing an effective water resource planning and management system, assessing soil erosion, sediment transport, and deposition in reservoirs must be given top priority (Singh et al. 2021b, 2024; Dananto et al. 2022). Having a clear understanding of the capacity and the expected lifespan of a reservoir is crucial for maximizing its utility in various applications, including irrigation and flood control (Fayaed et al. 2013). Traditional techniques for reservoir evaluation, such as inflow–outflow analysis, hydrographic surveys, and bathymetric surveys, are time-consuming, expensive, cumbersome, and require significant manpower (Dadoria et al. 2017). In contrast, geospatial techniques involving remote sensing and geographic information systems offer a cost-effective and timely means of estimating reservoir sedimentation (Singh et al. 2021b). The utilization of geospatial techniques has proven to be a valuable source of extensive and frequent data about the entire Earth (Shendge & Chockalingam 2016). Satellite imagery becomes particularly insightful when there are no cloud obstructions. This panoramic perspective directly reveals information about water coverage in reservoirs at specific elevations on the dates of satellite passage (Narasaya et al. 2012). However, the accuracy of sedimentation estimation through remote sensing is notably sensitive to the extent of the water body area (Jain & Jain 2011). Further, satellite data quality significantly affects the precision of reservoir sedimentation assessment through geospatial techniques. The higher the spatial resolution of the satellite sensor, the more accurate the estimates become.

The Kandi region of Punjab, which extends as a narrow belt along the northeastern border of the state, is a sub-mountainous zone encompassing the Punjab Shivaliks and the undulating land beneath the hills in districts such as Gurdaspur, Hoshiarpur, Fatehgarh Sahib, and Ropar (Rawat et al. 2013). Agriculture is the primary occupation in this region, which heavily relies on rainfall for both winter (rabi) and summer (kharif) crops. The Shivalik foothills, covering approximately 2.14 million ha, possess a unique and fragile ecosystem within the Himalayan range (Singh et al. 2021a). This region encompasses states such as Punjab, Haryana, Himachal Pradesh, Uttarakhand, and Jammu and Kashmir, with an average elevation ranging from 415 to 500 m above sea level (Singh et al. 2016). In the Kandi region, agriculture is the predominant livelihood, but limited soil and water resources are a major concern due to its susceptibility to erosion (Singh et al. 2021a). The intensive rainfall and hilly topography of this region exacerbate soil erosion, which reduces soil fertility and productivity. Numerous small and large seasonal tributaries, known as ‘choes’, traverse the region, making it prone to flooding (Singh & Kaur 2005). Approximately 40% of the rainfall in this area is wasted in floods, leading to significant crop damage and soil infertility (Gosal 2004). To address these challenges, dams have been constructed for flood control and irrigation purposes. However, the steep terrain causes a substantial portion (20–40%) of monsoon rainfall to flow into the catchments through ‘choes’, resulting in extensive gully and upland soil erosion (Sur et al. 1999; Singh 2019). Consequently, dam reservoirs experience a high rate of siltation in relation to the soil erosion from their catchments, with a gross storage capacity reduction of 1.0–1.3% annually (Sur et al. 1999). This alters the elevation–area–capacity curve of the reservoirs, leading to inaccurate quantification of water availability, faulty water use scheduling, and reservoir operations (Singh et al. 2023b).

Presently, many earthen dam reservoirs in the Kandi region (Shivalik foothills) of Indian Punjab are facing severe sedimentation issues caused by soil loss at rates ranging from 72.7 to 144 metric tons per hectare per year due to water erosion from their catchment areas. This predicament is primarily due to the undulating terrain, highly erodible soils, and frequent heavy rainstorms within the dam catchment areas of this region. Continuous soil loss has led to a substantial reduction (about 24% per decade) in the storage capacities of downstream dams and reservoirs due to silt inflow (Singh et al. 2021a). In this region, there are numerous small catchments ranging in size from 500 to 5,000 ha, and around 25 to 30 small earthen dams. Notably, large reservoirs like Pong and Bhakra Dams in the state and adjacent areas are monitored by the Central Water Commission (CWC) and the India Water Resources Information System (WRIS), providing data on sedimentation. However, limited efforts have been made in the past to monitor these small Dams due to financial constraints and management challenges, resulting in a lack of reliable information about them. Therefore, monitoring the storage capacities of downstream reservoirs is of utmost importance for understanding the hydrologic conditions of these catchments and the sediment load reaching the reservoirs through water erosion (Singh et al. 2023b).

Keeping the above information in view, a study was undertaken to estimate the capacity of the Chohal reservoir, located in the Kandi region of Punjab using Landsat 8 and Sentinel-2 imagery, and to compare satellite data quality for estimating reservoir capacity and sedimentation rate. The innovation of this study lies in the integration of multi-temporal remote sensing data (Landsat 8 and Sentinel-2) with geospatial techniques to assess capacity and sedimentation for the Chohal Dam, a critical water resource in the Kandi region of Punjab. Unlike previous studies, a detailed sedimentation assessment approach was developed, tailored to the hydrological and geomorphological characteristics of the Kandi region. The results provide useful insights for reservoir management and present a practical method that can be applied to similar sites around the world.

The Chohal Dam catchment features rugged terrain characterized by hilly landscapes with steep sandy rock slopes, ideal for efficient runoff. While roadways provide sufficient accessibility across the region, intermittent disruptions may occur due to spate flows along the choes. Nestled within the Shivalik hills, the upper watershed spans approximately 49.6 km², while the lower region, covering roughly 21 km², lies primarily within the administrative boundaries of seven villages, namely Chohal, Baroti, Sarain, Sainchan, Bhagowal, Adamwal, and Thathal. Moreover, segments of five additional villages, namely Saleran, Bassi Ballo, Bassi Maroof, Saila Ajowal, and Bassi Hussainpur, also intersect this area. The command area, predominantly flat and well-drained, may necessitate some leveling to optimize its suitability for irrigation purposes.

The climate within the Chohal catchment area is characterized as tropical, exhibiting high temperatures during the summer months and significantly lower temperatures during winter. It undergoes four distinct seasons, namely the monsoon season (June to September), the post-monsoon season (October and November), winter (December to March), and summer (April to May). During summer, the maximum temperature reaches 47.67 °C in June 1966, while the minimum temperature falls to −4.44 °C in November of the same year. The area is heavily influenced by the East–West monsoon. Although there is only one rain gauge station situated at the rest house of the Forest Department in Manguwal village, recently, installed numerous other rain gauge stations have been installed around both the catchment and command areas. Annual rainfall varies significantly, ranging from a minimum of 480.4 mm in 1965 to a maximum of 2082.0 mm in 1955. The mean annual rainfall recorded for the period spanning from 1950 to 1988 is 1079.3 mm. The majority of the annual precipitation occurs during the monsoon period, typically spanning from June to October, with a mean rainfall of 879.9 mm.

Landsat 8 data, featuring a 30-m spatial resolution, were obtained from the USGS website available at https://earthexplorer.usgs.gov. The Landsat satellite is widely used to map the water bodies. It is the most commonly used optical remote sensor, and it is freely available. The spatial resolution of the sensor used in this study is 30 m × 30 m. Data from Landsat 8 is available in 11 different bands as specified in Table 2.

Sentinel-2 data with a finer 10-m spatial resolution were sourced from the Copernicus Data Space Ecosystem website, accessible at https://dataspace.copernicus.eu. The Sentinel-2 satellite, part of the Copernicus Earth Observation Programme by the European Space Agency (ESA), offers high-resolution optical imagery for environmental monitoring and management. With 13 spectral bands, it provides detailed observation across diverse applications such as agriculture, forestry, land-use planning, and disaster management. Pixel sizes vary at 10, 20, and 60 m resolutions (Table 2), offering versatility for tailored analyses, from small-scale feature studies to broader geographical assessments.

Following the data acquisition process, extensive image processing techniques were applied using ERDAS Imagine and ArcGIS software suites. Furthermore, crucial information such as design specifications, the original elevation–area–capacity curve, water surface elevations, and bathymetric survey data pertinent to the designated dam/reservoir were obtained from the Department of Water Resources in Hoshiarpur, within the jurisdiction of the Government of Punjab, India. This data spans a comprehensive timeframe of 32 years, from 1987 to 2023.

Layer stacking, image-to-image registration, and defining the area of interest (AOI) are integral steps in the geospatial data processing workflow, aimed at enhancing the accuracy of classification and analysis.

Layer stacking was done to create a single multilayer image by combining multiple images to improve the accuracy of classification. Each of the 11 bands of data provided by Landsat 8 was utilized for different purposes. In this study, the images of Landsat 8 for the various water elevations and dates were downloaded, and layer stacking was done. This process was repeated for Sentinel-2 images, and the stacked images were exported into GeoTIFF format.

It is the process of geometrically aligning two or more images on a single coordinate system to fuse or integrate matching pixels that represent the same objects. WGS 1984 UTM ZONE 43 projection was used during this study for alignment of digital image data of the selected reservoirs. Digital images of the selected reservoirs were georeferenced and then used to align the satellite images on the same projection with the help of ERDAS 2014. Image-to-image registration was done by overlapping the delineated image over the stacked image to get the AOI.

In this study, the shapefile of the selected reservoir, along with its catchment, was created. The shape file was then used as an input to select the AOI. Using the Subset and Clip Tool in ERDAS 2014, the AOIs (subsets) of the selected reservoirs were prepared. Subsets for different dates were made for the preparation of Modified Normalized Difference Water Index (MNDWI).

An open water feature can be successfully extracted using MNDWI (Gautam et al. 2015). MNDWI suppresses and removes the build-up noise, vegetation noise, and soil noise (Xu 2006). The maximum reflection takes place in the visible region of the electromagnetic spectrum, and absorption is maximum in the SWIR (shortwave infrared) region. Visible band wavelength range is 0.4–07 μm, and SWIR wavelength ranges between 1.4 and 2.4 μm.

In this study, the Landsat satellite data were used due to the largest time series availability of digital data (Nath & Deb 2010), and Sentinel data were used due to its higher resolution of 10 m. To prepare the MNDWI, Band 3 (Green) and Band 6 (SWIR) of Landsat and Band 3 (Green) and Band 11 (SWIR) of Sentinel were used in this study. The maximum reflectance value is provided by the green band, and the SWIR band provides the lowest reflectance value to clearly identify the open water feature.

The MNDWI was employed to extract water bodies from Landsat 8 and Sentinel-2 satellite data for various dates spanning from 2006 to 2023. The MNDWI produces digital number (DN) values ranging from −1 to +1, specifically designed for identifying open water features (McFeeters 2007). To validate the accuracy of the MNDWI-derived DN values for both water and non-water pixels (representing features like buildings, vegetation, soil, etc.), the ‘Inquiry Tool’ in ERDAS Imagine software was utilized.

Thresholding serves as a pivotal technique for discriminating water body features, with established efficacy across global studies (Xu 2006). In this study, thresholding was employed to extract water spread areas within selected reservoirs. The determination of the most suitable threshold value involved pixel-by-pixel examination of DN values.

MNDWI emerged as a reliable method for estimating water spread areas (Magome et al. 2003). MNDWI pixels were categorized into non-water and water classes. Leveraging the ‘Model Maker Tool’ in ERDAS 2014, DN values of each image were organized from highest to lowest to establish optimal threshold conditions for the automated extraction of water pixels.

Raster images containing automatically extracted water spread areas were transformed into vector format through the utilization of ‘Raster to Polygon Tool’ and ‘Geometry Tool’ within ArcGIS 10.7. This facilitated the estimation of water spread areas for selected reservoirs, a critical step in accurately computing their capacities.

The cumulative reservoir capacity was calculated by adding the incremental volume of water at different elevations.

Reservoir capacity loss, annual depletion rate, and sediment yield can be estimated using the following relationships (Kumar 2021; Singh et al. 2023c):

• Reservoir capacity loss:

  

  (3)
• Percent capacity loss:

  

  (4)
• Annual depletion rate:

  

  (5)
• Sediment yield/inflow rate:

  

  (6)

  where is the capacity loss in time Δt (years); is the capacity of the reservoir at the time of construction (t_o); is the present capacity of the reservoir (t_c); is the percent capacity loss (%); D_a is the annual depletion rate (%); is the bulk density of the catchment near the reservoir (g/cm³); A_c is the catchment area of the reservoir (km²); and N is the number of years from inception of the reservoir/dam to till date (–).

The bulk density of the soil catchment bed of the reservoir was determined in the laboratory of Soil department, PAU, Ludhiana, by using the procedure explained by Michael (1978).

The statistical analysis involved estimation of the increase (%) or decrease (%) in estimated reservoir capacity using Landsat and Sentinel datasets.

This section deals with the presentation of the findings related to satellite data quality comparison (Landsat 8 and Sentinel-2 imagery) for estimation of water spread area, live storage capacity of the reservoir, capacity loss rate, and sedimentation rate for the Chohal Dam. The results obtained are described below, categorized into various subheadings.

Table 4 illustrates the estimated water spread area and capacity of the Chohal reservoir, derived from both Landsat and Sentinel sources. Leveraging remote sensing data, including Landsat and Sentinel imagery, enabled precise assessments of sediment accumulation and changes in reservoir capacity. The observed water elevation ranged from 372.9 to 381.5 m during the assessment period.

Analysis utilizing Landsat data showed a fluctuation in estimated area from 9.2 to 50.0 ha, while estimates from the Sentinel dataset ranged slightly broader, varying from 12.2 to 52.8 ha. Similarly, the estimated capacity of the reservoir exhibited variability across different water surface elevations. Using Landsat data, the estimated capacity ranged from 3.5 to 48.4 ha m, with the lowest and highest capacities observed at water elevations of 373.7 and 380.7 m, respectively. Assessments based on Sentinel data yielded estimates ranging from 4.3 to 49.9 ha m, showing similar variability at the same water elevations.

Table 4 demonstrates a consistent increase in both water spread area and reservoir capacity as water elevation rises, with estimates from Sentinel data surpassing those from Landsat data. For example, at an elevation of 372.9 m, the estimated water spread area increases by 33.3% using Sentinel data compared with Landsat data, and the estimated capacity increases by 32.3% (Table 4). This comparison highlights the potential advantages or improvements in estimation provided by Sentinel data over Landsat data, crucial for various applications such as hydrology, resource management, and environmental monitoring.

These findings underscore the sensitivity of estimations to the choice of satellite dataset, emphasizing the importance of considering such variations in reservoir management and planning. It also emphasizes the need for robust methodologies and careful validation processes to ensure the accuracy and reliability of capacity estimations. Furthermore, these insights inform decision-making processes and resource allocation efforts for sustainable water management in the Chohal reservoir area. Continued research and refinement of methodologies will further enhance our understanding and management of water resources in this region.

The study evaluated capacity loss and sedimentation rates in the Chohal reservoir, utilizing satellite data from Landsat 8 and Sentinel-2, providing crucial insights into the sustainable utilization of water resources and highlighting the need for effective management strategies in the Kandi region of Punjab. These insights are crucial for reservoir management and planning to ensure sustainable water resource utilization.

At a water surface elevation of 381.5 m, the live storage capacity of the Chohal reservoir, as derived from the bathymetric survey, is 293.8 ha m, compared with the original design live storage capacity of 355.0 ha m. At the same elevation, satellite data from Landsat and Sentinel indicate capacities of 247.9 and 265.5 ha m, showing reductions of 30.18 and 25.21%, respectively, over 32 years. The corresponding annual depletion rates are 0.94 (Landsat) and 0.79% (Sentinel), while the sedimentation rates are 23.31 (Landsat) and 19.47 Mg/ha/year (Sentinel). Sentinel estimates reflect a 16.5% lower capacity loss and sedimentation rate compared with Landsat, highlighting its higher resolution and accuracy. Sediment yield obtained for this reservoir exceeds that reported for the Dholbaha catchment (Singh et al. 2023c). The study integrates satellite data, bathymetric surveys, and sediment yield assessments to enhance sediment management for the Chohal reservoir. This highlights the critical need for effective sedimentation mitigation strategies to sustain reservoir functionality.

The results indicated that the Chohal reservoir exhibited an increase in water spread area and capacity with rising water surface elevations. Despite slight variations in the estimated values between Landsat and Sentinel datasets, likely attributed to differences in data resolution and accuracy, both datasets provided crucial insights into reservoir dynamics. These insights are crucial for reservoir management and planning to ensure sustainable water resource utilization. Furthermore, the study evaluated capacity loss and sedimentation rates, revealing a significant decrease in reservoir capacity over time primarily due to sedimentation.

The annual sedimentation rates of the Chohal Dam obtained from both Landsat and Sentinel datasets fall within the range of 0.1–1.0, 0.5–1.0, and 0.1–1.5%, as reported by Walling (2006), Keller et al. (2000), and Vishwakarma et al. (2015), respectively. Whereas, compared with the previous studies of this region by Sur et al. (1999), Prasad (2020), and Kumar (2021), the annual sedimentation rate obtained in the present study (for Chohal Dam) is notably lower, consistently remaining below 1.0%. Using Landsat data, the annual reduction in live storage capacity of the Chohal Dam (0.94%) is almost the same as that of the Dholbaha reservoir (0.85%), as reported by Singh et al. (2023c). The yearly decline in storage capacity of Chohal and Dholbaha Dams aligns closely with the reduction rate (0.84% per year) observed in one of the 24 federally constructed reservoirs situated in the central state of Kansas, USA, as documented by Rahmani et al. (2018).

The annual sedimentation rates in the reservoirs located in the Kandi region exhibit spatial variability. Approximately 24 years ago, Sur et al. (1999) reported an annual sedimentation rate of 1.0–1.3% in the reservoirs of this region. However, for the Saleran Dam, the sedimentation rate obtained from the SWAT model is notably higher at 1.77%, as reported by Prasad (2020), exceeding the previously observed range of 1.0–1.3%. Further, by using Landsat satellite data, Kumar (2021) reported the sedimentation rate of 1.5%, which is also beyond the earlier reported range. Numerous global studies have also examined sedimentation assessment in reservoirs. Wagh & Manekar (2021) reported an annual sedimentation rate of 0.27% in the Ujjani reservoir located in Maharashtra, India, using Resource Sat-2 and Resource Sat-2A data. The comparatively lower sedimentation rate in this reservoir may be attributed to the finer spatial resolution of the satellite data (5.8 m) compared with Landsat (30 m) and Sentinel-2 (10 m) or the lesser sediment inflow into the reservoir. The Ujjani reservoir is a large earth-cum-concrete gravity dam, while the Chohal reservoir is a smaller earth-filled dam. Similarly, Jagannathan & Krishnaveni (2020) reported a 0.31% annual sedimentation rate in the Wellington reservoir located in Tittagudi Taluk in Cuddalore district of Tamil Nadu, India. Ingole et al. (2015) reported an average annual capacity loss rate of 1.25 MCM (0.59%) in the Nanak Sagar reservoir, situated on the Deoha river in the Tarai region of Uttarakhand, India, utilizing multi-date remote sensing satellite imagery data (LISS-III sensor). Mukherjee et al. (2007) reported an average annual sedimentation rate of 0.75% (61.0 MCM) in the Hirakud Dam located in Orissa, India, using multispectral data of IRS-P6 satellites (LISS-III sensor). Reported the annual capacity reduction or sedimentation rate of 0.80% in the Srisailam reservoir, located in the confined valley of the Krishna River at Mahabaleshwar in the Western Ghats region of India, using multispectral satellite (LISS-III) data. Pandey et al. (2016) reported the annual capacity loss or sedimentation rate of 0.27% in the Patratu reservoir, located in the Jharkhand state of India, using multispectral satellite data (LISS-III sensor). Whereas, Dadoria et al. (2017) reported the annual sedimentation or capacity loss rate of 1.1% (0.32 MCM) in the Murrumsilli reservoir in Dhamtari, Chhattisgarh, India, using multispectral satellite data of LISS-III sensor.

Avinash & Chandramouli (2018) reported the sedimentation rate of 2.5% (0.72 MCM) in the Kabini reservoir in Karnataka, India, using Landsat 8 imagery. Whereas, Nyikadzino & Gwate (2021) reported a significantly higher annual sedimentation rate (2.7%) in the Chesa Dam located in Zimbabwe using Landsat 8 dataset. However, this estimate might be lower (<2.7%) when utilizing finer satellite data. Whereas, Prasad et al. (2018) reported the annual capacity loss or sedimentation rate of 0.14% (1.82 MCM) in the Ghataprabha reservoir in Karnataka, India, using Sentinel-1A SAR data.

These findings underscore the importance of implementing effective sediment management strategies to mitigate reservoir capacity loss. Therefore, remote sensing technique offers an economically viable, labor and time-efficient solution for reservoir sedimentation monitoring and management (Kummu & Varis 2007; Patni et al. 2017) when compared with traditional methods such as bathymetric survey, which is time-consuming, tedious, and costly operation, as reported by several researchers in the past (Goel et al. 2002; Vente et al. 2003; Thomas et al. 2009; Mupfiga et al. 2016). The outcomes of this study emphasize the need for proactive measures to ensure long-term water availability for irrigation and other purposes, highlighting the significance of sustainable water resource management in the context of evolving environmental conditions (Singh et al. 2023c).

The Chohal reservoir in the Kandi region of Punjab serves vital roles, including flood control, irrigation, water storage, and fisheries. However, seasonal rainfall-driven soil erosion in its catchment area has significantly increased sediment influx, reducing the storage capacity (live, dead, and gross) of the reservoir and impacting its operational lifespan. Addressing this issue requires a structured approach that identifies existing problems, their causes, and targeted management strategies. Over the past 32 years, sedimentation has caused a storage capacity decline of 30.2% (based on Landsat data) and 25.2% (based on Sentinel-2 data). Sedimentation rates of 0.94% annually (Landsat) and 0.79% annually (Sentinel-2) indicate a steady loss of reservoir capacity. The primary cause is unchecked soil erosion from the catchment, driven by intense seasonal rainfall and land degradation. Deforestation and land-use changes have left large areas exposed to erosion, increasing surface runoff and sediment transport into the reservoir. Additionally, unstable slopes and gullies further accelerate sediment movement during rainfall events. To mitigate sedimentation, optimized land-use planning tailored to the unique geomorphology of the Kandi region is essential. Reforestation of degraded areas and the establishment of vegetative buffer strips can stabilize soil and reduce erosion. Constructing check dams and gully plugs in erosion-prone zones helps slow down runoff, allowing sediments to settle before reaching the reservoir. Implementing land-use zoning based on geomorphological characteristics enhances soil conservation and water retention, ensuring long-term stability.

Sediment yield monitoring using high-resolution satellite imagery (Landsat 8, Sentinel-2) can provide continuous tracking of sedimentation trends, supporting adaptive management strategies. When conservation measures alone are insufficient, desilting and dredging operations become necessary. Mechanical and hydraulic dredging techniques effectively remove accumulated sediments, restoring lost storage capacity. Post-dredging stabilization through vegetation-based erosion control in dredged zones is crucial to prevent sediment re-entry and sustain reservoir functionality. Sustainable water management approaches further contribute to sediment control. Precision irrigation systems, such as drip irrigation, can minimize runoff and soil loss while ensuring water use efficiency. Additionally, constructing minor diversions and drainage structures can help channel excess runoff away from sediment-sensitive areas while maintaining adequate water availability. Engaging local stakeholders in watershed conservation initiatives can promote sustainable practices and long-term reservoir health.

By integrating advanced geospatial assessments with region-specific sediment management strategies, the Chohal reservoir can be preserved as a critical water resource. This approach ensures its functionality and sustainability for future generations.

The comparative analysis of Landsat 8 and Sentinel-2 satellite data for assessing reservoir capacity and sedimentation rates in the Chohal Dam, situated in the Kandi region of Punjab, India, not only sheds light on the critical role of geospatial techniques in water resource management but also underscores the pressing need for innovative solutions to address water scarcity challenges. Through meticulous examination, the study elucidates that while both Landsat 8 and Sentinel-2 satellite data offer valuable insights, the higher spatial resolution of Sentinel-2 data offers slightly broader ranges in estimated area and reservoir capacity compared with Landsat imagery. Over the span of 32 years, the datasets depict a concerning trend of significant declines in reservoir capacity, with Landsat indicating a loss of approximately 30.2% and Sentinel-2 showcasing a slightly lower decline of 25.2%, showcasing the enhanced accuracy of capacity estimation with higher resolution imagery. Furthermore, the analysis reveals annual sedimentation rates of 0.94% for Landsat and 0.79% for Sentinel-2, underscoring the pivotal role of high-quality data in precisely monitoring sedimentation processes. These findings not only highlight the need for the adoption of advanced remote sensing techniques in reservoir management but also emphasize the urgency of implementing sustainable conservation strategies. The significance of high-resolution satellite data in addressing sedimentation challenges emerges as a crucial aspect for ensuring the long-term availability of water resources not only in the Chohal reservoir area but also in similar regions grappling with water scarcity issues worldwide.

J.S.: Retrieval and analysis of geospatial data. M.C.S.: Planning, conceptualization, implementation, and manuscript preparation. A.M.: assistance in identifying the research problem.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.