Groundwater Level Variations Research Articles

Groundwater Chemical Trends Analyses in the Piedmont Po Plain (NW Italy): Comparison with Groundwater Level Variations (2000–2020)

The concentrations of chemicals in the groundwater chemical values in the Piedmont Po Plain (NW Italy) show significant temporal variability and need to be characterised due to the lack of regional-scale assessments. The aim of this study was to analyse the trends (period 2000–2020) in the main physicochemical parameters and main ions in 227 wells in the shallow aquifer and to identify the potential causes. The identification of change points (points of sudden change) and comparisons with groundwater level variations were also performed. Results highlight general increasing trends for Na, Cl and HCO3, decreasing trends for SO4 and NO3, stationary conditions for pH and heterogeneous behaviours for electrolytic conductivity, Ca and Mg. Change points occurred in at least 50% of the monitoring wells, mainly during the 2008–2011 period. The comparison between groundwater levels and chemistry highlights a direct proportionality. Superimposed processes that induce an absence of proportionality are shown. The comparison of results with those of previous studies conducted under similar conditions revealed similar variations.. In conclusion, the potential responsible factors (e.g., road-salt dissolution and agricultural practices) and the relevant role of groundwater level variation were identified.

GNSS and Sentinel-1 InSAR Integrated Long-Term Subsidence Monitoring in Quetta and Mastung Districts, Balochistan, Pakistan

Land subsidence (LS) is a global phenomenon that has affected several urban centres around the world such as Jakarta (Indonesia), Mexico City (Mexico), Xi’an (China), and Iron County (US). It has mainly been attributed to anthropogenic activities such as groundwater exploitation, especially in unconsolidated aquifer systems rich in highly compressible clay and silt. The platy clay minerals rearrange into horizontal stacks after dewatering, leading to a volume change due to overburden. In this study, land subsidence is investigated in the Quetta and Mastung districts, Balochistan, Pakistan, by employing Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS), and groundwater level (GWL) variations. This study represents the first attempt in Pakistan to measure the long-term land subsidence by fusing GNSS and InSAR data for improved validity. InSAR data from the Sentinel-1 satellite in the Ascending (195 scenes) and Descending (183 scenes) tracks were used to analyse LS from December 2015 to December 2022. High-accuracy Trimble NetRS GNSS receivers were used in five locations from October 2006 to December 2022. An average subsidence ranging from 3.2 cm/y to 16 cm/y was recorded in the valley mainly due to the GWL decline and clay-rich sediments, which are prone to compaction due to dewatering. An accumulative LS of 2 m was recorded by the permanent GNSS station in central Quetta from October 2008 to January 2023 (14.2 years). An acceleration in the subsidence from 12 cm/y to 16.6 cm/y after 2016 was recorded by the continuous GNSS. Additionally, the InSAR and GNSS values were compared for validation, resulting in a good correlation between both techniques. A GWL decline ranging from 1.7 m to 6 m was recorded by the piezometers in Quetta during the period 1987–2022. Large- and small-scale fissures were observed in the study area during the surveys. These fissures are responsible for damage to the city’s infrastructure and aquifer contamination. The subsidence profile also agrees with the subsurface lithology. Our assessment concludes that Quetta may be the fastest-sinking metropolitan city in Pakistan. The overexploitation of groundwater and the population explosion may be the main contributing factors for the land subsidence.

Loading effect of phreatic level variations induced by river tide on groundwater head fluctuations in tidal river aquifer systems

The unconfined groundwater level variations caused by the river tide can impact the fluctuation of the groundwater head in the underlying confined aquifer by loading effect. In this study, we proposed a mathematical model and provided an analytical solution for a confined aquifer system considering the loading effect when the aquifer system is eroded by the tidal river to varying extents. The results demonstrated that the loading effect tends to amplify the amplitude of the tide-induced head fluctuation while reduce its phase shift in the confined aquifer. These effects are more significant with a larger tidal loading efficiency, a wider tidal river, and a smaller tidal propagation parameter of the unconfined aquifer. The analytical solution was validated by the observed groundwater level fluctuations from a groundwater level monitoring well near a tidal river in the Peral River Delta of China, and provided reliable estimations of aquifer parameters in the region.

Physics-informed neural networks in groundwater flow modeling: Advantages and future directions

In recent years, there has been enormous development in soft computing, especially artificial intelligence (AI), which has developed robust methods for solving complex engineering problems. Researchers in the field of water resources engineering have applied these AI methods to solve a variety of hydrological problems. Despite their widespread use in the surface and atmospheric hydrology fields, groundwater hydrologists have not widely used AI methods in their routine field-scale modeling efforts. This is because AI models have been primarily considered black box models that lack physical meaning. Furthermore, using AI models to generate the space-time distribution of transient groundwater level variations is challenging and requires further flux balance and mass transport analyses. More recently, a new type of physics-informed neural network (PINN) model has been developed to address several limitations by integrating governing physics (groundwater flow equations) into the AI tools. This study presents the systematic advantages of the PINN algorithm for solving groundwater problems using a set of classic test problems. As discussed in detail in the article, these advantages and potentials are associated with the meshless nature of PINN, its continuous time and space dimensions, its independence from time-stepping and incremental marching in space, and its efficiency in running time. However, despite PINN's promising attributes, it is important to acknowledge its nascent stage of development and the inherent limitations of all neural network models, such as training challenges and hyperparameter selection. Thus, collaborative efforts between groundwater modelers and computer scientists are imperative to explore and exploit the full potential of PINN in tackling increasingly complex groundwater problems and nurturing PINN into a dependable modeling tool in industry and academia.

Effect of urbanization on groundwater resources hydrodynamics and bearing capacity: a case study from the Bayraklı region, İzmir, Türkiye

The bearing capacity of soil is a critical factor in the design of foundations for civil engineering structures. The bearing capacity depends on soil properties, as well as the location of the water table. A rise in the groundwater level can be dramatic, especially in highly urbanized regions, and can affect the bearing capacity of foundations. In this study, groundwater-level fluctuations in a highly urbanized region in İzmir, the third largest city in Türkiye, was monitored over a 1 year period, and its effect on reducing the bearing capacity, which is not considered in foundation design and construction, was investigated. For this purpose, four observation wells equipped with groundwater data loggers were used to determine the variations in groundwater level over 1 year. Using the Terzaghi approach to calculate the bearing capacity, normalized bearing capacity plots for various foundation width/depth ( B / D f ) ratios were generated for all four observation wells. Remarkable bearing capacity changes of 10.94, 8.21, 7.62 and 9.29% were observed in four different observation wells (OW-1, OW-3, OW-6 and OW-9, respectively). The study showed that changes in groundwater level in the region caused by urbanization poses a potential risk to the sustainability of previously constructed foundations.

Comparative estimation of a random error component of automatic well level gauges based on processing measurements of earth-tidal level of groundwater

Relevance. Introduction of autonomous digital level gauges allows significantly expanding the range of tasks solved in various fields of geology and ecology, by increasing the frequency of measurements and applying mathematical processing methods to avoid distortions of different nature. The expansion of the field of application of downhole level meters makes it a topical issue to choose a particular type of device to ensure the required measurement accuracy. Aim. To consider the approaches to comparative estimation of random error of several types of automatic level gauges based on their recording of earth-tidal variations of groundwater level. Methods. Methods of compensating the influence of variations in atmospheric pressure on groundwater levels; methods of calculating tidal level variations; methods of correlation and frequency analysis of the calculated and experimental data. Results. The authors have carried out a comparative assessment of the accuracy in readings of several digital level gauges, installed in one well, by comparing the measured values of earth-tidal variations of the groundwater level with the calculated ones. Based on the experimental data, tidal variations of the groundwater level were obtained by eliminating distortions from variations in atmospheric pressure and then filtering with the moving mean. From correlation and amplitude-frequency analysis of the calculated and experimental data, indicators were determined for making a comparative assessment of a random component of the error of the tested level gauges. Conclusions. The approach suggested allows choosing independent digital level gauges with a minimum random error component using the totality of calculated parameters. The most promising way to eliminate the distorting effects of tidal variation of groundwater level is the method based on the cutter frequency filtering of the level signal measured by the level gauge.

Sensing the dependable surficial signatures of temporal groundwater variations in arid coastal regions through geospatial techniques with respect to microclimate changes

Reports on Groundwater level variations and quality changes have been a critical issue, especially in arid regions. An attempt has been made in this study to determine the surface manifestations of groundwater variations through processing imageries for determining the changes in land use, Normalized Differential Building Index (NDBI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), along with Groundwater level (GWL) and Electrical conductivity (EC). Decadal variation between these parameters for 2013 and 2023 shows that the average water level had increased by 1.03amsl, while the EC values of groundwater decreased by 418 μS/cm. The decrease in EC values indicates freshwater recharge, promoting natural vegetation, thus reducing the LST values by 3.28 °C. In addition, urban landscaping and relatively lesser emissivity from built-up surfaces than the sandy desert have further reduced the LST. The interrelationship of the parameters indicates that an increase in LST correlates with an increase in NDBI and with less significant changes in NDVI. The lowering of the LST along the coastal regions was inferred to be due to the influence of Sea breeze, adjacent moisture from the ocean, shallow water level, and the shadow effect of the buildings. Further, the increase in water level was mainly attributed to the recent increase in rainfall and the extreme event in 2018. The higher EC in the lesser NDBI regions is attributed to the anthropogenic contamination from agriculture and landfill leachates. Though there was an increase in NDBI, the LST of the region was inferred to be reduced mainly due to the increase in water level and reduction of emission from desert sand by recent urban developments.

Spatio-temporal patterns of groundwater level changes in southwestern Indian Punjab

Abstract The study aimed to assess the dynamic behavior of groundwater levels in the southwestern districts of Indian Punjab, focusing on the spatial and temporal distribution of waterlogged and overexploited areas before and after the monsoon season. The study utilized groundwater level data spanning 48 years (1973 -2020), using GIS to map groundwater levels and visualize fluctuations throughout the study area. The findings revealed significant variations in groundwater levels within the southwestern district of Punjab during different seasons. The maximum waterlogged areas were found to be 97,350 ha (1973, Faridkot), 56,080 ha (1981, Ferozepur), 21,730 ha (1991, Sri Muktsar Sahib), 52,790 ha (2000, Sri Muktsar Sahib), 6,760 ha (2010, Sri Muktsar Sahib), and 2,910 ha (2020, Fazilka). However, the waterlogged and potential waterlogging area observed in Fazilka district covered about one-third (32.52%) of the district during 2020. The study identified that 45% of the study area faced the risk of overexploitation, 46% was considered safe, and 9% was either waterlogged or at risk of waterlogging. Over the 48 years, the study demonstrated the dynamic nature of waterlogged areas in the southwestern districts of Punjab, including Ferozepur to Fazilka via Faridkot and Sri Muktsar Sahib districts.

Monitoring the Impacts of Artificial Recharge Structures on Water Table at Ambedkar Nagar, Uttar Pradesh, India

To study the groundwater recharge through rainfall and artificial recharge structures in selected dry well in different locations of UP was experimented. The data was recorded (1998-2017) to execute the artificial recharge structure at the appropriate locations with best geological condition to enhance the recharge rate at least cost for control of declining groundwater level. Study reveals that the stochastic auto regressive lime series model is an effective tool for management of ground water resource at pre and post monsoon. The variation of pre and post monsoon ground water level is maximum as the physical soil characteristics including hydraulic conductivity may enhance the recharge rate at least cost.

Confining Pressure Forecasting of Shield Tunnel Lining Based on GRU Model and RNN Model

The confining pressure has a great effect on the internal force of the tunnel. During construction, the confining pressure which has a crucial impact on tunnel construction changes due to the variation of groundwater level and applied load. Therefore, the safety of tunnels must have the magnitude of confining pressure accurately estimated. In this study, a complete tunnel confining pressure time axis was obtained through high-frequency field monitoring, the data are segmented into a training set and a testing set. Using GRU and RNN models, a confining pressure prediction model was established, and the prediction results were analyzed. The results indicate that the GRU model has a fast-training speed and higher accuracy. On the other hand, the training speed of the RNN model is slow, with lower accuracy. The dynamic characteristics of soil pressure during tunnel construction require accurate prediction models to maintain the safety of the tunnel. The comparison between GRU and RNN models not only highlights the advantages of the GRU model but also emphasizes the necessity of balancing speed accuracy in tunnel construction confining pressure prediction modeling. This study is helpful in improving the understanding of soil pressure dynamics and developing effective prediction tools to promote safer and more reliable tunnel construction practices.

Integrating Remote Sensing Data and MODFLOW Modeling for Sustainable Groundwater Resource Management and Monitoring in the Guinea Region, West Africa

Groundwater is vital in ensuring water security, supporting agriculture, and sustaining ecosystems in the Guinea region of West Africa. However, the region faces increasing challenges related to groundwater availability and quality due to population growth and climate change. This research presents pixel-based spatial-temporal trends assessment of groundwater mappings using an integrated approach that combines remote sensing data with MODFLOW modelling. The study focuses on the Guinea region, characterized by diverse topography and hydrogeological conditions. Remote sensing data, including satellite imagery of shallow groundwater storage (GWS), relative humidity, temperature, NDVI, and rainfall, provided valuable insights into observing hydrological cycles and climate changes. The MODFLOW modelling framework was employed to simulate groundwater flow (Hydraulic Heads) and variability of groundwater levels. It enabled the investigation of groundwater recharge and discharge processes, as well as the impact of climatic variations on groundwater dynamics. Furthermore, the trend modelling approach facilitated and revealed that the direction of the western to the northern part of the study area suffered from a depletion rate of groundwater level. The integrated analysis of remote sensing data and MODFLOW modelling highlights the complex interactions between climate change and groundwater resources. This research underscores the importance of proactive and informed groundwater management strategies to ensure the sustainable use of this invaluable resource. By combining remote sensing technology and groundwater modelling, it offers a holistic approach to support evidence-based decision-making for groundwater resource management and monitoring in the Guinea region of the West Africa.

Coupled hydrogeological modeling and nitrate transport modeling in an anthropized valley, a case study of the lower Soummam valley (Bejaïa Northeast of Algeria)

Anthropogenic activities, including wastewater discharges and fertilizer use, were identified as the primary factors influencing nitrate concentrations in the lower Soummam Valley in the North-East of Algeria. This study aimed to investigate nitrogen pollution and simulate nitrate mass transport coupled with a numerical flow model. Hydrogeochemical findings revealed significant seasonal variations between the high-water period (May 2019 and 2021) and the low-water period (September 2019 to November 2020 and 2021). During the low-water period, both temperature and electrical conductivity increased. However, pH, dissolved oxygen, and nitrate levels decreased in the high-water period. Nitrites and ammonium exhibited irregular fluctuations. The numerical flow model was developed using PMWIN and coupled with a solute transport model simulated with the Model Transport 3 Dimensions (MT3D). Calibration was conducted under steady-state conditions from 2011 to 2021 and was validated by comparing piezometric heads and nitrate concentrations measured in May 2021. Three scenarios were devised to simulate transient conditions from 2011 to 2050, considering variations in both groundwater levels and nitrate concentrations. The simulation revealed that river and dependent head boundaries are the primary sources of groundwater recharge. These boundaries contributed to approximately 87% of the total inflow. The model also depicted the water exchange between the aquifer and the river. However, the constant head boundary represented the main outflow, accounting for 85.7% of the total. The nitrate model illustrated an increase in nitrate concentrations, reaching 3.8 mg/L in 27 out of the 29 simulated wells. Both the river and groundwater head boundaries were identified as the key factors governing the hydrodynamic aquifer system and nitrate transport in the alluvial aquifer of the lower Soummam Valley.

Simulation of cyclic slow-to-fast landslides based on the coupled creep and frictional slip model

Soft rock slopes may undergo long-term cyclic slow-to-fast landslides due to the softening of clay minerals and frictional sliding under hydromechanical disturbance. We proposed a meso/macroscale cyclic slow-to-fast landslide model by coupling creep and frictional sliding model to address the abovementioned problem. The model can reveal the macroscopic catastrophic phenomena caused by the accumulation of mesoscale damage in soft rocks, thus providing a scientific basis for the simulation of cyclic slow-to-fast landslides. For modeling validation, we applied the model to the Spriana soft rock slope, which undergoes long-term instability under periodic groundwater level variation. By adopting the coupled finite element method and distinct element method (FEM-DEM), the model can mimic the stable creep and fast sliding in different phases of matured slow-moving landslides. The difficulties in simulating cyclic accelerated sliding are solved, where the simulation error of displacement is less than 5.7 % compared with field monitoring data. It may serve as a prediction tool for cyclic slow-to-fast landslides.

Prediction of groundwater fluctuations in Chengalpattu district through machine learning

Groundwater, found beneath the Earth’s surface in saturated zones of soil, sediment, and rock, plays a crucial role in sustaining ecosystems and supporting human activities like agriculture and industry. Monitoring and managing groundwater resources are crucial for sustainable use. As of the latest update in January 2022, Chennai, a city in southern India, has been grappling with water scarcity issues. The city has faced recurrent water shortages due to various factors, including rapid urbanization, inadequate infrastructure, depleting groundwater levels, and irregular rainfall patterns. Chengalpattu district in Tamil Nadu, India, is known for its diverse geographical features, incorporating urban and rural landscapes, and is significant for agriculture and water resource management. This study focuses on predicting variations in groundwater levels in open wells at different locations in the Chengalpattu district, assessing the effectiveness of various machine-learning models. This paper utilizes the ARIMA model provided by the stats models library. This library is widely employed for statistical modelling and hypothesis testing in Python, and encompasses a range of tools for time series analysis, including the ARIMA model. In this context. ARIMA models are employed for predicting future depth which focus on depicting autocorrelations in the data. Additionally, a package consolidating various models, including Seasonal Naïve (a straightforward forecasting method for seasonal data that serves as a reliable benchmark by relying on the observation from the same period a season ago), was incorporated in this study.

Analyzing Coastal Groundwater Variability in Sindhudurg, Maharashtra: A Spatio-temporal GIS Approach+6

Due to a sharp increase in groundwater demands in coastal regions, groundwater levels are getting depleted day by day. The local groundwater table fluctuation eventually speeds up sea water intrusion. The present study investigates spatial and temporal groundwater level fluctuations to identify the area affected by sea water intrusion as well as for sustainable water resource management in coastal stretch of Sindhudurg district, Maharashtra, which is about 121 km. Using groundwater level data collected from Central Groundwater Board (CGWB), Sindhudurg for the period from 1991 to 2022, fluctuations of groundwater levels were examined. The collected levels were evaluated for monsoon, pre-monsoon & post-monsoon seasons and all levels were interpolated over the study area using the Inverse Distance Weighted Method as a spatial interpolation technique in QGIS 3.22.1, in order to obtain the actual differences in groundwater levels. According to the results, the groundwater level varies from 1.88m to 13.45 m below ground level (bgl) in the pre-monsoon season, from 0.1m to 7.05 m (bgl) in the post-monsoon season and from 1m to 10.6m (bgl) in the monsoon season. Using statistical tests like Mann-Kendall test & Sen's slope estimator, the pattern of variation of groundwater levels was anticipated. According to our findings, over 80% of the basin has a falling water table during the monsoon season. It was observed that the groundwater level in the pre-monsoon season was more as compared to those in the monsoon and the post-monsoon seasons in the entire study area. The overall outcome of the study demonstrates that the groundwater level fluctuations were non-uniform in the study area, which may cause an increase in the sea water intrusion, leading to a decrease in crop production. Keywords: GIS, Groundwater level, Sea water intrusion, Mann Kendall test, Spatial interpolation, Sen’s slope estimator.

Characterization of the Hydrological Balance and Assessment of the Recharge of Fractured Aquifers in the Context of Climatic Variability in Siguiri

Climate change in the Siguiri prefecture is having a considerable impact on the water balance. With the aim of determining the hydrological balance and the quantity infiltrated into the aquifers, groundwater resources can be assessed. To carry out the study, we focused our analyses on climatic parameters (rainfall, temperature), which led to the determination of groundwater recharge in the aquifers. We found an average temperature of 26°C, with a maximum temperature of 39°C in April. The minimum temperature from December to January was 13°C. Rainfall during the dry season (November, December, January, February, March and April) was 54 mm. The average rainfall during the wet season (May, June, July, August, September, October) is 1142 mm. The imbalance in the water balance is explained by the amount of water infiltrated (41 mm or 3% of rainfall). This value, which represents a volume of 717.5 million m3 per year, is deemed sufficient to meet the needs of the population of Siguiri (695,449 inhabitants with a growth rate of 3%). Groundwater resources have been affected by rainfall deficits and variability in groundwater levels over the last forty (40) years (1980-2020) in the various localities in the study area. Precipitation water runs off (90%) more than it infiltrates (3%) into the aquifer horizons of the Siguiri prefecture, making access to drinking water difficult during the dry season for the population of Siguiri.

PREDICTING THE IMPACT OF RAPID URBANISATION ON URBAN GROUNDWATER LEVEL VARIATIONS AND ANALYZING CLIMATE DYNAMICS IN PUNJAB'S CITIES, INDIA: AN APPROACH TOWARDS SUSTAINABLE CITIES

Abstract. In the past few decades, India has witnessed an unparallel population growth across its urban centres. The significant rise in urban population has led to acute demands for resources to sustain them, contributing to anthropogenic activities that impact urban resources and climate. This study was carried out to analyse the impact of rapid urbanisation on the groundwater level variation and climate dynamics. Focus was laid on Ludhiana and Patiala urban centres of Punjab state, India. Multi-temporal analysis of LULC was computed using Landsat-4, 5, 7 & 8 datasets to analyse urban sprawl during 1990–2023 for both the cities. A significant increase of 30% urban area was observed during 1990–2023. Further, to analyse the impact of urbanisation on ground water levels, the groundwater water storage variation was analysed using the GRACE and GRACE-FO mission datasets. It was found that the groundwater withdrawal was greater than the amount of total groundwater recharge during 1990–2023. Both these cities Ludhiana and Patiala has experienced an extreme decline in groundwater storage levels. Further, the time-series statistical relationships were explored to understand the relation between urban sprawl and annual temperature, rainfall patterns and total water storage for these cities during the years 2000-2022 for analysing the effect of rapid urbanization.

Comparison between numerical analysis and experiment for the dry density and shear resistance variations with different depths (groundwater level variations)

ABSTRACT Comparison between numerical analysis by the PLAXIS 3D – finite element method and experiment measurement by the Viet Nam Standard “TCVN 4195:2012; TCVN 4199:1995” for the dry density and shear resistance variations with different depths (groundwater level variations) is shown clearly with the particular process. Results show that dry unit weight (density) increases gradually with the increase in different depths. The maximum deviation of the dry unit weight (dry density) and shear resistance according to the constant loading (P) at the different depths included the dry density (γ k ) value of 16.26 kN/m2 and shear resistance (τmax) of 72.7 kN/m2 at 4.3 m, whereas shear resistance (τmax) of 41.6 kN/m2, 27.1 kN/m2, 91.8 kN/m2, 98.2 kN/m2, and 82.9 kN/m2 at 7.0 m, 18.0 m, 27.0 m, 30.3 m, and 39.6 m, respectively. Results show the small deviation together, but some values presented changes remarkably. Thus, this study provides reliable reference in geotechnical engineering for researchers in the future.

A New Method for Estimating Groundwater Changes Based on Optimized Deep Learning Models—A Case Study of Baiquan Spring Domain in China

Estimating groundwater level (GWL) changes is crucial for the sustainable management of water resources in the face of urbanization and population growth. Existing prediction methods for GWL variations have limitations due to their inability to account for the diverse and irregular patterns of change. This paper introduces an innovative approach to GWL prediction that leverages multisource data and offers a comprehensive analysis of influencing factors. Our methodology goes beyond conventional approaches by incorporating historical GWL data, examining the impacts of precipitation and extraction, as well as considering policy-driven influences, especially in nations like China. The main contribution of this study is the development of a novel hierarchical framework (HGP) for GWL prediction, which progressively integrates correlations among different hierarchical information sources. In our experimental analysis, we make a significant discovery: extraction has a more substantial impact on GWL changes compared to precipitation. Building on this insight, our HGP model demonstrates superior predictive performance when evaluated on real-world datasets. The results show that HGP can increase NSE and R2 scores by 2.8% during the test period compared to the current more accurate deep learning method: ANFIS. This innovative model not only enhances GWL prediction accuracy but also provides valuable insight for effective water resource management. By incorporating multisource data and a novel hierarchical framework, our approach advances the state of the art in GWL prediction, contributing to more sustainable and informed decision making in the context of groundwater resource management.

Land subsidence studies in the Godavari Delta regions of the East coast of India using ALOS and Sentinel 1 data

Subsidence in coastal deltas is a challenging issue globally, influenced by both climate and anthropogenic pressures. Owing to its rich economic value, the Godavari Delta on the east coast of India attracts the human population and is therefore a hub of developmental activities. We quantified the line of sight (LOS) rate of land deformation in the Godavari Delta, India, for two different periods using the time series interferometric analysis of Advanced Land Observing Satellite Phase Arrayed L-band Synthetic Aperture Radar (ALOS PALSAR) (2007–2011) and C-band Sentinel-1A (2017–2020) datasets. This approach was based on the Small Baseline Subset (SBAS) algorithm, which constructed 76 (ALOS) and 333 (Sentinel-1A) pairs of interferograms. The results indicated that the majority of deltas had varying LOS deformation rates in both periods. The southern and northern coastline of the deltas showed subsidence (-10 to -20 mm/yr) and uplift (5 mm to 7 mm/yr), respectively, from 2007 to 2011. The uplift (11 mm/yr) during 2017–2020 was attributed to coastal erosion and accretion during the study period. Statistical analyses showed that the majority of the coastline experienced erosion from 2006 to 2011, ranging from 0 ‐30 m/yr. Conversely, during 2017–2019, accretion ranging from 0-30 m/yr was dominant. The Kakinada spit has recently experienced an uplift of 8 mm/yr. Many factors contributing to deformation, such as groundwater fluctuations, rainfall, land use and land cover (LULC) changes, and water quality, were explored. Groundwater level variations were strongly correlated with the rate of deformation. A direct relationship was also observed between water recharge through rainfall and the ground deformation rate. The subsidence rate was estimated as 0 to ‐8 mm/yr from 2007-2011 and -10 to -20 mm/yr for 2017–2020 in the Godavari Delta. Increased urban areas and land conversion from the LULC analysis were the causative factors of subsidence in this region. Thus, the Godavari Delta will become highly vulnerable, as the region has shown various LOS deformation rates. Studies on land subsidence are highly relevant for effective coastal management planning and management.