Land Subsidence and Displacement Occurrence in the Urmia Plain Aquifer Using Interferometric Radar Technique and Hidden Faults

Land Subsidence and Displacement Occurrence in the Urmia Plain Aquifer Using Interferometric Radar Technique and Hidden Faults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Land Subsidence and Displacement Occurrence in the Urmia Plain Aquifer Using Interferometric Radar Technique and Hidden Faults Fariba Hemmati, Sara Khanjari, Akram Alizadeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4352949/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The occurrence of land subsidence in a number of Iranian cities has increased, mostly due to groundwater level fluctuations, a consequence of recent structural displacements, decreased precipitation and increased rate of population. Urmia Plain Aquifer has been suffering such environmental challenges, leading to extensive land displacement. Hence, this research investigates the extent of land displacement in the Urmia Plain Aquifer from 2015 to 2023 using the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique via the Sentinel Application Platform (SNAP) software and detected hidden faults. DInSAR is an efficient tool for assessing surface deformation, including land displacement. The results displayed a maximum land subsidence of 9.00 cm in the south and an uplift of 9.6 cm in the north of Urmia Plain Aquifer. Comparing the information obtained from InSAR with the groundwater level data reveals a positive strong correlation. The groundwater level in the southern parts is lower than in the north. This study indicated that land displacement, leading to the fluctuation of the groundwater level, can effectively be evaluated using InSAR, a less time-consuming and expensive technique. Land subsidence Interferometric radar SNAP software Urmia Plain Aquifer Groundwater Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Land subsidence, characterized by vertical downward movements and horizontal displacements, is a global threat induced mainly by groundwater depletion in arid and semiarid regions (Zhao et al 2024 ). Overexploitation of groundwater resources in urban areas exacerbates land subsidence, impacting infrastructures and increasing flood vulnerability ( Shokri et al 2023 ). Human-induced processes like groundwater extraction contribute significantly to accelerated land subsidence rates in coastal environments, affecting over 500 million people globally (Minderhoud et al 2023 ). Radar interferometric techniques and machine learning models have been utilized to predict and prioritize influential variables in land subsidence occurrence, revealing that factors beyond groundwater withdrawals, such as proximity to geological features like anticlines and faults, play crucial roles in susceptibility (Buffardi & Ruberti, 2023 ).This interdisciplinary research highlights the importance of understanding and managing land subsidence to mitigate environmental damages and socio-economic issues worldwide. Land displacement is a global issue. During recent years, land displacement in Iran has led to several problems in various sectors, mainly including agricultural lands, residential buildings, roads, and power transmission lines (Marselis et al. 2017 ). It has mainly been due to natural processes such as local active tectonics and anthropogenic activities( Robinson et al, 2018 ). In recent years, the Iranian plains, including the Urmia Plain, have significantly been impaired by a large number of land subsidence occurrences, especially in the residential and agricultural sectors (Khoshlahjeh et al. 2021). The land subsidence phenomenon has been assessed by a large number of researchers all over the world (Gharechaee et al. 2023 ). investigated land subsidence in Mexico City from 2014 to 2015 using the D-InSAR method and concluded that the subsidence rate in some locations exceeds 40 cm/y. In this study, the potential of Sentinel-1 images to support D-InSAR assessments across a large and diverse region was highlighted. In two different studies conducted in Jakarta, Indonesia, the land subsidence rate varied between 3–10 cm/y and 2–3 cm/y (Widodo et al. 2019 ). evaluated the land subsidence risk in Xi'an, China, using a risk matrix that integrates differential settlement, ground cracks, and land-use classification through radar techniques and optical images(Gharechaee et al. 2023 ).In Iran, several investigations have been conducted to evaluate the incidence and susceptibilities of land subsidence. Gharechaee et al. ( 2023 ) conducted a study on land subsidence susceptibility mapping in Iran's Bakhtegan basin. They utilized InSAR technology and machine learning models to uncover factors contributing to this phenomenon, which extend beyond groundwater extraction. Their findings highlighted the significance of geographical elements such as proximity to dams, faults, and mines. Karami et al. ( 2023 ) leveraged SBAS-InSAR and GIS methodologies to observe land subsidence and displacement in Shiraz, Iran. They were able to pinpoint areas with significant displacement, which could be attributed to building densities and population distribution. Ghorbani et al. ( 2022 ) employed the Interferometric Radar Technique to observe notable land subsidence resulting from groundwater depletion and shifts in climate within the Ardabil Plain, located in Iran, showcasing a peak rate of 45 mm per year. Panahi et al. ( 2022 ) conducted an assessment on the susceptibility of land subsidence in Iran through the utilization of InSAR and machine learning techniques. Areas such as Razavi Khorasan, as well as provinces like Hamedan and Khouzestan, exhibit notable susceptibility towards future occurrences of land subsidence. Rafiei et al. ( 2022 ) conducted an assessment of land subsidence in the Samalghan plain in Iran through the utilization of the DInSAR technique. The findings revealed notable subsidence attributed to the overexploitation of groundwater, resulting in a maximum subsidence of 34 cm in the year 2019. Mirzadeh et al. ( 2021 ) conducted a study on the characterization of irreversible land subsidence in the Yazd-Ardakan Plain, Iran spanning from 2003 to 2020 by utilizing InSAR time series. Their findings reveal a notable instance of land subsidence attributed to the excessive extraction of groundwater. Yousefi et al. (2021) utilized the interferometric radar technique for the purpose of monitoring land subsidence in Tehran, Iran. The integration of data from various SAR images enabled the assessment of stress and the impact of aquitard thickness, revealing a weak correlation with groundwater exploitation. Nasiri et al. ( 2021 ) conducted a study in Iran's Fahlian Basin, employing DINSAR to observe land subsidence. The findings indicated an average subsidence rate of 4 cm/year, with a 75% correlation between groundwater decline and subsidence. Moghimi et al. ( 2021 ) described the Urmia Plain as having a complex tectonic configuration characterized by numerous faults and fractures that significantly shape the region's geomorphology. The interaction between natural geological features such as faults and fractures, in conjunction with anthropogenic activities like the excessive extraction of groundwater resources, has played a significant role in the occurrence of land subsidence phenomena within the Urmia Plain. The Urmia Plain Aquifer has a complex tectonic structure, with numerous faults and fractures that noticeably govern the geomorphology of the area. The naturally occurring geologic features (e.g., faults and fractures) together with anthropogenic activities (e.g., the excess exploitation of groundwater) have considerably contributed to the land subsidence occurrence in the Urmia Plain Aquifer . The occurrence of hidden faults provides crucial information to estimate seismic hazards and land subsidence vulnerabilities. The subsidence occurrence of a faulted area can be assessed by studying the relationships between rock strata. The main factors contributing to land subsidence in the Urmia Plain Aquifer are:Groundwater depletion due to overexploitation for agricultural and domestic use(Raspini et al, 2022 )( Gharechaee et al, 2023 )( Caló et al,2017). As groundwater levels decline, the aquifer system compacts, leading to land surface lowering.The presence of significant fine-grained materials (e.g., clay and silt) in the unconsolidated aquifer system (Caló et al,2017) (Choubin et al, 2023 ).. These sediments are prone to compaction when groundwater is withdrawn, exacerbating subsidence.Hence, performing this research to identify more vulnerable regions through land displacement in the Urmia Plain Aquifer. sounds necessary. This research aims to assess the occurrence of Urmia Plain Aquifer. land subsidence and displacement and identify the regions with higher land subsidence vulnerability using groundwater level data and hidden faults information. For this reason, the interferometric radar technique (InSAR) via the SNAP (Sentinel Application Platform) software, an efficient tool to estimate land subsidence occurrence along with Arc GIS was applied. Research area The Urmia Plain Aquifer (Fig. 1 ), the West Azerbaijan province, is situated in the northwest of Iran (N 44° 15' to 45° 52' and E 37° 18' to 37° 47'). Urmia Plain Aquifer is encompassed by Lake Urmia to the east, the border mountains of Iran and Turkey to the west, chiefly including the Seir Mountains, Ghiz Ghaleh, Jahoudha, Chehel Mor Shahedan, Mah, Ali Panjeh-ye Si, and Ali Iman, the Zolachay and Kharkharechay watersheds to the north, and the Gadar Chay watershed to the south. Tectonically, the West Azerbaijan province is located within the Caucasus and Alborz Mountains at a distance from the Zagros Mountains (Hoseynalizadeh Alizadeh et al, 2017). There is no agreement regarding the actual geological setting of Azerbaijan (Ghorbani, 2013 ). Regarding Innocenti et al ( 1982 ), the northern part of West Azerbaijan includes the Caucasus and Pontus Mountains in Turkey while the southern part is comparable to Central Iran and Western Iran, and its tectonics extend into the Taurus Mountains in Turkey. A significant structural event occurring in the Early Devonian was accompanied by faulting and fragmentation that led to distinct sedimentary facies in the Azerbaijan domain (Eftekharnezhad, 1980 ). The shape and morphology of the Urmia Lake region are tectonically controlled by two main fault systems, one in the NE-SW direction and the other almost parallel to the strike of the Zagros Thrust System (Alizadeh, 2013 ). The eastern and central parts of the Urmia urban area lie on wide plains (Fig. 1 ), while other parts are mountainous, lying in the highlands (Shahrabi 1994 ). Except for alluvial deposits, lithological units in the Urmia region have been deformed by fold and fault systems. Meteorologically, the Urmia Plain Aquifers experiences relatively warm summers and cold winters. The rainfalls generally start in late October and continue until June. The Urmia Plain Aquifers are recharged by a number of rivers such as Nazluchay, Ruzehchay, Shahrchay, and Barandozchay. Lithostratigraphically, the plain encompasses a range of lithological units from Cenozoic mainly including salty flats, recent alluvial deposits, alluvial fans, thin-layered limy sandstones and marn, reefy and marl limestones, Miocene conglomerates, to Paleozoic chiefly comprising limestones, dolomitic limestones, laterite-bearing dolostones, shales, greenish siltstones and sandstones and reddish to greyish arkosic sandstones along with the post-Cretaceous and pre-Oligocene intrusive rocks (Geological Survey of Iran, 2007). Quaternary deposits around Urmia city consist of 10 lithological units (Sartipi et al. 2014 ): old terraces, sandy conglomerate and sandstone, Paleo-Urmia Lake deposits (marl, mudstone and conglomerate), young terraces, clay silt, freshwater limestone and travertine, clay and silty clay, recent alluvial fan deposits, old alluvial fan deposits, and recent alluvial deposits. Hydrologically, the plain aquifer is located in the vicinity of the western shore of Lake Urmia, with an area of 749 Km 2 and an average elevation of 1381 m from free seawater level. The watersheds of the Urmia Plain aquifer, from north to south, include Nazlu Chay, Ruzeh Chay, Shahr Chay, and Barandoz Chay. There are 160 logs from 61 locations (Fig. 3 ) and 12 tranches through the plain (Table S1). The average depth of wells described in the logs is 22m. The average depth of the groundwater level in the plain is 11.2 m (Table 2 ). Methodology Land subsidence is a widespread phenomenon globally, primarily resulting from the excessive exploitation of underground water resources and the consequent decline in their levels. The main objective of this research is to measure the extent of subsidence in the Urmia Plain aquifer using radar interferometry. To date, no comprehensive study has been conducted in the study area using this method. DInSAR (Differential Interferometric Synthetic Aperture Radar) is an effective technique for monitoring land subsidence caused by groundwater depletion (Gao et al, 2018 )( Caló et al,2017) (Zamiri-Aghdam et al,2022).DInSAR uses satellite radar images to detect and measure ground displacement over time with millimeter-level accuracy(Gao et al, 2018 )( Caló et al,2017). By comparing radar images acquired at different times, it can identify and quantify land subsidence.The technique is particularly useful for monitoring subsidence in areas with unconsolidated aquifer systems, where the presence of significant fine-grained materials can lead to compaction and land surface lowering as groundwater is withdrawn(Gao et al, 2018 ) (Choubin et al, 2023 ).DInSAR can be used to create detailed subsidence maps that show the spatial distribution and magnitude of land displacement (Gao et al, 2018 ) ( Caló et al,2017). These maps can help identify subsidence hotspots and track changes over time.By correlating DInSAR-derived subsidence data with groundwater level measurements, it is possible to establish a direct link between aquifer depletion and land surface lowering ( Caló et al,2017).This information is crucial for managing groundwater resources and mitigating subsidence-related hazards. In this research, Sentinel-1 radar images and SNAP software were used to determine the level of land subsidence, and water level data were utilized to evaluate the model. The following data and software were employed for this research:Digital Elevation Model (DEM SRTM) with 30 meters resolution, Geological map at a scale of 100 000 sheets of Urmia, Oshnavieh, and Saru (Gangchin),Underground water depth data in Excel format (from 2015 to 2022) (provided by the Water Organization of West Azerbaijan), Arc Map software version 10.8, SNAP software,Sentinel-1 images (downloaded from scihub.copernicus.eu) S1A_IW_SLC__1SDV_20150502T150041_20150502T150108_005744_0075FF_7E12 S1A_IW_SLC__1SDV_20230426T150122_20230426T150150_048269_05CDF2_28C2 To investigate the extent of subsidence in the study area, Sentinel-1 radar data were used, which have a wavelength of 5.54 centimeters. The images were taken during the descending pass on May 2, 2015, and April 26, 2023. The data used are in SLC format. Information related to the downloaded images is shown in Table (1). In this research, radar interferometry was used to determine the amount of displacement. For this purpose, the image dated May 2 was selected as the base image, and the image dated April 26 as the follow-up image. Table 1 Detailed information on images and their baseline distances. Polarization Orbit Mode Type Date Satellite VV Descending SLC 2015/05/02 Sentinel-1A PART 1 VV Descending SLC 2023/04/26 Sentinel-1A To evaluate the results obtained from InSAR, the depression of the groundwater in the study area from 2015 to 2023 was considered using the data taken from the West Azerbaijan Regional Water Authority. Therefore, a groundwater contour map, with the Inverse Distance Weighting (IDW) interpolation method, was generated using Arc GIS 10.8. To investigate the groundwater depression, first, a map was plotted for each annual water level and then the final map was provided for the selected period. Radar Data Analysis Using DInSAR Technique The radar interferometry is generally applied using the two methods of Single-path and Multi-path interferometry. In single-path interferometry, two antennas installed on a platform are responsible for simultaneously collecting data in both the parallel (along track) and perpendicular (cross-track) to the flight direction In multi-path interferometry, there is only an antenna in the platform to cover two different passes with nearly similar geometries. This method relies on precise flight path information and is applicable to monitor deformation, land subsidence, and volcanoes (Dehghani et al, 2009 ). The distance between two flight paths is referred to as the baseline. Each Synthetic Aperture Radar (SAR) imaging system has a fundamental baseline length limitation that should not be surpassed. Since the Sentinel-1 satellite data consists of separate blocks and pieces, a De-bursting process is applied to integrate the data. The radar interferometry is influenced by a number of factors including steep terrain, high incidence angle, low resolution, and short wavelength (Massonnet et al, 1993 ). Radar data is collected with a side-looking perspective during the motion of the Earth's surface, sweeping it with microwave signals. The received signals are composed of complex numbers representing amplitude and phase. The basics of interferometric radar is to utilize the phase information reflected from the Earth's surface. Deformations on the ground induce changes in the phase between two radar images collected from the same area at different times. By analyzing and modelling this phase difference, ground displacement can be quantified (Massonnet & Feigl, 1998 ). The interferogram is generated by subtracting the phases of two synthetic aperture radar (SAR) images acquired at different times. In general, the phase of an interferogram is composed of the following components (Hanssen, 2001 ): $$\varDelta \varnothing ={\varnothing }_{atm}+{{\varnothing}}_{Topo}+{\varnothing }_{Defo}+{{\varnothing}}_{orb}$$ where \({\varnothing }_{atm}\) , \({{\varnothing}}_{Topo}\) , \({\varnothing }_{Defo}\) and \({{\varnothing}}_{orb}\) are the atmospheric, topographic, deformation, and orbital phases, respectively. Atmospheric effects can be mitigated by utilizing repeated images or correction sources such as GPS. The orbital geometry errors can be reduced by the sensor orbital information. Hence, the topography information obtained from DInSAR analysis is necessary for identifying the land deformation. In this research, to generate an interferogram, Sentinel images were first imported into the software and a multilooking process to create square pixels in both azimuth and range directions and record orbital information was then performed. Finally, the slave image was geometrically recorded relative to the master image, resulting in a corresponding pixel-to-pixel matching between the two images. The filtering process using the Goldstein filter was applied in the azimuth direction due to the Doppler frequency difference, and in the range direction due to the variable viewing angles of the sensors. Moreover, based on the calculated geometric parameters, the slave image is resampled relative to the master image, aligning a pixel-to-pixel correspondence. The pixel-to-pixel images were then multiplied together to calculate the phase difference between the corresponding pixels. This phase difference was represented in the interferogram. As mentioned earlier, the final phase should be free from topographic, atmospheric, and orbital errors. To eliminate topographic errors, the SRTM digital elevation model (DEM) was used. The measured phase in interferometry is a fraction of 2π, and the number of phase cycles is unknown. Therefore, to assign absolute phase values to each pixel in the image, a phase unwrapping process in the phase recovery stage was performed. The resulting image from this stage included interferometric fringes. The distance between these fringes of the same colour is equal to half the wavelength of the sensor. The displacement map was finally provided. A coherence image was used to confirm the model's validity. Based on the selected coherence image, the accuracy was acceptable, with a coherence magnitude of 0.76 in the region of subsidence (-0.07). Results and discussion Initially, the subsidence map of the study area was prepared using the SNAP software, and subsequently, utilizing the piezometric well data received from the Regional Water Authority of West Azerbaijan Province, the groundwater depth map of the Urmia Plain aquifer was prepared and analyzed for the time period of the years 2015 to 2022. By using the information from the subsidence map, areas that are at risk can be identified and utilized in environmental studies by managers and planners. Additionally, the groundwater depth map can help us understand the relationship between water levels and subsidence, and ensure adequate monitoring in risk-prone areas. To estimate the extent of changes due to subsidence in the Urmia Plain aquifer, two Sentinel-1 satellite images were analyzed for the time period from May 2, 2015, to April 26, 2023. To facilitate processing speed, the study area, which is located in the IW 3 swath of Sentinel-1 images, was selected. To obtain ground displacement over a time period, orbital errors, topographic effects, and atmospheric noise must be removed from the interferogram. The interferogram phase contains the effects of topography, target displacement, and atmospheric influences. To remove noise from the interferograms, the Goldstein filter was used, and subsequently, phase unwrapping was performed to convert the phases into values that represent ground displacement. After obtaining the displacement map, a coherence image was used for model validation. According to the coherence image, the subsidence is -0.07 with a coherence of 0.76, indicating an acceptable level of precision. The interpolation map of the land subsidence rate in the Urmia Plain Aquifers from 2015 to 2023 using the DInSAR method is illustrated in Fig. 3 . The land displacement varies between − 0.09 and 0.96 m. The southern regions of the plain indicated a maximum subsidence rate of -0.09 m during the considered period, while the northern regions displayed an uplift of 0.96 m. This displacement in different regions of the plain plays an important role in the groundwater level fluctuations. The position of piezometric wells of the Urmia Plains Aquifer, including 60 boreholes, is shown in Fig. 4 . The groundwater levels in these wells were monitored from April 2015 to December 2023 and the interpolation maps of the groundwater levels for each year (Figs. 5 a-h) and for 2015 to 2023 were provided The annual groundwater levels varies in different parts of the plain, where the water level in the southern parts of the plain is lower than in the northern parts. Comparing the groundwater level during the considered time span with the estimated land subsidence of the Urmia Plains Aquifer suggests that there is an agreement between the groundwater level fluctuations and the land subsidence. Hence, both the highest groundwater depression and the maximum land subsidence occur in the southern parts of the plain. This reflects that the results obtained from the DInSAR method are in line with those from groundwater level data, considering interferometry as a strong tool in groundwater decline management studies. According to the maps prepared for each year, the area of classes for each map was obtained (Table 2 ). As observed in the table, the largest area is related to changes in the water level in Class 1 in the year 2019, with an area of 363.81km 2 . This indicates that in this year, 363.81km 2 had a water level between 0–5 meters, and a significant portion of this area experienced a decrease in water level. The smallest changes in the water level in Class 1 are associated with the year 2021, with a value of 219,57 km 2 . This means that in this year, 219,57 km 2 had a water level between 0–5 meters, and a comparatively smaller area experienced a decrease in water level. (In general, the elevation difference in the study area is 238 m and Class 1 was selected for comparison due to its lower elevation and proximity to Lake Urmia). In the southern regions of the Urmia Plains Aquifer, there is a significant subsidence, resulting in a lower groundwater level, confirming the correlation between subsidence and groundwater level. Table 2: Changes in Water Depth Based on Area During the Years 2015-2022. Class Area 7 ) KM 2 ( Class Area 6 ) KM 2 ( Class Area 5 ) KM 2 ( Class Area 4 ) KM 2 ( Class Area 3 ) KM 2 ( Class Area 2 ) KM 2 ( Class Area 1 ) KM 2 ( Year 13/16 41/14 19/67 27/42 73/35 239/81 334/52 2015 12/80 23/29 24/61 28/30 89/93 217/61 352/49 2016 8/75 24/68 27/96 31/27 107/20 303/96 254/31 2017 11/57 38/09 20/68 34/84 95/90 312/47 235/18 2018 8/69 20/21 24/08 25/16 84/32 222/82 363/81 2019 10/48 16/41 28/40 23/91 78/60 252/02 339/26 2020 13/20 36/17 20/56 34/54 105/25 319/73 219/57 2021 14/12 36/91 19/7 32/12 132/40 256/16 248/63 2022 Conclusions This study was carried out regarding the recent groundwater level fluctuations of the Urmia Plains Aquifer, resulting in land subsidence. Hence, the monthly and annual groundwater levels for eight years were integrated with the DInSAR technique to assess the Urmia Plains Aquifer land displacement. The results of the interferometric radar technique indicated a strong correlation with the groundwater level data. The maximum land subsidence was in line with the highest groundwater level decline, where the overexploitation of the groundwater occurs in the southern parts of the study area. This has been mainly due to the increasing agricultural activities in the southern sectors. Hence, using the DInSAR technique, more vulnerable regions of the Urmia Plains Aquifer were considerably identified. Although radar techniques are powerful tools to estimate various geological phenomena such as land displacement, the validity of the results is significantly affected by the spatial resolution of radar images and the accuracy of the radar data Hence, integrating radar techniques with the experimental and field data is suggested. Also, applying geophysical methods along with considering regional tectonics can efficiently improve the results obtained from this investigation. Furthermore, to mitigate the rate of land subsidence in Urmia Plains Aquifer, appropriate controlling attempts to prevent excess exploitation of the groundwater and illegal well drilling are strongly recommended. In this regard, placing and monitoring geodetic markers in areas with higher land subsidence rates may be considered a controlling tool to manage better and mitigate the consequent hazards of land subsidence in the study area. Recommendation Implement strict regulations on groundwater extraction to prevent further depletion of the aquifer system. Promote water conservation practices and efficient irrigation techniques in agriculture, which accounts for the majority of groundwater use in the region. Develop alternative water sources, such as surface water or treated wastewater, to reduce reliance on groundwater. Develop early warning systems to identify subsidence hotspots and track changes in subsidence rates over time, allowing for proactive management of the aquifer system. Implement artificial groundwater recharge programs, such as injection wells or infiltration basins, to replenish the aquifer system and mitigate subsidence. Promote natural recharge by preserving and restoring wetlands, lakes, and other surface water bodies in the region. Conduct detailed risk assessments to identify areas most susceptible to subsidence-related hazards, such as infrastructure damage or groundwater contamination. Develop and implement mitigation strategies, such as reinforcing infrastructure or grouting techniques, to minimize the impact of subsidence in high-risk areas. Involve local communities, farmers, and other stakeholders in the decision-making process for groundwater management and subsidence mitigation. Provide education and training programs to raise awareness about the importance of sustainable groundwater use and the risks associated with land subsidence. References Alizadeh, A. (2013). 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Investigation of the phenomenon of subsidence due to the decrease of groundwater level in the city of Urmia using the zoning of changes in the distribution of soil layers. Iranian journal of Ecohydrology, 8(3), 791-806. Nasiri, A., Shafiei, N., & Farzin Kia, R. (2021). Investigation of Fahlian aquifer subsidence and its effect on groundwater loss. Arabian Journal of Geosciences, 14(7), 637. Panahi, M., Khosravi, K., Golkarian, A., Roostaei, M., Barzegar, R., Omidvar, E., ... & Lee, S. (2022). A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning. Geocarto International, 37(26), 14065-14087. Rafiei, F., Gharechelou, S., Golian, S., & Johnson, B. A. (2022). Aquifer and land subsidence interaction assessment using sentinel-1 data and DInSAR technique. ISPRS International Journal of Geo-Information, 11(9), 495. Raspini, F., Caleca, F., Del Soldato, M., Festa, D., Confuorto, P., & Bianchini, S. (2022). Review of satellite radar interferometry for subsidence analysis. Earth-Science Reviews, 235, 104239. Robinson, D. T., Di Vittorio, A., Alexander, P., Arneth, A., Barton, C. M., Brown, D. G., ... & Verburg, P. H. (2018). Modelling feedbacks between human and natural processes in the land system. Earth System Dynamics, 9(2), 895-914. Sartipi, A. H., Haghfarshi, E., Karimi, H., Shiva, E., Seidi Sahbari, P., Vakil Baghmisheh, F., & Zamani mehr, S. (2014). Geological report of the Urmia map (1:25000); 5065 III SW. (In Persian). scihub.copernicus.eu. Shahrabi, M. (1994). Description of the Urumieh geological map (scale 1/250000): Geological Survey of Iran, B3, 90 p. Shokri, N., Mahdavi Ara, M., Ansari, S., & Sharifi, M. (2023, May). Toward prediction of land subsidence assisted by artificial intelligence approaches. In EGU General Assembly Conference Abstracts (pp. EGU-5025). Widodo, J., Herlambang, A., Sulaiman, A., Razi, P., Perissin, D., Kuze, H., & Sumantyo, J. T. S. (2019, April). Land subsidence rate analysis of Jakarta Metropolitan Region based on D-InSAR processing of Sentinel data C-Band frequency. In Journal of Physics: Conference Series (Vol. 1185, No. 1, p. 012004). IOP Publishing. Yousefi, R., & Talebbeydokhti, N. (2021). Subsidence monitoring by integration of time series analysis from different SAR images and impact assessment of stress and aquitard thickness on subsidence in Tehran, Iran. Environmental Earth Sciences, 80(11), 418. Zamiri-Aghdam1, F., Akhoondzadeh, M., Dehghanijabbarlou, M.(2022). Monitoring of Urmia Lake Bridge Subsidence during 2014- 2021 Using DInSAR-SBAS Method and GPS Data, Journal of Geomatics Science and Technology,11(4),97-105. Zhao, R., Arabameri, A., & Santosh, M. (2024). Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithms. Environmental Science and Pollution Research, 31(10), 15443-15466. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4352949","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374591475,"identity":"97ff2421-370b-49df-a810-90e4fee78e21","order_by":0,"name":"Fariba Hemmati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBAC9gYeCIONvQFIGlgQ1sJzAKzFQIKN5wCYJkELg0QCiEGMFgbeg49u1Pyp45N8fnXDjwIJBv727gQCWviSjXOOAR0mnVN2swdk25mzG/BqsWfgMZPOYQNrSbvBA9RiIJGLXwsPA4/575x/QC2SZ9Ju/iFSixlzbhtQiwT7sdvE2cLMYyyd22cs2caTw3ZbxkCCh6BfeNh7DD/nfJPjl28//uzmmz82cvztvfi1MDAjdBtAXEoCYH9AiupRMApGwSgYQQAAgNE6pYaTLlwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-4023-8614","institution":"Farhangian University","correspondingAuthor":true,"prefix":"","firstName":"Fariba","middleName":"","lastName":"Hemmati","suffix":""},{"id":374591476,"identity":"dcade5a4-e410-41c0-b828-356953ec35b9","order_by":1,"name":"Sara Khanjari","email":"","orcid":"","institution":"Urmia University","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Khanjari","suffix":""},{"id":374591477,"identity":"f184d50f-9cb5-4d0a-8d27-375fad36df0b","order_by":2,"name":"Akram Alizadeh","email":"","orcid":"","institution":"Urmia University","correspondingAuthor":false,"prefix":"","firstName":"Akram","middleName":"","lastName":"Alizadeh","suffix":""}],"badges":[],"createdAt":"2024-05-01 07:29:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4352949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4352949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69313211,"identity":"960aa993-12e6-4e8c-b0c1-de92a4b1850a","added_by":"auto","created_at":"2024-11-19 05:29:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":422015,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap showing position of the study area.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4352949/v1/3959d693a0da056d6d90766b.png"},{"id":69313210,"identity":"914af129-c667-40b1-ab8e-17ad03434b95","added_by":"auto","created_at":"2024-11-19 05:29:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":222464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReconstructed Geological Map of the study area usnig the 1:100,000 Geological Map of Urmia (after Geological Survey of Iran, 2007)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4352949/v1/ff72bdd5f5e5213a0f2c59f0.png"},{"id":69313206,"identity":"3125037c-d2c1-4ed0-bff4-3e9d6ff47aab","added_by":"auto","created_at":"2024-11-19 05:29:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":503964,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLand subsidence levels (in m) in the Urmia Plain Aquifer using the DInSAR method.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4352949/v1/1ad1d6de6acde46efedf4046.png"},{"id":69313208,"identity":"bfbec958-8556-431d-96c7-8d020cf5cc62","added_by":"auto","created_at":"2024-11-19 05:29:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":316897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of the Locations of Piezometric Wells in the Urmia Plain Aquifer Aquifer, Prepared from the Data of the Water Organization of West Azerbaijan Province.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4352949/v1/fde82ce210d00d230fafff1e.png"},{"id":69313602,"identity":"8f63215a-4e60-4070-aa77-d2531fdcc8b7","added_by":"auto","created_at":"2024-11-19 05:37:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1271046,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWater Depth Map for the Years 2015-2022 (in Meters)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4352949/v1/33c66931f1a022648a1dea3b.png"},{"id":74916930,"identity":"5e3caa69-0ae4-4e69-ba91-f9b7e927388a","added_by":"auto","created_at":"2025-01-28 10:08:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3717810,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4352949/v1/da0f42f1-3884-4174-95e1-e591d9efd70f.pdf"}],"financialInterests":"","formattedTitle":"Land Subsidence and Displacement Occurrence in the Urmia Plain Aquifer Using Interferometric Radar Technique and Hidden Faults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLand subsidence, characterized by vertical downward movements and horizontal displacements, is a global threat induced mainly by groundwater depletion in arid and semiarid regions (Zhao et al \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overexploitation of groundwater resources in urban areas exacerbates land subsidence, impacting infrastructures and increasing flood vulnerability ( Shokri et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e ). Human-induced processes like groundwater extraction contribute significantly to accelerated land subsidence rates in coastal environments, affecting over 500\u0026nbsp;million people globally (Minderhoud et al \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Radar interferometric techniques and machine learning models have been utilized to predict and prioritize influential variables in land subsidence occurrence, revealing that factors beyond groundwater withdrawals, such as proximity to geological features like anticlines and faults, play crucial roles in susceptibility (Buffardi \u0026amp; Ruberti, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).This interdisciplinary research highlights the importance of understanding and managing land subsidence to mitigate environmental damages and socio-economic issues worldwide.\u003c/p\u003e \u003cp\u003eLand displacement is a global issue. During recent years, land displacement in Iran has led to several problems in various sectors, mainly including agricultural lands, residential buildings, roads, and power transmission lines (Marselis et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It has mainly been due to natural processes such as local active tectonics and anthropogenic activities( Robinson et al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In recent years, the Iranian plains, including the Urmia Plain, have significantly been impaired by a large number of land subsidence occurrences, especially in the residential and agricultural sectors (Khoshlahjeh et al. 2021). The land subsidence phenomenon has been assessed by a large number of researchers all over the world (Gharechaee et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003einvestigated land subsidence in Mexico City from 2014 to 2015 using the D-InSAR method and concluded that the subsidence rate in some locations exceeds 40 cm/y. In this study, the potential of Sentinel-1 images to support D-InSAR assessments across a large and diverse region was highlighted. In two different studies conducted in Jakarta, Indonesia, the land subsidence rate varied between 3\u0026ndash;10 cm/y and 2\u0026ndash;3 cm/y (Widodo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). evaluated the land subsidence risk in Xi'an, China, using a risk matrix that integrates differential settlement, ground cracks, and land-use classification through radar techniques and optical images(Gharechaee et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).In Iran, several investigations have been conducted to evaluate the incidence and susceptibilities of land subsidence.\u003c/p\u003e \u003cp\u003eGharechaee et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted a study on land subsidence susceptibility mapping in Iran's Bakhtegan basin. They utilized InSAR technology and machine learning models to uncover factors contributing to this phenomenon, which extend beyond groundwater extraction. Their findings highlighted the significance of geographical elements such as proximity to dams, faults, and mines.\u003c/p\u003e \u003cp\u003eKarami et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) leveraged SBAS-InSAR and GIS methodologies to observe land subsidence and displacement in Shiraz, Iran. They were able to pinpoint areas with significant displacement, which could be attributed to building densities and population distribution.\u003c/p\u003e \u003cp\u003eGhorbani et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) employed the Interferometric Radar Technique to observe notable land subsidence resulting from groundwater depletion and shifts in climate within the Ardabil Plain, located in Iran, showcasing a peak rate of 45 mm per year.\u003c/p\u003e \u003cp\u003ePanahi et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted an assessment on the susceptibility of land subsidence in Iran through the utilization of InSAR and machine learning techniques. Areas such as Razavi Khorasan, as well as provinces like Hamedan and Khouzestan, exhibit notable susceptibility towards future occurrences of land subsidence.\u003c/p\u003e \u003cp\u003eRafiei et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted an assessment of land subsidence in the Samalghan plain in Iran through the utilization of the DInSAR technique. The findings revealed notable subsidence attributed to the overexploitation of groundwater, resulting in a maximum subsidence of 34 cm in the year 2019.\u003c/p\u003e \u003cp\u003eMirzadeh et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a study on the characterization of irreversible land subsidence in the Yazd-Ardakan Plain, Iran spanning from 2003 to 2020 by utilizing InSAR time series. Their findings reveal a notable instance of land subsidence attributed to the excessive extraction of groundwater.\u003c/p\u003e \u003cp\u003eYousefi et al. (2021) utilized the interferometric radar technique for the purpose of monitoring land subsidence in Tehran, Iran. The integration of data from various SAR images enabled the assessment of stress and the impact of aquitard thickness, revealing a weak correlation with groundwater exploitation.\u003c/p\u003e \u003cp\u003eNasiri et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a study in Iran's Fahlian Basin, employing DINSAR to observe land subsidence. The findings indicated an average subsidence rate of 4 cm/year, with a 75% correlation between groundwater decline and subsidence.\u003c/p\u003e \u003cp\u003eMoghimi et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) described the Urmia Plain as having a complex tectonic configuration characterized by numerous faults and fractures that significantly shape the region's geomorphology. The interaction between natural geological features such as faults and fractures, in conjunction with anthropogenic activities like the excessive extraction of groundwater resources, has played a significant role in the occurrence of land subsidence phenomena within the Urmia Plain.\u003c/p\u003e \u003cp\u003eThe Urmia Plain Aquifer has a complex tectonic structure, with numerous faults and fractures that noticeably govern the geomorphology of the area. The naturally occurring geologic features (e.g., faults and fractures) together with anthropogenic activities (e.g., the excess exploitation of groundwater) have considerably contributed to the land subsidence occurrence in the Urmia Plain Aquifer .\u003c/p\u003e \u003cp\u003eThe occurrence of hidden faults provides crucial information to estimate seismic hazards and land subsidence vulnerabilities. The subsidence occurrence of a faulted area can be assessed by studying the relationships between rock strata. The main factors contributing to land subsidence in the Urmia Plain Aquifer are:Groundwater depletion due to overexploitation for agricultural and domestic use(Raspini et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)( Gharechaee et al, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)( Cal\u0026oacute; et al,2017). As groundwater levels decline, the aquifer system compacts, leading to land surface lowering.The presence of significant fine-grained materials (e.g., clay and silt) in the unconsolidated aquifer system (Cal\u0026oacute; et al,2017) (Choubin et al, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).. These sediments are prone to compaction when groundwater is withdrawn, exacerbating subsidence.Hence, performing this research to identify more vulnerable regions through land displacement in the Urmia Plain Aquifer. sounds necessary. This research aims to assess the occurrence of Urmia Plain Aquifer. land subsidence and displacement and identify the regions with higher land subsidence vulnerability using groundwater level data and hidden faults information. For this reason, the interferometric radar technique (InSAR) via the SNAP (Sentinel Application Platform) software, an efficient tool to estimate land subsidence occurrence along with Arc GIS was applied.\u003c/p\u003e"},{"header":"Research area","content":"\u003cp\u003eThe Urmia Plain Aquifer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the West Azerbaijan province, is situated in the northwest of Iran (N 44\u0026deg; 15' to 45\u0026deg; 52' and E 37\u0026deg; 18' to 37\u0026deg; 47'). Urmia Plain Aquifer is encompassed by Lake Urmia to the east, the border mountains of Iran and Turkey to the west, chiefly including the Seir Mountains, Ghiz Ghaleh, Jahoudha, Chehel Mor Shahedan, Mah, Ali Panjeh-ye Si, and Ali Iman, the Zolachay and Kharkharechay watersheds to the north, and the Gadar Chay watershed to the south.\u003c/p\u003e \u003cp\u003eTectonically, the West Azerbaijan province is located within the Caucasus and Alborz Mountains at a distance from the Zagros Mountains (Hoseynalizadeh Alizadeh et al, 2017). There is no agreement regarding the actual geological setting of Azerbaijan (Ghorbani, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Regarding Innocenti et al (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), the northern part of West Azerbaijan includes the Caucasus and Pontus Mountains in Turkey while the southern part is comparable to Central Iran and Western Iran, and its tectonics extend into the Taurus Mountains in Turkey. A significant structural event occurring in the Early Devonian was accompanied by faulting and fragmentation that led to distinct sedimentary facies in the Azerbaijan domain (Eftekharnezhad, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). The shape and morphology of the Urmia Lake region are tectonically controlled by two main fault systems, one in the NE-SW direction and the other almost parallel to the strike of the Zagros Thrust System (Alizadeh, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The eastern and central parts of the Urmia urban area lie on wide plains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), while other parts are mountainous, lying in the highlands (Shahrabi \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Except for alluvial deposits, lithological units in the Urmia region have been deformed by fold and fault systems.\u003c/p\u003e \u003cp\u003eMeteorologically, the Urmia Plain Aquifers experiences relatively warm summers and cold winters. The rainfalls generally start in late October and continue until June. The Urmia Plain Aquifers are recharged by a number of rivers such as Nazluchay, Ruzehchay, Shahrchay, and Barandozchay.\u003c/p\u003e \u003cp\u003eLithostratigraphically, the plain encompasses a range of lithological units from Cenozoic mainly including salty flats, recent alluvial deposits, alluvial fans, thin-layered limy sandstones and marn, reefy and marl limestones, Miocene conglomerates, to Paleozoic chiefly comprising limestones, dolomitic limestones, laterite-bearing dolostones, shales, greenish siltstones and sandstones and reddish to greyish arkosic sandstones along with the post-Cretaceous and pre-Oligocene intrusive rocks (Geological Survey of Iran, 2007).\u003c/p\u003e \u003cp\u003eQuaternary deposits around Urmia city consist of 10 lithological units (Sartipi et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e): old terraces, sandy conglomerate and sandstone, Paleo-Urmia Lake deposits (marl, mudstone and conglomerate), young terraces, clay silt, freshwater limestone and travertine, clay and silty clay, recent alluvial fan deposits, old alluvial fan deposits, and recent alluvial deposits.\u003c/p\u003e \u003cp\u003eHydrologically, the plain aquifer is located in the vicinity of the western shore of Lake Urmia, with an area of 749 Km\u003csup\u003e2\u003c/sup\u003e and an average elevation of 1381 m from free seawater level. The watersheds of the Urmia Plain aquifer, from north to south, include Nazlu Chay, Ruzeh Chay, Shahr Chay, and Barandoz Chay. There are 160 logs from 61 locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and 12 tranches through the plain (Table S1). The average depth of wells described in the logs is 22m. The average depth of the groundwater level in the plain is 11.2 m (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eLand subsidence is a widespread phenomenon globally, primarily resulting from the excessive exploitation of underground water resources and the consequent decline in their levels. The main objective of this research is to measure the extent of subsidence in the Urmia Plain aquifer using radar interferometry. To date, no comprehensive study has been conducted in the study area using this method. DInSAR (Differential Interferometric Synthetic Aperture Radar) is an effective technique for monitoring land subsidence caused by groundwater depletion (Gao et al, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)( Cal\u0026oacute; et al,2017) (Zamiri-Aghdam et al,2022).DInSAR uses satellite radar images to detect and measure ground displacement over time with millimeter-level accuracy(Gao et al, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)( Cal\u0026oacute; et al,2017). By comparing radar images acquired at different times, it can identify and quantify land subsidence.The technique is particularly useful for monitoring subsidence in areas with unconsolidated aquifer systems, where the presence of significant fine-grained materials can lead to compaction and land surface lowering as groundwater is withdrawn(Gao et al, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) (Choubin et al, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).DInSAR can be used to create detailed subsidence maps that show the spatial distribution and magnitude of land displacement (Gao et al, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) ( Cal\u0026oacute; et al,2017). These maps can help identify subsidence hotspots and track changes over time.By correlating DInSAR-derived subsidence data with groundwater level measurements, it is possible to establish a direct link between aquifer depletion and land surface lowering ( Cal\u0026oacute; et al,2017).This information is crucial for managing groundwater resources and mitigating subsidence-related hazards.\u003c/p\u003e\n\u003cp\u003eIn this research, Sentinel-1 radar images and SNAP software were used to determine the level of land subsidence, and water level data were utilized to evaluate the model. The following data and software were employed for this research:Digital Elevation Model (DEM SRTM) with 30 meters resolution, Geological map at a scale of 100 000 sheets of Urmia, Oshnavieh, and Saru (Gangchin),Underground water depth data in Excel format (from 2015 to 2022) (provided by the Water Organization of West Azerbaijan), Arc Map software version 10.8, SNAP software,Sentinel-1 images (downloaded from scihub.copernicus.eu)\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003cul\u003e\n \u003cli\u003eS1A_IW_SLC__1SDV_20150502T150041_20150502T150108_005744_0075FF_7E12\u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003cul\u003e\n \u003cli\u003eS1A_IW_SLC__1SDV_20230426T150122_20230426T150150_048269_05CDF2_28C2\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eTo investigate the extent of subsidence in the study area, Sentinel-1 radar data were used, which have a wavelength of 5.54 centimeters. The images were taken during the descending pass on May 2, 2015, and April 26, 2023. The data used are in SLC format. Information related to the downloaded images is shown in Table (1). In this research, radar interferometry was used to determine the amount of displacement. For this purpose, the image dated May 2 was selected as the base image, and the image dated April 26 as the follow-up image.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetailed information on images and their baseline distances.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePolarization\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOrbit Mode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSatellite\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015/05/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSentinel-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePART 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023/04/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSentinel-1A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTo evaluate the results obtained from InSAR, the depression of the groundwater in the study area from 2015 to 2023 was considered using the data taken from the West Azerbaijan Regional Water Authority. Therefore, a groundwater contour map, with the Inverse Distance Weighting (IDW) interpolation method, was generated using Arc GIS 10.8. To investigate the groundwater depression, first, a map was plotted for each annual water level and then the final map was provided for the selected period.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eRadar Data Analysis Using DInSAR Technique\u003c/h2\u003e\n \u003cp\u003eThe radar interferometry is generally applied using the two methods of Single-path and Multi-path interferometry. In single-path interferometry, two antennas installed on a platform are responsible for simultaneously collecting data in both the parallel (along track) and perpendicular (cross-track) to the flight direction In multi-path interferometry, there is only an antenna in the platform to cover two different passes with nearly similar geometries. This method relies on precise flight path information and is applicable to monitor deformation, land subsidence, and volcanoes (Dehghani et al, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The distance between two flight paths is referred to as the baseline. Each Synthetic Aperture Radar (SAR) imaging system has a fundamental baseline length limitation that should not be surpassed. Since the Sentinel-1 satellite data consists of separate blocks and pieces, a De-bursting process is applied to integrate the data. The radar interferometry is influenced by a number of factors including steep terrain, high incidence angle, low resolution, and short wavelength (Massonnet et al, \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRadar data is collected with a side-looking perspective during the motion of the Earth\u0026apos;s surface, sweeping it with microwave signals. The received signals are composed of complex numbers representing amplitude and phase. The basics of interferometric radar is to utilize the phase information reflected from the Earth\u0026apos;s surface. Deformations on the ground induce changes in the phase between two radar images collected from the same area at different times. By analyzing and modelling this phase difference, ground displacement can be quantified (Massonnet \u0026amp; Feigl, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe interferogram is generated by subtracting the phases of two synthetic aperture radar (SAR) images acquired at different times. In general, the phase of an interferogram is composed of the following components (Hanssen, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\varDelta \\varnothing ={\\varnothing }_{atm}+{{\\varnothing}}_{Topo}+{\\varnothing }_{Defo}+{{\\varnothing}}_{orb}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varnothing }_{atm}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\varnothing}}_{Topo}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varnothing }_{Defo}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\varnothing}}_{orb}\\)\u003c/span\u003e\u003c/span\u003eare the atmospheric, topographic, deformation, and orbital phases, respectively.\u003c/p\u003e\n \u003cp\u003eAtmospheric effects can be mitigated by utilizing repeated images or correction sources such as GPS. The orbital geometry errors can be reduced by the sensor orbital information. Hence, the topography information obtained from DInSAR analysis is necessary for identifying the land deformation.\u003c/p\u003e\n \u003cp\u003eIn this research, to generate an interferogram, Sentinel images were first imported into the software and a multilooking process to create square pixels in both azimuth and range directions and record orbital information was then performed. Finally, the slave image was geometrically recorded relative to the master image, resulting in a corresponding pixel-to-pixel matching between the two images. The filtering process using the Goldstein filter was applied in the azimuth direction due to the Doppler frequency difference, and in the range direction due to the variable viewing angles of the sensors. Moreover, based on the calculated geometric parameters, the slave image is resampled relative to the master image, aligning a pixel-to-pixel correspondence.\u003c/p\u003e\n \u003cp\u003eThe pixel-to-pixel images were then multiplied together to calculate the phase difference between the corresponding pixels. This phase difference was represented in the interferogram. As mentioned earlier, the final phase should be free from topographic, atmospheric, and orbital errors. To eliminate topographic errors, the SRTM digital elevation model (DEM) was used.\u003c/p\u003e\n \u003cp\u003eThe measured phase in interferometry is a fraction of 2\u0026pi;, and the number of phase cycles is unknown. Therefore, to assign absolute phase values to each pixel in the image, a phase unwrapping process in the phase recovery stage was performed. The resulting image from this stage included interferometric fringes. The distance between these fringes of the same colour is equal to half the wavelength of the sensor. The displacement map was finally provided. A coherence image was used to confirm the model\u0026apos;s validity. Based on the selected coherence image, the accuracy was acceptable, with a coherence magnitude of 0.76 in the region of subsidence (-0.07).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eInitially, the subsidence map of the study area was prepared using the SNAP software, and subsequently, utilizing the piezometric well data received from the Regional Water Authority of West Azerbaijan Province, the groundwater depth map of the Urmia Plain aquifer was prepared and analyzed for the time period of the years 2015 to 2022. By using the information from the subsidence map, areas that are at risk can be identified and utilized in environmental studies by managers and planners. Additionally, the groundwater depth map can help us understand the relationship between water levels and subsidence, and ensure adequate monitoring in risk-prone areas. To estimate the extent of changes due to subsidence in the Urmia Plain aquifer, two Sentinel-1 satellite images were analyzed for the time period from May 2, 2015, to April 26, 2023. To facilitate processing speed, the study area, which is located in the IW 3 swath of Sentinel-1 images, was selected.\u003c/p\u003e\n\u003cp\u003eTo obtain ground displacement over a time period, orbital errors, topographic effects, and atmospheric noise must be removed from the interferogram. The interferogram phase contains the effects of topography, target displacement, and atmospheric influences. To remove noise from the interferograms, the Goldstein filter was used, and subsequently, phase unwrapping was performed to convert the phases into values that represent ground displacement. After obtaining the displacement map, a coherence image was used for model validation. According to the coherence image, the subsidence is -0.07 with a coherence of 0.76, indicating an acceptable level of precision.\u003c/p\u003e\n\u003cp\u003eThe interpolation map of the land subsidence rate in the Urmia Plain Aquifers from 2015 to 2023 using the DInSAR method is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The land displacement varies between \u0026minus;\u0026thinsp;0.09 and 0.96 m. The southern regions of the plain indicated a maximum subsidence rate of -0.09 m during the considered period, while the northern regions displayed an uplift of 0.96 m. This displacement in different regions of the plain plays an important role in the groundwater level fluctuations.\u003c/p\u003e\n\u003cp\u003eThe position of piezometric wells of the Urmia Plains Aquifer, including 60 boreholes, is shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The groundwater levels in these wells were monitored from April 2015 to December 2023 and the interpolation maps of the groundwater levels for each year (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea-h) and for 2015 to 2023 were provided The annual groundwater levels varies in different parts of the plain, where the water level in the southern parts of the plain is lower than in the northern parts. Comparing the groundwater level during the considered time span with the estimated land subsidence of the Urmia Plains Aquifer suggests that there is an agreement between the groundwater level fluctuations and the land subsidence. Hence, both the highest groundwater depression and the maximum land subsidence occur in the southern parts of the plain. This reflects that the results obtained from the DInSAR method are in line with those from groundwater level data, considering interferometry as a strong tool in groundwater decline management studies.\u003c/p\u003e\n\u003cp\u003eAccording to the maps prepared for each year, the area of classes for each map was obtained (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). As observed in the table, the largest area is related to changes in the water level in Class 1 in the year 2019, with an area of 363.81km\u003csup\u003e2\u003c/sup\u003e. This indicates that in this year, 363.81km\u003csup\u003e2\u003c/sup\u003e had a water level between 0\u0026ndash;5 meters, and a significant portion of this area experienced a decrease in water level. The smallest changes in the water level in Class 1 are associated with the year 2021, with a value of 219,57 km\u003csup\u003e2\u003c/sup\u003e. This means that in this year, 219,57 km\u003csup\u003e2\u003c/sup\u003e had a water level between 0\u0026ndash;5 meters, and a comparatively smaller area experienced a decrease in water level. (In general, the elevation difference in the study area is 238 m and Class 1 was selected for comparison due to its lower elevation and proximity to Lake Urmia). In the southern regions of the Urmia Plains Aquifer, there is a significant subsidence, resulting in a lower groundwater level, confirming the correlation between subsidence and groundwater level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Changes in Water Depth Based on Area During the Years 2015-2022.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable dir=\"rtl\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1795%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eClass Area\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e7\u003cspan dir=\"RTL\"\u003e)\u003c/span\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eClass Area 6\u003cspan dir=\"RTL\"\u003e)\u003c/span\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eClass Area 5\u003cspan dir=\"RTL\"\u003e)\u003c/span\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eClass Area 4\u003cspan dir=\"RTL\"\u003e)\u003c/span\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eClass Area 3\u003cspan dir=\"RTL\"\u003e)\u003c/span\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6603%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eClass Area 2\u003cspan dir=\"RTL\"\u003e)\u003c/span\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.141%;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eClass Area 1\u003cspan dir=\"RTL\"\u003e)\u003c/span\u003eKM\u003csup\u003e2\u003c/sup\u003e\u003cspan dir=\"RTL\"\u003e(\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.53205%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eYear\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1795%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e13/16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e41/14\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e19/67\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e27/42\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e73/35\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6603%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e239/81\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.141%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e334/52\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.53205%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2015\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1795%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e12/80\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e23/29\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e24/61\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e28/30\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e89/93\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6603%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e217/61\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.141%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e352/49\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.53205%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2016\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1795%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e8/75\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e24/68\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e27/96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e31/27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e107/20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6603%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e303/96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.141%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e254/31\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.53205%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2017\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1795%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e11/57\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e38/09\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e20/68\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e34/84\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e95/90\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6603%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e312/47\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.141%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e235/18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.53205%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2018\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1795%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e8/69\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e20/21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e24/08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e25/16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e84/32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6603%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e222/82\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.141%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e363/81\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.53205%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2019\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1795%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e10/48\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e16/41\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e28/40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e23/91\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e78/60\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6603%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e252/02\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.141%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e339/26\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.53205%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2020\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1795%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e13/20\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e36/17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e20/56\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e34/54\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e105/25\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6603%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e319/73\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.141%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e219/57\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.53205%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2021\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1795%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e14/12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e36/91\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e19/7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e32/12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6218%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e132/40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6603%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e256/16\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.141%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e248/63\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.53205%;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2022\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study was carried out regarding the recent groundwater level fluctuations of the Urmia Plains Aquifer, resulting in land subsidence. Hence, the monthly and annual groundwater levels for eight years were integrated with the DInSAR technique to assess the Urmia Plains Aquifer land displacement. The results of the interferometric radar technique indicated a strong correlation with the groundwater level data. The maximum land subsidence was in line with the highest groundwater level decline, where the overexploitation of the groundwater occurs in the southern parts of the study area. This has been mainly due to the increasing agricultural activities in the southern sectors. Hence, using the DInSAR technique, more vulnerable regions of the Urmia Plains Aquifer were considerably identified. Although radar techniques are powerful tools to estimate various geological phenomena such as land displacement, the validity of the results is significantly affected by the spatial resolution of radar images and the accuracy of the radar data Hence, integrating radar techniques with the experimental and field data is suggested. Also, applying geophysical methods along with considering regional tectonics can efficiently improve the results obtained from this investigation. Furthermore, to mitigate the rate of land subsidence in Urmia Plains Aquifer, appropriate controlling attempts to prevent excess exploitation of the groundwater and illegal well drilling are strongly recommended. In this regard, placing and monitoring geodetic markers in areas with higher land subsidence rates may be considered a controlling tool to manage better and mitigate the consequent hazards of land subsidence in the study area.\u003c/p\u003e"},{"header":"Recommendation","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eImplement strict regulations on groundwater extraction to prevent further depletion of the aquifer system.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePromote water conservation practices and efficient irrigation techniques in agriculture, which accounts for the majority of groundwater use in the region.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDevelop alternative water sources, such as surface water or treated wastewater, to reduce reliance on groundwater.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDevelop early warning systems to identify subsidence hotspots and track changes in subsidence rates over time, allowing for proactive management of the aquifer system.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eImplement artificial groundwater recharge programs, such as injection wells or infiltration basins, to replenish the aquifer system and mitigate subsidence.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePromote natural recharge by preserving and restoring wetlands, lakes, and other surface water bodies in the region.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eConduct detailed risk assessments to identify areas most susceptible to subsidence-related hazards, such as infrastructure damage or groundwater contamination.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDevelop and implement mitigation strategies, such as reinforcing infrastructure or grouting techniques, to minimize the impact of subsidence in high-risk areas.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInvolve local communities, farmers, and other stakeholders in the decision-making process for groundwater management and subsidence mitigation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProvide education and training programs to raise awareness about the importance of sustainable groundwater use and the risks associated with land subsidence.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlizadeh, A. 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Environmental Science and Pollution Research, 31(10), 15443-15466.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land subsidence, Interferometric radar, SNAP software, Urmia Plain Aquifer, Groundwater","lastPublishedDoi":"10.21203/rs.3.rs-4352949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4352949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe occurrence of land subsidence in a number of Iranian cities has increased, mostly due to groundwater level fluctuations, a consequence of recent structural displacements, decreased precipitation and increased rate of population. Urmia Plain Aquifer has been suffering such environmental challenges, leading to extensive land displacement. Hence, this research investigates the extent of land displacement in the Urmia Plain Aquifer from 2015 to 2023 using the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique via the Sentinel Application Platform (SNAP) software and detected hidden faults. DInSAR is an efficient tool for assessing surface deformation, including land displacement. The results displayed a maximum land subsidence of 9.00 cm in the south and an uplift of 9.6 cm in the north of Urmia Plain Aquifer. Comparing the information obtained from InSAR with the groundwater level data reveals a positive strong correlation. The groundwater level in the southern parts is lower than in the north. This study indicated that land displacement, leading to the fluctuation of the groundwater level, can effectively be evaluated using InSAR, a less time-consuming and expensive technique.\u003c/p\u003e","manuscriptTitle":"Land Subsidence and Displacement Occurrence in the Urmia Plain Aquifer Using Interferometric Radar Technique and Hidden Faults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 05:13:49","doi":"10.21203/rs.3.rs-4352949/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"589e49fb-b0fc-49cf-9baf-06afa0610d75","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-28T10:00:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 05:13:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4352949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4352949","identity":"rs-4352949","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}