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This study investigates the consequences of unsustainable groundwater extraction in the Kabodarahang plain, a region significantly impacted by geohazards, such as land subsidence and sinkhole formation due to excessive groundwater use for agricultural and industrial activities. Over 32 years (1990–2022), a dramatic decrease in groundwater levels by approximately ~41 m has been observed, leading to notable geohazards. Employing radar interferometry techniques with Sentinel-1 satellite radar imagery and the Sentinel Application Platform (SNAP) tool, complemented by field data, this research aims to quantify the rate of subsidence and evaluate the associated risks, particularly in urban and residential zones. Findings from 2017 to 2018 indicate a subsidence rate of 14.5 cm, predominantly in urban areas, thereby elevating the risk of this geohazard. The results underscore the critical need for sustainable groundwater management policies and practices. The study demonstrates the effectiveness of radar interferometry in monitoring subsidence in the Kabodarahang plain and suggests that integrating such techniques with field surveys and satellite data can enhance the detection and management of risks related to unsustainable groundwater usage. This research contributes to the understanding of the impacts of groundwater depletion on geohazards and supports the development of strategies for sustainable groundwater use to mitigate such risks.

This study investigates the consequences of unsustainable groundwater extraction in the Kabodarahang plain, a region significantly impacted by geohazards, such as land subsidence and sinkhole formation due to excessive groundwater use for agricultural and industrial activities. Over 32 years (1990–2022), a dramatic decrease in groundwater levels by approximately ~41 m has been observed, leading to notable geohazards. Employing radar interferometry techniques with Sentinel-1 satellite radar imagery and the Sentinel Application Platform (SNAP) tool, complemented by field data, this research aims to quantify the rate of subsidence and evaluate the associated risks, particularly in urban and residential zones. Findings from 2017 to 2018 indicate a subsidence rate of 14.5 cm, predominantly in urban areas, thereby elevating the risk of this geohazard. The results underscore the critical need for sustainable groundwater management policies and practices. The study demonstrates the effectiveness of radar interferometry in monitoring subsidence in the Kabodarahang plain and suggests that integrating such techniques with field surveys and satellite data can enhance the detection and management of risks related to unsustainable groundwater usage. This research contributes to the understanding of the impacts of groundwater depletion on geohazards and supports the development of strategies for sustainable groundwater use to mitigate such risks. [ABSTRACT FROM AUTHOR]

We present average ground‐surface velocities and strain rates for the 1.7 million km2 area of Iran, from the joint inversion of InSAR‐derived displacements and GNSS data. We generate interferograms from 7 years of Sentinel‐1 radar acquisitions, correct for tropospheric noise using the GACOS system, estimate average velocities using LiCSBAS time‐series analysis, tie this into a Eurasia‐fixed reference frame, and perform a decomposition to estimate East and Vertical velocities at 500 m spacing. Our InSAR‐GNSS velocity fields reveal predominantly diffuse crustal deformation, with localized interseismic strain accumulation along the North Tabriz, Main Kopet Dagh, Main Recent, Sharoud, and Doruneh faults. We observe signals associated with recent groundwater subsidence, co‐ and postseismic deformation, active salt diaprism, and sediment motion. We derive high‐resolution strain rate estimates on a country‐ and fault‐scale, and discuss the difficulties of mapping diffuse strain rates in areas with abundant non‐tectonic and anthropogenic signals. Plain Language Summary: Across the entire country of Iran, the ongoing convergence of the Arabian and Eurasian tectonic plates at about two cm every year is deforming the Earth's crust, producing earthquakes in this process of continental collision. Accurate measurements of how the ground is moving today are critical to understanding both the county‐scale deformation, and the local‐scale earthquake hazard, where the crust is deforming quickly and building up strain that is likely to be released in future major earthquakes. We combine multiple series of satellite radar images with GPS velocities to estimate East and vertical ground motion across all of Iran, at a higher level of detail than previous GPS‐only studies have been able to capture. Our velocity fields show a complex mix of ground motion signals, from crustal deformation on a country‐scale, to rapid land subsidence caused by the extraction of groundwater from aquifers. Some of the major faults are clearly building up strain for future earthquakes, but other regions are deforming much more diffusely, making it difficult to ascertain the locations of any future seismic hazard and ground shaking. Key Points: We generate high‐resolution East and vertical velocity fields for Iran using Sentinel‐1 InSAR and GNSS observationsRegional deformation is diffuse, with interseismic strain localized onto the Doruneh, Main Kopet Dagh, North Tabriz, and Sharoud faultsIran contains a wealth of time‐varying, short‐wavelength signals associated with groundwater extraction, salt diaprism, and sediment motion [ABSTRACT FROM AUTHOR]

For reasons of personal safety, artists are not always allowed to mention directly and articulately events in history and the issues that arise therefrom. One of the skills of the artist is to employ suggestiveness and allusiveness in regard to what they are contemplating. This method is used in contemporary Iranian art. Here I take a number of examples from six contemporary artists who have used different means to fly under the radar of state authorities to be expressive without being explicit. In each of these the central idea is allusion as a way of not mentioning the precise subject so as not to incriminate the artist. In time, a shared imaginary of the unmentionable becomes the characteristic 'language' of the Iranian artist. My subject is a study of visual periphrasis in Iranian art in the modern period. This first appeared long ago in literary form in classical Persian poetry and has recently re-emerged in several modernist and contemporary movements in the art of protest and polemic in periods of repression and state censorship. I argue that this is concealed in the barely observable, or in what is missing altogether, and is present only in allusion and suggestion. [ABSTRACT FROM AUTHOR]

Information about water resources is crucial for sustainable development, and this issue is considered to be one of the most important concerns worldwide due to rapid industrialization and population growth. Countries in the semiarid region of the western Asia, like Iran, are dependent on groundwater resources so access to these resources is vital. This study maps surface spring potential on the Nourabad–Koohdasht Plain of Iran using a deep-learning algorithm called convolutional neural network (CNN), and the result was compared to predictions made with five advanced data-mining models: logistic model tree (LMT), LMT hybridized with bagging (BA-LMT), LMT hybridized with dagging (DA-LMT), LMT hybridized with random subspace (RS-LMT), and LMT hybridized with AdaBoost (AB-LMT). Frequency ratio was used to assess the strengths of relationships of each subclass layer to groundwater presence and evidential belief function revealed their effects on model uncertainty. The locations of 2463 springs were determined and showed that the northern part of the plain has the highest groundwater potential based on the density of springs. The data representing each of the spring locations were used for prediction modeling. Receiver operating characteristic (ROC) and area under the ROC curve (AUC) were used to evaluate the strengths of the predictions produced by the models. The results show that CNN (AUC = 0.885) provided the best prediction of spring locations. AB-LMT (AUC = 0.877) was second best, and BA-LMT (AUC = 0.876), DA-LMT (AUC = 0.856), RS-LMT (AUC = 0.846), and the standalone LMT model (AUC = 0.827) followed in rank. It can be concluded that the hybrid LMT models increased the predictive strength of the standalone LMT model when used to predict spring locations. These hybrid modeling methods may be used to improve sustainable groundwater management in the study region and in other regions as well. [ABSTRACT FROM AUTHOR]

Geodiversity assessment provides a reliable basis for land planning and management at national and regional levels. The present study was conducted with the aim of evaluating and analyzing the geodiversity in Iran and comparing the two methods of kriging and hotspot analysis in creating a geodiversity map. To this end, five main factors, including, geology, geomorphology, soil, hydrography, and climate were used to generate a geodiversity map. The number of features was counted for each index of each grid. The values of each index and the sum of these values ​were analyzed using Kriging and Getis-Ord Gi* statistics methods. According to the Getis-Ord Gi* statistic, 16.2% of Iran’s area is located in hotspot clusters with a confidence level of more than 95%. The Zagros folded belt, Alborz mountains, Sanandaj-Sirjan zone, Zagros thrust belt, Uromieh-Dokhtar zone, mountains north of Kerman (Central of Iran), and Maku-Baneh zone were identified as the most important geodiversity hotspots in Iran. The results showed that the Getis-Ord Gi* method with a higher coefficient of determination and classification of clusters based on Z-score and P-value is more accurate than the kriging method for classifying hot spots of geodiversity. [ABSTRACT FROM AUTHOR]

Abstract We present average ground‐surface velocities and strain rates for the 1.7 million km2 area of Iran, from the joint inversion of InSAR‐derived displacements and GNSS data. We generate interferograms from 7 years of Sentinel‐1 radar acquisitions, correct for tropospheric noise using the GACOS system, estimate average velocities using LiCSBAS time‐series analysis, tie this into a Eurasia‐fixed reference frame, and perform a decomposition to estimate East and Vertical velocities at 500 m spacing. Our InSAR‐GNSS velocity fields reveal predominantly diffuse crustal deformation, with localized interseismic strain accumulation along the North Tabriz, Main Kopet Dagh, Main Recent, Sharoud, and Doruneh faults. We observe signals associated with recent groundwater subsidence, co‐ and postseismic deformation, active salt diaprism, and sediment motion. We derive high‐resolution strain rate estimates on a country‐ and fault‐scale, and discuss the difficulties of mapping diffuse strain rates in areas with abundant non‐tectonic and anthropogenic signals.