مؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Estimation of Sedimentary Thickness of Parts of Upper Benue Trough, Nigeria, using Integrated High Resolution Aeromagnetic and GRACE dataEstimation of Sedimentary Thickness of Parts of Upper Benue Trough, Nigeria, using Integrated High Resolution Aeromagnetic and GRACE data11210533910.22059/jesphys.2026.387436.1007656FAJ. UchennaAbangwuDepartment of Physics and Astronomy, University of Nigeria, Nsukka, Nigeria.Oliver U.EkwuemeFederal University of Technology Akure, Ondo State, Nigeria.Ngozi A.OkwesiliDepartment of Physics and Astronomy, University of Nigeria, Nsukka, Nigeria.Desmond O.UgborDepartment of Physics and Astronomy, University of Nigeria, Nsukka, Nigeria.Emmanuel A.IgweDepartment of Physics and Astronomy, University of Nigeria, Nsukka, Nigeria.Anthonia N.NwobodoDepartment of Industrial Physics Enugu State University of Science and Technology, Enugu State, Nigeria.Maximus C.UgwuanyiFederal University of Allied Health Sciences, Enugu State, Nigeria.Journal Article20241224High-resolution aeromagnetic and gravity data from the Gravity Recovery and Climate Experiment (GRACE) were analyzed to evaluate the sedimentary thickness and hydrocarbon potential of parts of the Upper Benue Trough in Nigeria. The study area is bounded by latitudes 9.0° to 10.0° North and longitudes 10.0° to 12.5° East, and covers approximately 30,250 km². Total Magnetic Intensity (TMI) and Bouguer gravity anomaly grids were generated using Oasis Montaj 8.4 software and subjected to polynomial fitting for regional–residual separation and qualitative interpretation. The magnetic and gravity fields exhibit similar regional trends, indicating that both deep and shallow sources are influenced by a common tectonic framework. Quantitative analysis using Source Parameter Imaging (SPI) and Euler deconvolution was performed to estimate the sedimentary thickness of the area. The magnetic and gravity SPI depth results ranged from -207.4 to -3523.0 m and -115.7 to -3767.3 m, respectively. The magnetic Euler depth values varied between 413.3 m and -3597.5 m, while gravity Euler depths ranged from 600.6 to -3606.4 m. The unconventional appearance of the gravity SPI and Euler depth maps is attributed to the coarse resolution and long-wavelength nature of satellite gravity data compared to the high resolution, near-surface sensitivity of aeromagnetic data. The maximum sedimentary thickness of -3767.3 m indicates adequate sediment accumulation for hydrocarbon generation. Areas such as Bashar, Yuli, Futuk, Dong, and Guyok are characterized by low gravity and magnetic anomalies with relatively deep basement depths, signifying zones of enhanced sedimentary deposition that are favorable for hydrocarbon entrapment.High-resolution aeromagnetic and gravity data from the Gravity Recovery and Climate Experiment (GRACE) were analyzed to evaluate the sedimentary thickness and hydrocarbon potential of parts of the Upper Benue Trough in Nigeria. The study area is bounded by latitudes 9.0° to 10.0° North and longitudes 10.0° to 12.5° East, and covers approximately 30,250 km². Total Magnetic Intensity (TMI) and Bouguer gravity anomaly grids were generated using Oasis Montaj 8.4 software and subjected to polynomial fitting for regional–residual separation and qualitative interpretation. The magnetic and gravity fields exhibit similar regional trends, indicating that both deep and shallow sources are influenced by a common tectonic framework. Quantitative analysis using Source Parameter Imaging (SPI) and Euler deconvolution was performed to estimate the sedimentary thickness of the area. The magnetic and gravity SPI depth results ranged from -207.4 to -3523.0 m and -115.7 to -3767.3 m, respectively. The magnetic Euler depth values varied between 413.3 m and -3597.5 m, while gravity Euler depths ranged from 600.6 to -3606.4 m. The unconventional appearance of the gravity SPI and Euler depth maps is attributed to the coarse resolution and long-wavelength nature of satellite gravity data compared to the high resolution, near-surface sensitivity of aeromagnetic data. The maximum sedimentary thickness of -3767.3 m indicates adequate sediment accumulation for hydrocarbon generation. Areas such as Bashar, Yuli, Futuk, Dong, and Guyok are characterized by low gravity and magnetic anomalies with relatively deep basement depths, signifying zones of enhanced sedimentary deposition that are favorable for hydrocarbon entrapment.https://jesphys.ut.ac.ir/article_105339_744ccffc4461b56e09859ce54b89a5d5.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Global-Scale Numerical Modeling of Seismic Wave Propagation Using PREM and Unstructured Triangular Meshing: Insights into the Layered Structure of EarthGlobal-Scale Numerical Modeling of Seismic Wave Propagation Using PREM and Unstructured Triangular Meshing: Insights into the Layered Structure of Earth132710584210.22059/jesphys.2026.397888.1007701FAAmirYazdanpanahSchool of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.MaysamAbediSchool of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.Journal Article20250701The Preliminary Reference Earth Model (PREM) provides a robust framework for understanding seismic wave propagation through Earth’s layered interior. This study employs numerical forward modeling to simulate P-wave and S-wave ray paths, travel times, and apparent velocity curves within a 2D unstructured triangular mesh based on PREM. The mesh was optimized for numerical stability, achieving a mean element quality of 0.91 and facilitating accurate interpolation of PREM’s velocity profiles. Seismic responses are compared with those from simplified homogeneous and linear gradient velocity models to highlight the influence of Earth’s layered structure. Validation against an analytical homogeneous benchmark yielded a mean travel-time relative error of only %, while comparative analysis revealed that the linear gradient model deviates from PREM by as much as % at the core-mantle boundary. Results demonstrate that PREM’s velocity heterogeneities and discontinuities, such as the core-mantle boundary and liquid outer core, produce complex ray paths and variable apparent velocities, contrasting the straight paths and uniform velocities of the homogeneous model and the smoother trends of the gradient model. These findings underscore the necessity of detailed velocity models and advanced unstructured discretization for realistic seismic simulations. By providing a reproducible computational framework, this study affirms the effectiveness of advanced numerical techniques in capturing Earth’s internal dynamics. It emphasizes the critical role of layering in seismic wave behavior, offering insights into the development of next-generation Earth models.The Preliminary Reference Earth Model (PREM) provides a robust framework for understanding seismic wave propagation through Earth’s layered interior. This study employs numerical forward modeling to simulate P-wave and S-wave ray paths, travel times, and apparent velocity curves within a 2D unstructured triangular mesh based on PREM. The mesh was optimized for numerical stability, achieving a mean element quality of 0.91 and facilitating accurate interpolation of PREM’s velocity profiles. Seismic responses are compared with those from simplified homogeneous and linear gradient velocity models to highlight the influence of Earth’s layered structure. Validation against an analytical homogeneous benchmark yielded a mean travel-time relative error of only %, while comparative analysis revealed that the linear gradient model deviates from PREM by as much as % at the core-mantle boundary. Results demonstrate that PREM’s velocity heterogeneities and discontinuities, such as the core-mantle boundary and liquid outer core, produce complex ray paths and variable apparent velocities, contrasting the straight paths and uniform velocities of the homogeneous model and the smoother trends of the gradient model. These findings underscore the necessity of detailed velocity models and advanced unstructured discretization for realistic seismic simulations. By providing a reproducible computational framework, this study affirms the effectiveness of advanced numerical techniques in capturing Earth’s internal dynamics. It emphasizes the critical role of layering in seismic wave behavior, offering insights into the development of next-generation Earth models.https://jesphys.ut.ac.ir/article_105842_1401b7f1a631ad19aa08d730d12d7ca0.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Statistical Assessment of Magnetic Precursors to the April 1, 2014 Chile 8.2 Earthquake Using Swarm Satellite DataStatistical Assessment of Magnetic Precursors to the April 1, 2014 Chile 8.2 Earthquake Using Swarm Satellite Data294410531410.22059/jesphys.2025.400455.1007714FAShahrokhPourbeyranvandDepartment of Seismological Research Center-Seismology, International Institute of Earthquake Engineering and Seismology, Tehran, Iran.Amir HosseinFirouzianDepartment of Seismological Research Center-Seismology, International Institute of Earthquake Engineering and Seismology, Tehran, Iran.Journal Article20250810Satellite-based monitoring of geomagnetic field variations offers a promising avenue for identifying earthquake precursors. The use of satellite data to detect such anomalies has garnered significant attention, given its global coverage and independence from ground-based limitations. In this study, we applied the characteristic curve method to Swarm satellite magnetic data to investigate pre-seismic anomalies preceding the Mw 8.2 Chile earthquake on April 1, 2014. After subtracting the International Geomagnetic Reference Field (IGRF-14), we identified a statistically significant anomaly in the northward magnetic component (BN) approximately 33 days before the event. The characteristic curve method, which is based on plotting the satellite data over the study area repeatedly on consecutive orbits, enabled quantification of this deviation, revealing a statistically significant signal while accounting for natural geomagnetic variability. Graphical analysis of the data highlighted this anomaly as a pronounced feature, distinguishable from typical fluctuations. This anomaly exceeded ±20 nT uncertainty bounds and persisted across multiple orbits. To validate its uniqueness, we analyzed two control intervals, three months before and after the study window, using the same method, confirming that no comparable anomalies occurred outside the precursor period. The anomaly’s directional specificity, temporal isolation, and amplitude support its interpretation as a genuine pre-seismic signal, likely linked to lithosphere-atmosphere-ionosphere coupling processes. These findings demonstrate the robustness of the characteristic curve method for satellite data and reinforce the potential of Swarm observations in short-term earthquake forecasting.Satellite-based monitoring of geomagnetic field variations offers a promising avenue for identifying earthquake precursors. The use of satellite data to detect such anomalies has garnered significant attention, given its global coverage and independence from ground-based limitations. In this study, we applied the characteristic curve method to Swarm satellite magnetic data to investigate pre-seismic anomalies preceding the Mw 8.2 Chile earthquake on April 1, 2014. After subtracting the International Geomagnetic Reference Field (IGRF-14), we identified a statistically significant anomaly in the northward magnetic component (BN) approximately 33 days before the event. The characteristic curve method, which is based on plotting the satellite data over the study area repeatedly on consecutive orbits, enabled quantification of this deviation, revealing a statistically significant signal while accounting for natural geomagnetic variability. Graphical analysis of the data highlighted this anomaly as a pronounced feature, distinguishable from typical fluctuations. This anomaly exceeded ±20 nT uncertainty bounds and persisted across multiple orbits. To validate its uniqueness, we analyzed two control intervals, three months before and after the study window, using the same method, confirming that no comparable anomalies occurred outside the precursor period. The anomaly’s directional specificity, temporal isolation, and amplitude support its interpretation as a genuine pre-seismic signal, likely linked to lithosphere-atmosphere-ionosphere coupling processes. These findings demonstrate the robustness of the characteristic curve method for satellite data and reinforce the potential of Swarm observations in short-term earthquake forecasting.https://jesphys.ut.ac.ir/article_105314_c5f86957f5043ad6dec2e9eb32d76eae.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Enhtheta: A new balanced filter for Edge Detection of Potential Field anomaliesEnhtheta: A new balanced filter for Edge Detection of Potential Field anomalies456410530910.22059/jesphys.2025.400924.1007716FAKamalAlamdarFaculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran.Journal Article20250818Magnetic and gravity data are types of potential field data. The magnetic method is based on variations in the magnetic field caused by lateral differences in the magnetization of the subsurface sources. Magnetic interpretation, similar to gravity interpretation, operates at several levels of complexity. It can range from simple identification and location of anomalous magnetic bodies in the subsurface (edge detection approaches) to three-dimensional modeling for complete characterization of an anomaly. The most commonly used edge detection filters for enhancing potential field data are vertical or horizontal derivatives. These derivative filters can be combined to produce a new edge detection filter (analytic signal and total horizontal derivative for example). On the other hand, balanced derivative filters (or local phase filters) are used to simultaneously emphasize potential field signals from sources at different depths. In this paper, an improved balanced filter, the Enhanced Theta filter (Enhtheta), is presented, which replaces the conventional THDR and ASA with balanced THDR and ASA in the Theta filter equation. In particular, the presence of overlying shallow and deep magnetic/gravity sources leads to the creation of strong and weak anomalies. Thus, if the observed data contain anomalies with a large variation in amplitude, then geologically important anomalies with small amplitudes may be hard to recognize. In such a dataset, closely-spaced sources are difficult to delineate due to the superposition effect. This new filtering technique balances the strong and weak anomalies in the original image, thereby producing a balanced theta map. The maximum value of this filter delineates the edges of the anomalies. Moreover, its total horizontal derivative (THDR_Enhthet) can be used as an edge detector filter. The maximum value of the THDR_Enhtheta filter shows the edges of the anomalies. The capability of the proposed algorithm is demonstrated using both noise-free and noise-contaminated synthetic magnetic data generated from prismatic models, as well as real aeromagnetic data from the Bushveld Complex, South Africa. The results of the new filter are compared with other edge detection filters, namely TDR, Theta and TDX. Enhtheta and its total horizontal derivative provide more accurate detection of the source edges compared to other filtering techniques. Therefore, interpretation of potential field data is facilitated using the Enhtheta filtering method.Magnetic and gravity data are types of potential field data. The magnetic method is based on variations in the magnetic field caused by lateral differences in the magnetization of the subsurface sources. Magnetic interpretation, similar to gravity interpretation, operates at several levels of complexity. It can range from simple identification and location of anomalous magnetic bodies in the subsurface (edge detection approaches) to three-dimensional modeling for complete characterization of an anomaly. The most commonly used edge detection filters for enhancing potential field data are vertical or horizontal derivatives. These derivative filters can be combined to produce a new edge detection filter (analytic signal and total horizontal derivative for example). On the other hand, balanced derivative filters (or local phase filters) are used to simultaneously emphasize potential field signals from sources at different depths. In this paper, an improved balanced filter, the Enhanced Theta filter (Enhtheta), is presented, which replaces the conventional THDR and ASA with balanced THDR and ASA in the Theta filter equation. In particular, the presence of overlying shallow and deep magnetic/gravity sources leads to the creation of strong and weak anomalies. Thus, if the observed data contain anomalies with a large variation in amplitude, then geologically important anomalies with small amplitudes may be hard to recognize. In such a dataset, closely-spaced sources are difficult to delineate due to the superposition effect. This new filtering technique balances the strong and weak anomalies in the original image, thereby producing a balanced theta map. The maximum value of this filter delineates the edges of the anomalies. Moreover, its total horizontal derivative (THDR_Enhthet) can be used as an edge detector filter. The maximum value of the THDR_Enhtheta filter shows the edges of the anomalies. The capability of the proposed algorithm is demonstrated using both noise-free and noise-contaminated synthetic magnetic data generated from prismatic models, as well as real aeromagnetic data from the Bushveld Complex, South Africa. The results of the new filter are compared with other edge detection filters, namely TDR, Theta and TDX. Enhtheta and its total horizontal derivative provide more accurate detection of the source edges compared to other filtering techniques. Therefore, interpretation of potential field data is facilitated using the Enhtheta filtering method.https://jesphys.ut.ac.ir/article_105309_e66567b92b3f007ef78461d917ed536e.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Geomagnetic Disturbance Impact on Magnetic Survey Tool Errors in High-Latitude Directional DrillingGeomagnetic Disturbance Impact on Magnetic Survey Tool Errors in High-Latitude Directional Drilling657310627410.22059/jesphys.2026.404857.1007734FAAndrei V.VorobevThe Geophysical Center of the Russian Academy of Sciences, Moscow, Russia.Gulnara R.VorobevaDepartment of Informatics, Ufa University of Science and Technology, Ufa, Russia.Journal Article20251023This paper presents an approach to develop a proactive probabilistic model for assessing extreme errors in magnetic surveying tools caused by geomagnetic disturbances in the Arctic aurora zone, with the aim of enhancing the efficiency of directional drilling and accounting for risks that go beyond reactive compensation methods.<br />Analysis of high-resolution geomagnetic data (2004-2005) from 12 auroral observatories reveals that the absolute additional error in magnetic declination (|Δ<em>D</em>|) follows a composite statistical law. The core of the distribution (~84% of data) is lognormal (shape parameter s = 0.87 for |Δ<em>D</em><sub>mean</sub>| and <em>s</em> = 1.02 for |Δ<em>D</em><sub>max</sub>|), indicating error formation via multiplicative ionospheric processes. Critically, the tails (~16%) are heavy and obey a Pareto distribution, signaling a substantial risk of extreme events. We quantify that the maximum synchronous error (|Δ<em>D</em><sub>max</sub>|) exceeds 5.67° with a 1% probability, even during the solar cycle's declining phase. A distinct diurnal pattern with dual maxima suggests an optimal time windows for precision drilling operations.<br />The established lognormal-Pareto model facilitates a paradigm shift towards proactive risk management in high-latitude drilling. Our findings underscore the statistical insufficiency of mean-error approaches and quantify a significant probability of extreme azimuthal errors due to space weather. This study provides a foundation for developing decision-support systems, optimizing operational schedules, and informing stricter metrological standards for Arctic drilling equipment.This paper presents an approach to develop a proactive probabilistic model for assessing extreme errors in magnetic surveying tools caused by geomagnetic disturbances in the Arctic aurora zone, with the aim of enhancing the efficiency of directional drilling and accounting for risks that go beyond reactive compensation methods.<br />Analysis of high-resolution geomagnetic data (2004-2005) from 12 auroral observatories reveals that the absolute additional error in magnetic declination (|Δ<em>D</em>|) follows a composite statistical law. The core of the distribution (~84% of data) is lognormal (shape parameter s = 0.87 for |Δ<em>D</em><sub>mean</sub>| and <em>s</em> = 1.02 for |Δ<em>D</em><sub>max</sub>|), indicating error formation via multiplicative ionospheric processes. Critically, the tails (~16%) are heavy and obey a Pareto distribution, signaling a substantial risk of extreme events. We quantify that the maximum synchronous error (|Δ<em>D</em><sub>max</sub>|) exceeds 5.67° with a 1% probability, even during the solar cycle's declining phase. A distinct diurnal pattern with dual maxima suggests an optimal time windows for precision drilling operations.<br />The established lognormal-Pareto model facilitates a paradigm shift towards proactive risk management in high-latitude drilling. Our findings underscore the statistical insufficiency of mean-error approaches and quantify a significant probability of extreme azimuthal errors due to space weather. This study provides a foundation for developing decision-support systems, optimizing operational schedules, and informing stricter metrological standards for Arctic drilling equipment.https://jesphys.ut.ac.ir/article_106274_bc66052711790be9ef4f7b3836f949f8.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Mahalanobis Distance Method for Enhancing Magnetotelluric Data Processing: A Case Study from the Rochechouart Impact Structure, FranceMahalanobis Distance Method for Enhancing Magnetotelluric Data Processing: A Case Study from the Rochechouart Impact Structure, France758810622210.22059/jesphys.2026.409669.1007750FAHosseinParnian-KhoyDepartment of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.Seyed HaniMotavalli-AnbaranDepartment of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.BanafshehHabibian DehkordiDepartment of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.PascalSailhacDepartment of Earth Sciences, Geosciences Paris-Saclay Laboratory (GEOPS), Université Paris-Saclay, Orsay, France.MostafaMousapour YasooriDepartment of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.YoannQuesnelCEREGE, Aix-Marseille Université, CNRS, IRD, INRAE, Aix-en-Provence, France.PhilippeLambertCIRIR – Center for International Research and Restitution on Impacts and on Rochechouart, Rochechouart, France.Journal Article20260108One of the primary challenges in magnetotelluric data processing is the presence of noise and outliers (anomalous values). These disturbances often come from human-made sources such as power lines, electronic devices, and nearby infrastructure. They can significantly affect the results related to apparent resistivity and phase, leading to unreliable models of subsurface electrical resistivity.<br />To identify and remove these outlier and noisy components, the Mahalanobis distance method is proposed as an effective solution. This approach — applied here in a four-dimensional feature space comprising the real and imaginary parts of two components of the impedance transfer function — calculates the distance of each data point from the dataset, meanwhile accounting for variances, covariances, and correlations between variables, thereby enabling the detection of anomalous points more accurately than simpler (2D) approaches.<br />In this study, to identify outliers, we applied the Mahalanobis distance method to real data from a single MT station located at the Chassenon Forage site within the Rochechouart impact structure, France. The results demonstrate that this approach not only improves the accuracy of subsequent analyses and enables the extraction of more precise information from subsurface structures, but also reduces processing time by efficiently eliminating contaminated windows before final impedance estimation.One of the primary challenges in magnetotelluric data processing is the presence of noise and outliers (anomalous values). These disturbances often come from human-made sources such as power lines, electronic devices, and nearby infrastructure. They can significantly affect the results related to apparent resistivity and phase, leading to unreliable models of subsurface electrical resistivity.<br />To identify and remove these outlier and noisy components, the Mahalanobis distance method is proposed as an effective solution. This approach — applied here in a four-dimensional feature space comprising the real and imaginary parts of two components of the impedance transfer function — calculates the distance of each data point from the dataset, meanwhile accounting for variances, covariances, and correlations between variables, thereby enabling the detection of anomalous points more accurately than simpler (2D) approaches.<br />In this study, to identify outliers, we applied the Mahalanobis distance method to real data from a single MT station located at the Chassenon Forage site within the Rochechouart impact structure, France. The results demonstrate that this approach not only improves the accuracy of subsequent analyses and enables the extraction of more precise information from subsurface structures, but also reduces processing time by efficiently eliminating contaminated windows before final impedance estimation.https://jesphys.ut.ac.ir/article_106222_6968b759e1dc5cb827aedfa6184699f5.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Spatial-Temporal Variations and Trends in Freezing Level Height in Iran: An Analytical PerspectiveSpatial-Temporal Variations and Trends in Freezing Level Height in Iran: An Analytical Perspective8910310174110.22059/jesphys.2025.386947.1007650FAHasanGhadermaziDepartment of Geography, Faculty of Literature and Humanities, Razi University, Kermanshah, Iran.JafarMasoompour SamakoshDepartment of Geography, Faculty of Literature and Humanities, Razi University, Kermanshah, Iran.0000-0001-9914-2145Seyed EhsanFatemiDepartment of Water Engineering, Campus of Agriculture and Natural Resources, Razi University, Kermanshah, Iran.MaryamHafezparastDepartment of Water Engineering, Campus of Agriculture and Natural Resources, Razi University, Kermanshah, Iran.Journal Article20241213This research investigates the temporal and spatial changes of freezing level height (FLH) in Iran over the statistical period from 1940 to 2023, using the modified Mann-Kendall non-parametric analysis method. The results indicate a significant upward trend in FLH from December to April. Except for summer, the upward trend is quite evident during autumn, winter, and spring. Annual changes in FLH show a significant upward trend during the study period. These significant upward trends at monthly, seasonal, and annual scale can show one of the consequences of climate change in Iran. Spatial analysis reveals an inverse relationship with the latitude, and as latitude decreases (towards southern regions of Iran), FLH increases. This relationship is more clear in winter, with the correlation coefficient between FLH and latitude being significant at the 0.05 alpha level. Additionally, FLH shows a direct relationship with longitude, increasing as one moves eastward. The highest freezing level height is observed in Chabahar (4347.5 meters) in southeastern Iran. Considering the decrease in snow cover and increase in heavy rainfall in Iran, monitoring variations in freezing level height can be useful for predicting these variables.This research investigates the temporal and spatial changes of freezing level height (FLH) in Iran over the statistical period from 1940 to 2023, using the modified Mann-Kendall non-parametric analysis method. The results indicate a significant upward trend in FLH from December to April. Except for summer, the upward trend is quite evident during autumn, winter, and spring. Annual changes in FLH show a significant upward trend during the study period. These significant upward trends at monthly, seasonal, and annual scale can show one of the consequences of climate change in Iran. Spatial analysis reveals an inverse relationship with the latitude, and as latitude decreases (towards southern regions of Iran), FLH increases. This relationship is more clear in winter, with the correlation coefficient between FLH and latitude being significant at the 0.05 alpha level. Additionally, FLH shows a direct relationship with longitude, increasing as one moves eastward. The highest freezing level height is observed in Chabahar (4347.5 meters) in southeastern Iran. Considering the decrease in snow cover and increase in heavy rainfall in Iran, monitoring variations in freezing level height can be useful for predicting these variables.https://jesphys.ut.ac.ir/article_101741_b5fbbfc74ea914fb2721fc5686c8ed87.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Analysis of Heat Waves in Iran: Patterns, Trends, and Characteristics in Frequency, Intensity, and Duration (1980-2024)Analysis of Heat Waves in Iran: Patterns, Trends, and Characteristics in Frequency, Intensity, and Duration (1980-2024)10512410173910.22059/jesphys.2025.387456.1007655FAAbbasRanjbar Saadat AbadiFaculty member of Atmospheric Science and Meteorological Research Center, Tehran, Iran.AminFazl KazemiDepartment of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.NasimHossein HamzehDepartment Meteorology, Air and Climate Technology Company (ACTC), Tehran, Iran.Journal Article20241223The increasing frequency and intensity of heat waves represent a pressing climate challenge globally. This study examines the patterns, trends, and characteristics of heat waves across 178 synoptic meteorological stations in Iran from 1980 to October 2024. Utilizing the Heat Wave Magnitude Index (HWMI), defined as periods where daily maximum temperatures exceed a specified threshold for three consecutive days, we observed a dramatic rise in heat wave occurrences—112.4%, 161.6%, and 391.1% in the second, third, and fourth decades compared to the first (1985-1994). Annually, approximately 28 heat waves were recorded, with an average magnitude of 6.4 and a duration of 23.8 days, particularly prevalent in the mountainous regions of the Zagros and Alborz ranges. This rise in heat waves in mountainous areas during colder seasons may be linked to feedback from reduced snow cover. The most severe heat wave occurred in summer 2024, marked by significant atmospheric features such as the clockwise rotation of the upper subtropical high's ridge line and intensified thermal low pressure in central Iran. These findings highlight the urgent need for adaptive resource management and climate resilience strategies. Policymakers should prioritize improved monitoring and predictive modeling to prepare communities for the growing impacts of extreme weather events, ensuring effective responses to the challenges posed by heat waves in Iran.The increasing frequency and intensity of heat waves represent a pressing climate challenge globally. This study examines the patterns, trends, and characteristics of heat waves across 178 synoptic meteorological stations in Iran from 1980 to October 2024. Utilizing the Heat Wave Magnitude Index (HWMI), defined as periods where daily maximum temperatures exceed a specified threshold for three consecutive days, we observed a dramatic rise in heat wave occurrences—112.4%, 161.6%, and 391.1% in the second, third, and fourth decades compared to the first (1985-1994). Annually, approximately 28 heat waves were recorded, with an average magnitude of 6.4 and a duration of 23.8 days, particularly prevalent in the mountainous regions of the Zagros and Alborz ranges. This rise in heat waves in mountainous areas during colder seasons may be linked to feedback from reduced snow cover. The most severe heat wave occurred in summer 2024, marked by significant atmospheric features such as the clockwise rotation of the upper subtropical high's ridge line and intensified thermal low pressure in central Iran. These findings highlight the urgent need for adaptive resource management and climate resilience strategies. Policymakers should prioritize improved monitoring and predictive modeling to prepare communities for the growing impacts of extreme weather events, ensuring effective responses to the challenges posed by heat waves in Iran.https://jesphys.ut.ac.ir/article_101739_64ebb85917dff21f9f634d971d749c06.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Assessment of Atmospheric Stability Indices for Thunderstorm Forecasting over Absheron PeninsulaAssessment of Atmospheric Stability Indices for Thunderstorm Forecasting over Absheron Peninsula12513110427610.22059/jesphys.2025.387703.1007658FANazimHuseynovDepartment of Aviation Meteorology, National Aviation Academy of AZAL CJSC, Baku, Azerbaijan.AygunBashirovaDepartment of Aviation Meteorology, National Aviation Academy of AZAL CJSC, Baku, Azerbaijan.Journal Article20241227This study examines the application of various thunderstorm indices to predict the occurrence of thunderstorms over the Absheron Peninsula. By considering thermodynamic indices such as the K index, lifted index (LI), Showalter index (SI), Thompson index, S index, Total Totals (TT) and SWEAT, and comparing their ability to forecast thunderstorms, more insight is gained into the vertical thermodynamic structure over the Absheron Peninsula.<br />Numerical weather prediction (NWP) models corresponding to the territory of Heydar Aliyev International Airport (UBBB) were used to calculate the indices. To facilitate the calculations, a software tool was developed in the C# programming language, which automates the process of selecting the required meteorological parameters. The generated data series are used as input for calculating the indices. The implementation of this software enhances the efficiency of the forecasting process, significantly reducing the time required by meteorologists. Based on the results of the study, a number of the indices produced relatively high skill scores and can serve as an initial guide for forecasters in determining the occurrence of thunderstorms. Among the indices, the SWEAT index demonstrated the highest forecasting skill, followed by LI, Thompson, S, SI, and TT. The K index, however, showed the lowest skill scores. These results are comparable to those of other studies that have examined thunderstorm occurrence.This study examines the application of various thunderstorm indices to predict the occurrence of thunderstorms over the Absheron Peninsula. By considering thermodynamic indices such as the K index, lifted index (LI), Showalter index (SI), Thompson index, S index, Total Totals (TT) and SWEAT, and comparing their ability to forecast thunderstorms, more insight is gained into the vertical thermodynamic structure over the Absheron Peninsula.<br />Numerical weather prediction (NWP) models corresponding to the territory of Heydar Aliyev International Airport (UBBB) were used to calculate the indices. To facilitate the calculations, a software tool was developed in the C# programming language, which automates the process of selecting the required meteorological parameters. The generated data series are used as input for calculating the indices. The implementation of this software enhances the efficiency of the forecasting process, significantly reducing the time required by meteorologists. Based on the results of the study, a number of the indices produced relatively high skill scores and can serve as an initial guide for forecasters in determining the occurrence of thunderstorms. Among the indices, the SWEAT index demonstrated the highest forecasting skill, followed by LI, Thompson, S, SI, and TT. The K index, however, showed the lowest skill scores. These results are comparable to those of other studies that have examined thunderstorm occurrence.https://jesphys.ut.ac.ir/article_104276_48900ec186bccc3dcb44804648203bed.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Dry Lightning Contributing to Wildfires in the Zagros Forested Area: A Meteorological and Environmental Analysis of Extreme Events in June 2019 and May 2020Dry Lightning Contributing to Wildfires in the Zagros Forested Area: A Meteorological and Environmental Analysis of Extreme Events in June 2019 and May 202013315210294310.22059/jesphys.2025.393620.1007679FASakinehKhansalariAtmospheric Science and Meteorological Research Center, Tehran, Iran.FarahnazFazel-RastgarDepartment of Physics, School of Chemistry and Physics, University of KwaZulu-Natal, Durban 4000, South Africa.RiadGuehazDepartment of Physics, School of Chemistry and Physics, University of KwaZulu-Natal, Durban 4000, South Africa.Center for the Development of Advanced Technologies (CDTA), Algiers, Algeria.VenkataramanSivakumarS. V. Raman Researchers Roadmap, Westville, Durban 4000, South Africa.Journal Article20250417Wildfires in forested and pasture areas of Iran have increased in recent years, raising concerns about their ignition sources and environmental drivers. Among natural causes, lightning—particularly dry lightning—plays a significant role in initiating wildfires under specific meteorological conditions. This study aims to analyze the contribution of dry lightning to major wildfire events in Iran’s Zagros region during May 2020 and June 2019. To achieve this, we utilized a combination of satellite-based fire data from FIRMS and lightning data from the Earth Networks Total Lightning Network (ENTLN). In addition, meteorological datasets and reanalysis models were employed to assess drought conditions and fire-conducive weather patterns. Key indices such as the Fire Weather Index (FWI), Fire Danger Index (FDI), Burned Area Index (BI), Keetch–Byram Drought Index (KBDI), and the Standardized Precipitation Evaporation Index (SPEI) were applied. Burn severity was evaluated using Landsat-8 and Sentinel-2 imagery. Our results reveal a strong correlation between wildfire activity and lightning occurrences, particularly in areas with dry vegetation, elevated temperatures, and minimal precipitation. Quantitative validation during two major fire events—June 2019 and May 2020—confirms that wildfires were most intense on days when dry lightning coincided with elevated Fire Weather Index (FWI) values. For instance, on June 6 and 7, 2019, 4332 and 4833 dry lightning strikes were recorded, with FWI values of 33.8 and 39.4, and over 900 high-confidence fire detections observed each day. Similarly, May 20 and 21, 2020 exhibited peaks in all three factors, with up to 5773 lightning strikes and FWI values exceeding 24. These findings substantiate the synergistic role of dry lightning and fuel flammability in wildfire ignition and highlight the importance of multi-variable monitoring for fire risk assessment. This study highlights the importance of integrating satellite data, lightning observations, and fire indices for wildfire risk assessment. Ultimately, this research provides valuable insight into the mechanisms of dry lightning-induced wildfires and contributes to developing early warning strategies and adaptation measures under changing climate conditions.Wildfires in forested and pasture areas of Iran have increased in recent years, raising concerns about their ignition sources and environmental drivers. Among natural causes, lightning—particularly dry lightning—plays a significant role in initiating wildfires under specific meteorological conditions. This study aims to analyze the contribution of dry lightning to major wildfire events in Iran’s Zagros region during May 2020 and June 2019. To achieve this, we utilized a combination of satellite-based fire data from FIRMS and lightning data from the Earth Networks Total Lightning Network (ENTLN). In addition, meteorological datasets and reanalysis models were employed to assess drought conditions and fire-conducive weather patterns. Key indices such as the Fire Weather Index (FWI), Fire Danger Index (FDI), Burned Area Index (BI), Keetch–Byram Drought Index (KBDI), and the Standardized Precipitation Evaporation Index (SPEI) were applied. Burn severity was evaluated using Landsat-8 and Sentinel-2 imagery. Our results reveal a strong correlation between wildfire activity and lightning occurrences, particularly in areas with dry vegetation, elevated temperatures, and minimal precipitation. Quantitative validation during two major fire events—June 2019 and May 2020—confirms that wildfires were most intense on days when dry lightning coincided with elevated Fire Weather Index (FWI) values. For instance, on June 6 and 7, 2019, 4332 and 4833 dry lightning strikes were recorded, with FWI values of 33.8 and 39.4, and over 900 high-confidence fire detections observed each day. Similarly, May 20 and 21, 2020 exhibited peaks in all three factors, with up to 5773 lightning strikes and FWI values exceeding 24. These findings substantiate the synergistic role of dry lightning and fuel flammability in wildfire ignition and highlight the importance of multi-variable monitoring for fire risk assessment. This study highlights the importance of integrating satellite data, lightning observations, and fire indices for wildfire risk assessment. Ultimately, this research provides valuable insight into the mechanisms of dry lightning-induced wildfires and contributes to developing early warning strategies and adaptation measures under changing climate conditions.https://jesphys.ut.ac.ir/article_102943_41dc04eb13f5d489cf283d94756b64d8.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Sustainable Restoration of Desert Ecosystems Through BIOGEMI-Based Soil Amendment: Improvements in Chlorophyll Content and Soil Properties in Haloxylon ammodendronSustainable Restoration of Desert Ecosystems Through BIOGEMI-Based Soil Amendment: Improvements in Chlorophyll Content and Soil Properties in Haloxylon ammodendron15316510530810.22059/jesphys.2025.394590.1007689FAShilaHajehforoshniaDepartment of Natural Resources Research, Isfahan Agricultural and Natural Resources Research and Education Center (AREEO), Isfahan, Iran.MasoudBorhaniDepartment of Natural Resources Research, Isfahan Agricultural and Natural Resources Research and Education Center (AREEO), Isfahan, Iran.MasoudTadayonnejadDepartment of Natural Resources Research, Isfahan Agricultural and Natural Resources Research and Education Center (AREEO), Isfahan, Iran.MajidHajirasoulihaGEO of Nano Vahed Sanat Persia, Isfahan, Iran.Journal Article20250506Drought is one of the most significant limiting factor for plant growth in arid regions, and traditional methods such as irrigation and fertilization offer limited effectiveness. In this context, BIOGEMI technology presents an innovative approach to mitigating drought stress by improving the biological and physical properties of soil and increasing its water-holding capacity. This study aimed to evaluate the effects of BIOGEMI on improving soil characteristics and chlorophyll content in Haloxylon ammodendron in the Fesaran area of the Sagzi Plain in Isfahan Province, a region characterized by saline and calcareous soils. The experiments were conducted using 144 Haloxylon seedlings, divided into BIOGEMI-treated and control groups. Parameters such as biomass, chlorophyll content, and relative water content (RWC) were measured and compared between treatments. Soil samples were also analyzed at the beginning and end of the experiments to assess physical and chemical changes. Results showed that chlorophyll a and b levels in the treated plants were 15% and 111% higher respectively, than in the control group, indicating a significant improvement in photosynthesis. The fresh and dry weight of aerial parts increased by 48% and 32.8%, respectively. Soil conditions also were improved after treatment: salinity decreased by 88.6%, and organic matter content increased by 50%. Additionally, the RWC in treated plants was 41.6% higher than in the control group, indicating enhanced drought tolerance. Overall, these findings confirm that BIOGEMI technology can serve as an effective tool for desertification control and the sustainable management of soil and water resources.Drought is one of the most significant limiting factor for plant growth in arid regions, and traditional methods such as irrigation and fertilization offer limited effectiveness. In this context, BIOGEMI technology presents an innovative approach to mitigating drought stress by improving the biological and physical properties of soil and increasing its water-holding capacity. This study aimed to evaluate the effects of BIOGEMI on improving soil characteristics and chlorophyll content in Haloxylon ammodendron in the Fesaran area of the Sagzi Plain in Isfahan Province, a region characterized by saline and calcareous soils. The experiments were conducted using 144 Haloxylon seedlings, divided into BIOGEMI-treated and control groups. Parameters such as biomass, chlorophyll content, and relative water content (RWC) were measured and compared between treatments. Soil samples were also analyzed at the beginning and end of the experiments to assess physical and chemical changes. Results showed that chlorophyll a and b levels in the treated plants were 15% and 111% higher respectively, than in the control group, indicating a significant improvement in photosynthesis. The fresh and dry weight of aerial parts increased by 48% and 32.8%, respectively. Soil conditions also were improved after treatment: salinity decreased by 88.6%, and organic matter content increased by 50%. Additionally, the RWC in treated plants was 41.6% higher than in the control group, indicating enhanced drought tolerance. Overall, these findings confirm that BIOGEMI technology can serve as an effective tool for desertification control and the sustainable management of soil and water resources.https://jesphys.ut.ac.ir/article_105308_2f354f13753c146c627995908e9d6089.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Flood Risk Spatial Analysis via Integrating SCS-CN and Logistic Regression Methods (Case Study: Kalateh-ye Qanbar Drainage Basin in Nishabur)Flood Risk Spatial Analysis via Integrating SCS-CN and Logistic Regression Methods (Case Study: Kalateh-ye Qanbar Drainage Basin in Nishabur)16718810540210.22059/jesphys.2026.401169.1007717FAMahnazNaemitabarDepartment of Climatology and Geomorphology, Faculty of Geography and Environmental Science, Hakim Sabzevari University, Sabzevar, Iran.Mohammad AliZangeneh AsadiDepartment of Climatology and Geomorphology, Faculty of Geography and Environmental Science, Hakim Sabzevari University, Sabzevar, Iran.LeilaGoli MokhtariDepartment of Climatology and Geomorphology, Faculty of Geography and Environmental Science, Hakim Sabzevari University, Sabzevar, Iran.Journal Article20250822Flood-prone area analysis within drainage basins plays a crucial role in flood risk management and planning. This study evaluated the performance of the Logistic Regression (LR) model and the Soil Conservation Service–Curve Number (SCS-CN) method for flood forecasting and zoning. A total of 17 criteria were incorporated, including elevation, slope, aspect, plan and profile curvature, precipitation, drainage density, distance to rivers, lithology, land use/cover, soil type, Normalized Difference Vegetation Index (NDVI), Slope Length Factor (SLF), Stream Power Index (SPI), Topographic Position Index (TPI), Terrain Roughness Index (TRI), and Topographic Wetness Index (TWI). In the SCS-CN model, groundwater infiltration (S) and surface runoff (Q) were estimated, while the Analytical Hierarchy Process (AHP) was applied to assign weights to the factors. Flood hazard maps for 5, 15, 25, and 50-year return periods were then generated based on weighted layers. The Receiver Operating Characteristic (ROC) curve was used to validate zoning results against the LR model. Findings indicated that the CN model outperformed LR, achieving RMSE = 0.143, MSE = 0.021, AUC = 0.921, and STD = 0.311. Within the LR model, lithology, proximity to rivers, and NDVI were the most influential predictors, while in the CN model, aspect, drainage density, precipitation, and land use were also dominant factors. Results further showed that 45–50% of the basin could be classified as having medium to high flood potential over the 5 to 50-year periods, particularly in downstream reaches and riverbeds. Given the concentration of settlements and agricultural activities, these areas represent critical zones for flood crisis management and mitigation.Flood-prone area analysis within drainage basins plays a crucial role in flood risk management and planning. This study evaluated the performance of the Logistic Regression (LR) model and the Soil Conservation Service–Curve Number (SCS-CN) method for flood forecasting and zoning. A total of 17 criteria were incorporated, including elevation, slope, aspect, plan and profile curvature, precipitation, drainage density, distance to rivers, lithology, land use/cover, soil type, Normalized Difference Vegetation Index (NDVI), Slope Length Factor (SLF), Stream Power Index (SPI), Topographic Position Index (TPI), Terrain Roughness Index (TRI), and Topographic Wetness Index (TWI). In the SCS-CN model, groundwater infiltration (S) and surface runoff (Q) were estimated, while the Analytical Hierarchy Process (AHP) was applied to assign weights to the factors. Flood hazard maps for 5, 15, 25, and 50-year return periods were then generated based on weighted layers. The Receiver Operating Characteristic (ROC) curve was used to validate zoning results against the LR model. Findings indicated that the CN model outperformed LR, achieving RMSE = 0.143, MSE = 0.021, AUC = 0.921, and STD = 0.311. Within the LR model, lithology, proximity to rivers, and NDVI were the most influential predictors, while in the CN model, aspect, drainage density, precipitation, and land use were also dominant factors. Results further showed that 45–50% of the basin could be classified as having medium to high flood potential over the 5 to 50-year periods, particularly in downstream reaches and riverbeds. Given the concentration of settlements and agricultural activities, these areas represent critical zones for flood crisis management and mitigation.https://jesphys.ut.ac.ir/article_105402_19899427a90be80d7f3793d34a1a0db9.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Observed impacts of Tropical Cyclones Paddy (2021) and Freddy (2023) on the oceanic conditions in the Southeast Indian OceanObserved impacts of Tropical Cyclones Paddy (2021) and Freddy (2023) on the oceanic conditions in the Southeast Indian Ocean18920610530410.22059/jesphys.2025.401391.1007719FAYusuf JatiWijayaDepartment of Oceanography, Faculty of Fisheries and Marine Science, Diponegoro University, Semarang, Indonesia.Nunik CorviaArumDepartment of Oceanography, Faculty of Fisheries and Marine Science, Diponegoro University, Semarang, Indonesia.0000-0001-9914-2145Ulung JantamaWishaCoastal Processes Research Group, Research Center for Oceanography, National Research and Innovation Agency (BRIN), Jakarta, Indonesia.Department of Physics and Earth Sciences, University of the Ryukyus, Nishihara, Japan.LilikMaslukahDepartment of Oceanography, Faculty of Fisheries and Marine Science, Diponegoro University, Semarang, Indonesia.0000-0003-3794-9212AlfiSatriadiDepartment of Oceanography, Faculty of Fisheries and Marine Science, Diponegoro University, Semarang, Indonesia.Annisa AuliaLukmanDepartment of Oceanography, Faculty of Fisheries and Marine Science, Diponegoro University, Semarang, Indonesia.KunarsoKunarsoDepartment of Oceanography, Faculty of Fisheries and Marine Science, Diponegoro University, Semarang, Indonesia.Journal Article20250827Tropical Cyclones (TC) Paddy and Freddy traversed the southern coast of Indonesia, each exhibiting distinct intensities and times. TC Paddy occurred in November 2021, while TC Freddy occurred in February 2023. This study utilized satellite data from multiple sources to assess the variations in the effects of TCs on aquatic environments, incorporating wind, chlorophyll-a (Chl-a), and sea surface temperature (SST) data. Both TC Paddy and Freddy exhibited comparable effects on the surface waters they traversed, specifically elevated Chl-a concentrations and a reduction in SST. Interestingly, a less significant rise in Chl-a, was observed as a consequence of TC Freddy, which was characterized by stronger winds than TC Paddy. This can be partly explained by the difference in translation speed between the two TCs. TC Paddy exhibited a slower translation speed of 1.07 m/s, in contrast to TC Freddy's 4.28 m/s, leading to prolonged turbulence of the surface water influenced by TC Paddy, hence facilitating a greater uplift of nutrients to the surface. The surface currents were also affected by the translation speed parameter. The strength would be affected by the slower (faster) translation speed, resulting in a stronger (weaker) outcome. Meanwhile, for wave parameters, the TC with greater (lesser) intensity produced higher (lower) significant wave heights.Tropical Cyclones (TC) Paddy and Freddy traversed the southern coast of Indonesia, each exhibiting distinct intensities and times. TC Paddy occurred in November 2021, while TC Freddy occurred in February 2023. This study utilized satellite data from multiple sources to assess the variations in the effects of TCs on aquatic environments, incorporating wind, chlorophyll-a (Chl-a), and sea surface temperature (SST) data. Both TC Paddy and Freddy exhibited comparable effects on the surface waters they traversed, specifically elevated Chl-a concentrations and a reduction in SST. Interestingly, a less significant rise in Chl-a, was observed as a consequence of TC Freddy, which was characterized by stronger winds than TC Paddy. This can be partly explained by the difference in translation speed between the two TCs. TC Paddy exhibited a slower translation speed of 1.07 m/s, in contrast to TC Freddy's 4.28 m/s, leading to prolonged turbulence of the surface water influenced by TC Paddy, hence facilitating a greater uplift of nutrients to the surface. The surface currents were also affected by the translation speed parameter. The strength would be affected by the slower (faster) translation speed, resulting in a stronger (weaker) outcome. Meanwhile, for wave parameters, the TC with greater (lesser) intensity produced higher (lower) significant wave heights.https://jesphys.ut.ac.ir/article_105304_a1743b6e001e3d85ba2568134b84c40b.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Assessing Teleconnections Effects on the Precipitation Seasonality at 44 Synoptic Stations across IranAssessing Teleconnections Effects on the Precipitation Seasonality at 44 Synoptic Stations across Iran20723110530510.22059/jesphys.2025.401809.1007722FAAli RezaSaadat MoghadasiDepartment of Irrigation and Reclamation Engineering, Faculty of Agriculture, College of Agriculture and Natural
Resources, University of Tehran, Karaj, Iran.Journal Article20250904Teleconnections—planetary‑scale conduits linking tropical diabatic heating to extratropical circulation—reconfigure jet structure, storm‑track geometry, and moisture transport, thereby modulating Iranian precipitation, which is sparse, highly seasonal, and sensitive to Mediterranean cyclogenesis. Using quality‑controlled 1991–2020 rainfall from 44 IRIMO synoptic stations and fourteen NOAA indices (AMO, AMM, AO, EAWR, EP‑NP, MEI, ONI, PDO, PNA, QBO30, SCAND, SOI, TNA, and TSA), we applied a hierarchical pipeline comprising canonical correlation analysis (CCA), seasonal partial least squares regression (PLSR), and k‑means regime clustering. CCA demonstrated coherent coupled variability between large‑scale modes and regional rainfall, justifying multivariate attribution. PLSR, calibrated separately for OND, JFM, and AMJ, yielded mean skill of R²=0.405 (0.155–0.746), 0.416 (0.203–0.888), and 0.287 (0.102–0.533), respectively, with one latent component sufficing at most stations but up to 12 required in the most teleconnection‑responsive winter sites. VIP diagnostics reveal a seasonal reordering of controls: AMO and EP‑NP, together with ENSO family indices, dominate JFM; SCAND is pre‑eminent in AMJ; and ENSO re‑intensifies during OND alongside EAWR. Station‑level maxima of |β| locate the strongest couplings, notably SOI/MEI over Kerman (|β|≈1.1–1.2, negative) and ONI over Kish (β≈−1.07) in winter, and PDO over Isfahan (β≈−0.56) in autumn. Clustering of normalized monthly fractions partitions stations into seven robust precipitation regimes (silhouette ≈0.62), separating Caspian bimodal climates, Zagros‑orographic spring peaks, and monsoon‑fringe southeastern tails. Collectively, results indicate that tropical Pacific forcing and Eurasian wave‑train modulation jointly shape Iran’s wet‑season predictability, while methodological pluralism is essential to retain low‑amplitude yet hydrologically consequential signals.<br /><br /><br />Teleconnections—planetary‑scale conduits linking tropical diabatic heating to extratropical circulation—reconfigure jet structure, storm‑track geometry, and moisture transport, thereby modulating Iranian precipitation, which is sparse, highly seasonal, and sensitive to Mediterranean cyclogenesis. Using quality‑controlled 1991–2020 rainfall from 44 IRIMO synoptic stations and fourteen NOAA indices (AMO, AMM, AO, EAWR, EP‑NP, MEI, ONI, PDO, PNA, QBO30, SCAND, SOI, TNA, and TSA), we applied a hierarchical pipeline comprising canonical correlation analysis (CCA), seasonal partial least squares regression (PLSR), and k‑means regime clustering. CCA demonstrated coherent coupled variability between large‑scale modes and regional rainfall, justifying multivariate attribution. PLSR, calibrated separately for OND, JFM, and AMJ, yielded mean skill of R²=0.405 (0.155–0.746), 0.416 (0.203–0.888), and 0.287 (0.102–0.533), respectively, with one latent component sufficing at most stations but up to 12 required in the most teleconnection‑responsive winter sites. VIP diagnostics reveal a seasonal reordering of controls: AMO and EP‑NP, together with ENSO family indices, dominate JFM; SCAND is pre‑eminent in AMJ; and ENSO re‑intensifies during OND alongside EAWR. Station‑level maxima of |β| locate the strongest couplings, notably SOI/MEI over Kerman (|β|≈1.1–1.2, negative) and ONI over Kish (β≈−1.07) in winter, and PDO over Isfahan (β≈−0.56) in autumn. Clustering of normalized monthly fractions partitions stations into seven robust precipitation regimes (silhouette ≈0.62), separating Caspian bimodal climates, Zagros‑orographic spring peaks, and monsoon‑fringe southeastern tails. Collectively, results indicate that tropical Pacific forcing and Eurasian wave‑train modulation jointly shape Iran’s wet‑season predictability, while methodological pluralism is essential to retain low‑amplitude yet hydrologically consequential signals.https://jesphys.ut.ac.ir/article_105305_9e8263ef622202b38e6f2b636946bbee.pdfمؤسسه ژئوفیزیک دانشگاه تهرانفیزیک زمین و فضا2538-371X51420260317Teleconnection Drivers of Extreme Precipitation over Iran: A Causal-Network Perspective (1980–2024)Teleconnection Drivers of Extreme Precipitation over Iran: A Causal-Network Perspective (1980–2024)23326310617010.22059/jesphys.2026.408553.1007744FAAli RezaSaadat MoghadasiDepartment of Irrigation and Reclamation Engineering, Faculty of Agriculture, College of Agriculture and Natural
Resources, University of Tehran, Karaj, Iran.Journal Article20251222This study provides a quantitative and process-oriented assessment of how large-scale ocean–atmosphere teleconnection patterns modulate extreme precipitation over Iran during 1979–2024. Using daily observations from 160 synoptic stations, precipitation extremes were characterized through ETCCDI indices (R10mm, R20mm, R95p, Rx5day), seasonal totals, and SPI-3, and subsequently aggregated into five quasi-homogeneous hydro-climatic clusters. A causal discovery framework based on the PCMCI+ algorithm was employed to infer directed and lagged relationships between twelve major teleconnection indices and regional precipitation metrics. Network diagnostics reveal a pronounced asymmetry between drivers and responses: teleconnection nodes exhibit higher total degree (≈19.3 ± 10.2) and betweenness centrality (≈0.07 ± 0.08) than precipitation nodes, confirming their dominant large-scale control. ENSO-related indices (Niño-3.4, ONI, MEI, SOI) jointly account for nearly 65% of the aggregated absolute causal strength over southern Iran, followed by the Indian Ocean Dipole (≈13%) and EAWR (≈10%). Short-duration intensity metrics (Rx1day and Rx5day) display the strongest cumulative teleconnection influence (exceeding 33% of the total signal), whereas moderate wet-day frequencies show weaker connectivity. Seasonally, robust links are concentrated in SON and MAM, with dynamically consistent and statistically supported signals also emerging in DJF, while JJA remains comparatively weakly connected. Composite analyses of upper-tropospheric wind at 250 hPa, mid-tropospheric geopotential height at 500 hPa (Z500), and lower-tropospheric moisture transport at 850 hPa indicate that teleconnections primarily act through reorganization of the subtropical and mid-latitude jet streams, modulation of Rossby wave-train structure, and alteration of mid-level baroclinicity and low-level moisture convergence. Overall, the results demonstrate that extreme precipitation over Iran is governed by a limited set of dynamically efficient teleconnections whose influence is seasonally modulated and regionally heterogeneous. These findings provide a quantitative basis for improving seasonal predictability and for evaluating process representation in climate-model simulations.This study provides a quantitative and process-oriented assessment of how large-scale ocean–atmosphere teleconnection patterns modulate extreme precipitation over Iran during 1979–2024. Using daily observations from 160 synoptic stations, precipitation extremes were characterized through ETCCDI indices (R10mm, R20mm, R95p, Rx5day), seasonal totals, and SPI-3, and subsequently aggregated into five quasi-homogeneous hydro-climatic clusters. A causal discovery framework based on the PCMCI+ algorithm was employed to infer directed and lagged relationships between twelve major teleconnection indices and regional precipitation metrics. Network diagnostics reveal a pronounced asymmetry between drivers and responses: teleconnection nodes exhibit higher total degree (≈19.3 ± 10.2) and betweenness centrality (≈0.07 ± 0.08) than precipitation nodes, confirming their dominant large-scale control. ENSO-related indices (Niño-3.4, ONI, MEI, SOI) jointly account for nearly 65% of the aggregated absolute causal strength over southern Iran, followed by the Indian Ocean Dipole (≈13%) and EAWR (≈10%). Short-duration intensity metrics (Rx1day and Rx5day) display the strongest cumulative teleconnection influence (exceeding 33% of the total signal), whereas moderate wet-day frequencies show weaker connectivity. Seasonally, robust links are concentrated in SON and MAM, with dynamically consistent and statistically supported signals also emerging in DJF, while JJA remains comparatively weakly connected. Composite analyses of upper-tropospheric wind at 250 hPa, mid-tropospheric geopotential height at 500 hPa (Z500), and lower-tropospheric moisture transport at 850 hPa indicate that teleconnections primarily act through reorganization of the subtropical and mid-latitude jet streams, modulation of Rossby wave-train structure, and alteration of mid-level baroclinicity and low-level moisture convergence. Overall, the results demonstrate that extreme precipitation over Iran is governed by a limited set of dynamically efficient teleconnections whose influence is seasonally modulated and regionally heterogeneous. These findings provide a quantitative basis for improving seasonal predictability and for evaluating process representation in climate-model simulations.https://jesphys.ut.ac.ir/article_106170_7e05be2a97157a3dd509021052a77187.pdf