فصلنامه فیزیک زمین و فضا
سال چهل و نهم شماره 3 (پاییز 1402)

GPS velocity fields consist of a set of geodetic observations of displacement with an irregular spatial distribution on the sphere. In this study, multiscale analysis based on spherical wavelet is used to estimate the GPS velocity field in the oblique collision zone of Arabia-Eurasia tectonic plates. Multiscale velocity field estimation is well suited for dense geodetic networks and is straightforward to implement.We show that for DOG wavelet a scale of 3 to 8 is suitable for GPS velocity field analysis in the study area. Estimation output can be used as a data layer in GIS analysis. Regularization is required to obtain a smooth estimated velocity field from the discrete observations. This is achieved through two possible actions. First, one can cull the set of possible spherical wavelets based on the coverage of observations. If each spherical wavelet has a sufficient number of observations constraining its coefficient, then no regularization is needed (λ = 0). Second, if all spherical wavelets are used for the inverse problem, then extensive regularization will be needed, since most wavelets will have zero observations for constraining their corresponding coefficients. In this research, we have chosen something in between these two extreme cases, where we at the outset eliminate many candidate spherical wavelets based on data coverage, but we still require a moderate amount of explicit regularization in the inversion.As the adopted spherical wavelets are analytically differentiable, spatial gradient tensor quantities such as strain rate, dilatation rate and rotation rate can be directly computed using the same coefficients. The gradient quantities are then calculated directly from the estimated field to identify potential deformation signals. The first factor controlling the estimation is the distance between the network stations. Wherever stations are dense, short-scale spherical wavelets participate in the estimation; and where stations are sparse, only long-scale spherical wavelets is used to do the estimation. As we allow shorter length-scale frame functions to be used in estimating the velocity field, the residual field vectors decrease in magnitude. The smallest residuals occur at observation points that are dense enough to fall within the support of the smallest length-scale frame functions which also have the smallest estimated uncertainties in the data. The largest residuals overall are associated with the largest uncertainties in the data.From the perspective of monitoring a GPS network, the residual map may be helpful in detecting spurious behaviour of singles at stations. If unusual strain, dilation, or rotation are observed around a station, such an observation would warrant additional analysis of the GPS time-series and error estimate. We remove a rotational field from the observation set to obtain the velocity field. We then estimate the horizontal velocity field. Once we have estimated the multiscale velocity field, we can readily compute other scalar quantities, such as dilatation, strain and rotation. The high density of stations near the fault system could capture the spatial gradient in the velocity field, and give rise to estimate strain-rates with a maximum of 1.410×10-7, an average of 1.786×10-8 and a standard deviation of 1.626×10-8 per year. The dilation rate is obtained with a maximum of -8.684×10-8, an average of -3.487×10-9 and a standard deviation of 1.144×10-8 per year and the rotation rate is obtained with a maximum of 7.771×10-8, an average of 7.720×10-9 and a standard deviation of 7.040×10-9 radians per year in the study area.Due to the shorter distance of observation stations in the southern part of central Alborz and northwestern Iran, the values of strain, dilatation and rotation rate can be observed in these areas on large scales.In multiscale estimation, the residual field between the original field and estimated field may reveal two key features. First, if there are systematic residuals in a particular region, then it is probable that one needs to include shorter-scale wavelets in the estimation. Second, if there is a strong residual at a single station, then the station is anomalous and is either malfunctioning or is capturing a signal that is not spatially resolved. The advantages of the multiscale approach are its ability to localize the deformation field in space and scale, as well as its ability to identify outliers in the set of observations. This approach can also locally match the smallest process obtained with the local density of observations, thus maximizing both the amount of information extracted and the possibility of comparing the resulting quantities in different regions of a scale. Multiscale estimation of the three-dimensional GPS velocity field is also possible using spherical wavelet frame functions. The vertical component, if any, should be used to estimate the velocity field, as deformation may not be predominant in horizontal directions. This formulation may be easily applied either regionally or globally and is ideally suited as the spatial parametrization used in any automatic time-dependent geodetic transient detector.

Flood is one of the most destructive natural hazards, especially in arid and semi-arid areas that experience great of rainfalls. This hazard is caused by the combination of several natural and human factors. Floods occur when the flow of the river increases to a certain extent. that the water crosses the banks of the river and inundates the nearby areas, as a result it affects the communities that live in the vicinity of the rivers. In addition to that, climate change increases the possibility of floods and their magnitude. Annual 75 world floods on average, affects millions of people worldwide. About 20,000 deaths per year, resulting in economic losses of $95 billion. In recent years, the frequency of floods has increased by more than 40%, as climate change significantly affects the intensity, pattern and magnitude of deadly floods. Especially Asian countries are exposed to floods and earthquakes with a relatively high impact. In Asia, more than 90% of human casualties are caused by natural disasters that are due to flooding. Recently, Iran has experienced catastrophic floods. In addition to the impact of climate change. The impact of human activities caused by the poor management of the watershed, including deforestation, excessive livestock grazing, the development of rural and urban settlements, have reduced the hydraulic space of rivers. In all these researches, the study of floodplains, different return periods, and inundation values in each application have been analyzed. Arid and semi-arid areas are definitely more prone to flooding due to heavy rains. In the present study, in order to investigate and distribute large-scale floods in Etrak river basin, the daily inundation data of 35 hydrometer stations were used over a period of 30 years. In order to extract large floods in the region, first, using the statistical method known as the inverse numerical filter method, the base runoff was separated from the total. The results of time series analysis extracted after applying the regression numerical filter method show that in 3 decades, different behavior of floods in the catchment area are observed. In the first decade, the floods had a higher intensity, and this high intensity also had a high frequency. In the second decade, the volume of floods decreased drastically, and in the third decade, the volume of floods was the same as in the first decade, but they occurred at longer intervals. The investigation of most severe floods showed that with the increase in the volume of the total runoff, there was a large difference between it and the base runoff, and the occurrence of this process is sudden and sometimes reaches more than 10 times the base runoff. The high volume of discharge in flood periods and on the other hand, the decade fluctuation in the occurrence of severe floods in the Atrak catchment area showed that for long-term forecasting, the inverse numerical filter method can be used.

The Oman Sea is the meeting point of the Eurasian and Arabian tectonic plates, where the Makran subduction zone is located. Knowledge of the behavior and local changes of the gravity field is of great importance in the study and modeling of the complex tectonic structure in this area, which has been less addressed. In this research, SARAL/AltiKa satellite observations have been used, which has higher spatial resolution and, as a result, higher range precision due to the measurement in the Ka frequency band. The SARAL/AltiKa satellite altimetry data is used for the preliminary process. Then atmospheric delays, geophysical effects, cross-examination and mean dynamic topography model were corrected. After performing these corrections and reaching the geoid height in the study area, the EIGEN6C4 global model was used to remove the long wavelengths of the gravity field up to the degree and order 180 from the geoid height signal. As a result, the residual geoid height (∆N) are prepared as an input signal for the marine gravity modeling. On the other hand, in most of the other methods used in earth gravity field modeling, to simplify the calculations, two assumptions of stationarity and isotropy of the gravity field are taken into account, which means that the gravity function does not depend on the changes in the azimuth and the position of the observations. These assumptions are not always valid. In this research, in the first step the long-wavelength of input signal is removed. Then the process continued with residual geoid height. Next, the improved covariance approach is used to increase the accuracy of determining the covariance. Also the idea of patching is applied. Those three steps provide a solution to reduce the negative effects of the stationary assumption in the local modeling of gravity field. The results of this research in 234 ship borne gravimetric observations were evaluated. The improvement of quality of the covariance with the idea of patching and improved covariance approach enhanced the local modeling results of the gravity field. It was found that with patching, the field modeling accuracy was increased by 25.1% (1.04 mgal) with the Tscherning-Rapp 1974 approach and 11.6% (0.33 mgal) with the improved covariance approach. Similarly, the improved covariance approach also improved the local modeling of gravity field. Using this approach increases the accuracy of local modeling by 31.3% (1.27 mgal) without patching and by 18% (0.56 mgal) with patching. As a result, it was found that removing long-wavelength and using improved covariance and patching increases the accuracy of the local modeling of the gravity field with more than 39% (1.6 mgal) as compared to the Tscherning-Rapp 1974 covariance function without patching in the Oman Sea. Moreover, applying the mentioned approaches in region 1 with independent covariance shows 47% (2.25 mgal) increase in accuracy in the local modeling of the gravity field. This improvement is equivalent to 11.3%(0.33 mgal) higher accuracy than the available global gravity models.

The non-inductive effects of structures with dimensions smaller than the measurement scale and at shallow depths prevent the correct observation of the regional electrical conductivity model and therefore make the modeling and interpretation of magnetotelluric data difficult and in some cases unreliable. The solutions that have been presented to estimate the intensity of these distortions and recover regional information have been mostly focused on two-dimensional modeling. Several studies have shown the adverse effects of galvanic distortions on 3D magnetotelluric inversion results. Removing or correcting these distortions in practice is, however, rarely done before 3D inversion due to the extreme under-determination of the problem of recovering non-distorted or regional information in 3D environments need to apply more constraints. In this research, the complexity of magnetotelluric data in the Sablan geothermal area, in the northwest of Iran, was studied. By fitting the 3D/2D/3D model in the region, shear and twist parameters have been evaluated for a part of the period interval in which the data show 2D behavior, according to skew angle values. In the next step, the same distortion parameters were applied to the three-dimensional part of the data and the components of the impedance tensor for the 3D structure were recovered. For this purpose, the phase tensor (Caldwell et al, 2004), the rotational invariants of the magnetotelluric tensor (Weaver et al, 2000) and the approach presented by Ledo et al (1998) have been used. In order to correct the distortion, in addition to the estimated values for the twist and shear angles and the period interval selected for matching the two-dimensional model, it should also be taken into account that in the two-dimensional model, the values of the distortion parameters, i.e. the torsion and shear angles, remain constant with changing period. With this criterion, despite the values of the skew angle showing a two-dimensional behavior, the average distortion parameters for a number of stations could not be selected due to high fluctuations. It seems notable to emphasize that the skew parameter is the only necessary and not sufficient condition to confirm the two-dimensional situation. The magnitude and phase of all the components of the recovered impedance tensor are different from before, indicating the importance of the distortion correction procedure before the 3D modeling and inversion. In addition, in more than half of the examines magnetotelluric sites, impedance tensor phase components are not located in the corresponding trigonometric quadrants that are constrained to be placed in them in normal 2D or even 3D earth conditions. There are numerous examples of observing and studying these effects in magnetotelluric data. This behavior is attributed to factors such as anisotropy, three-dimensional complexities, two-dimensional structures with large resistivity contrast and severe distortion (Egbert, 1990; Pina and Dentith, 2018; Wannamaker, 2005; Jones et al, 1998). The results have shown that these abnormal values are related to the rotation angle and distortion level. Some of these stations show very large distortion angles.

Iran is a wide compressional deformation and seismic activity zone along the Alpine-Himalayan orogenic belt resulting from the conversion motion between the stable Arabian and Eurasian plates. Northwestern Iran is part of a complex tectonic system within the Arabia-Eurasia collision zone. The main active fault of northwestern Iran is the Gailatu–Tabriz strike-slip fault system (GTFS) that extends ~400 km in length from north of Mianeh (a town in the East-Azarbaijan province of Iran) to the southwest and south of Kaghsman (in Turkey) to the northwest. It has a conspicuous history of seismicity and a controlling role in the geodynamics of the region. In this study, we utilize satellite images, DEM images, field evidence, earthquake information, and GPS data, to investigate the active tectonic characteristics of the GTFS. From the southeast to the northwest, GTFS consists of three main fault zones, named: North Tabriz Fault, Mishu-Tasuj Fault, and Gailatu-Siah Cheshmeh-Khoy Fault. Near Kaghsman, the northwestern end of the GTFS forms a horsetail splay structure, with many faults having normal components, and to the east, GTFS merges with the Bozghush fault zone. GTFS shows a variety of transtension and transpression tectonic structures (stepovers, bendings, pull-apart basins, and splay structures) formed in the dextral shear zone. North Tabriz fault zone is characterized by three main NW striking right-stepping en echelon segments (Bostan Abad, Shebli, and Tabriz fault segments) and is known as the causative fault of three destructive historical earthquakes on 1042/11/04 (Mw 7/6), 1721/04/26 (Mw 7/7), and 1780/01/08 (Mw 7/7). To the east, it joins the Bozghush thrust fault zone that caused the 2019/11/07 (Mw 6/0) earthquake on its Shalgun-Yelimsi left-lateral strike-slip fault segment. In the central part of the GTFS, the Mishu-Tasuj fault zone is formed as a transpressional bend. The macroseismic epicenter of the 1786/10 (Mw 6/2) earthquake is located near this fault zone. Thrust faults in the southern part of the Mishu-Tasuj fault zone are parallel with close distances, and have uplifted the land masses; probably representing the migration of thrust faulting into the southern plains; similar to the Esfarayen and Sabzevar thrust faults in northeastern Iran. Four pull-apart basins have been created due to the movement of fault segments along the Gailatu-Siah Cheshmeh-Khoy fault. The current kinematics of the GTFS plays a key role in the tectonic of northwestern Iran and accommodates part of the convergence movement between the Eurasian and Arabian plates. Earthquake history and geometry of different segments of the GTFS imply seismic gap, especially on the North Tabriz fault, and faults interaction (e.g., between Shalgun-Yelimsi left-lateral strike-slip fault and south Bozghush thrust fault, and Gailatu-Siah Cheshmeh-Khoy right-lateral strike-slip fault and Tasuj thrust fault) which are important issues in seismic hazard in northwestern, especially for Tabriz City with a population of about 1.5 million.

The main purpose of this research is to determine the thickness of the crust and lithosphere in the northeastern regions of the Iranian plateau. There are different methods to determine these two parameters. According to the definitions and how to determine this thickness, each of the methods has advantages and disadvantages. In this research, large-scale satellite data is used, so we do not need data acquisition that makes it a great advantage, and inversion in this way will give us the final model of the crustal and lithospheric structure in the shortest time.For this purpose, a joint inversion of geoid topography and gravity data is used. First, we obtain a 1D model that is close to reality using the simultaneous modeling of geoid data, topography, basic concepts of physics and mathematics and local isostasy, for the thickness of the Moho and lithosphere. Then we use a 3D inversion method to reduce the difference between the measured and calculated data. The initial model that is given to the program in 3D inversion is the output of 1D modeling.The studied area, the northeastern region of the Iranian plateau (including Kopeh-dagh) is an area with great potential in natural resources (mainly oil and gas). The highlands of northeastern Iran are formed in the Alpine-Himalayan folds and are similar to the Zagros mountains from a geological point of view. The Kopeh-dagh mountain range starts from the east of the Caspian Sea and enters Afghanistan after passing through Turkmenistan. The Kopeh-dagh mountain range separates the Turan plate from the central Iranian plate (a part of the Eurasian shield) and reaches a maximum height of 3000 meters. Most geologists consider Kupe-dagh to be the southern edge of the Turan shield and a part of the Eurasian plate. The main fault of Kopeh-dagh (Eshgabad) along the N130 direction forms the southern border of the Turan plate with the Kopedagh mountain range. A very small anomaly, the free-air gradient in the northeast of the Ashgabat fault, indicates the subduction of the southwest-trending Turkmenistan plate beneath Kupeh-dagh. In this way, less displacement is observed between the Turkmenistan plate and the southeastern Caspian lowlands compared to the displacement between the Turkmenistan and Iran plates that can be proved from the value of the gravity anomaly in the west.There are large gas fields shared by the three countries of Iran, Turkmenistan and Afghanistan in the Kopeh-dagh region and its surrounding areas. Huge gas fields in Iran, Daulatabad-Donmez, Ghazli, Shatlik, Mehri and Bayran Ali in Turkmenistan and Gogar in Afghanistan have been discovered in this area. Geographically, Kopeh-dagh is part of the eastern continuation of the Alborz Mountains, but its geological and structural features are different from the surrounding areas. One of the main goals in the exploration of oil resources is to describe the structure of the sedimentary cover and underground topography. Furthermore, oil production is very sensitive to heat storage by the source rock and thus to the tectonic evolution of the entire lithosphere.Results show that the thickening of the Moho is observed under the Kopeh-dagh mountain range and the thickness decreases gradually when moving towards the northeast and southwest of the Kopeh-dagh mountain range. The depth of the Moho in the studied area varies from 40 to 60 km. According to the modeling results, the lithosphere-asthenosphere boundary in the southwestern part of the studied area (Central Iran) is approximately 100 km, and it reaches approximately 200 km towards the northeast of the this area.

Nowadays, network structures are found in many natural and engineered systems, e.g., river networks, microchannel networks, plant roots, human blood vessels, etc. Therefore, providing efficient methods for modeling phenomena such as diffusion, advection, etc. is very practical. One of the most common tools for modeling this phenomena is numerical modeling, as mathematics software is well- developed and powerful nowadays. In this research, a new approach called Equation-Oriented Modeling has been presented. In this approach each branch of the network has its own differential equation, and these branches are connected or coupled by boundary conditions. In other words, unlike classical modeling, EOM does not solve through the discretization of the partial differential equation in the whole domain of the network, while in this approach, each branch of the network has its own differential equation with its own specific diffusion coefficient and cross section area, then the problem is solved as a system of PDE. The main point of EOM is to formulate a physical problem in the network into a system of differential equations, which is finally solved by the Method of Lines. MOL is an efficient computational method used to solve partial differential equations or PDE systems. MOL is generally implemented in two steps, in the first step spatial derivatives are replaced by algebraic approximation. In the second step, the ordinary differential equation system is integrated with respect to time using any method, for example, in this research, we use the Runge-Kutta 4th order method. EOM was implemented to solve the diffusion equation in three types of networks, including tree-shaped and loop network. Then modeling results for 3 networks were presented as spatial concentration profiles in different paths in the networks. The model had reasonable results in the boundaries and branches according to the boundary conditions, loading and concentration functions, as well as the continuity of concentrations and loading by diffusion in the output results was reasonable. The boundary conditions that apply at the intersections of the branches include the continuity of concentration and the continuity of loading due to the diffusion phenomenon. The results of test case 3 were compared with another numerical model for validation, and three types of Error Parameters were calculated at different times between these two models. R-Squared (R2) was calculated in path (1-2-3-5-9), and its value was 0.99-1, which was the optimal value. This coefficient shows that the results of the EOM and the other numerical model has the same trend. Then, RMSE and MAE were also calculated and their values were approximately zero for all times. The modeling results for 3 networks were presented as spatial concentration profiles in different paths in the networks. The first advantage of the EOM approach is that the choice of terms in the differential equation is left to the user rather than the software developer, so that a wider range of phenomena can be modeled and the effects of different terms can be seen in the modeling. The second advantage of this approach over classical modeling is that the equations are available to the user as tools and model elements, and modeling complex networks such as tree-shaped, and Loop networks is not as complicated as classical models. The third advantage of EOM is the tools available in mathematical programs for optimization or linking with other programs. Since the heat equation is similar to the diffusion equation, the results of this research can be used for other important topics, such as solving the heat equation in microchannel networks for cooling systems, modeling pollutant transport in river networks, or diffusion modeling of solutes in plant roots.

Different parts of the climate system as well as precipitation are subject to the global warming and climate change. The aim of present study is monitoring different aspects of changes and trends in precipitation over Iran, using MERRA2 reanalysis data from 1990 to 2020 based on 7 indices including Maximum 1-day precipitation (Rx1day), Simple precipitation intensity index (SDII), Maximum length of dry spell (CDD), Maximum length of wet spell (CWD), Annual count of days when PRCP ≥ 10mm (Rx10mm), Annual count of days when PRCP ≥ 1 mm (Rx1mm), and Annual total precipitation on wet days (PRCPTOT). MERRA2 is the second generation of Modern-Era Retrospective Analysis for Research and Applications (MERRA) by NASA’s GMAO (National Aeronautics and Space Administration, Global Modeling and Assimilation Office), with spatial resolution of 0.5o (approximately 50 kilometers) and temporal resolution up to hourly data. Correlation analysis between MERRA2 and observation data of 48 weather stations indicates acceptable results all over the country especially for the North, the North West and the Western parts. The highest values of R2 was 0.94 calculated for the wet regions of the North and Northwest, the least values were 0.63 for the arid regions of the South and the Central parts of Iran. Mann-Kendall non-parametric test was used for trend analysis and detection. Mann-Kendall test uses time series data for consistently increasing or decreasing trends in a variable and can works with all statistical distributions. The results show a decreasing trend on 0.95 significance level in 3 indices including PRCPTOT, Rx1day, Rx10mm, in the West and Northwest of Iran. Previous researches confirmed and reported the similar results. In the North East part of the country (parts of the Khorasan-e-Razavi, Semnan and Golestan provinces) positive increasing trends was detected. Considering this distinct regional discrepancy in calculated trends (trends in indices), it is necessary to focus on the effects of possible changes in pressure and synoptic systems on the precipitation changes over Iran. For RX10mm increasing trends was detected for the South and South East of the country. Due to the high absolute humidity in these regions, if condition is suitable for convection and air ascending, Sudan low pressure and Indian monsoon make possible high amount of intense convective rainfall origin from Indian ocean. Overall, MERRA2 shows an underestimate for the region of highest rainfall (North of the country) and an overestimate for arid regions. As a conclusion, based on the results of this study it is necessary to have a distinct precise plane not only for managing the decreasing trends in precipitation and water deficits, but also for mitigating impacts of intense rainfall and flash floods. Further studies needed to determine simultaneous (combined) effects of precipitation and temperature changes in other to manag water resources.

To assess thermal comfort of residences, several different factors as meteorological, physiological, and socio-cultural, should be considered. The integrated effect of these variables on thermal stress can be obtained and evaluated using thermal comfort indices. Thermal comfort as a bio-meteorological index is of special importance. The purpose of this research is to analyze the seasonality and trend of heat stress in Iran in the period from 1981 to 2020. In this research, the Universal Thermal Climate Index (UTCI) index as a common thermal index was evaluated for outdoor thermal comfort, using ERA5 dataset. ERA5 is the fifth generation ECMWF reanalysis data for the global climate and weather for the past 4 to 7 decades. The ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave, and land-surface quantities. An uncertainty estimate is evaluated by an underlying 10-member ensemble at three-hour intervals. Such uncertainty estimates are closely related to the information content of the available observing system which has improved considerably over time. They also indicate flow-dependent sensitive areas (Hersbach et al., 2020). Also, root mean square error (RMSE), Percent bias (PBIAS), Nash–Sutcliffe model efficiency coefficient (NSE), and Root Mean Standard Deviation Ratio (RSR) metrics were used to evaluate the quality of ERA5 dataset.The seasonal variability of the UTCI index shows that this index has significant regional heterogeneity in Iran. The increase in UTCI from the north to the south of Iran leads to an increase in thermal stress. The spatial distribution of areas that do not have thermal stress during the hot period of the year are mainly consistent with the altitudes. During the cold seasons of the year, areas with elevations of more than 3,000 meters in Iran have moderate cold stress. The investigation of the trend of thermal stress during the last four decades, which was analyzed with the modified Mann-Kendall test, shows that the UTCI in Iran has a dominant increasing trend. The UTCI index shows an increasing trend in the spring and autumn seasons by 100%, and in the winter and summer seasons at 99.83 and 99.75% of the country, respectively. The maximum significant increasing trend of the index at the level of 0.05 was achieved in the spring. In the same way, the highest value of Sen's slope estimator test of the area-averaged trend is also seen with the value of 0.52 oC in this season in the study period. The results of this study for climatology and the trend of the UTCI index in Iran show that: 1- There is a close relationship between heat and cold stresses in Iran and topography, but this relationship is not a linear; 2- Along with global warming, the UTCI index in Iran during the years 1981-2020 has shown an increasing trend; 3- In general, areas with UTCI cold stress in the country are decreasing and areas with heat stress are increasing; 4- One of the key findings in this study is the significant increase in trend of the UTCI index in the spring season.

The propagation of nonlinear waves such as ion acoustic, electron acoustic, dust acoustic in the plasma media have been studied in different equilibrium and non-equilibrium conditions. Meanwhile, the study of these waves in magnetized plasmas, due to the effect of the external magnetic field on the plasma with different angles of wave propagation has been less addressed. There are extensive studies on the propagation of acoustic waves in magnetized plasma, which show that when the intensity of the magnetic field is constant, these waves propagate as soliton waves with a stable profile in the plasma. In fact, the uniform magnetic field does not interfere with the fluctuations of the plasma particles to produce dilute and dense regions and wave propagation, and for this reason, the harmonic soliton wave is propagated in the plasma. We know factors such as heating, particles collision and viscosity that cause perturbation in plasma particles fluctuations. In this situation, the propagation of the acoustic wave will no longer be in the soliton form and a shock wave may appear. On the other hand, we know that in actual conditions, the magnetic field governing laboratory plasmas such as tokamaks and also astrophysical and space plasmas are not constant at all. As a realistic example, the Earth's magnetic field intensity varies from 30000 nT in 0 (latitude): +60 (altitude) to 45000 nT in 10 (latitude): +90 (altitude), where the magnetic field is almost horizontal. Therefore it would interesting to study the presence of a non-uniform magnetic field. For this purpose, we considered an ion-electron magnetized plasma model and numerically investigate the ion acoustic wave behavior in this medium, while the strength of the magnetic field is not the same in different parts of the plasma. In this situation, for simplicity in calculations, we assume the direction of the magnetic field to be constant. We use the second order Runge-Kutta method and by numerically solving the basic equations of the ion acoustic wave, it is shown that the stable behavior of the solitonic wave is perturbed in the presence of varying magnetic field and in this case, the wave propagates as a shock wave. Now we can introduce the non-uniform magnetic field along with factors such as viscosity, heating, collision etc. as the new sources of producing the acoustic shock waves in plasmas. We also studied the cases where the collisional terms and gyro frequencies of the particles are considered. In this condition, the effects of the non-uniform magnetic field are different. This subject can also be considered for other acoustic waves in different temperature and density models, various non-thermal plasmas and other features in astrophysical and laboratory plasmas.

In recent years, the importance of climate prediction has increased as a scientific source for understanding climate change and evaluating its consequences in political and economic decisions. Providing predictions with less uncertainty, especially for precipitation and temperature is of considerable importance for policymakers in time periods from several months to several decades. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability and variability. The DCPP consists of three components (A, B, and C). Component A comprises of the production and analysis of an extensive archive of retrospective forecasts. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts, and Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (Boer et al. 2016). The aim of this study is to predict precipitation extremes using the decadal Climate Prediction Project contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 2021 to 2028 over Iran. For this purpose, two types of data including 77 synoptic stations and three DCPP models (BCC-CSM2-MR, MPI-ESM1-2-HR, and MRI-ESM2-0) with a horizontal resolution of 100 km were used. The precipitation output of DCPP models, each with nine variants (27 members) were used for two time periods, including Hindcast (1981-2019) and Forecast (2021-2028). To evaluate DCPP models, we used the Root Mean squared error (RMSE), the Pearson correlation coefficient (PCC), the Mean Bias Error (MBE), the Percent bias (PBIAS), and the Taylor diagram methods. In addition, Direct Model Output (DMO) was corrected by the Delta Change Factor (DCF) method, and the Independent Weighted Mean (IWM) was used to generate a multi-model ensemble from 27 members. In this study, the ETCCDI indices including days with Heavy precipitation (R10mm), days with Very heavy precipitation days (R20mm), Simple daily intensity (SDII), The maximum 1-day precipitation amounts (Rx1day), The maximum 3-day precipitation amounts (Rx3day), The maximum 5-day precipitation amounts (Rx5day) were calculated to analyze precipitation extremes for all regions of Iran. Furthermore, the evaluation of the DCPP models showed that the output of mentioned models is acceptable for all regions of Iran. Also, the performance of CMIP6-DCPP-MME is higher than the individual models. The result of the prediction of precipitation extremes showed that the six studied extreme precipitation indices will increase for the next decade. The Southwest and Northeast are the two hotspots of positive anomaly. In contrast, the southern coast of the Caspian Sea for the R10mm index will experience a negative anomaly for the next decade. The findings show that the southeastern region of Iran, from the eastern borders to the north of the Strait of Hormuz, will be the main area of negative precipition anomalies in the country in the next decade. So that the indices of days with heavy (R10mm) and very heavy (R20mm) precipition will decrease by 2.7 and 0.3 days, and daily precipition intensity (SDII) will decrease by 2.6 mm/day.

In this study, a future projection of four extreme precipitation indices consists of total extreme precipitation (R95p), extreme precipitation days (R95d), the fraction of total rainfall from events exceeding the extreme precipitation threshold (R95pT) and extreme precipitation intensity (AEPI) over Iran was carried out using the reference period of 1990-2014 based on the multi-model ensemble approach and a rank-based weighting method using five models from the CMIP6 models (MPI-ESM1-2-HR, EC-Earth3, EC-Earth3-Veg, GFDL-ESM4, and MRI-ESM2-0). The weight of each model was calculated depending on its historical simulation skill and then weighted and unweighted groups were used for future projections. In this study, the threshold of heavy precipitation was calculated using percentiles, according to the method of Zhai et al. (2005). For this purpose, at each grid point, annual rainfall data (daily rainfall greater than or equal to 1 mm) were sorted in ascending order to obtain the sequence of annual rainfall for each year from 1990 to 2014. Then, the mean 95th percentile for the 25-year precipitation sequence at each grid point was defined as the extreme precipitation threshold (Rwn95). It should be noted that the heavy rainfall threshold for observations and CMIP6 models was calculated separately from observation data of synoptic stations and from each CMIP6 model. Based on this extreme precipitation threshold, four extreme precipitation indices were calculated. The results of the spatial skill of the simulation show that the EC-Earth3 model with MR_taylor equal to 0.65 and with a horizontal resolution of 0.7 degrees has the best skill in simulating the spatial pattern of extreme precipitation indicators, and the MPI-ESM1-2-HR model with MR_taylor equal to 0.5 and with a horizontal resolution of 0.938 degrees is the second suitable model among the five selected models used for simulating the spatial pattern of extreme precipitation indices. Also, the results of the simulation time skill test show the superiority of MPI-ESM1-2-HR and GFDL-ESM4 models with MR_IVS respectively equal to 0.6 and 0.5 compared to other studied models. It is important to note that the horizontal resolution of the model is not the only factor that determines the skill of the model in simulating extreme precipitation indicators in the study area. Because the model with above-average weight (>0.2) is not a high-resolution model and improvement in physical processes which are also needed. The results show that the probability of increasing the total extreme precipitation (R95p) and extreme precipitation intensity (AEPI) in the study area in the period 2026-2050 under four scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 with values greater than 0.5 is almost certain. According to the median value close to zero or even negative of the extreme index R95d, it is inferred that the priority of the increase of the extreme amount of precipitation is over the number of days of occurrence, and these extreme precipitations will occur in fewer days, which is a warning for the flood risk. A comparison between the weighted and unweighted ensemble means shows that the uncertainty in the study area is almost always reduced after applying the weighted scheme to future probabilistic projections.

Coronal heating mechanisms are generally classified as waves (AC: Alternating Current) and Direct Current (DC) models. Restricted evidences from both models have been observed over different structures of the solar atmosphere. However, a general model describing the global heating process in the whole solar atmosphere is missing. Active regions are the magnetic anchorages of the solar corona. It is believed that the Active Regions have important contribution in the heating of the corona. ARs are divided into two sub-regions: Warm coronal loops (~ 1 MK) and hot coronal loops (> 1.5 MK). There are strong observational evidence supporting the DC nanoflare model in the warm coronal loops. However, there is still no conclusive answer for the responsible heating mechanism of hot loops. There are some observational evidence indicating the existence of steady heating, some others indicating the impulsive heating, and very lately people have found MHD wave heating signatures in the hot loops. The intrinsic fuzzy nature of hot coronal emission lines made it impossible to isolate the hot loop structures and study their physical properties separately. Therefore, the only way to study the hot loops is to focus on their hot footpoints called “moss” areas, which was discovered in 1999 by Berger et al. using TRACE 171 Å images. The moss emission has a reticulated spongy structure. It is believed that when the hot coronal loops cover the cold plage regions, an inward radial thermal conduction gradient is formed which causes the plasma to heat up and radiate. This radiation is what we call ”moss”. The dark patches inside the bright moss areas are the cross sections of the cold spicular materials rising upward toward the corona.Using spectroscopic and imaging data of Interface Region Imaging Spectrograph (IRIS) and Solar Dynamic Observatory (SDO), dynamic properties of the moss areas over an AR is studied. Three boxes of moss regions are selected. The time variation of the intensity, Doppler shift, and line widths of C II 1335.7077 Å and Si IV 1402.770 Å emission lines are investigated. Time series of the intensities over the three selected moss regions are made from IRIS SJI 1400, and 2796, along with AIA/SDO 1700, 304, 1600, 171, 193, 211, 335, 94, and 131 channels. Using FFT technique we obtained oscillatory behavior over the all mentioned parameters. The results show oscillatory behaviors in the line width, Doppler shifts, and line intensities of C II and Si IV spectral lines with periods of 3.9 and 6.9 minutes over the moss areas. 3.9 min oscillations are observed over the AIA 211 passband, as well, which could be an indication of the presence of torsional Alfven waves coupled with kind mode. High frequency oscillations with 0.9 to 2 min periods are observed over the selected moss regions in AIA hot channels like 335, 131, and 94, as well as Si IV line. This could be an indication of occurring magnetic reconnections above the moss regions in the hot coronal lines, triggering the Alfven waves in this structure. Therefore, our results support the presence of MHD waves heating mechanisms in the studied moss structures.

The Sun is an external object that significantly impacts the earth's atmosphere and space weather conditions. Flares and coronal mass ejections are large-scale solar atmospheric features that mainly emerge at active regions above the sunspots and have influenced the earth. The sunspots, considered signatures of solar activity, are fascinating features related to the internal dynamics and activity of the Sun. The appearance of the sunspots in the photosphere shows the complexity of the magnetic field on the Sun. The frequency and size of sunspots change over time which show periods (e.g., eleven years periodicity) that may be a sign of the complex Sun. The time series of the sunspot numbers have been recorded for several centuries, and this time series is significantly changed over time. The complex network approach is a way to investigate the inherent property of complex time series, such as the sunspots time series.In this study, the growing complex network with the visibility condition is constructed using the time series of the sunspots (time and numbers) for 1922 to 2016 collected by SILSO. We compute the complex network parameters such degree of nodes, shortest path length, and clustering coefficients. We examine the sunspot complex network's scale-free, small-world, and assortative properties.We show that the degree distribution of the complex network for the time series of the sunspots obeys a power-law distribution function. We applied a method via maximum likelihood estimation in the Bayesian framework to obtain the power indicated. Therefore, the degree exponent is obtained larger than three, so the complex network for the time series of the sunspots is small-world and scale-free. The power-low behavior is an essential characteristic of self-organized or self-organized criticality systems. Limited productivity is a crucial property for these complex systems. The small word behavior indicates that the large sunspot numbers in the time series are clustered with several small values neighbors and linked with distinct large values in the time series.The small-world network represents a small characteristic path length with a high clustering coefficient. The scale-free and small-word behavior for the network of the sunspots time series may imply that the sunspots and sunspot groups forming via complex non-linear dynamics. Changing the magnetic polarity of the sunspots during the solar cycle can be a characteristic of such complex systems. The limited predictability in sunspots' time series, e.g., the intensity of activity within a solar cycle, may also be another sign of the complex Sun.The behavior of the degree of node distribution, clustering coefficient, and shortest path length indicates that the time series of sunspots is a non-random system. We showed that the degree of correlation is a function of the network size and can be considered as an assortative, dis-assortative, or neutral network.

Since one of the important steps in the management of natural areas is to know when and where the wildfire is more likely to occur or where it has a more negative effect, examining and preparing a fire risk map of sensitive and high-risk areas is crusial. In terms of wildfires, this is one of the main necessities in wildfires control and management. Wildfire risk indicators can be very useful for predicting areas with wildfire risk potential. One of the most widely used wildfire risk indices is the Forest service fire Weather Index (FWI), which has been based on the Canadian forest service fire risk rating system since 1971.In order to evaluate the effectiveness of FWI indicators in identifying wildfire-prone areas in the vegetation areas of Iran from the climate variables of temperature, relative humidity, wind speed, and precipitation of 53 meteorological stations were monitored on a daily basis for the period of 1981-2020. In order to analyze the climate of the vegetation areas under investigation, combined satellite product of burned areas and FWI indices during the period (1981-2020) were used. After downloading and an initial processing, the examined indicators were coilculated for the border of Iran. Then, their time series was analyzed, using zonal statistics. Than the area-averaged of each of Iran's forest regions was calculated, and their seasonal maps were prepared by the kriging interpolation method. Finally, the Pearson correlation coefficient method was used to determine the relationship and correlation between FWI indices and burned areas.The results of the analysis of the combined satellite product of the burned areas showed that the maximum extent of wildfire in the forest regions of Iran is related to the summer season. In this season, a large part of the country's forests, especially in the forest regions of Arsbaran, that has the occurrence of wildfires, and the minimum amount of wildfire was related to the spring season, which was observed in a limited area in the southwest of Iran. The values of atmospheric fire indicators vary according to the different characteristics of forest regions such as vegetation, topography, and climate. The drier parts of the south, east, and southeast of Iran mainly have the highest values of FWI indices, which shows that the increase in temperature and drought affects the moisture content and, as a result, increases the occurrence and spread of fire. The FWI indices in spring for all forest regions of Iran have a high efficiency for identifying areas with wildfire occurrences. Also, in the hot season of the year, when most wildfires occur in the vegetation areas of Iran, FFMC and FWI indices had the best performances.According to the climate variability and topography of Iran's forests, it is difficult to control the occurrence of fire. Hence identifying wildfire critical areas, and determining and preparing a map of fire-risk areas is an effective step in helping forest managers to plan and manage high-risk areas in terms of fire. In addition to this, wildfire atmospheric indices obtained with high horizontal resolution can help better understand the spatiotemporal variability of fire risk, especially in Iran with its complex and diverse topography.