فصلنامه فیزیک زمین و فضا
سال چهل و نهم شماره 1 (بهار 1402)

The process of converting rainfall into runoff in a region is complex and is affected by many different factors. There is a close relation between land use and erosion. Soil erosion involves the separation and transport of soil particles by runoff. Therefore, runoff production is an important process that is related to soil loss and environmental effects of agricultural operations due to its involvement in nutrient loss. As a result, it is necessary to study the amount of runoff produced as one of the main processes of soil erosion.Rainfall simulator allow repeated measurements in different fields to determine the factors affecting runoff and erosion. Despite the challenges, the use of simulators is common worldwide due to its many benefits in various fields of soil erosion and sediment production. In Iran this method was used in many researches with different targets. Kavian et al (2019) simulated the effect of herbaceous residues on runoff production, Golshan et al (2018) using the rainfall simulator compared the SWAT model and Regression model. In other countries other research can be mentioned the researches of Biddoccu et al (2016), Zhang et al (2018a) and Wang et al (2018). For this purpose, in the present study, which was carried out in one of the forest areas in the east of Ardabil province, called Fandoghlu forest area, the amount of runoff and sediment from the rainfall was measured and compared in different land uses using a rainfall simulator machine.  In this study, the relationship between land use and runoff and sediment production was investigated in the Fandoghlu forest with 4378 ha area, using a rainfall simulator machine. The used rainfall simulator has 1 m2 area with the ability to adjust the intensity and duration of rainfall. The rainfall simulator machine was installed at 104 points with 21.867 mm / hr. rainfall intensity during the 15 mints in the three different land use consisting of forest, rang and agriculture. Soil sampling was performed from each land use from a depth of 0-20 cm. 34 samples were taken for forest use and 35 samples of intact and untouched soil were collected for each of the agricultural and rangeland uses, and a total of 104 soil samples were collected. The location of the points was recorded via the Global Positioning System (GPS).Using laboratory soil samples, parameters of initial soil moisture, bulk density, organic carbon, soil particle size distribution, true bulk density, total porosity, saturated hydraulic conductivity, saturated moisture, field capacity, permanent and susceptible wilting point usage and weight average of aggregate diameter were measured. The mean values of soil bulk density in forest, rangeland and agricultural uses were 0.881, 1.067 and 1.355 g / cm3, respectively. In rangeland use, the increase in specific gravity of the soil relative to the forest can be attributed to the kicking of livestock due to uncontrolled grazing (Ferreras et al., 2006). The mean values of true specific gravity and total soil porosity in forest were obtained. The rangeland and agricultural uses are equal to 1.905, 2.018 and 2.162, and 53.60, 46.46, 37.09 respectively. The reason for the reduction of true specific gravity in forest use is because the organic part of the soil inherently has a small true specific gravity. As by increasing the share of soil organic matter, the actual specific gravity decreases (Zhang et al., 2018b). In forest, rangeland and agricultural uses, the amount of runoff was equal to 868.5, 925 and 1425 ml/m2. Also, the amount of sediment concentration in each of the land uses was 1.937, 8.889 and 44.222, respectively. The results showed that vegetation, slope, soil characteristics and land use change have a significant effect on runoff and sediment components in the study area. Field studies and direct statistics of harvesting are very important in watershed management and estimating runoff and sediment production. The rainfall simulator used in this research can be transported to difficult areas that can provide integrated information from the watershed. The distribution of soil particle size on runoff and sediment components showed that there is a significant difference between different amounts of silt, clay and sand in each of the land uses and runoff volume. So that the amount of clay and silt from the forest to the pasture and arable land is reduced and the amount of sand is increased. Silty soils have low permeability due to low adhesion and porosity and as a result, more runoff volume. Therefore, land use management in this area can play a very important role in reducing runoff and fertile soil.

Landslides may be considered a common natural hazard. The events in many cases lead to significant economic losses as well as fatality damages. Therefore, the Investigation of landslides in order to reduce damage in the preliminary studies of construction projects, especially linear structures in areas with landslide potential is of great importance. Electrical tomography or electrical resistivity tomography (ERI) is a geophysical technique for imaging sub-surface structures (sliding surface in this case) from electrical measurements made at the surface or boreholes. In recent decades, geophysical methods have been widely used in landslide investigations. Among the geophysical methods, ERT is very useful for landslide investigation. In this study, after the landslides in the Langar and TelmaDarreh regions which two sections of Kiasar-Semnan road were destroyed by these landslides, field surveys were conducted on the sites. In this study, a total of six ERT profiles were carried out for landslides (four profiles on Langar and two profiles on TelmaDarreh landslides) investigation. All ERT profiles were performed normally in the direction of the landslide. The Langar Landslide (located in the Langar region in Mazandaran province) occurred on the Kiasar-Semnan road at longitude 53 36 02 and latitude 36 13 03. Due to this landslide, about 300 meters of the Kiasar-Semnan main road was completely destroyed. The TelmaDarreh landslide (located in the TelamaDarreh region in Mazandaran province) also occurred on the Kiasar-Semnan road at latitude 53 41 38 and latitude 36 14 59. After the landslide, about 20-30 meters of the Kiasar-Semnan main road sunk and has been repaired. In this study, the first stage in our electrical tomography was sending an electric current into the ground based on different arrays (dipole-dipole, pole-pole arrays, and Vertical Electrical Sounding) and then measuring the response of the earth in voltage. Bad data points were easily viewed as they appeared as stand out points because the values were displayed in the form of profiles for each data level. These bad data points could be due to the failure of the relays at one of the electrodes, poor electrode ground contact due to dry, sandy, or stony ground, attaching electrodes to wrong connectors, or shorting across the cables due to very wet ground conditions. In the next step for building the inversed resistivity model, the algorithms of well-known IPI2win and Res2dinv software were used.It should be noted that geoelectrical techniques (such as ERT), like other geophysical methods, are used as a complement to other geotechnical methods such as drilling and sampling. Therefore, is noted that the results obtained are based only on the interpretation of electrical resistivity data. In the Langar landslide, according to the results of ERT models and the geology of the region, the existence of four main layers in sections was determined. The electrical resistivity tomography technique showed that the first layer is characterized by very low electrical resistivity (less than 20 ohmmeters) on the sections. This layer of clay is saturated with water. The second layer is with low electrical resistivity (20 to 100 ohmmeters). This layer is most likely a sand-clay layer with water-saturated sand. The third layer is with medium electrical resistivity (100 to 300 ohmmeters). This layer is composed of dense wet sand. The fourth layer has high electrical resistivity (more than 300 ohmmeters). This layer is the bedrock of the area. The depth of the bedrock increases along with the landslide mass from the landslide crown to the landslide heel so that the depth at the landslide center (middle of the valley) varies from 25 m (GH profile center) to 60 m (AB profile center) from the ground surface. The bedrock is located in the center of the landslide from the floor of the old road at a depth of about 50 to 60 meters. It is likely that the presence of a water-saturated clay layer on a layer of dense wet sand caused the Langar landslide in the area. Probably the rupture level starts from about 12 meters in the center of the GH profile and continues with 15 meters in the center of EF and reaches about 25 meters in the center of AB. The depth and topography of slip surfaces have been determined and expressed in all sections.In the landslide of TelmaDarreh at the southern shoulder of the road, the depth of the bedrock starts from at least 2 meters in the center of the landslide and reaches about 6 to 8 meters so that the separation surface between the clay mass and saturated sand with the bedrock is broken. In this place, the depth of rupture in the center of the landslide is about 8 meters from the road surface. Due to the slope of the mountain, this depth reaches about 20 meters above the road at the bottom of the road and at the location of the CD profile.

This study presents a new estimate for subsidence in the northeast of the country through the time series analysis of 11-year (from the beginning of 2006 till the end of 2016) of 31 stations of the Khorasan network, as part of the Iranian Permanent Geodynamic & GNSS Network (IPGN), located in northeastern Iran. The mentioned estimation is obtained from the velocity vector of network stations in the International Terrestrial Reference Frame of ITRF2014 based on time series analysis in two realms, i.e., deterministic model analysis and stochastic model analysis. The deterministic model analysis is comprised of jump detection, determination of station motion model parameters, the study of station trend, outlier detection, and statistical significance test to check the jumps magnitude. Due to the interdependence of these steps, the related calculations are performed iteratively. Noise analysis includes two phases, namely, spatial filtering and temporal filtering. In the first phase, the Common Mode Error (CME) parameter is calculated using the weighted stacking method and taking into account the data correlation coefficient and stations distance. In the second phase, using the maximum likelihood estimation (MLE) method, the optimal noise model is derived as a combination of white noise and flicker noise. As a result, the reliable velocities of the stations (resulting from a complete analysis of the deterministic model) and their realistic uncertainties (resulting from the selection of optimal stochastic models) are calculated. Based on this study we found that: (1) Each station during the 11-year study period has on average nine jumps, all of which are of non-seismic origin. (2) Including the data from all IPGN stations in spatial filtering, leads to better results and on average reduces the norm of post-fit residual vectors for east, north, and up coordinate components by 30.17%, 29.40%, and 17.90%, respectively. (3) Concerning the temporal filtering, we found that the noise of the up-component is significantly higher than the noise of the horizontal components. (4) Stochastic model analysis showed the realistic uncertainties of the east, north, and up components are 4.33, 4.44, and 3.70 times, respectively, greater than the uncertainties which are derived without application of stochastic modeling (optimistic uncertainties). (5) The vertical velocity of most of stations was found to be in the normal range of -5 to 5 mm/yr. (6) Five stations, namely, GOLM, GRGN, NFRD, NISH, and SHRN are having anomalous subsidence (up to 9 mm/yr). (7) The proximity of the three stations GOLM, NFRD, and NISH allows us to infer a regional subsidence for the area of their location. (8) The station GRGN, in addition to anomalous subsidence, shows distinctive features such as the nonlinear trend as well as large periodic signals in the up component of the station. Therefore, to find the reason for such vertical behavior of the earth's crust more permanent GNSS stations must be established in that area. (9) The estimated parameters of periodic signal of the stations demonstrate that the annual and draconitic year signals have the largest amplitudes in the three coordinate components. In addition, amplitude of the periodic signals of the up component is significantly larger than the other components.

Today, identifying new research trends in any scientific field is very important for researchers, universities and research institutes, research investors, industry, and scientific policymakers. Numerous studies have been conducted with scientometric approach in various branches of science (for example: Leydesdorff, 1987; Leydesdorff et al., 1994). Wegner and Leydesdorff (2003) examined the field of seismology in a study entitled "Seismology as a Dynamic and Distributed Field for Research" using the method of co-authorship and citation analysis between journals. The results of their study show that the scientific products of seismology are not different in terms of the degree of internationality compared to the older subject area of geophysics. Other studies in this field include the studies of Sagar et al. (2010), Wu et al. (2015), Amr (2018), Gizi and Putenza (2020) and He et al. (2021), which specifically examine a subfield of knowledge of Seismology (such as tsunamis, tectonic plates, studies related to a particular earthquake, or early earthquake-related warnings). The purpose of this study is to quantitatively and qualitatively evaluate the scientific products of Iran and the world in the Scopus citation database on the subject of Earthquake studies in the period 2019 to 2021. In this study, with the scientometric approach, VOSviewer software (Van Eck and Waltman, 2009, 2020) and co-occurrence analysis of keywords for scientific mapping and thematic exploring have been used.The present study shows that the number of researchers in the field of earthquakes, reported in the Scopus database, is equal to 112262 articles, which after being limited to 2019 to 2021, this number reached 15270 articles. In addition, the growth trend of research in the field of applied seismology in earth sciences has followed an exponential growth pattern and has grown significantly in the last three years. China, the United States, Japan, and Italy are ranked first to fourth in the world in research and production of scientific articles and are still at the forefront in the field of earthquake science and knowledge. A study of Iran's scientific production in comparison with other countries in two different periods shows that Iran, in the period 2007-2009 was not on the list of top 15 countries in the field of earthquake research, but in the last three years, it has ranked ninth and has reached almost the same level as Germany. However, there has been no significant change in other top countries, despite slight shifts in rankings.In addition, the number of articles by different research institutes in the field of earthquake knowledge in the period 2019-2021 is shown in Figure 6. As can be seen, most of the scientific output has been provided by Chinese research institutions, and several American and Asian universities have also contributed to producing earthquake knowledge in subsequent rankings. Most of the scientific products related to earthquakes in Iran are the results of research by researchers from the University of Tehran, the International Institute of Earthquake Engineering and Seismology (IIEES), and the Sharif University of Technology as the first to third ranks.The findings of this study are of great importance for directing suggestions and formulating technological research plans and projects to provide the ground for acquiring scientific authority of seismological knowledge in Asia. Therefore, the need to expand regional scientific cooperation in the policy-making of research institutes and researchers active in the field of earthquake sciences is inevitable to improve the scientific level of Iran among Asian and European countries.

Electrical resistivity tomography is a simple, cost-effective, and highly practical method for surveying near-surface properties. Today, this method is widely used in the discovery and exploitation of water resources, archeology, and environmental and hydro-geophysical studies (such as estimating the hydrogeological parameters of the aquifer). In electrical resistivity imaging, according to the purpose and location of data collection, the electrodes are placed in specific arrays, and data collection is performed. The collected data (potential distribution or apparent resistivity) is then transformed into a distribution of actual electrical resistivity values using inverse modeling methods. Imaging requires defining and solving a nonlinear inverse problem. In this strategy, we optimize the objective function, which consists of fitting field and theoretical data. First, the physics of the problem (forward model) is presented by solving Poisson's equation with the finite difference numerical solution method. An accurate and efficient forward calculation is the basis of most processes of the inversion. Calculation of resistivity forward responses is carried out using simulation of the current flow into the earth’s surface through solving Poisson’s equation. In this contribution, a finite-difference algorithm is applied to discretize the simulated models, restricted by a mixed boundary condition. One of the merits of the finite-difference method over the other methods is its well-known ability to quickly approximate the solutions for any arbitrary and complex structure models. The finite-difference method is relatively fast compared with the finite-element method. However, to include a general topography, the finite-element method becomes a better selection despite being computationally expensive. The partial differential equations governing the resistivity problem are obtained by using the principle of conservation of charge and the continuity equation.The inverse problem is then solved by linearizing the problem in different iterations. A significant part of this research is how to perform inverse modeling of electrical resistivity data. The formulation and solution of the forward and inverse problem in this dissertation have been programmed in MATLAB and part of the program has been written in the C language to increase the computing speed. The field data is noisy due to the non-ideal measuring instruments, improperly filed conditions, operator errors, and geological conditions. Noise values can play a pivotal role in the inversion of electrical resistivity due to the special properties of the inverse problem. A proper estimation of field measurements noise level prevents over- or under-fitting of the calculated data and field data during inversion. Improper fitting (i.e., fitting where the value of the parameter  is much more or less than one) leads to creating an artifact or loss of important details in the final inverted model. In this paper, to deal with the effect of noise level on the ERT inversion results, two methods of reciprocity error method and stacking error method have been used. The results of numerical modeling show that the appropriate estimation of the noise level leads to the estimation of subsurface resistivity models close to the ground reality. We also provide a comparison between the inversion results obtained with the presence of noise level and those derived without including the weighting matrix into the objective function.

Regional GPS networks are now becoming sufficiently dense that one can, with increasing confidence, calculate the full two-dimensional velocity gradient tensor rather than rely on one-dimensional transects. The two-dimensional tensor provides additional insight by allowing one to calculate the vertical axis rotation and dilatation rate tensors. Furthermore, the principal horizontal strain rate axes are not always obvious from examination of the velocity vectors alone. In this study, the Iranian plateau at the oblique collision zone of the Arabia-Eurasia tectonic plates has been selected as the study area. Deformation measured by regional GPS networks in the Iranian plateau reflects the geologic and tectonic variability of the region. Using GPS observations, the velocity gradient tensor can be obtained and by calculating the scalar quantities extracted from this tensor, we can study the continental deformation and interpret the tectonics of the region.For the Iranian collisional plateau, the regional strain and rotation rate are analyzed by inverting GPS velocity vectors to calculate the two-dimensional velocity gradient tensor. In the general case, estimated deformations using GPS data show characteristics of regional deformation. Principal shortening and extension rate axes, vertical axis rotation, and two-dimensional volume strain (dilatation) are very consistent with long-term geological features over large areas, indicating that the GPS velocity fields reflect processes responsible for the recent geologic evolution of the Iranian plateau. Differences between geological and GPS descriptions of deformation can be attributed either to GPS networks that are too sparse to capture local interseismic deformation, or to permanent deformation that accurs during strong earthquakes.Vertical axis rotation amplitude and sign changes are due to distributed deformation throughout the plateau. The presence of large regions with almost constant low amplitude rotation rates indicates a quasi-rigid bodies rotation inside the Iranian collisional plateau bounded by faults. The estimated compressive axis confirms the direction of the Arabia-Eurasia tectonic plates collision. The internal amplitude of shortening is lower than the boundaries of the plateau and has a negative two-dimensional dilation almost everywhere. A negative dilation rate is associated with vertical crustal uplift.The maximum value of negative volumetric strain was obtained in the southern part of Eastern Alborz. It was calculated as 8.1×10-3 and 4.6×10-3 per year, respectively, using the nearest neighbor and weighted distances methods.We show the superiority of the gridding method over the Delaunay triangulation method. On the other hand, to study the strain locally in regions with active deformation, if the number of GPS stations in there is high, the value of α in the distance-weighted method or the number of neighborhoods in the nearest neighbor method should be chosen less. Current two-dimensional GPS networks are adequate to resolve first-order regional-scale instantaneous strain variations. However, the resolution of some of the issues raised here must await the deployment of dense, continuous, and high-rate GPS networks. Such networks, scaled to the dimensions of the problems one wishes to study, will provide more regular temporal sampling allowing one to calculate more reliably near fault interseismic strain. More importantly, they will bring a critical third dimension of velocity measurement, allowing one to calculate the full three- dimensional velocity gradient tensor. Furthermore, the deployment of dense networks of continuous GPS stations can be used in conjunction with differential radar interferometry to provide a more spatially and temporally complete snapshot of tectonic deformation.

Rip current is one of the most important phenomena in coastal areas. Due to the importance of rip currents, which are directly related to human lives, they have been studied and evaluated from different approaches. This research aimed to determine rip currents' effect on the sediments' granulometry through sampling of the sediments in the rip channel and its surrounding area on the Caspian Sea coast. In this study, one station was selected in Noor city, Mazandaran province. Various factors such as lack of private property, easy access, lack of human manipulation, and knowledge of drowning rescuers about the location of the drowned were considered in the selection of the station. Then, in two seasons, winter (December and March) and spring (May and June), with the help of lifeguards and existing signs, the location of the rip canal was identified in the station and at least three sediment samples were taken from the channel and three samples from the environment around the channel using Grab. In addition, the water depths were estimated in the sediment sampling points using Echo sounder. The sediment samples were transferred to the laboratory and sediment particles were separated based on grain size using a shaker and multiple sieves (with a network mesh of 30, 50, 60, 80, 100, 140, 200, and 230). The data obtained from the shaker were entered into the excel page of GRADISTAT software and the characteristics of sediment samples including mean diameter (D50), mean degree of sorting, skewness, and kurtosis were calculated by Folk and Ward method. All sediment particle characteristics were compared between the rip channels and surrounding areas using an unpaired t-test. The results showed that among the sedimentation characteristics, no significant difference was observed between the channel and surrounding areas in the degree of sorting and kurtosis neither in the winter nor in the spring. In addition, the results of granulometry showed that D50, mean particle size, and skewness of grain distribution of sediment particles were significantly different between the rip channel and the surrounding areas in the spring. The highest amount of D50 (with an average of 185.8 mm), mean (with an average of 202.7 mm) and skewness (0.48) of sediment particles were observed in the rip channel. These significant differences in particle characteristics were not observed in the winter samples probably due to the turbulent weather and sea currents. In addition, the average water depth of the rip channel was obtained at ca. 120 cm, while the average water depth of surrounding areas was estimated at ca. 85 cm. Therefore, we can state that some of the channel sediments have been removed by water flow in the channel. The higher energy and velocity of the flow in the channel than the surrounding environment has caused the transfer of sediments. On the other hand, sediment transport is directly related to other parameters such as sediment grain size. The present study showed that rip channels have sediments with significantly different characteristics than the surrounding sediments. The higher flow rate in the rip channel probably causes the removal of some fine-grained sediments and is not able to remove coarse-grained sediments. Removal of fine-grained sediments causes a change in the texture of the remaining sediments towards larger grains and these changes have caused a significant granulometric change between the rip channel and the surrounding environment, especially in the spring.

The extent of the energy received from the sun to the earth is one of the main and important input parameters in the agricultural, ecological, hydrological, climatological, and environmental models. It has also a key role in most processes related to the soil-plant-atmosphere system such as net radiation, reference evapotranspiration, soil temperature, moisture, and snow melt. In this research, the efficiency of three categories of the global radiation empirical models including sunshine-based, air temperature-based, and cloudiness-based models was evaluated during the 1997-2021 period for the Sanandaj synoptic station and after these model’s calibration procedure, the better models were recognized. By adopting the estimated global radiation by better models as the input of modeling some processes including net radiation, reference evapotranspiration, soil temperature, and moisture and snow melt, the model outputs were compared with the case that the measured global radiation had been adopted as these models input. After the calibration of global radiation models and optimizing the empirical parameters of them, which were performed based on minimizing the RMSE index between the left and right sides of these equations, finally, three models including Angstrom–Prescott model, the Bristow-Campbell model, and the Black model were selected as the representative of each of the three mentioned categories. In the next step, cross validation was applied to these three models based on the MAE، MBE، R2 and Reff indices. The results showed a reasonable agreement between the measured and estimated global radiation based on all of these three selected models. The MAE index for Angstrom–Prescott, Bristow-Campbell, and Black models was 138.5, 227.14, and 251.3 kj.m-2.day-1, respectively which can be considered as the acceptable extent. The MBE index led to obtaining good results with no considerable bias for Angstrom–Prescott model (-3.9 kj.m-2.day-1) and the Bristow-Campbell model (+11.9 kj.m-2.day-1) but a positive overestimating bias using the Black model (+159 kj.m-2.day-1). For all three models, the R2 and Reff were respectively greater than 0.83 and 0.78 values. The best values of these two indices were obtained for the Angstrom–Prescott model. Regarding comparison of the outcome of estimated and measured global radiation when applying as the input of some models, the histogram of the errors (the difference between outputs of some of the processes models based on the estimated and measured global radiation inputs) for net radiation calculation showed the error range mostly from -50 to +50 kj.m-2.day-1 for all of the three global radiation model inputs, which as seemed to be a narrow difference between these two cases. For reference evapotranspiration calculation, the histogram of errors was mainly between very low values of -0.2 to 0.1 mm.day-1 for all of the three global radiation models. Regarding l temperature modeling, the Angstrom–Prescott model (with error range between -0.5 to 0.2 C) showed a better performance than the Bristow-Campbell and Black models (with error range between -1 to 1 C). For soil moisture modeling, the Angstrom–Prescott model showed very suitable performance with the most error values close to zero and the Bristow-Campbell and Black models showed relatively suitable and similar performance. The snow modeling performed based on some few snowy days, the Angstrom–Prescott model with focusing the histogram error between -2 to 0.5 cm can be considered as the best model and the Bristow-Campbell and Black models showed similar but not as good performance. On the whole, the results indicated that the appropriate outcomes were obtained when applying the global radiation estimated by Angstrom–Prescott model as the input of all of the processes models. Regarding Bristow-Campbell and Black methods, which led to obtaining relatively similar results, applying them as the inputs of different models led to a diversity of results including very appropriate (for reference evapotranspiration), appropriate (for net radiation), relatively appropriate (soil temperature and moisture) and not-appropriate (for snow).

The ionosphere is the upper part of the Earth's atmosphere, which is considered to be approximately 70 to 1000 km above the Earth's surface. Ionosphere modeling has been one of the goals of spatial geodesy since 1970. In many ionosphere modeling using satellite measurements such as GPS, total electron content (TEC) are used as observational input data. In recent years, modeling and prediction of the TEC have been considered by researchers with methods that have high speed and accuracy. One of the branches that has been able to show good capabilities in the field of estimation and modeling is machine learning methods (ML). Machine learning includes fuzzy inference systems (FIS), artificial neural networks (ANNs), genetic algorithms (GAs), support vector machines (SVMs), and evolutionary communications (ECs). Since 1993, with the advancement of computer technology, many new and hybrid algorithms, such as the adaptive neuro-fuzzy inference system (ANFIS), have been developed in ML. Another new effective approach in ML is the support vector regression (SVR) method. The SVR is a kernel-based ML method for classification and regression in which the risk of incorrect classification is minimized. The structure of an SVR network has a lot in common with the ANN, and the main difference is practically in the way of the training algorithm. In general, this method is divided into linear and nonlinear modes.In this paper, the TEC of the ionosphere is modeled and evaluated with ML models. Support vector regression (SVR) and artificial neural network (ANN) methods are used for local TEC modeling. In both models, the latitude and longitude of the GPS stations, day of the year (DOY), hours, AP, KP, DST, and F10.7 are considered an input vectors. Also, the value of VTEC is considered as the output of the models. The main innovation of this paper is in evaluating the effect of different physical parameters on the accuracy of ML models. Using observations of 15 GPS stations in the northwest of Iran from 193 to 228 in 2012, new models are evaluated. Also, the results of the new models are compared with the results of the global ionosphere map (GIM), the IRI2016, and NeQuick empirical models in two internal and one external control station. Statistical indices of root mean square error (RMSE), relative error, dVTEC, and correlation coefficient are used to evaluate the error of the models. Sensitivity analysis of SVR and ANN models to input parameters is performed and the importance of each physical parameter in spatio-temporal modeling of the ionosphere is investigated. The results obtained from this paper show that in both high and low geomagnetic and solar activities, the SVR model in internal control stations has a higher accuracy than other models. But at the external control station, the error of the SVR model is much higher than other models. Determining the parameters of the kernel function using observations at the territory of the studied network is the reason. Also, the sensitivity of SVR and ANN models is increased to the physical parameters F10.7, KP, DST, and AP, respectively. For precise local ionosphere modeling, the effect of these parameters must also be considered.

Numerous numerical experiments have been performed to cloud model seeding over the last two decades. Silver iodide nucleation has been parameterized using different methods in these studies. The results of these studies indicate that cloud seeding can change the distribution of precipitation in most cases. Moreover, most of these numerical simulations have been used only in the field of convective cloud seeding and are incapable of complete simulation of atmospheric conditions. For this purpose, the governing equations should be parameterized in three dimensions for the general case and be used in the appropriate model.In this study, the effect of cloud seeding, whether increasing or decreasing in rainfall, has been studied. For this purpose, the WRF numerical model has been developed to simulate the cloud seeding. Since, it is virtually impossible to repeat experiments under similar meteorological conditions, a model that can simulate the effect of cloud seeding on microphysical processes and precipitation could avoid many speculations or inaccurate estimates.The basic hypothesis of cloud seeding is based on the physical principle that at sub-freezing temperatures the equilibrium vapor pressure relative to ice is lower than the equilibrium vapor pressure relative to liquid water. Therefore, the saturated environment with 100% relative humidity relative to water (RHW = 100%) will be supersaturated relative to ice at temperatures below zero degrees Celsius (Pruppacher and Klett, 2010). As a result, in a cloud that is saturated with liquid water and composed of supercooled cloud water droplets, ice particles grow rapidly to form larger and heavier drops which could be fall as rain drops. In that environment, tiny, supercooled cloud droplets either grow in upward motion or evaporate to provide vapour for ice to grow. Therefore, in the cloud seeding with silver iodide, ice particles are expected to be produced and grow in the cold part of the cloud, and the liquid water of the cloud will be transformed into ice phase species more quickly.The operational cloud seeding project has been carried out in the northwest area of Iran. At the time of operational project, the seeding target area was under the influence of the eastern Mediterranean low pressure center, this trough has caused the formation of divergence in its downstream in the upper levels of the atmosphere in the target area and has led to the formation of severe upward movements. Stable and thick clouds have formed in the area. Under the above mentioned environmental conditions, 44 pyropatrons of 4% silver iodide were fired at the target area by a seeding aircraft. Silver iodide particles measuring 0.1 to 1 mm are very effective in freezing nuclei. In this study, the effect of seeding is coded based on the model of Meyers et. al (1995) and Seto et. al (2011) by applying the seeding conditions into the Morrison scheme code within the WRF model and changing the number density and mixing ratio of cloud ice due to the silver iodide injected into the atmosphere.By simulating the effect of cloud seeding, meteorological quantities, including precipitation under seeding conditions, are estimated by changing the Morrison microphysical scheme in the WRF model. The WRF numerical model was also run in control mode (without applying cloud seeding relations). By comparing the output rainfall of the numerical model in seeding mode with the output rainfall of the numerical model in control mode, the amount of cloud seeding effect was determined.The results showed that the changes resulting from seeding in the studied cloud seeding operation were not favorable in all stations, and in some cases, the decrease in precipitation was seen 2 hours after seeding. This decrease in some stations, such as Maragheh, Tabriz, Sahand, and Khoy, starts from seeding time and continues until the end. But in a station like Sarab, although the rainfall decreases slightly at the beginning of cloud seeding, over time, it increases to 7% after two hours. While seeding in Parsabad, and Ahar stations resulted in precipitation enhancement by 3%, 9%, and 27% two hours after seeding, respectively.

Major hurricanes occur in the Atlantic Ocean every year over a seasonal period known as the Atlantic hurricane season. There is evidence to suggest that the hurricanes can be affected by the North Atlantic Oscillation (NAO), a low-frequency phenomenon occurring in a large region over the North Atlantic. In turn, hurricanes can affect the North Atlantic storm track by transition to extratropical cyclones in the midlatitude regions. The objective here is to investigate the relationship between hurricanes and the North Atlantic storm track through NAO index. For this, the correlation coefficient between daily NAO index and 6-hourly “accumulated cyclone energy” (ACE) index related to the hurricanes are computed and analyzed for July to October 2017–2019. Then, in the dynamical study using JRA-55 data and from the energy point of view, the vertically-averaged “eddy kinetic energy” (EKE) and the main terms involved in its dynamical evolution are computed for the hurricane season. Also, by selecting one of the major hurricanes in September, which has different conditions in terms of being affected by the North Atlantic Storm track and entering the midlatitudes, the relationships between hurricanes and the North Atlantic storm track are further investigated.Results show that when hurricanes are active for only about a week, they are limited to the subtropical region and have a higher correlation coefficient (about 95%) with NAO. But when hurricanes are active for more than a week and involve an extratropical transition phase, they have a relatively lower correlation coefficient with NAO. Also, the long-term statistical study (1995–2019) shows that although the number of hurricanes in the positive phase of NAO is about 7% more than that in the negative phase, but the relative prevalence of the negative phase of NAO at the time of hurricane activity in the main development region is slightly higher than that of the positive phase. In addition, hurricanes in which all activity is in the positive phase of NAO stretch to the east coast of the United States and are reinforced there, while hurricanes that all of their activity coincide with the negative phase of the NAO, occur in the Sargasso Sea and the CapeVerde regions. Therefore, NAO phases affect hurricane track during extratropical transition.The monthly mean values of the vertically-averaged EKE and the main dynamical terms of its time evolution equation in September show that eddy activity is weak during summer in the hurricanes activity zone; however, in the east coast of the United States and Canada, there are significant changes in the dynamical terms. Also, in the extratropical transition, the dynamical terms determining EKE evolution at the entrance of the Atlantic storm track have large amounts in the Labrador Sea due to deep convection, which suggests a significant energy exchange with hurricanes in this area. Another result is that baroclinic conversion and divergence of ageostrophic geopotential flux are the most important terms determining EKE evolution. Also, geographical location of the hurricanes during transition has a significant effect on the changes of EKE. If hurricanes are intensified in the east coasts of North America and Canada and occur at the same time in the positive phase of NAO, they could play a very important role in strengthening the North Atlantic storm track.

Ultraviolet radiation is defined as electromagnetic radiation with wavelengths in the range of 200-400 nm and is divided into three different bands. UVC is related to the wavelength from 200 to 280 nm, while UVB is related to the wavelength ranging from 280 to 315 nm and UVA is related to the wavelength from 315 nm to the visible level (400 nm). Ultraviolet radiation has beneficial effects such as making vitamin D and disinfecting effects. On the other hand, it causes harm such as burns and skin cancer, and damage to the eyes and immune system. Predicting the amount of UV radiation based on the UV index can be of great help to people's health. In this study, the tropospheric ultraviolet-visible (TUV) model was used to predict the UVI index. This model requires ozone, whiteness, and Aerosol Optical Depth (AOD) to forecast UVI. WACCM model data was used for ozone and whiteness column values from the ozone data of the GFS and AOD global forecasting systems. 612 case studies in the whole year of 2020 were selected from each of the 12 months of the year from different parts of the country. GFS, WACCM, and OMI data were extracted for the mentioned dates and interpolated at the desired points. Because OMI data is available locally at noon everywhere, case studies have been selected for noon. Then the interpolated values along with the length, width, and height of the points were given as input to the TUV model, and the UVI value was predicted. Due to the lack of access to the actual value of UVI in the country, OMI data was assumed as observational data and used to compare with the predicted value. Conventional statistical measures ME, MAE, RMSE, and Pearson correlation coefficient were used to validate the prediction value with observational data. The results showed that in January, February, April, November, and December, which are the coldest months of the year and the day length is shorter and the sun is less intense, so the error rate is lower than in other months (warm months of the year). However, in general, the forecast is very accurate. So that in all selected study cases, the values of ME, MAE, RMSE, and R are 0.16, 0.85, 1.13, and 0.93, respectively, which indicates the high accuracy of the forecast. The results also showed that the forecast error has a linear relationship with the AOD value. Thus, the higher the AOD value, the more negative the forecast error and underestimated forecast value.In the warmer months of the year, the length of the day is longer and the intensity of the sun's radiation is higher, resulting in more errors. The amount of error is also related to the amount of light depth of the particles; the greater the AOD, the greater the error. The correlation coefficient diagram also showed that there is a high correlation between the forecast and observation values. This research is the first research in the field of forecasting the UV index in the country and has had satisfactory results.

In Reverse Osmosis (RO) desalination plants, the most important problem is the increase of salinity near the outfall. In this study, different scenarios of brine waste dispersion discharge of a desalination plant in terms of the depths of outlet system positions and physical properties of discharge system that meet the standard criteria of the Department of Environment of Iran are investigated.In this work, the effect of the desalination effluent discharge site in terms of the depth of discharge location and emission of pollutants on a Caspian Sea coastal area (Neka) has been investigated and various scenarios have been implemented and proposed. Here the effectiveness of desalination effluent discharge depths of different scenarios, using a numerical model, have been considered. The model simulates unsteady 3D flows, by taking into account density variations, currents and other hydrographic conditions. The model has a dynamical nesting facility which gives a possibility of making an increase in resolution in areas of special interest. For increasing numerical efficiency, structured nested grids with three sizes of 90, 30, and 10 meters and uniform vertical mesh size equals to 0.5 meters have been used. In comparison with other common works, in this research, using a 3D non-hydrostatic (fully hydrodynamic) mathematical model to simulate the dispersion of saline water effluent, is an important feature. The effective density variation between the effluent and the receiving environment and generation of vertical flows resulting from this density variation, cannot be simulated using simplified mathematical models as they may face serious errors. Lack of the rapid diffusion and ideal conditions for plume development, illustrates that the worst condition for brine dispersion is a calm sea with minimum currents in coastal areas. So, the effects of the sea waves have been neglected and longshore wind induced current has been assumed to be a minimum of approximate value of 0.03 m/s.The mean salinity in in the southern Caspian Sea is about 12.5 gr/lit and the desalination brine salinity has been considered as 25 gr/lit and the rate of fresh water and brine waste water production is about 6 m3/s. With these assumptions for rate of effluent discharge and sea conditions, different scenarios have been investigated using a 3D numerical model including different velocities and directions of a pair of jet fluxes in outlet system and outlet installation Reverse Osmosis (RO) desalination plants salinity near the outfall. In this study, depths of outlet system positions and physical properties of discharge system are mainly investigated.The results show that in acceptable scenarios (with higher jet discharge speed and vertical direction of 30˚ to the vertical axis), the receiving environment has high brine concentrated area with a radius less than 200 meters. The results of different scenarios of discharge depths show that regarding the depths of discharge studied in this work (5, 10 and 15 meters), when the jet injection is closer to the horizontal direction, there is no significant difference between the results of different depths. But, in selected conditions, i.e. conditions where the angle of the effluent discharge jet is closer to the vertical axis (vertical direction of the jets is 30˚ to the vertical axis), deeper dischages create better conditions in terms of salinity propagation horizontally in the environment.

Precipitable water vapor (PWV) is a key parameter in meteorological studies and forecasting of atmospheric events such as rain and flood. Due to the spatial limitations of GPS and meteorological stations, as well as observational discontinuities in the time domain, PWV modeling is of great importance. Obtaining PWV using direct measurements and water vapor measuring devices is a difficult task. The best way to get information on PWV variations indirectly is to use GNSS measurements. The GNSS meteorological technique can provide continuous and almost instantaneous observations of the amount of PWV around a GNSS station. Research has shown that the accuracy of weather forecasts can be improved using GNSS-dependent techniques. Models based on GNSS observations for estimating PWV are known as tropospheric analytical models. The tomographic model is one of the most famous and widely used tropospheric models. There are limitations such as a large number of unknown parameters, rank deficiency of design matrix and the inevitability of using regularization methods, assuming the amount of water vapor inside each voxel is constant and also, the need for initial amounts of water vapor inside the voxels in the voxel-based tomography (VBT) method. Such limitations have led researchers to use machine learning methods to estimate the spatio-temporal variation of PWV. In this paper, the spatio-temporal modeling of PWV is suggested using the generalized regression neural network (GRNN) model. The GRNN model is a type of artificial neural network (ANN) that uses radial basis functions (RBF) as an activity function in the hidden layer. As a result, its accuracy is higher than the ANN model. Eight parameters of longitude, latitude and height of GPS station, day of year (DOY), time (min.), relative humidity (RH), temperature (T) and pressure (P) are considered as inputs of GRNN and ANN models and the PWVs corresponding to these eight parameters are the outputs. After the training step, to evaluate the GRNN and ANN models, the observations of two GPS networks are used. In the GPS network of north-west of Iran, observations of 23 GPS stations in the period of 300 to 314 (winter season) from 2011 have been used. For the central Alborz GPS network, observations of 11 stations at the period of 162 to 176 (summer season) in 2016 are also used. Results obtained from GRNN and ANN models in two interior control stations, one exterior control station (outside the GPS network territory) and also in Tabriz and Tehran radiosonde stations are compared and evaluated with the results of VBT, ECMWF, Saastamoinen and GPT3 models. The statistical parameters of root mean square error (RMSE), relative error and correlation coefficient (R) are used to evaluate the accuracy of the models. At the north-west GPS network, the averaged RMSE values of GRNN, ANN, VBT, ECMWF, Saastamoinen and GPT3 models in the two interior control stations are calculated as 2.14, 2.57, 3.32, 3.36, 6.31 and 4.35 mm, respectively. For the central Alborz GPS network, the averaged RMSE of two interior control stations are computed as 2.01, 2.42, 3.24, 3.26, 6.00 and 4.06 mm, respectively. For the exterior control station, the GRNN model has less error than the ANN, VBT and Saastamoinen models, but more than the ECMWF and GPT3 model. The results of this paper show that the GRNN model has a very high accuracy compared to other analytical and empirical models of the troposphere. This model has the ability to show the spatio-temporal variations of precipitable water vapor with high accuracy at the GPS network territory and; it can considered as an alternative for the other analytical and empirical models.

Simultaneous observations of the Interface Region Imaging Spectrograph (IRIS) data, with a spatial resolution of less than one second consisting of ultraviolet (UV) spectra and images (SJI), make it possible to investigate solar chromosphere and transition region and provide valuable information about the dynamics of solar jets. IRIS combines numerical modeling, high resolution imaging, and UV spectroscopy. The interface region is the main place for the transfer of energy from the solar surface to the very hot corona. Of course, knowing the secret of energy transfer in the solar atmosphere is not the only goal of this mission, but also it examines the solar winds that is emitted from this area, which carry a rain of charged particles into space and also affect the Earth's climate. Information about the dynamic behavior of the physical phenomena of the solar atmosphere is obtained by studying the characteristics of spectral lines. For this purpose, it is necessary to obtain the information to identify and study spectral lines and how they are formed. The solar atmosphere is a plasma environment associated with a variety of transient events. Astrophysicists, especially in the field of solar dynamic physics, describe these events by magneto-hydrodynamics aspect. One of these phenomena is the bright spots of the solar atmosphere called jets. We identify and study the dynamics of a series of jets recorded on August 17, 2014, at Mg II k, C II and Si IV spectral lines corresponding to the 2796 Å, 1336 Å, and 1394 Å wavelengths, respectively. Jets are small-scale dynamic events that can be detected by non-Gaussian profiles of lines in the solar chromosphere and transition region. The production mechanism of these plasma jets is still being investigated. We use the temporal evolution analysis method to track the path of these structures and determine their apparent velocity. To calculate the Doppler velocity we perform Gaussian fitting at the same time on the spectral intensity profiles. The apparent velocity results show that these jets have quasi-periodic motions with speeds of approximately 10 to 110 kms-1. Spectral investigation of these jets also shows the periodic behavior that is associated with increasing in blue and red wings at the three wavelengths as -65 to 40, 60 to 50, and 80 to 60 kms-1, respectively. Simultaneous enhancements in the blue and red wings of the spectrum can be caused by two-directional upward currents caused by magnetic reconnection and amplified by waves with p-modes (compression modes). According to these results, it is suggested that the fluctuations in these events with increasing on one side of the spectrum and both sides of the wing are signs of spiral and rotational motions, respectively. The results of this research show that by using the data of the IRIS Telescope, it is possible to identify and extract the physical components of jets at different wavelengths and identify their dynamic behavior. These specifications will help us better understand the stratification of the solar atmosphere and how heat and matter are transferred to the sun's surface and the effects of such transitions on the Earth's atmosphere. The application of this study will be the goal of space research and is very important in identifying space and Earth’s climate.