نشریه نیوار
پیاپی 118-119 (پاییز و زمستان 1401)

One of the most important forms of precipitation in the hydrological cycle of mountainous regions is snow. In this research, after collecting statistics and information related to days with snow during the statistical period (1989-2018) in stations located in the three provinces of Kermanshah, Ilam and Lorestan. Also, the time distribution and frequency of snowy days and their change process were discussed monthly and annually, In order to reveal the trend of annual changes in snowy days, Man-Kendall trend determination tests and Sen's slope estimator were investigated. These tests were carried out for the 30-year time series examined on an annual and monthly scale. The results snow days trend in Middle Zagros showed that the snow days have changed during the statistical period of the study, that the highest and lowest changes correspond to the highest and lowest frequency of snowy days in the studied region, In fact, the southeastern and northeastern regions of the region, which had the highest number of snowy days, have also had the greatest changes. In general, based on the results of the investigation of the trend of snow days in the Middle Zagros, it can be stated that the number of snow days in the study area has a decreasing trend, and this decreasing trend in some stations and some series (months of January and series annually), has become significant at the 95% confidence level. Based on the Sen's slope estimator method, the changes of snow days in the region are noticeable and significant

The occurrence of heavy rains and extreme events due to climate change has had devastating effects on the economic, social context. Therefore, studying the variability and behavior of heavy rainfall events and heavy rainfall is of particular importance. In this study, we investigated the impact of climate change on heavy rainfall and baseline temperatures (1989-2008) with a 20-year future period (2011-2030) for five stations in Bijar, Khalkhal, Zanjan and Mianeh in Sefidrood catchment. Heavy rainfall and maximum and minimum temperature values were generated using the LARS-WG model and climate scenario A1B, A2, B1 for the next 20 years. Brooks method was used to identify heavy precipitation and future temperatures of observation and future periods. According to this method, the upper temperatures and heavy rainfall of the base and future periods were divided into four groups of five years and were classified using statistical methods. The results show that the upper temperatures over the next few years will see an increase in the number of events, a downward trend but in terms of quantity, and the global warming trend in the minimum temperature indices will be higher than the maximum temperature related trend. . Heavy rainfall will be upward during the next period in Bijar and Khalkhal stations and in Zanjan, Qorveh and Middle stations.

In this research, an estimate of the depth of 10 cm in the soil of the Synoptic Station of Tabriz in East Azerbaijan province was carried out using artificial neural network (ANN) and backward vector machine (SVM). Two main component analysis (PCA) and gamma (GT) tests were used for pre-processing data and input data. According to the results, for Tabriz station, 3 input variables were selected by gamma test. In the main components analysis method, four main components for the synoptic station of Tabriz were selected. The results of modeling indicate that the gamma-based gamma-ray machine (GT-SVM) model with a mean square error of 2.48 ° C can be selected as the selected model for the station. The most important variables known to estimate the temperature of the soil were the average temperature, sunshine, wind speed and relative humidity, respectively, by gamma test. Finally, according to the results, it can be concluded that the methods used for pre-processing the data in this study do not differ significantly in soil temperature prediction, and both methods have worked well. Also, the SVM model in all estimations has a more acceptable performance than the ANN model.

It has been tried to investigate and study the fluctuations of different meteorological parameters at different probability levels in the important stages of the agricultural calendar. To conduct this study, first, the traditional agricultural calendar was prepared through the reports of the Amol Rice Research Center and with the help of interviews with rice farmers and the cooperation of rice agricultural experts. Among the various stages in the agricultural calendar, 5 stages were selected as the main stages including: land preparation, treasury, transplanting, flowering and ripening of seeds and harvesting. Then, the data of temperature, precipitation, humidity, wind speed, sunshine hours and surface temperature in 9 weather stations as representative of the main rice growing areas in the province for the statistical period of 2005 to 2020 were received from the Meteorological Organization. . According to the agricultural calendar and the time frames for the 5 mentioned stages and the appropriate thresholds and weather conditions at that time, the compatibility of the favorable conditions for rice cultivation operations in different stages with the actual conditions of investigation and the expected values ​​of each parameter in each period It was calculated at different probability levels. The results show the amount and possible thresholds of the effective parameters in the 5 stages of the rice cultivation calendar. The results showed that the average temperature in the five mentioned stages is 12.8, 14.3, 18.7, 20.7 and 31.4 degrees Celsius, respectively, which is within the optimal thermal threshold range for all agricultural operations.

Changes in the land biosphere since 1970 are consistent with global warming: climate zones have shifted poleward in both hemispheres, and the growing season has on average lengthened by up to two days per decade since the 1950s in the Northern Hemisphere extratropic. In this research, projected change of growing season length (GSL) and the start date of the growing season over Yazd province are investigated using three GCMs (GFDL, HadGEM and MIROC5) under the RCP8.5 scenario during 2021-2040. The results indicate Yazd province will experience extended GSL in a warmer world. The thermal growing season has been projected to increase owing to the earlier onset of growth in late winter and spring. Therefore, the prolongation of GSL under the RCP8.5 is attributed to earlier onset. Future temperature and precipitation scenarios based on GFDL and HadGEM models under the RCP8.5 scenario show more unfavorable conditions for this province. Because of the sensitivity of agriculture to weather and climate conditions, these impacts can have substantial direct and indirect effects on production and profitability.

Numerical weather prediction (NWP) models are not completely accurate and error free, and there is always some uncertainty. The errors in weather forecasting stem from the limitations of human theoretical understanding of the atmosphere and the operational capacity to produce forecasts. It is necessary to make a forecast, along with an estimate of its uncertainty. This is accomplished by creating ensemble systems of weather forecasts differing in the initial conditions or physical formulation of NWP models. There are several methods for post-processing of ensemble forecasting, including Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) that they are more popular because of higher efficiency and accuracy. In this research, first, an 18-member ensemble system is formed, which each member is an independent run of the WRF model with different physical configurations. BMA method was used to estimate the density function of predicting 24-hour cumulative precipitation. Due to some hardware limitations and access to an ensemble system with fewer number and more efficient members, the size of the ensemble system has been reduced to 7 members. Using the BMA method, a weight is assigned to each ensemble member. The size of the ensemble system is reduced by removing the members who had less weight. The probabilistic prediction verification obtained from the 7-member ensemble system in a test period from 15 January 2020 to 15 May 2020 has been checked using reliability diagram. The results show that the probabilistic predictions are sufficiently skilled for 24-hour cumulative precipitation.

In this research, the flood-prone areas of East Azarbaijan province were identified using the flood damage information of regional water and crisis management for the period of 1378-1397, and a map of the spatial distribution and frequency of flood events was drawn. The highest frequency of floods occurred in the western half of the province, especially in the cities of Marand, Shabestar and Tabriz, among which the floods with a frequency of more than 10 days are related to the cities of Tabriz, Shabestar, Marand, Jolfa and Sarab. In order to determine synoptic patterns for each day of torrential and heavy rainfall in the province, maps of the upper levels of the atmosphere as well as indices of atmospheric instability were examined along with the skew-T map. The systems were identified based on the origin of the classification system and 7 systems, which include the high pressure of Siberia, the Black Sea, the Mediterranean and the Red Sea, as well as a combination of these systems. The highest number of systems is related to the Mediterranean system with 78 cases and the Mediterranean and Black Sea system with 54 cases in the second place. Precipitation systems are divided into two dynamic and thermal categories in terms of the air rising factor, and the frequency of systems leading to flooding is generally 58% related to dynamic systems and 42% of them related to thermal systems caused by the formation of clouds. They are local convection.

Land use and land cover maps are essentially needed for socio-economic development and environment protection. Accurate and up to date maps play an important role in urban planning. Synthetic Aperture Radar (SAR) sensors provides unique information from the Earth surface due to their imaging capabilities in all-weather condition. However, inherent speckle effect limits their application. In this study, the effect of speckle filtering on the land use/land cover (LULC) classification map in Bander-Mahshahr, Iran has been studied. Dual-polarimetric Sentinel 1-A (VH,VV) and multispectral Sentinel-2B were fused for classification purposes. Different speckle removing methods such as Boxcar, Median, Frost, Refined Lee, Lee Sigma, Intensity-Driven Adaptive-Neighborhood, Gamma Map, and Lee filters were applied on the Sentinel-1A dataset. The Gram–Schmidt (GS) fusion process was chosen to integrate the multispectral Sentinel-2 data and VH, VV bands of Sentinel-1 data. Then, the LULC (land use/land cover) was produced with a random forest classifier. IDAN filter has reached the highest overall accuracy (i.e., 76.64%) and Kappa coefficient (i.e., 0.72) on the combined VH polarization image and sentinel-2 bands. Also, in combining VV polarization with Sentinel 2 bands, the median filter provides the highest performance with overall accuracy of 76.6% and Kappa coefficient of 0.7. As the study area is located in a coastal environment and there is frequent cloud cover, the combination of two polarizations VV and VH without using Sentinel-2 bands was also studied. The highest performance was provided by the boxcar filter with an overall accuracy of 95.56% and a Kappa coefficient of 0.94. The obtained results confirm the high capabilities of SAR images in LULC mapping in a coastal city

Double Diffusion Convection (DD) structures, in two types of Salt-Fingering (SF) and Diffusive Convection (DC), occur due to vertical temperature and salinity gradients with different diffusion coefficients. And thermohaline circulation in the Strait of Hormuz often causes the formation of SF on the surface and in the northern or middle parts of the strait and DC structure in the depth and southern parts of the Strait of Hormuz. In this study, the formation of DD structures and their role in temperature changes and thermal energy of water in the Strait of Hormuz have been investigated. This effect depends on the formation conditions of diffusion structures and the average temperature and thermal energy of the water mass. Due to the fluctuations resulting from the DD structures in the hot days of the year, a 2-5% change in the temperature and thermal energy of water is observed. So that the maximum changes up to 5% (change in energy) occur in the southern parts of the strait. The heat exchange during the DC process is higher than that of SF (more than 10 times). However, the thermal energy is minimal at the place of the cold mass and strong convection, and the DC structure is weakened due to the increased energy exchange with the environment,. This heat exchange causes the internal energy of water to decrease by about 250 J. While during the growth of SF, the temperature and thermal energy of the environment (on a small scale) increases.

International Hamoon wetland has played an important role in social, economic, security, political and environmental activities in southeastern Iran as the 12th residential wetland. In this research, using the Google System inheritance of the Engine and the NDWI Index of Madis Sensor and its verification with Landsat and Satinel satellite imagery 2, the state of blue fluctuations of Hamoon triple wetlands with 1032 images in 2000 to 2022 was investigated The results show that the Hamoon wetland has had the most unstable water conditions compared to two other wetlands, and an average of 31.5 square kilometers, equivalent to 1.3% of its total area in the study period was in dewatering.In other words, 98.8% of its area is dry and alternately 226 months of 18.8 years was completely dry. The maximum water area of this wetland was 7.3% of its total area during this period of 183 square kilometers. Also, the Hamon wetland with the average of 243.7 square kilometers of 243.7 square kilometers equivalent to 15.6% of the total area of the wetland in the study period has been the most stable blue conditions, and in this period 194 months, 16.2 years has been alternately watered. Is. Also, the highest area of the wetland was recorded at 797 square kilometers of 51.1% of its total area in 2020. This dewatering level in the dried area of northern Sistan and Baluchestan, which is 50 mm aggregates, can be an important source of water supply.

Khuzestan province is affected by dust storms. The purpose of this study is to identify ‎the most significant climate and surface factors in the emission and transfer of dust in this province. By means ‎of remote sensing data including MERRA-2, GLDAS as well as Aqua, the ‎monthly average mass concentration of surface dust, air column dust and climate and surface ‎variables from 2011 to 2019 have been prepared and analyzed. The result revealed that the ‎region with an average vegetation cover index of 0.15 is susceptible to wind erosion and local dust. Also the ‎result demonstrated that the dust surface concentration and the dust column density declines from the ‎southwest to the northeast of the region due to the increased distance from the cross-border dust sources and ‎also the mountainous structure of the northeast. Analyzing of the annual distribution, showed a negative ‎relationship between the dust surface mass concentration and vegetation, and also positive/negative relationship ‎between dust column density and vegetation/ precipitation. In the monthly distribution of dust ‎ surface concentration and wind speed a positive correlation of 0.84 and 0.96 obtained, respectively. while ‎negative correlation values of -0.84, -0.81, and -0.54 were found Respectively. Therefore, dust surface concentration has the highest correlation with climate factors, which indicate the impact of cross-border ‎dust. Based on the results obtained, the annual distribution, showed a negative relationship with the dust surface concentration and vegetation. In addition, the relationship between dust column density and ‎vegetation and precipitation showed negative and positive values, respectively.‎

The amount of precipitation plays an essential role in the occurrence of floods. The more accurate the rainfall forecast is, the better the flood can be predicted. Numerical weather forecasting models such as WRF usually do not have suitable outputs for predicting the amount of precipitation in the first hours of implementation; This is intensified in the summer season on the southern shores of the Caspian Sea in the provinces of Gilan and Mazandaran. Because most of the heavy rains in this season occur in the form of convection and in small spatial and temporal dimensions. Using the extrapolation of GPM, TRMM satellite products to predict short-term rainfall up to 24 hours can be a suitable method to achieve the amount of rainfall with more appropriate resolution and accuracy. In this research, the occurrence of two daily precipitation systems of more than 40 mm in summer on the southern shores of the Caspian Sea was investigated. The results showed that the use of GPM, TRMM satellite products for short-term rainfall forecast up to 24 hours is more accurate compared to the model output. Statistical analysis showed a good correlation between model prediction and ground data and satellite data