جستجوی مقالات مرتبط با کلیدواژه « رطوبت خاک » در نشریات گروه « زمین شناسی »

Drought is a natural phenomenon that is mainly caused by less than normal precipitation over a long period that occurs in almost all climates. Since the late 1970s, the occurrence of drought has increased globally due to increased evaporation (caused by global warming). This phenomenon occurs slowly and can be short-term or terminated within a few months or continue for several years. drought has increased significantly in Iran in recent decades. The increase in droughts has many negative consequences in different areas of agriculture, water resources, and wildfire. The purpose of this study is to investigate the drought situation in the vegetation areas of Iran using the Keetch-Byram drought index (KBDI). For this purpose, the KBDI drought index derived from the data set of the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth edition (ERA5) with a horizontal resolution of 0.25o during the period of 1981-2020 has been used. Also, the combined product of surface soil moisture (SSM) from microwave sensors AMI-WS (ERS-1 and ERS-2 satellites) and ASCAT (MetOpA-B satellite) with a horizontal resolution of 0.25 o to evaluate the KBDI drought index results as well as the moisture condition. The results showed that the khalij-Omani and Irani-Turani vegetation areas have very low soil moisture and high drought in Iran. The amount of soil moisture in these areas is low and the area-averaged is equal to 12.65% and 18.42%, respectively, which has been somewhat constant in all months, in other words, dryness is the predominant climate feature of these areas. The monthly analysis of the KBDI index shows the spread of drought severity towards the west, i.e., the vegetation area of Zagros, in the hot months of the year. A decrease in soil moisture and dryness can be observed in a major part of Iran from early spring to mid-autumn. Also, in the Zagros region, from mid-summer to mid-autumn, increasing values of the drought index and decreasing soil moisture can be observed. The minimum drought index is in the vegetation areas of Hyrkani and Arsbaran. The correspondence between the results of the surface soil moisture (SSM) of the satellite product and the KBDI drought index obtained from ECMWF-ERA5 shows the appropriate efficiency of the KBDI drought index in identifying drought centers in Iran and investigating the spatio-temporal patterns of drought occurrence. frequent low-intensity drought may favor more drought-tolerant species and adapt forests and grasslands to future conditions without the need for management measures.

Drought has a huge impact on crop production. Therefore, a better understanding of drought and other atmospheric phenomena is important in order to reduce the damage caused by them. In Iran, most of the studies related to agricultural drought is carried out using remote sensing data due to the lack of in-situ soil moisture data. In fact, because of shortage of soil moisture data alongside a reasonable spatial distribution across the country, the researches pertaining to agricultural drought have been conducted less comparing to meteorological and hydrological drought. However, a few attempts were made to monitor agricultural drought by some researchers in Iran. This study aimed to monitor soil moisture at different depths using in-situ together with remotely-sensed data in Neyshabur City. In addition, the impact of soil moisture on rain-fed wheat yield in different crop growth stages was investigated.
Neyshabur city with an area of 869843.23 ha is extended from center to northwest of Khorasan Razavi province. It is in the neighborhood of Torbat Heydariyeh and Kashmar counties from the south, Sabzevar from the west, Faroj and Quchan from the north and Mashhad and Chenaran from the east. Its’ geographical location ranges from 35° 30 ʹ to 36° 59ʹ N latitudes and 57° 40ʹ to 59° 30 ʹ E longitudes. Farmers cultivate rain-fed wheat in early November and harvest it in late June. The observed data of rain-fed wheat growth stages from Neyishabur agrometeorological research station of 2010-2011 to 2014-2014 were used. The start and end dates of wheat growth stages of Sardari cultivar, together with measured biometric variables were used for further statistical analysis.
In addition, measured soil moisture was used at different depths of 10, 20, 30, 40, 50 and 70 cm, in a weekly base during 2010-2011 to 2013-2014. To investigate the impact of meteorological variables on wheat yield, the drought indices, including Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were calculated. Moreover, remotely-sensed based indices, including Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) were derived from different bandwidths of TM, ETM+ and OLI sensors of Landsat satellite. Statistical analysis was performed using Pearson correlation coefficient to find out the correlation among variables. Besides, the simple regression analysis was carried out to calculate the relationship between soil moisture at different depths and meteorological and remote sensing indices. The impact of soil moisture on rain-fed wheat yield was investigated using statistical regression analysis. The statistical analysis was carried out for three wheat growth stages including primary stage (from germination to four leaf), intermediate stage (from tillering to stem elongation) and final growth stage (from booting to physiological maturity).
The results revealed that the maximum and minimum NDVI values during December (germination and four leaf growth stages) were 0.769 and -0.355 in 2011-12 and 2010-11, respectively. It is interesting that wheat yield was 1645 and 3845 kg/ha in 2010-11 and 2011-12, respectively, which correspond to mentioned NDVI values. In addition, the maximum and minimum range of NDVI variations for whole growing season were for 2013-14 and 2011-12 with maximum and minimum wheat yield, respectively (3845 and 1645 kg/ha). Therefore, it could be partly due to appropriate temporal distribution of precipitation and partly because of adequate soil moisture at crop root zone at germination and four leaf growth stages.
The statistical regression analysis showed that soil moisture in 10 cm depth had a significant positive correlation with NDVImax and SPEI, with correlation coefficients of 0.595 and 0.780, respectively. In other words, 35 and 61 percent of variations of 10 cm soil moisture depth were accounted for NDVImax and SPEI, respectively. The results showed that, the relationship between rain-fed wheat yield and soil moisture depths was non-significant at different crop growth stages. Although, it should be noted that the April precipitation was important for soil moisture variations and consequently for rain-fed wheat yield. It coincided with start of booting stage of crop.
The results of this study revealed that NDVImax and SPEI had the most significant correlations with soil moisture at 10 cm depth. However, no significant relationships were found among soil moisture at 20, 30, 40, 50, and 70 cm depths with meteorological and remote sensing indices.

All objects whose temperature exceeds absolute zero (-273°C) can emit energy. The amount of energy emitted from the objects depends on their temperature and can be measured according to Stephan-Boltzmann's law. The maximum emission of this energy is at a certain wavelength defined by Planck's law. Regarding the surface temperature of the sun, it emits maximum energy at a wavelength of 0.48 microns, in the middle of visible waves, while the Earth emits its maximum energy at 10 microns (infrared) wavelengths. This radiation which starts from 3 microns and continues to 100 microns (infrared), is known as Outgoing Long Radiation (OLR). Measuring this radiation is very important for understanding the energy balance and the temperature of the Earth. Because of the difficulties in measuring this radiation, the use of remote sensing data can effectively help in understanding the tempo-spatial variations of OLR. The purpose of this study is to estimate the seasonal trend of Iran’s outgoing longwave radiation by using National Oceanic and Atmospheric Administration (NOAA) satellites. In this study, the daily mean outgoing longwave radiation data for the period 1988/3/21 to 2018/3/20, with 1° spatial coverage, was extracted on a global scale from the United States Climate Data Record (CDR) database. Then, based on nearly 700 million pixels, the seasonal mean of Iran’s outgoing longwave radiation was calculated for each year, and a time-space matrix was obtained with dimensions of 154*30, for each season. The rows of the matrix are locations (pixels) and the columns are the time (season). For each season of the year, the non-parametric test of Mann-Kendall was calculated at a confidence level of %90 for each individual pixel. The results showed that there was no negative trend in different seasons in Iran, and only in winter, Iran's territory has an extensive positive trend. Hence, the outgoing longwave radiation does not show trends in other seasons of the year. The positive trend of the outgoing longwave radiation during winter is due to cloudiness and snow in most of Iran. Also, in this study, the long-term mean outgoing longwave radiation pattern of Iran was calculated for each season, separately. Findings of the long-term mean of the seasons showed that outgoing longwave radiation depends on latitude and topography of the earth. So, the highest outgoing longwave radiation is seen in low and flat latitudes (especially in summer) and the lowest one is seen in high and uneven latitudes(especially in winter).

Soil moisture influences the partitioning of rainfall into evapotranspiration, infiltration and runoff, hence it is an important factor for determining the magnitude of flood events. The Soil Moisture Active and Passive (SMAP) mission is a microwave all-weather sensor with cloud penetration capability it can be harnessed for inundation mapping. On the other hand, Global Precipitation Measurement (GPM) is the first satellite that has been designed to measure light rain and snowfall, in addition to heavy tropical rainfall. The Integrated Multi-satellitE Retrievals for GPM (IMERG) products, with 0.1° × 0.1° spatial resolution and 30 min temporal resolution, are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account.
In this study, daily rainfall estimates from IMERG Early, Late, and Final runs (IMERG-E, IMERG-L and IMERG-F) are compared with daily precipitation measured by 22 synoptic rain-gauges over the northwest and western regions of Iran. Assessment is implemented for a period from April 2016 to February 2017. The assessment technique is using a contingency table that reflects the frequency of “Yes” and “No” of the satellite estimation. We have used two threshold values of 0.1 mm/day to define rain/no rain and 5.0 mm/day as moderate or higher rainfall events. The scatter plots of daily precipitation values, IMERG-E, IMERG-L and IMERG-F data against rain-gauge observations, indicate underestimation according to Bias for moderate or higher rainfall; this is more so when the precipitation threshold is increased. However, the IMERG-F shows better performance (i.e., closer to one-to-one line) in estimating moderate or higher rain. For the first threshold, all the three runs show approximately same performances; but some differences are seen at the second threshold. At this threshold, POD for IMERG-F is about 0.27 and for the two other products is about 0.14, which means larger fraction of the observed “Yes” events was correctly estimated by IMERG-F than IMERG-E and IMERG-L.
At the second part of this work, the synergistic use of satellite-based precipitation and soil moisture observations was dedicated to mapping of flood monitoring in the northwest of Iran on 14 April 2017. In this study, a value-added product was used that over-samples the SMAP volumetric soil moisture data with a spatial resolution of 40 km and posts it on a 9 km grid, SPL2SMP_E. The SMAP data maps show a pattern that is consistent with the precipitation maps; i.e., following the rainfall on 14 April, there is an increase in the saturated area and after that it begins to decay.
So together, SMAP and GPM can provide information on the surface water fluxes, an important quantity for assessing and monitoring the Earth’s fresh water resources. Therefore, integrated GPM and SMAP data can serve as a key tool for application users and emergency management to assess the extent and potential impact of flooding events among other hydrometeorological phenomena.

Soil temperature is the most important factor in sustainable development and agriculture. These factors are not controllable, but by examining the facts and figures it can be predictable and under control. The aim of this study was to evaluate soil temperature at synoptic stations in Sari, Mazandaran province. The study period is from 2003 to 2014. The database consisted of soil temperature at depths of 5, 10, 20, 30, 50 and 100 cm, dry air temperature, soil moisture and air in the above-mentioned period which is collected by the equipment used in meteorological stations. Neural network method is used to estimate the model. Results showed that soil temperature is directly related to air temperature But deep down near the surface of this dependency has been.and Soil temperature with air humidity has fluctuates. So that up to 50 cm rise but decreased with increasing depth. Soil moisture also examined and The results have shown that this agent is effective in high depths.