Showing 52 results for Temperature
L. Parviz , M. Kholghi, Kh. Valizadeh,
Volume 15, Issue 56 (7-2011)
Abstract
The determination of air temperature is important in the energy balance calculation, hydrology and meteorological studies. In this regard, the limited number of meteorological stations is one of the serious problems for air temperature determination on a large spatial scale. The remote sensing technique by covering large areas and using updated satellite images might be appropriate for estimation of this parameter. In this research, the negative correlation between land surface temperature and vegetation index (NDVI) has been used for air temperature estimation through TVX method in which the inference of air temperature is based on the hypothesis that the temperature of the dense vegetation canopy is close to air temperature. For investigation the performance of TVX method, images of MODIS sensor have been applied for the Sefidrod River basin in the years 1381- 1382-1384. The spilt window technique which was developed by Price has been used for land surface temperature calculation. The mean difference between observed and estimated land surface temperature using Price algorithm was about 6.2Co. This error can affect the air temperature values. Because of using NDVI index in TVX method, this method has the sensitivity to the vegetation density, though in the parts with sparse vegetation, the value of error increases. 4 percent variation of air temperature against the 0.05 increasing of maximum NDVI indicates the high performance of TVX method for air temperature estimation in large areas.
R. Roghani, S. Soltani, H. Bashari,
Volume 16, Issue 61 (10-2012)
Abstract
Southern Oscillation Index (SOI) and Sea Surface Temperature (SST) patterns affect rainfall in many parts of the world. This study aimed to investigate the relationship between monthly and seasonal rainfall of Iran versus SOI and Pacific and Indian sea surface temperature. Monthly rainfall data, from 50 synoptic stations with at least 30 years of records up to the end of 2007, were used. Monthly and seasonal time series of each station were divided to several groups by four methods (Average SOI, SOI Phases, Indian SST Phases and Pacific SST Phases) using Rainman software and with regard to 0-3 months lead-time. Significant differences among rainfall groups in each method were assessed by the non-parametric Kruskal-Wallis and Kolmogorov-Smirnov tests, and the significant relationship was validated using Linear Error in Probability Space (LEPS) test. The results showed that SOI during summer (July-September) was related to autumn (October-December) and October rainfall in the west and northwest of Iran and the west Caspian Sea coast. The El Niño (negative) phase was associated with an increase in rainfall and the La Niña (positive) phase was associated with a decrease in rainfall in these regions. Average SOI is a useful index for rainfall forecasting in the above-mentioned areas. However, Indian and Pacific SST phases are not suggested for rainfall forecasting in Iran, duo to weak or non-persistence relationships. In conclusion, Iran rainfall is not limited to SOI, Pacific and Indian SST therefore, Rainman could not be used as an aid to water resources management over a year in Iran. It is suggested that we study the teleconnection between Iran rainfall and other ocean-atmospheric oscillations developing a model similar to Rainman in order to that we investigating the variation in Iran rainfall with aid of other effective ocean-atmospheric indicators
N. Parsafar , S. Marofi,
Volume 16, Issue 62 (3-2013)
Abstract
In this research, we estimated soil shallow depths temperatures using regression methods (Linear and Polynomial). The soil temperatures at soil depths (5, 10, 20, 30, 50 and 100 cm) were correlated with meteorological parameters. For this purpose, temperature data of Hamedan station (in the period 1992-2005) were employed. Soil temperature data were measured on a daily basis at 3 PM, 9 PM and 3 AM. MS Excel was used for deriving the regressions between soil temperature and meteorological parameters (air temperature, relative humidity and sunshine hours). The results showed that the highest coefficient of determination (R2) of the linear regression was between soil temperature in 20 cm soil depth and air temperature at 3 AM (R2= 98.15%) and the lowest value in 100 cm soil depth at 3PM (R2= 83.96%). Also, the highest R2 of non-linear regression was observed between soil temperature in 10 cm soil depth and air temperature at 3 AM (R2= 98.45%) and lowest value in 100 cm soil depth at 3PM (R2= 84.11%). The results showed that the highest and lowest values of R2 of linear relations between meteorological parameters (relative humidity and sunshine hours) and soil temperature were observed in 10 cm soil depth (at 3 AM) and in 100 cm soil depth, respectively. Correlations of soil temperature with air temperature were greater than those with the other two parameters. Moreover, R2 values of non- linear relation were higher than linear relation.
M. A. Moradi, A. Rahimikhoob,
Volume 16, Issue 62 (3-2013)
Abstract
Reference evapotranspiration (ET0) is a necessary parameter for calculating crop water requirements and irrigation scheduling. In this study, a method was presented as ET0 is estimated with NOAA satellite imagery in the irrigation network. In this method, a pixel from a set of pixels within the irrigation network was chosen with the highest vegetation index, and its surface temperature (Ts) with extraterrestrial radiation parameter (Ra) was used as inputs of the model. The M5 model tree for converting Ta and Ra to ET0 was used as input variables. In this research, Gazvin irrigated area was selected as a case study. A total of 231 images of NOAA satellite related to irrigation season of the study area were used. The results obtained by the M5 model were compared with the Penman–Monteith results, and error values were found within acceptable limits. The coefficient of determination (R2), percentage root mean square error (PRMSE) and the percentage mean bias error (PMBE) were found to be 0.81, 8.5% and 2.5%, respectively, for the testing data set.
N. Zohrabi, A. Massah Bavani, E. Goudarzi, M. Heidarnejad,
Volume 20, Issue 77 (11-2016)
Abstract
Since climate change is regarded as a serious threat to different parts of life cycle, separation of factors intensifying this phenomenon seems necessary. This research has investigated the temperature and precipitation trend using the multiple trend test in the upstream Karkheh basin located in west of Iran. For this purpose, two-dimensional graphs of temperature and precipitation anomalies of the CGCM3 Model (1000-year data) were drown for the study area. Then, the attribution of changes in climate variables due to climate internal fluctuations or greenhouse gases affected by human factors were investigated. Based on the findings of this study, in different parts of the study area, the range of natural climate variables for temperature and precipitation changes (95% probability) in the west of the study area are
± 1.4ºC and ±76%, respectively.
The results showed increase and decrease in temperature and precipitation in most of the studied stations, respectively. The variables of temperature and precipitation are affected by climate change and as we approach latest years, especially in the western and central parts of the study area, the impact of greenhouse gases in increasing temperature and reducing precipitation becomes more evident. According to the current results it can be concluded that changes in land use in Iran caused by human interventions can be introduced as a significant factor for the ascending trend of temperature. However, it can be noted that the most important factors of the increased greenhouse gases in recent years are human activities such as land use changes. These changes certainly have affected water resources in the study area.
O. Babamiri, Y. Dinpazhoh,
Volume 20, Issue 77 (11-2016)
Abstract
Accurate estimation of ET0 in any region is very important. The aim of this study is to compare and calibrate the 20 empirical methods of estimating evapotranspiration (ET0) based on three categories in monthly timescale at the Urmia Lake watershed. These categories are: 1) temperature-based models (Hargreaves (HG), Thornthwaite (TW), Blaney-Criddle (BC), Linacre (Lin)), 2) radiation-based model (the Doorenbos-Pruitt (DP), Priestly-Taylor (PT), Makkink (Mak), Jensen-Haise (JH), Turc (T), Abtew (A), McGuinness-Bordne (MB)) and 3) mass transfer-based model (Meyer (M), Dalton (D), Rohwer (R), Penman (P), Brockamp-Wenner (BW), Mahringer (Ma), Trabert (Tr), WMO and Albrecht (AL)). For this purpose, the information of 10 synoptic meteorological stations during the period of 1986-2010 was used. Results from the above mentioned methods were compared with the output of the FAO Penman-Monteith (PMF-56) method. Performance of the methods evaluated using the R2, RMSE, MBE and MAE statistics. The best and worst methods of each category were determined for the study area. The best methods of each category were calibrated for the area under study. Results indicated that there is a significant difference between the results of selected methods of each category and the PMF-56 method. Performance of the selected methods remarkably increased after calibration. Among the temperature-based group, the HG method having the median R2 value of 0.9597 was recognized as the best method. After calibration the medians of RMSE, MBE, and MAE were 72.09, 3.14 and 10.70 mm/ month, respectively. After HG, the Lin and BC found to be the best second and third methods in the study area. The TW showed Large error, therefore, it was not a suitable method for ET0 estimation in study area. Among the radiation-based group, the DP model was selected as the best method in the study area. Furthermore, the median of R2 values was 0.982. In this method, the medians of RMSE, MBE and MAE after calibration were 7.89. -0.62 and 6.03 mm/month, respectively. Following DP, the PT method was recognized as the 2nd best one. The methods namely M, JH, T, A and MB were put in the 3rd to seventh rank of the radiation category. Finally, among the mass transfer-based group, having R2=0.8945, the Meyer method was selected as the best method of this group for the study area. In the mentioned method (after calibration) the medians of RMSE, MBE, and MAE were 21.8, -8.7 and 17.3 mm per month, respectively. From mass transfer based group, the D method was found as the second best method in the study area. The methods namely R, P, BW, Ma, Tr, WMO and A were ranked 3rd to 7th, respectively. In general, the performance of radiation based methods was superior than others in Urmia Lake basin. Temperature based methods and mass transfer based methods were ranked second and third, respectively. Further examination of the performance resulted in the following rank of accuracy as compared with the PMF-56: DP (Radiation based), HG (Temperature based) and Meyer (Mass transfer). In general, it can be concluded that after calibration the DP method is suitable to estimate reference crop evapotranspiration among 20 selected methods in the Urmia Lake basin.
Sh. Kouhestani, S, Eslamian, A. Besalatpour,
Volume 21, Issue 1 (6-2017)
Abstract
This study aims to investigate the changes of minimum and maximum temperature variables under the impact of climate change for time period of 2015-2100 in the Zayandeh-Rud River Basin. The outputs of 14 Global Climate Models (GCMs) under three green-house emission scenarios (RCP2.6, RCP4.5, and RCP8.5) are employed from the Fifth Assessment Report (CMIP5) of Intergovernmental Panel on Climate Change (IPCC). A novel statistical downscaling method using a Bayesian Relevance Vector Machine (RVM) is used to project the impact of climate change on the temperature variables at regional scale. The results of the weighting average of the GCMs show that the various models have different accuracy in the projecting the minimum and maximum temperatures in the study area. The results demonstrate that the MIROC5 and CCSM4 are the most reliable models in projecting the maximum and minimum temperatures, respectively. The highest increase for both maximum and minimum temperatures was obtained in winter.
On the annual basis, the maximum temperature will increase by 0.18-0.76 °C and 0.25-1.67 °C, respectively, in the near and long-term future periods under different emission scenarios. The annual minimum temperature will increase by 0.28 to 0.82 °C and 0.24-1.56 °C, respectively, in the near and long-term future periods. In a general view, changes in maximum temperature will be slightly higher than minimum temperature changes in the future.
N. Abbasi, A. A. Afsharian,
Volume 22, Issue 1 (6-2018)
Abstract
Gypsiferous soils are one of the problematic soils which, due to solubility and contact with water, are a threat to various civil structures, especially water structures. Various factors affect the rate and amount of gypsum particles solubility. Gypsum types, the soil texture, the amount of gypsum in soil, the hydraulic gradient, and temperature and flowing water from gypsum soil are the major factors affecting the quality and quantity of the gypsum solution. In this research, the effects of some peripheral conditions including water temperature and hydraulic gradient on the solubility of gypsum soils were studied. To this aim, samples of gypsum soils were provided artificially by adding various rates of the natural gypsum rock including 0, 5, 10, 20 and 30 percent by weight of clay soil. Then, all gypsum soils were leached under five hydraulic gradients levels including 0.5, 1, 2, 5 and 10. The results indicated that the rate of Gypsum in the soil had a direct effect on the rate of solution in a way that by increasing the percent of Gypsum, the rate of solubility was increased. Also, the rate of leaching (the rate of the derived Gypsum from soil to the primary rate of Gypsum) was decreased by increasing the rate of Gypsum. In addition, by increasing hydraulic gradient, the speed of water and its amount in soil environment within a specified time were raised; further the rate of gypsum was increased too. Also, it was found that the rate of the solubility was increased directly by the temperature. The solubility rate of the gypsum soil at 50 C0 was found to be 2.5 and 1.6 times greater than that of the soil at 5 and 20 C0, respectively.
Sh. Zand-Parsa, S. Parvizi, A. R. Sepaskhah, A. A. Kamgar Haghighi,
Volume 22, Issue 1 (6-2018)
Abstract
In this study, the values of moisture and soil temperature were estimated at different depths and times under unsteady conditions by solving the Richards’ equation in an explicit finite difference method provided in Visual Studio C#. For the estimation of soil hydraulic parameters, including av and nv (coefficients of van Genuchten’s equation) and Ks (saturated hydraulic conductivity), soil moisture and temperature at different depths were measured by TDR probes and the stability apparatus, respectively. The objective function [equal to Root Mean Square Error (RMSE)] was minimized by the optimization of a parameter separately, using the Newton-Raphson method, while, the other parameters were considered as the constant values. Then, by replacing the optimized value of this parameter, the same was done for other parameters. The procedure of optimization was iterated until reaching minor changes to the objective function. The results showed that soil hydraulic parameters (coefficients of van Genuchten’s equation) could be optimized by using the SWCT (Soil Water Content and Temperature) model with measuring the soil water content at different depths and meteorological parameters including the minimum and maximum temperature,, air vapor pressure, rainfall and solar radiation. Finally, the measured values of soil moisture and temperature were compared to the depth of 70cm in spring, summer, and autumn of 2015. The values of the normalized RMSE of soil water content were 0.090, 0.096 and 0.056 at the soil depths of 5, 35 and 70 cm, respectively, while the values of the normalized RSME of soil temperatures were 2.000, 1.175 and 1.5 oC at these depths, respectively. In this research, the values of soil hydraulic parameters were compared with other previous models in a wider range of soil moisture varying from saturation to air dry condition, as more preferred in soil researches.
N. Zough, M. Shirvani,
Volume 22, Issue 4 (3-2019)
Abstract
Alginate biopolymer, due to possessing a high capacity and affinity for heavy metals, is a suitable material for the removal of metals from polluted waters; however, the weak structural consistency of alginate hydogels limits the practical application of this natural polymer in water purification practices. In this study, sepiolite clay mineral was used as a solidifier of alginate hydrogel to produce hybrid materials with different clay:alginate ratios (1:2, 1:4 and 1:8). Subsequently, the sorption of Pb by the prepared hybrid materials was studied in different Pb concentrations (25 to
2000 mg/L) and temperatures (15, 25, 35 and 45 °C). The results showed that the Langmuir and freundlich equations could significantly describe Pb sorption data on the sorbents. Based on the Langmuir model estimation, alginate showed and sepiolite showed the highest and lowest capacities for Pb sorption, respectively; also, the hybrids were intermediates in this respect. The capacity and affinity of all sorbents were enhanced with increasing the temperature from 15 to 45 °C. Standard enthalpy changes (ΔH°) were found to be positive, confirming that the process of Pb sorption on the sorbents was endothermic. Positive values were also obtained for the standard entropy changes (ΔS°), suggesting increased randomness at the solid-solution interface during the sorption of Pb ions on the sorbents. The values of the standard free energy change (ΔG°) were negative for all different temperatures, thereby indicating that sorption on the sorbents was spontaneous and favorable. Overall, it could be concluded that modification of alginate with sepiolite might cause the decreased sorption capacity of alginate; however, the hybrid materials are good candidates for the Pb removal from aqueous solutions because of their high sorption capacities.
S. Shiukhy Soqanloo, S. Golshan, M. Khoshravesh,
Volume 22, Issue 4 (3-2019)
Abstract
The effects of climate change can be released from the surface to the soil depth, thereby affecting soil thermal regime. Thermal energy in the soil plays a very important role in causing climate changes. In this study, for the assessment and detection of the climate changes, soil depths temperature, the measured data related to the daily air temperature at a height of 2 meters (screen) during the years (1951-2014), and the soil depths daily temperature (5-10-20-30-50 to 100 cm), for 3, 9 and 15 hours, were obtained during a period (1992-2014) in Shahrud station. The climate change detection was employed to compare the treatment mean. As well, for detection of trends related to the annual, seasonal and monthly time series and their relation to the soil depths temperature, parametric methods (regression analysis and Pearson) and nonparametric (Mann-Kendall, Spearman) were applied. The results showed that the soil temperature was increased in all months except January, February and March. Also, in the seasonal time series, the soil depths temperature was increased in all seasons except winter. In fact, based on the results, the soil temperature in spring, summer and autumn was increased. Detection trends of the annual soil depths temperature showed that, except for the Pearson correlation coefficient method, soil temperature was increased at all soil depths.
N. Ganji Khorramdel, S. M. R. Hoseini,
Volume 23, Issue 2 (9-2019)
Abstract
Estimation of evapotranspiration is essential for planning, designing and managing irrigation and drainage schemes, as well as water resources management. In this research, artificial neural networks, neural network wavelet model, multivariate regression and Hargreaves' empirical method were used to estimate reference evapotranspiration in order to determine the best model in terms of efficiency with respect to the existing data. The daily data of two meteorological stations of Shahrekord and Farrokhshahr airport in the dry and cold zones of Shahrekord during the period 2013-2004 was used; these included the minimum and maximum temperature, the average nominal humidity, wind speed at 2 meters height and sunshine hours. %75 of the data were validated, and %25 of the data was used for testing the models. Designed network is a predictive neural network with an active sigmoid tangent function hidden in the layer. In the next step, different wavelets including Haar, db and Sym were applied on the data and the neural network-wavelet was designed. To evaluate the models, the method was used by the Penman-Montith Fao and for all four methods, RMSE, MAE and R statistical indices were calculated and ranked. The results showed that the wave-let- neural network with the db5 wavelet had a better performance than other wavelets, as well as the artificial neural network, multivariate regression and the Hargreaves method. The results of wavelet network modelling with the db5 wavelet in the Farrokhshahr station were calculated to be 0.2668, 0.2067 and 0.998, respectively; at the airport station, these were equal to 0.2138, 0.14 and 0.9989, respectively. The results, therefore, showed that the neural network-wavelet performance was more accurate than the other models studied in this study.
M. Madanian, A. R. Soffianian, S. Soltani Koupai, S. Pourmanafi, M. Momeni,
Volume 23, Issue 4 (2-2020)
Abstract
Land surface temperature (LST) is used as one of the key sources to study land surface processes such as evapotranspiration, development of indexes, air temperature modeling and climate change. Remote sensing data offer the possibility of estimating LST all over the world with high temporal and spatial resolution. Landsat-8, which has two thermal infrared channels, provides an opportunity for the retrieval of LST using the split- window method. The main objective of this research was to analyze the LST of land use/land cover types of the central part of Isfahan Province using the split- window algorithm. The obtained results demonstrated that the "other" class which had been mainly covered with bare lands exhibited the highest LST (50.9°C). Impervious surfaces including residential areas, roads and industries had the LST of 45°C. The lowest temperature was observed in the "water" class, which was followed by vegetation. Vegetation recorded a mean LST of 42.3°C. R2 was 0.63 when regression was carried out on LST and air temperature.
H. Siasar, T. Honar, M. Abdolahipour,
Volume 23, Issue 4 (2-2020)
Abstract
The estimation of reference crop evapotranspiration (ETo) is one the important factors in hydrological studies, irrigation planning, and water resources management. This study attempts to explore the possibility of predicting this key component using three different methods in the Sistan plain: Generalized Linear Models (GLM), Random Forest (RF) and Gradient Boosting Trees (GBT). The maximum and minimum temperature, mean temperature, maximum and minimum humidity, mean humidity, rainfall, sunshine hours, wind speed, and pan evaporation data were applied for years between 2009 to 2018. Using various networks, the ETo as output parameter was estimated for different scenarios including the combination of daily scale meteorological parameters. In order to evaluate the capabilities of different models, results were compared with the ETo calculated by FAO Penman-Monteith as the standard method. Among studied scenarios, M1 covering the maximum number of input parameters (10 parameters) showed the highest accuracy for GBT model, with the lowest RMSE (0.633) and MAE (0.451) and the maximum coefficient of regression (R = 0.993). Air temperature was found as the most sensitive parameters during sensitivity analysis of studied models. It indicated that accuracy and precision of temperature data can improve the results. Application of the GBT model could decrease the time consumed to run the model by 70%. Therefore, the GBT model is recommended for estimation of ETo in the Sistan plain.
M. H. Nasserzadeh, B. Alijani, M. Paydari,
Volume 24, Issue 2 (7-2020)
Abstract
Given the climatic changes and threats to food security in recent years, they have have become a major issue in agricultural climatology. The present study aimed to investigate the status of agricultural climate suitable for the cultivation of rice in the light of the influential climatic conditions in the past. Given the effect of temperature and the amount of precipitations on rice growth and the sensitivity of rice to these two variables, the study examined the predicted future temperature and rainfall and their effects on rice. Data related to the temperature and rainfalls were obtained from the Meteorological Organization. Additionally, the temperature and agricultural potential of the region were considered. By preparing the agricultural calendar for the cultivation of rice, the correlation between temperature, precipitation and rice productivity was calculated using the Spearman Correlation coefficient. By using the SDSM model, future data and temperature and precipitation return period were determined in the SMADA software. The results demonstrated that minimum spring temperature tended to be late spring. The minimum temperature had the highest impact in April, the maximum temperature had the highest impact in July and the maximum rainfall had the highest effect in both June and July. Based on the results of the prediction models, the studied region would experience an increase in temperature and rainfall by providing favorable conditions for the cultivation of rice. However, delays in the cold season and shortness of the growth period increased the risks associated with the cultivation of rice in this period.
A. H. Nasrollahi, H. Ahmadi, Y. Sabzevari, S. Nouri,
Volume 24, Issue 2 (7-2020)
Abstract
The Plant Water Resistance Index (CWSI) is a tool that can be used for the rapid monitoring of plant water status, which is a key requirement for the accurate product irrigation management.The purpose of this study was to calculate the CWSI index for bean hares in the Khorramabad region for two methods of surface irrigation and drip tape irrigation. For this purpose, a design was implemented in the form of randomized complete block design and split plot experiment. The main factors included drip tape irrigation (T) and surface irrigation (F), and the cultivars of Chibi cultivars including COS16 (C), Sadri (S) and diluted (K) served as sub-plots. By using the field measurements, the position of the upper and lower base lines was estimated for each treatment in different months and used to calculate the CWSI index. The results showed that CWSI values calculated in the surface irrigation during plant growth period were always higher than those in the drip tape irrigation. The highest value of CWSI index was obtained for the Sadri variety, which was equal to 0.20 and 0.26, for the type and surface method, respectively. Statistical analysis showed that the effect of irrigation method on the amount of water stress index was significant at 5% level, but there was no significant difference between different cultivars. According to the results of this study, the threshold values for CWSI were considered to be 0.19 and 0.24 for surface and drip tape irrigation respectively, and relationships were presented based on the differences in vegetation and air temperature to determine the irrigation time.
S. Banihashemi , S. S. Eslamian, B. Nazari,
Volume 25, Issue 2 (9-2021)
Abstract
The upcoming climate change has become a serious concern for the human society. These changes, caused and aggravated by the industrial activities of the international community and the increase in the concentration of greenhouse gases in the atmosphere, are seen as a threat to the food security and environment. Temperature change and precipitation are studied in the form of different probabilistic scenarios in order to have an outlook for the future. The present study was conducted to address the effects of climate changes on temperature and precipitation in Qazvin plain in the form of five AOGCMs including Hadcm3, CSIRO-MK3, GFDL, CGCM3 and MICROC3.2, and 3 greenhouse gas emission scenarios of A1B, A2 and B1, based on different possible scenario combinations in the next 30 years, 2021-2050 and 2051-2080 (near and far future). On basis of the study results, all 4 target stations, on average, will have experienced a change between two ratios of 0.5 and 1.4 of the observed precipitation period by the end of 2050, and the mean temperature will have had a change between -0.1 to 1.6 °C, relative to the observed period. By the end of 2080, the precipitation will also have fluctuated between the two proportions of 0.5 and 1.7 times of the observed precipitation period and the mean temperature will touch an increase between 0.6 and 2.6 °C. Both SPI and SPEI indices suggest the increment in the number of dry periods in the near and far future. However, the total number of negative sequences differed considering the 3, 12 and 24-month intervals at the stations level. Given the SPEI index, as compared to the base period, the total negative sequences of drought and number of dry periods will increase at 3 stations of Avaj, Bagh-Kowsar and Shahid-rajaei-powerhouse and decrease at Qazvin station in the future; however, SPI gives different results, such that for Bagh-Kowsar, there will be an increase in both total negative sequences of drought and number of dry periods, as compared to the baseline period; three other stations will have more dry periods, specifically, but less total negative sequences. The results reported that the drought events would become severe, and the wet events would become extreme in the future.
M. Kaffash, H. Sanaei Nejad,
Volume 25, Issue 2 (9-2021)
Abstract
Land Surface Temperature (LST) is an important parameter in weather and climate systems. Satellite remote sensing is a unique way to estimate this important parameter. However, satellite products have either low spatial resolution or low temporal resolution that limits their potential use in various studies. In recent years, the use of Spatio-temporal fusion techniques to produce high resolution simultaneous spatial and temporal images has been extensively investigated. In this study, a Flexible Spatio-temporal Data Fusion (FSDAF) was used to produce Landsat-like LST images with Landsat spatial resolution and MODIS temporal resolution. The quantitative and qualitative validation of the images was performed by comparing them with the Actual Landsat LST images. The results showed that the FSDAF algorithm has high accuracy in estimating daily LST data both qualitatively and quantitatively. The RMSE and MAE parameters of the images produced compared to the actual Landsat images were 1.18 to 1.71 and 0.88 to 1.29°C, respectively. The correlation coefficient above 0.87 and bias between -0.6 to 1.45°C also confirms the high accuracy of the algorithm in estimating Landsat-like land surface temperature on a daily time scale.
R. Jafari, H. Sanati,
Volume 25, Issue 3 (12-2021)
Abstract
The southern regions of Kerman Province have repeatedly encountered dust storms. Therefore, the objective of this study was to identify dust sources using effective parameters such as vegetation cover, land surface temperature, soil moisture, soil texture, and slope as well as to detect dust storms originating from these regions based on 31 MODIS images in 2016 and SRTM data. After normalizing parameters, the dust source map was prepared by fuzzy logic and assessed with an error matrix and available dust source map. Results showed that 30.5% of the study area was classified as a low source of dust, 39.55% as moderate, and 29.85% as severe-very severe. The overall accuracy of the produced map was about 70% and the producer and user accuracy of the severe-very severe class was more than 87%. The detection of dust storms originated from the identified dust sources also confirmed a crisis situation in the region. Due to the repeatability and continuity of obtained dust source map at pixel scale, it can be used to update available dust source maps and manage dust crisis in the region, properly.
H. Alipour, A. Jalalian, N. Honarjoo, N. Toomanian, F. Sarmadian,
Volume 25, Issue 4 (3-2022)
Abstract
Dust is one of the environmental hazards in arid and semi-arid regions of the world. In some areas, under the influence of human activities, dust is contaminated by heavy metals. In this study, the dust of 10 stations in the Kuhdasht region of Lorestan province in four seasons of spring, summer, autumn, and winter, as well as adjacent surface soils (a total of 40 dust samples and 10 surface soil samples), were sampled and some heavy metals including Zn, Pb, Cd, Ni, Cu, and Mn were analyzed. The results revealed that the amount of Zn in the dust was much higher than the surface soils of the region (800 vs. 85 mg/kg). Contamination factor index calculation indicated that high contamination of Cd and Zn, significant contamination of Ni and Pb, and lack of contamination by Cu and Mn. The annual enrichment factor of Cd (33.9) and Zn (24.6) was very high, Ni (11.3) was significant, Pb (6.4) was moderate, Mn (1) and Cu (0.82) were low. Based on the enrichment factor values, Cd, Zn, and Ni seem to have a human origin, Pb has both human activities and natural origin, and Cu and Mn have an only natural origin.
