جستجوی مقالات مرتبط با کلیدواژه "رطوبت نسبی" در نشریات گروه "آب و خاک"

given the prevalence of available data for only these two parameters in many parts of the country, various scenarios involving these parameters were studied. The best scenario for predicting relative humidity was obtained using the XGBoost model. To assess the accuracy of the model, the Bajgah region in Fars Province was chosen as a case study, and the accuracy of different scenarios was compared using data from the past 30 years (1993 to 2023). In this regard, missing data were estimated using the KNN Imputer model. The correlation between mean relative humidity of one to ten days before and the target variable (predicted  relative humidity on day t) was calculated using Pearson correlation. Based on the results indicating the insignificance of data from the fourth day and earlier, data from one to three days before were utilized.

Finally, by comparing the results based on six statistical criteria (RMSE, MAE, MARE, MXARE, NSE, and R2), it was determined the scenario based on relative humidity and the maximum and minimum temperatures of the preceding 3 days provides the best estimation.

The present study was conducted to examine the changes in monthly, seasonal, and annual trends in relative humidity of 13 meteorological stations in the Razavi-Khorasan Province from the beginning of the statistical period of each station until 2021. The study area includes Dargaz, Fariman, Golmakan, Gonabad, Kashmar, Khaf, Neyshabour, Mashhad, Quchan, Sarakhs, Sabzevar, Torbat-e Jam, and Torbat-e Heydarieh counties. Parametric linear regression and the nonparametric Mann-Kendall (MK) test were used to detect trends in relative humidity. The majority of stations have shown decreasing trends at 5% significance level in relative humidity in different time scales, and only in two stations, Quchan (June, December, and autumn season) and Sarakhs (autumn season), the relative humidity showed an increasing trend at 5% significance level. A decrease in monthly mean relative humidity for six stations has been found in the range of 0.7 to 3.5 percent/decade, while an increase for Quchan station 1.9 to 2.6  percent/decade was observed. In addition, most of the studied stations have experienced a decrease in relative humidity on a seasonal and annual scale. A decrease in Seasonal mean relative humidity for six stations has been found in the range of 0.6 to 2 percent/decade, while decreases in annual mean relative humidity stations ranged from 0.9 to 1.6 percent/decade. The results of this research indicate a decreasing trend in the average monthly, annual and seasonal relative humidity during recent years.

The behavior of daily changes of relative humidity is quite variable. We first draw the curve of this variable on a normal day. And it can be seen that the distribution of this variable is not normal. The curve of this variable is a skewed curve to the right. Therefore, the equal coefficients could be used only as an approximation for estimating the daily average of relative humidity. Climatic conditions of the meteorological stations are also another parameter to be considered. This research presents a new method for estimating the daily average of relative humidity in three climatic regions of Iran. The patterns for the sample stations in each climatic region were presented separately.

E. Eccel (2012) developed an algorithm to simulate the relative humidity of the minimum daily temperature in 23 weather stations in the ALP region of Italy. In this research, the base pattern was calibrated by temperature and precipitation measurement. Ephrath, et al. (1996) developed a method for the calculation of diurnal patterns of air temperature, wind speed, global radiation, and relative humidity from available daily data. During the day, the air temperature was calculated by: (1) (2) Where S(t): Dimensionless function of time, DL: Day Length h, LSH: the time of maximum solar high h, ta: current air temperature, P: the delay in air Tmax with respect to LSH h. Farzandi, et al. (2012) presented more accurate patterns for estimating daily relative humidity from the humidity of Iranian local standard hours and daily precipitation variables, the minimum, maximum, and average daily temperature in coastal regions. The purpose was to present linear and nonlinear patterns of daily relative humidity separately for different months (12 patterns) and annually in coastal regions (the Caspian Sea, the Persian Gulf, and the Oman Sea).Mirkamandar, et al. (2020) modified new patterns of diurnal temperature based on climatically clustering in Iran. The final pattern has an interception and new coefficients to estimate the daily average of temperature. Rezaee-Pazhand, et al. (2008) introduced new patterns for estimating the daily average temperature in arid and semiarid regions of Iran. The final pattern has an interception and new coefficients to estimate the daily average of temperature. (3) Veleva, et al. (1996) showed that the atmospheric temperature-humidity complex (T-HC) of sites located in a tropical humid climate cannot be well characterized by annual average values. Better information is given by the systematic study of daily changes of temperature (T) and relative humidity (RH), which can be modeled by linear and parabolic functions. Farzandi et al. (2011) divided Iran into three climatic clusters. Which were used in the present work. First, a classification that provides climatological clustering. This clustering was used the data of annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation, and three indices of Demartonne, Ivanov, and Thornthwaite. Iran was partitioned into three clusters i.e. coastal areas, mountainous range, arid and semi-arid zone. Several clustering methods were used and the around method was found to be the best. Cophenetic correlation coefficient and Silhouette width were validation indices. Homogeneity and Heterogeneity tests for each cluster were done by L-moments. The “R”, software packages were used for clustering and validation tests. Finally, a clustering map of Iran was prepared using “GIS”. The data of 149 synoptic stations were used for this analysis. Systematic sampling was done to select sample stations. The linear regression model was fitted after screening and data preparation. A model was presented for estimating the daily average temperature in each climatic region and sampling stations in each cluster. The best models were presented by reviewing the required statistics and analyzing the residuals. The calibration and comparison of the presented patterns in this paper with commonly applied models were undertaken to calculate the mean squared error. “SPSS.22” software was used for analysis.

The coefficient of determination (R2) and the Fisher statistics showed that the patterns had a good ability to estimate the daily average of relative humidity. The daily average of relative humidity patterns confirmed an interception in the equations. Standardized coefficients showed that predictor variables were not weighted in all of the patterns. The mean squares errors were a measure of the applicability of patterns. The accuracy of the estimating daily average of relative humidity recommended models in three climates was confirmed by calculating the mean squared errors. The proposed patterns of this study had less error than the common patterns.

The relative humidity at 3 pm was more effective in estimating the daily average. The independent assumption of the residual was confirmed with the acceptable value of Durbin-Watson statistics. The averages of the residuals in each pattern was zero. According to the graphs, stabilization of variance can be seen based on the residual on each pattern in each cluster. Proposed patterns were calculated according to mathematical principles. But the common patterns did not observe these mathematical principles. The mean squares errors (MSE) of proposed patterns were less than common patterns. Therefore, the patterns presented in this study are more powerful than common patterns.

In recent years, increasing population and limited water and arable land in Iran have led to significant investment in greenhouse production. Due to the effect of greenhouse microclimate conditions on the quantitative and qualitative yield of the product, the use of mathematical models to study and microclimate simulation of the greenhouse is necessary. The objective of this research is to apply and analyze an energy balance model using meteorological data outside the greenhouse to simulate microclimate conditions and the transpiration in a greenhouse. The study was conducted in a commercial greenhouse with plastic cover and an area of 4333 square meters. Temperature and relative humidity data were collected inside the greenhouse with 21 data loggers. For collected meteorological data outside the greenhouse, the meteorological station installed on the roof of the greenhouse and the data of the Tuyserkan synoptic meteorological station were used. A combination of energy balance and transpiration equation was used to estimate temperature, relative humidity and crop transpiration inside the greenhouse. Estimated transpiration was modified by combining the energy balance model and the Stanghellini transpiration equation. The pattern of temporal variation of the simulated and measured values of temperature and relative humidity inside the greenhouse was similar. The root mean square error of the simulated temperature for mechanical ventilation and natural ventilation was 0.72 and 0.67, 0.83 and 0.77 daily, and 0.49 and 0.49 degrees Celsius, respectively, for the daytime and night time. The root mean square error of relative humidity for mechanical and natural ventilation was 4.4% day and night, 3.0% for daytime, and 6.0% for night time. After modifying the simulation model, four days of microclimate inside the greenhouse showed that the temperature inside the greenhouse reaches 35 °C during the day and less than 16 °C at night, which are not in the optimal temperature range for greenhouse cucumbers. Due to high temperature (35º C) and low humidity (15% to 30%), evaporative cooling will be required at some hours of the day. The total simulated transpiration was 30.2 mm. The estimated temperature was acceptable, but the estimated relative humidity at night differed from the measured values due to thermal inversion. Estimated transpiration was modified by combining the energy balance model and the Stanghellini transpiration equation. After verifying and modifying the model, the microclimate conditions of the greenhouse were simulated and analyzed for four days. The results showed that by using the energy level model before the construction of the greenhouse, it is possible to estimate the microclimate conditions of the greenhouse and the water requirement of the crop.

The purpose of this research is to present a method based on global and experimental algorithms for estimating relative humidity in Sistan and Baluchestan province. For this purpose, Terra MODIS images and products (MOD05, MOD07) of 10-years (1-Jan-2009 to 31-Des-2018) under cloudless conditions were used as selected data. To validate the obtained layers (near ground surface temperature, ground surface pressure, total perceptible water, TPW, and relative humidity), the ground station and the radio sound data were used. The R2 and REMS values of the recorded layers from the sensor and the ground data were acceptable and the sensed data were in good agreement with ground station measurements. Results show that the altitude factor play an important role in temperature measurement. The minimum amount of TPW has been recorded to be 6 mm in the Khash, Mir Java and Saravan regions in the cold season and the maximum amount of TPW has been recorded to be 49 mm in the coastal areas in the warm season. The average relative humidity in the province is in equilibrium in the cold season, while it is out of equilibrium in the hot season, so that it is above 60% on the coast of Oman and less than 10% in the central province (Khash, Mir Java and Zahedan).

To prevent water stress in plants and have sustainable water management in the field, fast and accurate determination of irrigation time is one of the most important issues. Measuring soil moisture and leaf surface temperature are two methods of determining time of irrigation. In this research, by combination of these two methods, a model for planning and management of forage maize irrigation (cultivar SC-701) is presented. The air relative humidity (RH) and temperature (Ta), leaf surface temperature (TL), and soil moisture content (SM) were measured in 2013 and, by using artificial neural network model and multiple stepwise method, a regression model was developed. Experiments were carried out in 2014 with five treatments including 100%, 85%, 75%, 65%, and 35% total available water (TAW), with four replications, Irrigation was carried out when soil moisture content reached the treatments moisture level. Measurements of the previous year were repeated and the model was calibrated. The results of the first year showed a correlation (R2=0.87) between the parameters RH, Ta, TL, Ta-TL as independent variable and SM as the dependent variable. Then, using three input parameters of air temperature, leaf surface temperature, and relative humidity, Determination Coefficient of soil moisture content model was calculated as R2= 0.92. In this model, soil moisture has an inverse relation with (Ta) and (TL-Ta) variables, but a direct relation with RH. Soil moisture content was compared using the model for the second year treatments and compared with the measured values. The difference in soil moisture content measured and estimated by the model at the peak solar radiation time (at noon) was less than ±10%. The model estimated 75% TAW treatment data well, with very small difference compared to the measured value.

Desalination by solar radiation is one of the efficient ways to filter sea water and regarding the renewable solar radiation energy application as a way to provide sweet water is regarded in line with sustainable development and adaptable to environment. Condensation irrigation system is a developed modern idea which takes into consideration environmental issues together with application of sea water. This system is being used for providing drinking water and plantation. The major part of this system that is responsible for providing sweet water is solar still. Solar stills utilize solar radiation for sea water desalination. In the present research, the effect of atmospheric factors and solar still base surface parameter on the evaporation potentiality in solar stills has been investigated in a one-year statistic period that purpose of this study, is trying to determine and recommend using stills with high evaporation potential. Results of this study indicate high dependency degree of stills evaporation potential to atmospheric factors that the intensity dependency of this parameter is including: positive relationship of ambient air temperature (R=0.95), the positive relationship of the number of sunshine hours per day (R=0.73) and negatively relationship relative humidity (R=-0.72). These observations also showed that by reducing the solar stills surface, evaporation potentiality in solar stills will increase and Investigate the effect of solar still base surface by ANOVA results indicated significant differences at the one percent level between evaporation from Solar Stills.

Relative humidity is one of the thermodynamic parameters in atmosphere which has various effects on the climatic attributes in any region. Long term studying of this parameter can have useful results about the long term effects of climate change on the thermodynamic parameters of the atmosphere. In this research, correlation of relative humidity frequency was considered in three synoptic stations in Iran during the period of 55 years (from 1956 to 2010) using a new method called “Wavelet transform” and also the sequential Mann-Kendall test. Results show that long term stability of humidity status in arid and humid climates is more than semi-arid climates and this issue can be obviously seen in the low correlations of long term periods.

Bio-climate studies, from the viewpoint of human comfort is the foundation of management planning. In the present study of four climatic parameters including apparent temperature, Standard Effective Temperature, Thermo hygrometric and wind for the study of human comfort was used in 14 cities. Areas of study include Abadan, Ahvaz, Bandar Lengeh and Bandar Abbas, Bushehr, Dezful, Esfahan, Kerman, Sabzevar, Semnan, Zahedan, Kashan, Yazd and Zabol, which were classified in two summer climates of very hot and hot. Meteorological variables used included daily values of air temperature, dew point temperature, relative humidity and wind speed at a height of two meters of summer, in the period 1385-1350. Results showed that relative humidity as a factor in comparison with daily dew point temperature as a factor at night represents the human comfort level in most coastal areas and is non-coastal. Thus, the standard effective temperature index (SET) because of a combination of weathering study areas daily realities, is more compliant. Accordingly, Bio-climate spectrum study areas, was in the quarter from Bio-climate sultry summer, with a range 27.5-30, Bio-climate to cool, with a range 15.5-17.8. Overall index based SET, in June that the weather warming began, eight cities in descriptive categories were hot and very hot. In August, compared with urban warming July, the city increased to nine, and in September with completion in summer, the number of cities to the city six decreased. Also, the SET index was based on any of the cities and none of the months of summer heat stress and severe cold stress. In the cities of Bandar Abbas, Bushehr and Bandar Lengeh, sultry heat stress is due to close proximity to the Persian Gulf, seems logical.

Urbanization has led to significant changes in the urban environment than natural conditions that urban microclimates are result of these changes. The purpose of this study is microclimate zoning of Isfahan city based on land cover types and investigating the effect of land cover types on urban microclimates. Isfahan city was divided into cells with dimensions 250×250 m. Different layers were defined in ArcGIS 9.3 software. The area percentage of each layer was determined in each cell. After weighting each layer, air temperature and relative humidity data of the 5 meteorological weather stations installed in Isfahan city were used to verify the layers weights. Microclimate zoning of Isfahan city was performed with weights given to each layer. The results showed that land use and land cover type have a significant effect on urban microclimate. Microclimate zoning is efficient for detection of cells with different temperature and humidity conditions. Microclimate code was lower in downtown where the density of residential areas and city streets are high in comparison with the suburbs where the density of agricultural lands and gardens are high. The evaluation results indicated that the method used in microclimatic zoning of the city was accurate and microclimate map is able to distinguish air temperature and relative humidity in the city.

Leaf Wetness Duration (LWD) is a key parameter in agricultural meteorology. Because of difficulties involved in LWD being readily measured, several methods have been developed to estimate it from weather data. Among the employed to estimate LWD, those that use physical principles of dew formation plus dew and/or rain evaporation have shown to be especially transmittable and of sufficiently accurate results, but their complexity is a disadvantage for operational use. Alternatively, empirical models have been utilized despite their limitations. The simplest empirical models use only relative humidity data. The objective of this study was to evaluate the performance of an RH-based empirical model and a Penman-Monteith physical model to estimate LWD in Sarvestan automatic station located in Fars province. The results indicated that both models during warm seasons underestimate LWD, while during cold seasons, the physical and empirical models show overestimation and underestimation, respectively. The Mean Absolute Error (MAE) in empirical model was recorded to be less than that in physical model's estimations. An adjusted optimum threshold value of relative humidity was suggested for the study which improved the estimations. Using an RH-based empirical model led to more accurate LWD estimations with less errors as compared with the previous published data. Hourly comparisons also showed that the optimum threshold model was of a more acceptable performance as compared with the other models.