جستجوی مقالات مرتبط با کلیدواژه « meteorology » در نشریات گروه « آب و خاک »

The significant wave height is one of the basic parameters for engineering operations of coasts and marine structures. In this study, significant wave height was estimated based on three scenarios including variables 1- waves, 2- meteorological and 3- combination of the first and second scenario, in time steps without time delay, 12 and 24 hours using adaptive neural fuzzy inference system (ANFIS) and gene expression programming (GEP) and combining them with wavelet theory (WANFIS, WGEP). Also, the results indicate that due to de-noising and removal of uncertainty in the data, combined-wavelet models have provided better results than singular models. The performance improvement percentage of WGEP models compared to GEP considering the RMSE criterion was 11%, 35%, and 7% for the first to third scenarios, respectively. Most of the hydrological conditions in the seas depend on temperature changes and the amount of this parameter is an important determining factor in the environmental conditions of each region. Also, changes in temperature and surface wind change the density of sea water. Therefore, the climatic variables of the region can affect different scenarios. The results of this study and the presentation of the governing mathematical relationship for estimating the value of significant wave height by the method of gene expression programming can be very useful in the field of coastal and water resources management and engineering.

In Iran, pressurized irrigation systems cover a large area of agricultural land, but water use efficiency remains low because farmers tend to do deficit (over) - irrigation due to their lack of knowledge of crop water requirements. To address this issue, irrigation systems can be automated, and it is important to estimate crop water requirements accurately. This can be done based on soil moisture deficit or meteorological data. The water required can then be applied using a volume meter or by determining irrigation time based on the sprinkler flow rate. The study aimed to compare crop water requirement estimates based on soil moisture deficit and meteorological data, as well as the amount of water applied using volume and time-based methods.The study was conducted in the research farm of Razi University, Kermanshah, Iran, on a sprinkler irrigation system equipped with pressure and flow measuring devices, pressure switches, and electrical valves. The field was under corn cultivation, and four types of irrigation management were evaluated, which included a combination of two methods of determining crop water requirement (soil moisture deficiency and meteorological data) and two methods of irrigation application (time or volume). The four treatments were soil moisture - time (MT), soil moisture - volume (MV), weather - time (WT), and weather - volume (WV). The crop water requirement was calculated using the Penman-Monteith formula based on daily weather data. Soil moisture was measured at different depths one day before irrigation, and the soil moisture deficit was calculated to determine the crop water requirement based on soil moisture. The irrigation volume for each sprinkler in the irrigation cycle was calculated using equations that written in the paper.In the volumetric-based method (treatments WV and MV), the volume of water applied was measured using a water meter with a precision of 0.1 liters, and irrigation was stopped after passing the required volume of water. In the time-based method (treatments WT and MT), the irrigation time was calculated by dividing the irrigation volume by the average flow rate of the sprinklers (3 liters per second), and irrigation was stopped after the calculated duration. The actual sprinkler flow rate was calculated based on the volume of applied water and irrigation time in each treatment and irrigation round. Crop yield was measured at the time of harvest in the studied treatments and a control treatment managed by the Faculty of Agriculture. The irrigation treatments were not applied in the first month of the growth period due to field limitations.The results show that the crop water requirement calculated based on meteorological data at the beginning and end of the growing period was more than the method based on soil moisture. In total, the amount of crop water requirement calculated based on soil moisture was 8% more than the meteorological-based method. The volume of applied water in treatments of MT and WT was 14 and 8% more than in MV and WV treatments, respectively.The actual flow rate of sprinklers was different from the design flow rate due to irrigation situations in other parts of the farm. The average discharge of sprinklers (12 irrigation events) in WT, MT, WV, and MV treatments was 2.79, 3.03, 3.27, and 3.12 l/s, respectively. The irrigation time in volume and time-based methods also showed a significant difference. The irrigation time in MT and MV treatments was 10 and 18% longer than in WT and WV treatments, respectively. The study found that due to the non-uniformity of sprinkler discharge, applying irrigation by volume method is better than the time-based method. The results suggest that the MV treatment, which determined the amount of irrigation based on soil moisture deficit and applied it using a volumetric method, is a suitable option for automating sprinkler irrigation systems in the studied region.

Water resources are strongly influenced by the hydrological cycle and the estimation of evapotranspiration as the main component of the hydrological cycle plays an important role in water resources management. This phenomenon is nonlinear and very difficult to estimate in the sense that there are many parameters involved in its estimation. There are many methods to estimate evapotranspiration none of which is free from problems. Some of these methods, such as Lysimeter, are costly and time-consuming, and others, such as empirical methods only have local value. Therefore, the application of a method which is able to model this phenomenon based on its entity and with minimum data seems necessary. In recent years, the use of artificial intelligence models to simulate various problems has become very popular. In terms of performance, artificial neural networks are very efficient models whose computational speed is completely independent of the mathematical complexity of the algorithms or the method used. The purpose of this research is to compare artificial neural network models, neural network model optimized with genetic algorithm and experimental models in estimation of reference evaporation and transpiration using meteorological data in Ramhormoz synoptic station.

As mentioned above, the aim of this study was to compare the models of artificial neural network (ANN), artificial neural network optimized with genetic algorithm (ANN + GA) and experimental models (Hargreaves-Samani, Blaney-Criddle and Irmak) in estimating reference evapotranspiration compared to the results obtained from the Penman-Monteith FAO standard model by using Meteorological data in Ramhormoz synoptic station. For this purpose, meteorological parameters of Ramhormoz synoptic station were collected monthly during the years 2011 to 2018. This information includes: minimum temperature, maximum temperature, average temperature, wind speed at 2 meters, minimum relative humidity, maximum relative humidity and was sunny hours.Artificial neural networks are simplified models of the working system of the human brain, which are not comparable to natural systems. These models try to imitate human thought processes.The process of using artificial neural network models includes three stages of training, verification and testing. In the present study, 70% of the data was considered for training, 10% for validation and 20% for testing. Also, the stimulus function considered for the training and test phase is the sigmoid tangent.To extract better results from the artificial neural network model, it is necessary to optimize the parameters used. To determine the most optimal parameters required for the artificial neural network model, such as the number of layers, neurons and the weight of the layers, a lot of time is spent on their calibration using the trial and error method. For this reason, in this research, the combination of artificial neural network model and genetic algorithm (ANN+GA) was used in order to achieve the optimal parameters of the artificial neural network model. Minimizing the amount of simulation error as a function of the objective function and the number of iterations was considered as the stopping condition of the optimization algorithm.

Overall, the results showed that artificial neural network models to empirical models used to model higher correlation with the Penman-Monteith FAO model. In addition, among the neural network models used, the integrated neural network model with the genetic algorithm has a higher correlation with the Penman-Monteith FAO model. So that the value of R2 in Blaney Kridel, Hargreaves Samani, Airmak, ANN and ANN+GA models is 0.65, 0.819, 0.781, 0.969 and 0.973, respectively. The results of using scenarios using meteorological parameters as input for ANN and ANN + GA models showed that the highest accuracy of estimating reference evapotranspiration in both models is related to the scenario with input data such as temperature. The minimum is the maximum temperature, wind speed at a height of 2 meters, minimum relative humidity, maximum relative humidity and sunny hours, and the lowest accuracy of the model was in a scenario with two inputs of maximum temperature and minimum temperature. Among the experimental models, Hargreaves-Samani, Irmak and Blaney-Criddle models had the highest correlation with the standard Penman-Monteith FAO model, respectively.

Evapotranspiration is one of the important factors in the hydrological cycle and among the determining factors of energy equations on the earth's surface and water balance. In this regard, many researchers tried to estimate the amount of evaporation and transpiration with a suitable approximation using a cheap and easier method for different regions. The purpose of this research is to compare artificial neural network (ANN) models, artificial neural network optimized with genetic algorithm (ANN+GA) and experimental models (Blaney-Criddle, Hargreaves Samani and Irmak) in estimating reference evaporation and transpiration compared to the obtained results. It was done from the standard Penman-Monteith-FAO model, using meteorological data at Ramhormoz synoptic station. The general results of this research showed that the artificial neural network models have a higher correlation with the Penman-Manteith-Fau model than the used experimental models. In addition, among the used neural network models, the integrated neural network model with genetic algorithm has a higher correlation with the Penman-Manteith-Fau model than the artificial neural network model. Also, among the experimental models used, respectively, Hargreaves Samani, Irmak, and Blaney-Criddle models have the highest correlation with the standard Penman-Monteith-Fau method. In line with the results of the present research, it is suggested to compare the results of experimental models and artificial neural network with the data obtained from the evaporation pan.

Due to the water scarcity in areas with hot climates, an accurate estimation of evapotranspiration has a significant impact on water resources management and planning. In this paper, the gene expression programming (GEP) approach was used to estimate the daily evapotranspiration in Yazd (hot and dry climate) and Jiroft (warm and humid climate) synoptic stations. The results of this method along with the results of empirical relation of Hargreaves-Samani were validated with those of the empirical relation of Penman Mantis FAO-56. Statistical indices of coefficient of determination (R2), root-mean-square error (RMSE) and mean absolute error (MAE) were employed to evaluate the efficiency of the utilized methods. The results indicated that GEP model (with R2 = 0.951 and RMSE = 0.66 mm/day for Yazd and R2 = 0.958 and RMSE = 0.65 mm/day for Jiroft) had higher performance compared to Hargreaves-Samani relation. Therefore, the use of GEP as a model with high capabilities is recommended for estimation of evapotranspiration in areas with hot-dry and warm-humid climates

Proper utilization of available water resources and implementation of development plans requires an accurate database and updating of statistical and meteorological information. The process of measuring meteorological data until it is recorded in a database is an accurate, sensitive and time-consuming process. This study examines the country's meteorological network, which is managed under the supervision of the Ministry of Energy, and the pathology of the current state of meteorological measurements. Accordingly, network evaluation was performed in three major sections: network status evaluation in terms of statistics and field visits and evaluation of the measurement trustee. The survey of measurement system administrators varies around the world, and the focus of measurement varies from ministry to ministry, depending on the macro goals of water and water productivity in each country. A study of the statistics of the country's measurement network showed that one of the most important problems is the lack of stations in the highlands and the lack of a long-term statistical period. Although the construction of the stations is more than half a century old, proper management over time has not been able to observe a proper network. Field evaluation of meteorological stations in the country also showed that the lack of attention to the necessary equipment and infrastructure, the cost of the measurement process, lack of culture regarding the need for measurement, managers' inattention to the measurement process and etc. have been caused that the quality and quantity of measurements are affected. Establishing a proper monitoring network, although it has high initial costs, but the return on investment is necessary and important due to the necessity of statistics and information to implement any development plan.

Recognizing and studying the drought phenomenon with regard to the affecting factors led to better understanding of this phenomenon and paved the way for short-term and long-term planning in relation to encountering, controlling and predicting of this phenomenon.Since to determine the severity, duration, and frequency of drought, is need to determine the drought by using indicators, drought monitoring is essential and researchers have always followed the use of indicators for drought monitoring as a management and planning tool. Drought monitoring is one of the prime factors in drought management. Monitoring systems have important roles in creating the drought plans and its management. Therefore, due to this importance  and taking into account the ecological and geographical conditions of Lake Urmia during recent years, the study and monitoring of drought for the above area is necessary. Therefore, the purpose of this study was to investigate the drought characteristics and calculate the three (SPI, PNI, CZI) drought indexes and comparing their efficiency and accuracy in the Western Azerbaijan province, as well as evaluating the time trends of their changes over the entire province.

Climate change (CC) is one of the most important concerns for mankind in the current century. Increasing CO2 concentration and the proof of the greenhouse effect theory in which the type and composition of atmospheric gases which influence the earth temperature, are among undeniable facts makes the future climate change more possible. Impacts of Global warming on hydrological cycles and precipitation patterns would be more prominent in arid and semi-arid regions of the earth. For the arid and semi-arid nature and the poverty more fraction of Afghanistan suffer from, it is likely that the impacts of CC on the country will be more intense. This is while there is no credible and reliant research addressing the impacts of CC on agriculture and food security sector of Afghanistan. Studying the impacts of CC on agriculture, future changes in agroclimatic indices and application of crop growth simulation models intensively require a precise and adequate sets of meteorological data. Because of many reasons, Afghanistan's historical meteorological data coverage is really weak. In this research the applicability of AgMERRA as a gauge-satellite based dataset for filling the Afghanistan in-situ meteorological gaps is evaluated via goodness of fit measures, patterns of seasonal changes and the probability distribution functions.

This study is conducted on four major stations of Afghanistan (Kabul, Herat, Mazar Sharif and Qandahar in the east, west, north and south of the country, respectively) (Fig. 1 and table 1) which had the best in-situ meteorological data coverage. Observed Maximum (Tmax) and Minimum temperature (Tmin) and precipitation (PRCP) data is collected via Afghanistan Meteorological Authority (AMA) or other sources. AgMERRA database downloaded with .nc4 format and extracted with R statistical software or Panoply ver. 4.8.4, dependently. Then five goodness of fit (GOF) measures (RMSE, NRMSE, MBE, R2 and d) are calculated according to the equations 1 to 5. There are different norms and indices to measure the validity of a models, some based on Pearson correlation coefficient (R and R2) which indicate the degree of correlation between observed and predicted data but have some amounts of sensitivity to extreme values (outliers). Although, many other measures are considered to overcome the weaknesses but it is hard to distinguish the best.

The results of this research indicated the good potency, effectiveness and ability of AgMERRA for gap-filling of in-situ meteorological data and producing spatiotemporal data series. Several studies in this area have almost the same results. It is reported that AgMERRA is the most applicable dataset for reflecting precipitation data comparing with ERA-Interim, ERA-Interim/Land and JRA-55 datasets. Comparisons via NRMSE shows great (>10%) and good (>20%) amounts in all stations and temporal scales. Among other stations, Mazar Shrif showed the best conformity between AgMERRA and observed data, while Kabul station had the weakest, probably due to complex topographic situation of the Kabul airport station. The amounts of R2 for predicting temperature (Tmax and Tmin) were more than 0.86 in daily, 14-days and monthly temporal scales. The lowest amount of the coefficient of determination was obtained at Qandahar station for Tmean in daily temporal scale (R2=0.8) and the highest amount obtained for daily Tmax at Mazar Sharif station (R2=0.947). R2 for daily PRCP were inadequate, but increasing to adequate amounts in 14-days and monthly temporal scales. The highest spatiotemporal amount of Tmax,Tmin and Tmean was obtained in daily scale and the lowest amount was obtained for Tmean (1.8 and 0.9, respectively). The Index of agreement (d), also had adequate amounts for 14-days and monthly PRCP (>0.87). The amount of MBE for precipitation in Herat, Mazar Sharif and Kabul stations were negative, while it was positive in Qandahar station with a hot and dry climate. AgMERRA could show a good compliance with changes of observed seasonal patterns, however, some amount of over and under-estimates are obvious especially for Kabul station. This compliance with in-situ observed patterns was acceptable for daily temporal scale, although AgMERRA was unable to predict some of the fluctuations in probability distribution composition (with the range of 1 °C), especially fot Tmax and Tmin, but fot Tmean the fluctuations simulated well.

According to the results of the study, AgMERRA showed an acceptable potency to simulate the in-situ meteorological data in four major studied stations of Afghanistan. According to the stochastic nature of PRCP, the variable showed the weakest results in daily temporal scale but acceptable in 14-days and monthly. Given the weak coverage of in-situ meteorological data of Afghanistan, AgMERRA could be a valid dataset for producing well scaled spatiotemporal data series to be used in agroclimatic, CC and crop growth modeling studies.

This article provides small examples of how to use reliable and documented historical literature to help understand temperature and meteorological conditions. Anywhere information on frost, cold, temperature, precipitation (snow or rain) with date and day is included can be meteorological information. However, as with today's meteorological, the temperature or precipitation characteristics cannot be quantitatively extracted, Only qualitative information can be used. Thus, one can call this discovery a branch of meteorological knowledge, "historical meteorology". Findings from this knowledge can help to identify periods of famine, how to balance aquifers and lakes, and so on. Looking at this branch of meteorological knowledge is the solution to the drought problem.

Solar radiation is an essential factor in irrigation scheduling, hydrological cycle, crop growth simulation models and estimation of reference evapotranspiration. The aim of the present research was to investigate the accuracy of solar radiation estimation models and their effects on reference evapotranspiration. For this purpose, the meteorological data of 4 synoptic stations including Urmia, Takab, Salmas and Mahabad in West of Urmia lake catchment in daily scale were used. Solar radiation was estimated using seven models including, Hargreaves-Samani, Allen, Self-Calibrating, Samani, Annandale, Bristow-Campbell and Angstrom–Prescott. Then, the obtained values were used in FAO- Penman-Monteith equation to estimate the reference evapotranspiration. In order to evaluate the model's accuracy, the statistical indicators including root mean square error, mean bias error and determination coefficient were used. The evaluation results of the models showed that the Angstrom – Prescott model had the best performance, and the Samani method was the weakest method in the studied stations. The average values of the root mean square error for the Angstrom–Prescott and Samani methods in the studied region were obtained 0.48 and 1.43 mm/day, respectively.