Agricultural Meteorology
M. Fashaee; S.H. Sanaei Nejad; M. Quchanian
Abstract
Introduction Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote ...
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Introduction Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote sensing data are often provided from three sources: microwave, visible and thermal. Most satellite soil moisture-based algorithms rely on passive microwave images, active microwaves, or a combination of data from several different sensors. Among the various remote sensing methods, the microwave electromagnetic spectrum has fewer physical limitations than other spectrum in measuring soil moisture. However, microwave soil moisture data often have very large pixel dimensions (more than 10 km), making it difficult to use them on a small scale.Materials and Methods In this study, in order to calculate the agricultural drought index at the field-scale, AMSR2 Retrieval data were calibrated first using field moisture measurement data in the Neishabour plain during 2017 to 2019. During the research period, 560 soil samples (20 samples in 28 shifts) were collected and soil moisture was measured in the laboratory of the Department of Water Science and Engineering, Ferdowsi University of Mashhad. LPRM_AMSR2_ SOILM3_001 is one of the third level products of the AMSR2 sensor, which is produced on a daily basis with a spatial resolution of 25 × 25 km2. Land surface parameters including surface temperature, surface soil moisture and plant water availability were obtained by passive microwave data using the Land parameter Retrieval Method (LPRM). Then, by using Modis sensor images (NDVI and LST), linear downscaling equations were extracted. The dimensions of the AMSR2 images were reduced from 25 kilometers to 1000 meters using these equations. In next step, SMADI Agricultural Drought Index, which is a combination of vegetation characteristics, soil moisture and land surface temperature, was used to monitor agricultural drought at the field-scale. Statistical indicators such as coefficient of determination (R^2), mean absolute error (MAE) and root mean square error (RMSE) were also used to evaluate the statistical performance.Results and DiscussionBy visual analysis of the role of vegetation and land unevenness, it was found that these two factors affect the regression relationships extracted for calibration of remote sensing data. The RMSE and MAE values for the regression equations used in the calibration process were calculated in the range of 1.6 to 4%, which can be considered acceptable in comparison with the mean values of the soil moisture data (15 to 20). The results showed that changes in SMADI index in three land use zones including rainfed cultivation (R1), medium rangeland (R2) and poor rangeland (R3) have experienced a similar trend to precipitation changes, illustrating that precipitation is one of the most effective factors in major changes in SMADI agricultural drought index fluctuations. It was also observed that SMADI index changes with a delay of 1 to 8 days compared to the precipitation changes in all three zones. In all three zones, the SMADI index followed a similar trend to in-situ soil moisture changes. At mot 80% of the changes in SMADI-R1 index can be explained by in-situ SM-R1, and the rest of the changes were related to other environmental factors or measurement error. This decreases to 68% in the R3 zone. It should be noted that soil moisture monitoring can more accurately reflect the impact of environmental factors on the changes in agricultural drought index such as SMADI than other variables; because the rainfall recorded at the meteorological station does not necessarily occur uniformly throughout the study area. On the other hand, any amount of precipitation will not necessarily lead to an effective change in soil moisture storage. This also renders assessment of the performance of agricultural drought indicators difficult.Conclusion Examination of statistical indices of coefficient of determination (R2), mean absolute error value (MAE) and root mean square error (RMSE) showed that the algorithm used in downscaling as well as estimating SMADI agricultural drought index is well able to reflect the interactions between precipitation, soil moisture, vegetation and changes in canopy temperature profile. This feature justifies and strengthens its application in agrometeorological analysis.
Agricultural Meteorology
B. Mirkamandar; Seied Hosein Sanaei-Nejad; H. Rezaee- Pazhand; M. Farzandi
Abstract
Introduction: 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 ...
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Introduction: 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. Materials and Methods: 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. Results and Discussion: 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. Conclusion: 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.
Mahsa Sameti; Seied Hosein Sanaei-Nejad; Firoozeh Rivaz; Bijan Ghahraman
Abstract
Introduction: Drought is a very complex natural phenomenon which changes with time and space. Spatial and temporal variations of drought are analyzed separately. Geostatistical methods can be used for spatiotemporal analyses to find related spatial and temporal pattern changes. These methods, ...
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Introduction: Drought is a very complex natural phenomenon which changes with time and space. Spatial and temporal variations of drought are analyzed separately. Geostatistical methods can be used for spatiotemporal analyses to find related spatial and temporal pattern changes. These methods, which use the spatio-temporal data, considering the spatial position of the data relative to each other, also take into account their temporal dependence. If needed, they can estimate values of their variable at any location and any time. Moreover, the drought spatial variations in the studied region can be drawn at every desired period. On the other hand, it is expected that intervening of the time dimension in the equations of these methods, as compared to the purely spatial methods, provide more precision in estimating the values of drought indices, which is studied in this research.
Materials and Methods: Monthly rainfall data of 48 stations in the northeast of Iran for the period of 1981-2012 were used in this study. The SPI drought index is calculated for the 12-month time scale. Data were divided into two groups of training data from 1981-2011 and experimental data of 2012. After analyzing the data regarding their stationarity and isotropic assumptions, the spatiotemporal data were formed and their spatiotemporal empirical variogram was drawn. Furthermore, the purely spatial and temporal variograms for the zero space and time steps were also drawn. Then, four models of the spatiotemporal variogram functions were applied on the training data. The performance of these models was tested and compared by estimating the parameters of the model based on the Square Error (MSE). Moreover, three-dimensional fitted variograms were drawn for different models. Mean The best spatiotemporal variogram model was selected by comparing the models prediction with experimental data using the Mean Square Prediction Error (MSPE). Using spatiotemporal kriging method, the predicted values of experimental data were interpolated and that of the observed values were interpolated by kriging method. Cross validation on experimental data was also performed using RMSE, MAE, ME and COR. Then spatiotemporal and purely spatial variogram models were investigated and compared.
Results and Discussion: The results showed that the 12-month SPI index had no spatial trend but had a decreasing trend against the time. Hence, a simple regression equation was used for fitting the trend of the data. After detrending the data, the SPI index values were considered as the dependent variable, while the time was taken as the independent variable. On the other hand, drawing the variogram in different directions (0°, 45°, 90°, and 135°) had no significant effect relative to each other, and the hypothesis of isotropic state was accepted. The plots of purely spatial and temporal variograms showed that the spherical variogram for space and the linear variogram for the time would have the best fitting. The empirical 3-D and 2-D spatiotemporal variograms of the training data were plotted. The empirical 3-D variogram showed that the data had reached to its temporal sill in a 1-year time lag, and had reached to its spatial sill, in about 25-kilometers, which are in conformity with the purely spatial and temporal variograms. The comparison of different variogram functions showed that the MSE values of the separable, metric, product-sum and sum-metric models were 0.00139, 0.00295, 0.00111, and 0.00112, respectively, the last two of which had fewer errors. Drawing the spatiotemporal variogram of these functions showed that the spatiotemporal variogram of product-sum and sum-metric models have more similarity to the sample one. Regarding the selection of the best model, the MSPE statistics of the product-sum and sum-metric models were 0.281 and 0.389, respectively. Therefore, the product-sum model could be selected as the best model. The least rate of errors was found in the exponential variogram model for space, and in the linear variogram for the time. The parameters of the nugget effect, partial sill and range for the spatial variogram would be 0.00, 0.063, and 5.78, and for the temporal variogram would be 0.00, 0.635, and 1.044, respectively. After predicting values of 12-month SPI in 2012 by the product-sum variogram model and adding the values of the trend, they were interpolated by using the spatiotemporal kriging, and the observed values were interpolated by the use of kriging. The obtained plot from the predicted values had great similarity with that of the observed values, which indicates the appropriate capability of the model in predicting the unobserved values. The cross-validation of different spatiotemporal and the spatial models with 25 and 47 neighborhoods showed that the performance of the models had no significant differences relative to each other, and they also had no better performance relative to the purely spatial model.
Conclusion: The results of this study showed that the product-sum model had a better performance among different spatiotemporal variogram models in predicting the 12-month SPI values of 2012. However, the performances of different spatiotemporal models were quite close to each other. There is no significant difference that could be observed between spatiotemporal and purely spatial models. Also, it is proposed to use the dynamic spatiotemporal models and the results to be compared with the classical models.
B. Mirkamandar; Seied Hosein Sanaei-Nejad; H. Rezaee-Pazhand; M. Farzandi
Abstract
Introduction: The behavior of daily changes in temperature is not straightforward. We first drew the curve of this variable on a normal day. It can be seen that the distribution of this variable was not normal. The curve of this variable was a skewed curve to the right. Therefore, the equal coefficients ...
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Introduction: The behavior of daily changes in temperature is not straightforward. We first drew the curve of this variable on a normal day. It can be seen that the distribution of this variable was not normal. The curve of this variable was a skewed curve to the right. Therefore, the equal coefficients could be used only as approximation for estimating daily average temperature. Climatic conditions of the meteorological stations were also another parameter to be considered. This research presents a new method for estimating daily average of temperature in three climatic regions of Iran. The patterns for the sample stations in each climatic region were presented separately. Materials and Methods: E. Eccel (2012) developed algorithms 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, 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 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). Rezaee-Pazhand, et al. (2008) introduced new patterns for estimating daily average temperature in arid and semiarid regions of Iran. Final pattern has interception and new coefficients for estimate 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 with linear and parabolic functions. Farzandi et al. (2011) divided Iran into three climatic clusters used in the present work. First a classification which provides climatological clustering. This clustering was used the data of annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation and three indices of De Martonne, Ivanov and Thornthwaite. Iran was partitioned into three clusters i.e. coastal areas, mountainous range and arid and semi-arid zone. Several clustering methods were used and 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 testes. Finally 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 daily average of 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. Results and Discussion: The coefficient of determination (R2) and the Fisher statistics show that the patterns have a good ability to estimate the daily average of temperature. The daily average temperature pattern confirmed an interception in the equations. Standardized coefficients showed that predictor variables were not weighted in all of the patterns. The average values 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. The mean squared error is a measure of the applicability of patterns. The accuracy of the estimating daily average temperature by the recommended models in three climates was confirmed by calculating the mean squared error. The proposed patterns of this study had less error than common patterns. Thus, the patterns have a good ability to estimate daily average temperature. Conclusion: The maximum temperature in calculating daily average of temperature is more effective than the minimum temperature. The standardized coefficient (Beta) of the daily average temperature patterns in coastal cluster was 48.2% for the minimum temperature and 51.8% for the maximum temperature. The largest influence of the maximum temperature was 63.1% in mountainous cluster for estimating daily average temperature. Range of the interception in the equations was from -1.735 to 0.26. The independent assumption of the residual was confirmed with the acceptable value of Durbin-Watson statistics. The average of the residuals in each patterns was zero. According to the graphs stabilization of variance can be seen based on the residual on the each pattern in each cluster. The proposed patterns were calculated according to mathematical principles but the common patterns did not consider these mathematical principles. The mean squared error (MSE) of the proposed patterns are less than common patterns. Therefore, the patterns presented in this study are more powerful than common patterns. The largest difference between the proposed patterns and the common patterns for estimate the daily average of temperature was 24% in mountainous cluster. Climatic clustering was done for states. The monthly and annual average temperature can be reliably estimated by using the data of sample stations in each state. These findings can be used to estimate daily, monthly and annual average of relative humidity in three climates and sample stations. In addition, one can employ the method for estimating daily, monthly and annual average of relative humidity and temperature based on around climatological clustering of Iran and other stations. Annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation can also be applied to estimate daily, monthly and annual average of temperature and relative humidity more accurately.
mahboobeh farzandi; Seyed Hossein Sanaeinejad; Bijan Ghahraman; Majid Sarmad
Abstract
Introduction: Temperature and precipitation are two of the main variables in meteorology and climatology. These are basic inputs in water resource management. The length of the statistical period plays a pivotal role in the accurate analysis of these variables. Observation data at Iran's first ...
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Introduction: Temperature and precipitation are two of the main variables in meteorology and climatology. These are basic inputs in water resource management. The length of the statistical period plays a pivotal role in the accurate analysis of these variables. Observation data at Iran's first synoptic station from 1330 (1951) is available at the Iranian Meteorological Organization website The historical monthly precipitation and temperature of five stations in Iran is available since 1880 with missing data. These data measured by the Embassy of the United States and Britain from the Qajar period and recorded in World Weather records books. These synoptic stations include Mashhad, Isfahan, Tehran, Bushehr, and Jask. The monthly missing data were predominantly recorded during World War II (1941-1949). Unfortunately, these data have missing. Therefore, the accuracy of simulating these variables is very important. The current research aimed to predict the missing values of monthly temperature and precipitation in Mashhad station. The stations in the neighboring countries were selected due to the distance to Mashhad, relationship, and completeness of data since 1880, as the predictive variables. Monthly precipitation of Ashgabat from Tajikistan and Sarakhs, Kooshkah, Bayram Ali, Kerki and Repetek from Turkmenistan were selected as an independent variable in the making of Missing Rainfall in Mashhad. Also, the temperature of Ashgabat, Bayram Ali, Gudan, Sarakhs, and Tajan were selected to restore the monthly temperature of the Mashhad station. This research has fitted ten multiple regression models to monthly rainfall of Mashhad station and has fitted 6 multiple regression to the monthly temperature of Mashhad. then the parameters of these patterns are optimized by genetic and Ant Colony algorithm. Also, the Artificial Neural Network (MLP) model and Support vector regression have been selected and implemented in order to simulate monthly precipitation and temperature data of Mashhad.
Materials and Methods: In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover, and selection. Ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Artificial neural networks are one of the main tools used in machine learning. As the “neural” part of their name suggests, they are brain-inspired systems which are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize. In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting).
Results and Discussion: At the first stage, several multiple regressions were fitted to monthly precipitation (with coefficients ranging from 0.63 to 0.81) and six patterns for monthly temperature (0.986-0.993). Afterward, GA and ACO were applied to improve the accuracy of the selected regression models by optimizing their parameters. At the next stage, ANN and SVR were used to estimate the monthly missing values separately. Finally, the results of the previous stages were compared using the root mean square error (RMSE), and the optimal models were applied to determine the missing values of monthly temperature and precipitation of Mashhad. The results showed that the Genetic Algorithm and Ant Colony increase the accuracy of the estimation of missing rainfall data significantly more than the previous methods. The lowest error criterion (RMSE) between regression patterns is 9.8 millimeters. By genetic algorithm, this criterion is reduced to 2.56 mm, and by ant colony algorithm to 2.559.
Conclusion: Comparison of the above methods in restoration temperature and precipitation shows that evolutionary methods (GA and ACO) are the best for estimating the missing monthly precipitation and machine learning methods (ANN and SVR) are the best to imputation missing data of monthly temperature.
Mojtaba Shokouhi; Seied Hosein Sanaei-Nejad; Mohammad Bannayan Aval
Abstract
Introduction: Achieving sustainable practices of mitigation and adaptation to climate change requires accurate projections of climate change in each region. In this regard, Coupled Model Inter-comparison Project (CMIP) over the past 20 years has shown a good performance. Therefore, new CMIP5 climate ...
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Introduction: Achieving sustainable practices of mitigation and adaptation to climate change requires accurate projections of climate change in each region. In this regard, Coupled Model Inter-comparison Project (CMIP) over the past 20 years has shown a good performance. Therefore, new CMIP5 climate models are expected to be bases for many climate change studies. These models use a new set of emission scenarios called Representative Concentration Pathway (RCP) to project climate change. Climate change is expected to impact wheat production and food security in Iran. So far, no study has not been conducted to regionally project climate change based on new CMIP5 models and RCP scenarios over the major wheat-producing areas in Iran. Our objective was to evaluate the performance of CMIP5 climate models in simulating temperature and precipitation in these areas. In addition, different combinations of climate models were evaluated to select appropriate models in these areas.
Materials and Methods: According to the latest data, nearly 60% of rainfed wheat is produced within our study area. The mean monthly temperature and precipitation data were provided by Meteorological Organization of Iran for synoptic stations. Period of 1975-2005 was considered as a historical period (baseline period). We evaluated outputs from 21 GCMs from CMIP5 climate models for monthly values of total precipitation and mean surface air temperature. One in ten ensembles of each GCM model was evaluated as available. We used model outputs for two emission scenarios i.e. RCP-2.6 and RCP-8.5, for the future periods of 2045–2065 and 2080-2100 to project temperature and precipitation changes. We assigned the models into two groups, high resolution (models less than 2° latitude/longitude, high-re; 11 models) and low resolution (models greater than 2° latitude/longitude, low-re, 10 models). Output GCM models were used for a grid in which recorded data are available. We applied the equidistant quintile-based mapping method (EDCDF) to correct bias of monthly precipitation and temperature simulated by models in the historical period (1975-2005) and, then in the future periods. We also used the root mean square error (RMSE), the coefficient of correlation and the skill scores (SS) to evaluate the model performance.
Result and Discussion: Average of all ensembles of an individual model outperformed the other ensembles in simulating the historical climate. This superiority is largely caused by the cancellation of offsetting errors in individual ensembles of a model, and also reduces the effects of natural internal climate variability in simulations. Taylor diagram showed, contrary to a simulation of temperature, simulations of precipitation have great variability than observations and the standard deviation of simulated precipitation values was less than that of observations for most used models. The models simulated temperature much better than precipitation across the region. Contrary to precipitation, the simulated temperature did not show a significant difference among the models. Several combinations of models resulted in an improvement in precipitation and temperature simulations. Therefore, a combination of models can be used in regional climate change assessment studies. The models performance for simulating the historical climate was evaluated based on skill score (SS) and Δ (the Euclidian distance from perfect skill, point (1, 1, 1, . . . , 1)). Many different combinations of 21 GCM models were evaluated, which combination of 7 models as selected models yielded a lower Δ and higher skill scores. For multimodal ensemble (MME) mean (All, high-re, low-re and Selected, models) Δ value was less than that for individual models. SS values in the simulation of precipitation were more than -3 for 75% of models during the high precipitation months. Uncertainty in the simulation of precipitation during the low precipitation months was more than that of high precipitation months and it was even much more in southern areas (especially in August and September). Uncertainties in temperature and precipitation changes projections were affected by the scenario, the time period and models selected. All models showed biases indicating the fact that direct use of such models in climate change studies (without bias correction) is not recommendable. Although the use of statistical methods for bias correction resulted in a significant reduction of nonsystematic biases, systematic biases were not considerably influenced. Precipitation will increase in northern areas toward the end of the century and a higher reduction in precipitation is anticipated in the southern areas. The average, long-term (2080–2100) temperature increase was 5.5°C under RCP-8.5. Further, temperature increase will be greater in the southern regions.
Conclusion: Performance of 21 GCMs from CMIP5 climate models were evaluated in major rainfed wheat-production areas in Iran and temperature and precipitation changes were projected under RCP-2.6 and RCP-8.5. Taking into account all GCM’s initial conditions (if they are available) leads to a better performance. Simulations of models exhibited biases, so models output must be corrected before they can be used in regional climate change assessment studies. Although bias correction resulted in a significant reduction of nonsystematic biases, systematic biases were not significantly affected. The MME (All, high-re, low-re and Selected, models) consistently outperformed individual models for both precipitation and temperature suggesting that a smaller group of models can be used in regional climate change assessment. We recognized a subset of 21 models (7 selected models) based on performance that combination of them can provide the best performance and plausible future projections.
M. Shokouhi; Seied Hosein Sanaei-Nejad
Abstract
Introduction: Many researchers studied and emphasized on determining the importance of climatic factors that affect crop yield. As the most source of moisture in rainfed cultivation, precipitation is the most important climate factor. Spatial and temporal change of this factor effects crop yield. Standardized ...
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Introduction: Many researchers studied and emphasized on determining the importance of climatic factors that affect crop yield. As the most source of moisture in rainfed cultivation, precipitation is the most important climate factor. Spatial and temporal change of this factor effects crop yield. Standardized Precipitation Index (SPI) is useful to characterize the condition of the moisture supply before and during the growing season of crops. Studies have shown that in some areas there is little correlation between spring wheat yield and SPI, while in other areas there is significant relationship between wheat yield and SPI. This difference indicates SPI as an indicator of moisture supply, depend on the study area .The purpose of this study was to determine the most effective period of precipitation during growing season for rainfed barley using variables obtained from moisture supply and precipitation periods in Tabriz. The most effective period of precipitation can be used for the management of rainfed cultivation.
Materials and Methods: Daily temperature and precipitation data of Tabriz station were collected from Iran Meteorological Organization for the years 1955 to 2013. In addition, barley yields data were collected for the years 1977 to 2013. In this study, the occurrence of phenological stages (germination, tillering, anthesis, ripening and harvesting) were estimated using growing degree days (GDD). The SPI value for 28-week time scale of the first week after planting (SPI28) was considered as an indicator of the moisture supply during growing season. SPI28 values less than zero and greater than zero representing different classes of drought and humidity respectively. For correlation analysis, 128 weekly variables were defined at different time scales of daily precipitation data (Table 2). The relationship between the crop yield and precipitation variables were analyzed by linear correlation.
Results and Discussion: The correlation coefficient (r) between precipitation and annual rainfed barley yield were presented in Table 2. The highest correlation between yield and precipitation occurred during the 10-week period between 25 February and 6 May, which was mostly observed at the end of April to mid-May that was coincide with the beginning of anthesis. So it can be concluded that the anthesis stage was the most critical stage to water stress in barley. Based on the SPI28 value greater than zero (wet conditions) or less than zero (dry conditions), the amount of precipitation (between 25 February and 6 May) was divided into two groups. The amount of precipitation between 25 February and 6 May explained 78% of the yield variations when SPI28 was greater than zero (wet conditions). One mm increase in precipitation in this period increased the yield with the rate of 2/76 kg / ha. If early planting conditions is dry (SPI 28
N. Siabi; S.H. Sanaeinejad; B. Ghahraman
Abstract
Introduction temporal and spatial change of meteorological and environmental variables is very important. These changes can be predicted by numerical prediction models over time and in different locations and can be provided as spatial zoning maps with interpolation methods such as geostatistics (16, ...
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Introduction temporal and spatial change of meteorological and environmental variables is very important. These changes can be predicted by numerical prediction models over time and in different locations and can be provided as spatial zoning maps with interpolation methods such as geostatistics (16, 6). But these maps are comparable to each other as visual, qualitative and univariate for a limited number of maps (15). To resolve this problem the similarity algorithm is used. This algorithm is a simultaneous comparison method to a large number of data (18). Numerical prediction models such as MM5 were used in different studies (10, 22, and 23). But a little research is done to compare the spatio-temporal similarity of the models with real data quantitatively. The purpose of this paper is to integrate geostatistical techniques with similarity algorithm to study the spatial and temporal MM5 model predicted results with real data.
Materials and Methods The study area is north east of Iran. 55 to 61 degrees of longitude and latitude is 30 to 38 degrees. Monthly and annual temperature and precipitation actual data for the period of 1990-2010 was received from the Meteorological Agency and Department of Energy. MM5 Model Data, with a spatial resolution 0.5 × 0.5 degree were downloaded from the NASA website (5). GS+ and ArcGis software were used to produce each variable map. We used multivariate methods co-kriging and kriging with an external drift by applying topography and height as a secondary variable via implementing Digital Elevation Model. (6,12,14). Then the standardize and similarity algorithms (9,11) was applied by programming in MATLAB software to each map grid point. The spatial and temporal similarities between data collections and model results were obtained by F values. These values are between 0 and 0.5 where the value below 0.2 indicates good similarity and above 0.5 shows very poor similarity. The results were plotted on maps by MATLAB software.
Results Discussion In this study the similarity and geostatistical algorithm were combined to compare and evaluate spatio-temporal of predicted temperature and precipitation data by MM5 model with actual data. The analysis of the similarity map is based on the F values, the area and also the uniformity of distribution over the area. The similarity between predicted and actual data is higher when F values are low and distributed more uniform. The temperature similarity maps showed that F values are between 0.0 - 0.2 in cold seasons. It was shown that the values had spatial continuity and uniform distribution. A large part of area (almost 80%) is covered by lowest F value (F˂0.1), which shows very high similarity among temperature datasets. The highest values (0.15 < F < 0.2) occurred in the central of the study area. In the warm seasons F values were between 0.0 - 0.4. These values had spatial continuity and uniform distribution which is lower than cold season. The area of good similarity values (0.0˂F˂0.1) is almost 45% of the whole region. The highest values (F>0.3) in the central region indicate errors in the model predictions data. But generally prediction of model in both seasons for the temperature was good. In annual time scale, F values are between 0.0 - 0.25. The area of good similarity value (0.0˂F˂0.1) is almost 65% of the whole region with spatial continuity and uniform distribution. Accuracy of the model declined from temperature of the cold season to annual and then warm season respectively. The precipitation similarity maps showed that in cold season F values changes between 0.05 - 0.4. These values had less spatial continuity than temperature. In more than half of the area (60%) there was fairly good similarity where 0.05 < F < 0.15. The maximum values (0. 3 < F < 0.35) occur in mountainous regions of the study area. In warm seasons F values are between 0.1- 0.45. These values are not uniformly distributed and dispersed. The area of good similarity values (0.0˂F˂0.1) is zero percent. The highest values (F>0.3) in the central mountainous area and south part of region suggests the low similarity in the model predictions. Similarity between the cold seasons is much higher than the warm seasons, which is due to the variability of precipitation during the seasons. In the annual time scale, F values are between 0.05 - 0.3. F values (0.0˂F˂0.1) are almost 40% of the whole region with uniform distribution. Overall, the higher uniform distribution of annual similarity values showed that prediction of model for annual precipitation data is better than seasonal. The maximum F values identified the areas with modeling error for various reasons. In this study the central and the southern parts had maximum F values at different time steps. Plotted mean monthly values of similarity indicated minimum and maximum temperature F values were occurred in January and July while for precipitation was taken place in January and September respectively. This shows that MM5 model prediction was good in January.
Conclusion: In this paper, the similarity algorithm discovered spatial and temporal similarities between the predicted and actual data for temperature and precipitation variables. According to the obtained F values, the model predicts temperature was better than precipitation. Due to the upward movement of the convective zone and the effects of topography for both variables, the similarity between predicted and actual data is low in warm seasons. In small areas of the south and the central region of the study area, F values are between 2.0 and 4.0, respectively, which could be considered as a weak similarity. The area with high f values (F > 0.45) can be seen on every precipitation map, which suggests a large error values related to reporting of the station data.
Keywords: Algorithms, Numerical prediction models, Similarity comparison, Spatio- temporal
M. Makari; B. Ghahraman; S.H. Sanaeinejad
Abstract
The objective of this study is to analyze the sensitivity of ETo for five models including FAO-Penman-Monteith, modified Blaney-Criddle, Hargreaves, Hargreaves-Samani and Priestley –Taylor. Daily meteorological data of Bojnourd synoptic station including air temperature, relative humidity, actual duration ...
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The objective of this study is to analyze the sensitivity of ETo for five models including FAO-Penman-Monteith, modified Blaney-Criddle, Hargreaves, Hargreaves-Samani and Priestley –Taylor. Daily meteorological data of Bojnourd synoptic station including air temperature, relative humidity, actual duration sunshine and wind velocity were used for sensitivity analysis of five models. In order to produce random data at a specific range, Monte-Carlo simulation was performed. Annual and seasonal were calculated to indicate the sensitivity of ETo in simultaneous variations of meteorological variables in each model.The results obtained in this study showed that the sensitivity of in simultaneous variations of meteorological variables is higher in summer. In all models, the most sensitivity was seen in summer and spring and the least sensitivity was occurred in autumn and winter. Among the studied models, FAO-PM and BC models had the most annual sensitivity and PT model had the least annual sensitivity. All of the models had fairly high correlation coefficient with FAO-PM model but the quantity of and was different in each model. BC model had the most and the least and was seen in and PT. According to the findings in this study, it can be concluded that SH model is fairly suitable for estimation of in synoptic station.
M. Fashaee; Seied Hosein Sanaei-Nejad; K. Davary
Abstract
Introduction: Numerous studies have been undertaken based on satellite imagery in order to estimate soil moisture using vegetation indices such as NDVI. Previous studies suffer from a restriction; these indices are not able to estimate where the vegetative coverage is low or where no vegetation exists. ...
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Introduction: Numerous studies have been undertaken based on satellite imagery in order to estimate soil moisture using vegetation indices such as NDVI. Previous studies suffer from a restriction; these indices are not able to estimate where the vegetative coverage is low or where no vegetation exists. Hence, it is essential to develop a model which can overcome this restriction. Focus of this research is on estimation of soil moisture for low or scattered vegetative land covers. Trapezoidal temperature-vegetation (Ts~VI) model is able to consider the status of soil moisture and vegetation condition. It can estimate plant water deficit for weak or no vegetation land cover.
Materials and Methods: Moran proposed Water Deficit Index (WDI) for evaluating field evapotranspiration rates and relative field water deficit for both full-cover and partially vegetated sites. The theoretical basis of this method is based on the energy balance equation. Penman-Monteith equation of energy balance was used to calculate the coordinates of the four vertices of the temperature-vegetation trapezoid also for four different extreme combinations of temperature and vegetation. For the (Ts−Ta)~Vc trapezoid, four vertices correspond to 1) well-watered full-cover vegetation, 2) water-stressed full-cover vegetation, 3) saturated bare soil, and 4) dry bare soil. WDI is equal to 0 for well-watered conditions and equals to 1 for maximum stress conditions. As suggested by Moran et al. to draw a trapezoidal shape, some field measurements are required such as wind speed at the height of 2 meters, air pressure, mean daily temperature, vapor pressure-temperature curve slope, Psychrometrics constant, vapor pressure at mean temperature, vapor pressure deficit, external radiation, solar radiation of short wavelength, longwave radiation, net radiation, soil heat flux and air aerodynamic resistance is included. Crop vegetation and canopy resistance should be measured or estimated. The study area is selected in the Mashhad plain in Khorasan Razavi province of I.R. Iran. Study area is about 1,200 square kilometers and is located around the Golmakan center of agricultural research. In this study, water deficit index (WDI) was zoning by MODIS images in subset of Mashhad plain during water year of 2011-2012. Then, based on the close relationship between WDI and soil moisture parameter, a linear relationship between these two parameters were fitted. Soil moisture is measured by the TDR and every 7 days at 5 depths of 5, 10, 20, 30 and 50 cm from the surface. Remote Sensing (RS) technology used as a tool for providing some of the data that is required. The moderate resolution imaging spectroradiometer (MODIS) instrument is popular for monitoring soil moisture because of its high spectral (36 bands) resolution, moderate spatial (250–1000 m) resolution and various products for land surface properties. MODIS products used in the present study include: MOD09A1 land surface albedo data, MOD11A1 land surface temperature data, and MOD13A1 vegetation data. Using ArcMap 9.2 and ERDAS IMAGINE 2010 softwares, WDI was calculated pixel by pixel for 18 days (non-cloudy days and simultaneous with measurement of soil moisture at the station).
Results and Discussion: The results showed that the northeastern region is predominantly rainfed and irrigated farmlands are under water stress. Conversely, the southwestern part of the area is mountainous with less water stress. Based on NDVI, there is also less crop cover in the southwestern part of the region during the year. The results showed that about 44% of the index values are in the range of 0.2-0.3. Then about 22% of the index values are in the range of 0.3-0.4. Thus it can be concluded that over 66% of the index values are in the range of 0.2-0.4. According to the maximum index value (WDI=0.59 on the 201th day of year) and the minimum values (WDI=0.0004 on the 129th day of year) during the time period of study, it seems that water stress in the study area in the six-month period of observation is moderate. To validate the results, changes in precipitation, relative humidity and WDI values were compared. As expected, after the occurrence of any significant rainfall, water stress is decreased and decreasing in relative humidity, coincided with increase in water stress. In the next step, the linear relationship between measured values of soil moisture and WDI values were fitted in 2 depth of 5 and 10 cm. It should be noted that the average values of WDI of four pixels surrounding the Golmakan station was used in calculation of the regression coefficients Similar research has shown that Ts~VI trapezoid based WDI can accurately capture temporal variation in surface soil moisture, but the capability of detecting spatial variation is poor for such a semi-arid region like Mashhad. The high correlation coefficient (93%) obtained from soil moisture (5 cm) and WDI regression showed the good mutual impacts of these two parameters on each other. The correlation coefficient between WDI index and soil moisture at a depth of 10 cm was equal to 83%. Reducing the value of the correlation coefficient was probably due to the delay in transferring the soil moisture changes to underlying depth.
Conclusion: The similarity of the mean values of rainfall and relative humidity of the air showed good compliance with the WDI. Good correlation coefficient (93%) between WDI and soil moisture (measured at depth of 5cm in the station) certifies the accuracy of the results obtained from WDI. The results showed that Ts~VI trapezoid based WDI can well capture temporal variation in surface soil moisture, while in this study, spatial zoning was avoided because of the lack of soil moisture data within the study area.
A. Mianabadi; A. Alizadeh; Seied Hosein Sanaei-Nejad; M. Bannayan Awal; A. Faridhosseini
Abstract
Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation ...
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Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation of daily areal rainfall can be obtained by spatial interpolation of rain gauges data. However, direct application of these techniques may produce inaccurate results. In the last years, applying the remote sensing for estimation of rainfall have got so popular all around the word and many techniques have been developed based on the satellite data with high temporal and spatial resolution. In this paper, CMORPH model was validated for precipitation estimation over the northeast of Iran. Results showed that this model could not estimate precipitation accurately in daily scale, but in monthly and seasonal scale the estimation was more accurate. Farooj and Namanloo station had the highest correlation equal to 0.31 in daily scale. The highest correlation in monthly scale was equal to 0.62 for Barzoo, Namanloo and Se yekAb station. In Seasonal scale Gholaman station had the highest correlation which was equal to 0.63. Also, the probability of detection has been estimated accurately by CMORPH. But this technique did not have an accurate estimation for wet and dry days, mean annual precipitation and the number of non-rainy days.
H. Ghafourian; Seied Hosein Sanaei-Nejad; K. Davary
Abstract
Most of drought evaluation systems are based on precipitation data. However short period of measured data and inappropriate distribution of weather stations and undesirable quality of precipitation measurement networks reduce ability of showing the spatial pattern of drought. Therefore, it is necessary ...
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Most of drought evaluation systems are based on precipitation data. However short period of measured data and inappropriate distribution of weather stations and undesirable quality of precipitation measurement networks reduce ability of showing the spatial pattern of drought. Therefore, it is necessary to recognize others reliable climatic data resources. Then to overpass the difficulty, after verification, the data is used to complete or substitute the existing data. Accordingly, in this research to monitor drought in Khorasan Razavi province using data from 10 synoptic stations and 107 rain gauges around the province, the monthly data of TRMM satellite was validated. To do this, standardized precipitation index (SPI) of 1, 3, 6 and 12 months are calculated for a 13 years period (1998-2010) and compared with those of satellite for the same period. The evaluation was measured using CSI (%) (Critical Success Index) and R2 (Coefficient of Determination). The results showed that there was a very good consistency between earth borne and satellite borne SPIs for all time scales except for 1 month time scale. Consistency value for all time scales over most regions of the province is more than 50%. Based on the results, for achieving the accuracy more than 60%, time scales of 1, 3, 6 and 12 months should be used as below: 1 month only for the northern regions, 3 month for all regions except the eastern part, 6 month for all regions except the northern part and 12 month for all regions except the northern region and central part of the province.
M. Ghaemi; A. Astaraei; M. Nassiri Mahalati; S.H. Sanaeinejad; H. Emami
Abstract
Successful implementation of soil and crop management program requires quantitative knowledge of site characteristics and interactions that affect crop yield. Soil properties are one of the most important site variables affecting within- field yield variability. The objective of this research was to ...
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Successful implementation of soil and crop management program requires quantitative knowledge of site characteristics and interactions that affect crop yield. Soil properties are one of the most important site variables affecting within- field yield variability. The objective of this research was to identify intercorrelations among soil properties (chemical, physical and biological) using principal component analysis (PCA) and their relationships with maize yield variability in field. Site variables (18) and maize yield were measured in selected parts of Astan Quds agricultural fields in Mashhad city. The principal component analysis was used to reduce the site variables numbers and remove multicollinearity among variables. The first four PCs with eigenvalues>1 accounted for > 67% of variability in measured soil properties. Soil properties were grouped in four PCs as: (PC1) Soil highly descriptive fertility potential, (PC2) Soil moderately descriptive fertility potential, (PC3) Soil permeability potential, (PC4) Soil aggregation potential. The results showed that the factor of soil highly descriptive fertility potential explained 43% of variance and accounted for 77% of the yield variability in the field. Principal component analysis allows explaining a major part of crop yield variability by removing the multicollinearity.
nafise seyednezhad; Seied Hosein Sanaei-Nejad; B. Ghahraman; H. Rezaee Pazhand
Abstract
Regional analysis, estimating missing values, areal rainfall, estimating PMP and rainfall- runoff models in daily scale are essential in water resources and climatological researches. Modified inverse distance interpolation method based on Fuzzy Mathematics (MIDW-F) is a new, efficient method and independent ...
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Regional analysis, estimating missing values, areal rainfall, estimating PMP and rainfall- runoff models in daily scale are essential in water resources and climatological researches. Modified inverse distance interpolation method based on Fuzzy Mathematics (MIDW-F) is a new, efficient method and independent of complex preconceptions hypothesis. The purpose of this paper is applying the new interpolation equation for above essential needs by calibration the daily rainfall of Mashhad Plain catchment. Screening and normalizing distances and elevations were done, then effects of fuzzy operations (Max, Min, Sum, Multiplication and SQRT) are Checked out and optimizing the parameters of MIDW-F by Genetic algorithms. The 215 daily precipitations (49 rain gauge stations) were analyzed and were calibrated. The results showed that the best operators are Minimum (Share58%), multiplying (Share35%) and total contribution rate of others are 6%. The MIDW-F was compared with the three others conventional methods (the Arithmetic mean, Thiessen polygon and IDW) and results showed that the errors of MIDW-F method were reduced noticeably. Largest Regional Mean Square errors (RMSE) is for Arithmetic mean (Max. 90.45, Min. 5.76, variance 686.8 and 70% Cv) and smallest RMSE belong to MIDW-F (Max. 56.67, Min. 4.6, variance 340.92 and 57% Cv). Zoning of daily rainfall at 22/3/2009 and 23/2/2010 and with MIDW-F and IDW methods were conducted and evaluated. The results showed that the zoning by MIDW-F proposed more details. So this method\ is proposed for the interpolation of daily precipitation in a homogeneous region.
S. Noori; S.H. Sanaei Nejad
Abstract
Because most of the methods that have been proposed for estimating statues drought generate point estimate, so researchers were always looking for ways to achieve regional estimates for better manage this gradually creeping phenomenon. Recently, remote sensing and techniques proposed base on it could ...
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Because most of the methods that have been proposed for estimating statues drought generate point estimate, so researchers were always looking for ways to achieve regional estimates for better manage this gradually creeping phenomenon. Recently, remote sensing and techniques proposed base on it could estimate drought in regional scale well. In this paper, it’s tried to estimate drought and evaluation performance of the Temperature Vegetation Dryness Index (TVDI) and the Modified Temperature Vegetation Dryness Index (MTVDI) using the vegetation and temperature MODIS products in Northern Khorasan during two years 2004 and 2008 (as normal and dry years respectively). The results showed that MTVDI index has performed better than TVDI index. The result of linear correlation analyzed between indices and the cumulative precipitation of the currently 16 days, early 16 days and early 1 month, 2 month, 3 month, 5 month and 7 month showed the indices, specially MTVDI, had a close relationship with early 1 month precipitation than the others. This is due to the delayed response of vegetation to precipitation.
mehri shahedi; S.H. Sanaiinejad; B. Ghahreman
Abstract
The purpose of this study is regional frequency analysis of Annual Maximum 1-day Rainfall (AM1R) and Annual Maximum 2-day Rainfall (AM2R) using L-moments theory in Khorasan Razavi Province. For this purpose, the basic statistical tests include: homogeneity, independency and outlier data for AM1R and ...
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The purpose of this study is regional frequency analysis of Annual Maximum 1-day Rainfall (AM1R) and Annual Maximum 2-day Rainfall (AM2R) using L-moments theory in Khorasan Razavi Province. For this purpose, the basic statistical tests include: homogeneity, independency and outlier data for AM1R and AM2R were surveyed in 123 rainfall stations. The province was divided into four regions based on cluster analysis, topography and spatial pattern of precipitation. Hydrology homogeneity was also controlled by running heterogeneity test for each region. generalized extreme value (GEV), generalized logistic (GLO), Pearson type III (PE3) and Log Normal type III (LN3) probability distributions were estimated for every region. To select the appropriate distribution of AM1R and AM2R data, the fitness was judged using an L-moment ratio diagram and the Kolmogorov–Smirnov test and GEVdistribution select . The regionally quantile estimateions for GEV distribution were also calculated for AM1R and AM2R data. In all of the Homogeneous regions, the estimated values of AM1R and AM2R from the obtained relations are close enough to the real data of return periods less than 200 years (The largest MAE was 0.0386). The average absolute error between the estimated and the observation values in each region is favorable, showing a high accuracy of the estimation.
S. Koozehgaran; M. Mousavi Baygi; S.H. Sanaei-Nejad; M.A. Behdani
Abstract
Abstract
Knowledge of the coordination of the agricultural activities in every region with the weather and climate condition of that area is necessary for any kind of agriculture activity. Therefore, understanding the climate and analyzing the ecophysiological characteristics of plants are the most ...
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Abstract
Knowledge of the coordination of the agricultural activities in every region with the weather and climate condition of that area is necessary for any kind of agriculture activity. Therefore, understanding the climate and analyzing the ecophysiological characteristics of plants are the most important factors in production. Saffron is one of the most valuable plants, which is planted in special climate conditions and has a unique growth process. At the present, Iran produces of 90% of total saffron production. Despite its old culture compared to other crops produced in the country, production of saffron in Iran that has relied primarily on indigenous knowledge. Analysis of the effect of the weather parameters on the performance of saffron and determining the suitable areas for planting saffron according to these parameters are important for agriculture and the economy. The statistics and data of 20 years taken from all the weather station in the region and the ten years performance of saffron were used in this study. Regression analysis and create of equation using minimum, average, maximum temperature and the relation between these parameters by saffron yield were accomplished by the use of JMP4 software. The digital climate maps of zoning scheme using software ArcGIS9.2 were drawn. The results showed that minimum temperature was the most effective factor on the performance during the month of Mehr, Aban, Azar and Dey compared with the other months and considering average temperature, the most affected months are Mehr, Aban, Azar and Dey. Maximum temperature was most effective on the performance during the month of Aban, Azar, Dey and Esfand compared with the other months Also after analyzing the equation and the climate zonation maps and the final map it become obvious that the most of the areas of the province were able to be ranked as suitable. The north and north-eastern areas were the best areas regarding the parameters discussed in order to grow Saffron. The center of province was considered average region to grow Saffron and the southern and south-western areas were determined the least suitable for growing saffron.
Keywords: Minimum, Average, Maximum temperature, Saffron yield, GIS
S.H. Sanaei-Nejad; S. Noori; M. Hashemi nia
Abstract
Abstract
Evapotranspiration (ET) determination is a key factor for irrigation scheduling, water balance, irrigation system design and management and crop yields simulation. Therefore many scientists have tried to estimate evapotranspiration in different spatial and temporal scales. Remote sensing is ...
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Abstract
Evapotranspiration (ET) determination is a key factor for irrigation scheduling, water balance, irrigation system design and management and crop yields simulation. Therefore many scientists have tried to estimate evapotranspiration in different spatial and temporal scales. Remote sensing is a one of new technique in estimation of ET in regional scales. So, in this study it’s tried to estimate spatial distribution daily actual ET for Mashhad’s sub basin using MODIS image data related to 4th June, 1st July and 26th July 2009 and surface energy balance algorithm for land (SEBAL) taking into account topographic effects. The results showed that MODIS image data and SEBAL method were able to estimate actual daily ET in Mashhad sub-basin properly. Based on the results, areas which had dense vegetation and low temperatures had high ET rates, while in areas with sparse vegetation and high temperatures the ET rate was low.
Keywords: Evapotranspiration, MODIS, Remote sensing, SEBAL
H. Ansari; K. Davary; S.H. Sanaei-Nejad
Abstract
Abstract
Drought is a natural creeping event that starts due to lower moisture compared to normal condition. This phenomenon impacts all aspects of human activities. However there is neither any detailed definition nor a general and proper index for drought monitoring. In this study, fuzzy logic has ...
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Abstract
Drought is a natural creeping event that starts due to lower moisture compared to normal condition. This phenomenon impacts all aspects of human activities. However there is neither any detailed definition nor a general and proper index for drought monitoring. In this study, fuzzy logic has been applied to deal with inherent uncertainties of the real world data. We presented a fuzzy model to evaluate and analysis the drought. Using the Fuzzy logic for drought monitoring of Mashhad synoptic station showed its higher capability and efficiency compared to Boolean logic. We combined two membership functions related to SPI (Standardized precipitation index) and SEI (a presumable standardized index for evapotranspiration), to provide a new index (SEPI: Standardized Evapotrans-Precipitation Index). The results showed that fuzzy model which employed 81 rules with minimum of 2 and maximum of 4 rules is the most accurate approach. The new index (SEPI) not only covers all advantages of SPI, but also can be calculated using different time scales of available data. Moreover, it considers temperature effects on drought occurrence and severity too. Monitored drought using SPI and SEPI indices demonstrated high correlation (more than 90%) between these two indices across all time scales. Drought monitored by SEPI for Mashhad synoptic station, at 1 to 3 monthly scales showed high drought frequency but low duration. Increasing time scales resulted in low frequency but higher duration. Employing SEPI also showed that high intensity and frequency of drought occurred in years 2000 and 2001 across all time scales. The longest drought duration, by 3 years across all time scales, occurred between 1995 to 1998.
Keywords: Fuzzy logic, Drought index, Standardized Precipitation index (SPI), Standardized Evapotransprecipitation Index (SEPI).
M. Mousavi baygi; S.H. Sanaei-Nejad; A. Nezami
Abstract
Abstract
Weather and climate are the most important parameters which affect on growth and development of plants and are the non-control and effective factors in agriculture. Threshold tolerance of plants is limited to these climatic parameters and fluctuation of these parameters has significant effect ...
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Abstract
Weather and climate are the most important parameters which affect on growth and development of plants and are the non-control and effective factors in agriculture. Threshold tolerance of plants is limited to these climatic parameters and fluctuation of these parameters has significant effect on agricultural products directly and indirectly. During the year, different climatic hazards damaged Khorasan Razavi province especially on agriculture field. Heat stress is one of the hazards that effects on plants. In this paper, heat stress is studied and mapped in Khorasan Razavi province. For this reason and according to plant thermal requirements, 30, 35 and 40 ºC thresholds determined for maximum and 20 ºC for minimum temperatures. Then by using meteorological data in a 13 years period, number of days with temperature more than these thresholds determined. After statistical test in Jmp software for determining sample size of data, suitable equations extracted for mapping by using Digital Elevation Model. Results show that heat stress appears more in south and west of Khorasan Razavi province and in this region Sabzevar and Sarakhs are damaged more than other places.
Key words: Heat stress, Khorasan Razavi, Mapping
A. Mianabadi; M. Mousavi baygi; S.H. Sanaei-Nejad; A. Nezami
Abstract
Abstract
Plants growth and development and physiological activities occur in a certain air temperature range. Spit of this fact that zero temperature named as a freezing temperature, in agriculture meteorology, freezing happen in lower temperature which is different for plants that lead to their tissues ...
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Abstract
Plants growth and development and physiological activities occur in a certain air temperature range. Spit of this fact that zero temperature named as a freezing temperature, in agriculture meteorology, freezing happen in lower temperature which is different for plants that lead to their tissues damage. Early autumn freezing cause damage to harvesting of cotton and sugar beet and affect on time of wheat and barley planting in Khrasan Razavi. There are also many damages on agricultural products due to late spring freezing each year. This problem leads to loss the flowers during the trees flowering. In order to investing this phenomenon in Khorasan Razavi, the autumn, spring and winter freezing were considered based on suitable temperature threshold and the occurrence probability obtained and mapped. Results show that early freezing in autumn begins from the north and then spread toward the southern regions. The late freezing in spring ends in the south sooner than other areas. Results also show that the winter freezing more occurs in the north.
Keywords: early autumn freezing, late spring freezing, winter freezing, Khorasan Razavi, mapping.
M.S. Ghazanfari Moghadam; M. Mousavi baygi; S.H. Sanaei-Nejad
Abstract
Abstract
Topography is the most important parameter which produces minimum temperatures in complex terrain. Radiative inversion occurs in the mountains and produces radiative freezing. When the land surface is cooled, a boundary layer forms. Since cold air is heavier than warm air, therefore, it flows ...
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Abstract
Topography is the most important parameter which produces minimum temperatures in complex terrain. Radiative inversion occurs in the mountains and produces radiative freezing. When the land surface is cooled, a boundary layer forms. Since cold air is heavier than warm air, therefore, it flows toward the down slope, which is named Katabolic flow. When Katabolic flows are formed, cold air accumulates in the valleys and thereafter in places which do not have a good drainage of air. Based on thermodynamic equations a model was developed to consider the accumulation of cold air in each point of a complex terrain. Minimum temperature prediction model (MTPM) was developed and used to predict the minimum temperature in complex terrains. This thermodynamic model uses digital elevation model to produce minimum temperature maps. Running MTPM for North Mountains of Tehran showed a good correlation between modeled and actual minimum temperatures.
Key words: Katabolic flow, Minimum temperature, Freezing ponds, Complex terrain, Thermodynamic models
