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.
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.
nafise seyednezhad; mahboobeh farzandi; H. Rezaee-Pazhand
Abstract
Introduction: The analysis of extreme events such as first frost dates are detrimental phenomena which influence in various branches of engineering, such as agriculture. The analysis and probability predicting of these events can decrease damage of agriculture, horticulture and the others. Furthermore, ...
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Introduction: The analysis of extreme events such as first frost dates are detrimental phenomena which influence in various branches of engineering, such as agriculture. The analysis and probability predicting of these events can decrease damage of agriculture, horticulture and the others. Furthermore, this phenomenon can have a relation with other thermal indexes. The analyzing of first frost dates of all synoptic stations of Khorasan Razavi province is subject of this article. The frequency analysis applied to eight distributions. Then the relationship between first frost dates and thermal index were studied. Best relation was between minimum temperature and return periods of first frost dates.
Materials and Methods: The analyzing of first frost dates (origin is March 21) of all synoptic stations of Khorasan Razavi province is subject of this article. At first data of each station were screening. The basic properties such as homogeneity, randomness, stationary, independence and outliers must be tested. The eight distribution Normal, Gumbel type 1, Gamma 2-parameter, Log normal 2 or 3 parameters, Generalized Pareto, Generalized extreme values and Pearson Type 3 fitted to data and the parameters estimated with 7 methods by the name of the several types of Moments (5 methods), maximum likelihood and the maximum Entropy. The Kolmogorov – Smirnov goodness of fit test can be used to compare the best distribution. The return periods of first frost dates are major application in frequency analysis. There is maybe a relationship between periods and thermal index such as min, max and mean temperature. This relationship can be adapted by regression methods.
Results and Discussion: The statistical analysis for prediction probabilities and return periods of the first frost dates for all synoptic stations in Khorasan Razavi province and the relationship between annual temperature indicators and this phenomenon is the aim of this article. The origin date of this phenomenon is March 21. First, data were screened. Then basic hypothesis test were applied which including the Runtest (randomness), the Mann-Whitney test (homogeneity and jump), the Wald-Wolfowitz test (independence and stationary), the Grubbs and Beck test (detection Outliers) and the three sigma methods (Outlier). The results were: 1-The Sabzevar, Mashhad and Gonabad had lower Outliers that will not cause any problem in data analysis by their skewness. The first frost data of all station were without upper outlier. 2- The independence of all stations was accepted at the 10% level. 3-All stations were Randomness, Independence and homogeneous and lack of jump. Eight probability distributions (Normal, Gumbel type 1, 2-parameter gamma, 2 and 3 parameters log-normal, the generalized Pareto, the generalized extreme values and the Pearson type 3) were applied. The skewness coefficients for all stations were more than 0.1 so Normal distribution was rejected. Also the7 methods of estimation (five different methods of moments, maximum likelihood and maximum entropy methods) were used. The ks fit test was applied. The ks for some stations were closed together at several estimations methods. The results are as follows: GPA (4 times), PT3 (4 times), LN2 (4 times), GA2 (3 times). Generalized Pareto distribution had the best fitted to data (60% of cases compared to the other functions). The results significantly indicated that the occurrence of first frost on the first day of process is in place. The first frost in the period of 2 years at all stations, not occur earlier than Aban(October 28). The 100-year return period event does not occur earlier than first of Mehr(September 22). There is no significant relationship between first frost in the period of 2 years with other factors such as altitude, latitude, longitude, temperature and precipitation as well.
Conclusion: Date of the first fall frost is one of the unfavorite climate influences that cause reduction in crop products. The purpose of this paper is to analysis the frequency occurrence of first frost day in several Khorasan’s synoptic stations as study area. Screening and initial basic tests such as randomness homogenity, independence, etc. were done. Eight distribution function, namely Normal, Gumbel type 1, Gamma 2 parameters, Log normal 2 and 3 parameters, Generalized Pareto and Pearson type III were fitted to data with five probability distributions methods (Ordinary Moments, Maximum Likelihood method, Modified Moments, Probability Weighted Moment and Maximum Entropy). Goodness of fit test was Kolmogorove-Smirnov test. PWM and ModM methods revealed relatively superior results compared to the rest of methods. Generalized Pareto distribution had the best fitted to data (60% of cases compared to the other functions). The results significantly indicated that the occurrence of first frost on the first day of process is in place. The first frost in the period of 2 years at all stations, not occur earlier than Aban. The 100-year return period event does not occur earlier than first of Mehr. There is no significant relationship between first frost in the period of 2 years with other factors such as altitude, latitude, longitude, temperature and precipitation as well.
najmeh khalili; Kamran Davary; Amin Alizadeh; Hossein Ansari; Hojat Rezaee Pazhand; Mohammad Kafi; Bijan Ghahraman
Abstract
Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. ...
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Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. For this purpose, weather generators can be used to enlarge the data length. Among the common weather generators, two models are more common: LARS-WG and ClimGen. Different studies have shown that these two models have different results in different regions and climates. Therefore, the output results of these two methods should be validated based on the climate and weather conditions of the study region.
Materials and Methods:The Sisab station is 35 KM away from Bojnord city in Northern Khorasan. This station was established in 1366 and afterwards, the meteorological data including precipitation data are regularly collected. Geographical coordination of this station is 37º 25׳ N and 57º 38׳ E, and the elevation is 1359 meter. The climate in this region is dry and cold under Emberge and semi-dry under Demarton Methods. In this research, LARG-WG model, version 5.5, and ClimGen model, version 4.4, were used to generate 500 data sample for precipitation and temperature time series. The performance of these two models, were evaluated using RMSE, MAE, and CD over the 30 years collected data and their corresponding generated data. Also, to compare the statistical similarity of the generated data with the collected data, t-student, F, and X2 tests were used. With these tests, the similarity of 16 statistical characteristics of the generated data and the collected data has been investigated in the level of confidence 95%.
Results and Discussion:This study showed that LARS-WG model can better generate precipitation data in terms of statistical error criteria. RMSE and MAE for the generated data by LAR-WG were less than ClimGen model while the CD value of LARS-WG was close to one. For the minimum and maximum temperature data there was no significant difference between the RMSE and CD values for the generated and collected data by these two methods, but the ClimGen was slightly more successful in generating temperature data. The X2 test results over seasonal distributions for length of dry and wet series showed that LARS-WG was more accurate than ClimGen.The comparison of LARS-WG and ClimGen models showed that LARS-WG model has a better performance in generating daily rainfall data in terms of frequency distribution. For monthly precipitation, generated data with ClimGen model were acceptable in level of confidence 95%, but even for monthly precipitation data, the LARS-WG model was more accurate. In terms of variance of daily and monthly precipitation data, both models had a poor performance.In terms of generating minimum and maximum daily and monthly temperature data, ClimGen model showed a better performance compared to the LARS-WG model. Again, both models showed a poor performance in terms of variance of daily and monthly temperature data, though LAR-WG was slightly better than ClimGen. For lengths of hot and frost spells, ClimGen was a better choice compared to LARS-WG.
Conclusion:In this research, the performances of LARS-WG and ClimGen models were compared in terms of their capability of generating daily and monthly precipitation and temperature data for Sisab Station in Northern Khorasan. The results showed that for this station, LARS-WG model can better simulate precipitation data while ClimGen is a better choice for simulating temperature data. This research also showed that both models were not very successful in the sense of variances of the generated data compared to the other statistical characteristics such as the mean values, though the variance for monthly data was more acceptable than daily data.
vajiheh mohammadi sabet; Mohammad Mousavi Baygi; Hojat Rezaee Pazhand
Abstract
Introduction: The Southern Oscillation is a large scale phenomenon that changes the Normal oscillating air pressure on both sides of the Pacific Ocean. It disrupted the normal conditions and the patterns of temperature and precipitation change in the nearby region and other regions of the world. This ...
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Introduction: The Southern Oscillation is a large scale phenomenon that changes the Normal oscillating air pressure on both sides of the Pacific Ocean. It disrupted the normal conditions and the patterns of temperature and precipitation change in the nearby region and other regions of the world. This phenomenon is caused by changing the water slope in the Pacific Ocean between Peru (northwestern South America) and Northern Australia (about Indonesia and Malaysia). ENSO phenomenon is formed of Elnino (warm state) and La Niña (cold state). There is high pressure system in the East and low pressure system in the West Pacific Ocean in normal conditions (Walker cycle). The trade winds blow from East to West with high intensity. ENSO start when the trade winds and temperature and pressure balance on both sides of the PacificOcean change. High pressure will form in the west and low pressure will form in the East. As a result, west will have high and east will have low rainfall. Temperature will change at these two locations. Enso longs about 6 to 18 months. This research investigated the impact of ENSO on monthly precipitation and temperature of Mashhad.The results showed that temperature and rainfall have a good relation with ENSO.This relation occurs in 0-5 month lag.
Materials and Methods: The severity of ENSO phenomenon is known by an index which is called ENSO index. The index is the anomaly of sea surface temperature in the Pacific. The long-term temperature and precipitation data of Mashhad selected and analyzed. The Rainfall has no trend but temperature has trend. The trend of temperature modeled by MARS regression and trend was removed.The rainfall data changed to standard and temperature changed to anomaly for comparison with ENSO index. The 2016 annual and monthly temperature of Mashhad is not available. The 2016 Annual temperature was forecasted by ARMA (1,1) model. Then this forecast disaggregated to monthly temperature. For each period of occurring high ENSO, these three indexes (ENSO index, standardized rainfall and anomalies temperature) were compared. The co-variation of these indexes was compared. Also, the correlation and cross correlation for each period of occurring ENSO, with rain and temperature of Mashhad was calculated.
Results and Discussion: Mashhad monthly temperature and precipitation were compared with the extreme values of ENSO index in periods of the occurrence this phenomenon (1950-2016). In addition, the correlation and cross-correlation between ENSO-Rainfall index and ENSO-temperature index for this period were calculated.Forecasted temperature for 2016 by ARMA (1,1) was 13.2 Degrees Celsius, which has 0.2 degree increase in comparison to last year. Results showed thatthere is no an obvious relation between ENSO-Temperature and ENSO-Rainfall in interval (-1, +1). But there are good relation between ENSO-Temperature and ENSO-Rainfall beyond of (-1,+1). The results of Elnino showed that the monthly precipitation and temperature increase with a lag of 2 to 5 months and 0 to 4 months, respectively. The results of Lanina showed that the monthly precipitation and temperature decrease with a lag of 3 to 5 months and 1 to 4 months, respectively. Also when ENSO index is located in the interval (-1, +1), there is no certain harmony with temperature and precipitation of Mashhad.
Conclusions: The aim of this study was evaluating the effect of the ENSO phenomenon on monthly temperature and precipitation of Mashhad.Mashhad monthly temperature and precipitation, respectively, for 132 and 124 years were available.Precipitation was static and has no trend, but temperature was not static and has two changed (jumped) point in 1976 and 2000. MARS regression was used for patterning the process. Removing the trend was done by MARS model and the data was obtained without trend. Monthly ENSO index since 1950 from reliable websites worldwide (NOAA) was obtained. Mashhad monthly temperature data was animalized and precipitation data was standardized. This was performed for comparing Temperature and Rain with ENSO index. The effect of the ENSO phenomenon on Mashhad precipitation and temperature in both graphical and cross-correlation was performed.As a final result, there is a good relation with latency zero up to 5 months for temperature and precipitation of Mashhad beyond the interval (-1, + 1). It cannot be claimed that after the phase of La Nina, El Nino must be entered and vice versa. This note is important for forecasting the temperature and precipitation of 2016coming months. If ENSO index in the coming months, especially in autumn and winter, decrease and inter in La Nina phase, the winter will be cold with low rainfall.
N. Seyyednezhad Golkhatm; H. Rezaee Pazhand
Abstract
Introduction: The analysis of extreme events such as last frost dates are detrimental phenomena which influence in various branches of engineering, such as agriculture. The analysis and probability predicting of these events can be decrease damage of agriculture, horticulture and the others. Furthermore, ...
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Introduction: The analysis of extreme events such as last frost dates are detrimental phenomena which influence in various branches of engineering, such as agriculture. The analysis and probability predicting of these events can be decrease damage of agriculture, horticulture and the others. Furthermore, this phenomenon can have a relation with other thermal indexes. The analyzing of last frost dates of all synoptic stations of Khorasan Razavi province is subject of this article. The frequency analysis applied to eight distributions. Then the relationship between last frost dates and termal index were studied. Best relation was between minimum temperature and return periods of last frost dates.
Materials and Methods: The analyzing of last frost dates (origin is 23th september) of all synoptic stations of Khorasan Razavi province is subject of this article. First data of each station were screening. The basic properties such as homogeneity, randomness, stationary, independence and outliers must be test. The eight distribution distribution Normal, Gumbel type 1, Gamma 2-parameter, Log normal 2 or 3 parameters, Generalized Pareto, Generalized extreme values and Pearson Type 3 fitted to data and the parameters estimated with 7 methods by the name of the several types of Moments (5 methods), maximum likelihood and the maximum Entropy. The Kolmogorov – Smirnov goodness of fit test can compared the best distribution. The return periods of last frost dates are major application in frequency analysis. There is maybe a relationship between periods and termal index such as min., max. and mean temperature. This relationship can be adapted by regression methods.
Results and Discussion: The statistical analysis for prediction probabilities and return periods of the last frost dates for all synoptic stations in Khorasan Razavi province and the relationship between annual temperature indicators and this phenomenon is the aim of this article. The origin dates of this phenomenon are 23th September. First, data were screened. Then basic hypothesis test were applied which including the Runtest (randomness), the Mann-Whitney test (homogeneity and jump), the Wald-Wolfowitz test (independence and stationary), the Grubbs and Beck test (detection Outliers) and the three sigma methods (Outlier). The results were: 1-The Golmakan, Kashmar and Torbatejam had lower Outliers that will not cause any problem in data analysis by their skewness. 2- The independence of all stations was accepted at the 10% level. 3-The Gonabad data was not homogeneous and removed. Eight probability distributions (Normal, Gumbel type 1, 2-parameter gamma, 2 and 3 parameters log-normal, the generalized Pareto, the generalized extreme values and the Pearson type 3) were applied. The skewness coefficients for all stations were more than 0.1 so Normal distribution was rejected. Also the7 methods of estimation (five different methods of moments, maximum likelihood and maximum entropy methods) were used. The ks fit test was applied. The ks for some stations were closed together at several estimations methods. The results are as follows: GPA (4 times), PT3 (4 times), LN2 (4 times), GA2 (3 times). The obtained results were: 1- The shortest duration of frost date was belonged to the Sarakhs station, but the longest return periods were not same. 3- The interior station ranges were 32 to 50 days for all return periods, with a mean of 41, standard deviation 9.3 and the coefficient of variation 5.9%, which represents the damping of the phenomenon within the station. 4-Pearson type 3, which has been recommended by some researchers, can not be generalized. 5- The major method of estimation was MOM (8 cases). The relationship between the last frost days and other meteorological factors such as, minimum, average and maximum temperature were investigated in this paper. The linear relationship between last frost days and the average annual minimum temperature were the best-fit.
Conclusion: The last frost dates analyzing of all Khorasan Razavi province synoptic stations is subject of this article. The data screening and basic tests were applied and data accepted as random samples. The 8 distributions with 7 methods of estimation were fitted to data. The best fitted distribution at all stations mainly included GPA, PT3, LN2. The major estimation method was MOM. The relationship between last frost periods and minimum temperature was the best linear models. So, we can predict the return period from this temperature as well.
mahsa noori; Saeed Reza Khodshenas; H. Rezaeepajand
Abstract
Introduction: Dam failure and its flooding is one of the destructive phenomena today. Therefore, estimating the peak outflow (QP) with reasonable accuracy and determining the related flood zone can reduce risks. Qp of dam failure depends on important factors such as: depth above breach (Hw), volume ...
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Introduction: Dam failure and its flooding is one of the destructive phenomena today. Therefore, estimating the peak outflow (QP) with reasonable accuracy and determining the related flood zone can reduce risks. Qp of dam failure depends on important factors such as: depth above breach (Hw), volume of water above breach bottom at failure (Vw), reservoir surface area (A), storage (S) and dam height (Hd). Various researchers have proposed equations to estimate QP. They used the regression method to obtain an appropriate equation. Regression is a mathematical technique that requires initial test and diagnosis. These researchers present a new regression model for a better estimation of Qp.
Materials and Methods: The data used in this study are related to 140 broken dams in the world for 34 of which sufficient data are available for analysis. Dam failure phenomenon is a rapidly varied unsteady flow that is explained by shallow waters equations. The equations in the one-dimensional form are known as Saint-Venant equations and are based on hydrostatic pressure distribution and uniform flow under rectangular steep assumption. Although hydraulic methods to predict the dam failure flood have been developed by different software, due to the complex nature of the problem and the impossibility of considering all parameters in hydraulic analysis, statistical methods have been developed in this field. Statistical methods determine the equations that can approximate the required factors from the observed parameters. Multiple regression is a useful technique to model effective parameters in Qp, which can examine the statistical aspects of the model. This work is done by different tests, such as the model coefficients necessity test, analysis of variance table and it creates confidence intervals. Data analysis in this paper is done by SPSS 16 software. This software can provide fit model, various characteristics and related tests in the Tables.
Results and Discussion:This paper proposes a new relationship with better estimation of discharge peak (Qp) based on Hw and Vw factors. Results showed how to choose the appropriate model (fitting the model) and the initial required tests, according to the diagnostic model. And it compares the estimated error (relative efficiency) of the researchers’ models with the proposed models. The number of models can be classified to three convenient linear, multiplicative and transformed bases on Vw, Hw and Qp (nonlinear terms Qp). The best models for each of the three models were selected. Their corrected determination coefficients (Adj R2) are close together and are between 0.86 until 0.864. The relative efficiency criteria based on the root mean square error (RMSE) was used to determine the best model. This standard was also used for other researchers’ models. RMSE of the three models presented in this article is lower than that of other models (from 745 to 759). Diagnostics analysis of the three models is not possible due to the large volume, so some statistical analysis for the model 2 are presented in detail. The results are given in the following Tables. Test level has been assumed to be 5%. From the point view of hydraulics, it can be said that the final equation for Qp should be proportional to Hw 1.5. So although the model (2) has the lowest RMSE, but the model (3) of the hydraulics viewpoint seems more logical and its RMSE is not very different from the model (2), so this model can be selected as the best model. Figure 1 show diagnostics diagrams of model (3). The right Figure shows the homogeneity of residuals (follow the normal law) as a histogram. This homogeneity is confirmed by the crouch graph (center Figure). The left graph shows the stabilization of residual variance. According to the preliminary and diagnostics tests results, the model (3) has been selected. Its determination coefficient (0.864) also shows good strength.
Table 1- Top models presented in this research
Model1
Model2
Model3
,
Note:
Table 2- Statistical characteristics of the proposed models
model Adjusted R Square Durbin
Watson F VIF Std.
Residual Cook's Distance Centered Leverage
1 0.862 1.716 104.383 1.283 [-1.975 , 2.908] [ 0,0.569] [0,0.363]
2 0.860 1.744 102.545 1.283 [-1.824 , 2.834] [0,0.608] [0,0.363]
3 0.864 1.687 211.048 1 [-2.202 , 2.699] [0,0.527] [0,0.335]
Figure 1- Model 3 diagnostics pattern diagrams: histogram (right), crouch diagram (middle) the estimated residuals (left)
Conclusion: In this study, data from 140 broken dams were used to provide an appropriate model for estimating the peak outflow of dam failure. Standard statistical principles including preliminary tests, diagnostic and the efficiency of the models are the innovations of this paper. Analysis showed that the three models are competitive, and that the best of them was selected. The determined coefficient of these models was from 0.86 to 0.864 ranges. Relative efficiency was calculated by the RMSE index. The results showed that these models are more accurate than the models presented by other researchers. The model (3) was presented in this research, the best result was estimated for Qp and its error was less than the other models.
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.
neda yousefi; Saeed Reza Khodshenas
Abstract
Predicting behavior and the geometry of the channels and alluvial rivers in which the erosion and sediment transport are in equilibrium is one of the most important topics in river engineering. Various researchers have proposed empirical equations to estimate stable river width (W). Empirical equations ...
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Predicting behavior and the geometry of the channels and alluvial rivers in which the erosion and sediment transport are in equilibrium is one of the most important topics in river engineering. Various researchers have proposed empirical equations to estimate stable river width (W). Empirical equations were tested with a comprehensive available data set consisting of 1644 points collected from 29 stable rivers. Moreover, these equations are compared with new model in this article for estimating W. The data set covers a wide range of flow conditions, river geometry, and bed sediments. These data set is classified in two groups (W
J. Tabatabee-Yazdai; H. Rezaee- Pazhand; H. Khatami-Mashhadi
Abstract
Abstract
This papare is resaults of analysing the data which reported from a harvesting project. The site for collecting data is buildings' roofs of Azad Islamic University of Mashhad. A rain gauge and a small reservoir (capacity is 4 cubic meters) were set at this site. Then the amount of precipitation ...
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Abstract
This papare is resaults of analysing the data which reported from a harvesting project. The site for collecting data is buildings' roofs of Azad Islamic University of Mashhad. A rain gauge and a small reservoir (capacity is 4 cubic meters) were set at this site. Then the amount of precipitation and its runoff were measured during two years (from 1386, Dey, 23 to 1388, Tir, 3).The sample size is 35 observations. The relationship between runoff and rainfall was estimated by regression methods. The best model was chosen by analyzing residuals and testing the Models. Then the runoff were estimated through this model. The 55-year annual rainfalls of Mashhad synoptic station (1951-2005) were selected and frequency analysis was done on them. Then the roofs' runoff was estimated by this frequency analysis for both wet and dry years. The total area of roofs and their mean annual runoff in ordered are 18680 square meters and 8428 cubic meters.
Key words: Water harvesting, Roof, Runoff, Azad Islamic University of Mashhad
