Agricultural Meteorology
M. Amirabadizadeh; Mahdieh Frozanmehr; M. Yaghoobzadeh; Saeideh Hosainabadi
Abstract
IntroductionNowadays, climate change is one of the human challenges in the exploitation and management of water resources. Temperature along with precipitation is one of the most important climatic elements and is one of the main factors in zoning and climatic classification. Due to location of ...
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IntroductionNowadays, climate change is one of the human challenges in the exploitation and management of water resources. Temperature along with precipitation is one of the most important climatic elements and is one of the main factors in zoning and climatic classification. Due to location of Iran within the drought belt and proximity to the high-pressure tropical zone, this country has an arid and semi-arid climate and suffers from drought in majority of years. Therefore, temperature fluctuations and variability are important issues, and make the study of temperature changes a necessity. In the current study, four data mining algorithms in selecting predictors for downscaling of maximum temperature in Birjand synoptic station have been studied, compared and the superior algorithm has been introduced. As the number of large scale features are high, selection of machine learning algorithm will play as an important role in statistical downscaling of climatic variables such as maximum temperature. Materials and MethodsToday, the data set is such that many variables are used to describe the climatic phenomenon in environmental studies. As the number of data is huge, choosing the predictors is one of the most important steps in preprocessing machine learning. In this study, four machine learning methods including stochastic approximation of simultaneous turbulence (SPSA), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Gradient Boosting Method (GBM) in selecting important features in downscaling of maximum temperature in Birjand synoptic station during the statistical period of 1961-2019 were studied and compared. It is a mechanism to find a combination of predictors that with a minimum number of predictors can produce an acceptable evaluation index in estimating the variable under study. For the present study, the weather information of Birjand Synoptic Meteorological Station has been prepared by the Meteorological Organization of Iran. In order to calibrate and validate the machine learning algorithms, 70% and 30% of the available monthly data, respectively, were allocated for this purpose. To conduct this research, coding in R-Studio environment and Caret and Fscaret packages were used. In this study, to evaluate the performance of the algorithms, three indices includes relative Nash-Sutcliffe Efficiency (rNSE), Volume Efficiency (VE) and Kling-Gupta Efficiency (KGE) were used.Results and DiscussionBefore using the algorithms in selecting large-scale predictors, the correlation between these variables and the maximum observational temperature at Birjand station was investigated. Large scale variables mslp, P1_v, P8_v, P8_u, P850 Temp, with a maximum correlation temperature of 0.6 showed that the correlation is acceptable given the complexity of the climate change phenomenon. In addition, these results show that all the algorithms used the important factors including F1, F2, F15, F16, F18, F20 and F26 by more than 50% and the first variable (mean pressure at the ocean surface) was the most important parameter in downscaling of maximum temperature. Also, the highest importance was for P1_v and the lowest value related to P5_u, as 73.2% and 15%, respectively. Violin plots of downscaled maximum temperature in validation step of different algorithms along with the observed maximum temperature in Birjand synoptic station in each of the algorithms showed that the values of the first and third quartiles in the output data of SPSA algorithm compared to other algorithms were closer to the observed data. According to the evaluation criteria, SPSA algorithm has a higher performance than other algorithms in reproducing the maximum monthly temperature values in Birjand synoptic station. Also, based on the volumetric efficiency evaluation criteria and relative Nash-Sutcliffe, GBM algorithm was more successful in selecting predictors than Ridge and LASSO algorithms. It is also observed that SPSA algorithm shows different results than other algorithms. In comparison of mean and variance of downscaled and observed maximum temperature, the results of t-test and F-test showed that SPSA algorithm has higher efficiency than other algorithms in regenerating mean and variance of observed maximum temperature in Birjand synoptic station at the 5% significance level.ConclusionThe data used in this study included large scale atmospheric variables and the maximum observed temperature at Birjand station. The algorithms were used to select important predictors and the performance of these methods in the validation part. According to the results of this study, the highest importance among large-scale variables is related to P1_v and the lowest value is related to P5_u, the values of which were 73.2% and 15%, respectively. The SPSA algorithm also performs better than other algorithms in selecting predictors and consequently the maximum temperature.
S. Kouzegaran; M. Mousavi Baygi; iman babaeian
Abstract
Introduction: Global warming causes alteration of climate extreme indices and increased severity and frequency of incidence of meteorological extreme events. In most climate change studies, only the potential trends or fluctuations in the average long run of climatic phenomena have been examined. However, ...
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Introduction: Global warming causes alteration of climate extreme indices and increased severity and frequency of incidence of meteorological extreme events. In most climate change studies, only the potential trends or fluctuations in the average long run of climatic phenomena have been examined. However, the study of affectability and pattern change of extreme atmospheric events is also important. Changes in climatic elements especially extreme temperature factors have a significant influence on the performance of farming systems. Accordingly, understanding changes in temperature parameters and extreme temperature indices is the prerequisite to sustainable development in agriculture and should be considered in management processes. Investigation of extreme values for planning and policy for the agricultural sector, water resource, environment, industry, and economic management is important. Materials and Methods: To evaluate the extreme temperature indices trend, some indices of temperature, recommended by the CCl/CLIVAR Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI), were considered using Rclimdex software. In this study, daily minimum and maximum temperature data retrieved from MPI-ESM-LR global climate model were used to predict future climate extreme events over the next three periods of 2026-2050, 2051-2075, and 2076-2100 based on IPCC scenarios of RCP4.5 and RCP8.5 of the studied area, covering South Khorasan province and southern part of Razavi Khorasan province, located in the east of Iran. The modified BCSD method was used to downscale extreme temperature data. Results and Discussion: Results showed an increasing trend of warm climate extreme. According to the output of Rclimdex for RCP4.5 scenario in the period of 2026-2050, it was observed that SU25 index, summer days, has a positive trend at all studied stations. This index was found to be significant and increased at all stations in the mid-term future period, and it had an increasing trend in the far future period, which was not significant. The number of Tropical Nights (TR20) index had a positive trend at all. In the mid-term future period, there was a significant increasing trend for some stations, while there were some negative and insignificant trends at some stations in the far future. The maximum monthly daily maximum temperature (TXx) and the maximum monthly daily minimum temperature (TNx) indices also had an increasing trend at all stations, and the mid-term future period had a significant increasing trend, while the trend was decreasing in the far future period. Results for temperature extreme indices under RCP8.5 scenario showed that SU25 index had a positive trend at all stations studied in the near future, mid-term, and far future period. Index of tropical nights (TR20) had an upward trend, which was significant in mid-term and far future periods at most stations. Percentage of days in which maximum temperature is below than 10th percentile (TX10P), indicating a decrease in cold days, had a negative trend for all stations in the near future period. In the mid-term and far future periods, this trend was significant at all stations. The maximum monthly daily maximum temperature (TXx) and the maximum monthly daily minimum temperature (TNx) indices also had an increasing trend at all stations and all three periods, and the trend was significant in the mid-term future. Conclusion: Minimum and maximum daily temperatures of MPI-ESM-LR global climate model were used to predict climatic extreme events during three future periods of 2026-2050, 2051-2075, and 2076-2100 under RCP4.5 and RCP8.5 scenarios at some stations located in South Khorasan province and southern part of Khorasan Razavi province. During the three studied future periods, extreme temperature indices changed significantly. The results showed that in both periods over the future years under the both scenarios, hot extreme indices would increase and cold extreme indices would decrease. It was observed that hot extreme indices, such as summer day index, the number of tropical nights, warm days and nights increased, while cold extreme indices had a decreasing trend in the period of study, which shows a decrease in the severity and frequency of cold events.
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.
shideh shams; Mohammad Mousavi baygi
Abstract
Introduction: Air temperature as an important climatic factor can influence variability and distribution of other climatic parameters. Therefore, tracking the changes in air temperature is a popular procedure in climate change studies.. According to the national academy in the last decade, global temperature ...
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Introduction: Air temperature as an important climatic factor can influence variability and distribution of other climatic parameters. Therefore, tracking the changes in air temperature is a popular procedure in climate change studies.. According to the national academy in the last decade, global temperature has raised 0.4 to 0.8⁰C. Instrumental records show that, with the exception of 1998, the 10 warmest year (during the last 150 years), occurred since 2000, and 2014 was the warmest year. Investigation of maximum and minimum air temperature temporal trend indicates that these two parameters behave differently over time. It has been shown that the minimum air temperature raises noticeably more than the maximum air temperature, which causes a reduction in the difference of maximum and minimum daily air temperature (daily temperature range, DTR). There are several factors that have an influence on reducing DTR such as: Urban development, farms’ irrigation and desertification. It has been shown that DTR reduction occurs mostly during winter and is less frequent during summer, which shows the season’s effect on the temperature trend. Considering the significant effects of the climatological factors on economic and agricultural management issues, the aim of this study is to investigate daily air temperature range for yearly, seasonal and monthly time scales, using available statistical methods.
Materials and Methods: Daily maximum and minimum air temperature records (from 1950 to 2010) were obtained from Mashhad Meteorological Organization. In order to control the quality of daily Tmax and Tmin data, four different types of quality controls were applied. First of all, gross errors were checked. In this step maximum and minimum air temperature data exceeding unlikely air temperature values, were eliminated from data series. Second, data tolerance was checked by searching for periods longer than a certain number of consecutive days with exactly the same temperatures. Third, a revision of internal consistence was done, verifying that daily Tmax always exceeds daily Tmin. Fourth, the temporal coherency was tested by checking if consecutive temperature records differ by more than 8 degrees. The homogeneity of the series was tested by means of the Standard Normal Homogeneity test, the Buishand range and the Pettitt tests, on yearly, seasonal and monthly time scales. Breakpoint can be detected by means of these methods. In addition, Von Neumann ratio test was used to explore the series’ randomness. Having investigated data’s randomness in this study, series’ trend was determined by the Kendal-Tau test. Furthermore, the slope of the series’ trend was calculated using the Sen’s slope method.
Results Discussion: Results indicated a decreasing trend in DTR during last 60 years (1951-2010) in Mashhad climatological station. Moreover, the results revealed that the slope of yearly DTR was decreasing (-0.029 ⁰C per year), which indicates that minimum air temperature values raise more maximum air temperature values. A breakpoint was detected during 1985. During 1951-1985, the average amount of DTR was 14.6⁰C, while this parameter reduced to 12.9⁰C for the period 1985-2010. The Kendall-Tau test was used to obtain the significance of trend during 1951-2010, 1951-1985 and 1985-2010. The results showed that during 1951-2010, DTR significantly reduced at a rate of 0.29oC per decade. However, between 1951 and 1985, DTR trend increased at a rate of 0.61oC per decade, while DTR trend between 1985 and 2010 reduced at a rate of 0.19 ⁰C per decade, which was not significant (P-value=5%). In the seasonal DTR series, the highest trend’s slope was calculated for the summer data (-0.43 ⁰C in a decade), while the lowest one accrued in spring (-0.15⁰C in a decade). From 1951 to 1985, DTR had an increasing trend, due to minimum air temperature’s downward trend. But from the late 1980 to 2010, as it was expected, downward DTR trend was observed, because during this period minimum air temperature increases more than the maximum air temperature, thus the difference between Tmax and Tmin was reduced. Monthly DTR analysis also revealed a decreasing trend from 1951 to 2010, except for March and April, which had a non-significant increasing trend. In monthly DTR series, as it was expected, similar to the yearly and seasonal time series, the breakpoints accrued around 1985 in 8 out of 12 months. During February, March, April and November no significant breakpoint was detected.
Conclusion: DTR decreasing trend indicated that minimum air temperature increase was greater than maximum. This can cause a significant effect on the agricultural sector, hence in an appropriate agricultural management, these points should be considered. For example, changing the sowing time is one of the decisions which a manager can make.
Sh. Shams; Mohammad Mousavi baygi
Abstract
Mashhad is Iran second most populous city, where in terms of tourism, economy and agriculture is very important. Regarding to the importance of the change of climatic factors and its effect on future policy, in this study the max and minimum temperature changes in the scale of yearly, seasonally, monthly ...
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Mashhad is Iran second most populous city, where in terms of tourism, economy and agriculture is very important. Regarding to the importance of the change of climatic factors and its effect on future policy, in this study the max and minimum temperature changes in the scale of yearly, seasonally, monthly and daily, was investigated by means of SNHT, Buishand, Pettitt, Von-neumann and kendall-tau. The results of this study indicate a temperature increase of Mashhad, comparison of the results showed that during the past 60 years (1951-2010), minimum temperature increased 2times more than maximum temperature (0.062 versus 0.031). Test results also showed temperature increasing in all seasons, but just winter maximum temperature increasing trend was not significant in 95% confidence level. Also the highest rate of temperature increasing was belonged to autumn minimum temperature, with the slope of 0.074. Like the difference between annual series, in all season minimum temperature increasing trend is higher than maximum trend, comparing trends in monthly maximum and minimum temperatures show similar results. It also was shown that the minimum temperature trend rose approximately near the year 1985, while maximum temperature break point is near 1995.
