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.
seyed javad rasooli; Mohammad Taghi Naseri Yazdi; reza ghorbani
Abstract
Introduction: Environmental factors whichaffect crop yield areone of the most important factors in increasing yield.Accurate prediction of crop yield for economic management and farming systems is of particular importance.
Materials and Methods: This research was done in order to statistically model ...
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Introduction: Environmental factors whichaffect crop yield areone of the most important factors in increasing yield.Accurate prediction of crop yield for economic management and farming systems is of particular importance.
Materials and Methods: This research was done in order to statistically model and predict the canola growth and yield in Mashhad region based on 5 agricultural meteorology indicesand 12 climatic parameters during 1999 - 2014period. The date of planting determined with regard to the optimum temperature at planting with probability of 75% based on Weibull formula. Beginning and the end of the phenological stages of canola (germination, emergence, Single leaf, rosette, stemming, flower, poddingand ripening) were calculated on the basis of growing degree days (GDD) for each set. Calculation and statistical equations was done usingMinitab Ver. 13.0, 16.Ver SPSS and Excelsoftwares. Correlation analysis,statistical models andmultivariate models were used to determine the relationship between the annual yield of canolaand independent variables, includingclimaticparameters and agricultural meteorologyindices during the growing season between 1999- 2000 and2009-2010for each phenological stage (8stages).The bestmodel was selected with respect to the values of the coefficient of determination (R2) and root mean square error (RMSE).If the predictive power is estimated of the model RMSE values of less than 10% excellent, between 10 and 20% good, 20 to 30% average, and higher than 30% weak. The model tested by estimating the yield of canola for the 2010 to2014 years and the correction factor was calculated and the effect.
Results and Discussion: Canola planting date wascalculated for 23 September in Mashhad region. The phenology of canola was calculated based on growing degree days (GDD) above 5 ° C.Germination calculatedfor25 September, emergence in 3 October, appearance single leaf in 7 October, rosette in 6 March, stemming in 4 April, floweringin 21 April, podding in 15 May and ripening in 4 Jun. The time of the phenological stages of cereals is virtually the same time. Therefore, due to the water scarcity in the studied region -canola can be used in crop rotation. Average, the highest and the lowest yield of canola were1329.5, 2159 and 835.5 kg per hectare,respectively.Canola crop yield showed a rising trend during 1999 – 2014period due toimprovingfarming techniques and mechanization. All models are significant regression coefficients were tested normal, alignment and line.Each model in the absence of proof of any of these hypotheses was removed and the 9remaining models were compared.Model 1 predicted canola crop yield in the single leaf stagewith an average yield of canola evapotranspiration ((Mpet, absolute maximum wind speed (FFabsmax) and the sum of the vapor pressure deficit (VPD).Model 5 predicted canola yield in the floweringstage based on the absolute lowest temperature (Tabsmin), average daily wind speed (FF) and total sunshine hours (SH). Model 3 predicted canola yield in the rosette stage based on the average of daily minimum temperature (Tmin), the number of days with precipitation greater than 1 mm R (day) and total pressure loss water vapor (VPD). Model 7 predicted canola yield during the whole growing season based on the average of daily maximum temperature (Tmax) and total precipitation (R).After R2 models with higher coefficient of 1, 5, 7 and 3, respectively, with coefficients of determination 0.902, 0.902, 0.868 and 0.866 respectively.Then F and RMSE were evaluated forecasting models 1 and 7 excellent, 5 good model and version 3 was average. Model 7due to lower RMSE and the number of parameters during growing season was the most appropriate model. Model validatedby means ofrecordedcrop yieldsduring 2011 and2014 years. The simulated yieldswere 1470, 1639 and 1226 with average of 1445 kg per hectare. Error percent was 45.1, 9.3 and -7.1for the following years with an average of 15.7. RMSE was 9.4, 2.6 and 2.3 with average of 7.4. The predictive value of the model was excellent for all these years.
Conclusion: Model predicted the yield of canola based on the average maximum temperature (Tmax) and total precipitation (R)with error correction to reduce15.7. These variables described 86.8percent yield in the growing season and were significant at 5 percent. Canola planting date wascalculated for 23 September. Time phenology was germinated 25 September until ripening 4 Jun.
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.
