Journal of Agricultural Meteorology
Year:2014 | Volume:2 | Issue:1|Papers Count:6

Many climate change impact studies require information at a finer resolution than that provided by Global Climate Models (GCMs). In this study, performance of two statistical models namely, ANFIS and ASD, for downscaling daily precipitation (occurrence and amount) and temperature has been compared. A combination method of Genetic algorithm and ASD was employed to identify downscaling predictors that have the most significant influence on the study variables for a 30 years period of 1971 to 2000 in Kerman Station, south east of Iran. The first 15 years of data (1971 to 1985) were used for calibration and rest was kept for evaluation. One of the main steps in downscaling is choosing the most dominant variables. The results revealed that in case of precipitation, these variables are relative and specific humidity at 500 HPa, surface airflow, strength, 850 HPa zonal velocities and 500 HPa geopotential heights. For modeling temperature, mean sea level pressure, surface vorticity and 850 HPa geopotential heights were the most dominant variables. Outputs from the third generation Canadian Coupled Global Climate Model (CGCM3 were used to test two models over the current period (i.e.1971-2000), and comparing the results with observed temperature and precipitation in Kerman station. Results indicated that the agreement of simulations with observations depends on the GCMs atmospheric variables used as ‘‘predictors’’ and the performance of the statistical downscaling model vary for different seasons. The results showed a slight increase in temperature in future period comparing to baseline (1971-2000). The comparison of ANFIS and ASD models indicated that they performed well for temperature with almost similar results, but ASD model performed better in projecting precipitation than ANFIS.

The purpose of this study is to quantify and mapping climatic damage risk of last spring frost for different agricultural and horticultural crops at various phonological stages over Iran and to develop an applicable model for determining crop insurance premium rates. For this purpose daily minimum temperature data of 137 meteorological stations since their establishment date to 2004 were collected and submitted to quality control. Missed or debt full data were reconstructed using a cross control model. Finally the time series of daily minimum temperatures are formed for each station and then the date of the occurrence of these daily data were expressed by TDN (Thermal Day Number), using a thermal year calendar (from first of August each year until end of July next year). Thus the last spring frost (LSF) date of occurrence in each year is expressed by TDN. The time Series of LSF are accomplished for 13 standard temperature threshold q, from -9.8 to +4.8 Celsius degrees and for each station. Based on these series, the normalized values of TDNi named Zi is defined as a measure of frost risk severity for each thermal year i and each critical temperature (q). At next step, based on Zi values, each thermal year was placed in one of the four risk classes of very low, low, medium and high frost damage risk group. Then the probability of their occurrences (P0 (q), P1 (q), P2 (q) and P3 (q)) were calculated for each station and all critical temperatureq. Finally the parameter P23 (q)= P2 (q)+P3 (q) i.e. the probability (in percent) of moderate and high frost risk were considered as spring frost risk criterion for various values of q. It has been shown that P23 (q) for all 13 values of q has a significant linear relationship with longitude, latitude and elevation. Based on these regression equations and using error-correction procedures, spring frost risk maps with spatial resolution of 1×1 km2 were constructed in GIS environment for 13 critical temperatures over Iran. Afterwards critical temperatures (q) at different phonological stages based on FAO threshold data were coupled to frost risk maps. Ultimately, for practical applications, a new agro-climatic risk tool named AgroClimRisk was developed which can be combined with geo-statistical methods to calculate the quantities of maximum, minimum and averages of frost risk for 27 different crops and for three phonological main stages i.e. germination, flowering and fruit forming, over agricultural zones of Iran.

Frequent anomalies on air pressure and atmospheric circulation patterns sometimes impose their impacts on a location far from its starting point. This kind of atmospheric disturbance is known as “Teleconnection”. Madden-Julian Oscillation is a teleconnection pattern and dominant form of tropical and sub-tropical intra-seasonal variations that plays an important role in atmosphere-ocean cycle system. Since, climate change causes changes in meteorological parameters; therefore, reference evapotranspiration and in general, crop water requirement might change accordingly. Having considered the issue of water scarcity in Iran and importance of evapotranspiration irrigation management, the impact of different MJO phases on reference evapotranspiration in several southern sites of Iran (during 1979 to 2008) was evaluated by different statistical methods in this study. The daily ET0 values were estimated by REF-ET software and Penman-Monteith FAO-56 method. The MJO data, according to Wheeler and Hendon (WH) definition, were obtained from National Oceanic and Atmospheric Administration (NOAA) database. The ET0 and MJO statistical pairs, in different phases and months were sorted for each study sites accordingly. Using Pearson and Spearman correlation methods, linear regression and selection of forward method, the impact of MJO signal on ET0 for each month and all WH phases was extracted. Results indicated the significant effect of MJO signal on ET0 values for the study stations. The maximum number of correlations was observed for Bandar-e-Lenge station. In addition, the larger effects were obtained for phases close to southern coasts of Iran. In this regard, the phases 1 and 8 showed the highest significant correlation coefficients and largest mean b values. It was found that the all-site average of correlation coefficients between the statistical pairs of MJO-ET0 is 0.26. During the course of the year, the highest and lowest significant impact of MJO on ET0 was occurred in July and March respectively.

Evapotranspiration (ET) plays a key role in agriculture planning and water resources management, hence the simulation and pattern studying of this parameter has been a major field of interest for researchers. Considering the data repeatability of evapotranspiration, The Box-Jenkins method is an appropriate method for ET modeling. In this study, the mean monthly values of evapotranspiration for five stations of Kermanshah province, Iran was calculated by FAO 56 Penman-Monteith equation using a 21 years period meteorological dataset. Then, a time series analysis was carried out to explore the variation of the evapotranspiration series. Finally, using SARIMA stochastic model, the corresponding parameters of normal condition, stochastic, white noise, whiteness of the residual, and the final evapotranspiration patterns for the each station were obtained. The final evapotranspiration patterns were obtained as SARIMA (2, 1, 0)×(1, 1, 1)12, for Kermanshah station, SARIMA (3, 1, 1) × (1, 0, 1)12 for Kangavar, SARIMA (2, 1, 2)×(1, 1, 1)12 for Sar pol zahab station, Ravansar station SARIMA (3, 1, 1)×(1.0.1)12 and Eslam Abad Gharb station SARIMA (1, 1, 1)×(0, 1, 1)12. Besides the selected models were used to predict the evapotranspiration of 12 months which had not been used in model development stage (Training) and the results were compared with the actual values. The corresponding values of RMSE and r indices for year 2009 were 0.38 mm and 0.99 in Kangavar station, 0.37 mm and 0.99 in Sarpol-Zahab and 0.40 mm and 0.99 in Kangavar respectively, which indicated a better performance in these three stations comparing to Ravansar and Eslam Abad stations with less satisfactorily results.

Orange (Citrus sinensis) is a subtropical fruit which grows in a wide range of climates. Radiation climate could affect physical and biochemical characteristics of fruit. Therefore, the aim of this research is to evaluate the effects of geographical aspects and fruit location within the canopy on orange fruit quality. A complete block design with split plots was employed to conduct this research in Sari vicinity, Semeskandeh, in 2012. Geographical aspects of north and south were assigned to main plots and location of fruit, deep within the canopy or sunlit, were assigned to subplots with three replications. The results showed that geographical aspect and fruit location significantly affected fruit anthocyanin and carotenoids. As fruit reached its ripening stage the a, b and a*b chlorophyll activity reduced. Both treatments had no significant influence on total soluble solids (TSS), Titratable Acidity percentage (TA%) and the ratio of TA/TSS during the trial.

Each year, frost causes extensive damage to all aspects of agricultural production. Therefore, quantification of frost risk occurrence is one of the most important factors in implementing agricultural insurance policies. In this study, a hierarchical algorithm for detecting advection and radiation frost has been presented using data of a network consisting of 51 meteorological stations of Iran during the period 1951-2009. Using 3-hours interval data, the percentage of the frost events occurrence were determined and categorized into three frost classes (i.e. advection, radiation and mixed frost).These occurrence percentages, known as frost risk, were further analyzed according to their geographical characteristics (latitude, longitude and altitude). Finally, frost risk map of Iran was generated. Analysis of frost risk maps showed that occurrence risk of radiation and mixed frost are more than advection frost across the country. Besides, it was found that the maximum values of risk for radiation frost with the threshold temperatures of 4 and 0oC are 10.62% (Shahrekord station) and 7.23% (Koohrang station). Corresponding values for advection frost are 7.22% and 4.07% respectively, both observed in Ardebil station.