نشریه هواشناسی کشاورزی
سال دهم شماره 2 (پاییز و زمستان 1401)

The World Meteorological Day 2023's theme is “The Future of Weather, Climate and Water across Generations” and it refers to the situation that the future will face. These three components together with the soil element form the main fundamentals of food security. The current note states that although the quantitative and qualitative unfavorable variations of these three components have global dimensions and covers many regions of the world , but what differentiates countries from each other in terms of the degree of damages due to these changes is the ways of managing and balancing water resources and water consumptions, as well as the implementation of resistance, resilience and adaptation programs in the face of crises. This study deals with the damages caused to the weather, water and climate in Iran and emphasizes that climate change and drought alone are not the cause of environmental crises. Especially, the effects of drought and climate change in the occurrence of water crisis are much less compared to the effect of water resources management. It has been concluded that drought like wet year is a natural, expectable and repeatable climatic phenomenon. Based on the 120 years rainfall data analysis of four old stations in Iran (Isfahan, Bushehr, Tehran and Mashhad) it has been concluded that the one, two, three, four and five consecutive years lasting droughts in Iran, regardless of their severity, occur once every four, eight, 17, 33 and 40 years in average. In fact, the water wealth of the country is the rainfall that the spatial average of which fluctuates between 140 and 350 millimeters in different years and it is necessary to consider this limitation as fundamental assumption in weather climate, water and soil challenges of the present and future generations.

Global space coverage of the TRMM satellite imagery has provided a good opportunity to use the precipitation data estimated by this satellite in the country. Various studies have been conducted in the country to evaluate the accuracy of the above data in comparison to the measured data at ground stations. However, few studies have examined the efficacy of postprocessing methods in correcting TRMM precipitation data. The purpose of this study was to evaluate the performance of quantile mapping methods in improving TRMM precipitation data compared to ground data. For this purpose, 10 quantile mapping methods were applied to the gridded TRMM precipitation data in Kermanshah province on a monthly time scale (Apr-Oct) from 2005 to 2012. The ground precipitation data for the same time periods were collected from 13 synoptic weather stations and 82 rain gauges. The results showed that non-corrected estimations of TRMM precipitation data in the elevated (lowland) areas higher (lower) than the ground data. In addition, it was found that the difference between satellite- and ground-based estimates of precipitation in high-precipitation months was much greater than low-precipitation ones. The Parametric transformation of scale method with the least error, among the others, was introduced as the most appropriate quantile correction method. The results of data post-processing showed that the mentioned method could improve the accuracy of TRMM precipitation data. In addition, the correlation coefficient between ground measurements and satellite precipitation data varied in the range of 0.73 to 0.94 (significant at the 5% level), with the highest correlations obtained compared to the precipitation of synoptic and TRMM stations.

In this study, a modified version of standard precipitatıon index (SPI), namely Agriculture-SPI,or aSPI was used to assese the impacts of drought on rainifed wheat yield in North Khorasan province,northeast of Iran. In the aSPI index, the effective rainfall is used instead of precipitation. The USDA-SCS, method was used for effective rainfall calculation. Both indices were worked out , using 30-years (1989-2018) monthly rainfall and temperature data. Rainfed wheat yield data for the period of 2004 to 2018 were also retrived from local oranizations and standardized for comparisons in different SPI time windows of 1, 3, 4, 5, 6, 7, 8, 9 and 12 months .The obtained annual yields were correlated with SPI and aSPI values in selected timescales. The results showed that in Bojnord station, the rainfall druring November-May period, in Shirvan, Faruj and Esfarayen stations April-June, in Maneh-Semelgan station, November-December period and in Jajarm station, October-June period rainfall, are the most signifcant variables affteting the rainfed wheat according to drought indices used in study region.

One of the climate variables with relatively large gaps in observation and significant importance in estimation of evapotranspiration is sunshine hours. In the present study, in order to reconstruction the sunshine hour data of several selected stations in Tabriz province, Iran namely,Tabriz, Sarab, Sahand and Maragheh during the period of 1990 to 2019, skill of intelligent approaches of SVR, ANN and RF was compared with statistical methods of normal ratio, geographical coordinates and weight correlation coefficient. Statistical indices of R, RMSE, MAD and Taylor diagrams were used for evaluation of comparisons. The obtained results showed that ANN and geographical coordinate methods have the highest accuracy in reconstruction sunshine hours among the selected intelligent and statistical methods, respectively. In Tabriz and Sahand stations, the geographical coordinate method with RMSE of 1.04 and 1.13 hours, respectively, in the Sarab station SVR with RMSE of 1.58 hours and in Maragheh station the normal ratio method with RMSE of 1.45 hours showed the highest accuracy in generating sunshine hours. Besides, RF method had the lowest accuracy in reconstruction of sunshine hours data. It can be concluded that in Tabriz, Sarab and Sahand stations, both types of intelligent and statistical methods have almost same accuracy, but in Maragheh station, statistical methods provided slightly better estimations.

This study aims to estimate the yield of spring chickpea using machine learning methods of linear regression models in Kermanshah region, west of Iran. The meteorological variables, agrometeorology and remotely-sensed based indices as predictor variables and yield data of Agricultural Jihad Organization of Kermanshah as a response variable were used for four growth stages during 1990-1991 to 2017-2018. Twenty four and three years data were used for training and model validation, respectively. The results revealed that among the linear models, Lasso model with a coefficient of determination of 67% and a standard error of 59.8 kg.ha-1 was chosen as a best model for crop yield estimation in the emergence to 50% of flowering stages. This model has relative deviations of 0.4, -0.3 and 3.5 for the years 1997-1998, 2005-2006 and 2010-2011, respectively.

One of the main challenges in hydrological and meteorological studies is determination of the air temperature in in large scale and high altitudes regions with limted stations. The remote sensing images have been widely used for estimating air temperature providing large scacle,precise data. In this study, in order to evaluate the accuracy of TRMM satellite air temperature estimates, the monthly data of two years of 2012 and 2013 were used for calibration and the 2015 dataset for validation. 36 TRMM satellite images of monthly temperature were retrieved and compared with observations of of 24 meteorological stations in Mazandaran province The correlation coefficents of satellite temperature estimations and observation data in most months was more than 0.50. Also, the spatial distribution of TRMM satellite and froundbased were almost same For the accuracy evaluation the MBE and RMSE indices were used. The highest errors in 2012 and 2013 study years were correspond to February and December, respectively, with a RMSE value of 7.4 and 4.8 °Celsius, respectively, The lowest error were observed in July and September, i.e. 3.4 and 2.9 ° Celsius, respectively. Similariy, based on the MBE index, the highest and lowest error was belonged to December and April, with values of -3.4 and +0.1, respectively, and the highest and lowest error based on RMSE was observed in months of May and December, with the values of +5.1 and +2.5 degrees Celsius, respectively. The results of this study showed that the use of remote sensing can be recommended in the regions with limited weather stations.

The pan evaporation is one of the major components of hydrological cycle. It is quite important in agricultural water management and water balance estimations. The current research was performed with two main goals. First, to study the trend of pan coefficient during baseline period of 1993-2018 and second, projection of pan evaporation during three future periods under RCP climate change scenarios in 5 selected stations across Iran, namely Mashhad, Bushehr, Ahvaz, Kerman. In part one; the monthly trend of Kp values were studied using Man-Kendal and Sen’s slope estimator in warm season (Spring and Summer).During baseline period a significant decreasing trend in pan evaporation was observed Mashhad,Bushehr and Ahvaz stations.In second part, for projection of pan evaporation under RCP scenarios, the PenPan model, a modified form of P-M equation, was used. The required projected climate data were retrieved from CNRM-C5 model outputs under RCP 4.5 and RCP 8.5 scenarios. The trend analysis of Kp values using Man-Kendal test during the baseline period showed a significant decreasing trend except for the Yazd station with the least coefficient of -1.88 mm. The greatest decreasing value based on Sen’slope estimator was observed in Bushehr station. The results of Man-Kendal test revealed a decreasing trend in pan evaporation in Ahvaz, Mashhad, Bushehr and increasing trend in Kerman stations. For future periods of 2020-2049, 2050 to 2079 and 2080 to 2100, an increasing trend of pan evaporation in Kerman station and decreasing trend in 3 other stations was detected. According to Sens’s slope estimator test, during the baseline period the most increasing trend was observed in Kerman station. For future period the most increasing trend was observed in Mashhad station (+0.64). It is projected that mean pan evaporation values during near, middle and far future periods would decrease 4.7, 6.2 and 8.8%, respectively. Despite of projected increase of air temperature in Mashhad, Bushehr and Ahvaz stations, a reduction in pan evaporation was observed which might be attributed to reduced received radiation, confirmed in previous studies as evaporation paradox.