نشریه هواشناسی کشاورزی
سال هشتم شماره 1 (بهار و تابستان 1399)

The recent statement of the Iranian Academy of Sciences on the irreversibility of the country's water resources situation has a damage-controlling vision of how water is used and allocated in various consumption sectors, especially in the agricultural one. The main concern is that in recent decades, the amount of water abstraction from renewable resources has exceeded the allowable limit, and this has not only led to a quantity loss of water storage, but it also, in some places, has led to a decline in water quality as well. The statement took into consideration the causes of such situations, and also made available considerable useful and appropriate approaches. The present memoir, in line with confirming the provisions of the statement, presents a picture of the limitation of the available facilities and possible solutions. In Iran, during the last sixty years, average rainfall has fluctuated between 164 and 342 mm per year (average annual rainfall within the country's borders) and the number of wet and dry years has been almost equal. It is obvious that if in the past we had more extended and dense forests and full-fledged aquifers from the same amount and the same rainfall system, but nowadays it is no longer the case, it should be mainly due to consumption mismanagement and reckless and unpredictable harvests from underground aquifers. Therefore, now we have no choice except using each drop of water more efficiently by making coherent laws, creating an effective water consumption culture, using successful global experiences, and taking into account the climatic advantages for crops cultivations, especially for more water-consuming crops such as rice. We also should rely on young experts and researchers’ problem-solving potential in effectiveness of water consumptions. By the time in balancing water harvesting and water consumption step by step, it is a must to look for new water resources to compensate water shortages. Although measures such as cloud seeding or deep and fossil water extraction cannot be approved, desalination and the use of open waters in the southern seas for drinking and industrial purposes could be considered.

Solar radiation plays an important role in surface energy balance and agroclimatic studies. In order to estimate daily global solar irradiance, Adaptive Neuro-Fuzzy Inference System (ANFIS) was used and compared with extended Angstrom Prescott model using 11-years data (2007-2017) climatic data of 6 meteorological stations across Iran namely, Mashhad, Kerman, Shiraz, Yazd, Zanjan and Bandar Abbas. The daily weather data including sunshine duration(n), Relative humidity (RH%), Dew point temperature (Td), water vapor pressure, maximum/Minimum air temperature, mean air temperature, cloudiness and soil temperature were used as inputs of ANFIS model to estimate the daily solar radiation, as the output. Performance of two models was evaluated using t-student index. According to the results, ANFIS model did a better job in estimation of solar radiation comparing to Angstrom-Prescott model as it showed closer agreement with observed data. For evaluation of each model’s skill in estimating solar radiation at daily and monthly time scales, the statistical indices of RMSE, MBE, r, t were used .Finally, the obtained values of t-test statistic of radiation estimation using ANFIS model, in Kerman, Mashhad, Shiraz, Zanjan, Yazd and Bandar Abbas station in daily time scale were 4.78, 7.52, 3.25, 2.20, 93.3, and 0.26, respectively. The corresponding monthly values were 0, 0.29, 0.59, 0.44, 0.21, and 0.20 respectively. Similarly, the t-index of radiation estimations by Angstrom model compared to observations for daily time scale were also 20.16, 36.31, 28.90, 2.39, 34.91 and 49.96, respectively. For monthly series, t values were 6.74, 9.53, 4.68, 0.59, 8.38 and 9.71, respectively. According to the findings of this study, the nonlinear modeling process of ANFIS model improves the estimation of solar radiation compared to the Angstrom-Prescott method.

Global warming could affect snow-covered watersheds and the temporal and spatial distribution of snow, and thus the water needs below the basin. Therefore, the aim of this study is to investigate the spatio-temporal changes of snow cover and snow retention in the northern aspects of Central Alborz. The Normalized Difference Snow Index (NDSI) extracted from MODIS satellite images was employed to estimate snow cover during 2000 - 2018 intervals. The findings of this study showed that there is a significant and increasing trend in the temperatures of May and June. On the other hand, although there was no trend in the snow cover, the Sen's slope estimator showed that the snow cover in January decreased by about 220 km per year, which had a high negative correlation with the increase in the average temperature of this month. (r = - 0.77), while in March, the snow cover increased by nearly 60 kilometers a year. There was a significant positive correlation (r = 0.81) between snow cover and snow depth, and significant negative correlations (r = -0.80 to r = -0.60) were found between the annual snow cover and the average annual air temperature. These findings indicate that the effect of temporal and spatial changes in temperature on the extent and persistence of snow cover is meaningful. By reduction of the basin’s height from the west in Haraz, with an average height of 2059 m, to the east in the Tajan basin, with an average height of 980.7 m, the snow cover area and its durability reduced. From the findings it is concluded that temperature changes in this region follow the global warming trend, while this warming has affected on the variance of the snow cover extend.

Drought is a natural phenomenon, therefor Drought monitoring and forecasting, especially the precise timing of its onset and duration, is of particular importance in water resources management and planning to mitigate drought effects. The purpose of this study is to evaluate, the meteorological droughts in the whole country of Iran using the Joint Deficit Index (JDI) and compared its with SPImod. For this purpose, the monthly precipitation data from 41 meteorological stations in Iran were used in the period of 1971-2017. The results showed that the SPImod index can describe seasonal variations of precipitation which is an advantage for this index. On the other hand, analysis based on the SPImod is sensitive to time scales, as it may present contradictory results in different time scales. To correct these defects the Joint Deficit Index (JDI) can be used. The basis of the JDI index is to create a joint distribution of SPImod indices with time scales of 1 to 12-month using copula functions. The results of the JDI index showed that the number of dry months (JDI <0) in the west, northwest, and some northern provinces were more than other parts of the country. In the next step, the drought characteristics, including severity, duration and the inter-arrival time were extracted from the JDI index time series. The results indicated that the correlation among drought characteristics were more than 0.7. In order to trivariate analysis of drought characteristics, the fitness of nine different copula functions were examined that the Farley- Gamble- Morgenstern copula was specified as the best function for constructing the trivariate distribution.

Short-term prediction of minimum temperature is important in mitigation chilling and frost injury in agriculture. In current study, the frequency of early autumn and late spring frosts in Sanandaj synoptic station, Iran were worked out. Then, using six variables, i.e. dry and wet bulb temperature, relative humidity, wind speed, wind direction, and cloud cover as the inputs, a multilayer perceptron artificial neural network model (MPL/ANN) based on the Levenberg-Marquardt training algorithm of MATLAB software package was applied for prediction of the minimum temperature for the next 3, 6, 9 and 12 hours ahead. The selected Transfer Functions were hardlims, logsig, polsin, radbas, satlins, softmax, tansig, and tribas. The statistical measures of MAD, MSD, RMSD, and R were used for comparisons. The results showed that in case of late spring frost, the poslin, logsig, tansig, and satlin functions in April with a correlation coefficient greater than 0.8 and error values of 1.17, 1.61, 1.88 and 2.00 (◦C) for the different times steps, respectively are the best options. Similarly, in October, the radbas, poslin, poslin, and tribas functions with a correlation more than 0.7 and error values of 1.60, 1.96, 1.99, and 1.36, were found to be the most suitable ones for prediction of the minimum temperature at 21:30, 00:30, 03:30 and 06:30 local time. Also, among the selected functions, the poslin with the highest frequency has the best performance in predicting nocturnal frosts in Sanandaj. The results confirmed the good performance of the ANN approach in short-term prediction of minimum temperature and frost occurrence in study region.

Drought is one of the most widespread and devastating phenomenon which has been more frequent due to climate change consequences in recent decades. The aim of this study is to project the agricultural drought in Birjand station, Iran using outputs of five GCM models approved in IPPC fifth assessment report, AR5, namely MIROC-ESM, GFDL-ESM2M, HADGEM2-ES, CSIRO-MK3.6 and IPSL-CM5A-LR under RCP4.5 and RCP8.5 scenarios. The retrieved outputs were downscaled using LARS-WG statistical model for the future period (2055-2025). For monitoring the drought the Self Calibrating Palmer Drought Severity index (SC-PDSI) during the baseline (1975- 2005) and future period was used. The results indicated that occurrence of droughts during future period will increase comparing to baseline, which might be caused by climate change in the study region. Models projections under both scenario revealed that the (2026-2035) period would experience more severe drought comparing to remaining years. According to results, the highest number of drought events in future period was projected by MIROC-ESM and CSIROMK 3.6, models under RCP8.5 scenario.

Quantification of the adverse effects of climatic and environmental indices on agricultural production is quite important in agrometeorology and economics sciences. The aim of current study is to investigate the interaction carbon dioxide emissions, as an indicator of environmental degradation, and crop production. For this purpose, panel data for 31 provinces of Iran during the period of 1977-2018 using Autoregressive Distributed Lag (ARDL) model were applied. Based on the statistical analysis, the negative coefficient of agricultural value added term in long and short term periods, i.e. -0.027 and -0.312, respectively, the Kuznets environmental curve (EKC) hypothesis was confirmed in the agricultural sector of study regions. The results showed that, almost 90% of the agricultural production shocks might be mitigated over one period (e.g. growing season) and will return to their long-term equilibrium levels in less than two periods. The coefficients of precipitation and temperature terms in greenhouse gas emissions equation were determined -0.038 and -0.302 respectively, indicating the significant impact of climatic variables on emission. Further studies on interaction of copping pattern and machinery utilization on emission and agricultural production are recommended.

For projection of possible climate change impacts on agriculture and water resources climate models are used but there exist uncertainties in the outputs of these models. The aim of this study is to project the possible changes of precipitation and uncertainty involved in Hashem- Abad station, north of Iran, using general circulation models. For this purpose, precipitation observations for the baseline period of (1990–2010) were collected. Then the outputs of the 8 GCM models (including ACCESS1-0, CanESM2, CMCCCMS, GISSE2H, HadGEM2CC, MIROC5, MIROCESM, and IPSLCM5AMR) under the two scenarios of RCP 4.5 and RCP 8.5 were downscaled using change factor method and compared. The uncertainties of the GCM models for both emission scenarios were also investigated. Comparison of means of precipitation time series for the period 2010–2040 was performed by t-test. The results indicated a decreasing trend in most of the months. The GISSE2H model projected the most significant decreasing trend of total monthly precipitation. The highest uncertainty was observed in July for the both scenarios. Besides, although the outputs of the models didn't show a significant difference in 82% of the cases, the models’ uncertainty band indicated a major difference among the results. Further uncertainty analysis may be recommended for more scrutiny.