نشریه هواشناسی کشاورزی
سال هفتم شماره 1 (بهار و تابستان 1398)

Monthly rainfall forecasting plays a major role in the water resources management and agroclimatic studies. The main purpose of this study is to assess the accuracy of NMME (North American Multi-Model Ensemble) in forecasting monthly rainfall in Sefidrood basin, North of Iran. For this purpose, the historical predicted data of NMME models for the period 1982 to 2017 were retrieved from, University of Columbia website, and compared with observed data obtained from the Iranian Meteorological Organization. The accuracy of NMME models predictions was evaluated by comparing them with the observed data, using statistical indices. The results showed that the single NMME model is not accurate, where the average value of determination coefficient (R2) was equal to 0.6. The models combination improved the accuracy of predictions, such that the determination coefficient increased to 0.7. Furthermore, for evaluation of the precipitation uncertainty, seventy-eight ensembles of the prediction models were investigated. The results of this evaluation showed that the models overestimated rainfall upto 80%. In addition, the uncertainty analysis of prediction models showed that the combination of models may reduce the uncertainty range.

Water vapor plays an important role in the atmospheric water balance and precipitation formation process in the lower troposphere. In this study, trend of the total precipitable water vapor (TPWV) which is column-integrated water vapor, is analyzed using radiosonde data of synoptic Shiraz station during the period 1990-2017. Besides, the TPWV data from the ERA- Interim of the ECMWF and NCEP-NCAR reanalysis datasets were retrived and used for comparison. The Mann Kendall (M-K) test, showed no significant trend in TPW data for both Radiosonde and ECMWF data for all months, except for Radiosonde data of August. Generally a non-significant decreasing trend in radiosonde time series of TPWV was observed for most of months. However, both linear regression and M-K tests showed a significant increasing trend in annual TPW of NCEP-NCAR data. The rate of decrease and increase in PW for Radiosonde and NCEP-NCAR data are 0.29 and 0.52 mm per decade, respectively. For all months (except April), there is a positive significant Pearson correlation between Radiosonde TPW series and ERA- Interim at 5% significance level. However, the Pearson correlation coefficients were not significant for NCEP-NCAR. The root mean square error of PWV data and ERA- Interim is generally smaller than NCEP-NCAR. The latter, showed an overestimation comparing to radiosonde. In general, ERA- Interim data has higher accuracy and more agreement with observed Radiosonde data.

Global warming has changed the pattern of temperature and precipitation distribution and will affect the production of crops such as rainfed wheat. In current study, by using correlation analysis and Iterative Chi-square analysis the wheat yield’s relationship with temperature and precipitation variables (in Saqez, Sanandaj, Bijar, and Zarrine obato Stations) was determined, and used to project the crop yield under climate change scenarios. The daily outputs of seven selected CMIP5 models under RCP8.5 scenario for the period 2045 to 2065 were used and corrected by Equidistant Cumulative Distribution Function matching (EDCDFm) method. The results showed that precipitation at the time of planting and during the flowering stage, the maximum temperature just before winter dormancy stage and during the flowering, the minimum temperature of January and February months are the most significant factors affecting the crop yield. The projected climatic conditions showed that the maximum temperature rise in November, December, April, and May will be unfavorable for crop production. Increased both maximum and minimum temperatures of months of January and February may provide more favorable conditions. Also, the projection of precipitation indicated that decreased rainfall of April and May, would lead to unfavorable moisture conditions. Unlike, increased precipitation in November could improve the moisture conditions at the time of sowing which favors greater yield.

Accumulation of greenhouse gases in the Earth’s atmosphere resulted in global warming and climate change. To mitigate climate change and cope with its consequences several strategies has been proposed. Silviculture and organic matters decomposition management, respectively, propose to sequestrate carbon and mitigate atmospheric greenhouse gases. The aim of this study is to evaluate the influence of tree species on soil greenhouse gas (CO2, CH4, N2O) emissions within forest plantations. For this purpose 20-year old maple, alder, oak, poplar and cypress species plantations were chosen in Sari region, Iran, and soil CO2, CH4, N2O emission rates measured during summer and winter times. Within each plantation area, 3 collecting gas chambers were installed at 10 cm soil depth randomly. The kind of emitted gases and their emission rates measured using gas chromatography. The results indicates that there are highly significant differences (P≤0.01) between tree plantations for CO2, CH4 and N2O gas emissions in the summer time, while during winter significant difference (P≤0.05) observes for CO2 emission. The TOPSIS ranking indicates that oak, maple, cypress, alder and poplar, respectively, are the more favorite sites in declining greenhouse gas emissions. Therefore, it concluded that in future silviculture attempts this issue should be taken into considerations.

Climate change is a major challenge for ecosystems and in particular, its impact on agriculture varies depending on regional characteristic and farming systems. Hence new challenges for production are emerging which should be carefully examined. The aim if this study is to project future spatial changes in current maize producing regions of Khuzestan Province, southwest of Iran under RCP scenarios by 2050s. The applied methodology is based on agro-ecological classification. This classification approach consists of combining two groups of environmental components including climatic (temperature under different scenarios) and non-climatic (land use) components including soil, slope, irrigated land, and type of farm management, by using the analytic hierarchy process. The baseline and future climate data were retrieved from the WorldClim database and ensembles for RCP 2.6, RCP 4.5 and RCP 6 for 12 selected GCMs. Results suggested that by 2050, the regions suitable for growing maize will be reduced by 65% in AEZ1 and by 36% in AEZ2. Maize will be grown mainly in the northwestern regions, and the southeastern regions will lose their suitability for maize cultivation. Presently, the southeastern part of the province is the main production region, in which the continuous growing of maize would be almost impossible under future climatic conditions.

The aim of this study is obtaining continuous weekly values of maize RUE by novel in-situ measurements of radiation. A semi-maturing cultivar of maize was cultivated in University of Tehran research farm in Karaj. Dry biomass and leaf area index were measured during 15 consecutive weeks of growing season. Then, based on in-situ complete pyrheliometry, by using insulation simulation and image processing, daily average of maize canopy reflectivity, extinction coefficient and the absorbed visible light of the canopy were estimated using a multi-layer model. During growing season infield continuous measurement of full spectrum irradiation, was measured continuously by TSR for all daylight hours. The diurnal insolation was simulated for that plant by an angular scanner (illuminator) during night time between 21:00 to 21:30 hrs. By applying an image processing technique, the visible band reflectivity of maize cover was estimated for the first seven weeks of study period. The measurements were continued till full closure of canopy and 12 different weekly values of RUE were calculated using the field measured data. The proposed approach allows generating continuous graph of RUE values. The research results indicate that even in the potential production conditions, using a constant value of radiation use efficiency for entire growing season of maize is not possible. During the growing season, the range of maize radiation-use efficiency reached 36.1 g/MJ, but the weekly values median of this quantity was equal to 3.5 g/MJ, which its occurrence probability in half of the growing season is 100%.