The bud burst phase of orchard trees is the most critical phase in relation to low temperature and frost since the most parts of the bud, especially the ovary, are very sensitive to low temperatures. Therefore, predicting the time of bud burst is important. If a model can predict the time of budding, it would be possible to protect buds from late spring frosts. In this study, the budding time of apple trees at two agrometeorological stations in northeast and northwest Iran was predicted by using a chilling and forcing model. Data for years 2002–2006 were used to calibrate the bud burst prediction model and respective information for the years 2007 and 2008 was used to validate it. For this purpose, five threshold temperatures (T_C) and 11 chill requirements (C_R) were used. Among 55 combinations of T_C and C_R, the combination with minimum Root Mean Square Error (RMSE) was selected for predicting bud burst of apple for each region. Meanwhile, the probability of last date of frost in spring was estimated by statistical distribution. By comparing the probability of frost occurrence with the date of predicted bud burst, the risk of frost damage on apple budding was estimated. Copyright © 2009 Royal Meteorological Society

This paper applies HEC-HMS to the Karkheh River basin (KRB), Iran, and facilitates the calibration of a continuous hydrologic model (CHM) with soil moisture accounting (SMA) and snowmelt degree-day parameters. Manual calibration was performed to ensure the physical relevance of HEC-HMS parameter values. Because manual calibration entails changing each parameter value in a user-defined setting, it is often a time-consuming procedure complicated by multitude of interacting parameters. To address this setback, an event-based calibration technique (EBCT) was implemented in KRB and its interior sub-basins whereby the governing parameters of specific fall, spring, and winter events were initially estimated in a precalibration step and used as inputs to facilitate calibration of the CHM. Model performance analyzed based on goodness-of-fit criteria with respect to peak flows, low flows, and hydrograph shape reflects uncertainties associated with streamflow naturalization and use of average annual parameter values for the snowmelt component. Sensitivity analysis provided insights into the basin’s snowfall and melt characteristics, distinguishing antecedent temperature index (ATI) cold rate coefficient and baseflow recession coefficient as key parameters affecting hydrograph shape and magnitude of the peak flow, respectively. Results based on goodness of fit metrics suggest that event-based parameter estimation using seasonal characteristics improved the efficiency and accuracy of the continuous HEC-HMS model (CORRL and NSE 0.78–0.87 and 0.5–0.7, respectively) while facilitating application to a large, data-poor river basin with heterogeneous climatic conditions.

To answer the question “what has changed the surface temperatures of water bodies around Iran under climate change conditions,” Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) data with 9.2-km spatial resolution and monthly time step were evaluated. A significant increasing trend was obtained using Pearson’s correlation coefficient and Mann-Kendall trend test on data from the Caspian Sea (June) and the Southern Gulfs (September). High correlations of 0.69 and 0.97 between remotely sensed data and ERA-Interim reanalysis data were also obtained for June and September, respectively. To assess the impact of some climatic parameters on SST variability in those two months, zonal and meridional wind at 10 m, wind speed at 10 m, air temperature at 2 m, and mean sea level pressure were evaluated. High correlations between meridional wind and the Southern Gulf SST, and meridional wind and Caspian SST were −0.86 and 0.75, respectively. Moreover, the results showed an important role of air temperature (0.96) and sea level pressure (−0.77) in the rising temperature trends of the Southern Gulfs. Wind speed was also detected in both regions as a booster of the trend.

Purpose Tehran, Iran, is an interesting location for aerosol studies because it is affected by anthropogenic pollution and desert dust aerosols. The aim of this study was to discriminate the aerosol types using satellite data over the city. Method The study was performed using Level-2 daily Aerosol Optical Depth (AOD) and Ångström Exponent (AE) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the Terra and Aqua satellites for the years 2011 to 2015. As the Deep Blue (DB) AE retrievals are more reliable than the Dark Target (DT) AE retrievals, the study was performed using DB data. Results The number of granules with successful retrievals (at least in two pixels with AODs >0.2 over Tehran with high quality assurance) was 200, which indicates that aerosols could be observed in 5.47% (200 from 3652 of Terra and Aqua granules) of the overpasses during the study period. The maximum and minimum values of AOD occurred during May (0.32 ± 0.27) and August (0.18 ± 0.07), respectively. Based on the AOD vs. AE data, aerosols were classified into three different categories: urban/industry (UI), Desert Dust (DD) and Mixed (Mix). To improve the accuracy of the aerosol classification, the analysis was limited to retrievals with AOD values larger than 0.2. The DD, UI and Mix types had 48.5%, 30.5% and 21% contribution in the aerosol days, respectively. Conclusions The maximum DD frequency was observed in the spring and summer seasons, while the UI type had its maximum during the cold season. The AOD of the DD type (over Tehran) correlated well with the AOD observations done at the Aerosol Robotic Network (AERONET) site in Zanjan (300 km northwest from Tehran). For the UI type, no relationship with the AERONET AOD was detected. This gives confidence in our aerosol typing as the contribution of dust in the aerosol load is mainly from long-range transport, whereas the urban aerosols originate from local sources. Back trajectories ending in Tehran show that the northeast and west trajectories are two main transport routes for the dust to the study area.