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The frost and chilling phenomena are the important climatic and environmental hazards that cause a lot of damage to the agricultural sector every year. In this respect, the present study intends to highlight the possibility of attributed occurrence to large-scale atmospheric indices. In this study, the probability of last spring frost (LSF) and chilling (LSC) at a critical temperature of + 4.4 to − 3.3 °C and different phases of El Nino Southern Oscillation index (ENSO) were extracted using Weibull Distribution Function (WDF) at 18 synoptic stations in a period of 59 years from 1961 to 2019. Then, the anomaly of LSF and LSC occurrence date was investigated in different ENSO phases (EP) and intensities (EI) and finally the LSF and LSC occurrence trend and the factors affecting this trend were analyzed using Mann–Kendall method and Sen’s slope estimator. Different EP and EI were classified using Oceanic Nino Index (ONI) in Nino 3.4 region. The results showed that the occurrence date and probability of the LSF and LSC with different base and critical temperatures in EP are associated with precedence and latency over the whole period and the anomaly of LSF and LSC occurrence date fluctuates between + 17 to − 12 and + 15 to − 17 days, respectively. In addition, the frequency and anomaly of the LSF and LSC at different base temperatures in different EI have been intensified compared to its different EP; therefore, the occurrence date of LSF and LSC can be predicted based on the EP and EI. On the other hand, the trend analysis of the date of the LSF and LSC indicates its retreat towards early spring or last winter at the main studied stations, which is due to global warming. According to the results obtained in some of the studied areas, the delay of LSF and LSC is also observed, which is due to local topographic characteristics, altitude and proximity to moisture sources.

The assessment of drought hazards is important due to their socio-economic impacts on water resources, agriculture, and ecosystems. In this study, the effects of drought on changing water area and canopy of the Lake Urmia watershed in the northwest of Iran have been monitored and evaluated. For this purpose, the Standardized Precipitation Index (SPI) was calculated in short and medium periods (1-month and 3-month) to determine the dry-spell periods in the Lake Urmia basin. In reviewing this analysis, the annual average has been examined and evaluated. Furthermore, Moderate Resolution Imaging Spectroradiometer (MODIS) and remote sensing data were used to calculate the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Normalized Difference Water Index (NDWI), and the Temperature–Vegetation–Dryness Index (TVDI) to identify the area of water body, water level, and vegetation changes during 20 years (2000–2020). The Pearson correlation coefficient was also employed to explore the relationship between the drought and the remote sensing-derived indices. According to the results of drought analysis, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, and 2020 had experienced dry spells in the Lake Urmia basin. The NDWI changes also showed that the maximum area of the Lake Urmia happened in 2000, and its minimum was recorded in 2014. The variation of NDVI values showed that the highest values of vegetation cover were estimated to be 2,850 km2 in.2000, and its lowest value was 1,300 km2 in.2014. The maximum EVI and TDVI were calculated in 2000, while their minimum was observed in 2012 and 2014. Also, the correlation analysis showed that the SPI had the highest correlation with NDVI. Meanwhile, 1-month SPI had a higher correlation than the 3-month SPI with NDVI and EVI. As a concluding remark, NDVI and NDWI were more suitable indices to monitor the changes in vegetation and drought-related water area. The results can be used to make sound decisions regarding the rapid assessment of remote sensing-derived data and water-related indices.

The objective was to investigate if changes in annual, monthly, and seasonal precipitation are associated with emergence of declining oak trees in Iran. Daily precipitation data were obtained from 20 synoptic stations distributed over the Zagros area from 1988-2019. Non-parametric Mann-Kendall (MK) test and Sen's Slope estimator (Qmed value) were applied to identify significant trends in the precipitation data. De Martonne climate classification (i.e., De Martonne aridity index (Idm) was used for climate classification. Although most stations showed decreasing trends in annual precipitation during the studied period (1988-2019), these trends were statistically significant at only two stations. The mean number of events per year pre- and post- oak decline was not significantly different (68 events before against 71 events after decline). Most of the annual precipitation in the Zagros region falls in winter and spring (80% in total). However, this ratio decreased after the year 2000 by 6% (not significant) compared with before. The difference between the average annual precipitation, before (19882000) and after (2000-2019) the emergence of the oak decline phenomenon, were not statistically significant in any of the climate types (semi-arid: 406 mm vs. 378 mm), Mediterranean (530 mm vs. 489 mm), and humid (924 mm vs. 912 mm) as well as in whole Zagros region (537 mm vs. 508 mm). Although our data suggested insignificant trends in precipitation for most stations, future research should investigate if rising temperature in the Zagros area has resulted in higher evaporation and drier soil thereby accelerating the oak tree decline.

After precipitation, reference evapotranspiration (ET O ) plays a crucial role in the hydrological cycle as it quantifies water loss. ET O significantly impacts the water balance and holds great importance at the basin level because of the spatial distribution of managing water resources. Large scale teleconnection indices (LSTIs) play a vital role by influencing climatic variables and can be pivotal in determining ET O and its predictive variables. This study aimed to model and forecast annual ET O in Iran’s basins by utilizing LSTIs and employing various machine learning models (MLMs) such as least squares support vector machine, generalized regression neural network, multi-linear regression (MLR), and multi-layer perceptron (MLP). Initially, climate data from 122 synoptic stations covering six and 30, main and sub basins were collected, and annual ET O values were computed using the Food and Agriculture Organization 56 (PMF 56) Penman–Monteith equation. The correlations between these values and 37 LSTIs were examined within lead times ranging from 7 to 12 months. Through a stepwise approach, the most influential predictor indices (LSTIs) were selected as input datasets for the MLMs. The findings revealed the significant influence of factors such as carbon dioxide (CO 2 ), Atlantic multidecadal oscillation, Atlantic Meridional Mode, and East Atlantic on annual ET O . Overall, all MLMs performed well in terms of the Scatter Index during both training and testing phases across all sub-basins. Furthermore, the MLP and MLR models displayed superior performance compared to other models in the training and testing evaluations based on various assessment metrics.

The intensive use of wood resources is a challenging subject around the world due to urbanization, population growth, and the biodegradability of wooden materials. The study of the climatic conditions and their effects on biotic wood degradation can provide a track of trends of wood decay and decomposition at regional and global scales to predict the upcoming responses. Thus, it yields an overview for decision-makers and managers to create a precise guideline for the protection of wooden structures and prolonged service life of wooden products. This study aimed at investigating the decay hazard in Iran, its decadal changes, and how it is affected by different phases of the El Niño Southern Oscillation (ENSO). Therefore, the risk for fungal decay of wood was estimated based on the Scheffer Climate Index (SCI) at 100 meteorological stations located in Iran, for the period 1987–2019 (separately for first, second, and third decade as decadal analysis). Subsequently, SCI value trends were analyzed using the Mann–Kendall and Sen’s slope method. Finally, the relationship between SCI and climatic parameters (temperature and precipitation) was explored. Generally, the SCI fluctuated between 2 and 75 across the region. The decay risk was ranked as low in most parts, but moderate in the northern part of the country along the Caspian Sea coastlines. Decadal analysis demonstrated that the highest mean SCI values took more place in the third decade (58% of stations) and the lowest mean SCI values in the second decade (71% of stations). Furthermore, the highest and the lowest SCI values occurred at 70 and 66% of stations in El Niño and Neutral phase, respectively. Trend analysis of SCI values showed that large parts of several provinces (i.e., Markazi, Tehran, Alborz, Qazvin, Zanjan, Ardebil, East Azarbayjan, West Azarbayjan, Kurdestan, Kermanshah, and Ilam) exhibited a significantly increasing decay hazard with a mean SCI of 2.9 during the period of 33 years. An analysis of causative factors (climatic parameters) for these changes revealed that all the meteorological stations experienced a significant increase in temperature while the number of days with more than 0.25 mm precipitation increased at some stations but decreased at others. However, in summary, the SCI increased over time. Hence, in this study, the effect of precipitation on SCI was confirmed to be greater than the temperature. Analysis of the results shows that the correlation between the SCI and ENSO was positive in most of the stations. Moreover, the results of spectral coherent analysis of SCI and ENSO in different climates of Iran showed that the maximum values of SCI do not correspond to the maximum values of ENSO and are associated with lag time. Therefore, the extreme values of the SCI values cannot be interpreted solely on the basis of the ENSO.

Occurrence of extreme climatic phenomena such as frost will cause significant risks and costs to many sectors, especially agriculture, horticulture, and forests. Frost will cause the worst damage when it occurs at the critical stages of crops, especially in spring. The frost phenomena are one of the important climatic and environmental hazards that cause a lot of damage to the agricultural sector of Iran every year. In this respect, the present study intends to highlight the projection of late spring rost by global circulation models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6). For this purpose, minimum temperature data of 17 synoptic stations were used in the period 1985–2014 in cold regions of Iran. For projecting the changes of LSF, the ACCESS-ESM1-5 and Nor-ESM2-LM Models were used under three (Shared Socioeconomic Pathway (SSP)) scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 for the next three periods (i.e., 2020–2049, 2050–2079, and 2080–2099). Then, the changes were compared to the historical period (1985–2014). The root mean square error (RMSE), mean bias error (MBE), correlation coefficient (CC), and Nash-Sutcliff efficiency (NSE) indices evaluated the models’ performances. The results revealed that the latest and earliest dates of LSF during the base period occurred in the western and central parts of Iran, respectively. The model evaluation indicated that the performance of ACCESS-ESM1-5 (MBE = 0.3, CC = 0.87, and NSE = 0.68) exhibited a higher accuracy than the NorESM2-LM model. Based on both GCM under all three SSP scenarios, the projection of changes in future periods (compared to the base period) indicated that the date of occurrence of LSF would be earlier than the base period, with the highest and lowest changes projected based on NorESM2-SSP5-8.5 and ACCESS-ESM1-5-SSP1-2.6 in Arak, Isfahan, Khorramabad, Sabzevar, Shahrekord, and Shahroud stations. In general, depending on the model and climate scenario, the LSF phenomenon occurs earlier or later in cold regions of Iran, and its changes would be between − 76 and + 19 days in the future period.

This study investigates the spatiotemporal characteristics of meteorological drought over the past 50 years in Iran's four distinct climates: Hyper-arid, Arid, Semiarid, and Humid. Employing three drought indices (Standardized Precipitation Index, Reconnaissance Drought Index, and Standardized Precipitation Evapotranspiration Index) at multiple timescales (1-, 3-, 6-, 9-, and 12-months), the analysis utilizes data from 41 synoptic meteorological stations spanning the period from 1969 to 2019. Results reveal a temporal increase in the duration and intensity of drought events, particularly post the 1998–99 period. The longest extreme drought, lasting 40 months, occurred during Dec 1998–Mar 2002 and Jan 2018–Mar 2018. Spatial patterns indicate a uniform rise in drought intensity across timescales and indices, transitioning from humid and semiarid to arid and Hyper-arid regions. Average drought durations for SPI, SPEI, and RDI indices are 9, 12, and 9 months, respectively, while mean drought frequencies stand at 14%, 17%, and 13% for SPI, SPEI, and RDI indices. Notably, SPEI exhibits greater duration and frequency of drought events, especially in arid and Hyper-arid regions. The research underscores the pivotal role of climatic variables in delineating drought characteristics and emphasizes the significance of selecting appropriate drought indices across diverse climates.

Precipitation forecasts are of high significance for different disciplines. In this study, precipitation was forecasted using a wide range of teleconnection signals across different precipitation regimes. For this purpose, four sophisticated machine learning algorithms, i.e., the Generalized Regression Neural Network (GRNN), the Multi-Layer Perceptron (MLP), the Multi-Linear Regression (MLR), and the Least Squares Support Vector Machine (LSSVM), were applied to forecast seasonal and annual precipitation in 1- to 6-months lead times. To classify precipitation regimes, precipitation was clustered using percentiles. The indices quantifying El Niño-Southern Oscillation (ENSO) phasing showed the highest association with autumn, spring, and annual precipitation over the studied areas. The MLP and LSSVM algorithms provided satisfactory forecasts for almost all cases. However, our results indicated that the performance of LSSVM decreased in testing data, implying the tendency of this algorithm towards overfitting. The MLP showed a more balanced performance for the training and testing sets. Consequently, MLP seems best suited to be used for forecasting precipitation in our study area. The modeling algorithms provided less reliable forecasts for the regions corresponding to the 10–40th percentiles, mostly located in hyper-arid and arid environments. This underscores the inherent difficulty of precipitation forecasting in the hyper-arid and arid areas, wherein precipitation is very erratic and sparsely distributed. Our findings illustrate that clustering precipitation regimes to consider microclimate seems vital for reliable precipitation forecasting. Moreover, the results seem useful to design preventive drought/flood risk management strategies and to improve food-water security in Iran.

Sunn pest, Eurygaster integriceps Puton is a key pest of wheat and barley in Iran. In this study, the impact of site-specific spraying on control of sunn pest damage and densities of the natural enemies was compared with the whole-field spraying method in 2009 and 2010. Three plots were assigned to each spraying method and two others were left untreated as control. The plots were divided into 11 × 11 m grids. Adults of E. integriceps were sampled using the distance-walk method. Coccinellids, Chrysoperla carnea and nymphs of sunn pest were sampled using a sweep net. Spatial analysis of datasets was done using Geostatistical Analyst extension of ArcGIS 9.3. The spatial analysis indicated that the adults and nymphs of E. integriceps had aggregated distribution in space and that site-specific spraying was applicable. Whole-field spraying was carried out when the mean density of E. integriceps in plots exceeded the economic threshold. In the site-specific spraying method, decamethrin ([cyano-[3-(phenoxy) phenyl] methyl] 3-(2,2-dibromoethenyl)-2,2-dimethylcyclopropane-1-carboxylate) was applied to the grid cells with densities above the economic threshold. Site-specific application reduced the insecticide input by ca. 40–50%. The numbers of C. carnea and coccinellids were higher in site-specifically sprayed plots compared with whole-sprayed plots after treatment. The mean numbers of nymphs were not significantly different ( P  < 0.01) in whole-field and site-specifically sprayed plots. Percent damaged grain was below the economic damage threshold in all treated plots. It can be concluded that site-specific spraying has the potential to control E. integriceps at an acceptable level along with reducing the amount of insecticide used. It also conserved natural enemies in untreated refuges.