This study analyzes six frontal dust storms in the Middle East during the cold period (October–March), aiming to examine the atmospheric circulation patterns and force dynamics that triggered the fronts and the associated (pre- or post-frontal) dust storms. Cold troughs mostly located over Turkey, Syria and north Iraq played a major role in the front propagation at the surface, while cyclonic conditions and strong winds facilitated the dust storms. The presence of an upper-atmosphere (300 hPa) sub-tropical jet stream traversing from Egypt to Iran constitutes also a dynamic force accompanying the frontal dust storms. Moderate-Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations are used to monitor the spatial and vertical extent of the dust storms, while model (Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), Copernicus Atmospheric Monitoring Service (CAMS), Regional Climate Model-4 (RegCM4)) simulations are also analyzed. The WRF-Chem outputs were in better agreement with the MODIS observations compared to those of CAMS and RegCM4. The fronts were identified by WRF-Chem simulations via gradients in the potential temperature and sudden changes of wind direction in vertical cross-sections. Overall, the uncertainties in the simulations and the remarkable differences between the model outputs indicate that modelling of dust storms in the Middle East is really challenging due to the complex terrain, incorrect representation of the dust sources and soil/surface characteristics, and uncertainties in simulating the wind speed/direction and meteorological dynamics. Given the potential threat by dust storms, more attention should be directed to the dust model development in this region.

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There has been a severely negative impact on soil water resources in temperate forests caused by the introduction of the type of heavy machinery in the forestry sector used for forest harvesting operations. These soil disturbances increase the raindrop impact on bare mineral soil, decrease infiltration rate, detach soil particles, and enhance surface flow. According to several studies, the role of slope gradient influence on runoff and soil loss continues to be an issue, and therefore more study is needed in both laboratory simulations and field experiments. It is important to define and understand what the impacts of slope gradient in harvesting practices are, so as to develop guidelines for forest managers. More knowledge on the key factors that cause surface runoff and soil loss is important in order to limit any negative results from timber harvesting operations performed on hilly terrains in mountainous forests. A field setting using a runoff plot 2 m2 in size was installed to individualize the effects of different levels of slope gradient (i.e., 5, 10, 15, 20, 25, 30, 35, and 40%) on the surface runoff, runoff coefficient, and sediment yield on the skid trails under natural rainfall conditions. Runoff and sediment yield were measured with 46 rainfall events which occurred during the first year after machine traffic from 17 July 2015 to 11 July 2016 under natural conditions. According to Pearson correlation, runoff (r = 0.51), runoff coefficient (r = 0.55), and sediment yield (r = 0.51) were significantly correlated with slope gradient. Results show that runoff increased from 2.45 to 6.43 mm as slope gradient increased from 5 to 25%, reaching to the critical point of 25% for slope. Also, further increasing the slope gradient from 25 to 40% led to a gradual decrease of the runoff from 6.43 to 4.62 mm. Runoff coefficient was significantly higher under the plot with a slope gradient of 25% by 0.265, whereas runoff coefficient was lowest under the plot with a slope gradient of 5%. Results show that sediment yield increased by increasing the slope gradient of plot ranging 5% to 30%, reaching to the critical point of 30%, and then decreased as the slope gradient increased from 35% to 40%. Runoff plot with a slope gradient of 30% (4.08 g m−2) ≈ plot length of 25% (3.91 g m−2) had a significantly higher sediment yield, whereas sediment yield was lowest under the plot with a slope gradient of 5% and 10%. A regression analysis of rainfall and runoff showed that runoff responses to rainfall for plots with different slope gradients were linearly and significantly increased. According to the current results, log skidding operations should be planned in the skid trails with a slope gradient lower than the 25 to 30% to suppress the negative effect of skidding operations on runoff and sediment yield.

This study investigates four types of synoptic dust events in the Middle East region, including cyclonic, pre-frontal, post-frontal and Shamal dust storms. For each of these types, three intense and pervasive dust events are analyzed from a synoptic meteorological and numerical simulation perspective. The performance of 9 operational dust models in forecasting these dust events in the Middle East is qualitatively and quantitatively evaluated against Terra-MODIS observations and AERONET measurements during the dust events. The comparison of model AOD outputs with Terra-MODIS retrievals reveals that despite the significant discrepancies, all models have a relatively acceptable performance in forecasting the AOD patterns in the Middle East. The models enable to represent the high AODs along the dust plumes, although they underestimate them, especially for cyclonic dust storms. In general, the outputs of the NASA-GEOS and DREAM8-MACC models present greater similarity with the satellite and AERONET observations in most of the cases, also exhibiting the highest correlation coefficient, although it is difficult to introduce a single model as the best for all cases. Model AOD predictions over the AERONET stations showed that DREAM8-MACC exhibited the highest R2 of 0.78, followed by NASA_GEOS model (R2 = 0.74), which both initially use MODIS data assimilation. Although the outputs of all models correspond to valid time more than 24 h after the initial time, the effect of data assimilation on increasing the accuracy is important. The different dust emission schemes, soil and vegetation mapping, initial and boundary meteorological conditions and spatial resolution between the models, are the main factors influencing the differences in forecasting the dust AODs in the Middle East.

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