Saeid Varamesh

Climate change has increased the vulnerability of arid and semi-arid regions to recurrent and prolonged meteorological droughts. In light of this, our study has sought to assess the nature of future meteorological drought in Lake Urmia basin, Iran, within the context of future climate projections. To achieve this, data from 54 general circulation models (GCMs) was calibrated against both in situ and Global Precipitation Climatology Centre datasets. These GCMs were then employed to project drought conditions expected over 2016–2046 under RCP2.6 and RCP8.5 as the most optimistic and pessimistic scenarios, respectively. To provide a comprehensive analysis, these RCPs were combined with two different time scale Standardized Precipitation Index (SPI), leading to eight different scenarios. The SPI was calculated over two temporal scales for the past (1985–2015) and future (2016–2046), including the medium-term (SPI-6) and long-term (SPI-18) index. Results showed that while precipitation is expected to increase by up to 34%, parts of the basin are projected to face severe and prolonged droughts under both RCPs. The most severe drought event is expected to occur around 2045–2046 under the most pessimistic RCP8.5 scenario. Severe droughts with low frequency are also anticipated to increase under other scenarios. By characterizing meteorological drought conditions for Lake Urmia basin under future climate conditions, our findings call for urgent action for adaptation strategies to mitigate the future adverse effects of drought in this region and other regions facing similar challenges. Overall, this study provides valuable insight into the impacts of climate change on future droughts that can adversely influence water resources in arid and semi-arid regions.

Land surface temperature (LST) and land surface albedo (LSA) are the two key regional and global climate-controlling parameters; assessing their behavior would likely result in a better understanding of the appropriate adaptation strategies to mitigate the consequences of climate change. This study was conducted to explore the spatiotemporal variability in LST and LSA across different land use/cover (LULC) classes in northwest Iran. To do so, we first applied an object-oriented algorithm to the 10 m resolution Sentinel-2 images of summer 2019 to generate a LULC map of a 3284 km² region in northwest Iran. Then, we computed the LST and LSA of each LULC class using the SEBAL algorithm, which was applied to the Landsat-8 images from the summer of 2019 and winter of 2020. The results showed that during the summer season, the maximum and minimum LSA values were associated with barren land (0.33) and water bodies (0.11), respectively; during the winter season, the maximum LSA value was observed for farmland and snow cover, and the minimum value was observed in forest areas (0.21). The maximum and minimum LST values in summer were acquired from rangeland (37 °C) and water bodies (24 °C), respectively; the maximum and minimum values of winter values were detected in forests (4.14 °C) and snow cover (−21.36 °C), respectively. Our results revealed that barren land and residential areas, having the maximum LSA in summer, were able to reduce the heating effects to some extent. Forest areas, due to their low LSA and high LST, particularly in winter, had a greater effect on regional warming compared with other LULC classes. Our study suggests that forests might not always mitigate the effects of global warming as much as we expect.

Snowstorms are disturbance agents that have received relatively little research attention rather than significant disturbances that they pose to forest ecosystems. In this study, we modeled the interactions between snowstorms and different characteristics of a forest stand in northern Iran and spatially visualized the susceptibility of the stand to damage caused by snowstorms using the random forest (RF) and logistic regression (LR) methods. After a severe snowstorm in November 2021 that caused stem breakage and uprooting of individual trees, the location of 185 damaged trees was identified via field surveys and used for generating an inventory map of snowstorm damage. The thematic maps of fourteen explanatory variables representing the characteristics of damaged trees and the study forest were produced. The models were trained with 70% of the damaged trees and validated with the remaining 30% based on the area under the receiver operating characteristic curve (AUC). The results indicated the better performance of RF compared to LR in both training (0.934 vs. 0.896) and validation (0.925 vs. 0.894) phases. The RF model identified slope, aspect, and wind effect as the variables with the greatest impacts on the forest stand sustainability to snowstorm damage. Approximately 30% of the study area was categorized as high and very high susceptible to snowstorms. Our results can enable forest managers to tailor more informed adaptive forest management plans in readiness for snowstorm seasons and recovery from their damage.