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Globally, there have been overwhelming concerns about the effects of climate changes on various species’ ecosystems and habitats. In the marine realm, significant loss and degradation of coral reefs have been identified. Along the coastlines of the Oman Sea, coral reefs are highly threatened and degraded. The deterioration of this marine ecosystem has occurred as a direct response to a variety of environmental and natural threats. Consequently, mass bleaching and coral mortality have been reported in several sites causing loss of live coral cover and their values and services. Although anthropogenic impacts on coral reefs across the northern Oman coasts have been examined, explicit spatial modeling of environmental stressors on coral reefs has not been yet conducted. In this study, a combination of Geographical Information Systems techniques and a quantitative Analytical Hierarchical Process have been utilized to investigate spatial patterns of environmental risks posed to coral reef cover. A multiple risk index was calculated, and a map which represents risk classes was produced based on various vulnerability, exposure, and hazard criteria. The key findings of this analysis revealed that specific coral reef sites were severely susceptible to environmental threats, particularly sites at Muscat, Sur, Matrah and Ras Al Sawady. The deep exposure of coral reefs to natural threats was found to be highly triggered by thermal stress, coastal pollution, and proximity to potential sedimentation sources. This research may inform beneficial policy actions to lessen the outcomes of natural threats to coral cover across the Oman Sea coasts, specifically in terms of the development of risk maps and spatial strategies to protect the most vulnerable, fragile, and sensitive coral reef ecosystems.

Over the last two decades, globally coastal areas have urbanized rapidly due to various socioeconomic and demographic driving forces. However, urban expansion in towns and cities of the developing world has been characterized by entangled structures and trends exacerbating numerous negative consequences such as pollution, ecological degradation, loss of agricultural land and green areas, and deprived settlements. Substantially, spatial simulation of urban growth and their consequences on coastal areas particularly in Egypt is still very rare. Geospatial modelling coastal urban growth is crucial and has enormous potential for coastal land use transformation and urban sustainability. The key aim of this study was to analyze spatiotemporal changes (2010–2020) and simulate future dynamics (2030 to 2050) of land use/land cover (LULC) in Alexandria Governorate, Egypt. Artificial Neural Network–Multiple Layer Perceptron (ANN-MLP) and Markov Chain techniques were employed within the GIS platform to assess processes of land transitions and predict urban growth trends, patterns and dimensions. The forecasting process was based on three maps of LULC derived from classified Landsat images of 2000, 2010 and 2020. In addition, topographical, demographic, accessibility, proximity factors were generated and developed in the form of raster spatial parameters of urbanization driving forces. The findings revealed that the observed expansion of the built-up area during one decade (2010–2020) was 12,477.51 ha, with a decline in agricultural area (7440.39 ha) and bare land (4904.91 ha). The projected change was forecasted to be 71,544 ha by 2030 and 81,983 ha in 2040 with a total of 35,998 ha increase in the built-up area and residential expansion by 2050. Despite this expected pattern of rapid changes, urban growth will be shaped by the key drivers of proximity to coastline and agricultural land transformation. The analysis indicates that the vertical urban growth will be most likely dominant along the coastal zone due to the lack of vacant lands, whereas the horizontal urban expansion will primarily take place towards the east-northeastern and south-southeastern directions of the city. The present work provides a holistic framework for establishing initial coastal land use plans not only for planners and urban administrators in Alexandria but also for policymakers and coastal municipalities in developing nations.

In recent years, socioeconomic transformation and social modernisation in the Gulf Cooperation Council (GCC) states have led to tremendous changes in lifestyle and, subsequently, expansion of urban settlements. This accelerated growth is pronounced not only across vegetated coasts, plains, and mountains, but also in desert cities. Nevertheless, spatial simulation and prediction of desert urban patterns has received little attention, including in Oman. While most urban settlements in Oman are located in desert environments, research exploring and monitoring this type of urban growth is rare in the scientific literature. This research focuses on analysing and predicting land use–land cover (LULC) changes across the desert city of Ibri in Oman. A methodology was employed involving integrating the multilayer perceptron (MLP) and Markov chain (MC) techniques to forecast spatiotemporal LULC dynamics and map urban growth patterns. The inputs were three Landsat images from 2010 and 2020, and a series of covariate layers based on transforms of elevation, slope, population settlements, urban centres, and points of interest that proxy the driving forces of change. The findings indicated that the observed LULC changes were predominantly rapid across the city during 2010 to 2020, transforming desert, bare land, and vegetation into built-up areas. The forecast showed that area of land conversion from desert to urban would be 5666 ha during the next two decades and 7751 ha by 2050. Similarly, vacant land is expected to contribute large areas to urban expansion (2370 ha by 2040, and 3266 ha by 2050), although desert cities confront numerous environmental challenges, including water scarcity, shrinking vegetation cover, and being converted into residential land. Massive urban expansion has consequences for biodiversity and natural ecosystems—particularly in green areas, which are expected to decline by approximately 107 ha by 2040 (i.e., 10%) and 166 ha by 2050. The outcomes of this research provide fundamental guidance for decision-makers and planners in Oman and elsewhere to effectively monitor and manage desert urban dynamics and sustainable desert cities.

The year 2020 witnessed the arrival of the global COVID-19 pandemic, which became the most devastating public health disaster in the last decade. Understanding the underlying spatial variations of the consequences of the pandemic, particularly mortality, is crucial for plans and policies. Nevertheless, few studies have been conducted on the key determinants of COVID-19 mortality and how these might vary geographically across developing nations. Therefore, this research aims to address these gaps by adopting the Geographically Weighted Poisson Regression (GWPR) model to investigate spatial heterogeneity of COVID-19 mortality in Oman. The findings indicated that local GWPR performed better than global Ordinary Least Square (OLS) model, and the relationship between risk factors and mortality cases varied geographically at a subnational scale. The local parameter estimates of the model revealed that elderly populations, respiratory diseases, and population density were significant in predicting mortality cases. The elderly population variable was the most influential regressor, followed by respiratory diseases. The formulated policy recommendations will provide decision-makers and practitioners with key factors related to pandemic mortality so that future interventions and preventive measures can mitigate high fatality risks.

The Sultanate of Oman overlooks three water bodies: The Arabian Sea, the Sea of Oman and the Arabian Gulf with a coastal face of more than 3000 km. Due to the recent global climate change, storm intensity has increased and inundation of coastal areas is inevitable. The pattern of coastal flooding depends on the geomorphologic and oceanographic characteristics of the coastal zone. The current research aims to delineate the susceptibility of Omani coast to the sudden sea level rise from cyclones and tsunamis using the coastal vulnerability index (CVI). Five physical parameters were implemented to perform the CVI, namely: The coastal geomorphology, elevation, slope, tidal range and bathymetry of the nearshore zone. Data were extracted from remotely sensed images and government resources assisted by field surveying. Geospatial analysis using geographical information system (GIS) was performed to manipulate and process the CVI from the collected data. Results showed that high vulnerable coastal regions to sea level rise account for 805 km of the coast, mostly along Al-Batinah plain in the north and along some scattered sectors at the eastern coast of the country. Major settlements and infrastructures are located at high CVI category. Moderate vulnerable coasts total 695 km mostly at the headlands along the Arabian Sea, whereas the low vulnerability coasts include the remaining shores along Musandam Peninsula and the eastern coast. This study provides a national map of the coastal vulnerability to the sea level rise, which is important for urban planning and decision supports for a sustainable management of Omani coastal zone.

Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the well-reported challenges of pixel overglow and saturation influence the applicability of the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) for accurate population mapping. This paper integrates three remotely sensed information sources, DMSP-OLS, vegetation, and bare land areas, to develop a novel index called the Vegetation-Bare Adjusted NTL Index (VBANTLI) to overcome the uncertainties in the DMSP-OLS data. The VBANTLI was applied to Riyadh province to downscale governorate-level census population for 2004 and 2010 to a gridded surface of 1 km resolution. The experimental results confirmed that the VBANTLI significantly reduced the overglow and saturation effects compared to widely applied indices such as the Human Settlement Index (HSI), Vegetation Adjusted Normalized Urban Index (VANUI), and radiance-calibrated NTL (RCNTL). The correlation coefficient between the census population and the RCNTL (R = 0.99) and VBANTLI (R = 0.98) was larger than for the HSI (R = 0.14) and VANUI (R = 0.81) products. In addition, Model 5 (VBANTLI) was the most accurate model with R2 and mean relative error (MRE) values of 0.95% and 37%, respectively.

The COVID-19 pandemic greatly impacted socioeconomic life globally. Nighttime-lights (NTLs) data are mainly related to anthropogenic phenomena and thus have the ability to monitor changes in socioeconomic activity. However, the overglow effect is a source of uncertainty and affects the applicability of NTL data for accurately monitoring socioeconomic changes. This research integrates the NTL and fine bare-land-cover data to construct a novel index named the Bare Adjusted NTL Index (BANTLI) to lessen the overglow uncertainty. BANTLI was used to measure the post-pandemic resumption of religious rituals and socioeconomic activity in Makkah and Madinah at different spatial levels. The results demonstrate that BANTLI significantly eliminates the overglow effect. In addition, BANTLI brightness recovered during the post-pandemic periods, but it has remained below the level of the pre-pandemic period. Moreover, not all wards and rings are affected equally: wards and rings that are near the city center experienced the most explicit reduction of BANTLI brightness compared with the suburbs. The Hajj pilgrimage period witnessed a larger decrease in BANTLI brightness than the pandemic period in Makkah. The findings indicate that (i) BANTLI successfully mitigates the overglow effect in the NTL data, and (ii) the cultural context is important to understand the impact of COVID-19.

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too.

Drought is one of the most devastating natural disasters, and its consequences affect various human and environmental aspects. In both arid and semi-arid regions, the impact of drought poses direct threats to livelihoods and socio-economic activities. For drought mitigation purposes, spatially accurate predictions of the areas to be affected are essential. By utilising an Artificial Neural Network (ANN) within a Geographic Information Systems (GIS) environment, this research aimed to project drought severity across Oman throughout the twenty-first century. Drought severity during the rainy season (DJF) was characterised using the Standardized Precipitation Evapotranspiration Index (SPEI) calculated for February at a three-month timescale. SPEI was computed based on the monthly data for a set of climatic variables (i.e. maximum and minimum air temperatures, total precipitation, wind speed, relative humidity) derived from the climate forecast system reanalysis (CFSR) dataset at a grid interval of 0.25° for the period between 1998 and 2012. The ANN model was forced with drought classes (i.e. mild, moderate, severe, extreme, and very extreme) employed as a dependent variable, while a wide spectrum of climatic (e.g., air temperature, precipitation, wind speed), topographical (e.g., elevation, aspect) and geographical (e.g., distance to coasts, vegetation cover) variables were used as independent variables. For consistency in projecting drought changes, the dependent and independent variables were re-gridded to a common grid interval (0.25 °C) using a spline interpolation algorithm. Our findings show that the ANN model provided a realistic simulation of drought occurrence incorporating the relevant climatic, topographical and geographic parameters across Oman. Regarding the projected spatial patterns of drought, the northern parts of the study area (e.g., North and South Al-Batinah governorates) are exposed to the severe and extreme intensification of drought, whilst predominately medium and low levels of droughts are expected to occur across the south and south-west areas of Oman. In a water-scarce region like Oman, the results of this study could have particular policy implications, specifically in terms of management of water resources, food production, agriculture, water supply, hydropower energy and biodiversity, amongst others. The projected changes in drought occurrence in Oman make it necessary to develop effective national initiatives to mitigate the impacts of drought and to build society's capacity for drought preparedness. Graphical abstract

Environmental DNA (eDNA) is increasingly being used to assess community composition in marine ecosystems. Applying eDNA approaches across broad spatial scales now provide the potential to inform biogeographic analyses. However, to date, few studies have employed this technique to assess broad biogeographic patterns across multiple taxonomic groups. Here, we compare eDNA‐derived communities of bony fishes and invertebrates, including corals and sponges, from 15 locations spanning the entire length of the Omani coast. This survey includes a variety of habitats, including coral and rocky reefs, and covers three distinct marine ecoregions. Our data support a known biogeographic break in fish communities between the north and the south of Oman; however, the eDNA data highlight that this faunal break is mostly reflected in schooling baitfish species (e.g., sardines and anchovies), whereas reef‐associated fish communities appear more homogeneous along this coastline. Furthermore, our data provide indications that these biogeographic breaks also affect invertebrate communities, which includes corals, sponges, and broader eukaryotic groups. The observed community shifts were correlated with local environmental and anthropogenic differences characteristic of this coastline, particularly for the eDNA‐derived bony fish communities. Overall, this study provides compelling support that eDNA sequencing and associated analyses may serve as powerful tools to detect community differences across biogeographic breaks and ecoregions, particularly in places where there is significant variation in oceanographic conditions or anthropogenic impacts. We conducted a multi‐marker eDNA metabarcoding survey across the coastline of Oman to assess whether eDNA‐derived fish and invertebrate communities reflected a biogeographic break between the north and the south of Oman. The eDNA data indicated that this faunal break is mostly reflected in schooling baitfish species but also included corals, sponges, and broader eukaryotic groups. The observed community shifts showed some correlation with local environmental and anthropogenic differences characteristic of this coastline, particularly for the eDNA‐derived fish communities.