The characteristics of near surface air temperature and wind field over the Southeastern Levantine (SEL) sub-basin during the period 1979–2018 were simulated. The simulation was carried out using a dynamical downscaling approach, which requires running a regional climate model system (RegCM-SVN6994) on the study domain, using lower-resolution climate data (i.e., the fifth generation of ECMWF atmospheric reanalysis of the global climate ERA5 datasets) as boundary conditions. The quality of the RegCM-SVN simulation was first verified by comparing its simulations with ERA5 for the studied region from 1979 to 2018, and then with the available five WMO weather stations from 2007 to 2018. The dynamical downscaling results proved that RegCM-SVN in its current configuration successfully simulated the observed surface air temperature and wind field. Moreover, RegCM-SVN was proved to provide similar or even better accuracy (during extreme events) than ERA5 in simulating both surface air temperature and wind speed. The simulated annual mean T2m by RegCM-SVN (from 1979 to 2018) was 20.9 °C, with a positive warming trend of 0.44 °C/decade over the study area. Moreover, the annual mean wind speed by RegCM-SVN was 4.17 m/s, demonstrating an annual negative trend of wind speed over 92% of the study area. Surface air temperatures over SEL mostly occurred within the range of 4–31 °C; however, surface wind speed rarely exceeded 10 m/s. During the study period, the seasonal features of T2m showed a general warming trend along the four seasons and showed a wind speed decreasing trend during spring and summer. The results of the RegCM-SVN simulation constitute useful information that could be utilized to fully describe the study area in terms of other atmospheric parameters.

With the fast and highly growing demand for all possible ways of remote work as a result of COVID19 pandemic, new technologies using Satellite data were highly encouraged for multidisciplinary applications in different fields such as; agriculture, climate change, environment, coastal management, maritime, security and Blue Economy.This work supports applying Satellite Derived Bathymetry (SDB) with the available low-cost multispectral satellite imagery applications, instruments and readily accessible data for different areas with only their benthic parameters, water characteristics and atmospheric conditions.  The main goal of this work is to derive bathymetric data needed for different hydrographic applications, such as: nautical charting, coastal engineering, water quality monitoring, sediment movement monitoring and supporting both green carbon and marine data science.  Also, this work proposes and assesses a SDB procedure that makes use of publicly-available multispectral satellite images (Sentinel2 MSI) and applies algorithms available in the SNAP software package for extracting bathymetry and supporting bathymetric layers against highly expensive traditional in-situ hydrographic surveys. The procedure was applied at SAFAGA harbor area, located south of Hurghada at (26°44′N, 33°56′E), on the Egyptian Red Sea coast.  SAFAGA controls important maritime traffic line in Red Sea such as (Safaga – Deba, Saudi Arabia) maritime cruises.  SAFAGA depths change between 6 m to 22m surrounded by many shoal batches and confined waters that largely affect maritime safety of navigation.  Therefore, there is always a high demand for updated nautical charts which this work supports.  The outcome of this work provides and fulfils those demands with bathymetric layers data for the approach channel and harbour usage bands electronic nautical chart of SAFAGA with reasonable accuracies.  The coefficient of determination (R2) differs between 0.42 to 0.71 after applying water column correction by Lyzenga algorithm and deriving bathymetric data depending on reflectance /radiance of optical imagery collected by sentinel2 missions with in-situ depth data values relationship by Stumpf equation.  The adopted approach proved to give  highly reasonable results that could be used in nautical charts compilation. Similar methodologies could be applied to inland water bodies.  This study is part of the MSc Thesis of the first author and is in the framework of a bilateral project between ASRT of Egypt and CNR of Italy which is still running.Keywords: Algorithm, Bathymetry, Sentinel 2, nautical charting, Safaga port, satellite imagery, water depth, Egypt.

Reliable assessment of shorelines extracted from medium-resolution satellite imagery requires independent high-resolution reference data and statistical methods that account for spatial dependence. This study compared three conventional analyst-assisted shoreline-extraction workflows—histogram thresholding, band ratio, and the Normalised Difference Water Index (NDWI)—at Coronation Park, Lake Ontario, Canada, using Sentinel-2 Level-2A imagery. A manually digitised shoreline derived from a UAV-based orthomosaic acquired approximately 27 h before the Sentinel-2 scene served as the independent reference. The UAV-based reference and each Sentinel-2-derived shoreline were divided into 31 ordered segments. For each Sentinel-2-derived segment midpoint, the shortest planar Euclidean distance to the nearest UAV-based reference midpoint was calculated and used to derive mean absolute error (MAE) and root mean square error (RMSE). Residual spatial autocorrelation was assessed using Moran’s I with 9999 permutations. Because the paired differences departed from normality, the Friedman test was treated as the primary overall comparison, while contiguous spatial-block permutation tests across block sizes of two to eight shoreline locations assessed robustness to local spatial dependence. NDWI achieved the highest positional agreement (MAE = 5.645 m; RMSE = 6.429 m), followed by band ratio (MAE = 14.303 m; RMSE = 14.797 m) and histogram thresholding (MAE = 26.167 m; RMSE = 26.910 m). Significant positive residual spatial autocorrelation was identified for all three methods (Moran’s I = 0.587–0.832, all p < 0.001). The Friedman test confirmed a significant extraction-method effect, χ²(2) = 49.226, p < 0.001, Kendall’s W = 0.794, and the effect remained significant across all tested spatial-block sizes, with empirical p-values ranging from 0.000007 to 0.004630. Among the three conventional methods tested at this large-lake site, NDWI provided the highest positional agreement and therefore offers a defensible baseline for evaluating future Sentinel-2 image-enhancement approaches.