Ocean responses to Shaheen, the first cyclone to hit the north coast of Oman in 2021

Nadhairi, Rahma Al; Hassan, A. K.; Abdelsattar, Amal; Bruss, Gerd; Akhazami, Suleiman Al

doi:10.1016/j.dynatmoce.2023.101358

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…The following is an explanatory overview of them. Regarding chlorophyll-a products, Nadhairi et al [59], for example, employed the daily mean chlorophyll-a satellite data (spatial resolution of 4 × 4 km) that were provided by the Copernicus Marine Environmental Monitoring Center (CMEMS). Regarding the digital elevation model, Xu et al [60], for example, compared some products that play a critical role in flood simulation.…”

Section: Available Productsmentioning

confidence: 99%

“…Most of the papers (79%) monitored wind data over time (e.g., [153]), whereas 41% retrieved this parameter from several remote data (e.g., [172]) and 38% compared different methods or different products to minimize errors (e.g., [173]). Moreover, 13 papers combined remote sensing techniques with models (e.g., [59]) or GIS techniques (e.g., [102]).…”

Section: Wind Datamentioning

confidence: 99%

“…] -SST (1) Effects of extreme events -Yes [59,153] --SST (1) Fishing zones MODIS (0.5-1 km) Yes [303,382] --SST (1) Giant kelp NOAA Yes --[228] SST (1) Green tide AVHRR (5 km) Yes - [199] -SST (1) Green tide Landsat (15-30 m) No -- [200] SST (1) Green tide MODIS (0.5-1 km) No --[200] SST (1) Harmful algal bloom MODIS (0.5-1 km) Yes [304] [305]…”

Section: Parametermentioning

confidence: 99%

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Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Cavalli

2024

Remote Sensing

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Since 1971, remote sensing techniques have been used to map and monitor phenomena and parameters of the coastal zone. However, updated reviews have only considered one phenomenon, parameter, remote data source, platform, or geographic region. No review has offered an updated overview of coastal phenomena and parameters that can be accurately mapped and monitored with remote data. This systematic review was performed to achieve this purpose. A total of 15,141 papers published from January 2021 to June 2023 were identified. The 1475 most cited papers were screened, and 502 eligible papers were included. The Web of Science and Scopus databases were searched using all possible combinations between two groups of keywords: all geographical names in coastal areas and all remote data and platforms. The systematic review demonstrated that, to date, many coastal phenomena (103) and parameters (39) can be mapped and monitored using remote data (e.g., coastline and land use and land cover changes, climate change, and coastal urban sprawl). Moreover, the authors validated 91% of the retrieved parameters, retrieved from remote data 39 parameters that were mapped or monitored 1158 times (88% of the parameters were combined together with other parameters), monitored 75% of the parameters over time, and retrieved 69% of the parameters from several remote data and compared the results with each other and with available products. They obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. They combined 17% of the parameters that were retrieved with GIS and model techniques. In conclusion, the authors addressed the requirements needed to more effectively analyze coastal phenomena and parameters employing integrated approaches: they retrieved the parameters from different remote data, merged different data and parameters, compared different methods, and combined different techniques.

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Section: Available Productsmentioning

confidence: 99%

Section: Wind Datamentioning

confidence: 99%

Section: Parametermentioning

confidence: 99%

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Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Cavalli

2024

Remote Sensing

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Investigation of Meteorological Characteristics of Tropical Supercyclone Shaheen Insights from High-Resolution Satellite Observations

Meer,

Mishra,

Nagaraju

2024

J Indian Soc Remote Sens

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Investigating the Role of the Key Conditioning Factors in Flood Susceptibility Mapping Through Machine Learning Approaches

Al-Kindi,

Alabri

2024

Earth Syst Environ

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This study harnessed the formidable predictive capabilities of three state-of-the-art machine learning models—extreme gradient boosting (XGB), random forest (RF), and CatBoost (CB)—applying them to meticulously curated datasets of topographical, geological, and environmental parameters; the goal was to investigate the intricacies of flood susceptibility within the arid riverbeds of Wilayat As-Suwayq, which is situated in the Sultanate of Oman. The results underscored the exceptional discrimination prowess of XGB and CB, boasting impressive area under curve (AUC) scores of 0.98 and 0.91, respectively, during the testing phase. RF, a stalwart contender, performed commendably with an AUC of 0.90. Notably, the investigation revealed that certain key variables, including curvature, elevation, slope, stream power index (SPI), topographic wetness index (TWI), topographic roughness index (TRI), and normalised difference vegetation index (NDVI), were critical in achieving an accurate delineation of flood-prone locales. In contrast, ancillary factors, such as annual precipitation, drainage density, proximity to transportation networks, soil composition, and geological attributes, though non-negligible, exerted a relatively lesser influence on flood susceptibility. This empirical validation was further corroborated by the robust consensus of the XGB, RF and CB models. By amalgamating advanced deep learning techniques with the precision of geographical information systems (GIS) and rich troves of remote-sensing data, the study can be seen as a pioneering endeavour in the realm of flood analysis and cartographic representation within semiarid fluvial landscapes. The findings advance our comprehension of flood vulnerability dynamics and provide indispensable insights for the development of proactive mitigation strategies in regions that are susceptible to hydrological perils.

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Ocean responses to Shaheen, the first cyclone to hit the north coast of Oman in 2021

Cited by 6 publications

References 47 publications

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Investigation of Meteorological Characteristics of Tropical Supercyclone Shaheen Insights from High-Resolution Satellite Observations

Investigating the Role of the Key Conditioning Factors in Flood Susceptibility Mapping Through Machine Learning Approaches

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