There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.

Relationships between watershed characteristics and mean wadi flood peaks in arid regions are investigated. Mean flood peak discharge was derived for 12 watersheds ranging from 64 to 1730 km² in Oman using 270 flood events from 10 years of record. Fourteen watershed characteristics were automatically extracted from a digital elevation model, and multiple regression was used to investigate the effect of these characteristics on wadi mean peak flow (Q _MPF) and 5-, 10-, 20-, 50- and 100-year return period flood peaks. Drainage area (DA), wadi slope (WS), watershed mean elevation (BE) and agricultural/farm area (FR) were found to be the key variables affecting flood flows. As return period increased, the influence of BE on flood-peak estimation decreased. In addition, urbanization is increasing alongside increasing agricultural areas, and the inclusion of FR in the relationship improved the variance explanation by 11% over models using only traditional variables such as DA and BE. Citation Al-Rawas, G. A. & Valeo, C. (2010) Relationship between wadi drainage characteristics and peak flood flows in arid northern Oman. Hydrol. Sci. J. 55(3), 377–393.

Recognition of the carbon dioxide (CO₂) concentration variations over time is critical for tracing the future changes in climate both globally and regionally. In this study, a time series analysis of atmospheric CO₂ concentration and its relationship with precipitation, relative humidity (RH), and vegetation is investigated over Oman. The daily XCO₂ data from OCO-2 satellite was obtained from September 2014 to March 2019. The daily RH and precipitation data were also collected from the ground weather stations, and the Normalized Difference Vegetation Index was obtained from MODIS. Oman was studied in four distinct regions where the main emphasis was on the Monsoon Region in the far south. The CO₂ concentration time series indicated a significant upward trend over different regions for the study period, with annual cycles being the same for all regions except the Monsoon Region. This is indicative of RH, precipitation, and consequently vegetation cover impact on atmospheric CO₂ concentration, resulting in an overall lower annual growth in the Monsoon Region. Simple and multiple correlation analyses of CO₂ concentration with mentioned parameters were performed in zero to three-month lags over Oman. They showed high correlations mainly during the rainfall period in the Monsoon Region.