Natural resources of the Mekong River are essential to livelihood of tens of millions of people. Previous studies highlighted that upstream hydro-infrastructure developments impact flow regime, sediment and nutrient transport, bed and bank stability, fish productivity, biodiversity and biology of the basin. Here, we show that tidal amplification and saline water intrusion in the Mekong Delta develop with alarming paces. While offshore M₂ tidal amplitude increases by 1.2–2 mm yr⁻¹ due to sea level rise, tidal amplitude within the delta is increasing by 2 cm yr⁻¹ and salinity in the channels is increasing by 0.2–0.5 PSU yr⁻¹. We relate these changes to 2–3 m bed level incisions in response to sediment starvation, caused by reduced upstream sediment supply and downstream sand mining, which seems to be four times more than previous estimates. The observed trends cannot be explained by deeper channels due to relative sea level rise; while climate change poses grave natural hazards in the coming decades, anthropogenic forces drive short-term trends that already outstrip climate change effects. Considering the detrimental trends identified, it is imperative that the Mekong basin governments converge to effective transboundary management of the natural resources, before irreversible damage is made to the Mekong and its population.

This study presents two machine learning models, namely, the light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), for the first time for predicting flash flood susceptibility (FFS) in the Wadi System (Hurghada, Egypt). A flood inventory map with 445 flash flood sites was produced and randomly divided into two groups for training (70%) and testing (30%). Fourteen flood controlling factors were selected and evaluated for their relative importance in flood occurrence prediction. The performance of the two models was assessed using various indexes in comparison to the common random forest (RF) method. The results show areas under the receiver operating characteristic curves (AUROC) of above 97% for all models and that LightGBM outperforms other models in terms of classification metrics and processing time. The developed FFS maps demonstrate that highly populated areas are the most susceptible to flash floods. The present study proves that the employed algorithms (LightGBM and CatBoost) can be efficiently used for FFS mapping.

The generation and processes of wadi flash floods are very complex and are not well understood. In this paper, we investigate the relationship between variations in geomorphometric and rainfall characteristics and the responses of wadi flash floods. An integrated approach was developed based on geomorphometric analysis and hydrological modeling. The Wadi Qena, which is located in the Eastern Desert of Egypt, was selected to validate the developed approach and was divided into 14 sub-basins with areas ranging from 315 to 1488 km2. The distributed Hydrological River Basin Environment Assessment Model (Hydro-BEAM) was used to obtain a good representation of the spatial variability of the rainfall and geomorphology in the basin. Thirty-eight geomorphometric parameters representing the topographic, scale, shape and drainage characteristics of the basins were considered and extracted using geographic information system (GIS) techniques. A series of flash flood events from 1994, 2010, 2013, and 2014, in addition to synthetic virtual storms with different durations and intensities, were selected for the application of this study. The results exhibit strong correlations between scale and topographic parameters and the hydrological indices of the wadi flash floods, while the shape and drainage network metrics have smaller impacts. The total rainfall amount and duration significantly impact the relationship between the hydrologic response of the wadi and its geomorphometry. For most of the parameters, we found that the impact of the wadi geomorphometry on the hydrologic response increases with increasing rainfall intensity.