This study investigates how projected changes in tropical cyclone (TC)-induced storminess, defined by extreme wind, surge, and wave events, will affect coastal flood hazards along the Sofala coastline in Mozambique under the near-future SSP5-85 climate scenario (up to year 2050). While global studies have suggested intensified storm characteristics in the Sofala region, no study to date has assessed their implications for regional coastal flood hazard in conjunction with sea-level rise (SLR). Using the synthetic STORM dataset in combination with probabilistic extreme value analysis and a storyline-based TC Idai simulation, this study quantifies the projected increase in TC-induced hazards and evaluates the relative contribution of storminess change and SLR to future flood depth and extent. Results show a consistent upward shift in the 100-year return levels for wind, surge, and wave. These changes can be mainly statistically attributed to the exceedance frequency (i.e., how often extremes occur) rather than exceedance intensity (i.e., how extreme they are when they occur). As such, the effective return period of historical 100-year events is found to be 45–60 years on average under the near-future scenario, suggesting that extreme events will become significantly more frequent. Although the upper tail dependence between surge and wave slightly weakens, the likelihood of joint surge–wave extremes still increases, lowering the joint return period from an average of 200 years to 120 years. In the TC Idai storyline, SLR accounts for 60–90% of the increase in total water levels, with a magnitude of around 0.25 m. However, storminess change contributes up to 40% in some areas, and is shown to substantially heighten flood extent and population exposure. The findings underscore the importance of integrating TC-induced storminess change in Sofala’s future coastal hazard assessments, in addition to SLR. Recommendations include extending analyses to additional synthetic TC models and emissions scenarios (e.g. SSP2-45), improving hydrodynamic model validation, and incorporating rainfall and fluvial forcing to capture the full spectrum of compound flood hazards under a changing climate.

Under future warmer climates, drought events are projected to occur more frequently with increasing impacts in many regions and river basins. This study focuses on exploring the potential of the LSTM deep learning (DL) approach for operational streamflow drought forecasting for the Rhine River at Lobith with a lead time (LT) of up to 46 days.
The research investigates optimal spatial resolution, input and target variables, and loss functions. Four LSTM-based model architectures are developed and tested, incorporating both historical observation and forecast data to generate 46-step forecasts simultaneously. The robustness and stability of the models are assessed through cross-validation, and their performances are compared. Subsequently, the performance of the LSTM-based model is compared to the physically-based models, namely Wflow-Rhine and FEWS-Rhine, in forecasting streamflow drought.
The results suggest that utilizing a subbasin spatial resolution, including historical discharge as input, and training the model on time-differenced data enhance the forecast skill. Among the evaluated models, the model architecture with two LSTMs in cascade exhibits stable and robust performance across the forecast horizon and is considered for operational use in this study. Comparisons between the DL model and physically-based models indicate that: 1) When using observed meteorology forcing from ERA5, the DL model demonstrates a notable performance compared to Wflow-Rhine simulation using the same forcing data. 2) When utilizing SEAS5 for forecasting, the DL model demonstrates skill over Wflow-Rhine in predicting discharge levels during the dry season up to 10 days ahead, as well as for discharges between 950 and 2200 m3/s across the entire forecast horizon. However, for discharges between 700 and 950 m3/s with longer LTs beyond 20 days, Wflow-Rhine shows skill over the DL model. 3) While FEWS-Rhine successfully forecasts drought events in 2018 throughout the forecast horizon, it tends to produce more Type I errors (false positives). The DL model, forecasting with SEAS5, accurately predicts drought events in 2018 for LTs up to 30 days and generally has higher precision values. Despite using different forcing datasets, the DL model can predict the timing and trend of past drought events, indicating its potential in capturing streamflow patterns.
This study contributes to operational water management in the Netherlands by employing the LSTM deep learning approach in an operational framework for drought forecasting. By leveraging historical observation data and forecasted meteorology forcing data, these models achieve skillful performances for streamflow drought forecasts. Future research could focus on further enhancing model performance, exploring the applicability of the LSTM-based models in other river basins, and validating the results in real operational settings.