Water is a limited resource but essential to sustaining life and supporting economic development. Only 2.5% of all the water on Earth is freshwater and can be used to meet basic human needs such as a clean supply of water for drinking, cooking, and bathing. Water scarcity is the result of an imbalance between supply and demand. Efficient water resource management is definitely of interest to research and is a practical topic. At the same time, water-related disasters such as floods and droughts cause the loss of life and property. Disasters increase the difficulty of effective water resource management. An increase in climate extremes can also increase the risk of floods and droughts. This overview covers 150 peer-reviewed journal publications from the last twenty years focusing on risk-reduction strategies for floods and droughts. First, a definition and classification of flood and drought was introduced. Second, studies and techniques associated with risk reduction were grouped into three themes and discussed: prediction and warning; monitoring; and impact assessment, response, and management. As a result, many studies were solely focused on, and achieve excellence in, their own themes. Special attention was needed to find in these studies what can convert the adverse impacts such as flood water to positive outcomes such as drought relief. Multidisciplinary cooperation is necessary to achieve sustainability and to adapt to climate change. Finally, advanced techniques such as artificial intelligence (AI) and the internet of things (IoT) were foreseen to have a tremendous impact on future disaster risk reduction.

Urban inundation forecasting with extended lead times is useful in saving lives and property. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, predicting peak flows and to allow response agencies to take necessary preparatory measures. However, ensemble precipitation forecasting is time- and resource-intensive. Using rainfall thresholds to estimate urban areas' inundation risk can decrease this complexity and save computation time. This study evaluated the performance of this system using 352 townships in Taiwan and seven typhoons during the period 2013–2015. The levels of forecast probability needed to issue inundation alerts were addressed because ensemble forecasts are probability based. This study applied six levels of forecast probability and evaluated their performance using five measures. The results showed that this forecasting system performed better before a typhoon made landfall. Geography had a strong impact at the start of the numerical weather modeling, resulting in the underestimation of rainfall forecasts. Regardless of this finding, the inundation forecast performance was highly contingent on the rainfall forecast skill. This study then tested a hybrid approach of on-site observations and rainfall forecasts to decrease the influence of numerical weather predictions and improve the forecast performance. The results of this combined system showed that forecasts with a 24 h lead time improved significantly. These findings and the hybrid approach can be applied to other hydrometeorological early warning systems to improve hazard-related forecasts.

An efficient inundation model is necessary for emergency flood responses during storm events. Cellular automata (CA)-based flood models have been proven to produce rapid results while maintaining a certain degree of accuracy. However, the need for computational resources dramatically increases when the number of grid cells increases. Digital elevation model (DEM)-based models generate results even faster, but the simplified governing equations within the models fail to reflect temporal flood evolution. To achieve rapid flood modeling while maintaining model simplicity, a novel two-dimensional hybrid inundation model (HIM) was developed by combining the CA- and DEM-based concepts. Given the temporal flood evolution generated by the CA concept, final finer-scale predictions were obtained by applying the DEM-based concept. The performance of this model was compared to those of widely used, physically based hydraulic models using three UK Environment Agency (EA) benchmark test cases. The HIM yielded consistent prediction results but was faster than the CA-based model. Finally, a comparison was made against flood observations, and the overall root mean squared error (RMSE) for flood depth was 0.388–0.400 m. Considering the uncertainty in the observed flood depths, the HIM shows promising potential to serve as an intermediate tool for emergency response in practical cases.

This study proposes a simplified model for non-riverine flood routing using a digital elevation model. The model has the advantage of running with only a few types of input, such as topographic data and cumulative rainfall. Given its ease of use, the model is stable and reliable for developing a real-time inundation forecasting system. This model uses two approaches to determine the collection of cells from which flooding is assumed to originate: (1) A traditional “lowest-elevation approach” that assumes flooding originates from the lowest elevations and that is only based on topographic data; and (2) a novel “D-infinity contributing area approach” that assumes flooding originates at the cells toward which the flow moves and that considers in situ topography and upslope information. The flood water is transferred based on the flat-water assumption that the water levels of adjacent cells are equalized. The performance was evaluated by comparing the simulated results with those from a complex inundation model. The simplified model with the lowest elevation assumption has limited applicability in flat areas and did not provide reasonable locations of the source of the flood. The D-infinity approach can improve the simplified inundation model and extend its application in various topographical areas.