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.

The accurate prediction of hourly runoff discharge in a watershed during heavy rainfall events is of critical importance for flood control and management. This study predicts n-h-ahead runoff discharge in the Sandimen basin in southern Taiwan using a novel hybrid approach which combines a physically-based model (HEC-HMS) with an artificial neural network (ANN) model. Hourly runoff discharge data (1200 datasets) from seven heavy rainfall events were collected for the model calibration (training) and validation. Six statistical indicators (i.e. mean absolute error, root mean square error, coefficient of correlation, error of time to peak discharge, error of peak discharge and coefficient of efficiency) were employed to evaluate the performance. In comparison with the HEC-HMS model, the single ANN model, and the time series forecasting (ARMAX) model, the developed hybrid HEC-HMS–ANN model demonstrates improved accuracy in recursive n-h-ahead runoff discharge prediction, especially for peak flow discharge and time.

Low-lying coastal regions and their populations are at risk during storm surge events and high freshwater discharges from upriver. An integrated storm surge and flood inundation modeling system was used to simulate storm surge and inundation in the Tsengwen River basin and the adjacent coastal area in southern Taiwan. A three-dimensional hydrodynamic model with an unstructured grid was used, which was driven by the tidal elevation at the open boundaries and freshwater discharge at the upriver boundary. The model was validated against the observed water levels for three typhoon events. The simulation results for the model were in reasonable agreement with the observational data. The model was then applied to investigate the effects of a storm surge, freshwater discharge, and a storm surge combined with freshwater discharge during an extreme typhoon event. The super Typhoon Haiyan (2013) was artificially shifted to hit Taiwan: the modeling results showed that the inundation area and depth would cause severe overbank flow and coastal flooding for a 200 year return period flow. A high-resolution grid model is essential for the accurate simulation of storm surges and inundation.

Global climate change has resulted in a gradual sea level rise. Sea level rise can cause saline water to migrate upstream in estuaries and rivers, thereby threatening freshwater habitat and drinking-water supplies. In the present study, a three-dimensional hydrodynamic model was established to simulate salinity distributions and transport time scales in the Wu River estuary of central Taiwan. The model was calibrated and verified using tidal amplitudes and phases, time-series water surface elevation and salinity distributions in 2011. The results show that the model simulation and measured data are in good agreement. The validated model was then applied to calculate the salinity distribution, flushing time and residence time in response to a sea level rise of 38.27 cm. We found that the flushing time for high flow under the present condition was lower compared to the sea level rise scenario and that the flushing time for low flow under the present condition was higher compared to the sea level rise scenario. The residence time for the present condition and the sea level rise scenario was between 10.51 and 34.23 h and between 17.11 and 38.92 h, respectively. The simulated results reveal that the residence time of the Wu River estuary will increase when the sea level rises. The distance of salinity intrusion in the Wu River estuary will increase and move further upstream when the sea level rises, resulting in the limited availability of water of suitable quality for municipal and industrial uses.