No abstract

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.

During an extreme event, having accurate inflow forecasting with enough lead time helps reservoir operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate reservoirs could help maximize flood protection while saving water for drier times of the year. This study combines ensemble quantitative precipitation forecasts and a hydrological model to provide a 3-day reservoir inflow in the Shihmen Reservoir, Taiwan. A total of six historical typhoons were used for model calibration, validation, and application. An understanding of cascaded uncertainties from the numerical weather model through the hydrological model is necessary for a better use for forecasting. This study thus conducted an assessment of forecast uncertainty on magnitude and timing of peak and cumulative inflows. It found that using the ensemble-mean had less uncertainty than randomly selecting individual member. The inflow forecasts with shorter length of cumulative time had a higher uncertainty. The results showed that using the ensemble precipitation forecasts with the hydrological model would have the advantage of extra lead time and serve as a valuable reference for operating reservoirs.

Rainfall during typhoons is one of the most important water resources in Taiwan, but heavy typhoon rainfall often leads to serious disasters and consequently results in loss of lives and property. Hence, accurate forecasts of typhoon rainfall are always required as important information for water resources management and rainfall-induced disaster warning system. In this study, a methodology is proposed for providing quantitative forecasts of 24 h cumulative rainfall during typhoons. Firstly, ensemble forecasts of typhoon rainfall are obtained from an ensemble numerical weather prediction (NWP) system. Then, an evolutionary algorithm, i.e., genetic algorithm (GA), is adopted to real-time decide the weights for optimally combining these ensemble forecasts. That is, the novelty of this proposed methodology is the effective integration of the NWP-based ensemble forecasts through an evolutionary algorithm-based strategy. An actual application is conducted to verify the forecasts resulting from the proposed methodology, namely NWP-based ensemble forecasts with a GA-based integration strategy. The results confirm that the forecasts from the proposed methodology are in good agreement with observations. Besides, the results from the GA-based strategy are more accurate as compared to those by simply averaging all ensemble forecasts. On average, the root mean square error decreases about 7%. In conclusion, more accurate typhoon rainfall forecasts are obtained by the proposed methodology, and they are expected to be useful for disaster warning system and water resources management during typhoons.

Hydrological ensemble prediction systems (HEPSs) can provide decision makers with early warning information, such as peak stage and peak time, with enough lead time to take the necessary measures to mitigate disasters. This study develops a HEPS that integrates meteorological, hydrological, storm surge, and global tidal models. It is established to understand information about the uncertainty of numerical weather predictions and then to provide probabilistic flood forecasts instead of commonly adopted deterministic forecasts. The accuracy of flood forecasting is increased. However, the spatiotemporal uncertainty associated with these numerical models in the HEPS and the difficulty in interpreting the model results hinder effective decision-making during emergency response situations. As a result, the efficiency of decision-making is not always increased. Thus, this study also presents a visualization method to interpret the ensemble results to enhance the understanding of probabilistic runoff forecasts for operational purposes. A small watershed with area of 100 km2 and four historical typhoon events were selected to evaluate the performance of the method. The results showed that the proposed HEPS along with the visualization approach improved the intelligibility of forecasts of the peak stages and peak times compared to that of approaches previously described in the literature. The capture rate is greater than 50%, which is considered practical for decision makers. The proposed HEPS with the visualization method has potential for both decreasing the uncertainty of numerical rainfall forecasts and improving the efficiency of decision-making for flood forecasts.