资源环境科技发展态势分析平台

Flood modeling at the regional to global scale is a key requirement for equitable emergency and land management. Coupled hydrological-hydraulic models are at the core of flood forecasting and risk assessment models. Nevertheless, each model is subject to uncertainties from different sources (e.g., model structure, parameters, and inputs). Understanding how uncertainties propagate through the modeling cascade is essential to invest in data collection, increase flood modeling accuracy, and comprehensively communicate modeling results to end users. This study used a numerical experiment to quantify the propagation of errors when coupling hydrological and hydraulic models for multiyear flood event modeling in a large basin, with large morphological and hydrological variability. A coupled modeling chain consisting of the hydrological model Hydrologiska Byrans Vattenbalansavdelning and the hydraulic model LISFLOOD-FP was used for the prediction of floodplain inundation in the Murray Darling Basin (Australia), from 2006 to 2012. The impacts of discrepancies between simulated and measured flow hydrographs on the predicted inundation patterns were analyzed by moving from small upstream catchments to large lowland catchments. The numerical experiment was able to identify areas requiring tailored modeling solutions or data collection. Moreover, this study highlighted the high sensitivity of inundation volume and extent prediction to uncertainties in flood peak values and explored challenges in time-continuous modeling. Accurate flood peak predictions, knowledge of critical morphological features, and an event-based modeling approach were outlined as pragmatic solutions for more accurate prediction of large-scale spatiotemporal patterns of flood dynamics, particularly in the presence of low-accuracy elevation data.

Plain Language Summary Floods are among the most devastating natural hazards, affecting multiple regions and millions of people each year. Accurate inundation predictions are vital information for land and emergency management. This objective can be achieved through a cascade of numerical models. However, each model is subject to uncertainties from different sources (e.g., input data, model structure, and parameters), and an understanding of how these uncertainties are propagated through each step of the modeling cascade is pivotal to improving inundation prediction accuracy. This study investigated the impact of uncertainties in streamflow predictions on the accuracy of floodplain inundation predictions. For this purpose, the Murray Darling Basin (Australia), a large basin that is affected by destructive floods, was used as a case study. The analysis illustrated the high sensitivity of floodplain inundation predictions to predicted streamflow peak values. Moreover, when attempting to model a long time series of low- and high-flow periods, uncertainties in the inundation patterns increased over time and from upstream to downstream areas of the basin. These results demonstrated the need for accurate predictions of streamflow peak values and suggested that focusing on the modeling of each large flood event separately is a more effective strategy for reliable inundation predictions.