Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods

Abstract

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 Byråns 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.

Hydrological Tracking Model for Amazon Surface Waters

“…Furthermore, regional to global scale river hydrologic‐hydrodynamic models showed improvements in the simulation of large‐scale flooding processes and stress the complexity of several floodplains and wetlands systems. For example we can mention studies in the Pantanal (da Paz et al, 2011), semi‐arid Niger Inner Delta (Fleischmann et al, 2018), Negro River Basin (Fleischmann et al, 2020), Southeast Australia (Grimaldi et al, 2019), and South America (Siqueira et al, 2018) but also at global scales (Getirana et al, 2017; Hoch et al, 2017; Zhao et al, 2017).…”

Trade‐Offs Between 1‐D and 2‐D Regional River Hydrodynamic Models

“…This is certainly the case for the Negro basin where rainfall rates are among the highest in the Amazon and associated to major uncertainties (Getirana et al, 2011). Recent studies have discussed the role of runoff (and other water balance components) uncertainty on regional hydrodynamic models (Bermúdez et al, 2017; David et al, 2019; Grimaldi et al, 2019). Adding to this literature, we have performed tests on the role of an online (two‐way) coupling between hydrologic and hydrodynamic processes.…”

A Novel Approach for the Integral Management of Water Extremes in Plain Areas

“…Ref. [52] studied the impact of uncertainties in streamflow predictions on a large basin affected by destructive floods. They found a high sensitivity of the model to long time data series of low-and high-flow periods and increased uncertainties in the inundation patterns, both spatially and temporally.…”

“…Currently, different approaches have been developed for better predictions by means of the use of modelling techniques. Some examples are the development of an enhanced extreme learning machine model for river flow forecasting [46], a support vector regression (SRV) model and a hybrid SVR-based firefly algorithm model to simulate evaporation [47], the grading of the eutrophic state of a reservoir using an Environmental Fluid Dynamics Code (EFDC) model [48], a model for sea-bottom change simulations in coastal areas with complex shorelines [49], a model for turbulent fluvial systems [50], a decision-making model for reservoir flood control [51] and the coupling of hydrologic and hydraulic models for modelling floods [52].…”