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Despite considerable efforts and progress in increasing resilience to natural hazards, the adverse socio-economic impacts of extreme weather events continue to increase globally. As climate change progresses, disaster risk management needs alignment with adaptation measures. In this perspective paper, we discuss emerging complications in disaster risk management during recent events from an interoperability perspective. We argue that a lack of interoperability between data and models, information and communication, and governance are barriers to successful integrated disaster risk management and climate adaptation. On this basis, we take a detailed look at the challenges involved and suggest that trans-disciplinary knowledge co-production is key to promoting interoperability between these components. Finally, we outline a framework for enabling knowledge co-production to enhance risk governance by improving ways of facilitating trans-disciplinary stakeholder engagement and draft a novel open-source federated data infrastructure, which allows stakeholders to consolidate and connect relevant data sources, models and information products. [ABSTRACT FROM AUTHOR]

In recent years, urban areas have been increasingly affected by more frequent and severe pluvial floods, attributed to climate change and urbanization. This trend is expected to continue in the future, underscoring the critical importance of flood warning and disaster management. However, pluvial flood forecasts on a communal level do not exist in practice, mainly due to the high computational run-times of high-resolution flood simulation models. Here, we showcase the capability of the hydrodynamic model RIM2D (Rapid Inundation Model 2D) to deliver highly detailed and localized insights into expected flood extent and impacts in very short computational processing times, enabling operational flood warnings. We demonstrate these capabilities using the case of the June 2023 torrential rain and resulting flood event in the city of Braunschweig, located in Lower Saxony, Germany. During this event, the city experienced intense rainfall of 60 liters per square meter within a short timeframe, resulting in widespread inundation, significant disruption to the residents' daily life, and substantial economic costs to the city. This study serves as a clear indication that different dimensions of flood consequences can be simulated at very high resolutions in extremely short computational times and that models such as RIM2D, along with the necessary hardware for their operation, have reached a level of maturity suitable for integration into operational early warning systems and impact-based forecasting systems for such floods. [ABSTRACT FROM AUTHOR]

Flash floods pose a distinct challenge compared to traditional fluvial flooding, with infrastructure-based solutions proving less effective. Effective responses hinge on advanced early warning systems providing actionable information, emphasising the necessity for computational flood forecasting models. However, hydrodynamic models, renowned for accuracy and completeness, face limitations due to computational intensity. This study explores two 2D flood forecasting models, RIM2D and SERGHEI, both with GPU implementations which allow us to maximise the forecast lead time. While RIM2D is less computationally intensive, suitable for operational use, SERGHEI, with higher computational costs, targets large-scale high-performance computing (HPC) systems. The assessment of applicability and trade-offs is carried out on the 2021 Eifel flood event, particularly in the lower Ahr valley. A set of simulations were performed at various resolutions from 1 to 10 m, which reveal similar accuracy among both models at coarser resolutions, yet discrepancies arise at finer resolutions due to the distinct formulations. Both models exhibit a rapid computational cost escalation, but at resolutions equal to or coarser than 5 m, forecasts are remarkably faster than the real-time ideal for operational use, paving the way for their use in early warning systems. However, higher resolutions necessitate multi-GPU and HPC capabilities, underlining the importance of embracing such technology in addressing broader flood domains. [ABSTRACT FROM AUTHOR]

Due to the long memory of snow processes, statistical seasonal streamflow predictions in snow-dominated catchments typically rely on snowpack estimates. Using mountainous catchments in Central Asia as a case study, we demonstrate how seasonal hydrological forecasts benefit from incorporating large-scale climate oscillations (COs). First, we examine the teleconnections between the major COs and peak precipitation season in eight catchments across the Pamir and Tian-Shan mountains from February to June. We then employ a machine learning framework that incorporates snow water equivalent (SWE) and dominant COs indices as predictors for mean discharge from April to September. Our workflow leverages an ensemble technique that uses multiple SWE estimates from near-time global data sources and diverse types of explainable machine-learning models. We find that the winter states of the El Niño-Southern Oscillation and the North Atlantic Oscillation enhance SWE-based forecasts of seasonal discharge in the study catchments. We identify three instances in which the inclusion of COs as additional predictors could be instrumental for snowpack-based seasonal streamflow forecasting: 1) when forecasts are issued at extended lead times and accumulated SWE is not yet representative of seasonal terrestrial water storage; 2) when climate variability during the forecasted season plays a larger role in shaping seasonal discharge; and 3) SWE estimates for a catchment are subject to larger uncertainty. Our approach provides a novel way to reduce uncertainties in seasonal discharge predictions in data-scarce snowmelt-dominated catchments. [ABSTRACT FROM AUTHOR]

After a flood disaster, the question often arises: "What could have happened if the event had gone differently?" For example, what would be the effects of a flood if the path of a pressure system and the precipitation field had taken a different trajectory? In this paper, we use alternative scenarios of precipitation footprints shifted in space, the so-called "spatial counterfactuals" to generate plausible but unprecedented events. We explore the spatial counterfactuals of the deadly July 2021 flood in the Ahr Valley, Germany. We drive a hydrological model of the Ahr catchment with precipitation fields of this event systematically shifted in space. The resulting discharge is used as a boundary condition for a high-resolution two-dimensional hydrodynamic model. We simulate changes in peak flows, hydrograph volumes, maximum inundation extent and depths and affected assets and compare them to the simulations of the actual event. We show that even a slight shift of the precipitation field by 15–25 km eastwards, which does not seem implausible due to orographic conditions, causes an increase in peak flows at the gauge Altenahr of about 32 % and of up to 160 % at the individual tributaries. Also, significantly larger flood volumes of more than 25 % can be expected due to this precipitation shift. This results in significantly larger inundation extents and maximum depths at a number of analyzed focus areas. For example, in the focus area around Altenahr, the increase of mean and maximum depth of up to 1.25 m and 1.75 m, respectively, is simulated. The presented results should encourage flood risk managers as well as the general public to meet precautionary measures for extreme and unprecedented events. [ABSTRACT FROM AUTHOR]

Flash floods pose a distinct challenge compared to traditional fluvial flooding, with infrastructure-based solutions proving less effective. Effective responses hinge on advanced early warning systems providing actionable information, emphasising the necessity for computational flood forecasting models. However, hydrodynamic models, renowned for accuracy and completeness, face limitations due to computational intensity. This study explores two 2D flood forecasting models, RIM2D and SERGHEI, both with GPU implementations which allow to maximise the forecast lead time. While RIM2D is less computationally intensive, suitable for operational use, SERGHEI, with higher computational costs, targets large-scale High-Performance Computing (HPC) systems. The assessment of applicability and trade-offs is carried out on the 2021 Eifel flood event, particularly in the lower Ahr valley. A set of simulations were performed at various resolutions from 1 m to 10 m, which reveal similar accuracy among both models at coarser resolutions, yet discrepancies arise at finer resolutions due to the distinct formulations. Both models exhibit a rapid computational cost escalation, but at resolutions equal to or coarser than 5m, forecasts are remarkably faster than real-time—ideal for operational use, paving the way for their use in early warning systems. However, higher resolutions necessitate multi-GPU and HPC capabilities, underlining the importance of embracing such technology in addressing broader flood domains. [ABSTRACT FROM AUTHOR]

High-resolution flood maps are needed for more effective flood risk assessment and management. Producing these directly with hydrodynamic models is slow and computationally prohibitive at large scales. Here we demonstrate a new algorithm for post-processing low-resolution inundation layers by using high-resolution terrain models to disaggregate or downscale. The new algorithm is roughly 8 times faster than state-of-the-art algorithms and shows a slight improvement in accuracy when evaluated against observations of a recent flood using standard performance metrics. Qualitatively, the algorithm generates more physically coherent flood maps in some hydraulically challenging regions compared to the state of the art. The algorithm developed here is open source and can be applied in conjunction with a low-resolution hydrodynamic model and a high-resolution DEM to rapidly produce high-resolution inundation maps. For example, in our case study with a river reach of 20 km, the proposed algorithm generated a 4 m resolution inundation map from 32 m hydrodynamic model outputs in 33 s compared to a 4 m hydrodynamic model runtime of 34 min. This 60-fold improvement in runtime is associated with a 25 % increase in RMSE when compared against the 4 m hydrodynamic model results and observations of a recent flood. Substituting downscaling into flood risk model chains for high-resolution modelling has the potential to drastically improve the efficiency of inundation map production and increase the lead time of impact-based forecasts, helping more at-risk communities prepare for and mitigate flood damages. [ABSTRACT FROM AUTHOR]