the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief Communication: Rapid high-resolution flood impact-based early warning is possible with RIM2D: a showcase for the 2023 pluvial flood in Braunschweig
Abstract. 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.
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RC1: 'Comment on nhess-2024-139', Anonymous Referee #1, 23 Sep 2024
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This study shows a newly developed/combination technique that has the potential to enhance flood early warning systems based on the level of impacts. The topic is important, the writing is clear, the logic is sound, and the results appear to be significant. Overall, I suggest only minor revisions are needed before it can be considered for publication in NHESS as a Brief Communication paper. Specifically, I have a few comments:
1. While I understand the challenges of quantitatively validating the modeling results due to the lack of systematic data, such validation is crucial before asserting that the predictions of this technique are evident, as the authors claim in the conclusion. Could the authors explore indirect approaches to quantify the results? For instance, calculating the ratio of coherent versus incoherent grids based on the predicted and observed inundation areas (using only the grids with observations) could be akin to a confusion matrix analysis.
2. As the authors suggest, providing an uncertainty map for this tool is both important and useful. Why was this map not included in the exemplary case presented in the study?
3. The manuscript currently contains over 20 references, which exceeds the limit for a Brief Communication in NHESS. Additionally, there are a few typographical errors that need correction.
Citation: https://doi.org/10.5194/nhess-2024-139-RC1
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