| 07 Jul 2025
Enabling Real-Time High-Resolution Flood Forecasting for the Entire State of Berlin Through RIM2D’s Multi-GPU Processing
Abstract. Urban areas are increasingly experiencing more frequent and intense pluvial flooding due to the combined effects of climate change and rapid urbanization—a trend expected to continue in the coming decades. This highlights the growing need for effective flood forecasting and disaster management systems. While recent advances in GPU computing have made high-resolution hydrodynamic modeling feasible at the urban scale, operational use remains limited, particularly for large domains where single-GPU processing falls short in terms of memory and performance.
This study demonstrates the capabilities of RIM2D (Rapid Inundation Model 2D), enhanced with multi-GPU processing, to perform high-resolution pluvial flood simulations across large urban domains such as the whole state of Berlin (891.8 km2) within operationally relevant timeframes. We evaluate RIM2D’s performance across spatial resolutions of 2, 5, and 10 meters using GPU configurations ranging from 1 to 8 units. Two flood scenarios are analyzed: the real-world pluvial flood of June 2017 and a standardized 100-year return period (HQ100) event used for official hazard mapping. Results show that RIM2D can deliver detailed flood extents, flow characteristics, and impact estimates fast enough to be integrated into real-time early warning systems, even at fine spatial resolutions. Multi-GPU processing proves essential not only for enabling high-resolution simulations (e.g., 2 m or finer), but also for making simulations at resolutions finer than 5 m computationally feasible for flood forecasting and early warning applications. Additionally, we find that beyond 4 GPUs, runtime improvements become marginal for 5 and 10 m resolutions, and similarly, more than 6 GPUs offer limited benefit at 2 m resolution, illustrating the balance between computational nodes of the used GPUs and number of raster cells of the model. Moreover, simulations at a finer 1 m resolution demand more than 8 GPUs to be run. Overall, this work demonstrates that large-scale, high-resolution flood simulations can now be executed rapidly enough to support operational early warning and impact-based forecasting. With models like RIM2D and the continued advancement of GPU hardware, the integration of detailed, real-time flood forecasting into urban flood risk management is both technically feasible and urgently needed.
The authors present an interesting article on high-resolution (2, 5, 10 m) rainfall-runoff simulations in Berlin with the RIM2D model using GPUs. The study presents a step forward with regard to capabilities of shallow water flow modelling in urban areas. The paper is worth to be published after minor revision.
l. 11: Abstract: when you speak about integration in a real-time early warning system, you should add some numbers on computational time of your 1h simulation
l. 101: t the 1D domain decomposition for the 2D is somehow unclear, explain more
l. 131ff: if you use 2m resolution, 1 cell has 4m², Berlin area of 900km2 then would require~225 mio. cells, why is your number ~double as high, similar for the other resolutions
l. 183, 205: rain is not a boundary condition but a source term, check in the document
sec. 3.1: the performance gain using several GPUs is several times quite poor, give some explanation why, any parallel overheads ?
l. 360: compare Berlin to the similar approaches of other federal states such as North Rhine-Westphalia
l. 366: these deep uncertainties must be mentioned / discussed, otherwise I would delete or rephrase
l. 383: criterion for affected persons, is this your definition or from the literature
Fig 8: how is the number of effected persons computed, is it per cell, how can it be smaller than 1
Sec. 3.4: comment more on uncertainties, friction, infiltration, sewer system ? as your model is that fast, you could do parameter variations eg for friction and infiltration
l. 426: you argue that such speeds are only achievable through multi-GPU; but such speed are also achievable through HPC cluster with many cores / CPU; add this here and also earlier where you argue similarly
in the context of real time prediction, you should also mention that there are several promising machine / deep learning / artificial neural network approaches
Minor:
- sometimes you speak about the state of Berlin, sometimes about the city, I suggest to unify
- l. 97: can you give a reference ?
- unify all headlines, sometime 1st small, sometimes capital
- l. 119: it is larger 3.8 or 3.9 check
- sec 2.2: add a reference to Fig 1, in principal to each figure
- l. 141: give a reference and / or explain
- l. 144: unit -1/3 should be exponent
- sec. 2.4: add references to Tab 1+2, in principal to all tables
- l. 336: sometime you write dx = 2 m, sometimes as here without dx -> unify in document
- further typos, minor comments are in an attached pdf, no need to comment on them