ISBN: 978-90-824846-6-3 | PAGE: 177
OPTIMIZATION AND VISUALIZATION OF NUMERICAL MODELS OF FLASH FLOODS IN
STEEP NORWEGIAN RIVERS
1Norwegian University of Science and Technology (NTNU), Trondheim, Norway
*Correspondance : email@example.com
Visualizing results is more important than ever in scientific dissemination. The analysis and communication of com-
plex phenomena such as flash floods requires new approaches. The target is using a state-of-the-art model with a
fast and robust predictive capability, which has been tested in small, steep rivers affected by recent flash floods, and
visualizing this model in a relatable manner. To do so, this research project aims to achieve optimized simulations
that could be carried out in the prototype of a serious gaming engine. The incentive for such optimized simulations is
that complex hydraulic modelling is data- and computationally costly. This clashes with the need for low complexity
solutions in the real-time based scenarios that an immersive experience and on-site decision-making requires.
flash floods, steep rivers, hydraulic modelling, optimization techniques, visualization of numerical models.
Visualizing the dynamics during a flash flood based on hydraulic modelling, as well as enabling more efficient
numerical simulations, would enhance flood risk assessment and its communication. Available models for floods
in steep Norwegian rivers do not convey the estimated risk based on a user-friendly and three-dimensional real
world. Moreover, 3D visualizations that could be used for educational or training purposes are often not based
on precise hydraulic data.
The target is to implement the optimized numerical models into the prototype for a serious gaming flood platform
(e.g. Virtual Reality or Augmented Reality). Thus, the flood scenario will be complimented by gamification,
storytelling and immersive narrative techniques, which will provide a better user experience and improved risk
perception. The current paper provides an overview of ongoing research, where several methods have been
implemented to address the need for faster hydrodynamic models in two steep Norwegian rivers.
Storelva in Utvik municipalities (south and west of Norway, respectively):
Parallel CPU-based computing of 2D models;
GPU-based computing of 2D models;
Machine Learning (ML) for 1D emulation models.
indexes and parallel CPU-based 2D numerical modelling. The river’s geometry was modified and its slope was
virtualized into constant values along the modelled reach to identify the influence of other parameters in the river
response. The results of such study and its applications are described in detail in (Moraru et al., 2019, 2020).
Such critical areas have the potential of being further combined into a locally refined, yet faster-solving, model.
The flash flood that affected Storelva river (in Utvik) in 2017 (Bruland, 2020) has been modelled in 2D (i.e. HEC-
RAS, Iber; 1(a)) and also visualized in 3D (i.e. Blender, Unity, Autodesk 3ds Max). The focus of the 2D models
was to understand the dynamics during the flood event in comparison to on-site documentation of the flood, as
well as searching for an optimal numerical method for small and steep rivers. The HEC-RAS model was carried
out on multiple parallel CPU threads, while the Iber numerical model was carried out on both CPU (single and
multi-thread), as well as on GPU (1(b)). Furthermore, the obtained 2D Iber model was used as input data for a
ML emulation model, and their performance was subsequently compared by Son (2020). Section 3 highlights
especially the outcome of using Iber CPU- and GPU-based computing in Storelva in Utvik.
PRELIMINARY RESULTS: CPU VS GPU FOR THE 2017 FLASH FLOOD
GPU computing, as well as the use of ML, are state-of-the-art optimization techniques (Moraru et al., 2020). The
computing tool used for numerical modelling will determine greatly the performance of the model, nevertheless.
For instance, preliminary results of a comparison of the computing performance of different CPUs and GPUs
in a small model of the 2017 flash flood (1(a)) shows that GPU-based computing is generally quicker than CPU-
based computing and that the dispersion of the data is larger in the CPU tests than in the GPU tests (1(b)).
Utvik flash flood modelled on GPU
80 1 Thread 80
70 3 Threads 70
60 8 Threads 60
GPU - Threads
CPU1 CPU2 CPU3 CPU4 CPU5 GPU-a GPU-b GPU-c GPU-d GPU-e GPU-f GPU-g
Preliminary CPU and GPU comparison for Utvik study case
Figure 1: (a) Study case, (b) average computing time per step for different CPUs (sing le- and multi-thread) and GPUs.
CPU- and GPU-based simulations need different computational effort based on the number of threads available
in the processing unit, i.e. the CPUs shown in 1(b) have from 1 to 8 threads, whereas the respective GPUs
have from 384 to 4352 threads. As the simulation will experience a speedup until all threads are fully used, the
speedup will be larger in GPU-based simulations than in CPU-based ones. In conclusion, this makes a personal
computer with a GPU suitable for fast hydrodynamic simulations of flash floods in steep Norwegian rivers.
The authors wish to thank an anonymous reviewer for the valuable feedback. This publication is part of the
World of Wild Waters (WoWW) project, which falls under the umbrella of Norwegian University of Science and
Technology (NTNU)’s Digital Transformation initiative.
Bruland O. (2020). How extreme can unit discharge become in steep Norwegian catchments? Hydrology Research 51(2), 290–307.
W. Uijttewaal et al. (Eds.), 10th International Conference on Fluvial Hydraulics (River Flow 2020), Delft, the Netherlands, pp. 1231–1238.
Taylor & Francis Group, ISBN 978-0-367-62773-7.
Moraru A., Usman K. R., Bruland O., Alfredsen K. (2019). River idealization for identification of critical locations in steep rivers using 2D
hydrodynamic modelling and GIS. In 22nd Northern Research Basins Workshop and Symposium, Yellowknife, Canada, pp. 144–153.
Son S. (2020). Optimization of hydraulic flood simulations in steep rivers using GPU and Machine Learning. Msc thesis, Universitat
Politecnica de Catalunya - Norwegian University of Science and Technology.
ISBN: 978-90-824846-6-3 | PAGE: 178
Average time per step (s)