All of Fast Hazard’s models are grounded in solid scientific research. The methodologies and algorithms underpinning each model are based on peer-reviewed scientific publications, ensuring transparency, rigor, and credibility.
These models are not only built on scientific principles—they are also suitable for use in academic research themselves. The input data used within the models is derived from reputable scientific sources. For detailed information about the specific datasets and parameters used in each application, please visit the application webpage and navigate to Docs → Datasources.
Our commitment to scientific integrity is further strengthened by Bastian van den Bout’s ongoing role at the University of Twente. His active presence in the academic world and strong network ensure that we stay closely connected to the latest research and innovations in environmental modelling. As a result, several of our models have been featured in scientific publications, contributing back to the academic community and supporting further developments in the field.
Article on the updated algorithms:
Exponentially cascaded cross-sectional volume estimates from elevation data, Bout & Glas, 2024 (under review)
The full article describing the method (Pre-Print):
A breakthrough in fast flood simulation, Bout et al., 2023 (under review)
The published article is available on Journal of Environmental Software and Modelling.
The original EGU talk in 2021:
van den Bout, B. (2022, May). Super-fast flash flood simulation using steady-state flow solvers. In EGU General Assembly Conference Abstracts (pp. EGU22-6953).
Some of the research that led to the methods development:
Bout, V. B., & Jetten, V. G. (2018). The validity of flow approximations when simulating catchment-integrated flash floods. Journal of hydrology, 556, 674-688.
The following documents detail master students whose research made use of the FastFlood tool.
Assessing the Impacts of Compounding Hazards Like Heat Waves and Floods in Kerala, India (F. Kolaparambil, 2024)
The Influence of Urban Morphology on Flood Susceptibility in Slums in a Data Scarce Environment Using Machine Learning (J. Munyi, 2024)
Development of Digital Twin Framework for Decision Support in Flood Risk Management (S. Rajendran, 2024)
Integrating Probabilistic Weather Forecasting for Flood Early Warning in Dominica (K. van Roon, 2024)
A short course session was organized at the EGU in spring 2024, see https://meetingorganizer.copernicus.org/EGU24/session/49447