Global automated calibration procedures for the FastFlood and FastSlide rapid hazard models

Calibration and validation are necessary steps in the field application of physically-based models for floods and landslides. These processes validate the model assumptions and adjust the parameterization to align with historical observations. However, calibration remains a time-consuming task due to the lack of globally available datasets directly linked to the calibration schemes of physically-based models.

As part of the FastFlood.org and FastSlide.org rapid modeling platforms, we have developed a built-in calibration scheme that leverages global observational datasets and data-driven approaches. This approach links the results of global calibration datasets to a standardized calibration scheme for flood and landslide models, enabling automatic calibration globally based on these datasets.

In this paper, we outline the setup, global dataset processing workflows, and the initial outcomes of this research. The global datasets are derived from multiple observation types. For example, discharge data for return period events in rivers is based on GloFAS reanalysis data, bias-corrected using an extreme-value analysis performed globally on GRDC discharge station time series. Flood extents from the Global Surface Water Explorer are included but corrected for the underrepresentation of flash flood events in the observational data. For landslide processes, landslide inventory collections and data-driven global landslide susceptibility maps are utilized to provide built-in calibration functionality.

To streamline the calibration process, specific automated schemes have been developed to preprocess these global datasets, enabling direct comparison with model outputs without user intervention. Furthermore, we explore some initial results of this calibration scheme, comparing its accuracy and relative performance against other calibration methods.