• A global hydrodynamic model (CaMa-Flood) was applied to assess regional flood hazards in Kalimantan, Indonesia. • The model was forced with ERA5 runoff and MERIT Hydro/DEM data at ∼1 km spatial resolution. • Simulated results captured major river networks and general flood patterns in large tropical basins. • Validation against AWLR discharge and Sentinel-1 flood extents showed underestimation of flood magnitude. • The study demonstrates the potential and limitations of global datasets for flood hazard assessment in data-scarce regions. This study uses the CaMa-Flood global hydrodynamic model to simulate flood hazards in Kalimantan at a spatial resolution of 1 arcminute (approximately 1.8 km), at a regional scale. The model uses runoff data from ERA-5 (0.25 resolution) and topographic data, including the MERIT DEM and MERIT Hydro river network. Model validation was conducted by: (1) comparing the discharge simulation results with observational data from six stations in each province in Kalimantan, (2) evaluating the CaMa-Flood input data model in simulating river flow, and (3) comparing the inundation map simulation results with Sentinel-1 imagery data. Comparison of streamflow between simulation and observation indicates that the average discharge is 8-63% higher at five observation points (Mahakam Melak, Lamandau, Landak, Sei Langsat, Nanga Taman) and 124% lower at one observation point (Sei Langsat), which potentially highlights the data quality of Indonesian river observations. The river network input data model captures 90.1% of the variability in river width data from Google Earth imagery. Comparison between CaMa-flood simulation results and Sentinel imagery shows that the total simulated inundation area is 16.04% higher than the Sentinel-1 SAR inundation area. Overall, the CaMa-Flood hydrodynamic model can be a global flood-routing model for assessing regional flood risk, particularly in areas with limited data, such as Kalimantan. The model results provide an initial overview of seasonal patterns in Indonesia and the distribution of flood inundation, which can be used to identify affected areas for monitoring and early flood warning measures.
Shafiya et al. (Sun,) studied this question.