Comparison of Digital Elevation Models, Stream Networks, ­ and Land Surface Model Input Datasets in Flood-Mapping ­ Case Studies: The 2022 Pakistan and 2024 North ­ Carolina Floods

Flood forecasting and predicting the sustaining impacts is vital in protecting people and infrastructure, particularly as increased intensities and frequencies of precipitation events continue to threaten populations globally. Flood inundation maps are an important component of predicting and communicating the impact of floods. While there are some existing tools and frameworks, there are still several limitations and variations in mapping global flood events such as precipitation forcings, streamflow estimates, and effects of existing infrastructure. Digital elevation model (DEM) data accuracy is another critical limitation in flood inundation modeling. With growing investments in remote sensing, new elevation datasets are becoming more readily available. Therefore, in this study, we test three global DEM products and two stream networks to ascertain their effectiveness in flood inundation mapping using a model cascade to produce regional streamflow estimates and flood inundation extents where the DEM and stream network datasets are the primary inputs. Simulations of recent large flooding event in Pakistan (2022) and North Carolina, USA (2024) served as the test domain for this study. DEM source proved to be the most significant factor in improving overall accuracy, with little impact noted between the sources of runoff data and stream networks used in this study. Coupling stream network derived from the same DEM product used in the mapping improved accuracy within each DEM subsets. This research provides insight into which datasets are appropriate to use for large spatial domains, with the aim of reducing the errors in flood modeling, ultimately aiding in situational awareness in response to these large-scale events.