Evaluating topography-based approaches for fast floodplain mapping in data-scarce complex-terrain regions: Findings from a Himalayan basin

Abstract

Despite a proven record of accurate floodplain mapping, complex hydrodynamic models lack efficiency due to their data-intensive approach and high computation time. As fast-computing low-complexity alternatives, topography-based hydrogeomorphic approaches are being increasingly used in recent times. Yet, there is little evidence to suggest whether and to what extent such topography-based approaches, simply relying on Digital Elevation Models (DEMs), can effectively delineate floodplains/flood hazard zones in data-scarce, highly complex mountainous terrains. We fill this knowledge-gap by conducting a floodplain mapping comparative assessment with three topography-based approaches along a 56-km river reach in the North West Himalayas (Beas River, India). Specifically, we generated floodplain extent using Height Above the Nearest Drainage (HAND), Topographic Wetness Index (TWI), and Slope Position (SP), and estimated their relative differences compared to a reference inundation extent simulated by MIKE11 hydrodynamic model. The unique feature of our study was an extensive field survey to collect longitudinal and cross-sectional elevation data across 223 locations, which allowed us to verify vertical accuracy across multiple alternative DEMs and subsequently improve the selected DEM via bathymetry integration. Statistical measures estimating the degree of consistency across three topography-based approaches suggested that HAND and TWI produce more reasonable floodplain maps than SP. HAND-based floodplain demonstrated greater agreement with the model-simulated reference map. TWI showed moderate predictability with a tendency of underestimating floodplains. SP was largely inconsistent, demarcating floodplains in areas where the hydrodynamic model found uplands and vice versa. Our study can offer critical insights to support floodplain hydrological, biological, and habitat studies across the world’s developing countries where locally relevant delineation of floodplains remains challenging due to data scarcity.