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Water Surface Elevation Constraints in a Data Assimilation Scheme to Infer Floodplain Topography: A Case Study in the Logone Floodplain
Author(s) -
Shastry Apoorva,
Durand Michael
Publication year - 2020
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl088759
Subject(s) - floodplain , digital elevation model , flood myth , data assimilation , remote sensing , elevation (ballistics) , environmental science , context (archaeology) , meteorology , hydrology (agriculture) , geology , cartography , geography , mathematics , paleontology , geometry , archaeology , geotechnical engineering
Accuracy of inundation extents from flood models are directly related to the quality of topographic information. Globally available digital elevation models (DEMs) are not accurate enough to support flood modeling. Satellite derived flood maps provide indirect information about ground elevations. Previous work proposed a data assimilation algorithm to leverage flood maps for improving topography but was tested only in context of a numerical experiment with synthetic data. Here we show for the first time that a data assimilation algorithm leveraging real remote sensing measurements to modify floodplain DEMs improves floodplain simulations. Over our study area, we observe a significant increase in inundation prediction capability using the updated DEM, with 9–19% average improvement in skill assessment characteristics. This algorithm is promising and fairly straightforward and can be used in data‐poor floodplains where high‐resolution DEMs do not exist.