ABSTRACT:

This workshop introduces FLDPLN, a low-complexity flood inundation mapping model developed at the University of Kansas, and walks through a complete end-to-end mapping workflow using the 2018 Labor Day flood on Wildcat Creek, Kansas as a worked example. FLDPLN sits between full hydrodynamic models and simpler approaches: it takes a hydro-conditioned DEM as its primary input and combines two flooding mechanisms — backfill flooding ("water seeks its own level") and spillover flooding ("water flows downhill") — to produce depth-to-flood values across the floodplain. Using small depth increments and allowing spillover between iterations resolves the discontinuities that arise from ridgelines when backfill is used alone. The model represents the floodplain as a many-to-many relationship between flood stream pixels (FSPs) and floodplain pixels (FPPs), with depth-to-flood values stored in a precomputed library. At map time, observed or forecasted gauge stages are interpolated along the stream network — using either linear interpolation between gauges or a volume-based method with segment stage-volume rating curves — and a flooded pixel's depth is computed from the maximum difference between FSP flow depth and its DTF. The hands-on portion covers the full Kansas FIM workflow through four Jupyter notebooks: preparing the DEM and building the Python environment, generating a segment-based FLDPLN library, tiling that library for efficient lookup, mapping the 2018 Wildcat Creek event against USGS gauges and the NOAA/NWS reference inundation map, and producing specialized outputs such as minimum depth-to-flood, FSP count, and depression depth maps. Materials include notebooks and a Python package on GitHub, an ArcGIS toolbox, and a MATLAB Runtime–based installer; the operational Kansas Flood Mapping Dashboard, which serves real-time FIM products across eight river basin libraries statewide, illustrates how the same workflow scales beyond a single creek.

Acknowledgements:
This research was supported by the Kansas Water Office (KWO) / KDEM and by the Cooperative Institute for Research to Operations in Hydrology (CIROH) with funding under award NA22NWS4320003 from the NOAA Cooperative Institute Program. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the opinions of NOAA.

ABSTRACT:

This resource provides a training and test module for applying FLDPLN to flood inundation mapping in Tuscaloosa, Alabama.
The resource is intended to support model training, workflow testing, and reproducible evaluation of FLDPLN-based flood mapping procedures in a local study area.
Users can use the materials provided in this resource to become familiar with the FLDPLN workflow, test model execution, and examine flood inundation mapping outputs for the Tuscaloosa application.