The Flood Inundation Mapping (FIM) track offers comprehensive training on the operational OWP HAND-FIM framework as well as other flood inundation mapping models and supporting tools.The workshop structure follows the FIM application and development lifecycle, including FIM compilation, model and database development, evaluation, and real-world application.
Leads: Sagy Cohen, University of Alabama
Workshop Listings
Day 1 Session 1
Pitamber Wagle, PhD candidate, Brigham Young University
This workshop demonstrates how to generate, integrate, and visualize flood inundation maps using a unified database framework. Participants will create HAND-based flood maps for a selected area and explore model-generated flood products linked to the database, with all datasets stored and shared through HydroShare. The session highlights how multi-source flood maps can support early warning and strengthen NWM inundation forecasting, while providing hands-on experience with CIROH-supported tools.
Using Streamflow Forecasts to Produce Flood Impact Forecasts
Day 1 Session 2
Joseph Gutenson, Follum Hydrologic Solutions
Mike Follum, Follum Hydrologic Solutions
With recent advancements in streamflow forecasting, informatics, and software, it is now possible to anticipate the economic consequences that floods may have on the built environment. By transforming streamflow forecasts into forecasted flood-inundation maps, we can better predict how flooding will impact communities and infrastructure. In this workshop, we will introduce attendees to a practical workflow for converting streamflow forecasts into building-level flood-impact forecasts. This newly developed workflow integrates streamflow predictions, automated inundation mapping, and impact assessment tools to estimate potential flood damage. It can be deployed anywhere within the continental United States.
Flood Inundation Mapping with FLDPLN
Day 2 Session 1
Xingong Li, University of Kansas
David Weiss, University of Kansas
Jude Kastens, University of Kansas
Flood inundation mapping is critical for risk assessment, emergency response, infrastructure planning, and flood-plain management. In this workshop, we introduce participants to FLDPLN — a computationally efficient, terrain-based low complexity flood inundation model — and guide them step-by-step through the process of building the FLDPLN library and generating inundation depth maps using stream stage observations or forecasts for a study area.
Topographically Derived High-Water Thresholds for Flood Inundation Mapping
Day 2 Session 2
Noelle Patterson, USU
Operational flood forecasting by the National Water Center relies on the identification of high-water thresholds for the initiation of Flood Inundation Mapping (FIM). More accurate representation of high-water thresholds at scale will result in more accurate triggers for FIM generation, a key public-facing OWP product, and response activation. Furthermore, paired high-water stage and flow thresholds can improve HAND-FIM performance through the calibration of synthetic rating curves. This workshop introduces “INFLection-based Elevations from Channel Topography” (INFLECT), a python application for identifying high-water thresholds from topographic data, and provides training on how to apply INFLECT to improve HAND-FIM performance. Using data from an example watershed, attendees will: 1) apply INFLECT to identify high-water thresholds at the hydrofabric segment scale, 2) calculate corresponding bankfull flow and recurrence interval values from nearest USGS gage flow records, 3) use the identified bankfull stage and flow as a calibration point to adjust a HAND-derived synthetic rating curve, and 4) compare flood inundation maps before and after calibration. The course will be facilitated through a Python Jupyter notebook, preloaded with results so attendees may perform all processing steps or simply inspect sample results along each step of the workshop.
Day 3 Session 1
Anupal Baruah, University of Alabama
Supath Dhital, University of Alabama,
The National Oceanic and Atmospheric Administration (NOAA)-Office of
Water Prediction’s (OWP) Operational HAND (Height above the nearest drainage) based FIM (Flood Inundation mapping) is a terrain-based flood inundation model that runs using the national water model (NWM) streamflow producing flood inundation maps at the watershed scale. FIMserv v1.0 is an automated, open-source, user-friendly, and cloud-enabled Python package to run this operational framework with some additional functionalities for producing FIMs across the country. The framework provides seamless integration of retrospective NWM, forecasted, recurrence streamflow and USGS gauge streamflow in flood inundation mapping. In FIMserv, we have integrated a deep learning-based surrogate model to enhance the FIM predictions by integrating the operational low-fidelity FIMs with other hydrological attributes. The workshop will provide hands-on training to the participants on generating FIM using the FIMserv v 1.1. Participants will learn how to generate FIMs and integrate the surrogate model to postprocess the FIM outcomes for specific AOI.
Day 3 Session 2
Dipsikha Devi, The University of Alabama,
Supath Dhital, The University of Alabama,
Accurate Flood Inundation Mapping (FIM) is essential for forecasting and evaluation.
Traditional pixel-based approaches can be time-intensive and error-prone. Here, we introduced the Flood Inundation Mapping Evaluation Framework (FIMeval), an open-source toolset for large-scale FIM evaluation. FIMeval links to a benchmarking database (FIMbench) that includes high-quality FIM benchmarks across the Contiguous United States, derived from remote sensing and high-fidelity model-predicted datasets.
It takes the advantage of comparing multiple target datasets with large benchmark datasets. This package also includes an option to incorporate permanent waterbodies as non-flood pixels with a user input file or pre-set dataset FIMeval supports multiple methods for generating flood extents, allowing users to assess how different delineation techniques influence evaluation outcomes.In addition to conventional performance metrics, FIMeval can quantify the number of inundated buildings based on user-supplied or pre-set building footprint data. New updates in the FIMeval includes different evaluation techniques to test the uncertainties with the performance metrics.