The track will provide well-rounded training on FIM, focusing on the operational OWP HAND-FIM framework as well as other FIM models and supporting tools. The workshop lineup and schedule are designed to follow the FIM application and development schema: compilation, evaluation, development, and application. Workshops will include an introduction session, software setup, OWP HAND-FIM and remote sensing application, evaluation against benchmark datasets, dissemination, and tool development.
Flood Inundation Mapping Workshops
In this session, attendees will get an overview of the development activities creating the flood inundation mapping from the National Weather Service to support flood forecasting operations. The process of development, verification, enhancement, and distribution to field forecasters will be discussed. Development will focus on HAND, but other modeling methodologies will be discussed as well as a glimpse into what is under development for future releases of flood mapping. Its bridge to operations and utility during hazardous flooding will also be presented displaying the lift from development to operations that guide a lot of enhancement priorities.
Hands on training NOAA-OWP Operational HAND-FIM
Day 2 Session 2
Anupal Baruah
Pranav Sundararajan
Carson Pruitt
The baseline flood estimation method employed by the National Water Center (NWC) FIM services is based upon the Height Above Nearest Drainage (HAND) method at HUC-8 scale. As explained by Zheng et al. (2018) and Liu et al. (2018), HAND is a geoprocessing technique that converts a Digital Elevation Model (DEM) to a Relative Elevation Model (REM) depicting the elevation of the surrounding terrain above the river to which it drains. The NWC FIM services implementation of HAND relies upon a hydrographic network, such as the NHDPlus, to define the local drainage (https://github.com/NOAA-OWP/inundation-mapping/wiki/3.-HAND-Methodology).Finaly, REMs are used in conjunctions with the NWM retrospective streamflow data to generate the flood maps for single and multiple events.
AutoRoute – Rapid flood inundation mapping using tools developed for the U.S. Military
Day 3 Session 1
Michael Follum
The Military has a keen interest in understanding and mapping historical, current, and forecasted flood events. The AutoRoute model is a raster-based model often used by the U.S. Military to rapidly simulate high-resolution flood maps over large domains. This class will guide the user through the steps from downloading data to creating a flood inundation map. Also discussed in the class will be the advantages of ensemble-based flood inundation maps and the ability to create a synthetic bathymetry surface that can be used in more-advanced 2-D hydraulic models (https://doi.org/10.1016/j.jhydrol.2023.129769).
Learn how to extract and document Flood Inundation Maps from HEC RAS-2D, SRH-2D, and other models in general to a common data structure and visualization tools.
Remote Sensing Flood Inundation Mapping and Enhancement with High-Resolution DEM
Day 4 Session 1
Dan Tian
Sagy Cohen
This workshop includes two components, a Google Earth Engine web app that can automatically delineate flood inundation areas from satellite images using a pretrained multilayer perceptron (MLP) model and an algorithm to enhance the flood maps directly derived from remote sensing images.
Quickly mapping the flood is critical for rescue during a flooding event and assessment of property loss and damage due to the hazards. Google Earth Engine (GEE) provides a cloud-based geospatial processing platform for timely environmental monitoring and analysis. However, most current flood mapping applications on GEE rely on simple thresholding techniques or spectral index-based approaches. The GEE web app introduced in this workshop use a multilayer perceptron (MLP) model trained locally based on a global dataset. It can quickly and automatically delineate inundation areas based on Sentinel-1 Synthetic Aperture Radar (SAR) and Height Above the Nearest Drainage (HAND). The only user inputs are the interested date and location. This application can be used by both scientists and non-scientists, including those without knowledge and skills of remote sensing image processing.
Flood maps directly derived from remotely sensed images are often imperfect with various errors, owing to the landscape complexity and image separability limitation. Environmental factors, such as clouds, terrain, and tree shadows on the remote sensing images, often result in information gaps. Flooded tree canopy and buildings tend to be erroneously classified as non-flooded areas, causing serious omission errors on image-derived flood maps. In the second part of this workshop, we introduce a hydrologically guided region growing algorithm to enhance remote sensing flood maps by incorporating high-resolution Digital Elevation Models (DEMs). This hydrologically guided region growing algorithm can detect flooded forests and buildings, and fill out data gaps caused by clouds, and hence greatly improve the accuracy and reliability of the initial flood map derived from remote sensing imagery. This algorithm also produces a flood depth map in addition to the flood extent.
This workshop will present a new approach for evaluating predicted Flood Inundation Maps (FIM) using Remote Sensing-derived FIM (RS FIM) as the benchmark. Remote sensing-derived FIMs can be useful for evaluating the predictive skills of FIM models. There are, however, inherent biases and limitations in RS FIM which are often ignored by modelers, leading to improper use and erroneous evaluation results. Simply put, in many locations across the flooded domain, RS FIM can misclassify flooding conditions while the model prediction may be right. This issue is especially acute under dense vegetation and the built environment. During the workshop, we will provide a short overview and examples of the issues with RS FIM use in model evaluation. We will then provide the background for a new strategy for more robust FIM evaluation using RS FIM. Building on the ‘Remote Sensing Flood Inundation Mapping and Enhancement with High-Resolution DEM’ workshop, we will use our RS FIM to run the RS FIM enhancement and evaluation tools