A new tool has been made available that can help Americans forecast and study floods.
The Cooperative Institute for Research to Operations in Hydrology (CIROH) has released Flood Inundation Maps as a Service, or FIMserv. CIROH is a joint venture between the National Oceanic and Atmospheric Administration's Office of Water Prediction and the University of Alabama. The research paper details how this tool can deliver timely forecasts while taking into account national data sets.
"FIMserv is poised to be an efficient tool for a broad range of scientists and partitioners, including social scientists, economists, and individuals, enabling them to generate high-resolution (10m) operational flood maps with minimal effort," said the paper.
FIMserv is robust enough to simultaneously generate simulations of multiple watersheds for different kinds of flood events, apply a range of hydrological models to make predictions, and incorporate both historical data and forecasts in those results.
NOAA estimates water flow of rivers and streams in a framework called the National Water Model. FIMserv effectively combines this with terrain elevation data to give the public access to flood mapping and forecasting. It uses a method called Height Above the Nearest Drainage (HAND) to do that. This model takes every point on a landscape and checks its vertical distance to the closest stream or river in order to determine which areas are at risk of submersion.
The FIMserv code is being made available as an open-source toolset, allowing software developers to create better and tailor-made programs. Better still, the tool can be equally operated on a local machine or in the cloud.
Floods are becoming more frequent and intense, so predicting them and vulnerable areas is key to building up protections. Luckily, the development of these kinds of forecasting tools has been accelerating. Some developers are leveraging AI to build their tools. Others hinge on speed, trying to generate warnings in 60 minutes. Others are even trying to lean on quantum computing to improve predictions.
Those interested in getting into the weeds can access FIMserv here.
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