The risk of flooding in the continental United States increases each spring. Experts point to deep snowpack, late winter storms, and rapid melting.
In 2023, the spring floods in California got a lot of media attention. But such danger is universal in most of the forty-eight states south of Canada and north of Mexico.
The National Weather Service’s spring flooding estimates in 2023 show that 146 million Americans are at risk, not including Hawaii and Alaska.
The agency’s hazard map predicts flooding in a wide swath of twenty-eight states in the middle of America, plus patches in eight mountain and far western states.
Even minor flooding carries the risk of injury or death from drowning, electrocution, respiratory illness, and injuries resulting from power outages.
A team of researchers at the Pacific Northwest National Laboratory (PNNL) has developed a new infrastructure design and flood modeling method to help predict flooding at a local scale.
Adding snow to flood calculations
To date, most small-scale engineering solutions for directing stormwater runoff—catch basins, swales, detention ponds, etc.—have been designed based on rainfall-only events. This decades-old predictive design standard is called the precipitation Intensity-Duration-Frequency curves technique.
The new generation PNNL method is based on a dataset that considers not only precipitation, but also regional snowpack and rain-on-snow events.
To create their dataset, PNNL researchers used more than 200,000 data points obtained from rain and snow forecasts across the country. The data is based on simulation grids that are less than 4 square miles each.
“What makes our method unique,” said Ning Sun, a hydrologist at PNNL, “is that it can provide the exact timing and magnitude of snowmelt in different locations.”
‘Simple, elegant, and useful’
Mark Wigmosta, a PNNL chief scientist, planned the project in 2017. Since then, he has collaborated with Sun and PNNL surface water hydrologist Hongxiang Yan to write a nine-paper series of studies that tells the story of the method’s evolution.
This includes a proof-of-concept study (2018), an introduction of the idea to the engineering community (2019), and the first paper proving the new method (2019).
The interest is immediate. A 2018 commentary on Water Resources Research called PNNL’s approach “simple, elegant, and useful.”
The team’s latest paper came out in April 2022. It shows that the PNNL datasets show how much water reaches the earth’s surface due to separate contributions from precipitation, snowmelt, and rain-on-snow. events.
Next: Land cover data
The team is working on the next evolution of the method, which will include eight new categories of land use and land cover. The original data of their new generation method was based on “open-area” land cover – that is, an imaginary surface with little vegetation.
“The vegetation canopy prevents some of the snowfall, which affects the time and final amount of water that reaches the soil surface,” Wigmosta said. “It is important to pre-calculate the curves for all these variables.”
Even denser and more comprehensive datasets are part of future versions of the method, Yan said. “We have more datasets, more mature datasets, and we’ve done more theoretical work.”
The PNNL framework has matured beyond the original 2018 data, representing only 376 observations in the western US. The current dataset represents more than 200,000 sites. Soon there will be millions, thanks to simulations based on increased plant cover.
The Department of Defense begins
The PNNL project sets out to optimize water control infrastructure at military bases. These critical defense operations are the size of small towns. The initial focus is on proof-of-concept. Now it’s on to application and outreach.
PNNL research uses the 327,000-acre US Army Yakima Training Center in eastern Washington State as a test case. The resulting approach to infrastructure design will soon benefit civilian communities operating hydrologic infrastructure on a similar scale.
“We want to get these data,” Wigmosta said.
Using the web tool
Currently, the PNNL scheme is only available for beta testing by defense authorities. However, the PNNL team has developed a web tool through Amazon Web Services. It will be available to the wider water engineering community later in 2023.
“The (PNNL) dataset is the engine behind the web tool,” Yan said. “On the back side, there’s a sophisticated model, but you don’t need to learn how to use it.”
A local water engineer simply fills in the fields related to location, vegetation cover, forest type, the desired time scale (24, 48, or 72 hours), and other factors. In return, the engineer obtains estimates of runoff intensity at any location of interest.
Meanwhile, “we can update the web tool and use it as a way to run our model,” Wigmosta said. “We can advance the state of science, regenerate all the (intensity, duration, and frequency) curves, and release them to the public.”
Coming soon: Climate change considerations
The web tool and expanded datasets are ready, Yan said. “Now we can consider the future.”
To do that, the PNNL team collaborated with the National Center for Atmospheric Research in Colorado to add climate change data to a newer version of the scheme.
Over time, researchers have also sought to make better scale resolutions available to users – from the current 4 square miles or so to one-sixth the size – such as size of a large city park or a college campus.
Better resolutions and more accurate flood models are essential in a warming world. Experts anticipate a future of more rain-and-snow events at higher elevations, stronger storms, and greater flood risks.
To improve small hydrologic infrastructure, PNNL’s approach will evolve to be more comprehensive and useful, Yan said. “We want to continue.”
Provided by the Pacific Northwest National Laboratory
Citation: Adding snow to spring flood estimates (2023, July 13) retrieved on July 13, 2023 from https://phys.org/news/2023-07-adding.html
This document is subject to copyright. Except for any fair dealing for the purpose of private study or research, no part may be reproduced without written permission. Content is provided for informational purposes only.