New model incorporates
topographical data for more accurate forecast of road disruption
Date:
May 15, 2019
Source:
Rensselaer Polytechnic
Institute
Summary:
As more rain falls on a
warming planet, a new computer model shows that it may not take a downpour to
cause widespread disruption of road networks. The model combined data on road
networks with the hills and valleys of topography to reveal 'tipping points' at
which even small localized increases in rain cause widespread road outages.
As more rain falls on a
warming planet, a new computer model shows that it may not take a downpour to
cause widespread disruption of road networks. The model combined data on road
networks with the hills and valleys of topography to reveal "tipping
points" at which even small localized increases in rain cause widespread
road outages.
The findings, which were
tested using data from the impact of Hurricane Harvey on the Houston area, were
published today in Nature Communications.
"To prepare for climate
change, we have to know where flooding leads to the biggest disruptions in
transportation routes. Network science typically points to the biggest
interactions, or the most heavily traveled routes. That's not what we see
here," said Jianxi Gao, an assistant professor of computer science at
Rensselaer Polytechnic Institute, and lead author of the study. "A little
bit of flood-induced damage can cause abrupt widespread failures."
Gao, a network scientist,
worked with environmental scientists at Beijing Normal University and a physicist
at Boston University to reconcile traditional network science models that
predict how specific disruptions impact a road network with environmental
science models that predict how topography influences flooding. Traditional
network science predicts continuous levels of damage, in which case knocking
out minor roads or intersections would cause only minor damage to the network.
But because of how water flows over land, adding topographical information
yields a more accurate prediction.
In Florida, an increase from
30mm to 35mm of rainfall knocked out 50 percent of the road network. And in New
York, Gao found that runoff greater than 45mm isolated the northeastern part of
the state from the interior of the United States.
In the Hunan province of
China, an increase from 25mm to 30mm of rainfall knocked out 42 percent of the
provincial road network. In the Sichuan province, an increase from 95mm to
100mm in rainfall knock out 48.7 percent of the provincial road network. And
overall, and an increase from 160mm to 165mm of rainfall knocked out 17.3
percent of road network in China and abruptly isolated the western part of
mainland China.
The researchers validated
their model by comparing predicted results with observed road outages in
Houston and South East Texas caused by Hurricane Harvey. Their model predicted
90.6 percent of reported road closures and 94.1 percent of reported flooded
streets.
"We cracked the data.
Hurricane Harvey caused some of the most extensive road outages in U.S. history,
and our model predicted that damage," Gao said. "Adding 3D
information causes more unusual failure patterns than we expected, but now we
have developed the mathematical equations to predict those patterns."
Gao was joined in the research
by Weiping Wang and Saini Yang of Beijing Normal University, and H. Eugene
Stanley of Boston University. At Rensselaer, the research was funded by the
Office of Naval Research, and a grant from the Knowledge and Innovation Program
at Rensselaer.
Story Source:
Materials provided by Rensselaer Polytechnic Institute. Original written by Mary
L. Martialay. Note: Content may be edited for style and length.
Journal Reference:
Weiping Wang, Saini Yang, H.
Eugene Stanley, Jianxi Gao. Local floods induce large-scale abrupt
failures of road networks. Nature Communications, 2019; 10 (1) DOI: 10.1038/s41467-019-10063-w
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