YSM Issue 95.1

IMAGE COURTESY OF JACK RUSK
Map of multi-hazard risk in the Himalayas.
prone to landsliding, potentially
leading to disastrous outcomes.
Understanding the Results
IMAGE COURTESY OF JACK RUSK AND MEREDITH REBA VIA JENNY WONG
Priyankar Chand (center touching the tablet), and
Karen Seto (behind Chand) working with people in a
workshop.
www.yalescientific.org
Rusk ultimately found that maximum entropy
modeling would be the best for his data.
Maximum entropy modeling works by finding
the most uniform hazard distribution for
the entire region while accounting for the environmental
variables. This methodology has
several advantages. For one, it works without
knowing where hazards did not happen,
which negated the issue of inconsistent reporting.
Additionally, it can handle both categorical
and continuous environmental factors—for
example, specific types of soil and
total precipitation are both variables in the final
model. It also does not lose accuracy when
fed irrelevant or correlated factors, allowing
for a consistent set of factors to be used for
all three hazards. Finally, it outputs a single
probability for each hazard at each location.
This simple output allowed Rusk’s team to use
a consistent methodology for defining “risk”
for all three hazards, allowing them to combine
the three hazard maps into one. When
the model was constructed using a subset of
the historical data, it was able to predict patterns
in the rest, an early indication of success.
Rusk’s results must be placed in the
context of Himalayan urbanization patterns
to make sense. Himalayan urbanization
often occurs as micro-urbanization,
a term coined by Seto to describe
the growth of settlements that are small,
scattered, and removed from existing cities.
Tzu-Hsin Karen Chen, a postdoctoral
fellow at the Seto lab who collaborated
on Rusk’s study, attributes micro-urbanization
in the Himalayas to a feedback
loop initiated by road construction. “Villages
[near a road] will have a lot of
new products that are transported to
the market in the urban area, and
therefore they have more capital
to expand,” Chen said. Thus, people
flock to settlements in
thin, fertile valleys
that are
convenient
places
for expanding existing
cities and optimal places to lay
roads leading to them.
However, these valleys are also the most hazardous
parts of the Himalayas. Their moist,
fertile soils take less water to saturate in a flood.
Their steep hillsides and low elevation make
them prone to landsliding, especially as settlement
on the valley bottoms forces people
to move up the hills and cut terraces for arable
land. These valleys also have hotter temperatures
than higher elevations do, making them
more prone to wildfires during droughts.
And yet, millions still inhabit these hazardous
areas. “There are reasons to be near
these urban agglomerations that aren’t related
directly to the presence of hazards—
access to education, access to healthcare,
access to the money economy,” Rusk said.
People choose these opportunities for socioeconomic
mobility, despite the hazards, in
the hopes of connecting with a wider world.
Where Do We Go From Here?
Rusk is the first to admit that his work
would have been impossible without his
fellow researchers physically located in the
Himalayas. “I’ve been humbled by the opportunity
to work with such an amazing
group of collaborators,” he said. Truly understanding
the impacts of hazards requires
talking to people where they happen; machine
learning models can only go so far
since they don’t explain why hazards happen
in certain patterns or how
they affect people.
“In all of this work,
you just have to
shuttle between largescale
patterns and everyday life
on the ground,” Rusk said.
The Yale team’s next project will zero in
on how urbanization changes the landscape
locally and affects hazards—the second
part of the Urban Himalaya project’s
overall goal. “We have one map that assesses
overall hazard patterns across the past
three decades,” Chen said, referring to the
output of the current model. “But now we
want to have a map for every year, from
1992 to the present.” These maps will allow
the researchers to see both hazards and urbanization
change together.
And humans are not only changing the
environment on a local scale but also on
a global scale. As climate change increases
extreme precipitation and lengthens
droughts, existing hazards will also grow
in frequency and destructiveness.
Managing multi-hazard risks requires
the coordination of normally independent
national governments, local agencies
managing separate hazards, and individuals
alike. Rusk’s group has helped illustrate
what progress can be made with an integrated,
multi-talented team looking at the
big picture. Now it is time to do the same
back on the ground. ■
ART BY DANIELLE DE HAERNE
ABOUT THE AUTHOR
Environment / Climate Change
DANIEL MA
DANIEL MA is a junior History and Ecology & Evolutionary Biology double major in Franklin College.
In addition to writing for YSM, he plays for Yale’s quiz bowl team and is an executive editor of the Yale
Historical Review. He is also a historical geography research assistant at the Digital Tokugawa Lab under
Dr. Fabian Drixler.
THE AUTHOR WOULD LIKE TO THANK Jack Rusk and Dr. Tzu-Hsin Karen Chen for the time they
offered to be interviewed and their general enthusiasm about their research.
FURTHER READING
Rusk, J., Maharjan, A., Tiwari, P., Chen, T.-H. K., Shneiderman, S., Turin, M., & Seto, K. C. (2022). Multi-hazard
susceptibility and exposure assessment of the Hindu Kush Himalaya. Science of The Total Environment,
804, 150039. https://doi.org/10.1016/j.scitotenv.2021.150039
Grainger, C., Tiwari, P. C., Joshi, B., Reba, M., & Seto, K. C. (2021). Who is vulnerable and where do they
live? Case study of three districts in the Uttarakhand region of India Himalaya. Mountain Research and
Development, 41(2). https://doi.org/10.1659/mrd-journal-d-19-00041.1
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March 2022 Yale Scientific Magazine 13