We are proud to have Dr. Steven R. Fassnacht, a professor of Snow Hydrology from Colorado State University, visit the University of Waterloo to present his research. Please join us in EV1-221 at 1:30 pm on Tuesday, February 5th, 2019 for his talk titled “Integration of Multi-disciplinary Data Sources to Understand Hydro-Climatic Change”.
Title: “Integration of Multi-disciplinary Data Sources to Understand Hydro-Climatic Change”
Abstract:
Across
the
globe,
station-based
data
are
analyzed
to
estimate
the
rate
of
change
in
temperature,
precipitation,
snowpack,
and
other
hydro-meteorological
variables.
However,
in mountainous
terrain
and
in
sparsely
populated
regions,
such
as
Mongolia,
stations
are
few
and
far
between,
leaving
significant
gaps
in
station-derived
precipitation
patterns
across space
and
over
time.
We
are
combining
social
and
physical
datasets
to
better
understand
variability
and
uncertainty.
Several
datasets
were
analyzed.
We
used
observations
of
Mongolian
herders,
who
live
on
the
land
and
observe
nature
and
its
changes
across
the
landscape,
as
well
as
individuals
who
live
on
the
Front
Range of
Colorado.
Both
groups
provided
their
observations
of
changes
in
climate. These
observation
of
changes
were
based
on
a
closed
ended
questionnaire
and
open
ended
questions, and
were
summarized
using
the
Potential
for
Conflict
Index
(PCI2).
This
statistical
tool
computes the
mean
response
and
the
consensus
amongst
responses. We
compared
these datasets
to
station-based
trends
computed
with
the
Mann-Kendall
significance
and
Theil-Sen’s
rate
of
change
tests.
We
found
a
variety
of
results.
For
the
Mongolian
data,
the
herder
had
similar
responses
for
temperature
and
precipitation
changes,
but
not
seasonality.
In
Colorado,
people tended
to
have
a
poor
understanding
of
climatic
conditions
and
changes,
yet
women
had
a
better
understanding
than
men.
We
also
used
the
PCI2
tool
to
evaluate
uncertainty. There
was
similar
uncertainty
among
the
herders
and
the
station
data
for
some
variables
and
in
some
locations.
This
combination
of
datasets
provides
a
new
window
into
both change
and
variability
of
climate.