Resolving spatiotemporal variation in climate warming of mountain streams
using dense sensor arrays and air temperature microclimate models
Daniel Isaak, Zachary Holden1, Charles Luce, Dona Horan, Sherry Wollrab, Brett Roper2
U.S. Forest Service, Rocky Mountain Research Station, Boise, ID USA.
Boise Aquatic Sciences Laboratory
1U.S. Forest
2U.S.
Service, Region 1, Missoula, MT, USA.
Forest Service, Fish and Aquatic Ecology Unit, Logan, UT, USA.
a)
Introduction
Air temperature increases from global warming are
expected to increase temperatures in the Earth’s rivers and
streams this century. Where long-term monitoring records
are available from minimally altered mountain streams (i.e.,
no urbanization, flow regulation, or recent wildfires), it is
often the case that stream temperatures change at slower
rates than air temperatures (Figure 1).
Results from the microclimate model in the Boise River
basin lend support to the decoupling hypothesis (Figure
2) for explaining the discrepancy between long-term
trends in air and stream temperatures (Figure 1).
However, the differences observed between valley floor
and ridgeline air temperatures were not so large that
factors associated with the differential sensitivity
hypothesis may be discounted. Empirical measurements
of stream temperatures across the Boise River basin, for
example, indicate that cold, high elevation streams
usually warm less than lower elevation streams across
years (i.e., converging regression slopes in Figure 8).
This pattern could arise from the proximity of high
elevation streams to more persistent snowpacks and
colder, or more abundant, groundwater inputs.
Geomorphic considerations related to channel slope and
valley morphology or stream size and riparian conditions
have
alsowebeen
put forth
potential
factors
affecting
As an aside,
began harvesting
some as
of the
full year stream
temperature
data this year from sensors
first deployed in 2010 in the Boise basin. Comparison of year over year changes among sites between the
stream
temperature sensitivity. More research is needed
strongly contrasting summers of 2011 and 2012 suggest most streams change in a consistent fashion across
climate
years, which
supports
the way
we’re
currently
modeling temporal
to
resolve
these
details
and
fully
understand
the variation
arrayin the regional model.
The changes below are associated with an increase of 2.5 C air temperature and a 50% change in discharge,
of
cause
stream
responses
to become available over
so afactors
large rangethat
of climate
variation
pushingtemperature
these changes. As more
of these data
the next year,
it will be possible to precisely describe the degree to which streams are differentially
climate
forcing.
Fig 5. Maps showing inter-annual change in mean August air temperatures
between 2010 and 2011 for the RCM (a) and the microclimate model in the Boise
River basin (b). The microclimate model predicts that valley temperatures
change less than ridgelines.
Air Temperature Trend
Stream Temperature Trend
0.3
The basic prediction from the microclimate model was
subsequently validated with empirical sensor measurements
during the summers of 2011 and 2012. Mean June/July air
temperatures across the basin in 2012 were warmer than
2011 (+2.1 ˚C) but air temperatures measured in valleys
changed 20% less (+2.0 ˚C) than those at ridgelines (+2.4 ˚C;
Figure 6).
0.2
0.1
0
Stream temperatures change
~60% as fast as air temperatures
measured at remote stations
-0.1
Air Temperature Models
-0.2
Spring
Summer
Fall
Air temperature data from the sensor network were
used to parameterize mixed effects microclimate
regression models (Holden et al. In prep; 2011) that
downscaled gridded air temperatures from the 15
kilometer resolution RegCM3 climate model (Hostetler
et al. 2011) and 4 kilometer gridded air temperatures
(Abatzoglou 2012) to a 30 meter resolution. The
microclimate models (fit at both monthly and daily
timesteps) treated gridded air temperatures and
topographic covariates as fixed effects and sensor site
as a random effect. Additionally, the daily timestep
model used humidity, atmospheric pressure, and
vorticity in the downscaling process to capture
interactions between terrain, cloud cover and largescale pressure patterns.
Winter
Fig 1. Comparison of 30-year trends (1980-2009) in air and stream
temperatures for 7 minimally altered streams across the Northwest U.S. Air
temperatures were measured from stations 10 – 100 kilometers away from
stream sites; stream temperatures were measured at USGS gages (figure
modified from Isaak et al. 2012).
Two hypotheses may explain why stream temperatures
change at slower rates than air temperatures. It could be
that air temperatures in valley bottoms are partially
decoupled from regional climate patterns and change less
than ridgeline temperatures or the free-air masses modeled
by global and regional climate models (Figure 2a;
decoupling hypothesis). Alternatively, it could be that
groundwater inputs or other geomorphic/riparian features
buffer streams from climate warming and that some
streams are more buffered than others (Figure 2b;
differential sensitivity hypothesis).
Fig 2. Different air
temperature forcing? (a)
Data Collection
Precise, local air and stream temperature measurements to
test these hypotheses can be collected by deploying dense
monitoring networks comprised of inexpensive digital
sensors (Holden et al. 2011; Holden and Jolly 2012; Isaak et al.
2010; Isaak and Horan 2011). Figure 3 shows example sensor
networks deployed in 2010 across a topographically complex
6,900 km2 mountain river basin in central Idaho.
Observed Air Temperature
(Monthly Average ˚C)
a)
sensitive, link that sensitivity to specific covariates, and then develop future climate scenarios that
directly incorporate it. One thing to notice in these relationships is the slight steepening of the slope in the
warmer year, suggesting lower elevation, warmer streams change more than cold streams (next slide).
3.0
2.5
20
16
September
October
y = -0.006x + 19.1
12
2.0
8
1.5
4
y = -0.0046x + 14.0
1.0
0
1000
Valley bottom
(n = 24)
1500
Ridgeline
(n = 12)
2000
Fig 6. Differences in mean June/July air temperatures
between 2011 and 2012 measured at 36 air sensor sites
across the Boise River basin. Difference in mean
temperatures at valley sites (left box plot) were
significantly smaller than those at ridgeline sites (right
box plot; p = 0.03).
Discussion
With only a few years of data from a network of
inexpensive sensors, it is possible to develop and validate a
microclimate model that enables high-resolution historical
climate reconstructions (Figure 5) or forecasts through
linkage to existing RCMs (Figure 7). Microclimate models
allow us to better understand and predict climate effects
on stream temperatures.
b)
25
y = 0.92x + 1.51
R² = 0.94
20
15
10
5
0
-10
-5
-5
-10
0
5
10
15
20
-10
25
y = 1.07x - 0.68
R² = 0.95
20
15
10
5
0
-5
0
10
2500
Summer 2011
Summer 2012
16
y = -0.0075x + 23.7
12
8
y = -0.0063x + 19.4
4
1000
1200
1400
1600
1800
2000
2200
Elevation (m)
Scatterplots in Figure 4 show similar temporal
relationships between predicted and observed air
temperatures from the regional climate model (RCM)
and the microclimate model at a monthly timestep.
These relationships, however, obscure important
heterogeneity in air temperature changes across the
river basin. Most notably for stream temperatures—air
temperatures are predicted to change less in valley
bottoms than on ridgelines (Figure 5).
Different sensitivity? (b)
Glacial valley buffering?
Inter-annual change in mean
June/July air temperature (˚C)
Fig 3. Locations of air (valley bottom and ridgeline) and stream temperature
sensors deployed across the Boise River basin in central Idaho in 2010.
Sensors have been continuously recording hourly temperatures for the last
two years. Inset photos shows example temperature sensors, which cost $20
- $110 per unit.
Observed Air Temperature
(Monthly Average ˚C)
Warming rate ( C / decade)
0.4
b)
Stream temperature (˚C)
Air, Water &
Aquatics Program
20
-10
Microclimate Predicted Air Temp
RCM Predicted Air Temp (Monthly
(Monthly Average ˚C)
Average ˚C)
Fig 4. Comparison of monthly mean air temperatures observed at 44 sensor sites
across the Boise River basin (13 month period from August 2010–August 2011) to
predictions from the RCM (a) and the microclimate model (b).
Fig 7. Map of differences in the Boise River basin for maximum August air
temperature between historic (1979-2010) and future periods (2036-2046)
predicted with the microclimate model forced by the RCM.
Fig 8. Stream temperature changes in the Boise River basin between the
summers of 2011 and 2012. Warmer streams at low elevations usually
change more than cold streams at high elevations. Slope parameters of
regression equations describe the stream temperature lapse rate in ˚C/m.
References
Abatzoglou, J.T. 2012. Development of gridded surface meteorological data for
ecological applications and modeling. International Journal of Climate 32:772-780.
Holden Z.A., J.T. Abatzoglou, C.H. Luce, L.S. Baggett. 2011. Empirical downscaling of
daily minimum air temperature at very fine resolutions in complex terrain.
Agricultural and Forest Meteorology 151:1066-1073.
Holden, Z.A., and W.M. Jolly. 2012. Modeling topographic influences on fuel moisture
and fire danger in complex terrain for improved wildfire management decision
support. Journal of Forest Ecology and Management 262:2133-2144.
Hostetler, S.W., J.R. Alder, and A.M. Allan. 2011. Dynamically downscaled climate
simulations over North America: Methods, evaluation and supporting
documentation for users: U.S. Geological Survey Open-File Report 2011-1238, 64 p.
RegCM3 data available at: http://regclim.coas.oregonstate.edu/index.html
Isaak, D.J., and D.L. Horan. 2011. An evaluation of underwater epoxies to
permanently install temperature sensors in mountain streams. North American
Journal of Fisheries Management 31:134–137.
Isaak, D.J., D.L. Horan, and S. Wollrab. 2010. A simple method using underwater
epoxy to permanently install temperature sensors in mountain streams. Available
at: http://www.fs.fed.us/rm/boise/AWAE/projects/stream_temperature.shtml.
Isaak, D.J., S. Wollrab, D. Horan, and G. Chandler. 2012. Climate change effects on
stream and river temperatures across the northwest U.S. from 1980–2009 and
implications for salmonid fishes. Climatic Change 113:499-524.
Websites for more…
http://www.fs.fed.us/rm/boise/AWAE/projects/air_temperature_r1.shtml
http://www.fs.fed.us/rm/boise/AWAE/projects/stream_temperature.shtml
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