Simulating stream temperature response to environmental change
Ryan J MacDonald, Sarah Boon, James M Byrne, Dept. of Geography, University of Lethbridge, ryan.macdonald@uleth.ca
RESULTS – INTER SITE COMPARISON
BACKGROUND
Water temperature directly influences water quality, ecosystem productivity, and the
physiological functioning of aquatic organisms 4,6,11. Changes in stream thermal regime
due to environmental disturbance (wildfire, climate change, timber harvest, and
mountain pine beetle) pose a significant threat to aquatic ecosystems 8,9,10. Although
stream temperature is recognized as a critical environmental variable4, few studies have
quantified how the physical processes driving stream temperature can be spatially
represented5. This is largely because stream temperature results from complex
interactions between groundwater, surface water, physiographic and atmospheric
conditions11. While most studies define these controls at the reach scale, managementrelated decision making requires that the spatial scale of interest be expanded from the
reach to the watershed scale6.
HYDROLOGICAL CONTROLS ON TEMPERATURE
Table 1. Summary of site physical characteristics
Elevation
Bed
Site
(masl)
Slope (%)
Star west upper
1690
10
Star east
1543
11
Star main
1480
9
Stream
orientation
N
NE
N
Mass
balance
Gaining
Losing
Gaining
Sky view factor
(ratio)
0.71
0.73
0.68
Physiographic data from each site indicate that each represents a different reach type with
distinguishable characteristics. This allows us to quantify the effect of each reach type on the
streams thermal regime. Star west upper represents a high-elevation gaining reach, Star east
represents a mid-elevation losing reach, while Star main represents a mid-elevation gaining
reach. All reaches have similar bed slope, orientation, canopy structure, and substrate type.
Currently no stream temperature modelling studies have been conducted on the eastern
slopes of the Canadian Rocky Mountains. This region supports sensitive salmonid
species such as the westslope cutthroat trout (Oncorhynchus clarkii lewisii) and bull trout
(Salvelinus confluentus), which are listed as ‘threatened’5, and ‘species of special
concern’1, respectively. Both are cold water species, thus increases in stream
temperature would be detrimental to their survival.
STUDY SITE AND METHODS
•These results
demonstrate hydrological
events play a key role in
governing thermal regimes.
Figure 13. Stream temperature and streamflow plotted for the period from May 15, 2010 to
October 8, 2010. Shaded boxes represent periods where changes in streamflow prompted
dramatic shifts in stream temperature.
Purpose
The goal of this project is to develop a spatial stream temperature model that accounts
for atmospheric, hydrologic and biophysical controls and can be applied to assess the
possible effects of environmental change on salmonid habitat.
•Field measurements show
that changes in streamflow
have a substantial impact
on stream temperature
(Fig. 13).
Rosgen
Substrate type
Classification
Cobble, boulder
F3b
Cobble, gravel
F3b
Cobble, gravel
B3a
Figure 5. Comparison of hourly stream
temperature for Star east (SE), Star main
(SM) and Star west upper (SWU) from May
15, 2010 to October 8, 2010.
Figure 6. Comparison of hourly streamflow
for Star east (SE), Star main (SM) and Star
west upper (SWU) from May 15, 2010 to
October 8, 2010.
Figure 7. Comparison of hourly air
temperature for Star east (SE), Star main
(SM) and Star west upper (SWU) from May
15, 2010 to October 8, 2010.
Comparisons of hourly stream temperature, streamflow and air temperature (Fig. 5, 6, 7)
demonstrate that each site represents different hydrological and meteorological conditions. Ttests indicate that, with the exception of air temperature at Star east and Star main (p=0.208), all
mean hourly values are statistically different between sites(p<0.0001). Linear regression (Fig. 8,
9, 10) demonstrates that hourly air temperature and stream temperature are not highly correlated,
particularly for Star east. These results suggest regression may not be the most appropriate
method for simulating stream temperature response to environmental change.
•By accurately
representing streamflow,
the effect of these events
on stream temperature can
be simulated.
•The proportion of
groundwater to streamflow
can be used to derive the
coefficient for simulating
the effect of groundwater
on stream temperature
(Fig. 14).
•Lower coefficients during
high flow (low proportion
of groundwater) and
higher coefficients during
low flow (high proportion
of groundwater).
Figure 14. Baseflow separation from May 9, 2010 to October 8, 2010 for Star main, conducted
using a method derived by Arnold and Allen (1999).
DISCUSSION AND CONCLUSIONS
•This work demonstrates that controls on stream temperature vary spatially and
temporally, thus, presents a significant challenge to modellers.
Figure 2. Hydrometeorological
station located at Star west upper.
Figure 8. Hourly air temperature vs stream
temperature at Star west upper.
Figure 9. Hourly air temperature vs
stream temperature at Star east.
Figure 10. Hourly air temperature vs
stream temperature at Star main.
RESULTS – STREAM TEMPERATURE MODELLING
Figure 3. Cross section piezometers
located at Star east.
Figure 1. Study area. Located in the headwaters of the Oldman River watershed, Alberta, Canada.
•Analysis of model output was
used to determine how each
component of the mass and
energy balance can be
represented in a spatial model.
Y = 0.81x
R2 = 0.32
• The model underestimates
stream temperature during
baseflow periods (Fig. 11).
•Three sites in Star Creek
watershed (Fig. 1) were
instrumented to measure the
mass and energy balance
components driving stream
temperature at the hourly time
step (Fig. 4).
•Data from the three sites were
used to drive a model developed
by Leach and Moore (2010).
•The initial simulation of daily
stream temperature (Fig. 11)
results in good simulations during
the spring period.
Q* (Shortwave flux)
Q* (Longwave flux)
Tw(Surface water temp)
F (Surface water flow)
Qh (Sensible heat flux)
Qe (Latent heat flux)
W (Width)
Tin(Groundwater temp)
qin(Groundwater flow)
b
Figure 4. Schematic of and mass and energy balance model (Leach and Moore, 2010).
•The underestimate is likely due
increases in shallow groundwater
temperature downstream of Star
west upper and misrepresentation
of the proportion of groundwater
to streamflow.
•Physically based models are an essential tool for simulating stream temperature response
to environmental change as they are process based and responsive to changes in both
mass and energy balance components.
•Once described in terms of hydrology, the appropriate contributions of each of the mass
and energy balance components can be applied to model stream temperature.
•Hydrological conditions play a key role in governing stream thermal regimes. Therefore,
the hydrological regimes of individual streams should be explicitly described in physical
models.
•A new method for deriving weighting coefficients from streamflow records in the absence
of measurements can be applied.
Figure 11. Daily stream temperature simulation from June 1, 2010 to October 8, 2010 using the
original Leach and Moore (2010) energy and mass balance model.
Y = 0.97x
R2 = 0.69
•Increasing the relative
contribution of groundwater (qin)
by multiplying qin by a coefficient
during baseflow improves stream
temperature simulations(Fig. 12).
• This enables both groundwater
temperature and flow to be
increased without observations.
•Simple regression based models, although applicable in some cases, may not be the best
method for simulating stream temperature response to environmental change, particularly
due to the non-stationarity of natural systems. This is particularly relevant in groundwater
dominated watersheds.
•Further work on model development is required to adequately represent the thermal
regime of streams in order to accurately predict the impacts of environmental change on
stream temperature.
a
•The use of GIS, physical modelling, and detailed field studies enables watershed-scale
research that is applicable in a management context.
ACKNOWLEDGEMENTS & REFERENCES
Funding for this work was provided by: the Alberta Conservation Association, Trout Unlimited Canada, the Alberta Water Research Institute, the
Natural Science and Engineering Research Council of Canada, Alberta Sustainable Resources Development (Forest Management Branch), and
the Western Economic Diversification Fund. Dez Tessler, Dave Dixon, Katie Burles, Chris Williams, Dr. Uldis Silins, Mike Wagner, Jolene Lust, and
Dr. Kevin Bladon have provided invaluable field and technical assistance.
Figure 12. Daily stream temperature simulation from June 1, 2010 to October 8, 2010 using the
modified Leach and Moore (2010) energy and mass balance model.
References:
1ASRD.
2002. Alberta species at risk; 2Arnold and Allen. 1999. JAWRA. 35: 411-424;3Buttle et al. 2009. Can. Water Res. J. 34: 113-126; 4Caissie. 2006. Freshwater.
Biol. 51: 1389-1406; 5COSEWIC. 2009. Canadian wildlife species at risk. 92 pp; 6Cox and Bolte. 2007. Env. Model. Software. 22:502-514; 7Leach and Moore
Hydrol. Proc. DOI: 10.1002/hyp.7854; 8Moore et al. 2005. Hydrol. Proc. 19: 2591-2608; 9Morrison et al. 2002. J. Hydrol. 263: 230-244; 10Poole and Berman. 2001.
Environ. Manage. 27: 787-802; 11Webb et al. 2008. Hydrol. Proc. 22: 902-918.
University of Lethbridge Mountain Hydrology Lab