Spatiotemporal Response of Transpiration to Climate Variation
in a Snow Dominated Mountain Ecosystem
Lindsey Christensen(1), Christina L. Tague(2), Jill S. Baron(3)
(1)Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523,(2) Donald Bren School of Environmental Science & Management, University of California,
Santa Barbara, CA 93106, (3)US Geological Survey, Colorado State University, Fort Collins, CO 80523.
Project Overview
Transpiration in mountain ecosystems is an important controlling factor of the underlying hydrologic cycle, including streamflow patterns and water storage.
Complex topography, variable climate, and the difficulties in collecting data create large uncertainties in how transpiration varies within a watershed (Kane 2005,
Whitaker et al. 2003) . It is important that this variable nature of transpiration is understood for better estimates of the current and future distributed water balance
(Boisvenue and Running 2006, Eder et al. 2005, Shär and Frei 2005, Gurtz et al. 2003) . We used the Regional Hydro-Ecological Simulation System model, a
process-based ecosystem model, to simulate the spatiotemporal response of transpiration to climate in the Upper Merced River basin in Yosemite National Park,
CA. By focusing on spatial variation we offer insight into the details of water distribution throughout a watershed with variable terrain and the causes of significant
spatial differences in the relationship between transpiration and climate forcing.
Results
Analysis
◙ Basin transpiration was lowest in the driest and wettest years, and highest in years of
◙ Model simulations of the Upper Merced River, Yosemite National
Vegetation, and
Zone Division
Maps
◙ Calibration and validation
DEM
30
gw1
0-1
gw2
0-1
error
%
NS
logged NS
Calibration
60
3.6
0.29
0.33
12
0.71
0.72
Validation
60
3.6
0.29
0.33
6
0.77
0.75
Streamflow (mm)
m
m/d
Soils
Veg
Normalized Streamflow
(mm per day)
90
80
70
60
50
40
30
20
10
0
Modeled
2
3
4
400
350
300
250
500
1000
Peak Snowdepth (MAXSNOW) (mm)
1500
17
18
19
20
21
GS Mean Temperature (GSmeanT) (C)
22
◙ Average annual transpiration was highest in low riparian and mid-elevations, and
decreased at high elevations (mid-elevations had a broad range of transpiration).
Vegetation was normalized by LAI to evaluate extent to which elevation differences are
due to vegetation biomass, similar results were seen. Elevational differences were
attributed to differences in vegetation and vegetation water use.
Simulated
10
0
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
◙ The relationship between annual transpiration and MAXSNOW for separate elevation
zones showed four distinct responses, where transpiration was indifferent at low and
high elevations, and middle elevations displayed distinct curvilinear trends.
Transpiration showed a positive significant trend with GSmeanT and low and high
elevations (R2 0.3 and 0.7, p<0.001, respectively), and no trend at mid-elevations.
relationship between simulated annual transpiration and climate
variation at basin scale.
Daily streamflow
1
450
15
◙ Annual and seasonal climate indices were used to examine the
Drain
12
300
500
20
1) average basin transpiration from 1926-2003 and
2) transpiration for 54 different elevation zones for water years
1990-2001. Elevation zones were 50 meters each, ranging from
1200 to 3950 m. Patch-scale estimates of transpiration and other
water flux estimates were averaged for each elevation zone.
Canopy
Processes
11
350
5
Hydrologic
Processes
10
400
Observed
25
K
m/d
Disturbance
History
Meteorological
Processes
LAI
450
0
◙ RHESSys was used to compute:
GIS
Inputs
550
500
250
We calibrated against observed streamflow to determine values of
input parameters for soil hydrologic patterns.
The Nash-Sutcliffe model efficiency coefficient was used to
assess hydrological model’s prediction of streamflow
Validated from 1970 to 1979 against observed streamflow.
RHESSys Model
600
550
Annual Transpiration (mm m-2)
-2
DEM,
Regional Hydro-Ecological Simulation System
Climate Time
Series Data
600
Annual Transpiration (mm m )
1) How does climate variability affect transpiration in a high elevation
mountain ecosystem at a basin wide scale,
2) How does transpiration vary within a basin based on an elevation
gradient, and
3) What are the drivers that cause this variability at separate elevations,
and how do they differ temporally and spatially?
moderate precipitation levels, creating a curvilinear relationship between transpiration
and MAXSNOW and APRCP (R2 = 0.32 and 0.29 respectively, p < 0.001). MAXSNOW
depth and annual precipitation in this basin were highly correlated (R2 0.97, p < 0.001),
thus for the period of historic record these indices are interchangeable as predictors. No
such trend existed with growing season mean temperature.
Park, CA.
RHESSys uses vegetation, soil, and Digital Elevation Model (DEM)
spatial maps for the spatial analyses of C, N, and hydrological
processes. MODIS LAI data was used to initialize LAI at start
of simulation.
Questions addressed:
5
Month
6
7
8
9
Observed
APRCP
AmeanT
°C
mm
0.289***
Acctrans
0.118
Variation
0.287***
0.065
CV (1st)
0.089**
0.155***
"1st" represents linear regressions, remainder
Snow off
GSmaxT
day
°C
0.204***
0.162*
0.269***
0.129
0.143***
0.011
are 2nd order regressions.
GSmeanT (1st)
°C
0.09**
0.031
0.191***
GSprecip
mm
0.075
0.079
0.004
MAXSNOW
mm
0.323***
0.327***
0.103**
P values *** < 0.001, ** < 0.01, and * < 0.1
A GIS based computer simulation of spatially distributed ecosystem
processes at the watershed scale, designed to simulate water, carbon
and nutrient fluxes.
Captures the temporal and spatial variability of ecosystem processes
at a daily time step over multiple years by applying a set of physically
based process models over spatially variable terrain.
http:// geography.sdsu.edu/Research/Projects/RHESSYS/
Tague, C.L. and L.E. Band. 2004
Acknowledgements
This research is a product of the Western Mountain Initiative, funded by the USGS
under contract # 04CRAG0004 / 4004CS0001, and EPA STAR grant Agreement
Number: R829640.
Corresponding author: (lindsey@nrel.colostate.edu)
-347
Conclusions
◙ At the basin scale, annual water transpired was lowest in driest and wettest years, and greatest in years of
moderate precipitation.
◙ Response of transpiration to climate along an elevation gradient show:
a) low elevations had little sensitivity to year-to-year climate variation;
b) middle elevations (low) had a unimodal relationship with climate and peaked at moderate precipitation,
reflecting combined effect of contrasting controls (temperature and precipitation);
c) middle elevations (high) had similar response to (b), yet with greater sensitivities to temperature; and
d) high elevations were more strongly affected by temperatures than precipitation indices.
◙ Transpiration is intimately linked with plant production, and other investigators have reported similar patterns of plant response to climate
along elevation gradients. Forest growth patterns in the North Cascades showed insensitivity to climate variability at low elevations (Peterson
and Peterson 2001) while Dunne et al. (2003) found soil moisture was not and temperature was a limiting factor on plant functional response.
Vegetation was most responsive to climate indices at mid elevations of mountain slopes with negative correlations of growth with temperature
at higher elevations (Case and Peterson 2005).
◙ Spatial patterns of relative sensitivity of transpiration to climate variation show middle elevations are
projected to have greatest change in the water balance.
◙ Simulation analysis was used to:
a) pinpoint where transpiration is sensitive to climate differences, and
b) detect vulnerable areas to shifts in water loss due to changes in climate.
0
170
◙ Spatial maps of the differences in transpiration between warmest and coldest years
(a), where greatest reductions in transpiration with warmer temperatures occur in lowmiddle elevations while high elevations show an increase in transpiration.
Reductions in transpiration for a wet versus average year (b) are greatest at the
higher-middle elevations, whereas transpiration reductions in dry versus average year
(c) are greatest at lower-middle elevations.
References
Boisvenue, C and SW Running (2006) Impacts of climate change on natural forest productivity-evidence since the middle of the 20th century. Global Change
Biology, 12, 862-882.
Case MJ, Peterson DL (2005) Fine-scale variability in growth-climate relationships of Douglas-fir, North Cascade Range, Washington. Canadian Journal of Forest
Research, 35, 2743-2755.
Dunne JA, Harte J, Taylor KJ (2003) Subalpine meadow flowering phenology responses to climate change: integrating experimental and gradient methods.
Ecological Monographs, 73, 69-86.
Eder, G, M. Fuchs, HP Nachtnebel, and W Loibl (2005) Semi-distributed modeling of the monthly water balance in an alpine catchment. Hydrological Processes,
19, 2339-2360.
Gurtz, J, H Lang, M Verbunt and M Zappa (2003) The use of hydrological models for the simulation of climate change impacts on mountain hydrology. In: Huber,
U.M., H. K. M. Bugmann, M.A. Reasoner (eds), Global Change and Mountain Regions, an Overview of Current Knowledge. Springer, The Netherlands, 343-354.
Kane, DL (2005) High-latitude hydrology, what do we know? Hydrological Processes. 19, 2453-2454.
Shär, C and C Frei (2003) Orographic precipitation and climate change. In: Huber, U.M., H. K. M. Bugmann, M.A. Reasoner (eds), Global Change and Mountain
Regions, an Overview of Current Knowledge. Springer, The Netherlands, pp 255-266.
Tague CL, Band LE (2004) Regional hydro-ecologic simulation system: an object-oriented approach to spatially distributed modeling of carbon, water and nutrient
cycling. Earth Interactions, 8, 1-42.
Whitaker, A, Y Alila, J Beckers, D Toews (2003) Application of the Distributed Hydrology Soil Vegetation model to Redfish Creek, British Columbia: model
evaluation using internal catchment data. Hydrological Processes, 17, 199-224.