Interacting controls on eco-hydrologic responses
to warming in mountain ecosystems
Upper Merced (K. Heyn, Dec. 2007)
Christina Tague
Bren School of Environmental Science and Management
University of California at Santa Barbara
Implications of hillslope-scale climate variation for estimating ecohydrologic responses to warming
Climate change effect on vegetation water use
How will climate change alter vegetation water
use in snow-melt dominated mountain
environments of the Western US?
 Tree Ring Analysis
 Higher elevations temperature
 Transitioning to water-limited at
lower elevations
(Natawakaski and Peterson, 2006)
 Climate effects on growth likely also on water use
 Evidence of widespread drought
stress related forest mortality
(Allen et al., 2010)
Can we model these types of responses using coupled mechanistic hillslope
hydro-carbon cycling models
http://fiesta.bren.ucsb.edu/~rhessys/
Tague and Band, 2004
Vertical and Lateral drainage in RHESSys
Carbon and Nitrogen Cycling in RHESSys
Transpiration (Penman-Monteith)
s(Rn + S) + ρC p (es − ea ) * ga
LE =


ga 

s + γ 1+
 g 

s
Photosynthesis (Farquhar)
F(Ac,Aj) - both of which include Ci
(concentration of carbon in leaves)
which depends on gs
Stomatal Conductance (Jarvis Model)
gs = f(Tmax,Tmin,LWP, atm C02, Radiation, VPD)
gs_canopy = gs*LAI
LWP - related to soil water availability
Linked with distributed hydrologic model and it’s parameterization
Gross PSN - f(light, nutrient availability,
conductance), and leaf area
Respiration: maintenance,
and growth f(T, N and
biomass)
varies with type and size of
plant components
NPP - Allocated to leaves, stems and roots; which
impact photosynthetic capacity and respiration costs
Potential complex dynamics because you have a
system with feedbacks and multiple controls
That carbon cycling models give you
“reasonable” forest biomass for particular sites is
not trivial; suggest that carbon cycling (rather
than structural or some other mechanism) can
explain growth and equilibrium size of stands
NM – Drought Stress Forest Mortality
 McDowell et al (2009) – 3 plots of
ponderosa pine in Bandelier National Park
 During 2000 drought, low elevation trees
died, upper did not
 Within 10km, elevation (2700, 2300,
2000m)
 Can eco-hydrologic model capture
 pre-drought difference in LAI and annual basal
area increment (productivity) between high, mid
and low elevation sites
 Reduced carbon-sequestration leading to death
by “carbon starvation”
RHESSys estimates of stem biomass vs. measured BAI
RHESSys estimates capture cross-site differences in productivity
WHY DO TREES DIE
Carbon Starvation hypothesis (McDowell et al., 2008): “stomatal
closure minimizes hydraulic failure during drought, causing
photsynthetic C uptake to decline to low levels, thereby promoting
carbon starvation as carbohydrate demand continue for maintenance
and metabolism and defense - the plant either starves outright or
succumbs to attach by insects or pathogens” (McDowell, 2010, New
Phytologist, 186:264-266)
The alternative proposed by McDowell is hydraulic failure - death due
to embolisms (stomates stay open; high tension cause embolisms and
widespread failure)
How much about climate controls on inter-annual variability in growth
and mortality can we explain by carbon cycling and carbon starvation
Carbon-cycling model captures greater carbon loss during 2004
drought is consistent with observed mortality at low elevation site
Min LAI
Where does NPP go?
Classic
Carbohydrate Storage?:
Reserve for use in “bad” years;
benefit is resilience to drought;
cost is less production of useful
biomass (leaves, stems roots) in good
year
http://mff.dsisd.net/Environment/PICS/GrowZones.jpg
Maintenance and
Growth Respiration
Q: Could carbohydrate storage be an
important variable to consider in
thinking about carbon starvation
mortality)?
Q: Would accounting for carbohydrate
storage change modelled response of
Ponderosa Pines to climate variation at
Frijoles site?
Note: we know even less about carbon
hydrate storage then we do about
allocation to roots, stems, leaves
(Sala et al., and McDowell, New
Pythologist 2010)
Impact of Percent NPP to Reserves on Minimum LAI
(from 1998-2007)
Assumptions about carbohydrate storage do not impact mortality in low
elevation site but do control whether mid site would withstand drought
Scaling UP: Bandelier National Park
Use RHESSys estimates to
scale to larger Bandelier
National Park and consider
warming scenarios
Peak Snow water
equivalent depth; Note
elevational change in
variance as well as mean
Warming scenarios +2, +4
RHESSys estimates of NPP for Ponderosa Pine
yellow = high probability of mortality
Corresponding implications for average (50year) stream hydrographs
What do spatial variation in energy, moisture drivers + hydrologic
connection tell us about spatial variation in climate sensitivity
Sierra Nevada Mountain watershed (183ha),
Elevation range 1800-2700m, conifer
(Jeffrey and Lodgepole pine and
fir with substantial meadows)
http://sagehen.ucnrs.org/Photos/scenics/index.html
Mean Monthly Precipitation
Mean Monthly Precip (mm)
Study Site: Sagehen Experimental
Watershed (UC Berkley Field Station)
RHESSys hydrologic model Performance –
post-calibration
Streamflow (1960-2000)



NSE (0.5)
R2 (0.8)
Inter-annual August R2 (0.8)
4 Drainage Parameters
K - saturated hydraulic conductivity
M - decay with depth
Pa - water potential at air entry
Po - pore size index
Relationship between modeled snowpack
(water year max 15-day average)
and observed August streamflow
Conclusion: spatial (between
watershed) differences in
subsurface drainage rates leads
to significant and substantial
differences in the sensitivity of
summer low flow to a change in
snow-melt recharge
Tague et al. 2008. Deep groundwater mediates streamflow response to
Climate warming in the Oregon Cascades. Climatic Change 86: 189-210.
Ecological Recession:
 Day of Water Year (Nov. 1 to Oct. 31) at which 7 day moving average of
transpiration goes to 50% of peak growing season value
(T on cloudy days removed)
Note at PET does not
Reach 50% of its peak
Day of
Ecological
Recession
Image taken from Schaeffer
and Williams, American Met.
Society Symposium, 1998.
 Timing of ecological recession – can be compared with sap-flow estimates
(without necessarily having to scale to whole tree transpiration)
For riparian patch, a 2°C
temperature increase
results, on average of
20 years, a 1 week
earlier timing of water
stress recession
For a patch with same
local conditions and LAI,
but disconnected from
upslope drainage area, a
2ºC temperature
increase results in a 2
week earlier timing of
water stress recession
Med snow yr
High snow yr
Low snow yr
Impact on vegetation water
use in late summer
Complex interaction
between amount of
precipitation, how much is
snow and late summer
water stress
2C Warming Scenarios – change in ET across elevation
Key Uncertainty – Scaling of Precipitation with Elevation
Daily Pi = base station precip * (k * (elevi-elevb)+1.0)
i = any model unit
k = scale factor
Scale Factor
Ind. Lake M et
Sagehen M et
Derived elevation scaling factor
0.0007
Impact of 2C Warming Scenarios on ET; sensitivity to precip
scale factor
low
mid
high
Spatial variation in precip: important control on ET response
to w arming
Bias as Percent
Calibrate precip scale factor based on predicted
streamflow bias:
Precip scale factor
Estimated scale factor 0.0007!!!
Climate change problem = PUB problem
One option is to think about streamflow as an integrater of
precip (high uncertainty) – models allow us to do that
“ Doing hydrology backw ards” (Kirchner, J., 2010)
Understand differential role of geology and precip variation
as a control on streamflow behavior is a pre-requisite for
doing this
Why we care about fine scale climate variation
: an ecohydrologic perspective
Within and between st order
watershed (1km-10km)
substantial spatial variation in
climate (T,P, controls on snow)
can be challenging to resolve
Co-spatial variation of these
climate drivers with vegetation
structure and function and
geology at these length scales
can lead to non linear responses
to climate variability and change
Next Steps


Utilize coupled-ecohydrologic framework to estimate whether
biological response to warming (directly and indirectly via
disturbance influence) are likely to be a “big” influence on
summer streamflow responses
Link model estimates of vegetation water use and NPP with
measurements of sapflow, tree ring C13 isotope (and others)
and mortality data (Western Mountain Initiative, Sierra CZO
other sites)
https://snri.ucmerced.edu/CZO
Contributors
UC Santa Barbara



Janet Choate
Aubrey Dugger
Elizabeth Garcia
Jim Kirchner
Craig Allen
Nate McDowell
Sarah Godsey
Linkages between changes in productivity and hydrologic processes