2015 CUAHSI Spring Cyberseminar Series - Adrian Harpold
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Transcript of 2015 CUAHSI Spring Cyberseminar Series - Adrian Harpold
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Water Vapor Fluxes from Snow Covered Landscapes: The Importance of Biotic
and Abiotic-Mediated Processes
Adrian A. Harpold Natural Resources and Environmental Science
University of Nevada, Reno CUAHSI Cyberseminar 4/17/2015
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Ecohydrological Paradoxes and Tradeoffs of Water Vapor Fluxes in Snowy Systems
How can snowmelt be both an efficient irrigator of vegetation and streamflow generation?
Will warming temperatures generate more/less vapor loss?
What are the tradeoffs between canopy interception and snowpack sublimation?
What are the tradeoffs between abiotic and biotic vapor losses on overall water budgets?
What can we learn from natural experiments (i.e. forest disturbance) to answer these questions?
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Acknowledgements
Paul Brooks, University of Utah
Joel Biederman, USDA ARS
Patrick Broxton, University of Arizona
John Knowles, University of Colorado
Theo Barnhart, University of Colorado
Noah Molotch, University of Colorado, JPL
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1981-2010 Mean Precipitation
Topography, Water, and Carbon Co-vary
Sierra Nevada
S. Rockies Appalachians
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1981-2010 Mean Precipitation
5
1981-2010 Min Temperature
Topography, Water, and Carbon Co-vary
Sierra Nevada
S. Rockies Appalachians
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1981-2010 Mean Precipitation
6
1981-2010 Min Temperature
Topography, Water, and Carbon Co-vary
Kellendorfer et al., 2011, RSE
Sierra Nevada
S. Rockies Appalachians
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Dry Year P=73 cm
ET Q
The Water Balance of Snow-Dominated Systems
Water balance is dynamic Example: Upper Truckee, CA
Simplified equation P=ET+Q
Wet Year P=155 cm
ET Q
From USFS
30-Year Average
ET Q
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Interception
Snow sublimation
Soil evap Transpiration
Storage
Streamflow
Dry Year P=73 cm
ET Q
The Water Balance of Snow-Dominated Systems
Water balance is dynamic Example: Upper Truckee, CA
More resolved equation:
How do we partition P into various stores and fluxes?
P=Esoil+Vinterception+Vsublimation+T+Q+S
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Snowmelt Timing Westerling et al., 2006
Biotic Water Demand is Changing: Effects of Forest Fire
Year 2000 Fire Regime
Schmidt et al., 2002
Beetle Outbreaks (Meddens et al., 2012)
Large departure from historical means Small departure from historical means
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Snowpack Dynamics Are Changing: Changes in AccumulaIon
Change in snow to rain Nov to March 1949-2004
Knowles, 2006, Journal of Climate
Change SWE 1950-2000
Mote et al., 2009
More rain, less
snow
Smaller April 1 snowpacks
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Snowpack Dynamics Are Changing: Changes in AblaIon
Trends over the last 30 years (1980-2010) Shorter snowmelts (SM50=Ime 50% melt occurs) Increased sublimaIon (SWE: Winter P raIo)
Harpold et al., 2012, WRR
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Paradox/Tradeoff 1: How can snowmelt be both an efficient
irrigator of vegetation and streamflow generation?
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Snow is a More Efficient Streamflow Generator Than Rain
Snowy watersheds show a range of ET/P Snowy watersheds generate more
streamflow when normalized to climate
Berghuijs et al., 2014, Nature CC
Long-term average for one watershed (red=snowy, green=rainy)
Over generates streamflow
Under generates streamflow
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Snow is a More Efficient Streamflow Generator Than Rain. Why?
259 sites, 2100+ station years
Snowmelt is responsible for peak soil moisture (PSM) response across varying stations
Consequently, soil field capacity most likely to be exceeded during snowmelt
Harpold and Molotch, in prep
1:1 relationship between peak soil moisture and snow
disappearance
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Snow is a More Efficient Streamflow Generator Than Rain. Why?
Model results suggest that more rapid snowmelt generates more streamflow (less vapor losses)
1100'0"W1200'0"W
450'0"N
400'0"N
350'0"N
350Kilometers
Barnhart et al., in prep
High snowmelt rates efficiently generate streamflow
High snow fractions show range of response
Study Area
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Snowmelt is an Efficient Irrigator
Niwot Ridge Ameriflux carbon measurements
Corresponding snow depth
Synchronicity between carbon uptake and snow water availability
0 50 100 150 200 2500
100
200Sn
ow d
epth
cm
2007
Ordinal Day0 50 100 150 200 250
5
0
NEE
umol/
m2 /s
Snow
Dep
th (c
m) NEE (um
ol/m2/s)
Snowmelt begins & NEE increases
Maximum annual NEE occurs at snow disappearance
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Snowmelt is an Efficient Irrigator Longer growing season
lead to less net ecosystem productivity (NEP)
Snow water used for transpiration throughout the year
GPP
(g C
m-2
wee
k-1 )
Hu et al., 2010, GCB
Snow derived Rain derived
Less SWE, less NEP
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Paradox/Tradeoff 2: Will warming temperatures generate more/less
biotic vapor losses?
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BioIc Controls: LimitaIons
Most of the snow-dominated Western U.S. has both temperature and water limitaIons on transpiraIon
Boisvenue and Running, 2006; GCB
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Biotic Controls: Changes in Temperature Limitations
Most runoff is generated at high elevations
Reduced temperature limitations can increase ET and decrease runoff
Assumes forests move up in elevation
Goulden and Bales, 2014, PNAS
Most runoff comes from high elevations
Increased ET w/ warming
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Biotic Controls: Importance of Alpine and Subalpine Area
Knowles, Harpold, et al., (in review), Hydro. Proc.
Spatial distributions matter! 35% of catchment area generates 60% of streamflow Catchment water balance will depend on how these
ecosystems respond and adapt to warming temperatures
From niwot.colorado.edu
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Paradox/Tradeoff 3: What are the tradeoffs between canopy interception and snowpack
sublimation?
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Abiotic Controls: Tradeoffs Between Interception and Snowpack Ablation
Distribution of snow in healthy forests reflects interception and sublimation losses
Pea
k S
WE
to P
Rat
io
(SW
E/P
)
Canopy sublimation
Snowpack sublimation
Distance (m)
Canopy cover
Canopy cover
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Forest Structure Influences on Abiotic Vapor Losses
Lidar shows impacts of interception and ablation across mosaic of canopy structure and topography
Canopy position matters!
Snow%Depth%(cm)%
0.4%
0.3%
0.2%
0.1%
%%%0%20%%%%%%%40%%%%%%%60%%%%%%%%80%%%%%%%100%%%%%%120%%%%%140%Pr
obab
ility%of%O
ccurrence%
Under%Canopy%Near%Canopy%Distant%Canopy%
Observed:%Solid%Line%Modeled:%Dashed%Line%
1000 m
100 m
Broxton et al., Ecohydrology, 2015 0"
100"
200"
Snow"Depth"(cm)"
Near canopy Distant NUnder canopy
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Predicting Abiotic Vapor Losses: Snow Physics and Laser Mapping (SnowPALM)
Broxton et al., Ecohydrology, 2015
Topography and canopy structure parameterized at 1-m resolution
Forced by tower micrometeorology
Verified with snow depth at 1-m scale
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0%
10%
20%
30%
40%
50%
Snow sublimation Interception Total vapor losses
Frac
tion
of W
inte
r P
Jemez, NM Boulder, CO
PredicIng AbioIc Vapor Losses: Site-Level Controls
Climate and forest structure lead to differing tradeoffs between interception and snow sublimation at each site
Snowpack Vapor Loss (mm)
39 65 92
Snowpack Vapor Loss (mm)
119 166 212
Broxton et al., Ecohydrology, 2015
Boulder, CO Jemez, NM
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50%
55%
60%
65%
70%
75%
1 meter 3 meter 10 meter 30 meter 100 meter
Frac
tion
of to
tal V
fr
om s
ublim
atio
n
Jemez, NM Boulder, CO
Smart Forest Management: Fine-Scale Canopy Matters For Water Partitioning Higher resolution leads to different
estimates using the same physics Characterizing canopy as under or
open is insufficient
1000 m
100 m
Sublimation increases 15%
Jemez River
Boulder Creek
Broxton et al., Ecohydrology, 2015
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Paradox/Tradeoff 4: What are the tradeoffs between abiotic and biotic
vapor losses in snow dominated systems?
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Tradeoffs In Abiotic and Biotic Vapor Losses: Forests and Alpine
Response of water budgets depend strongly on distribution of abiotic and biotic-mediated processes
Changes in runoff generation in alpine areas from warming could overwhelm changes in subalpine forests
!
!
From niwot.colorado.edu
Knowles, Harpold, et al., (in review), Hydro. Proc.
Abiotic: more efficient streamflow generator, less sensitive to climate
Biotic: less efficient streamflow generator, more sensitive to climate
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What can we learn from natural experiments to answer understand paradoxes and tradeoffs in vapor
losses from snow-dominated systems?
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Forest Disturbance in the Southern Rockies: A Natural Experiment
Can we use forest disturbance to learn about: Tradeoffs between
interception and snowpack sublimation?
Tradeoffs between abiotic and biotic vapor losses?
Denver, CO
MPB impacts Chimney Park, WY
Heavy fire impacts Las Conchas, NM
Impacted study catchments
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Effects of Disturbance: Expectations from Mountain Pine Beetle (MPB)
(weeks)
Gray Attack
Interception
(months) (years)
Hyd
rolo
gic
Par
titio
ning
Ano
mal
y E
nerg
y A
nom
aly Radiationsw
Wind
Courtesy: J. Biederman
Transpiration
Hypothesis 1: Larger Snowpacks
Hypothesis 2: Increased Streamflow
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CanEvap Soil Evap
Total ET
0
200
400
600
CLM4 Noah CLM4 Noah
Sensi&vity to Vegeta&on Change Chimney Park, WY
Impacted LAI=1
Healthy LAI=4
Water (cm)
Courtesy of D. Gochis, NCAR
LAI=1
LAI=4
Eects of Disturbance: Model Fidelity Model experiment using two land-surface models
CLM Noah
Different total partitioning of vapor losses between models
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0
5
10
15
20
New
sno
w e
vent
(cm
)
Healthy Post-fire
Eects of Disturbance: Tradeos Between IntercepIon and Snowpack SublimaIon
Individual snow event shows evidence of interception changes following disturbance
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0
10
20
30
40
Healthy Pre-Fire
Wat
er (c
m)
Winter P Maximum SWE
SWE:P = 68% SWE:P = 67%
0
5
10
15
20
25
Healthy Post-fire
Wat
er (c
m)
Winter P Maximum SWE
Eects of Disturbance: Tradeos Between IntercepIon and Snowpack SublimaIon
0"
10"
20"
30"
40"
50"
Healthy" MPB"Die4O"
Water"(cm)"
April"2011"Snow"Survey"
SWE:P"="74%""
SWE:P"="69%""
Winter precipita&on (cm)
0"
10"
20"
30"
40"
50"
Healthy"MPB"Die4O
"
Water"(cm)"
April"2011"Snow"Survey"
SWE:P"="74%
""
SWE:P"="69%
""
Peak SWE (cm)
Surprisingly, peak snowpacks did not change after disturbance
SWE:P = 56% SWE:P = 62%
2012 POST-FIRE (survey) 2011 POST MPB (survey) Biederman et al., 2015, Ecohydro. Harpold et al., 2015, Ecohydro.
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MPB below/above 12% 20% Healthy below/above 9% 11%
Site
Wind Speed (m/s)
Rsw (W/m2)
NWT Above Canopy Sub-canopy
CP Above Canopy
Sub-canopy
4.1 (2.4) 0.38 (0.15)
3.6 (1.3) 0.43 (0.30)
131 (65) 14 (9)
126 (65) 25 (15)
Change from sublimation in canopy (Healthy) to sublimation of the snowpack (Disturbed)
Energy to snowpack increased following disturbance
Biederman, Harpold, et al., WRR
0%
10%
20%
30%
40%
50%
2010 2011 2012
Win
ter V
apor
Flu
x/Pr
ecip
itatio
n (c
m/c
m)
Healthy MPB die-off
Eects of Disturbance: Tradeos Between IntercepIon and Snowpack SublimaIon
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Larger snowpack sublimation post-disturbance
Snowpack sublimation compensates for lower interception (total vapor losses still 30-45%)
Canopy sublimation
Snowpack sublimation
Healthy Forest
Canopy sublimation
Post-Fire Forest
Snowpack sublimation
Eects of Disturbance: Tradeos Between IntercepIon and Snowpack SublimaIon
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Eects of Disturbance: Tradeos Between AbioIc and BioIc Vapor Losses
~50% of water budget to summer vapor loss
Similar cumulative vapor losses in post-disturbance forest
Biederman, Harpold, et al., 2014, WRR
0%
10%
20%
30%
40%
50%
60%
70%
80%
2010 2011 2012
Sum
mer
Vap
or F
lux/
Prec
ipita
tion
(cm
/cm
)
Healthy MPB die-off
-
Effects of Disturbance: Tradeoffs Between Abiotic and Biotic Vapor Losses
Evidence of kinetic fractionation indicative of evaporation ONLY in disturbed sites
Biederman, Harpold, et al., 2014, WRR
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0%
10%
20%
30%
40%
50%
2010 2011 2012
Run
off E
ffici
ency
(cm
/cm
)
Healthy Q* MPB die-off Q* MPB die-off Q
Eects of Disturbance: Tradeos Between AbioIc and BioIc Vapor Losses
No evidence for increased streamflow using both measured streamflow (Q) and water balance approach (Q*=P-ET)
Biederman, Harpold, et al., 2014, WRR
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Eects of Disturbance: Tradeos Between AbioIc and BioIc Vapor Losses
Tradeoffs between interception and snowpack sublimation Increased energy inputs to
under canopy snowpacks
Potential sources of growing season vapor losses: Greater soil evaporation Compensation by trees Recovering vegetation
Soil evaporation
Remaining forest
Regrowth
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Tradeoffs in Abiotic and Biotic Vapor Losses: Disturbed Forests
Eight watersheds in Colorado were investigated following severe MPB disturbance
Biederman, Harpold, et al., (in review, WRR)
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Tradeoffs in Abiotic and Biotic Vapor Losses: Disturbed Forests
We infer abiotic-mediated vapor losses mediate decreases in transpiration using three different methods
Biederman, Harpold, et al., (in review)
Only significant changes were towards less runoff
following disturbance
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Take Home Points Snowmelt effectively infiltrates the soil profile
thus maximizing storage (for transpiration) and water subsidies (for runoff)
Tradeoffs between interception and snowpack sublimation depend strongly on climate and vegetation structure
In semi-arid climates (i.e. Rocky Mountains) abiotic-mediated vapor losses are likely compensating for changes in biotic-mediated vapor losses following disturbance
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Questions and Comments
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References Berghuijs, W. R., Woods, R. A., & Hrachowitz, M. (2014). A precipitation shift from snow towards rain
leads to a decrease in streamflow. Nature Climate Change, 4(7), 583-586. Hu, J. I. A., Moore, D. J., Burns, S. P., & Monson, R. K. (2010). Longer growing seasons lead to less
carbon sequestration by a subalpine forest. Global Change Biology, 16(2), 771-783. Goulden, M. L., & Bales, R. C. (2014). Mountain runoff vulnerability to increased evapotranspiration with
vegetation expansion. Proceedings of the National Academy of Sciences, 111(39), 14071-14075. Harpold, A.A. and N.P. Molotch. Timing of snowmelt differentially influences soil moisture response in
Western U.S. mountain ecosystems. Knowles, J., A.A. Harpold, et al. The relative contributions of alpine and subalpine ecosystems to the
water balance of a mountainous, headwater catchment in Colorado, USA
Broxton, P., A.A. Harpold, J. Biederman, P.D. Brooks, P.A. Troch, &N.P. Molotch. (2015) Quantifying the effects of vegetation structure on wintertime vapor losses from snow in mixed-conifer forests. Ecohydrology. doi: 10.1002/eco.1565
Harpold, A.A., J. Biederman, K. Condon, M. Merino, Y. Korganokar, T. Nan, L.L. Sloat, M. Ross, and P.D. Brooks. (2014) Changes in winter season snowpack accumulation and ablation following the Las Conchas Forest Fire. Ecohydrology. 7: 440-452. doi: 10.1002/eco.1363.
Biederman, J.A., A.A. Harpold, D. Reed, D. Gochis, B. Ewers, E. Gutmann, & P.D. Brooks. (2014) Increased evaporation following widespread tree mortality limits streamflow response. Water Resources Research. 50, 53955409, doi:10.1002/2013WR014994.
Biederman, J., P.D. Brooks, A.A. Harpold, D. Gochis, E. Gutman, D. Reed, E. Pendall, & B. Ewers. (2014) Multi-scale Observations of Snow Accumulation and Peak Snowpack Following Widespread, Insect-induced Lodgepole Pine Mortality. Ecohydrology. doi:10.1002/eco.1342.
Biederman, J., Somor, A., A.A. Harpold, et al. Streamflow response to insect-driven tree mortality in subalpine catchments.