trend attribution of eurasian river discharge to the arctic ocean hydro group seminar, may 5...
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Trend Attribution of Eurasian River Discharge to
the Arctic Ocean
Hydro Group Seminar, May 5
Jennifer Adam
Dennis Lettenmaier
Study Domain
Mean Annual Air Temperature, C-18 -12 -6 0 6
Lena
Yenisey
Ob’
Study Period
1930-2000
Indigirka
Severnaya Dvina
Observed Stream Flow Trends
• Discharge to Arctic Ocean from six largest Eurasian rivers is increasing, 1936 to 1998: +128 km3/yr (~7% increase)
• Most significant trends during the winter (low-flow) season
• Purpose of study: to investigate what is causing this
Dis
cha
rge,
km
3/y
r Annual trend for the 6 largest rivers
Peterson et al. 2002
J F M A M J J A S O N D
10
20
30
40
Dis
cha
rge,
m3/s
GRDCMonthly Means Ob’
1950 1960 1970 1980
Dis
cha
rge,
km
3
Winter Trend, Ob’
• Currently experiencing system-wide change: All subsystems affected!
– Rivers, temperature, precipitation, permafrost, snow, wetlands, glaciers, vegetation zonation, fire frequency, insect infestations…
Climate and the Arctic
• Implications to global climate:(1) Albedo feedback
(2) Greenhouse gas emissions/uptake
(3) Ocean circulation feedback
www.noaa.gov
Thermohaline Circulation(heat) (salt)
Freshening of the Arctic Ocean deep water formation in the Northern Atlantic slowed-down or “turned-off”
Stream Flow Trend Attribution
• Hypothesized contributors –1.Acceleration of the hydrologic cycle:
P , E?2.Permafrost Degradation: dS/dt , E?3.Reservoir Operation: dS/dt?, E? 4. Other: fires, land use, wetlands, clouds, …
• Published authors to date all say, “we don’t know”: McClelland et al. (2004), Berezovskaya et al. (2004), Pavelsky and Smith (2006)…
• Water Balance:dS
P Q Edt
Storage,S:
ground water/ice, lakes, surface
ice…
? ? ?
Permafrost Primer
Frozen
Frozen
Unfrozen
Unfrozen
Permafrost:Coldest climates
Seasonally Frozen Ground:Moderate to Cold climates
Active Layer Depth (ALD)The hydrologically active layer
Warming can cause the ALD to increase and/or the extent of permafrost to decrease – both affect runoff generation
Affects of Permafrost Change on Stream Flow
• Seasonal effects:– Increased ALD, delay of freeze-up
Increase in late fall/winter stream flow?
• Annual increase via melt of excess ground ice: ice in excess of the volume
of the soil pores had the soil been unfrozen
* massive ice
* flakes or thin layers
* expanded soil pores
Lena: 100% permafrost (all types)
Yenisey: 89% permafrost (all types)
Ob’: 26% permafrost (all types)
Permafrost Distribution
Continuous , 90-100%Discontinuous, 50-90%Sporadic, 10-50%
Seasonally Frozen GroundIsolated, <10%
Brown et al. 1998
Annual Air Temperature/Stream Flow Correlation
Discontinuous Permafrost, %0 5 10 15 20
T/Q
Cor
rela
tion 0.4
0.2
0.0
-0.2
-0.4
-15 -10 -5 0Air Temperature, C
T/Q
Cor
rela
tion 0.4
0.2
0.0
-0.2
-0.4
(+) Correlation
(-) Correlation
COLD: no T control on Q
THRESHOLD: T control through permafrost melt
WARM: T control through Evapotranspiration
Annual Precipitation/Stream Flow Correlation
• “P-PET” is indicator of ΔE sensitivity to ΔP
• (P-PET) << 0 indicates high sensitivity, therefore ΔP contributes more towards ΔE than ΔQ, and P/Q correlation is low
• linear relationship for “warm” basins indicates few dS/dt effects
• scattered points for other basins (not shown) indicates more significant dS/dt effects
ΔE sensitivity to ΔP
ΔQ sensitivity to ΔP
Hypothesis FormulationCOLD: no T control on Q
ΔE ~ 0 ?
ΔdS/dt ~ 0
ΔP ~ ΔQ
THRESHOLD: T control through permafrost melt
ΔE ?
ΔdS/dt < 0, according to amount of “threshold”
ΔP < ΔQ
WARM: T control through Evapotranspiration
ΔE = f (ΔP , ΔT , P-PET)
ΔdS/dt ~ 0
│ΔP │ > │ ΔQ │, depending on ΔT, P-PET
permafrost
• Selection of trend test:
* Sensitive to seasonal differences in trend
• Varying periods between 1936 and 1998• Test for 99% significance, two-tailed• Calculate trends for precipitation,
temperature, and stream flow (gauged and reconstructed (McClelland et al. 2004))
Trend Analysis
Linear Regression
Mann-Kendall/ Sen Slope
Seasonal* Mann-Kendall/Sen
Slope
Annual Data Annual Data Monthly Data
Normally distributed
Non-parametric Non-parametric
Temperature Trends, 99%
Precipitation Trends, 99%
mm/yearC/year
Lena
Yenisey
Ob’
Sec
onda
ry B
asin
s
Stream Flow Trends, 99%
Len
a
Ob’
Ye
nise
y
Aldan (Lena)
Lena (head)
S. Dvina
Ob’ (head)
Indigirka
mm/year
Precipitation Trends (for periods with stream flow 99%)
Len
a
Ob’
Ye
nise
y
Aldan (Lena)
Lena (head)
S. Dvina
Ob’ (head)
Indigirka
mm/year
Str
eam
Flo
w T
rend
, m
m/y
r
Precipitation Trend, mm/yr
Stream Flow/Precipitation Trends
Gauged
Recon.
Gauged
Lena(1)Reservoir
(2)Precipitation
(3)Permafrost?
(4)ET?
Yenisey(1)Permafrost
(2)Reservoir
(3)Precipitation?
Ob’(1)Precipitation
(2)ET
(3)Reservoir?
Aldan (Lena)
(1)Permafrost
(2)Precipitation?
Lena (head)
(1)Precipitation
Severnaya
Dvina(1)Precipitation
(2)ET?
Basin Most Likely ControlsLena Reservoir, Precipitation, Permafrost, ET?,
other?
Yenisey Permafrost, Reservoirs, Precipitation?
Ob’ Precipitation, Reservoirs?, ET?
Indigirka other and Precipitation (little change)
Aldan (Lena) Permafrost, Precipitation?
Lena (head) Precipitation, Permafrost?, other?
Nizhn. (Yenisey)
? (change is small)
Pod. (Yenisey)
Precipitation and other (ET?)
Ob’ (head) Precipitation (and Reservoir?)
Irtish (Ob’) Precipitation and other (ET?, Reservoirs?)
Tobol (Ob’) ? (change is small)
S. Dvina Precipitation and other (ET?)
Lena at Kusur
Vilyuy at Khatyrik-Khomo
Vilyuy at Chernyshevskiy
Vilyuiskoe Reservoir
Reservoir filling: 1966-1970
Q Differences: (1970-1994)-(1959-1966)(post-dam) – (pre-dam)
-6000
-4000
-2000
0
2000
4000
cu
bic
me
ter
pe
r s
ec
on
d
Dam
Vilyuy
Lena
Modeling Application
Su et al. 2005
• VIC 4.1.0 r3
• Lakes
• Frozen soil
• Blowing snow
• EASE 100 km
• Calibration / Validation:
• Su et al. 2005
• river discharge, snow cover extent, ice freeze-up/break-up, ALD (with problems)
Simulated Q Trend ValidationLena
Yenisey
Ob’
ObservedSimulated
Naturalized
• VIC land surface hydrology model – complete water and energy balance
• Controls handled:
(1)Precipitation: YES
(2)Temperature on evaporation: YES
(3)Temperature on Permafrost: SOON
(4) Reservoirs: NO
Ann
ual S
trea
m F
low
, 10
3 m
3 /s
Simulated Stream Flow Trends, 99%
Len
a
Ob’
Ye
nise
y
Aldan (Lena)
Lena (head)
S. Dvina
Ob’ (head)
Indigirka
mm/year
Observed Stream Flow Trends, 99%
Len
a
Ob’
Ye
nise
y
Aldan (Lena)
Lena (head)
S. Dvina
Ob’ (head)
Indigirka
mm/year
Gauged
Recon.
Gauged
Obs
erve
d T
rend
, m
m/y
r
Simulated Trend, mm/yr
Observed/Simulated Stream Flow Trends
Lena: X
Ob’: ~
Ob’(head): ~Irtish:
S. Dvina: ~
Study Domain
Mean Annual Air Temperature, C-18 -12 -6 0 6
Lena
Yenisey
Ob’
Study Period
1930-2000
Indigirka
Severnaya Dvina
Ob’: 1950 to 1980 and S. Dvina: 1960-1995
Ob’ 1950 - 1980
Severnaya Dvina 1960 - 1995
ΔQ
Fraction Explained
by ΔP
Fraction Explained
by ΔE
Fraction Explained by ΔdS/dt
Historical P/T
Variability
Historical P Variability /
Climatology T
Historical T Variability /
Climatology P
• Cherkauer finite difference algorithm
• solving of thermal fluxes through soil column
• infiltration/runoff response adjusted to account for effects of soil ice content
• parameterization for frost spatial distribution
• tracks multiple freeze/thaw layers
• can use either “no flux” or “constant flux” bottom boundary
current set-up:
• constant flux – damping depth of 4m, Tb defined as annual ave air temperature, 15 nodes utilized
• spatial frost turned on
“Noflux” On• Motivation: Bottom boundary temperature no longer constrained – model is free to predict this as well as how this responds to various changes in climate, ground cover, and soil state.
• Necessitates deepening simulation depth to ~3x the annual damping depth (so, needs to be 10-20m)
• For nodes below bottom of third soil layer, total moisture derived from bottom soil moisture layer
Dp = 4 m, Tb(init) = -12 °C Dp = 15 m, Tb(init) = -3 °C
Tem
pera
ture
, °C
Tb Sensitivity to Tb(init): therefore init at zero, spin-up full 70 years at 1930’s
climatology
Effect of exponential node distributions (18 nodes, 15 m)
Time (one year)
Dep
th
Linear Exponential
Use of Russian Soil Temperature Data (Zhang, NSIDC)
Dep
th,
m
Temperature, °C
• temporal: 1800’s through 1990, but not continuous
• monthly data
• depths: 2cm, 5cm, 10cm, 15cm, 20 cm, 30 cm, 80 cm, 1.6 m, 3.2 m
• Simulated versus observed soil temperatures, Ob’ station for 9/1960, linear node distribution (18 nodes, dp = 15m, tb,init = zero)
Month
Tem
pera
ture
, °C
• Yenisey stations
• mean monthly biases
• bias varies with month and with depth
Global Soil Moisture Database (Robock)
From other datasets:
• snow depth
• soil temperature
• air temp, precip
• radiation data
Two sites selected for detailed analysis – red circles
0-10 cm
0-20 cm
0-50 cm
0-100 cm
Excess Ground Ice in VIC(ice in excess of the volume of the soil
pores had the soil been unfrozen)
• Segregation Ice: • the first to respond to warming (i.e. usually exists in expanded soil pores – most often in clays)• Initialize model with ice-filled expanded soil pores
• according to ground ice content maps• as ice thaws due to climatic warming, allow the soil pores to collapse to natural state by updating porosity (and accounting for 9% volume change from liquid to solid)
• Intrusive Ice:• can be found as massive ice – often the last and slowest response to warming• add a soil layer of pure ice to VIC
Ground Ice Conditions
Ongoing Modeling Foci• Off-line macro-scale hydrologic land surface
modeling
- Explore contributions to stream flow trends outside permafrost regions (Ob, S. Dvina)
- Problems with permafrost simulations identified:
(1)Needs dynamic bottom boundary temperatures (at soil damping depth)
(2)Investigate using observed soil (and other) data
(3)Needs incorporation of excess ground ice
• Stream Flow Predictions – using downscaled GCM output
Questions?
Parameter
Aldan (Basin 5) Irtish (Basin 10)
Baseline Adjusted Diff. Baseline Adjusted Difference
binf 0.003 0.003 0 0.025 0.027 0.002
d1 0.003 0.004 0.001 0.025 0.029 0.004
d2 0.003 0.004 0.001 0.025 0.007 -0.018
d3 0.003 0.003 0 0.025 0.024 -0.001
Dsmax 0.003 0.003 0 0.025 0.024 -0.001
Ws 0.003 0.004 0.001 0.025 0.022 -0.003
Ds 0.003 0.003 0 0.025 0.024 -0.001
Acknowledgements: Xiaogang Shi
Sensitivity of Q Trend to Calibration Parameters
sn
kks SS
1
1
1 1
)sgn(k kn
i
n
ijjkikk XXS
)var(
)sgn(
s
ss
S
SSZ
1
1 11
218
)52)(1()var(
s ss n
i
n
ijij
n
k
kkks
nnnS
ji
XXD jkikijk
for all pairs ),( jkik XX
(normally distributed, mean of zero)
snk 1 , and knji 1
. )( ijkDmedianB
where
Seasonal Mann-Kendall
Calculation of Slope Estimator, B:
Stream Flow Data
UW Data Development
mm
/yea
r
Lena
monthly climatology
long-term variability
short-term variability
++
Reconstructed Gauged 1940 1960 1980 2000
Lena300
200mm
/yea
r
80
40
0mm
/mon
th
3 6 9 12
Precipitation DataLena
1940 1960 1980 2000
600
400
mm
/yea
r
100500m
m/m
onth
Gauge-Based UW (gauge-based) Reanalysis
3 6 9 12
High Quality Precipitation
Stations
High Quality Temperature
Stations
ID
Precipitation (mm year-1) Temperature (°C year-1)
UDel UW UDel UW
1 98% 0.42 NA 0.24 NA -0.006 NA -0.010
2 NA 0.12 NA -0.08 NA 0.002 95% 0.011
3 NA 0.41 NA 0.23 NA 0.007 95% 0.014
4 NA -0.14 NA -0.32 NA -0.005 NA -0.005
5 99% 0.91 98% 0.68 NA -0.002 NA -0.010
6 NA 0.22 NA 0.11 NA -0.004 NA -0.006
7 NA 0.62 NA 0.40 NA -0.005 NA 0.006
8 NA 0.36 NA 0.11 NA -0.001 NA 0.011
9 NA 0.43 NA 0.19 NA 0.010 95% 0.012
10 NA 0.34 NA 0.19 95% 0.013 99% 0.020
11 NA 0.41 NA 0.34 NA 0.010 90% 0.014
12 NA 0.12 NA 0.28 NA -0.003 NA 0.001
Stream Flow/Precipitation Trend Compatibility
can be explained by Observed Precipitation
can be explained by Reanalysis Precipitation
cannot be explained by any Precipitation Product
Lena Ob’Yenisey
0%
100%
Gau
ged
Rec
onst
ruct
ed
0%
100%
Fre
quen
cy o
f P
erio
ds