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Budyko guide to exploring sustainability of water yields from catchments under changing environmental conditions
Irena Creed and Adam Spargo Western University, London, ON CANADA
2
And a consortium of catchment scientists, including:
USA Mary Beth Adams (FER) Emery Boose (HFR) Eric Booth (NTL) John Campbell (HBR) Alan Covich (LUQ) David Clow (LVW) Clifford Dahm (SEV) Kelly Elder (FRA) Chelcy Ford (CWT) Nancy Grimm (CAP) Jeremy Jones (BNZ) Julia Jones (AND) Stephen Sebestyen (MAR) Mark Williams (NWT) Will Wolheim (PIE) Meryl Alber (GCE) John Blair (KNZ) William Bowden (ARC) Ward McCaughey (TEN) Teodora Minkova (OLY) Dan Reed (SBC) Leslie Reid (CAS) Phil Robertson (KBS) Jonathan Walsh (BES)
CANADA Fred Beall (TLW) Tom Clair (KEJ) Robin Pike (CAR) John Pomeroy (MRM) Patricia Ramlal (ELA) Rita Winkler (UPC) Huaxia Yao (DOR)
Budyko Curve
Russian climatologist 1920 –2001
Budyko Curve describes the theoretical energy and water limits on the catchment water balance (P-ET=Q).
Budyko Curve provides a “business as usual” reference condition for the water balance.
If we assume it depicts the expected partitioning of P into ET and Q,
then we can begin to account for the reasons why sites depart from the baseline.
Can the Budyko Curve be used to identify catchments undergoing shifts in water yields or at risk of undergoing these shifts?
3
Water limit (AET=P);
a site cannot plot above
the blue line unless there
is input of water beyond
precipitation.
Energy limit (AET=PET); a
site cannot plot above the
red line unless
precipitation is being lost
from the system by means
other than discharge.
Budyko Curve 4
Energy limited (wet); AET is limited by the amount of thermal energy that is available.
Water limited (dry); AET is limited by the amount of water that is available.
Budyko Curve 5
HORIZONTAL deviations reflect a change in the climatic conditions
(temperature, precipitation)
Warmer, drier
Less
ru
no
ff
VER
TIC
AL
dev
iati
on
s re
flec
t a
chan
ge in
par
titi
on
ing
b
etw
een
ET
and
Q
Budyko Curve 6
North American Network
• Network of catchments across North America
• Represent longest existing paired records of meteorology and hydrology.
• Provide opportunity to explore effects of climate on water yields in headwaters
• Backdrop: P-PET (30-yr climate normals, 1971 to 2000)
7
Hypotheses
(1) Under stationary conditions (naturally occurring oscillations), catchments will fall on the Budyko Curve.
(2) Under non-stationary conditions (anthropogenic climate change), catchments will deviate from the Budyko Curve in a predictable manner.
8
Do the catchments fall on the Budyko Curve?
9
Budyko Curve
Distribution of sites on Budyko Curve based on “common” 10 year period of data
Jones JA et al. (2012). Ecosystem processes and human influences regulate streamflow response to climate change at long-term ecological research sites. BioScience 62: 390–404.
10
Deviations from Budyko Curve
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
HB
RA
ND
FER
TLW
MA
RN
TLO
LYC
AS
ELA
DO
RSB
CK
EJM
RM
MA
YK
BS
PIE
TEN
CW
TU
PC
GC
EN
WT
CA
RK
NZ
BN
ZB
ESSE
VLU
QFR
ALV
WC
AP
AR
C
Lon
gte
rm A
vera
ge A
ET/P
d
evia
tio
n f
rom
th
e B
ud
yko
Long term average deviation in AET/P (i.e., partitioning between ET and Q)
11
Reasons for falling off the Budyko Curve
1. Inadequate representation of P and T (Loch Vale)
2. Inadequate representation of ET (Andrews)
3. Inadequate representation of Q (Marcell)
4. Forest conversion (Coweeta)
5. Forest disturbance (Luquillo)
1. Today’s Focus: Response to changing climatic conditions
Deviations from Budyko Curve 12
Climate related deviations: the “d” statistic
We assume that the Budyko Curve represents the reference condition for the time period prior to anthropogenic climate change being detected in water yields.
13
Climate related deviations: the “d” statistic
For naturally occurring climate oscillations, the partitioning between ET and Q should move up and down with the Budyko Curve.
14
Climate related deviations: the “d” statistic
If the partitioning between ET and Q moves away from the Budyko Curve,
then this can be attributed to anthropogenic climate change.
15
Climate related deviations: the “d” statistic
The “d” statistic, the deviation in AET/P due to climate change, is calculated.
16
Climate related deviations: the “d” statistic
We know that not all catchments fall on the Budyko Curve for reasons unrelated to climate (i.e., o and ô on plot).
We assume these offsets are constant before and after climate change, and so this term becomes zero.
17
Climate related deviations: the “d” statistic
Negative d represents a downward shift and an increase in Q (more water yield).
Positive d represents an upward shift an a decrease in Q (less water yield).
18
Can we identify catchment properties that influence water yields?
19
Inter-annual variation along Budyko Curve
Spider plots showing year-to-year deviations from long term average
Jones JA et al. (2012). Ecosystem processes and human influences regulate streamflow response to climate change at long-term ecological research sites. BioScience 62: 390–404.
20
Inter-annual variation along Budyko Curve
Spider plots showing year-to-year deviations from long term average
Jones JA et al. (2012). Ecosystem processes and human influences regulate streamflow response to climate change at long-term ecological research sites. BioScience 62: 390–404.
21
Pre climate change responsivity 22
Responsivity is measured as the maximum range in AET/P after accounting for natural deviation in the Budyko Curve.
23
HIGH RESPONSIVITY Water yields are
synchronized to P
LOW RESPONSIVITY Water yields are
not synchronized to P
High vs. low responsivity
Pre climate change responsivity vs. “d”
PREDICTION #1: Larger deviations in catchments with higher responsivity
(catchments cannot buffer against climate change and water yields strongly linked to the atmosphere).
24
Pre climate change elasticity 25
Elasticity is measured as the ratio of range of PET/P to AETP/P.
High vs. low elasticity 26
HIGH ELASTICITY (>1) large PET/P range
relative to AET/P range
LOW ELASTICITY (<1) small PET/P range
relative to AET/P range
Pre climate change elasticity vs. “d”
PREDICTION #2: Larger deviations in catchments with lower elasticity (catchments cannot acclimate/adapt to changing climatic conditions)
27
Elasticity
0.0
0.5
1.0
1.5
2.0
2.5
0.5 0.6 0.7 0.8 0.9 1.0
Elas
tici
ty
Responsivity
Responsivity does not imply elasticity
Y = 4.90x – 2.86 R² = 0.039 P = 0.07
28
Applying these metrics to test hypotheses
29
Defining the onset of anthropogenic climate change to
identify pre vs. post behavior
Wang and Henjazi adopted a constant breakpoint (1970) to detect the effects of global environmental change on
water yields across USA
Wang D and M Hejazi. (2011). Quantifying the relative contribution of the climate and direct human
impacts on mean annual streamflow in the contiguous United States. Water Resour. Res. 47: W00J12,
doi:10.1029/2010WR010283.
30
Record length of P, ET (T) and Q data highly variable among the catchments
Constant breakpoint 31
Constant breakpoint 32
Record length of P, ET (T) and Q data highly variable among the catchments A 1970 breakpoint captures only 7 headwater sites!
Constant breakpoint: pre vs. post changes
Some sites observing net positive changes while others observing net negative changes.
33
Constant breakpoint: deviation (“d”)
POSITIVE deviations (lower water yields) in coniferous forests and NEGATIVE deviations (higher water yields) in deciduous forests
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
Deciduous Coniferous
d
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
MA
R S
5
MA
R S
2
FER
WS4
HB
R W
S3
CW
T W
S18
HB
R W
S6
MR
M
AN
D W
S02
AN
D W
S08
d
34
Constant breakpoint: responsivity vs. “d”
y = 0.12x - 0.06R² = 0.39p = 0.07
y = 0.12x - 0.05R² = 0.85
0.00
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.00 0.50 1.00
Ab
solu
te v
alu
e o
f d
Responsivity
With Hubbard WS06Without Hubbard WS06
PREDICTION #1
35
Constant breakpoint: elasticity vs. “d”
y = 0.02x + 0.02R² = 0.48p = 0.04
y = 0.01x + 0.03R² = 0.57p = 0.03
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.00 1.00 2.00 3.00
Ab
solu
te v
alu
e o
f d
Elasticity
With Hubbard WS06Without Hubbard WS06
PREDICTION #2
36
Elasticity
Ford CR, SH Laseter, WT Swank, JM Vose. (2011). Can forest management be used to sustain water-based ecosystem services in the face of climate change? Ecological Applications 21: 2049–2067.
Variable breakpoint
Use AutoRegressive Integrated Moving Average technique to check for breakpoints at each year from 1960 to 2000 (Ford et al. 2006).
No breakpoint Breakpoint at 1981
1981
37
Variable breakpoints allowed inclusion of additional sites (CAR, NTL), but also exclusion of sites with no detectable breakpoint (AND).
Variable breakpoint 38
Variable breakpoint: deviation (“d”)
-0.15
-0.10
-0.05
0.00
0.05
0.10
Car
nat
ion
Mar
cel W
S2
Mar
cel S
5
Fern
ow
Hu
bb
ard
WS3
Hu
bb
ard
WS6
Co
wee
ta W
S18
Co
wee
ta W
S 1
7
No
rth
tem
per
ate…
Mar
mo
t
d
39
Similar separation of deciduous (positive water yields) vs.
coniferous (negative water yields) in the variable breakpoint analysis, except Carnation Creek.
Variable breakpoint: responsivity vs. “d”
y = 0.07x - 0.01R² = 0.05
y = 0.14x - 0.07R² = 0.48
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.00 0.20 0.40 0.60 0.80 1.00 1.20A
bso
lute
val
ue
of
d
Responsivity
With Carnation
Without Carnation
Linear (With Carnation)
Linear (Without Carnation)
40
PREDICTION #1
y = 0.01x + 0.03R² = 0.06
y = 0.01x + 0.01R² = 0.69
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.00 1.00 2.00 3.00 4.00 5.00 6.00A
bso
lute
val
ue
of
dElasticity
Without Carnation
With Carnation
Linear (Without Carnation)
Linear (With Carnation)
Variable breakpoint: elasticity vs. “d” 41
PREDICTION #2
Elasticity
Is the outlier key to our understanding?
Constant vs. variable breakpoints
Similar relationships observed between responsivity and elasticity vs. absolute value of “d”.
Variable breakpoint relationships reveal a “significant” outlier (Carnation Creek, BC).
Need to delve further into the data to identify causes for this outlier – examine magnitude of temperature increase after the breakpoint.
42
Rate of temperature increase vs. “d” 43
Average increase in temperature estimated by
slope of line after breakpoint
As temperature increases above 0, d increases
Rate of temperature increase vs. “d” 44
Average increase in temperature estimated by
slope of line after breakpoint
As temperature increases above 0, d increases
Rate of temperature increase vs. “d” 45
Tipping point will vary based
on species tolerance
ranges
Average increase in temperature estimated by
slope of line after breakpoint
As temperature increases above 0, d increases
Outlier: Carnation Creek, BC
• Western hemlock
• Largest “d” and highest rate of anthropogenic climate change (2°C/decade)
Is water yield of the outlier catchment at greater risk because its tree species is “at the edge” of its climate tolerance?
Climatic Parameter Parameter Mean Parameter Range (min and max)
Annual mean temperature
5.13 °C -4.66 to 12.93 °C
Annual total precipitation
1600 mm/year 237 to 4196 mm/year
46
Baseline 1971 - 2000
Prior to anthropogenic climate change, Carnation Creek fell at the edge of the range of Western Hemlock.
5th – 95th Percentile
Maximum Range
47
McKenney DW, et al. (2007). Potential impacts of climate change on the distribution of North
American trees. Bioscience 57: 939-948.
First 30 years, 2011 - 2040
Based on CGCM model simulations, the range of Western Hemlock will recede on Vancouver Island.
5th – 95th Percentile
Maximum Range
48
McKenney DW, et al. (2007). Potential impacts of climate change on the distribution of North
American trees. Bioscience 57: 939-948.
Second 30 years, 2041 - 2070 5th – 95th Percentile
Maximum Range
49
Based on CGCM model simulations, the range of Western Hemlock will recede on Vancouver Island.
By mid 21st century, Carnation Creek will lie at the edge of the maximum range of Western Hemlock.
McKenney DW, et al. (2007). Potential impacts of climate change on the distribution of North
American trees. Bioscience 57: 939-948.
Third 30 years, 2071 - 2100 5th – 95th Percentile
Maximum Range
50
Based on CGCM model simulations, the range of Western Hemlock will recede on Vancouver Island.
By mid 21st century, Carnation Creek will lie at the edge of the maximum range of Western Hemlock.
By end 21st century, Carnation Creek will lie outside the maximum range for Western Hemlock.
McKenney DW, et al. (2007). Potential impacts of climate change on the distribution of North
American trees. Bioscience 57: 939-948.
Responsivity and/or elasticity
Ab
solu
te v
alu
e o
f d
LARGE DEVIATIONS IN WATER YIELDS Unresponsive inelastic
SMALL DEVIATIONS IN WATER YEILDS Responsive Elastic
+ 0.5 deg C/yr
+ 1.0 deg C/yr
+ 1.5 deg C/yr
+ 2.0 deg C/yr
Shifting to alternative stable states?
A better conceptual model of climate change effects on water yields 51
Acknowledgements
• NSERC Discovery Grant and Canada Research Chair Program
• “LTER Synthesis Workshops” funded by the LTER Network Office
• NSF LTER grants to participating USA sites
• USFS and USGS for initial establishment and continued support of watershed studies at many of the study sites
• NCE-SFM funded project on HydroEcological Landscapes and Processes (HELP) and the participating Canadian sites
52
53
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Evap
ora
tive
Ind
ex
(AET
/P)
Dryness Index (PET/P)
Annual Raw DataAdiabatic Lapse Rate
(1) Inadequate representation of P, T 54 54
Loch Vale (LVW): Failure to apply adiabatic lapse rate to meteorological data to account for orographic effects results in shift away from the curve.
!
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Evap
ora
tive
Ind
ex
(AET
/P)
Dryness Index (PET/P)
PET Penman-MonteithPET Hamon
55 (2) Inadequate representation of ET
HJ Andrews (HJA): Failure to consider net radiation, relative humidity and/or wind speed results in shift away from the curve.
Formula Variables
Thornthwaite T
Hamon T
Priestley-Taylor T, NR
Penman-Monteith
T, NR, RH, WS
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Evap
ora
tive
Ind
ex
(AET
/P)
Dryness Index (PET/P)
DischargeDischarge + Groundwater
56 (3) Inadequate representation of Q
Marcel (MAR): Failure to consider surface vs. groundwater losses of precipitaton.
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Evap
ora
tive
Ind
ex
(AET
/P)
Dryness Index (PET/P)
DeciduousConiferous
(4) Forest management effects 57
Coweeta (CWT): Conversion of forest from deciduous to coniferous forest results in shift away from the curve.
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Evap
ora
tive
Ind
ex
(AET
/P)
Dryness Index (PET/P)
Before hurricane1-5yrs after hurricane>5yrs after hurricane
(5) Natural disturbance effects 58
Hurricane Hugo hit the site in
September 1989 reducing
above ground biomass by 50%.
Luqillo (LUQ): Disturbance Effects
Constant breakpoint findings
• Responsivity and elasticity were directly correlated to climate related deviations from the Budyko Curve (among the catchments studied)
• Catchments where P and Q are synchronised catchments are more sensitive to climate change, that is they are tightly coupled with the atmosphere.
59