Budyko guide to exploring sustainability of water yields from ......Budyko guide to exploring sustainability of water yields from catchments under changing environmental conditions

Budyko guide to exploring sustainability of water yields from catchments under changing environmental conditions

Irena Creed and Adam Spargo Western University, London, ON CANADA

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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?

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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)

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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.

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Do the catchments fall on the Budyko Curve?

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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.

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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)

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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.

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For naturally occurring climate oscillations, the partitioning between ET and Q should move up and down with the Budyko Curve.

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If the partitioning between ET and Q moves away from the Budyko Curve,

then this can be attributed to anthropogenic climate change.

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The “d” statistic, the deviation in AET/P due to climate change, is calculated.

16


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


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).

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Can we identify catchment properties that influence water yields?

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Inter-annual variation along Budyko Curve

Spider plots showing year-to-year deviations from long term average


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Inter-annual variation along Budyko Curve

Spider plots showing year-to-year deviations from long term average


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Pre climate change responsivity 22

Responsivity is measured as the maximum range in AET/P after accounting for natural deviation in the Budyko Curve.

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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).

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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)

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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

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Applying these metrics to test hypotheses

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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.

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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.

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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

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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

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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

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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

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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)

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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.

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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






Tipping point will vary based

on species tolerance

ranges




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

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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

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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

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Second 30 years, 2041 - 2070 5th – 95th Percentile

Maximum Range

49


By mid 21st century, Carnation Creek will lie at the edge of the maximum range of Western Hemlock.



Third 30 years, 2071 - 2100 5th – 95th Percentile

Maximum Range

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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.



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

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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)


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)


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)


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)


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.

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Budyko guide to exploring sustainability of water yields from ......Budyko guide to exploring sustainability of water yields from catchments under changing environmental conditions