water balance partitioning at the catchment scale: hydrosphere-biosphere interactions

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Water balance partitioning at the catchment scale:

Hydrosphere-biosphere interactions

Peter Troch, Ciaran Harman and Sally Thompson

2009 Hydrologic Synthesis Reverse Site VisitAugust 20-21 2009

Arlington, VA

2009 Hydrologic Synthesis Reverse Site Visit – Arlington, VA

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Motivation: another Horton index…

Horton, 1933 (AGU)

H constantV

W V : Growing-season vaporization (E+T)

W : Growing-season wetting (P-S)

“The natural vegetation of a region tends to develop to such an extent that it can utilize the largest possible proportion of the available soil moisture supplied by infiltration” (Horton, 1933, p.455)

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Horton Index vs. Humidity IndexMean Horton Index Std. Horton Index

53% with Std(H)<0.0674% with Std(H)<0.0783% with Std(H)<0.0893% with Std(H)<0.10

Troch et al., 2009 (HP)

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Climate Vegetation Geology Topography

The Horton Index

EcosystemProductivity

CatchmentBiogeochemistry

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

What controls the Horton index?

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

The Horton Index

Precip

“Fast” runoff

“Slow” runoff

ET

Wetting

Annual Evapotranspiration

Annual WettingHI =

Proportion of available water vaporized

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Three approaches explain HI

FunctionProcessPattern

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pre

dic

ted

HI_

50

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

HI_50

HI

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

... all three predict the mean remarkably well

ProcessFunctionPattern

Uncalibrated

Calibrated

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

HI was predictable based on static or mean catchment properties

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pre

dic

ted

HI_

50

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

HI_50

Pattern

HI = f ( )

Humidity index P/EP

Mean Topographic Index<Log (a / tan β)>

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Function

Functional model predicts mean, variance of HI

Wetting potentialFast flow threshold

P

S

U

ET

W

Functional model:

→ S and U have thresholds

→ ET and W have upper limit

…and using a conceptualization of annual partitioning of precip…

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Process ... and using a stochastic model based on filtering of storm events.

Storagecapacity

Calibrated storage capacity

CalibratedUncalibrated

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

We gained insight into controls on HI

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Empirical HI model

HUI

CT

I

0.1

0.2 0

.3

0.4

0.5

0.6

0.7

0

.8

0.9

1

1 2 3 4

34

56

78

Empirical CV(HI) model

HUI

CT

I

0

0.0

2

0.0

4

0.0

6

0.08

0.1

0.12

0.14

0.16

0.18

1 2 3 4

34

56

78

Regression models suggest that climate and topography are primary controlsPattern

Humidity IndexHumidity Index

Topographic Index

Mean: Climate (except in steep, arid regions)CV: topography (humid regions)

Mean HI CV HI

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Functional model suggests catchment capacity to vaporize and store water are

basic controls

Ep λs = λu = 0

λs = λu = 0.05

Function

Mean: - vaporization potential (~ energy) - catchment “wetability” (to a point)

P = 1000mm

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Process model also suggests keys are that climate and capacity to store water from storm eventsProcess

Mean HI: Humidity Index, storage capacityVariance: only sensitive in arid regions

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Prediction of interannual variability opens up questions about other factors

Timing of rainfall, vegetation response, landscape change, …?

ProcessFunctionPattern

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Key unresolved questions:

How does variability scale in time?

What timescales are important?

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Key unresolved questions:

What is the role of vegetation in hydrologic partitioning?

Are we only able to make predictions because of the co-evolution of vegetation, soils and geomorphology constrained by climate, geology and time?

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Variability and Vegetation

Learning from Data-Rich Sites

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Working ParadigmClassic ecohydrological approach:

ETmax ~ f(Rn, VPD, LAI,T)

ET ~ ETmax * f(θ)

“Water-limited” paradigm? Plant control of ET?

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

A Parsimonious Model Penman Monteith

Model

Rn VPD LAI U P T

Emax

E

T

Interception Model

PPT

Runoff

Drainage

Infiltration

Multiple Wetting Front ModelRoot Water Uptake Model

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Interannual variability

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Sub-daily variability

ET

(m

m/h

r)

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Seasonal variabilityE

T (

mm

/hr)

Month

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Soil Moisture Drydown v ET

0 50 100 150 200 250 300 350 4000

0.5

1

1.5

2

2.5

3

E TS o il Mo is ture

800 900 1000 1100 1200 1300 1400-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Kendall

Sky OaksET increases as soil moisture declines! ET

Soil Moisture

ET correlates to soil moisture

Days

Days

ET

(m

m/h

r) o

r θ

%E

T (

mm

/hr)

or

θ %

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Adding Groundwater Improves PredictionE

T (

mm

/hr)

Month

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Phenology Changes Seasonality of ET

10 20 30 40 500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

17

DOY

Nor

mal

ized

ET

, LA

I, R

n

L A I

E T

R n

0 50 100 1500

0.02

0.04

0.06

0.08

0.1

0.127

Radiation

ET

0 50 100 1500

0.02

0.04

0.06

0.08

0.1

0.129

Radiation

ET

0 1000

0.04

0.08

0.1213

Radiation

ET

A

B

C

A

B

C

Week

No

rmal

ized

ET

, LA

I an

d R

n

Howland Forest, Maine

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Phenological Effects are Predictable

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

13

Norm alized Cum ulative GDD

Nor

mal

ized

ET

0 0.2 0.4 0.6 0.8 10.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

110

Norm alized Cum ulative GDD

Nor

mal

ized

ET

0 0.1 0.2 0.3 0.4 0.5 0.6 0.70.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

111

Norm alized Cum ulative GDD

Nor

mal

ized

ET

Kendall Grasslands Donaldson Coniferous Forest Morgan Monroe Mixed Forest

Poorly correlated Well correlated

ET v Cumulative Growing Degree Days for first 150 Days of the Year

Onset of plant growth?Or leaf maturity?

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

0 0.5 1 1.5 2 2.5 30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

S ky O aks

Mo rg an Mo nro e

Harvard

G o o d win C re e k

Can Patches Predict Catchments?

Humidity Index

Ho

rto

n I

nd

ex

S.O. Catchment

M.M. Catchment

H.F. Catchment

G.C. Catchment

Sky Oaks

Morg. Monroe

Harvard Forest

Goodwin Crk.

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Conceptual Upscaling Approach Multiple Buckets – different topography,

veg, soil etc.

PPT, Energy, C

ET, Energy, C

Deep Drainage, Water Table, Lateral Redistribution

Surface redistribution

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Ecohydrological catchment classification?

Sky Oaks

Fort Peck Goodwin Creek

Howland Forest

Donaldson

Kennedy

Kendall

Austin Cary

Metolius

Harvard Forest

0.5 1 1.50

Morgan Monroe

Humidity Index

HuIRadiationPhenologyGW AccessSeasonality

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Discussion Points

• What does all this mean for predicting water cycle dynamics in a changing environment?– Mean behavior of hydrologic partitioning is

surprisingly predictable, and– Knowing hydrologic partitioning improves

prediction of vegetation response, yet– The inter-annual variability is poorly understood

and calls for higher understanding of ecosystem control on water cycle dynamics (do we need to replace the old paradigm?)