Water balance partitioning at the catchment scale: Hydrosphere-biosphere interactions
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Transcript of 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 constantVW
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
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Pre
dict
ed H
I_50
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1HI_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
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Pre
dict
ed H
I_50
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1HI_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
CTI
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0.2 0.3
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0
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1
1 2 3 4
34
56
78
Empirical CV(HI) model
HUI
CTI
0
0.0
2 0
.04
0.0
6
0.08
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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 Model Root Water Uptake Model
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Interannual variability
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Sub-daily variabilityET
(mm
/hr)
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Seasonal variabilityET
(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
ETSo il Mo is ture
800 900 1000 1100 1200 1300 1400-0.2
0
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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 %
ET (m
m/h
r) o
r θ %
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Adding Groundwater Improves PredictionET
(mm
/hr)
Month
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Phenology Changes Seasonality of ET
10 20 30 40 500
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DOY
Nor
mal
ized
ET
, LA
I, R
n
L A I
ET
R n
0 50 100 1500
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0.04
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0.08
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0.127
Radiation
ET
0 50 100 1500
0.02
0.04
0.06
0.08
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0.129
Radiation
ET
0 1000
0.04
0.08
0.1213
Radiation
ET
A
B
C
A
BC
Week
Nor
mal
ized
ET,
LA
I and
Rn
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
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Norm alized Cum ulative GDD
Nor
mal
ized
ET
0 0.2 0.4 0.6 0.8 10.2
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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
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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
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Sky O aks
Mo rg an Mo nro e
Harvard
G o o d win C re e k
Can Patches Predict Catchments?
Humidity Index
Hor
ton
Inde
x
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?)