11/1/2011

• Summary statement on runoff generation– You summarized the classic “named”

mechanisms– My view – 2 requisite conditions

• 1• 2

• Key task: incorporate process knowledge into predictive models

• Next Project

How to Apply Process Information to Improve Prediction

• Improved prediction and improved process understanding are mutually reliant

time

Pre

cip

itat

ion

time

flo

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Lumped Model Distributed Model,

Physics based

Semi-Distributed Model, Conceptual),),(()( CAtPftQ

p

q

REW 1

REW 2 REW 3

REW 4

REW 5

REW 6

REW 7

Data Requirement:

Computational Requirement:

Small Large

Process Representation:Parametric Physics-Based

Predicted States Resolution:Coarser Fine

ssQUt

).().(

Perceived Intellectual Value:Small Large

q q

Modified from Mukesh Kumar

Lumped Model Distributed Model,

Physics based

Semi-Distributed Model, Conceptual),),(()( CAtPftQ

p

q

REW 1

REW 2 REW 3

REW 4

REW 5

REW 6

REW 7

Outcome:Right for Wrong

Reasons

Wrong for Right Reasons

ssQUt

).().(

q q

History:Mathematical

LumpingProcess

Understanding

Future:? Process

Understanding

Modified from Mukesh Kumar

How do we use Process Knowledge or data in this scene?

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cip

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How do we use Process Knowledge or data in this scene?

• Calibration– Assumes “model” is correct, forces

parameters to give the right answer

• Rewrite model to properly represent processes

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cip

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ion

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In Defense of Hydrologic Reductionism… an approach to understand the nature of complex things by reducing them to the interactions of their parts…

…a philosophical position that a complex system is nothing but the sum of its parts, and that an account of it can be reduced to accounts of individual constituents …

My PastBerkely Catchment Science Symposium 2009

My Past:In Defense of Reductionism

• Newton was right

• Model failures result from poor characterization of heterogeneous landscapes leads to– No emergent properties

• Our community struggles to identify grand, overarching questions because…there are no grand unknowns

• Hydrology is a local science

The Response Ciaran Harman, Catchment Science Symposium, EGU 2011

The Response Ciaran Harman, Catchment Science Symposium, EGU 2011

Catchments Lump Processes

Emergent Behavior

Decades of case studies have documented the many ways that water moves downhill

Recent work has identified many Physically Lumped Properties that are manifestations of the system of states and fluxes

-A physical basis for lumped parameter modeling

Physically Lumped Properties(emergent behavior)

• Connectivity

• Thresholds

• Residence Time

Physically Lumped Properties

• Connectivity

• Thresholds

• Residence Time

0

200

400

600

7/2 8/31 10/30 12/29 2/27 4/28 6/27

Dep

th (

mm

)

Total Precipitation

Water Input

Evapotranspiration

0

0.1

0.2

7/2 8/31 10/30 12/29 2/27 4/28 6/27

So

il M

ois

ture

0

10

20

30

Bed

rock F

low

(m

m)

15 cm30 cm65 cmBedrock Flow

0

20

40

60

7/2 8/31 10/30 12/29 2/27 4/28 6/27

Str

eam

flo

w

(lit

ers/

min

)

5

8

(m

g/L

)

Streamflow

dissolved solids

Threshold responses

0

1

0 10 20 30 40 50Moisture content (%)

Ru

no

ff r

atio

Satellite

Tarrawarra

Courtesy of Roger GraysonRoger Grayson, pers. Com.

Physically Lumped Properties

• Connectivity

• Thresholds

• Residence Time

Res

iden

ce T

ime

Met

hods

Figure from Jim Kirchner

)(tCin

)(tCout

This approachThis approachsimplifiedsimplified

Model Theory: The Convolution IntegralModel Theory: The Convolution Integral

Input Function:

Derived from precipitation 18O signal

Represents 18O in water that contributes to recharge

System Response Function:

Time distribution of water flow paths

Predicted or simulated output 18O signature

t

in dtgt )()()(

Soil water residence time

2 4 1 0 5 5 0 2 4 1 0 6 0 0 2 4 1 0 6 5 0 2 4 1 0 7 0 0 2 4 1 0 7 5 0 2 4 1 0 8 0 0

5 9 0 1 8 5 0

5 9 0 1 9 0 0

5 9 0 1 9 5 0

5 9 0 2 0 0 0

5 9 0 2 0 5 0

5 9 0 2 1 0 0

2 .0

4 .0

6 .0

8 .0

1 0 .0

1 2 .0

1 4 .0

1 6 .0

N ear S trea m

P it 5

P it A

T en s io m e te r N e tw o rk

ln (a /tan)

R a in g au g e

Annual Data

P 2250 mm

Q 1350 mm

E 850 mm

Average Data

Slope 34o

Relief 100-150m

Ksat 5 m/hr

Soils DataDepth 1 m

Strong catenary

sequence

Soil water Residence

Time

-4

-8

-12

-16

18O

‰

-4

-8

-12

-16

18O

‰

Soil WaterPrecipitation

Average -9.4‰Amplitude 0.1‰Std Dev. 3.4 ‰

Average -9.4‰Amplitude 1.2‰Std Dev. 0.6 ‰

If bedrock quite impermeableMRT and distance from the

divide2 4 1 0 5 5 0 2 4 1 0 6 0 0 2 4 1 0 6 5 0 2 4 1 0 7 0 0 2 4 1 0 7 5 0 2 4 1 0 8 0 0

5 9 0 1 8 5 0

5 9 0 1 9 0 0

5 9 0 1 9 5 0

5 9 0 2 0 0 0

5 9 0 2 0 5 0

5 9 0 2 1 0 0

2 .0

4 .0

6 .0

8 .0

1 0 .0

1 2 .0

1 4 .0

1 6 .0

N ear S trea m

P it 5

P it A

T en s io m e te r N e tw o rk

ln (a /tan)

R a in g au g e

0

40

80

120

160

0 10 20 30 40 50 60 70 80

Distance from divide (m)

Mea

n R

esid

ence

tim

e (d

ays)

MRT = 1.9(Distance) + 19.0r 2̂ = 0.88

Vache and McD WRR 2005

Lumped Model Distributed Model,

Physics based

Semi-Distributed Model, Conceptual),),(()( CAtPftQ

p

q

REW 1

REW 2 REW 3

REW 4

REW 5

REW 6

REW 7

ssQUt

).().(

q q

History: Mathematical Lumping

Process Understanding

Future:? Process

Understanding

Modified from Mukesh Kumar

Physically Lumped ModelDistributed Model,

Physics based),),(()( CAtPftQ

q

ssQUt

).().(

History: Mathematical Lumping

Process Understanding

Future:Process

Understanding

Physically lumped properties

Physically lumped properties

Modified from Mukesh Kumar

How to Apply Process Information to Improve Prediction

• Retain the computationally efficiency and lumped philosophy of systems models

• Observe how catchments create physically lumped properties

• Replace mathematical lumping approaches with physically lumped properties

– Use as “processes” , not data as validation targets– Build “processes” into new model structures

What do we do with this awareness?

ConnectivityThresholds

Residence Time

Lump the lumpsIt’s about Storage

P-ET-Q =dS/dt

ConnectivityThresholds

Residence Time

Storage

A Tale of Two Catchments

A Natural Storage Experiment

0

20

40

60

Tha

w D

epth

(cm

)

0

2

4

6

13-Jun 3-Jul 23-Jul 12-Aug

Flo

w (

cms)

65

75

85

13-Jun 13-Jul 12-Aug

% O

ld W

ater

0.0

0.2

0.4

13-Jun 13-Jul 12-Aug

Run

off R

atio

Storage Capacity

A Natural Storage Experiment

0

20

40

60

Tha

w D

epth

(cm

)

0

2

4

6

13-Jun 3-Jul 23-Jul 12-Aug

Flo

w (

cms)

65

75

85

13-Jun 13-Jul 12-Aug

% O

ld W

ater

0.0

0.2

0.4

13-Jun 13-Jul 12-Aug

Run

off R

atio

• We should focus on Runoff Prevention mechanisms in addition to runoff generation mechanisms

• We should concern ourselves with how catchments Retain Water in addition to how they release water

Storage CapacityP-ET-Q =dS/dt

The Storage Problem

• Storage is not commonly measured

• Storage is often estimated as the residual of a water balance

• Storage is treated as a secondary model calibration target

Improved storage characterization will lead to improved prediction

Reynolds Creek Dry Creek

Distributed Soil Moisture Measurements - Aspect