Transcript Isotope & Watershed Modelling

Hydrologic
Models
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Isotope Hydrology Shortcourse
Isotopes in
Hydrological Models
Prof. Jeff McDonnell
Richardson Chair in Watershed Science
Dept. of Forest Engineering
Oregon State University
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Hydrologic
Concluding
Catchment
Isotope
Models
Statements
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Basics

Day 1




Morning: Groundwater Surface Water Interaction, Hydrograph separation
basics, time source separations, geographic source separations, practical
issues
Afternoon: Processes explaining isotope evidence, groundwater ridging,
transmissivity feedback, subsurface stormflow, saturation overland flow
Day 3



Morning: Introduction, Isotope Geochemistry Basics
Afternoon: Isotope Geochemistry Basics ‘cont, Examples
Day 2


Outline
Morning: Mean residence time computation
Afternoon: Stable isotopes in watershed models, mean residence time and
model strcutures, two-box models with isotope time series, 3-box models
and use of isotope tracers as soft data
Day 4

Field Trip to Hydrohill or nearby research site
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Example 1
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
Isotopes in model validation
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Svartberget Sweden
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
Isotopes in model validation
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Svartberget Sweden
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Example 2
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
Maimai New Zealand
Isotopes in model calibration
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Maimai Watershed in New Zealand
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Process Study Findings
…process experimental work at Maimai for past 25

Channel stormflow is usually 85-90% old water (Pearce et al., 1986
WRR)

Most subsurface stormflow is via soil pipes at the soil bedrock
interface (Mosley, 1979 WRR; McDonnell, 1990 WRR).

Riparian zone, hillslopes and hollows have distinct 18-O (McDonnell
et al., 1991 WRR and base cation concentration (Grady et al., in
review).

Mean age of baseflow is 3 mo (Stewart and McDonnell, 1991 WRR).
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Hillslope
Hollow
Riparian Zone
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A Three Box Model
P E
P E
P E
Hillslope box
Runoff
Hollow box
Riparian box
Umax
U
Umin
Seibert and McDonnell, 2002 WRR
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Geochemical End Members
100
90
Hillslope
Stream
Rain
Riparian Zone
Soil-Ridge
Soil-Hollow
80
K (mmol/L)
70
60
50
40
30
20
Riparian Zone
Hollow
10
0
0
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50
100
150
Na (mol/L)
200
250
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Cluster Analysis of Deuterium Concentration
in Subsurface Water
Hillslope
Hollow
Riparian
McDonnell et al.,1991; W
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A Three Box Model
P E
P E
P E
Hillslope box
Runoff
Hollow box
Riparian box
Umax
U
Hillslope type
discussed earlier…
Umin
Seibert and McDonnell, 2002 WRR
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The hollow box
Storm Rainfall Sd18O = -10o/oo
dS/dZ
y<0
y<0
d18O = -4.5o/oo
d18O = -5o/oo
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The time series
Q [mm/h]
Groundwater level [m]
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Catchment
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Scale
3
2
Model efficiency 0.
Hillslope
Hollow
Riparian
1
0
6
4
Observed Q
Simulated Q
2
0
28-Sep
18-Oct
17-Nov 27-Nov
….but8-Oct
does it
work 28-Oct
for the 7-Nov
right reasons???
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But does this agree with our conceptual
picture of the how the watershed works
based on our isotope information????
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Model performance with hard data
calibration
Goodness measure
1
0.8
0.6
0.4
Runoff efficiency
GW hard
GW soft
Parameter values
New water
0.2
0
Increasing soft data
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How can we use the process knowledge
Soft Data: Qualitative knowledge from the
experimentalist that cannot be used directly
as exact numbers (e.g. % new water, soil
depth, reservoir volume, macropore flow, etc
Rainfall/snow
Soils
Bypass flow
and mixing
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Pipeflow of old
water
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

Type of soft data
New water contribution to peak runoff
Range of groundwater levels, min/max, fraction of
saturated soil

Frequency of groundwater levels above a certain level

Parameter values

Fraction of riparian, hillslope, hollow

Porosity of riparian, hillslope, hollow

Soil depth of riparian, hillslope, hollow

Threshold level in hollow zone
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Dialog between the experimentalist and modeler
Evaluation rules
Experimentalist
Modeller
Values for evaluation rules (ai)
“Degree of acceptability”
a2
1
0
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a1
a3
a4
Model value or parameter
Different ways of evaluating model
acceptability
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Acceptability according to:
Example
A1 Fit between simulated and
Runoff
Efficiency
observed data
A2 Agreement with process
New water
Percentage of
(qualitative) knowledge
contribution
flow
A3 Reasonability of parameter
Spatial extension
Fraction
of
Combined
objective function:
values according to
of riparian zone
catchment area
n1 n 2 n3
n
experimentalist
A = A1 A2 A3
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Measure
peak
with n = n1  n2  n3
Seibert and McDonnell, 2002 AGU Monograp
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0
 x-a
1

 a2 - a1

m ( x) = 1
a -x
 4
 a4 - a3
0

Soft data discussions
if x  a1
if a1  x  a2
Fuzzy Rules
- new water at peak
- reservoir volumes, Ksat et
- range of gw levels
- hollow threshold level
if a2  x  a3
0.06
a2
if a3  x  a4
if x  a4
0.03
a1
0.12
a3
0.15
a4
(30/9/87 event, McDonnell et al. 91; WRR
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Hydrologic
Catchment
Models
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Improvement of model performance with
soft data
Goodness measure
1
0.8
0.6
0.4
Runoff efficiency
GW hard
GW soft
Parameter values
New water
0.2
0
3
2 ater ater GW
1
Q
A
A
A
d
d
t
w
w
f
n
n
a
a new new d so
2
1
A
A
& and Q an
A1,
W
e.g. from Seibert and McDonnell, 2
G
Q
d
ar
h
,
Q
Increasing soft data
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Q [mm/h]
Groundwater level [m]
Hydrologic
Catchment
Models
Plot
Scale
Scale
Model efficiency 0.
Hillslope
Hollow
Riparian
3
2
1
0
6
Observed Q
Simulated Q
4
2
0
28-Sep
8-Oct
18-Oct
28-Oct
7-Nov
17-Nov
27-Nov
….but does it work for the right reasons???
© Oregon State University
Q [mm/h]
Groundwater level [m]
Hydrologic
Catchment
Models
Plot
Scale
Scale
Model efficiency 0.92
Hillslope
Hollow
Riparian
3
2
….maybe not?
1
0
6
Observed Q
Simulated Q
4
2
0
28-Sep
© Oregon State University
8-Oct
18-Oct
28-Oct
7-Nov
17-Nov
27-Nov
Q [mm/h]
Groundwater level [m]
Hydrologic
Catchment
Models
Plot
Scale
Scale
2
Model efficiency 0.93
Hillslope
Hollow
Riparian
….maybe not???!!
1
0
6
Observed Q
Simulated Q
4
2
0
28-Sep
© Oregon State University
8-Oct
18-Oct
28-Oct
7-Nov
17-Nov
27-Nov
Hydrologic
Catchment
Models
Plot
Scale
Scale
Improvement of model performance with
soft data
Goodness measure
1
0.8
0.6
0.4
Runoff efficiency
GW hard
GW soft
Parameter values
New water
0.2
0
3
2 ater ater GW
1
Q
A
A
A
d
d
t
w
w
f
n
n
a
a new new d so
2
1
A
A
& and Q an
A1,
W
e.g. from Seibert and McDonnell, 2
G
Q
d
ar
h
,
Q
Increasing soft data
© Oregon State University
Hydrologic
Catchment
Models
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Scale
Example 3
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
Brugga Basin, Germany
Isotopes in model structure
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Catchment
Hydrologic
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Models
Scale
Beyond a 1km2 research watershed

Obtain as much map info as possible

Do synoptic survey of stream flow (if possible, temp, pH,
EC etc)

Gauge trib junctions

Measure mean age of water

Dominant runoff generation processes


Example Rietholzbach catchment
Translation into model elements
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Topography
Hydrologic
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Soils and Geology
Geology
Mor äne de r W ürm- Ve rglet scherung
Würm , Niederterass e
OSM , Tös swald Schicht en
OSM , Öhninger S chichten
Schw em mke gel
S o il
Re gosol
S a u re B ra u n e rd e
Braun erde
K a lk b r a u n e r d e
V e r b r a u n te r u . B u n te r G l e y
F a h le r g le y
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Digital Elevation Model
Topography
Slope
Topo Index
Curvature
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Process Identification
Hortonian
Overland
Flow
Saturation
Overland
Flow
Subsurface
Flow
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Catchment
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Scale
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Dominant Runoff Processes
Horton
rarely saturated
sometimes saturated
Frequently saturated
Often saturated
Always saturated
Subsurface flow
Drained areas
No runoff
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MRT and watershed modeling
Discharge
.... following Uhlenbrook et al. (2002) WRR
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Conceptualization of Runoff Processes
.... following Uhlenbrook et al. (2002) WRR
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Runoff Generation
flat hilltop, weathering profile
base layer, solifluction
main layer, solifluction
course top layer
boulder field
accumulation zone
saturated area
ha
rd
roc
ka
qu
i fe
r
.... following Uhlenbrook et al. (2002) WRR
© Oregon State University
Hydrologic
Concluding
Catchment
Isotope
Models
Statements
Plot
Scale
Scale
Basics

Day 1




Morning: Groundwater Surface Water Interaction, Hydrograph separation
basics, time source separations, geographic source separations, practical
issues
Afternoon: Processes explaining isotope evidence, groundwater ridging,
transmissivity feedback, subsurface stormflow, saturation overland flow
Day 3



Morning: Introduction, Isotope Geochemistry Basics
Afternoon: Isotope Geochemistry Basics ‘cont, Examples
Day 2


Summary
Morning: Mean residence time computation
Afternoon: Stable isotopes in watershed models, mean residence time and
model strcutures, two-box models with isotope time series, 3-box models
and use of isotope tracers as soft data
Day 4

Field Trip to Hydrohill or nearby research site
© Oregon State University