ASSESSING HILLSLOPE RESPONSE
MECHANISMS USING STABLE
ISOTOPES
C. Freese, SA Lorentz, J van Tol & PAL le Roux
1Centre
for Water Resources Research, University of KwaZulu-Natal, 3201
2University of Fort Hare
3Department of Soil Crop and Climate, University of the Free State, Bloemfontein, 9301.
*Corresponding author (email carl.freese@gmail.com)
Introduction
Site specific nature of previous studies makes transfer to ungauged sites difficult due to:
•
•
1: Spatial and temporal complexity
2: Current lack of tools
Residence time distribution equations
• generalized descriptors of catchment hydrology
• Spatially transferrable
• Potentially low data intensity
Develop generalized descriptors of subsurface for use in a catchment scale model
• δ18O isotope data
• two-step algorithm ( derive Dp and τ)
• parameterize hillslope sub catchments in the ACRU Intermediate zone model
• comparative ACRU simulations to assess the ability of Dp and τ
Methodology
Methodology
Convolution integral relates the output isotope time series to the input isotope time series
•
simulating the probability distribution for a conservative tracer molecules
Where:
δ(t)
=
output δO18 signal
t’
=
integration parameter describing
entry time of the tracer into the system
t
=
calendar time
δin
=
input δO18 signal
g(t - t’)
=
residence time distribution
Where:
g(t)
Dp
τ
=
=
=
response function
Dispersion coefficient
mean response time.
Where:
N
αi
Pi
δi
δgw
=
=
=
=
=
number of time steps/samples
recharge factor
precipitation amount (mm)
precipitation δO18 value (‰)
ground water δO18 value (‰)
Methodology
Results δin
4
2
0
δO18
-2
-4
-6
-8
-10
04-Oct-09
13-Nov-09
input rfl
23-Dec-09
hillslope 1
hillslope 2
01-Feb-10
hillslope 3
13-Mar-10
hillslope 4
Results δ(t)
3
simulated
2
lc04 seep
1
4L
δO18
0
-1
-2
-3
-4
-5
-6
01-Feb-09
08-Feb-09
15-Feb-09
22-Feb-09
01-Mar-09
08-Mar-09
3
simulated
lc04 seep
4L
2
1
δO18
0
-1
-2
-3
-4
-5
-6
1-Feb-10
16-Feb-10
3-Mar-10
18-Mar-10
2-Apr-10
Results
3
simulated
2
uc 2A
δO18
1
uc 2B
0
uc 01
-1
-2
-3
-4
-5
-6
1-Feb-10
16-Feb-10
3-Mar-10
18-Mar-10
2-Apr-10
3
2
simulated
1
uc 3/4
δO18
0
-1
-2
-3
-4
-5
-6
1-Feb-10
16-Feb-10
3-Mar-10
18-Mar-10
2-Apr-10
Results
Hillslope Site
Lower
1
catchmen
t
Upper
2
3
catchmen
t
4
Date
Dispersion
Mean response time R2
coefficient (D)
(τ)
LC 04
February 2009
0.002
18
0.81
LC 04
March 2012
0.003
12
0.24
LC 08
February 2009
0.0015
12
-
LC 08
March 2012
0.002
12
0.27
UC 01
February 2009
0.30
10
-
UC 01
March 2012
0.30
10
0.19
UC3/4
February 2009
0.09
9
-
UC3/4
March 2012
0.09
9
0.41
Results (ACRU 2000)
30
0
10
25
Discharge (mm)
20
30
15
40
R2= 0.68
10
50
60
5
70
0
80
rainfall
simulated
observed
Rainfall (mm)
20
ACRU Intermediate zone model
Results (ACRU Int)
Results (ACRU Int)
0
10
25
20
20
30
15
R2= 0.71
10
40
50
60
5
70
0
80
rainfall
simulated
observed
Rainfall (mm)
Discharge (mm)
30
Conclusions
• Low Dp high τ
– event pulse responses of the lower catchment.
• High Dp low τ
– sustained drainage of upper catchment.
• ACRU Int improvement on baseline simulations.
– Peak flows (ACRU 2000 & Int)
– Low flows (ACRU Int)
– Improved simulation of soil water discharge to stream
Proposal
• Initial field setup/ maintainence
– December 2014-February 2015
• Improved data sets
– Analyse for a range of tracers (EC, silica, N etc.)
– Temporal sampling density (tracers)
• Rainfall
• Streamflow
• Soil water
• Monitor Mooi hillslopes
– Hillslopes across different geologies
– Identify similar/typical hillslopes
Proposal
• Further ACRU Int testing
–
–
–
–
Refine input data set (tracers)
Increase detail of Weatherley simulations (more landsegments)
Define typical hillslopes within certain parts of the Mooi
Parameterise & model Mooi hillslopes
• Further insight into transferability of Dp and τ
– Capability to represent hydrological process across scales
– Linked to existing classification systems