Transcript Document

CLIMATE AND LANDSCAPE CONTROLS ON SPATIAL (SUB-BASIN)
VARIABILITY OF WATER BALANCE WITH CHANGING TIME SCALES:
APPLICATION OF THE DOWNWARD APPROACH
D. Farmer and M. Sivapalan
Centre for Water Research, University of Western Australia
Abstract
The south-west Western Australia has a semi-arid, Mediterranean climate with cold, wet winters and hot,
dry summers. Both the climate and the landscape features are highly variable over relatively short
distances (i.e.. 50 to 100km). Except for a few water resources catchments the landscape is sparsely
gauged for rainfall and streamflow.
This poster deals with the application of a “downward” or top-down approach to a systematic analysis of
the space-time variability of observed streamflows around a 7,000 km2 basin. The method is used to
discover the key climatic and landscape (soils, topography and vegetation) controls on this variability,
with a view to eventually making predictions for ungauged sub-catchments within the basin. These
predictions are ultimately used to describe patterns of within-basin spatial and temporal variability.
First the systematic analysis is carried out for a number of representative, gauged catchments in and
around the basin of interest. In the first instance this involves the analysis of rainfall-runoff data to
assess general behaviour differences in signatures of streamflow variability at various timescales (i.e.,
inter-annual, intra-annual or mean monthly, flow duration curves). .
A series of conceptual, storage based, water balance models of increasing complexity with decreasing
timescales (annual, monthly daily etc.) are then used to investigate the ability of the models to reproduce
the various signatures. The required parameters for the models are estimated from available landscape
information and recession data. The combination of model complexity and sensitivity analysis on key
parameters provides feedback that is subsequently used to conduct “first pass” distributed modelling.
Results demonstrate significant east-west trends driven by climate variability, and systematic and
random variations caused by soils, topography and vegetation, including land use.
Murray Basin, Western Australia
Avon
Perth
Murray
Collie
Albany
Murray River basin (SW-WA)
at Dwellingup
6840 km2
Two major upstream catchments:
Hotham 4500 km2, Williams 1700 km2
Many smaller tributaries ~60-200 km2
Dwellingup area – high rainfall, natural Jarrah
landscapes, deep laterite profiles, mature pine
plantations.
P= 1000 mm/yr Ep = 1500 mm/yr
Eastern portion – low rainfall, shallower soils,
less relief, large scale clearing for agriculture,
small pockets of remnant veg.
P = <600mm/yr Ep = 1600-1800 mm/yr
Notional east-west changes in soils, landscape,
rainfall and vegetation.
Soils / landscape
Vegetation
Introduction
Initial analysis of river gauging stations on the Hotham, Williams and Murray
Rivers identified behaviours that were not easily explained at catchment scales .
•Why does the drier Williams catchment have greater yield than the wetter Murray
catchment (Fig A and B) ?
•How does increasing dryness to the east impact upon the 4,000
catchment (Fig D) ?
km2
Hotham
A
•What role does the decreasing soil storage capacity play (Fig E) ?
It was also found that the catchments within the Murray Basin were generally
drier than catchments from Western Australia and around Australia where the
downward approach had been previously applied (Fig C).
C
D
B
E
Analysis of key signatures
Observed data from nine representative catchments inside of and
adjacent to the Murray Basin (Fig A) were used to produce
derivative datasets. Data for five of these are shown. Name
colours correspond to colours used in the figures. The remaining
four had behaviours similar to ‘Dwellingup’ and ‘South’.
Comparisons were made of annual (Inter-Annual variability, Fig
B), average monthly (Intra-annual variability, Fig C), flow
duration (Fig D) and recession (Fig E) behaviours.
A
IntraAnnual (average monthly) yield
Inter-annual variability of Annual Yield [1983-1992]
15
100
1200mm-Dwellingup
540mm-North
700mm-South
500mm-Cuballing
400mm-East
90
70
10
60
MonthQ (mm)
•Yield differences between catchments North and South, and
Dwellingup and East.
80
Annual Q [mm]
The data identified subtle differences between the test catchments,
these included:
1200mm-Dwellingup [for]
540mm-North
[AG]
700mm-South
[AG]
500mm-Cuballing
[AG]
400mm-East
[AG]
[For]
[AG]
[AG]
[AG]
[AG]
50
40
5
30
20
10
0
0
1
2
3
4
5
6
AnnualQ: lowest-highest
7
8
9
•A notable lag in the Dwellingup catchment (Fig C).
10
Flow Duration Curves - Murray Reference Cats
2
0
10
6
month
8
1
10
12
C
Master Recession Curves - Murray Reference Cats
1
450mm-East
[Agriculture]
500mm-Cuballing [Agriculture]
1150mm-Dwellingup [Forest]
540mm-North
[Agriculture]
700mm-South
[Agriculture]
0
0
a=10,b=0.5
log Q
log[ Q / meanQ ]
Recession analysis identified a general trend in ‘a’ values (Fig E).
Higher values for Dwellingup were confirmed by nearby
catchments (purple line in Fig. E).
4
700mm-South
[Agriculture]
540mm-North
[Agriculture]
500mm-Cuballing [Agriculture]
1150mm-Dwellingup [forest]
10
10
2
B
•Some variability in Cuballing catchment (Figs B, C and D).
10
0
10
10
-1
10
10
-1
a=5,b=0.5
-2
a=3,b=0.5
a=3,b=0.5
10
-3
10
0
0.2
0.4
0.6
exceedence probability
0.8
1
-2
10
0
D
Conclusion: some general trends existed but data alone could not explain all variability.
1
10
time (days)
10
2
E
Yarragil : Inter Annual
Representative catchment modelling analysis
Falls Farm : Inter Annual
100
150
90
Annual Q [mm]
50
60
50
40
30
20
10
0
The model was first run using mean basin values for soil depth
(pink lines). These showed that local variability could not be
explained by climate alone.
1
2
3
4
5
6
0
7
1
Yarragil : Intra Annual (mean monthly)
10
16
9
AvMonthQ (mm)
8
7
4
6
3
2
1
0
2
4
6
Month
8
10
0
12
Yarragil : flow duration
2
10
10
0
2
4
6
8
10
12
Falls Farm : Flow Duration
2
1
logQ
Log Q
0
10
10
-2
10
10
6
5
2
10
5
6
4
10
4
7
10
10
3
8
12
0
2
Falls Farm : Intra Annual (mean monthly)
18
14
Models were then run using ‘best estimates’ parameter sets. Model
sensitivity analysis (green lines) indicated that the prediction of
general catchment behaviour was not substantially impacted for
small variations to realistic model parameters estimates.
Independent testing on remaining gauged catchments showed that
while predicted data may not be perfect they did capture the local
behaviour. Predictive uncertainty was smallest for longer timescale
signatures and this gave confidence in the ability of the models to
predict mean annual and seasonal patterns using interpolated
parameter sets.
70
100
MonthQ (mm)
Farmer et al (2003) used the model framework described below on
two catchments within the Collie Basin immediately south of the
Murray. Catchment ‘South’ was one of these. The approach had
also been previously run on Catchment ‘North’ for another project.
Annual Q [mm]
80
-4
10
0
0.1
0.2
0.3
0.4
0.5
Exceedence Probability
0.6
0.7
0
-1
-2
-3
0
0.1
0.2
0.3
0.4
0.5
0.6
Exceedence Probability
Comparison of results from Yarragil (‘Dwellingup’ catchment) and Falls farm (‘Cuballing’ catchment) identified
differences. The deeper soils for ‘Dwellingup’ tended to promote a long wetting up and late winter flows. Summer
depletion was exacerbated by evaporation from the native forest. The shallower soils of ‘Cuballing’ and lack of
substantive vegetation meant that less rainfall was needed before parts of the catchment became wetted and runoff
began to occur. Baseflow contributions were required before low flows were captured. Realistic parameter estimates
were needed to capture these behaviours. Similar outcomes were identified for ‘North’ and ‘South’.
Predicted long-term average yield
Individual model runs were made on the 70 sub-catchments. Soil and
recession parameters were interpolated from key data points. Measured
vegetation cover was used. Rainfall data for each sub-catchment was
derived from 31 well distributed daily rainfall gauges. Evaporation data
was derived from long term pan estimates (see Farmer et al 2003) .
Model data was produced for a 10 year period from 1980-1989. The
resulting data highlighted the role of vegetation in evaporating water and
reducing net runoff. Cleared catchments in the wetter western portion of
the Williams catchment were found to produce the highest yields (Q/P).
Catchments in the eastern areas were found to be drier.
Since all sub-catchments are of equal size it is possible to assess net
contribution to total basin yield.
The long-term averages tend to reflect the eastward decline in annual
rainfall. While they provided some indication of spatial variability they
did not serve to explain observed differences at smaller temporal scales.
Seasonal behaviour and runoff generation
Aggregation of sub-catchment predicted data permitted water
balance signatures to be generated for the Murray, Hotham and
Williams catchments. These showed a reasonable reproduction
of the observed behaviours, including flow (Fig A and Fig B).
Comparison of intra-annual behaviour between the western and
eastern parts of the basin shows the lag seen in the earlier
analysis (Fig C and Fig D). This lag reflects the longer period of
wetting up associated with the deeper soil profiles of the Darling
Range. In the shallower soil profiles runoff occurs more closely
to winter rainfall patterns.
A
Darling Range
B
Eastern Murray Basin
Bi-monthly analysis of rainfall and yield over the critical JuneJuly wetting up period provided a visual summary of the
situation (Fig E and Fig F). While rainfall totals are typically
higher in the western parts of the basin, yields are low.
C
D
Blue = observed data, red = predicted
Predominantly cleared catchments immediately
east of the forested catchments and in the eastern
basin areas are quite active, while areas of remnant
vegetation are predicted to be lower yielding.
Further investigation has shown that due to lower
soil capacities, moderate rainfall and slightly lower
evaporation the Williams River is most likely to
respond to a larger storm event. This has
implications for flood risk assessment.
E
F
Background to the Method
Farmer et al (2003) introduced a model framework that can be used
in a parsimonious manner to assess the impact of climate and
landscape interactions upon key water balance signatures (Figure).
The parameters associated with the model framework could be
related to identifiable catchment characteristics (see Table right).
Values could therefore be subjectively estimated from available
information and spatially distributed to reflect landscape transition .
This study uses the model framework to examine sub-basin impacts
resulting from spatial variability of key parameters. Interaction
between climate inputs and gradually changing parameters across
the basin causes subtle changes in process dominance and thus an
observable variation in behaviour.
P
E
Parameters were estimated for reference sub-catchments from soillandscape reports for the Murray Basin and some catchments.
Vegetation cover was estimated from satellite imagery. Recession
parameters were adopted from the recession analysis.
This knowledge was then spatially distributed in order to derive
indicative parameter sets for each of the 70 sub-catchments. Model
combinations are then run for each sub-catchment in order to obtain
output data that can be used to examine spatial and temporal
variation across the basin.
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Reference:
Farmer, D., M.Sivapalan and C.Jothityangkoon, 2003, Climate, Soil and Vegetation Controls upon the Variability of Water Balance in
Temperate and Semi-Arid Landscapes: Downward Approach to Hydrological Prediction, Water Resources Research, 39(2)
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Summary
Data and model analysis of the various temporal signatures at five sub-catchment locations established key differences
between landscapes within the basin. Independent testing on remaining catchments showed that while results may not
be perfect local behaviour was adequately captured. Subsequent distributed modelling made it possible to predict the
general (large scale) trends that cause detectable basin scale impacts.
The study identified that variability within the Murray Basin resulted from changing interactions between soil water
storage capacity, rainfall and evaporation. Though model structure remained essentially similar, small changes in
parameters and process significance contributed to observed spatial and temporal variability. Responses were
significantly altered in the presence of vegetation.
Variations in behaviour were identified between the vegetated deeper soil catchments in the west, the shallow drier
catchments in the east and the shallow active catchments in the south-east. Of note is the way that different parts of
the basin transition from dry hot summer states to peak wetness during winter-spring. These trends impact upon
runoff generation, streamflow contribution and space-time patterns of catchment wetness.
Using the downward approach provided:
1. Confidence that parsimonious model configurations could capture the essential behaviour.
.
2. Insight into local process and parameter significance.
3. A means to assess model sensitivity to landscape derived parameters and parameter change.
4. The ability to incorporate understanding from outside-of-basin gauges.
5. The means to derive the maximum benefit from limited basin datasets before making predictions.
6. Support for decisions needed to make spatially distributed predictions for ungauged catchment areas.