1. science
2. weather
3. meteorological disaster
4. flood

1
2
Rainfall and streamflows in Greater Melbourne catchment area: variability and recent
3
anomalies
4
5
Authors: B. Timbal1, M. Griffiths1 and K.S. Tan2
6
7
8
9
10
Version 12: Final review in response to Climate Research reviewer's comments
11
12
13
14
15
Corresponding author:
16
Bertrand Timbal
17
[email protected]
18
19
1
20
Bureau of Meteorology
21
GPO Box 1289, Melbourne VIC3001, Australia
22
2
23
24
990, La Trobe Street, Docklands, VIC 3008, Australia
: Centre for Australian Weather and Climate Research
: Melbourne Water
1
25
Abstract
26
27
Observed rainfall and water availability is investigated across several catchments northeast of
28
Melbourne using gridded rainfall data over the last 113 years and reconstructed streamflow
29
observations for the last 100 years, focusing on the 1997-2009 record-breaking rainfall deficits,
30
associated record low streamflows and subsequent recovery from 2010 to 2012. The influence of
31
large-scale tropical modes of climate variability affecting rainfall, and subsequently reservoir
32
streamflows, are shown to be modulated by the orographic features marking this region. Since these
33
remote large-scale tropical climate forcings contributed strongly to the recovery, it helps explain the
34
uneven recovery since 2010. However, across these catchments these large-scale modes of natural
35
variability do not explain the long-term deficit in streamflows in the last 15 years. Annual streamflow
36
in these wet catchments can skilfully be reconstructed month by month using catchment-wide
37
observed rainfall. The year-to-year variability, decline during the last 30 years and magnitude of the
38
deficiency during the Millennium Drought are reasonably well captured but not fully accounted for by
39
the linear combination of the rainfall of the current month, the previous month and the previous 12
40
months. Maximum temperature does not have a sizeable additional impact when added to the
41
reconstruction, while the previous 12 months of rainfall is as critical information to capture many of
42
these statistics.
43
2
44
45
1. Introduction
46
South-eastern Australia (SEA), defined as continental Australia south of 33.5°S and east of 135.5°E
47
(Timbal, 2009), had its lowest 13-year rainfall between 1997 and 2009 compared to the entire 1900-
48
2012 period. The 1997-2009 annual average of 512.4 mm was 12% below the 1900-2012 average
49
annual of 582 mm (CSIRO, 2012). We will use the terminology Millennium Drought (MD) as used
50
by Leblanc et al. (2011) to name that extremely low rainfall period occurring between 1997 and 2009
51
at the time of the transition to the new millennium. Two other low rainfall periods were seen in the
52
instrumental record, which extends as far back as 1865 (Timbal and Fawcett, 2013), one around the
53
time of the federation of the Australian States (in 1901, Federation Drought) and the other during an
54
extended period preceding and encompassing World War II (WWII Drought). The MD was
55
unprecedented as it was more severe than either of these two previous droughts and, in contrast to the
56
previous droughts the rainfall deficit in SEA happened during the cooler part of the annual cycle from
57
April to October with the largest anomalies in autumn (Timbal, 2009).
58
59
Timbal and Hendon (2011) have shown that the MD could not be accounted for by naturally occurring
60
modes of variability generated within the tropics such as the El Niño-Southern Oscillation (ENSO)
61
and the Indian Ocean Dipole (IOD). This is consistent with other calculations (Smith and Timbal,
62
2012) and the fact that tropical modes of variability do not relate well with autumn rainfall in SEA
63
(Timbal and Murphy, 2007). Timbal et al. (2010) demonstrated that the rainfall deficit observed across
64
SEA was related to an observed strengthening of the belt of high pressure – a feature evident at every
65
longitude but for this study measured above the eastern part of the Australian continent following
66
Drosdowsky (2005). This belt of high pressure, known as the sub-tropical ridge (STR), is a key
67
controller of rainfall across southern Australia during the cool months of the year from April to
68
October. The 20th and early 21st century evolution of the ridge has been observed to evolve similarly to
69
global average temperatures and to account for about two-thirds of the observed deficit across SEA
70
during the MD (Timbal and Drosdowsky, 2013).
3
71
72
The MD concluded in 2009; the spring/summer of 2010/11 saw one of the strongest La Niña events on
73
record combined with a negative IOD event and a positive Southern Annular Mode (SAM) event – i.e.
74
all three key influences were in their wet phases in terms of expected rainfall impacts on south-eastern
75
Australia (CSIRO, 2012). These conditions, coupled with the warmest sea-surface temperatures on
76
record to the north of the Australian continent, contributed to making 2010 to 2011 Australia’s wettest
77
two-year period on record (Bureau of Meteorology, 2012). While the extensive flooding of 2010/11
78
was, by and large, consistent with natural variability (Hendon et al., 2013), it is possible that ongoing
79
global warming contributed to the magnitude of the event through its impact on ocean temperatures
80
(Evans and Boyer-Souchet, 2012). The spring and summer of 2011/12 saw the re-emergence of
81
another (weaker) La Niña event, combined with a positive SAM event during early summer, but this
82
time the Indian Ocean played a lesser role. Despite the La Niña events of 2010/11 and 2011/12, which
83
resulted in above average annual rainfall totals across the region, late autumn and winter rainfalls in
84
2011 continued to be below average, which is consistent with the trends associated with the observed
85
evolution of the STR associated with the expansion of the Hadley circulation (Nguyen et al., 2013); in
86
2010 to 2012, the intensity of the STR averaged 0.3, 1.1 and 1.2 hPa above the 20th century average
87
respectively in autumn, winter and spring.
88
89
The eastern area of Australia is dominated by the Great Dividing Range (GDR) – a range of
90
mountains, up to 2,000 m high, running approximately parallel to the coast from western Victoria in
91
the south to mid-Queensland in the north, 200-300km inland (Figure 1a). The southern part of the
92
GDR, within SEA, is where a large fraction of the run-offs and streamflows into dams is generated. In
93
this study we will focus on the central southern Victoria region (Figure 1b) where Melbourne and a
94
large part of the state of Victoria collect surface run-off into large dams North East of Melbourne. In
95
the area of interest the GDR runs basically west-east; on the northern side of the GDR, Lake Eildon
96
receives surface run-off from a large catchment (Fig. 1b); while on the south side lies the area
97
managed by Melbourne Water. There is a significant north-south orientated range connecting to the
4
98
GDR in a location named “the Triangle” separating the Thomson catchment area in the east from the
99
other major Melbourne Water catchments in the Yarra Basin to the west: Upper Yarra, O’Shannassy
100
and Maroondah (comprising streamflow from Watts River and diversion from Graceburn Creek).
101
These major water supply catchments all feed into the Yarra River, we will referred to them as “Yarra
102
catchments”, note we do not include some minor Melbourne water supply catchments and tributaries
103
within the Yarra Basin (e.g. Armstrong, Starvation and McMahon creeks). The various orientations of
104
these catchments in relation to the GDR may generate significant differences in term of how large-
105
scale climate modes of variability are impacting the rainfall across these catchments and how future
106
climate change may impact them. While the entire south-eastern Australia has been extensively
107
studied (CSIRO, 2012); a specific study on these important catchments is lacking. It is therefore of
108
interest to put the rainfall variability and in particular the magnitude of the MD across these major
109
water supply catchments in perspective and compare them to the entire SEA region and the broader
110
Murray-Darling Basin (MDB) which extend further North. Beside the importance of this region for
111
water management and the provision of drinking water to Melbourne, all these catchments are inter-
112
connected (water transfers are enabled from east to west and from north to south), it offers interesting
113
water supply operations and management opportunities and challenges for which improved
114
understanding of variability and changes are valuable.
115
116
While average annual rainfall during the MD declined by an average of 12% across south-eastern
117
Australia, the impact on streamflows was even more extreme. Due to non-linear hydrologic processes,
118
a magnified response of streamflows relative to rainfall (the “elasticity factor”) is expected (Chiew,
119
2006) and are generally two to three times the magnitude of the respective decline seen in rainfall for
120
the MDB region (Post & Moran, 2013). However, non-stationarity was also observed during the MD
121
as the elasticity response was much larger than during previous dry periods (Potter et al., 2010). This
122
magnification of the streamflow response has been related to various factors including the absence of
123
wet years, increasing temperature and the change in seasonal rainfall patterns, which means that the
124
land surface is not well moistened in autumn, resulting in a substantial reduction in runoff generation
5
125
during subsequent seasons possibly leading to poorly understood hydrological processes (e.g. changes
126
in ground water levels and connectivity with surface runoff). Some hydrological studies have focused
127
on the Victorian and Melbourne Water catchments (Preston and Jones, 2008; Howe et al. 2006) and
128
many studies have focused on the hydrological implications of the MD (Kiem and Verdon-Kidd, 2009
129
and 2010; CSIRO 2010, 2012) but there is a lack of study focusing on the impact of the MD and
130
subsequent recovery for the catchments close to Melbourne. Furthermore, in light of the inability of
131
hydrological model to fully reproduce the impact of the MD on streamflow across the region and in
132
particular the increase in the elasticity factor (CSIRO, 2012), we decide to investigate an alternative
133
approach and evaluate how well simple statistical relationships between rainfall and streamflow could
134
capture the hydrological response to the MD in these important catchment east of Melbourne.
135
136
The aims of this research are as follow: 1) document the characteristics of the MD and subsequent
137
recovery from 2010 to 2012 across the catchments east of Melbourne with a focus on the importance
138
of the marked orography across these various catchments which have different orientation: north of the
139
GDR (Lake Eildon catchment), and south of the GDR (Melbourne Water major catchments) with a
140
separation between the south-west oriented catchment (the Yarra Basin catchments), and the south-
141
east oriented catchment (the Thomson catchment), 2) for the same area evaluate how the large-scale
142
drivers of the rainfall are being modulated by the orography, and 3) evaluate how simple relationships
143
between streamflows and rainfall can capture the specificities of these catchments and the marked
144
variability across the observation record and in particular in the recent decades.
145
146
This paper is structured as follows: first a description of the dataset used and their possible short
147
comings is provided, with additional details on the area being studied. Then a section presents the
148
results focusing on 3 directions: the rainfall and streamflow records over the region, investigating the
149
seasonal and year-to-year variability with a particular focus on the MD and the 2010 to 2011 recovery;
150
the relationship between observed local variability for rainfall and streamflows and large-scale climate
151
indices; and finally, the possibility to use observed rainfall and temperature data to reconstruct the
6
152
observed streamflows.
A discussion section follows to synthesis the findings and draw some
153
conclusions from this investigation.
154
155
156
2. Data and Methods
157
158
We use the Australian Bureau of Meteorology’s current operational high-resolution monthly rainfall
159
analyses, generated as part of the Australian Water Availability Project (AWAP) (Jones et al. 2009).
160
These analyses are at 0.05°×0.05° resolution, or approximately 5 km × 5 km. The analysis
161
methodology employed in these analyses is a hybrid one, merging two-dimensional Barnes successive
162
correction analyses (Jones and Weymouth 1997) of fractions of monthly mean rainfall and three-
163
dimensional thin-plate smoothing spline analyses (Hutchinson 1995) of climatological monthly
164
rainfall.
165
166
The number of stations varies considerably during the period. For the purpose of understanding the
167
changes in real stations feeding the gridded rainfall in the vicinity of the area of interest, records from
168
stations have been combined, such that stations within 0.05 degree (approx. 5km) of each other are
169
grouped. Station record duration and location varies with time (Figure 2). A station is considered
170
active if there is data for at least 10 months of the year and for at least 75% of the years during that
171
time period. Focusing on three regions, covering Eildon, Thomson and the Yarra catchments; the time
172
evolution of the number of stations reporting for each of these three stations is likely to influence the
173
gridded rainfall as the numbers of stations has grown steadily until the 1970s before levelling off or
174
decreasing (Figure 2, lower left panel). Of particular concern is the coverage in the more mountainous
175
parts of the catchment. The average altitude of stations within each of these regions shows that it has
176
been increasing with time (Figure 2, lower right panel). This non-stationarity is likely to have an
177
effect on the time series of the AWAP catchment average as higher elevation stations tend to be wetter
7
178
than low elevation stations. It is therefore expected that the rainfall in the early record is likely to be
179
lower due to the station coverage. One would expect this effect to generate a spurious upward trend for
180
rainfall across these catchments (the relevance of this data issue will be discussed further in section 4).
181
182
The streamflow data at the Melbourne Water major water supply catchments (Yarra and Thomson
183
catchments) were provided by Melbourne Water, and the data for the Eildon catchment were provided
184
by the Department Environment and Primary Industries (DEPI). All streamflow data span the period
185
1913 to 2012. Monthly streamflow record at the Melbourne Water major harvesting reservoir sites are
186
based on reservoir mass balance approach calculated using hydrological models and water level
187
gauging records (taking into account of the streamflow, outflow and change in storage). However, the
188
earlier part of the monthly streamflow record prior to the construction and operation of the reservoirs
189
(pre-1984 for Thomson, pre-1957 for Upper Yarra, and around pre-1926 for O’Shannassy and
190
Maroondah) were derived based on flow correlation with nearby stream gauging record or catchment
191
area adjustment. For Graceburn Creek, the entire recorded monthly flow just upstream of the weir
192
(unimpacted flow) was used in this study as some of the flow was diverted at the weir into the
193
Maroondah Reservoir since 1941 depending on operational conditions. The monthly flow record at the
194
Melbourne Water major harvesting reservoir sites represents the main sources of streamflow from
195
Melbourne's water supply catchments that are not impacted by upstream diversions, but may be
196
impacted by changes to catchment (see further elements in the discussion section).
197
198
The long-term annual mean gridded rainfall as well as the mean for the four calendar seasons from
199
1900-2012 is shown in its native grid (approx. 5 km squares) across the area of interest (Figure 3). As
200
expected, rainfall is higher over the significant topography, and the highest rainfall is observed in
201
winter. This is consistent with the main characteristics of the mid-latitude rainfall systems (frontal and
202
cut-off lows) travelling from west to east across the region. In particular, in winter the maximum
203
rainfall is on the west facing slopes at high elevation – O’Shannassy and the southern part of the
8
204
Eildon catchment area. Spring is wetter than autumn. This picture is very relevant for the Murray part
205
of the MDB as most of the run-off is generated in these high elevations areas.
206
207
Factors known to influence the run-off are the rainfall, the soil moisture and the evaporation. In this
208
study we use the rainfall of the current month as the first predictors of the monthly streamflow data
209
continue with additional contribution to the pre-conditioning of the soil by using the rainfall of the
210
immediate past month as well as the rainfall over the previous 12 months. In addition, we also
211
consider the role that maximum temperature can play on streamflow via the evaporation by using the
212
average maximum temperature over the current month (based on the same operational gridded Bureau
213
of Meteorology dataset). While the role of temperature on evaporation is well established by the
214
Penman-Monteith equation, its impact on the complex evapo-transpiration aggregated over a
215
vegetation covered landscape is debateable (see Smith and Power, 2014). Some early studies
216
suggested that warmer temperatures made an important contribution to the anomalously dry conditions
217
experienced in the Murray Darling Basin region of eastern Australia during the prolonged drought of
218
1996 to 2010 (Nicholls, 2004; Cai et al., 2009). However, this explanation has been disputed by
219
Lockart et al. (2009). There is no evidence that temperature increases can explain this changed
220
relationship. A more likely explanation, as proposed by others, is that it reflects the combined effects
221
of changes in timing of rainfall events throughout the year, the absence of very heavy rainfall events
222
and, most likely, long-term changes in groundwater levels (Larsen and Nicholls, 2012). Studies
223
combining all these effects with temperature anomalies suggested a much more reduced contribution
224
from temperature than originally thought (CSIRO, 2012 and reference within)
225
226
Temperature data are available from 1910 onward, rainfall data from 1900 onward and streamflows
227
data from 1913 onward. A multi-linear regression approach was used based on the observed
228
relationship between the atmospheric variables and streamflows for the full 1913 to 2012 record.
229
Several attempt were made to linearly reconstruct the monthly streamflow data using these predictors,
9
230
five are reported here in order to document the importance of several factors as well as the possibility
231
of spurious skill due to over fitting of the dataset:
232
(1) R 1 is the most complete reconstruction and combine current monthly rainfall with the rainfall of
233
the previous month, the rainfall of the previous 12 months (not including the current month) as
234
well as the temperature of the month,
235
(2) R 2 repeats R 1 but using a fully cross-validated approach,
236
(3) R 3 repeats R 1 but with the influence of the temperature of the month removed,
237
(4) R 4 repeats R 3 but with the influence of the rainfall of the previous 12 months removed; and
238
(5) R 5 repeats R 4 but with the influence of the rainfall of the previous month removed.
239
While these are very clear simplifications of complex hydrological processes occurring at small sub-
240
catchment scale, the focus here is to see how much of the catchment-wide streamflow can be captured
241
with this simple approach as an alternative to hydrological model of various complexity (e.g. storage
242
routing) which are used in many studies.
243
244
245
246
3. Results
a. Rainfall and streamflow variability in the recent decades
247
In order to characterise the specificities of the MD across the catchments that were considered in this
248
study and to put it into the broader perspective of the SEA and MDB, we compare the MD to the
249
previous driest periods on record for each area. It was found that the earlier driest periods on record
250
were centred around 1940 for the MDB and SEA, and around 1910 for the Melbourne Water
251
catchments and for Eildon. Of particular interest are the rainfall deficit as a percentage of the long-
252
term mean, the magnitude of the wettest years during both dry periods and the seasonality of the
253
decline (Table 1).
254
10
255
In the Eildon catchment, the previous largest rainfall deficit was 8% (over the 13-year period 1902-
256
1914). This is a remarkably small number compare with the deficit of 18% observed during the MD.
257
For Melbourne Water catchments, previous driest 13-year periods were even less remarkable, never
258
exceeding 5% deficits. Another clear difference between the MD and previous historical droughts is
259
the absence of very wet years: e.g. in both Eildon and Yarra catchments, there was no single year
260
above the long-term average for the 13 years of the MD. In comparison, during previous long-term
261
drought, well above average rainfall years were observed (up to 15% above the long-term average).
262
The seasonality of the rainfall deficit is also markedly different. During the MD, the autumn deficit is
263
the largest in percentage terms, almost 30% whilst the other seasons have 7 to 16% decreases. For the
264
1902-1914 drought, the decline was predominantly in summer (about 10%), the other seasons all had
265
declines of less than 5%; winter being the key season for streamflow was close (within ±2%) to the
266
long-term average for Eildon and all Melbourne Water catchment areas.
267
268
The annual cycle of rainfall in each of the four catchment areas highlights the differences between the
269
broader MDB which has a broad summer maximum for rainfall and a smaller early winter peak in
270
June and the catchments across the GDR where rainfall is predominantly a cool-season (April to
271
October, peaking in July) phenomenon (Figure 4). The bi-modal nature of the annual cycle reflects the
272
large latitudinal extent of the MDB (from about 38oS to 24oS) with a northern basin dominated by
273
warm season rainfall and the southerly Murray Basin dominated by cool season rainfall. During the
274
MD, the dry conditions in the southern catchments were apparent over the whole year but worse for
275
the entire half of the year when the monthly temperature is below the annual mean (i.e. the cool
276
season), whilst the dry conditions in the MDB were limited to the cool season with a small increase
277
during the warm season.
278
streamflows in the three southerly catchments is very large. The largest reduction in streamflow in
279
absolute terms is in winter and spring which is the peak season for streamflow while the largest
280
rainfall decline occur in autumn. In percentage terms nevertheless, the annual cycle of the streamflow
The consequence of the rainfall deficiency during the MD on the
11
281
reductions match the rainfall deficiency being largest in autumn, this illustrates the importance of
282
autumn rainfall to saturate the catchment prior to the winter rains
283
284
2010 when the MD ended together with 2011 were the wettest two years recorded across Australia
285
(BoM, 2012). For the two catchment areas north of the GDR, Murray Darling Basin and Eildon, 2010
286
was extremely wet ranking 1st and 7th respectively out of the 110-year record (Table 2). Further south
287
(Yarra and Thomson) the 2010 rainfall was above the long-term mean but only ranked 29 and 26
288
respectively for the 113-year record (decile 8), well short of the highest values on record established
289
during the 1950s or the 1970s depending on catchments. That north-south difference in ranking was
290
less apparent in 2011 with rainfall ranking between 11 and 36 for all catchments considered. These
291
annual totals hide very contrasting seasonal behaviours. Dividing the rainfall into the warm months of
292
the year (November to March), and the cold months of the year (April to October), the record-breaking
293
rain came during the warm months of the year whereas the cool month rainfall was close to or below
294
the long-term average in 2010 and 2011 across the Yarra and Thomson catchments. While in Eildon,
295
the cool month rainfall was above average in 2010 but below in 2011 similar to SEA and the entire
296
MDB. The rainfall patterns for the average of 2010 and 2011 when compared to the long-term mean
297
(Figure 5) underlines that across the catchments of interest the largest differences occurred in summer
298
and spring while the winter and autumn maps are similar to the long-term mean. During the 2-year
299
wet period, total rainfall during the warm season across the area was mostly higher than the total
300
rainfall during the cool season, and this contrast is significant in the entire MDB and the Eildon north
301
of the GDR in comparison with the Yarra and Thomson catchments south of the GDR.
302
303
The size of the recovery in comparison of the size of the MD deficit differ markedly between the
304
entire MDB and the Eildon north of the GDR and the Yarra and Thomson catchments south of the
305
GDR. These quantities are expected to oscillate around zero but for the 16 years (192 months) shown
306
(Figure 6) sizeable deficits are obvious. Across the southerly catchments, the deficit for the MD (1997
307
to 2009) is only partially offset by the subsequent recovery in 2010 to 2011 and at the end of the 1612
308
year period; the accumulated deficit is still larger than at any time in the past instrumental record prior
309
to the onset of the MD in 1997 (the previous lowest 16-year period drawn from the 1900 to 1996
310
record is considered in Figure 6 to provide that historical benchmark). The magnitude of the record-
311
breaking nature of the MD for the 3 southern catchments is clearly shown. In contrast, the recovery
312
from the long-term perspective is relatively modest in all three catchments except for the Eildon
313
catchment north of the divide where the recovery is sizeable.
314
315
316
317
b. Large-scale modes of natural variability on rainfall and reservoir
streamflows
318
Australia is affected by many types of weather systems, the frequency and magnitude of these are
319
often influenced by some large-scale features of the atmospheric circulation (Risbey et al, 2009). Best
320
known and documented such natural large-scale modes emanate from tropical oceans: primarily in the
321
Pacific (the El Niño-Southern Oscillation: ENSO) and its response in the Indian Ocean, as measured
322
by the Indian Ocean Dipole (IOD) index proposed by Saji et al. (1999). To investigate these large-
323
scale influences on the high elevation catchments we are using two indices based on tropical sea
324
surface temperatures. Niño4 (N4) is the classic index of the ENSO variability based on central Pacific
325
SST anomalies, the tripole is an index developed by Timbal and Hendon (2011) which encompasses
326
the anomalies of all tropical SSTs with a strong relationship with the SEA rainfall and hence capture
327
the additional effect of the IOD on Australian rainfall.
328
329
In addition, regional indicators are used which in part respond to remote modes of variability but for
330
other part respond to broad scale change of the mean meridional circulation which influence the
331
climate of SEA through the role of mean sea level pressure (MSLP) in controlling local rainfall and
332
streamflow (CSIRO, 2012).
333
Drosdowsky (2005) has been shown to be tight controller of SEA rainfall during the cool season
334
(Timbal and Drosdowsky, 2013) and is used in this study. The geographical location of these indices
335
are summarised in Figure 7.
The sub-tropical ridge (STR) intensity and position computed by
13
336
337
Monthly correlation coefficients between both rainfall and streamflow (not shown) and the four
338
climate indices for each of the four catchments are very weak and not significant from November to
339
March but are statistical significant from April to October in most instances. This is explored further
340
for the key seasons of winter and spring (Figure 8) when the relationships are usually stronger and
341
statistically significant and when the bulk of the streamflow is generated. Higher correlation
342
coefficients with the tripole rather than N4 are noted everywhere and are consistent with the fact that
343
the tripole was constructed to maximise these correlations across SEA. The added value of the tripole
344
versus the classic N4 is obvious in winter where correlation coefficients for N4 fail to reach the 99%
345
significance level across the entire region.
346
347
However, by and large, both the STR position and intensity are more important in controlling year-to-
348
year variability across the Victorian catchments than tropical modes. Contrary to tropical influences,
349
the STR has a stronger relationship across the entire region in winter rather than in spring. South of the
350
GDR, the reduction from winter to spring is marginal and hence by spring the area where the strongest
351
control on the rainfall from the STR shift from being north of the GDR to being south of it. Spatial
352
features are very similar for STR intensity and position; the stronger relationship is always with the
353
intensity rather than with position, consistent with previous study (Timbal and Drosdowsky, 2013;
354
Whan et al., 2013).
355
356
The relationship between streamflow and the state of the tropical oceans was investigated by building
357
composites from both the long-term historical period and during the more recent MD and subsequent
358
recovery in 2010 and 2011. We compute composite based on the tripole annual values which by and
359
large are consistent with known ENSO and IOD years (Figure 9). Few noteworthy exceptions are
360
1928 and 1929 when, a negative tripole was associated with El Niño years, and 1933 when a positive
361
tripole was associated with La Niña years, and 1999 when a high positive tripole year was neither
362
associated with La Niña or IOD years and 1918, 1930, 1960 and 1961 when negative tripole years
14
363
were also not linked to ENSO or IOD variability. Composites were computed based on positive or
364
negative tripole years and compared with averages; the data were split in two periods: 1910 to 1996
365
and from 1997 to 2012 (Figure 10). This compositing demonstrates that tropical modes of variability
366
continue to have the same effect on a year-to-year basis i.e. positive tripole years are wetter than
367
average and negative tripole years are drier than average but it is happening with a reduced mean
368
streamflow post-1996. The shift is particularly marked for Melbourne Water catchments with almost
369
no overlaps between the two envelopes a clear indication of the role of the GDR in limiting the
370
tropical influence across Melbourne catchment and making the long-term drying trend since 1996
371
more prominent. Beside the overall reduction, it is also noted that the peak streamflow month is now
372
delayed from August for the pre-1997 period to September in the post-1996 period. This could
373
possibly be the effect of drier autumn with lower antecedent catchment condition at start of winter
374
months, and coupled with reduced rainfall, leading to reduced runoff generation efficiency and a delay
375
in peak monthly streamflow.
376
377
378
c. Using catchment rainfall to estimate reservoir streamflow
379
In this section we explore the relationship between rainfall and streamflow: using simple linear models
380
described in an earlier section we attempt to reconstruct the streamflow using the high resolution
381
gridded rainfall and temperature data. Key statistics for reconstructed streamflows are presented and
382
compared to observed rainfall and streamflow (Tables 3 and 4). The ability to reproduce the observed
383
streamflow decreases when the number of explanatory hydro-climate variables is reduced. For all
384
statistics, rainfalls at lag are important either from the previous month (R 4 versus R 5 ) and previous 12
385
months (R 3 versus R 4 ); the impact of temperature is very marginal (R 1 versus R 3 ).
386
387
Explained variances (i.e. squared correlation coefficients) are high and significant (Table 3) even for
388
the simplest of approach but grow to exceed 0.9 when multiple explanatory variables were used. This
389
is not reduced by a full cross-validation done for the most complete reconstruction (R 2 versus R 1 ). The
15
390
improvement in the amount of variance reconstructed is noteworthy rising to close to 80% for the
391
multiple variable reconstructions, however there it is worth noting that a full cross-validation reveals
392
that part of the gain may be spurious (compare R 2 and R 1 amount of observed variance reproduced in
393
Table 3). In addition to the year-to-year variability, it is interesting to investigate how well these
394
reconstructions capture trends (both long and short term). We have chosen to look at 3 different time
395
periods: the Millennium Drought (1997-2009), the recent period (1980-2012) and the entire period for
396
which we have data (1913-2012). Results from the fully cross-validated approach (R 2 ) are not
397
meaningful from the full historical period and are not reported.
398
399
There has been a net decrease in the streamflow for each of the time periods and for each catchment
400
(Table 4). Over the last 30 years the most complicated reconstruction captures the trend well (R 1 ).
401
Similarly the magnitude of the decline in streamflow during the MD which has been reported to be
402
larger than what is expected from the well-established rainfall-runoff elasticity (Potter et al., 2010;
403
CSIRO, 2012 and reference within) is well captured (in particular for Eildon and Yarra, less so for the
404
Thomson catchment). For these two results, the cross-validation using data prior to the last 30 years
405
(R 2 ) ensures that these results are not spurious.
406
407
Contrary to the recent trend and intensity of the MD, only half the secular trend is captured at best by
408
the more comprehensive multiple variable model. In light of the model’s ability to reproduce the more
409
pronounced trends observed since 1980, it does raise the issue that long-term trends are partly affected
410
by the non-stationarity in the number of actual observations used in the rainfall grid (see data section
411
and Figure 2 for details) thus preventing the model to match the trend in the longer-term streamflow
412
record.
413
414
415
4. Discussion and conclusions
16
416
417
a. Severity of the Millennium Drought
418
South Eastern Australia experienced, from January 1997 to December 2009, its worst long-term
419
drought on record: the Millennium Drought (MD) the driest 13-year period since reliable climate
420
records from the mid-18th century. It is different from previous droughts in several ways including
421
lower year-year variability and the seasonality of the rainfall decline. The MD was more severe than
422
previous historical drought across the entire SEA, but more remarkably so for the catchments dotted
423
across the Great Dividing Range (GDR), in the State of Victoria, east of Melbourne: by a sizeable
424
factor of 3 to 4 compare to the previous historical drought. The GDR acts as a physical barrier in terms
425
of natural variability and is the source of high run-off and streamflows into large dams which have
426
been constructed to provide surface water supply to Melbourne (Yarra and Thomson catchments) and
427
the rest of Victoria north of the GDR (Eildon catchment). The long-term rainfall deficiencies (and
428
streamflow reductions) have led to challenges in water management in Melbourne and across the SEA
429
(Post et al. 2014). The previous driest period on record was only a third (Thomson catchment) to less
430
than a quarter (Yarra catchments) of the severity of the rainfall deficit during the MD shading new
431
light on the extreme severity of the MD for these high elevation, high yield catchments.
432
433
This finding confirms earlier study about the very unusual nature of the MD. The question about the
434
stationarity of the rainfall record in the recent decades compared to the full historical record was raised
435
by Timbal and Fawcett (2013) whom demonstrated that to be very unlikely to be the case across the
436
broader south-east of Australia using a longer historical perspective (from 1865 to 2012). Furthermore,
437
using paleoclimate proxy records from the Australasian region, Gallant and Gergis (2011) developed a
438
reconstruction of streamflow for the Murray River from 1783 to 1988, and estimated that the 1998–
439
2008 streamflow deficit has an approximate 1 in 1,500 year return period. For Melbourne’s major
440
water supply catchments, Tan and Rhodes (2013) highlighted that the 10-year (1997-2006) drought
441
streamflow was estimated to have less than a 0.2% probability of occurrence (worse than 1 in 500-
442
year) based on historical streamflow record since 1913, but could become a commonly occurring event
17
443
in a changing and variable climate assuming a ‘return to dry scenario’ similar to that experienced
444
during the recent MD.
445
446
b. Recovery following the Millennium Drought
447
The MD concluded in early 2010. In 2010 and 2011, very high rainfall across the entire Australian
448
continent (highest 2 year rainfall total across the continent) brought relief to SEA and most reservoirs
449
located in the catchments considered in this study recovered. In 2012, at the end of the refilling season
450
(November), the MDB authority reported its storage capacity at 100%, Lake Eildon also reached full
451
capacity while Melbourne Water storages exceeded 80%. These are clear indications that from a water
452
management perspective, the abundant rainfall in 2010 and 2011 provided the required relief from the
453
MD and the recovery was complete.
454
455
However, from a purely climatic perspective, cumulative monthly rainfall anomalies tell a different
456
story regarding the magnitude of the recovery in comparison with the severity of the MD rainfall
457
deficiency; the accumulated rainfall deficit has not recovered anywhere close to the previous historical
458
accumulated deficits. For all these high elevation catchments on both sides of the GDR, the recovery
459
of the reservoir levels hides some important facts. Despite the 2010 to 2011 recovery, across the
460
Eildon, Yarra and Thomson catchments, accumulated rainfall deficiencies are still the largest at any
461
time within the historical record prior to the onset of the MD. The rainfall during the recovery was
462
predominantly a warm season phenomena while the long-term deficit is primarily a cool season
463
phenomena. Finally, the recovery was un-even spatially with the largest recovery observed north of
464
the GDR and not as much south of the GDR in Melbourne’s water supply catchment areas.
465
466
c. Role of large-scale drivers
467
It is well established that rainfall in SE Australia decreases during El Niño years and during years with
468
positive IOD, and it has been suggested that the MD was due to an extreme number of these years
469
(Ummenhofer et al., 2009); although that idea has since been discarded by several studies (Nicholls,
18
470
2009, Smith and Timbal, 2012, Timbal and Hendon, 2011). The analysis of inter-annual variability of
471
rainfall and streamflow across these catchments highlights the importance of the GDR. Large-scale
472
modes on natural variability emanating from Tropical Pacific and Indian oceans are an important
473
source of the observed inter-annual variability of rainfall and streamflow but this importance is
474
reduced south of the GDR. The tropical modes show a significant north-south gradient of influence
475
with stronger correlations to the north. Using a tripole index (Timbal and Hendon, 2011), it was
476
shown that the SST to the north of Australia and the Indian Ocean response to ENSO play a key role at
477
the onset of the winter-spring period; by spring, the relationships with the tropical indices are stronger
478
throughout the region than in winter but the strong influence of the GDR in limiting the relationship
479
with tropical influences further south across Victoria remains obvious. There is a similar effect further
480
north along the GDR between coastal and inland SEA, which has been explained in terms of moisture
481
flux anomalies and opposing influences between the direct influence of the Pacific tropical ocean and
482
the remote influence of the Indian Ocean (Pepler et al., 2014).
483
484
On the contrary to teleconnections with tropical oceans, local control of the rainfall due to the belt of
485
high pressure systems hovering across SEA (the sub-tropical ridge) is particularly strong for the entire
486
winter and spring period in these high elevation catchments. Rainfall is primarily generated during the
487
cool time of the year by rain bearing systems travelling from west to east and their ability to penetrate
488
inland and deliver rainfall is being reduced if the STR strengthens or shifts poleward. From a seasonal
489
prediction perspective, the correlations with rainfall are consistently stronger than those with
490
streamflow in particular with the STR indices, consistent with the lack of predictability and hence
491
memory from month to month for the STR behaviour. The difference is not as pronounced for tropical
492
indices where correlations are very similar for rainfall and streamflows in spring thus confirming that
493
because of the predictable and persistent nature, tropical modes of variability can be used to predict
494
streamflows and complement the memory from month to month which exist in streamflow (Robertson
495
and Wang, 2012).
496
19
497
For the catchments east of Melbourne, since 1997 there is a definite shift to lower streamflow during
498
the June to November dam filling season. This is the period of greatest rainfall and is therefore critical
499
to water management. Since 1997, high streamflow years due to a positive influence from the tropics
500
(i.e. positive tripole) provide similar streamflow to low streamflow years due to a negative tropical
501
influence (i.e. negative tripole) prior to 1996. It is clear from that analysis that while tropical modes of
502
variability remain important to discriminate between high streamflow and low streamflow years, the
503
marked reduction of streamflow during the period including the MD and the recovery in 2010-12 is
504
not explained by these modes. Instead, we continue to observed year-to-year variability driven by
505
these modes (as were the case during the wet 2010 and 2011 La Niña episodes or during the very dry
506
2006 to 2009 repeated positive IOD episodes) but applied to a different and lower baseline for mean
507
streamflow. In the meantime, other studies have reported that the reduction of rainfall across SEA
508
which is driving the streamflow reduction in these southerly catchments is primarily due to a
509
strengthening of the STR potentially associated with global warming (Timbal and Drosdowsky, 2013).
510
511
d. Relationships between rainfall, temperature and streamflow
512
The ability to reproduce monthly streamflow variability using solely the rainfall observed across the
513
catchments over different time scales and combined with temperature was investigated. For this part of
514
the study, the focus was on Eildon, Thomson and Yarra catchments. These southern catchments are
515
smaller in scale and have higher rainfall than the Murray Darling Basin and thus it is more likely that
516
the streamflow can be successfully captured by using catchment wide rainfall averages. In addition,
517
because of the complex made-man influence across the Murray Darling Basin it is unlikely that a
518
simple relationship can be found between rainfall, temperature and streamflow. It was found that when
519
current rainfalls, as well as rainfall from the previous month and the previous 12 months are used as
520
explanatory variables in the multiple linear regression models, most of the annual streamflow
521
variability can be reasonably reproduced including the decline in the last 30 years and the severity of
522
the streamflow decline during the MD. On the contrary to rainfall, temperature does not appear to play
523
a significant role, as discussed previously by Potter and Chiew (2011) and Smith and Power (2014)
20
524
and reference within. This was tested several times using various combinations with and without
525
temperature (not all results were shown here for the sake of brevity). However, it is fair to say that in
526
all attempts made rainfall of the month was always used and hence it is possible that due to the
527
dependence between rainfall and temperature (i.e. wet days are cold days), once rainfall is used the
528
effect of temperature on streamflow is already captured. Overall for future use of this simple approach,
529
testing both the optimal combination (which includes temperature) and a reduced combination
530
(without temperature) will be worthwhile. An important point as well is that the ability of the most
531
complete linear combination to reproduce streamflow characteristics during the last 30 years was
532
confirmed not to be a statistical artefact using a fully cross validated approach.
533
534
The long-term memory for rainfall (previous 12 months), which represents the groundwater or deeper
535
antecedent soil moisture conditions, is important while short-term and current rainfalls are not
536
sufficient. It was found to be a useful variable to simulate the long-term persistence in streamflow
537
decline observed during the MD which is in supplement of the long-term rainfall-runoff elasticity
538
observed in the past. This idea could be pushed further (i.e. using the prior 10-years rainfall mean) to
539
capture decadal shift linked to the Inter-decadal Pacific oscillation (IPO) which has a signature of SEA
540
streamflow (Pui et al., 2011). The fact that antecedent catchment conditions ranging from a few
541
months to a year can have large impacts on streamflow is well known and has been shown previously
542
for Melbourne Water catchments.
543
Melbourne’s major water supply catchments under two vastly different climate scenarios. The results
544
indicated that given the moderately wet antecedent catchment conditions at the start of 2012, the
545
simulated runoff assuming a repeat of the 2006 observed daily rainfall sequences (a very low rainfall
546
year) would remain higher for three to four months before returning to the very low runoff conditions
547
similar to a dry year like 2006. In comparison, the simulated runoff assuming a repeat of the 2011
548
observed daily rainfall sequences (a relatively high rainfall year) would remain lower for a longer
549
period of about six to eight months before returning to the higher runoff conditions similar to a wet
550
year like 2011.
Tan et al. (2012) investigated the runoff responses for the
21
551
552
It was also noted that an observed secular decline in streamflows was not well captured by these
553
reconstructions but there is credible evidence that it is likely due to a data issue. In the data section, it
554
was noted that for rainfall, the coverage was sparse in the earlier part of the record with an absence in
555
high elevation stations. This is likely to result in a spurious positive trend and hence it is possible that
556
the actual long-term rainfall declining trends is more pronounced than the one deduced from the
557
gridded observed rainfall and used in the reconstruction, leading to an underestimation of the
558
streamflow reduction trends. However other possibilities are worth mentioning. For example changes
559
in the accuracy of the streamflow data overtime cannot be ruled out (given the flow data prior to dam
560
construction in the Thomson and Yarra catchments were mostly derived based on flow correlation
561
with nearby stream gauging record or catchment area adjustment). It is also possible that differences
562
in catchment soil depth, non-linear hydrologic processes and thresholds effects as well as external
563
changes such as changes in land use or vegetation cover due to bushfires impacts which cannot be
564
captured by a simple multiple linear regression model have played a part in recent history. Such
565
factors may be at play in the fact that while the streamflow reduction during the MD is fully captured
566
for the Yarra catchments (-35% reconstructed for -36% observed), it is less so for the Thomson
567
catchment (-30% reconstructed for -37% observed).
568
569
Overall, for these high rainfalls high yield catchments, simple linear combination of observed rainfall
570
across the catchment can reproduce streamflow at the monthly time-scale. This simple analysis opens
571
up the possibility of providing a simple estimate of the impact of climate change on these catchments
572
using downscaled climate change projections on similar grid. This can be done using currently
573
available statistical downscaling technique (Timbal et al., 2009) which provide projections for future
574
changes of rainfall on such a high resolution grid (Timbal et al., 2011). Such work is underway and
575
will provide a benchmark against which to evaluate more complex approaches relying on several
576
hydrological models of various complexities fed with these downscaled rainfalls (Teng et al., 2012).
22
577
578
e. Conclusions
579
The additional knowledge gain in this study on the magnitude of the MD and the size of the 2010-12
580
recovery and how it differs on different side of the GDR is also very valuable; although very little
581
differences were found south of the GDR between the Yarra and Thomson on either side of a north-
582
south ridge. The significant contrast in the rainfall patterns during the 2010 and 2011 recovery
583
between the entire MDB and the Eildon north of the GDR, in comparison with the Yarra and Thomson
584
catchments south of the GDR opens up water management opportunities as all these catchments are
585
inter-connected by the water supply system connections (e.g. north-south connection). Advancing our
586
understanding of how the rainfall varies on inter-annual to longer timescale can help better inform the
587
management of these systems and potentially enhance water security, such as through inter-basin
588
transfer strategy during times of droughts and floods, and in responding to climate change.
589
590
591
Acknowledgement
592
This work has been supported by the Victorian Climate Initiative (VicCI). Inflow data were provided
593
by the Department of Environment and Primary Industries (DEPI, Victoria) courtesy of Rae Moran
594
and by Melbourne Water courtesy of Bruce Rhodes. Ian Smith and Mohammed Bari (BoM) have
595
contributed to improve an earlier version of this manuscript. We thank three anonymous reviewers for
596
very useful criticisms of an earlier version of this manuscript.
597
23
598
References
599
600
Bureau of Meteorology (2012) Australia’s wettest two-year period on record; 2010-11, Special
601
Climate
602
www.bom.gov.au/climate/current/statements/scs38.pdf.
Statement
No.
38,
National
Climate
Centre,
Bureau
of
Meteorology,
603
604
Cai, W., Cowan, T., Briggs, P., Raupach, M., 2009: Rising temperature depletes soil moisture and
605
exacerbates severe drought conditions across southeast Australia, Geophys. Res. Lett. 36, L21709.
606
http://dx.doi.org/10.1029/2009GL040334.
607
608
Cai, W, van Rensch, P., Cowan, T., and Hendon, H. 2011. Teleconnection Pathways of ENSO and the
609
IOD and the Mechanisms for Impacts on Australian Rainfall. J. Climate, 24, 3910–3923.
610
611
Chiew, F.H.S., 2006: Estimation of rainfall elasticity of streamflow in Australia. Hydrol. Sci., 51(4),
612
613-625
613
614
CSIRO, 2010: Climate variability and change in south eastern Australia - A synthesis of findings from
615
Phase 1 of the South Eastern Australian Climate Initiative (SEACI), SEACI report, 36pp, available on-
616
line at: http://www.seaci.org
617
618
CSIRO (2012) Climate and water availability in south-eastern Australia: A synthesis of findings from
619
Phase 2 of the South Eastern Australian Climate Initiative (SEACI), CSIRO, Australia, September
620
2012, 41 pp.
621
622
Drosdowsky, W., 2005: The latitude of the subtropical ridge over eastern Australia: the L index
623
revisited, Int. J. Climatol., 25, 1291-1299.
24
624
625
Evans, J. P., and I. Boyer-Souchet (2012), Local sea surface temperatures add to extreme precipitation
626
in northeast Australia during La Niña, Geophys. Res. Lett., 39, L10803, doi:10.1029/2012GL052014.
627
628
Gallant, A.J.E. and Gergis, J., 2011: An experimental streamflow reconstruction for the River Murray,
629
Australia, 1783–1988, Water Resources Research, 47, doi:10.1029/2010WR009832.
630
631
Hendon H. H., Lim E.-P., Anderson D. L. T. and J. Arblaster, 2013: Causes and predictability of
632
record wet spring Australia 2010, Mon. Wea. Rev., submitted
633
634
Howe, C., R.N. Jones, S. Maheepala and B. Rhodes, 2005: Implications of Potential Climate Change
635
for Melbourne’s Water Resources. A collaborative Project between Melbourne Water and CSIRO
636
Urban Water and Climate Impact Groups
637
638
Hutchinson, M. F. 1995: Interpolating mean rainfall using thin plate smoothing splines. Int. J. Geog.
639
Inf. Sys., 9, 385-403.
640
641
Jones, D.A. and Weymouth, G. 1997: An Australian monthly rainfall dataset, Technical Report 70.
642
Bureau of Meteorology: Melbourne, Australia, 19pp.
643
644
Jones, D.A., W. Wang and R. Fawcett, 2009: High quality spatial climate data sets for Australia, Aust.
645
Met. Oceanogr. J., 58, 233-48.
646
647
Kiem, A.S., Verdon-Kidd, D.C., 2009. Climate drivers of Victorian streamflow: Is ENSO the
648
dominant influence. Aust. J. of Wat. Res., 13(1), 17-29
649
25
650
Kiem, A.S., Verdon-Kidd, D.C., 2010. Towards understanding hydroclimatic change in Victoria,
651
Australia – preliminary insight into the "Big Dry". Hydro. Earth. Syst. Sci., 14, 433-445
652
653
Larsen, S.H. and N. Nicholls, 2012: The nature of the recent rainfall decrease in the vicinity of
654
Melbourne, southeastern Australia, and its impact on soil water balance and groundwater recharge,
655
Australian Meteorological and Oceanographic Journal, 62(1), 11-18
656
657
Leblanc, M., S. Tweed, A. Van Dijk and B. Timbal, 2011: A review of historic and future hydrological
658
changes in the Murray-Darling Basin, Global Planetary Change, doi:10.1016/j.gloplacha.2011.10.012.
659
660
Lockart, N., D. Kavetski, and S. W. Franks, 2009: On the recent warming in the Murray-Darling
661
Basin:
662
doi:10.1029/2009GL040598.
Land
surface
interactions
misunderstood,
Geophys.
Res.
Lett.,
36,
L24405,
663
664
Nguyen, H, B. Timbal, I. Smith and C. Lucas, 2013: The Hadley Circulation in Reanalyses:
665
climatology, variability and expansion, J. of Climate, 26(10), 3357-3376
666
667
Nicholls, N., 2004: The changing nature of Australian droughts, Climatic Change, 63(3), 323-336
668
669
Nicholls, N., 2009: Local and remote causes of the southern Australian autumn-winter rainfall decline,
670
1958–2007, Climate Dynamics, DOI 10.1007/s00382-009-0527-6
671
672
Pepler, A., C. Rakich, B. Timbal and A. Coutts-Smith, 2014: A mechanism to explain the absence of
673
relationship between tropical modes of variability and Australian rainfall along the Eastern seaboard,
674
Journal of Climate, in press.
675
26
676
Pui, A., A. Lal and A. Sharma, 2011: How does the Interdecadal Pacific Oscillation affect design
677
floods in Australia?, Water Resources Research, 47(5), doi:10.1029/2010wr009420
678
679
Post, D.A., Moran R.J. 2013: Provision of usable projections of future water availability for south-
680
eastern Australia: The South Eastern Australian Climate Initiative. Australian Journal of Water
681
Resources, 17.2, 135-142
682
683
Post, D., B. Timbal, F. Chew, H. Hendon, H. Nguyen and R. Moran, 2014: "Decrease in south-eastern
684
Australia water availability linked
685
DOI: 10.1002/2013EF000194
to
ongoing Hadley cell
expansion",
Earth
Future,
686
687
Potter N.J., Chiew, F.H.S., Frost, A.J., 2010: An assessment of the severity of recent reductions in
688
rainfall and runoff in the Murray-Darling Basin, J. of Hydro., 381, 52-64
689
690
Preston, B. and R. Jones, 2008: Evaluating sources of uncertainty in Australian runoff-projections.
691
Advance Water Resources, 31(5), 758-775
692
693
Risbey, J.S., M.J. Pook, P.C. McIntosh, M.C. Wheeler and H.H. Hendon, 2009: On the Remote
694
Drivers of Rainfall Variability in Australia, Monthly Weather Review, 137, 3233-3253
695
696
Robertson D.E. and Q.J. Wang, 2012: A Bayesian approach to predictor selection for seasonal
697
streamflow forecasting, Journal of Hydrometeorology, 13, 155-171, doi: 10.1175/JHM-D-10-05009.1.
698
699
Saji N.H., Goswami, B.N., Vinayachandran P.N. and Yamagata, T. 1999: A dipole mode in the
700
tropical Indian Ocean, Nature, 401, 360–3.
701
27
702
Smith, I. and B. Timbal, 2012: Links between tropical indices and southern Australia rainfall, Int. J. of
703
Clim., 32(1), 33-40, DOI: 10.1002/joc.2251
704
705
Smith, I. and S. Power, 2014: Past and future declines in rainfall and inflow into Perth (Western
706
Australia) dams, Journal of hydrology: Regional studies, 2, 84-96
707
708
Tan K.S., Crawford-Wilson P. and Rhodes B., 2012: Assessing catchment runoff responses under dry
709
and wet year scenarios – a case study, Proceedings of 34th Hydrology and Water Resources
710
Symposium, 19-22 November, Sydney, Engineers Australia, p1157-1164.
711
712
Tan K.S. and Rhodes B.G., 2013: Drought severity estimation under a changing climate, Australian
713
Journal of Water Resources, 17(2), 143-151.
714
715
Teng, J., F.H.S. Chiew, B. Timbal, Y. Wang, J. Vaze, B. Wang, 2012: “Assessment of an analogue
716
downscaling method for modelling climate change impact on runoff”, Journal of Hydrology,
717
doi:10.1016/j.jhydrol.2012.09.024
718
719
Timbal B. and Murphy, B. 2007: Observed climate change in South-East of Australia and its relation
720
to large-scale modes of variability, BMRC Res. Lett., 6, 6-11.
721
722
Timbal, B., 2009: “The continuing decline in South-East Australian rainfall – Update to May 2009”,
723
CAWCR Res. Let., 2, 4-11
724
725
Timbal, B., Fernandez, E. and Li, Z, (2009): Generalization of a statistical downscaling model to
726
provide local climate change projections for Australia. Environmental Modelling & Software, 24 (3),
727
341-358
728
28
729
Timbal, B., J. Arblaster, K. Braganza, E. Fernandez, H. Hendon, B. Murphy, M. Raupach, C. Rakich,
730
I. Smith, K. Whan and M. Wheeler, 2010: Understanding the anthropogenic nature of the observed
731
rainfall decline across south-eastern Australia, CAWCR Technical Report 26, 180pp.
732
733
Timbal B. and H. Hendon, 2011: The role of tropical modes of variability in the current rainfall deficit
734
across the Murray-Darling basin, Water Resources Research, doi:10.1029/2010WR009834.
735
736
Timbal, B., Y. Wang and A. Evans, 2011: “Downscaling climate change information: an essential
737
ingredient to incorporate uncertainties into adaptation policies”, In Chan, F., Marinova, D. and
738
Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation.
739
Modelling and Simulation Society of Australia and New Zealand, December 2011, pp. 1652-1658.
740
ISBN: 978-0-9872143-1-7
741
742
Timbal, B. and W. Drosdowsky, 2013: “The relationship between the decline of South Eastern
743
Australia rainfall and the strengthening of the sub-tropical ridge”, Int. J. of Climatol., 33, 1021-1034,
744
DOI: 10.1002/joc.3492
745
746
Timbal, B. and R. Fawcett, 2013: “An historical perspective on South Eastern Australia rainfall since
747
1865”, J. of Climate., 26, 1112-1129
748
749
Ummenhofer, C. C., M. H. England, P. C. McIntosh, G. A. Meyers, M. J. Pook, J. S. Risbey, A. S.
750
Gupta, and A. S. Taschetto, 2009: What causes southeast Australia’s worst droughts?, Geophys. Res.
751
Let., 36, L04706, doi:10.1029/2008GL036801
752
753
Whan, K. B. Timbal and J. Lindsay, 2013: “Linear and nonlinear statistical analysis of the impact of
754
the sub-tropical ridge intensity and position on south-east Australian rainfall”, Int. J. of Climatol.,
755
DOI: 10.1002/joc.3689
29
756
757
758
30
759
List of Figures
760
761
762
763
764
Figure 1: Map of South Eastern part of the Australian continent (upper panel) detailing the catchment
boundaries for the Murray Darling basin (red line), the Eildon catchment and the main Melbourne
Water catchment areas (enlarged in the lower panel). Colours show the height above sea level in
metres. Dark blue shows the location of the reservoirs. The gridded observation resolution is shown
by the white crosses. The location of "The triangle" is shown with a red symbol. ............................... 37
765
766
767
768
769
770
Figure 2: Map of the area of interest (upper panel) showing the location of weather stations
contributing to the data set and how they have changed over time. Red circles show 1900-1970, green
circles 1970-1980, blue circles 1980-2010, red triangles 1900-1980, green triangles 1970-2010 and
blue triangles 1900-2010. The rectangles identify the three regions shown in the bottom panels: time
series of the number of stations in the each of the three regions (left) and the average altitude of
stations in each of the three regions (right)............................................................................................ 38
771
772
773
Figure 3: 1900 to 2012 mean rainfall based on the Bureau of Meteorology operational 0.05 degree
gridded rainfall for the annual total (top panel) and the four seasonal means: summer (middle left),
autumn (middle right), winter (bottom left) and spring (bottom right). ................................................ 39
774
775
776
777
Figure 4: Monthly 1900-2012 rainfall climatology (left column) compared with the 1997-2009 values
and anomalies for each of the four catchment areas (MDB: top, and then Eildon, Yarra and Thomson).
Same number but for the streamflows in three of the catchments; the monthly streamflow anomalies
are provided in absolute term (green bars) and percentage (orange bars). ............................................ 40
778
Figure 5: As per Figure 3 but for an average of 2010 and 2011. ........................................................... 41
779
780
781
782
Figure 6: Accumulated rainfall anomalies for each of the catchment areas for three different time
periods. The red curve shows the deficit for the 1997 to 2012 period. The blue curve shows the 19942009 period, encompassing the MD. The black curve shows the 16 year period with the lowest deficit
prior to 1994........................................................................................................................................... 42
783
784
785
786
787
Figure 7: Location of indices of large-scale modes of variability used in this study: Niño4 SST region
(the red rectangular), Saji et al. (1999) Dipole Mode Index (DMI, yellow boxes), Timbal and Hendon
(2011) tropical tripole (blue shapes), Drosdowsky (2005) subtropical ridge position (green lines): the
mean annual position is shown as a solid line, with northern (winter) and southern (summer) extent
shown as dashed lines. ........................................................................................................................... 43
788
789
790
791
Figure 8: Correlation coefficients in winter (left) and spring (right) between the rainfall and climate
indices (N4, top row, the tripole, second row, the STR position, third row and intensity, fourth row).
Correlations coefficients larger than +/-0.25 are significant at the 99% level. Correlations coefficients
are computed on time-series from 1913 to 2012. .................................................................................. 44
792
793
794
Figure 9: Time series of the tripole index. The bars show the value of the tripole index for each year.
The colours of the bars show significant El Nino (red) and La Nina (blue) years, with yellow being
neutral, the coloured stars show significant positive (red) and negative (blue) IOD years. The
31
795
796
horizontal dashed lines show 0.8 standard deviation of the tripole index. The vertical dashed line
marks the start of 1997........................................................................................................................... 45
797
798
799
800
Figure 10: Eildon, Thomson and Yarra composite streamflows based on the tripole index values.
Blue lines are computed using years prior to and including 1996; red lines show the composites
computed since and including 1997. Lines with stars are the mean over the period; lines with
diamonds, are years with a positive tripole, and with triangles, years with a negative tripole. ............. 47
801
802
List of Tables:
803
804
805
806
807
808
Table 1 Characteristics of the Millennium Drought (1997-2009) compared to the next most intense
lowest 13-years period on record not overlapping with the MD. Anomalies are given as percentage
from the long-term mean. The annual rainfalls of the wettest year embedded within the 13-year
droughts are indicated as well as the ranking of that year and the anomalies form the long-term mean
in percentage term. For both the MD and previous lowest 13 years on record, seasonal anomalies are
also indicated in percentage terms. All statistics are based upon the complete 1900 to 2012 dataset. . 33
809
810
811
812
813
814
Table 2: Statistics for 2010 and 2011 rainfall across 5 areas of interest: The Murray-Darling Basin, the
Eildon catchment, the Melbourne Water major catchments (Thomson and Yarra) and the SouthEastern Australia as a whole. Highest rainfall on record prior to 2010 (1900 - 2009) are indicated as a
bench mark. Annual rainfall and ranking are provided for 2010 and 2011 (ranking are based on the full
1900 to 2011 database) cool months (April to October) and warm months (January to March and
November to December). Bold font indicates record breaking statistics .............................................. 34
815
816
817
818
819
Table 3: Inter-annual variance explained (squared correlation coefficients) and percentage of the
observed variance reproduced by the reconstructions. All statistics are computed using the entire series
from 1913 to 2012 and for the 5 linear reconstructions: R 1 to 5 : predictors are rainfall of the month
(R), rainfall from the previous month (R -1 ), rainfall from the previous 12 month (R -12 ) and temperature
of the month (T). .................................................................................................................................... 35
820
821
822
Table 4: Statistics for observed rainfall and streamflow (in bold) as well as for the 5 linear streamflow
reconstructions (see main text for details) for the three catchment areas: linear trends from the 1913 to
2012, from 1980 to 2012 and for the magnitude of the deficit during the MD. .................................... 36
823
32
824
Tables
825
826
Previous 13-year dry (PD) period
Rainfall deficit
Wettest year
Summer anomaly
Autumn anomaly
Winter anomaly
Spring anomaly
MD
PD
MD Total
(rank)
Anomaly
PD Total
(rank)
Anomaly
MD
PD
MD
PD
MD
PD
MD
PD
MDB
19341946
-15 %
-13 %
577
(15)
23%
543
(25)
16%
-6%
11%
-24%
-16%
-4%
-7%
+3%
-26%
Eildon
19021914
- 18 %
-8 %
1090
(62)
-2%
1284
(23)
15%
-8%
-10%
-29%
-1%
-16%
+1%
-12%
-5%
Yarra
19021914
- 17 %
-4%
1476
(61)
-2%
1735
(19)
15%
-8%
-8%
-25%
+1%
-13%
2%
-14%
-5%
Thomson
19021914
- 15 %
- 5%
1420
(49)
2%
1566
(22)
13%
-8%
-9%
-24%
-4%
-12%
+1%
-13%
-3%
SEA
19371949
-12%
-8%
611
(44)
5%
697
(13)
20%
-6%
+8%
-26%
-5%
-9%
-11%
-6%
-13%
827
828
Table 1 Characteristics of the MD (1997-2009) compared to the next most intense lowest 13-years
829
period on record not overlapping with the MD across 5 areas of interest: the Murray-Darling Basin, the
830
Eildon catchment, the Melbourne Water major catchments (Thomson and Yarra) and the South-
831
Eastern Australia as a whole. Anomalies are given as percentage from the long-term mean. The annual
832
rainfalls of the wettest year embedded within the 13-year droughts are indicated as well as the ranking
833
of that year and the anomalies form the long-term mean in percentage term. For both the MD and
834
previous lowest 13 years on record, seasonal anomalies are also indicated in percentage terms. All
835
statistics are based upon the complete 1900 to 2012 dataset.
836
33
837
838
MDB
Highest annual rainfall
between 1900 and 2009
2010 annual rainfall (ranking)
2011 annual rainfall (ranking)
784 (1956)
Eildon
Yarra
Thomson
1724 (1956) 2275 (1952) 2245 (1952)
SEA
878 (1956)
807 (1)
600 (13)
1516 (7)
1226 (36)
1702 (29)
1721 (26)
1557 (26)
1602 (18)
811 (4)
718 (11)
Longterm
mean
Warm month rainfall
259
439
626
591
261
Cool month rainfall
212
676
879
799
323
1997
2009
Warm month rainfall
255
363
520
501
236
Cool month rainfall
191
556
730
675
279
Warm month rainfall
Cool month rainfall
Warm month rainfall
Cool month rainfall
538 (1)
269 (21)
432 (5)
168 (86)
800 (1)
717 (48)
664 (7)
564 (87)
818 (11)
884 (57)
886 (6)
833 (69)
86 (
781 (11)
773 (65)
816 (6)
785 (60)
469 (1)
342 (42)
450 (3)
267 (84)
2010
2011
839
840
Table 2: Statistics for 2010 and 2011 rainfall and compare with highest rainfall for 1900 to 2009 as a
841
bench mark for the five areas of interest as per table 1. Annual rainfall and ranking are provided for
842
2010 and 2011 (ranking are based on the full 1900 to 2011 database) cool months (April to October)
843
and warm months (January to March and November to December). Bold font indicates record-
844
breaking statistics
845
34
846
847
Reconstruction
Predictors
Eildon
Yarra
Thomson
R1
R, R -1 , R -12 , T
0.90 / 84%
0.85 / 78%
0.77 / 74%
R2
R, R -1 , R -12 , T
0.90 / 80%
0.85 / 72%
0.81 / 51%
R3
R, R -1 , R -12
0.90 / 84%
0.85 / 78%
0.77 / 74%
R4
R, R -1
0.85 / 64%
0.74 / 45%
0.74 / 53%
R5
R
0.79 / 34%
0.69 / 18%
0.69 / 27%
848
849
Table 3: Inter-annual variance explained (squared correlation coefficients) and percentage of the
850
observed variance ranges reproduced by the reconstructions. All statistics are computed using the
851
entire series from 1913 to 2012 and for the 5 linear reconstructions (see main text for details):
852
predictors are rainfall of the month (R), rainfall from the previous month (R -1 ), rainfall from the
853
previous 12 month (R -12 ) and temperature of the month (T).
854
35
855
856
1997 – 2009
anomaly
(% from longterm mean)
1980 to 2012
linear trend
As percentage
of 1980-2012
mean
1913 to 2012
linear trend
(as a percentage
of mean)
(mm/decade)
Rainfall
Inflow
R1
R2
R3
R4
R5
Rainfall
Inflow
R1
R2
R3
R4
R5
Rainfall
Inflow
R1
R2
R3
R4
R5
Eildon
-18
-42
-40
-41
-40
-30
-20
-13
-38
-50
-49
-49
-42
-29
-6
-34
-17
N/A
-17
-14
-9
Yarra
-17
-36
-35
-34
-34
-21
-13
-11
-23
-29
-29
-29
-21
-12
-8
-34
-16
N/A
-16
-10
-6
Thomson
-16
-37
-30
-28
-30
-22
-15
-7
-18
-19
-15
-17
-14
-10
-8
-36
-15
N/A
-15
-11
-8
857
858
Table 4: Statistics for observed rainfall and streamflow (in bold) as well as for the 5 linear streamflow
859
reconstructions (see main text for details) for the three catchment areas: magnitude of the deficit
860
during the MD, linear trends from 1980 to 2012 and from 1913 to 2012.
861
36
862
863
864
Figure 1: Map of South Eastern part of the Australian continent (upper panel) detailing the catchment
865
boundaries for the Murray Darling basin (red line), the Eildon catchment and the main Melbourne
866
Water catchment areas (enlarged in the lower panel). Colours show the height above sea level in
867
metres. Dark blue shows the location of the reservoirs. The gridded observation resolution is shown
868
by the white crosses. The location of hill top called "The triangle" is shown with a red symbol.
869
37
870
871
872
873
874
875
Figure 2: Map of the area of interest (upper panel) showing the location of weather stations
876
contributing to the gridded data set. Red circles show stations in existence from 1900 to 1970, green
877
circles from 1970 to 1980, blue circles from 1980 to 2010, red triangles from 1900 to 1980, green
878
triangles from 1970 to 2010 and blue triangles from 1900 to 2010. The coloured rectangles on the top
879
panel identify the three regions for which two statistics are provided in the bottom panels: time series
880
of the number of stations (left panel) and the average altitude of stations (right panel).
881
38
882
883
884
885
886
887
888
889
Figure 3: 1900 to 2012 mean rainfall based on the Bureau of Meteorology operational 0.05 degree
890
gridded rainfall for the annual total (top panel) and the four seasonal means: summer (middle left),
891
autumn (middle right), winter (bottom left) and spring (bottom right).
892
39
893
894
895
Figure 4: Monthly 1900-2012 rainfall climatology (left column) compared with the 1997-2009 values
896
and anomalies for each of the four catchment areas (MDB: top, and then Eildon, Yarra and Thomson).
897
Same number but for the streamflows in three of the catchments; the monthly streamflow anomalies
898
are provided in absolute term (green bars) and percentage (orange bars).
40
899
900
901
902
903
904
905
906
907
Figure 5: As per Figure 3 but for 2010 and 2011 averaged.
908
41
909
910
911
912
Figure 6: Accumulated rainfall anomalies for each of the catchment areas for three different time
913
periods. The red curve shows the deficit for the 1997 to 2012 period. The blue curve shows the 1994-
914
2009 period encompassing the MD which in all cases is the lowest 16-year period on record. The
915
black curve shows the 16 year period with the lowest accumulated deficit prior to 1994.
42
916
917
918
919
920
921
922
923
924
Figure 7: Location of indices of large-scale modes of variability used in this study: Niño4 SST region
925
(the red rectangular), Saji et al. (1999) Indian Ocean Dipole (IOD, yellow boxes), Timbal and Hendon
926
(2011) tropical tripole (blue shapes), Drosdowsky (2005) subtropical ridge position (green lines): the
927
mean annual position is shown as a solid line, with northern (winter) and southern (summer) extent
928
shown as dashed lines.
929
930
931
43
932
933
934
935
936
937
938
939
940
Figure 8: Correlation coefficients in winter (left) and spring (right) between the rainfall and climate
941
indices (N4, top row, the tripole, second row, the STR position, third row and intensity, fourth row).
942
Correlations coefficients larger than +/-0.25 are significant at the 99% level. Correlations coefficients
943
are computed on time-series from 1913 to 2012.
44
944
945
946
947
948
Figure 9: Time series (coloured bars) of the annual tripole index. The colours of the bars show
949
significant El Niño (red) and La Niña (blue) years, with yellow being neutral, the coloured stars show
950
significant positive (red) and negative (blue) IOD years. The horizontal dashed lines show ±0.8
951
standard deviation of the tripole index separating either positive or negative tripole years from neutral
952
years. The vertical dashed line marks the start of 1997.
953
954
45
955
956
957
958
46
959
960
961
Figure 10: Eildon, Thomson and Yarra composite streamflows based on the tripole index values.
962
Blue lines are computed using years prior to and including 1996; red lines show the composites
963
computed since and including 1997. Lines with stars are the mean over the period; lines with
964
diamonds, are years with a positive tripole, and with triangles, years with a negative tripole.
965
47