1
Appendix 1: Methodological details and park specific results from analysis of
2
temperature and rainfall data for national parks in South Africa
3
4
Extended methods
5
Sixty-four weather stations within and adjacent to parks were selected from the South African
6
Weather Service’s (SAWS) network of stations. These included all SAWS stations within national
7
parks. Electronic data for the longest available series were obtained from SAWS. For most stations
8
there were five variables available: humidity, pressure, rainfall, temperature and windspeed and
9
direction, of which the rainfall series was the longest in all cases. Temperature data were available as
10
daily minima and maxima, while rainfall was available as a daily total. Humidity, pressure and wind
11
data were not considered in the current analyses because none of these data were available for a
12
sufficiently long time period to detect meaningful changes.
13
14
All data were manually checked for errors and where these occurred, they were deleted. This
15
included deleting all invalid data points (e.g. negative rainfall or minimum temperature > max
16
temperature and highly unlikely 0’s [e.g. maximum temperature in summer. 0 appears to be used in
17
some instances in place of missing data]). Where possible, outlier values were compared to data from
18
nearby weather stations if such stations existed. Percentage of missing data was determined for each
19
series. Datasets with many outliers and data gaps were not included in the analysis. As an additional
20
check of the consistency of the data across the stations, we used the package ‘climatol’ (Guijarro,
21
2011) to run standard normal homogeneity tests (SNHT) of precipitation data from seven stations in
22
the Garden Route area (see next paragraph). No breaks were found in the data based on the
23
threshold value of 25 for the SNHT, which confirms that the data are homogenous for all seven
24
stations.
25
26
To determine whether using weather data from single stations was meaningful at a park level (i.e. at a
27
scale of ~35km2 in Bontebok, the smallest park to ~19 000km 2 in Kruger, the largest) and to verify
28
periods of high and low rainfall, we compared rainfall data from two stations in Addo Elephant
29
National Park (Alexandria-bos and AGR stations) and seven stations throughout and adjacent to the
30
Garden Route National Park. The correlation between the Addo stations (r = 0.73) was surprisingly
31
strong given that the stations are 64km apart and in quite different habitat, and therefore likely to
32
experience some differences in climate. Extremely high rainfall events were mirrored at both stations.
33
For the Garden Route, the minimum distance between any two stations was 9.5km and the maximum
34
distance was 178km. Garden Route has the greatest altitudinal range of the parks (0 – 1578m) but
35
the available stations were found between 0 and 412m. Pairwise tests of correlation were carried out
36
in R (R Development Core Team, 2011) for all 21 combinations of the seven stations. We detected
37
strong highly significant (p < 0.001) correlations between monthly and daily rainfall data between all
38
stations in the region (for monthly data mean r = 0.77 ± 0.09SD). There was also a highly significant
39
trend in these correlations over distance (r = -0.88, p < 0.001), with closer stations having more highly
40
correlated rainfall. These results provide support for the validity of using individual stations to infer
1
1
past climate trends for each park, especially for smaller parks over distances of less than 100km,
2
though results will depend on local topography.
3
4
Of the 64 stations, 35 were excluded due to a high percentage of missing data, too short time series
5
or where sufficient data from a more appropriate station were available (but see next paragraph) and
6
thus only 29 were included in the final analysis. The Agulhas data used in this study were recorded on
7
a private farm near to the Park, due to data inconsistencies in the SAWS data for the Agulhas station.
8
The duration of the time series ranged from 17 years for data from the station in Beaufort West (Karoo
9
National Park), to 110 years for rainfall data from several stations. A list of the weather stations used,
10
their GPS location, the variables available and length of data for each is provided in Appendix Table 1
11
in the main paper.
12
13
Multivariate analyses
14
To assist in understanding the similarities and differences of possible climate changes between parks
15
at different spatial scales, and as a basis for grouping parks to report and/or interpret results, we
16
quantified the relationships in rainfall patterns across the parks. Rainfall patterns in South Africa have
17
previously been categorized into eight homogenous regions (Kruger, 1999; Mason, 1998). We
18
conducted a multivariate analysis of the rainfall time series across the parks (with >80 years of data)
19
that allowed us to further rationalize the rainfall patterns into four major regions using correspondence
20
analysis in the ‘ade4’ package (Dray & Dufour, 2007) in R (Figure S1, a duality diagram which
21
graphically represents the joint relationships between the variables). The clusters differentiate the
22
following four rainfall regions: a winter rainfall region (S1 far left) in the south-western part of the
23
country, two summer rainfall regions – one arid (Figure S1 top right), one sub-tropical (S1 bottom
24
right), and an aseasonal region that can receive rain at any time of the year which occurs in a
25
southern to eastern coastal belt (S1 centre left).
26
2
1
2
Figure S1: Canonical correspondence analysis of monthly rainfall data illustrating relationships in
3
rainfall patterns across parks. Subjectively allocated clusters (dotted lines) group parks which
4
experience similar rainfall seasons. The parks at the top right of the cluster are arid, but receive
5
summer rainfall, while the parks at the bottom right are wetter, but also receive summer rainfall. The
6
parks in the centre can receive rain at any time of year, while those to the far left of the diagram
7
receive winter rainfall.
8
9
Trend analyses
10
All data were averaged/totalled per month as well as annually to obtain monthly and annual data for
11
the following variables: total rainfall, mean minimum, absolute minimum, mean maximum and
12
absolute maximum temperature and daily temperature range (DTR). In addition, the longest dry spell,
13
the percentage of dry days and the average rainfall event size were calculated per year. The longest
14
dry spell (consecutive dry days) was calculated as the longer of (1) the longest spell without rain in
15
the current calendar year and (2) the overlapping period from last rain in the previous year to first rain
16
in the current year. Where more than three readings were missing in a month, the data for that month
17
were not considered (recorded as missing data). All analyses were conducted in the R statistical
18
freeware (R Development Core Team, 2011). Exploratory analyses were also conducted using the
19
source code RClimDex (Zhang & Yang, 2004).
20
21
Averaged and totalled monthly data were analyzed using linear regression across years (le Roux &
22
McGeoch, 2008) as well as locally-weighted polynomial regression – LOWESS (Cleveland, 1979;
23
Cleveland, 1981), which was used for annual data to reduce the influence of extreme events on the
24
trend and to allow the gradient of the trend line to vary over time. A variety of moving averages were
3
1
plotted for the rainfall to investigate the presence or absence of longer term cyclic patterns within the
2
data. An 18-20 year quasi-periodic oscillation has been observed in rainfall within South Africa
3
(Kruger, 1999; Tyson et al., 1975), while the El Nino/La Nina phenomenon is said to occur in an
4
approximate five year cycle (two to seven years, IPCC, 2007).
5
6
To determine whether any changes had taken place in the variability of rainfall, we used the
7
coefficient of variation (CV) of 20-year moving averages of annual rainfall (New et al., 2000). The
8
period of 20 years was chosen to include the full rainfall cycle of 18-20 years (Tyson et al., 1975).
9
Shorter cycles were tested but had little effect on the outcome of the analysis. For a 20 year period
10
with one or two years of missing data, missing data were replaced by the mean annual rainfall for
11
those 20 years. Twenty year periods with more than two years of missing data were excluded
12
(counted a missing data). A linear regression model was run on the resultant series of CV values to
13
determine whether any change had taken place in rainfall variability over time.
14
15
Extreme Events
16
For temperature we assessed both the occurrence of temperatures above 35oC (in parks where the
17
maximum temperature seasonally exceeds this threshold: n = 10 for parks with 20 years or more of
18
data) and temperatures below 0oC. There were twelve parks where temperatures were known to drop
19
below zero annually, but for seven of these parks, the average number of below zero days was less
20
than three. Changes in below zero days were therefore only evaluated for the five parks where
21
temperatures consistently dropped below 0 in winter. For rainfall, we used a slightly different threshold
22
approach to identify the months and years in which very high rainfall was recorded. The threshold
23
used to identify months of high rainfall was 1.5 times the average rainfall of the wettest month and for
24
years, 1.2 times the average annual rainfall. A similar approach was used to identify dry spells.
25
However, there were concerns that missing data (recorded as 0’s) could be identified as dry spells.
26
Therefore, in addition to identifying years where less than 0.8 times the annual average rainfall fell, a
27
three month moving rainfall average was also used to cross-check dry periods, ameliorating the effect
28
of missing data. These 3-month moving averages were normalized relative to the three-month rainfall
29
average for those months. Drought events were defined as the periods in the bottom 2.5% of the
30
normalized values. Patterns of occurrence of dry and wet spells were examined across the country.
31
32
Although using thresholds was useful for explicitly identifying exceptionally wet (possible flood) and
33
hot
34
wet/dry/warm/cool months (e.g. months of higher than average rainfall in the dry season or moderate
35
temperatures in winter). We therefore identified months that have lower or higher than expected
36
rainfall or temperatures relative to the same month in other years. We normalised monthly
37
temperature/rainfall data with respect to all other data in the same month across years and identified
38
the top and bottom 2.5% of the normalized values for each variable. We then linked the occurrence of
39
these extremes to the year and month in which they occurred. For temperature, we plotted the
40
frequency of low and high extremes per year and for rainfall, we plotted the extremes on a year by
(or
dry/cold)
“in-season”
events,
it
would
not
identify
seasonally
uncharacteristic
4
1
month scatterplot. This allowed for detection of possible changes in the timing of the occurrence of
2
extremes.
3
4
Seasonality
5
Most studies that consider changes in climate seasonality have focused on growing season length,
6
with less focus on shifts in rainfall patterns aside from tracking shifts in seasonality itself (see Pryor &
7
Schoof, 2008; Sumner et al., 2001; Walsh & Lawler, 1981). We used two approaches to determine
8
whether any shifts have taken place in the seasonality of rainfall in each of the rainfall regions. Firstly,
9
we used Walsh & Lawler’s (1981) seasonality index to quantify the seasonality of rainfall at a number
10
11
of stations. Using this method, seasonality is calculated as:
1
SI i 
Ri
n 12
| X in 
n 1
Ri
|
12
12
where Ri is the average annual precipitation for period i and X in is the average monthly precipitation
13
for month n over period i. The index can vary between 0 (rainfall is spread exactly equally throughout
14
the year) and 1.83 (rainfall is very strongly seasonal with all rainfall occurring in a single month, Table
15
S1). We divided the data in two equal time periods (1920-1964 and 1965-2009), including those parks
16
with at least 80+ years of data, and calculated the average seasonality index for both these periods.
17
We also calculated the seasonality index for the last 20 years (1990-2009) and compared SI for each
18
of these periods to determine whether there were any differences in the seasonality of rainfall
19
between these time periods (Pryor & Schoof, 2008).
20
21
Table S1: Seasonal rainfall regimes as determined by the Seasonality Index (SI) (Walsh & Lawler,
22
1981)
SIi
Rainfall regime
<0.19
Rainfall very equable, i.e. spread throughout the year
0.20–0.39
Rainfall spread throughout the year, but with a definite wetter season
0.40–0.59
Rather seasonal with a short drier season
0.60–0.79
Rainfall seasonal
0.80–0.99
Rainfall markedly seasonal with a long dry season
1.00–1.19
Most precipitation occurs in < 3 months
>1.20
Seasonality is extreme with almost all precipitation in 1–2 months
23
24
The second approach was analogous to methods used for detecting shifts in growing season (see
25
Donnelly et al., 2004; Linderholm, 2006), tracking changes in the start and end of the rain season. We
26
defined the ‘start’ and ‘end’ of the rainfall season as the days of the year by which 25% and 75% of
27
that year’s rain had fallen respectively (see Pryor & Schoof, 2008). This analysis did not take into
28
account how much rain fell in the year, but rather the distribution of rainfall over that year. The length
29
of the rainfall season was calculated as the number of days between the ‘start’ and ‘end’ date
30
determined above. A ‘rainfall year’ for the winter and aseasonal parks (winter being June to August)
5
1
was defined as a calendar year from January to December, while for the summer rainfall areas
2
percentiles were calculated between June and May of the following year to span the continuous
3
rainfall season. The locally-weighted polynomial regression was run to investigate any patterns of
4
change that may have occurred in the start, end or length of the season in the four major regions (see
5
Figure S1).
6
7
Results
8
Results from the linear and LOWESS trend analysis of annual temperature and rainfall data are
9
presented in the main paper. Here we significant annual trends in temperature (Figure S2) and rainfall
10
(Figure S3) as well as LOWESS trends in mean minimum and maximum temperature for parks with
11
>20 years of data (Figure S4).
12
13
Figure S2: Spatial distribution of significant annual trends in temperature-related variables for the 19
14
parks (the start year of data is indicated in parentheses): 1 – Richtersveld (1990), 2 – Namaqua
15
(1990), 3 – West Coast (1973), 4 – Table Mountain (1960), 5 – Kalahari Gemsbok (1960), 6 –
16
Augrabies Falls (1978), 7 – Tankwa Karoo (1986), 8 – Bontebok (no data), 9 – Agulhas (1960), 10 –
17
Karoo (1993), 11 – Garden Route (data insufficient, available only from 1996), 12- Mokala (1991), 13
18
– Camdeboo (no data), 14 – Mountain Zebra (1985), 15 – Addo Elephant (1960), 16 – Marakele (no
19
data), 17 – Golden Gate Highlands (1980), 18 – Mapungubwe (1960), 19 – Kruger (1960). Grey-
20
shaded parks had less than 20 years (or no) temperature data available. DTR = Daily temperature
21
range. Parks with no associated symbols demonstrated no significant annual trends in temperature.
6
1
2
3
Figure S3: Spatial distribution of significant annual trends in rainfall related variables for the 19 parks
4
(the start year of data is indicated in parentheses). 1 – Richtersveld (1952), 2 – Namaqua (1953), 3 –
5
West Coast (1973), 4 – Table Mountain (1900), 5 – Kalahari Gemsbok (x= 1960), 6 – Augrabies Falls
6
(1945), 7 – Tankwa Karoo (1933), 8 – Bontebok (1900), 9 – Agulhas (1900), 10 – Karoo (1993), 11 –
7
Garden Route (1900), 12- Mokala (1914), 13 – Camdeboo (1993), 14 – Mountain Zebra (1985), 15 –
8
Addo Elephant (1919), 16 – Marakele (1937), 17 – Golden Gate Highlands (1977), 18 – Mapungubwe
9
(1927), 19 – Kruger (1920). Grey-shaded parks had less than 30 years of rainfall data available.
10
Parks with no associated symbols demonstrated no significant annual trends in rainfall over the
11
available time period. The four major rainfall regions identified through multivariate analysis (Figure
12
S1) are depicted by dotted lines.
13
14
7
1
2
Figure S4: LOWESS trends in annual mean maximum and minimum temperature for all parks with at
3
least 20 years of data.
4
5
Results from a moving average analysis of rainfall data revealed a definite cyclic pattern with an
6
average cycle length of approximately 15-18 years (Figure S5). However, the cyclic patterns differed
7
between rainfall regions, with the most obvious and consistent (between parks) pattern observable in
8
the summer rainfall region (Figure S5c, d). The winter rainfall region showed an above average
9
rainfall period during the 1950s but no significant trend across the whole time series.
10
8
1
2
Figure S5: Five year moving averages for two winter rainfall parks (a), two aseasonal parks (b), two
3
summer rainfall parks (c) and two arid summer rainfall parks (d), as well as the LOWESS trend in
4
annual data for each park (e-h).
5
6
Monthly trends per park
7
Significant increases in the temperature of at least one month were detected in 15 of the 16 national
8
parks with sufficient data. Increases in temperature happened throughout the year, though February
9
experienced the highest number of significant increases across parks and the four temperature
10
variables (Figure S6a). Significant decreases in temperature were only detected in three national
11
parks: Mountain Zebra where the mean minimum temperature decreased in April (Figure S6b; the
12
figure also shows that the trend for other months is decreasing) and the absolute minimum in June
13
over 25 years; Namaqua, where the mean minimum of two months (May and September) and the
14
absolute minimum of March decreased over 20 years; and Tankwa Karoo where the September
15
minimum temperature decreased. Tankwa Karoo National Park was the only national park not to
16
experience an increase in temperature in any months (based on 23 years of data). Increases in the
9
1
highest recorded maximum temperature occurred mainly outside of summer, and this was also true
2
for all decreases in temperature (which occurred in the autumn, winter and early spring, Figure S6a).
3
Daily temperature range increased in at least one month in six parks, all in the west, south or south
4
central part of the country and decreased significantly only in Addo Elephant in December. The parks
5
in the southwest and north to north-east parts of the country as well as Addo Elephant showed the
6
most consistent increases in temperature trends with increases in all or nearly all months (Figure
7
S6b).
8
9
Trends in monthly rainfall patterns were similar to those of annual trends, with aseasonal rainfall parks
10
showing drying trends in some months. Significant declines in rainfall were detected in both Bontebok
11
and Garden Route in September and in Bontebok in February. Trends were less obvious in the
12
seasonal rainfall parks.
13
10
1
2
3
Figure S6: (a) The number of parks where significant changes occurred in four temperature variables
4
(mean and maximum maximum temperature and mean and minimum minimum temperature). Warm
5
colours indicate increases in temperature, while cool colours show decreases. (b) Trends in mean
6
minimum and mean maximum temperature per month in Mountain Zebra (1985-2009) and Addo
7
Elephant, Kalahari Gemsbok and Table Mountain National Parks (1960-2009). The direction of the
8
bar (up or down) indicates the direction of the trend, while grey bars indicate a significant trend (p <
9
0.05).
11
1
Changes in extremes
2
Temperature
3
Details of changes in the occurrence of temperatures below 0 oC and above 35oC are described in the
4
main text (also see Figure S7a). In addition we assessed the occurrence of unseasonally warm and
5
cool months. While the absolute minimum and maximum temperature ever recorded per park were
6
not restricted to the 1990s or 2000s (Table S3), for most parks there was a clear trend for the most
7
extreme minimum and maximum temperatures to be recorded in the latter half of each data series.
8
Data for Table Mountain for example (Figure S7b,c) show that of the lower 2.5 percentiles of minimum
9
and maximum monthly temperatures, only 17% and 10%, respectively, occurred during the second
10
half of the time-series (i.e. since 1985, Figure S7). In contrast, 97% of the occurrences of extreme
11
high (top 2.5 percentile) maximum temperatures and 85% of the extreme high (top 2.5 percentile)
12
minimum temperatures have been recorded occurred in this latter period.
13
14
Rainfall
15
Uncharacteristic rainfall by month
16
Our first analysis of rainfall extremes identified uncharacteristic rainfall for each month. In the summer
17
rainfall parks (e.g. Kruger and Mapungubwe), nearly all the uncharacteristically wet months occurred
18
during the usually dry winter season (Figure S8). However, uncharacteristically high rainfall was also
19
recorded in some of the usually wet months, notably January 1958 and February 2000, indicating
20
flood events (both verified in archives) in both Kruger and Mapungubwe at these times. Lower than
21
expected rainfall in Mapungubwe occurred especially between October and December in the latter
22
half of the time series, likely corresponding with the late arrival of summer rains (Figure S8). The
23
asesaonal rainfall region showed more evenly distributed extreme events through the year, although
24
most of the wet months occurred prior to the 1950s with more dry extremes in recent decades. Lower
25
than expected winter rainfall in Agulhas and Table Mountain (in the winter rainfall region) occurred
26
throughout the time series, though the 1950s and 70s were very wet, even compared to winter
27
(rainfall) months in other years. In addition, Table Mountain in particular showed a slight increase in
28
the number of wetter than usual months in recent decades (Figure S8).
12
1
2
Figure S7: (a) Change in the number of days in a year above 35 oC in parks where this threshold is relevant (y-axis scale differs regionally); and the frequency
3
of extreme high events (within the top 2.5 percentile of normalised monthly values) and extreme low (within bottom 2.5 percentile) average (b) minimum and
4
(c) maximum temperatures recorded in Table Mountain National Park between 1960 and 2009.
13
1
2
Figure S8: The occurrence of uncharacteristically low (open symbol) and high (closed symbol) rainfall
3
months (within the top or bottom 2.5 percent for that month across the series) over time. These
4
months represent both extremely wet and dry months within season (e.g. the wet winter period in the
5
1950s in the winter rainfall parks) as well as unseasonally dry and wet months (e.g. the drier than
6
expected early wet season – October to December – in the latter half of the Mapungubwe data). The
7
vertical line on each of the latter plots indicates the year where the data commences.
8
9
14
Table S3: Absolute minima and maxima recorded per park per climatic variable as well as the average number of extremely wet months per decade in the time series.
Park
Annual total
Annual total
rainfall max (mm)
rainfall min (mm)
Daily rainfall (mm)
Max temp (oC)
Min temp (oC)
Average (± SD)
Number of
wet months1
decades in
per decade
series
Addo Elephant
1526 mm (1932)
334.6 mm (1999)
287.5 mm (1952)
45.5 oC (1987)
-2.2 oC (1970)
13 ± 2.9
9
Agulhas
1816.2 mm (1954)
258.6 mm (1928)
241.3 mm (1956)
38.3 oC (1960)
0.8 oC (1981)
9 ± 8.4
11
41.8 oC
-4 oC
9 ± 3.1
6
Augrabies Falls
348.4 mm (1976)
8.4 mm (1951)
80.7 mm (1956)
Bontebok
1447.7 mm (1906)
396 mm (1969)
419.1 mm (1906)
-
-
14 ± 4.6
11
Camdeboo
398.6 mm (2000)
177.6 mm (1998)
62.2 mm (2006)
-
-
-
-
Garden Route
1755.7 mm (1906)
568.4 mm (1949)
231.1 mm (1916)
-
12 ± 4.6
11
(1992)
7 ± 0.6
3
-
-
7 ± 2.3
5
Golden Gate
(1960)
(2002, 06)
-
963.4 mm (2000)
456.2 mm (1982)
115.6 mm (1992)
35.4 oC
397 mm (1996)
157.4 mm (19)
84.4 mm (2002)
43 oC (1995)
-3.6 oC (1996)
-10.3 oC
(2009)
-12.5 oC
Highlands
Karoo
Kalahari Gemsbok
555.3 mm (1974)
74.4 mm (1997)
79 mm (1989)
45.4 oC
Kruger
1101.2 mm (2000)
264.2 mm (2003)
178.1 mm (1951)
45.6 oC (1992)
-4.2 oC (1972)
8 ± 2.1
9
45.1 oC
-3.8 oC
6 ± 2.0
8
6 ± 4.0
7
(1981)
Mapungubwe
939.5 mm (2000)
92.1 mm (1935)
291.9 mm (1936)
Marakele
925.5 mm (1967)
120 mm (2003)2
204 mm (2009)
Mokala
1026.7 mm (1974)
117.2 mm (1922)
150.1 mm (1941)
40.9 oC (1997)
-8.5 oC (2003)
7 ± 2.0
9
Mountain Zebra
501.2 mm (1988)
196.6 mm (1992)
56 mm (1988)
43 oC (1998)
-4.1 oC (1996)
-
-
Namaqua
432.1mm (1996)
75 mm (1969)
75 mm (1990)
40.9 oC (1993)
-1.4 oC (1999, 03)
9 ± 3.3
5
46.2 oC
-5.4 oC
12 ± 1.3
5
Richtersveld
126.5 mm
Table Mountain
(1976)3
(1969)3
(2007)
(1966)
-
(1995)
(1972)
-
7.1 mm (1998)
44 mm
736.8 mm (1944)
135.8 mm (1947)4
97 mm (2005)
37.6 oC (2000)
1 oC (1981)
6 ± 4.5
11
Tankwa Karoo
619.5 mm (1976)
117.5 mm
(2003)5
68 mm (2007)
40 oC
-5.2 oC
11 ± 4.2
7
West Coast
383.4 mm (1974)
154.2 mm (1978)
57 mm (1974)
43.2 oC (2004)
5 ± 1.2
3
(2008)
(2007)
(1992, 05)
0 oC (1994, 95)
1
Months that received in excess of 1.5 times the average rainfall of the wettest month for that park
Highly unlikely due to several consecutives months of 0 rainfall; 2002 (255mm) also has several 0 months, while 1984 (269) could be plausible
3 Some unreasonably high rainfall events were excluded from analyses when no supporting evidence could be found for these, but it is still uncertain whether several
occurrences of very high rainfall throughout the record are accurate
4 Highly unlikely because missing data very likely (6 months in a row of 0 rain in 1947), 187 mm (1973) likely more accurate
5 Highly unlikely – 11 months of “0” recorded in a row in 2002/2003 and all years of low rainfall have suspiciously high numbers of 0 rainfall months
2
15
1
Extreme wet and dry months
2
There was no clear pattern of increase or decrease in the occurrence of extremely wet months (i.e. above
3
the set threshold) over time although Bontebok and Addo Elephant showed a slight decline in the number of
4
extreme months. The 1950s, 70s and 2000s stood out as decades with multiple extreme months in multiple
5
parks in the summer rainfall region. On average, the aseasonal parks had a higher number of wet months
6
exceeding the threshold of rainfall in excess of 1.5 times the average rainfall of the wettest month, compared
7
with the seasonal parks (Table S3). This highlights the higher variability across months of the rainfall in the
8
aseasonal region. The eastern summer rainfall areas had the lowest average number of months with
9
extreme rainfall (Table S3). In the winter rainfall parks, there appeared to be a slight increase in the number
10
of very wet months over time and the 50s and 70s had a number of high rainfall months. Similarly, the
11
1900s, 30s, 50s and 70s had a high number of extremely wet months across the aseasonal region. These
12
time periods fall within the region’s cyclical rainfall pattern (Figure S5).
13
14
To reduce the potential effect of missing data recorded as zero rainfall, a drought event was defined as a
15
three-month period in the bottom 2.5 percent of normalized average rainfall for those three months. The
16
1990s and 2000s had a number of dry periods in the aseasonal parks, while the 40s and 50s showed similar
17
dry periods, especially prominent in the Garden Route. The summer rainfall regions had drought periods
18
during the1980s and 90s, while the winter rainfall parks had a number of dry periods in the 60s. In Kruger,
19
there was a high concentration of drought periods before 1935, but this could be as a result of missing data.
20
21
Extreme years
22
While one or two high rain months might have caused a flood event, the year itself might not be considered a
23
wet year as a whole. A wet year was defined as a year where 20% more rain fell than the annual average
24
rainfall, while the opposite was true for defining dry years (80% of usual rainfall). For a high proportion of
25
stations in the aseasonal region there weren’t very many years where the annual rainfall exceeded the 20%
26
threshold. This is despite parks in this region having the highest number of extreme months, i.e. despite the
27
clear variability in monthly rainfall and rainfall timing, the annual total rainfall is fairly consistent. The dry
28
years in this region were quite variable across the parks, with the 1940s, 60s, 80s, 90s and 2000s all having
29
dry periods. 1984 was the record dry year at 3 stations along the Garden Route. Data from the Garden
30
Route support documented flood records fairly well, with high overlap between years in which major floods
31
occurred and years with 20% higher than mean rainfall (‘extreme years). The following years between 1900
32
and 2003 have seen major floods in the region (Templehof et al. 2009): 1905 (extreme year at Bloukrans,
33
Gouveldbos), 1910, 1916 (extreme year at Bloukrans, Gouveldbos and Witelsbos), 1929, 1931 (extreme
34
year at Karatara and Witelsbos), 1932 (extreme year at Karatara and Humansdorp), 1940, 1954 (extreme
35
year at Gouveldbos and Karatara), 1961, 1967 (extreme year at Keurbooms), 1977 (extreme year at
36
Humansdorp), 1981 (extreme year at Bloukrans, Gouveldbos, Humansdorp, Karatara, Keurbooms and
37
Witelsbos and the most extreme year on record at three of these stations), 1998 and 2003.
38
39
The year 2000, in which floods caused significant damage to infrastructure in Kruger and elsewhere
40
(Smithers et al. 2001), was the record rainfall year in three parks in the summer rainfall region (Kruger,
41
Golden Gate Highlands and Mapungubwe). The 2000s as a whole had a number of wet years in this region
42
(see for example Kruger, Figure S9), as did the 1970s. The drought years for this region were spread
16
1
through the time series in most cases. In the winter rainfall region, the 1970s, and especially the 1950s had a
2
high number of wet years, while the dry years were scattered throughout the time series.
3
4
5
Figure S9: The number of extreme rainfall years per decade in Kruger (extreme years identified as those
6
years that had more than 1.2 times the average rainfall for the entire data series).
7
8
Trends in seasonality
9
The seasonality index (SI) for each park (Table S4) corroborated the results of the correspondence analysis
10
(Figure S1) that grouped parks into four major rainfall regions. Comparison of SI over time showed that the
11
seasonality of the winter rainfall parks was quite stable. The aseaonal parks were also quite stable although
12
Addo Elephant appeared to be becoming even more equable. Both savanna parks (eastern summer rainfall
13
region) showed a trend towards stronger summer rainfall seasonality, with a dry season of increasing length
14
(Table S4).
15
16
Table S4: Rainfall seasonality index (SI) for eight national parks for each of three timespans, as well as
17
Kalahari Gemsbok for two timespans (included for comparison with literature)
Park
SI 1920-
SI 1964-
SI 1990-
Seasonality
1964
2009
2009
regime
Trend
Garden Route
0.130
0.130
0.184
Equable
No trend
Bontebok
0.154
0.1133
0.184
Equable
No trend
Equable
Trend towards even
Addo Elephant
0.161
0.118
0.101
more equable rainfall
Rainfall throughout
Agulhas
0.299
0.287
0.287
the year but with
No trend
17
Park
SI 1920-
SI 1964-
SI 1990-
Seasonality
1964
2009
2009
regime
Trend
marked wet season
Seasonal but with
Table Mountain
0.511
0.517
0.507
shorter dry season
No trend
Seasonal but with
Mokala
0.598
0.580
0.590
Kalahari
Gemsbok
Kruger
Mapungubwe
0.754
0.699
0.820
0.722
0.804
shorter dry season
No trend
Seasonal
No trend discernable
0.777
0.817
0.945
given shorter series
Seasonal becoming
Trend towards more
marked seasonal
strongly seasonal
with longer dry
rainfall with longer
period
drier season
Marked seasonality
Trend towards more
with long dry
strongly seasonal
season
rainfall with longer
drier season
1
2
Patterns in the start, end and length of the rainfall season showed large amounts of variation (Figure S10),
3
but were largely consistent with trends in SI and trends within each region were fairly consistent between
4
parks, especially in the asesonal region. LOWESS regression indicated some subtle changes in rainfall
5
seasonality over the past 5-11 decades, although a linear regression did not detect any significant changes.
6
The arid summer rainfall parks showed the highest variation in the timing of rainfall of all the regions. Of the
7
three parks in the region, the Kalahari Gemsbok rainfall pattern was the most consistent (50% of rainfall is
8
received between day 205±37 and day 283±24). In the winter rainfall region, there was no detectable change
9
in the ‘start date’ of the season (day at which 25% of that year’s rainfall had fallen) over time (mean day of
10
the year = 133±22 in Table Mountain), while the ‘end’ of the season appeared to be occurring earlier in the
11
year at the latter end of the time series, although this had little influence on the length of the season (Figure
12
S10; Table S4). The pattern in the eastern summer rainfall parks was not completely consistent between
13
parks, but generally showed limited change in the start date of the rainfall season (Kruger data indicated a
14
slight decrease), and a decrease in the date at which 75% of rain had fallen. Although the LOWESS trend
15
indicates little overall change in season length (Figure S10), the SI for the last 20 years indicates that the
16
season may indeed be getting shorter (Table S4).
17
18
Figure S10: Changes in seasonality of rainfall over time for one park from each of the three major rainfall regions of South Africa.
19
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