METADATA
Title
0.01° stack of climate layers for continental analysis of biodiversity pattern,
version 1.0
DATASET NAME
Individual attributes are raster datasets named as listed in the summary table
below
Custodian
CSIRO Ecosystem Sciences
Jurisdiction
Australia
DESCRIPTION
Abstract
This metadata statement documents a related set of datasets (attributes) derived
from a common source and software tools. These data provide rasterised layers
of climatic variables hypothesised to explain spatial patterns in biological diversity
at continental scales for use with statistical modelling tools (e.g. see methods
described in Elith et al. 2006). Specifically, these data were derived for modelling
the compositional pattern of multiple species with environmental factors such as
climate, soil and topography using the statistical technique Generalised
Dissimilarity Modelling (Ferrier et al. 2007) applied to continental Australia
(Williams et al. 2010a; b). Monthly climate layers were derived using version 3 of
the national 9 second DEM (Hutchinson et al. 2008), resampled to 0.01 degrees,
and the ESOCLIM module of ANUCLIM version 6.0 (beta) (Hutchinson et al. 2000
and T. Xu pers. comm.) with the Australian Climate Surfaces version 2.1
(Hutchinson and Kesteven 1998). The derived climate variables are monthly
mean values for minimum temperature, maximum temperature, precipitation,
solar radiation, evaporation, wind and others. Some of these monthly variables
were used to generate growth indices (using the GROCLIM module of ANUCLIM)
and a number of other variables related to mean and extreme conditions of
atmospheric and soil moisture conditions and climatic seasonality (Williams et al.
2010a; b). The Australian Climate Surfaces version 2.1 for the ANUCLIM-BIOCLIM
package are interpolated 75-year means (1921 to 1995) for rainfall and
temperature and 25-year means for other variables (1970-1995), representing
the period prior to the onset of measureable climatic warming. These data for
GDM analysis were masked to consistently define data/nodata values and
supplied in DIVA-GIS floating-grid format in the WGS84 geographic reference
system. Spatial extent and mask is the same as the substrate/terrain grids
described elsewhere (Williams 2010a).
This metadata statement describes the data in a formal metadata style. More
detail is given in the project report “Harnessing Continent-Wide Biodiversity
Datasets for Prioritising National Conservation Investment” (Williams et al.
2010a; b). Two other metadata statements describe the substrate/terrain grids
(Williams 2010a) and biotic attributes (Williams 2010b) which derived from
National vegetation mapping.
OTHER METADATA
ANZCW0703011541: GEODATA 9 Second Digital Elevation Model Version 3
(Hutchinson et al. 2008)
Stein J. (2008) Metadata: Environmental attributes compiled for the continental
GDM analysis. Fenner School of Environment and Society, The Australian National
University, Canberra.
Williams K. J. (2010) 1km resolution terrain and substrate layers for continental
analysis of biodiversity pattern (metadata for digital spatial data). CSIRO
Sustainable Ecosystems, Canberra.
Williams K. J. (2010b) 1km resolution vegetation attribute layers for continental
analysis of biodiversity pattern (metadata for digital spatial data). CSIRO
Ecosystem Sciences, Canberra.
GEOGRAPHIC EXTENT
Geographic Extent Name(s)
Australia
GEOGRAPHIC BOUNDING BOX
North Bounding Latitude
-9.0
South Bounding Latitude
-43.8
East Bounding Longitude
153.64
West Bounding Longitude
112.9
DATA CURRENCY
Beginning date
1921
Ending date
1995
Dataset Status
Complete
Maintenance and Update Frequency
Not planned
Data Format
DIGITAL - ESRI Arc/Info binary GRID (floatgrid format) and DIVA-GIS
(http://www.diva-gis.org/)
Lineage
Monthly climatic layers were computed with the ESOCLIM module of ANUCLIM
version 6.0 (beta) (Hutchinson et al. 2000) for each grid cell of a 0.01 degree
resolution DEM derived by resampling the national 9 second DEM version 3 with
the Arc/Info RESAMPLE function using bilinear interpolation. Annual growth
indices were computed using the GROCLIM module of ANUCLIM, a derivative of
GROWEST (http://fennerschool.anu.edu.au/publications/software/growest.php).
These and other climatic variables are described below.
The values of each layer were consistently expanded around the coastline using
the focalmean function in Arc/INFO GRID with neighbourhood shape defined by a
circle of radius two cells with DATA values. In each case, the original values of the
grids were retained and values were extrapolated outwards from the coast, or to
fill data voids where applicable. The data were expanded around the coast
because of the coarseness of the 1km grid which results in removal of areas of
coastline when viewed at finer scales.
Data were masked to consistently define data/nodata values across all climatic
and substrate/terrain layers (for the latter, see Williams 2010a).
A coastline mask with resolution 0.01 degrees (OZCSTM_1KM) was generated
from the current extent of the Australian coastline and surrounding islands
defined by the GEODATA Coast 1:100,000 topographic vector data series
(Geoscience Australia 2004) (details given in Williams et al. 2010a). This
coastline incorporates some minor land and island locations that are not yet
captured by the 9-second digital elevation model (Hutchinson et al. 2008). It was
created to mask model outputs to better reflect the GEODATA coastline, rather
than the expanded coastline mask.
Annual Climate Statistics
A wide range of bioclimatic indices (climatic factors that influence living
organisms) are considered relevant to the distribution of biodiversity,
encompassing essential resources (water) or conditions determining growth and
survival (frost, heat, drought, wind) or linked to disturbance events (e.g. fire,
cyclone, flood). As a result, a large number of bioclimatic indices have been
developed for modelling biodiversity patterns (Busby 1991; Nix 1986). Currently
35 bioclimatic indices have been incorporated into the ANUCLIM software package
(Hutchinson et al. 2000) as derivatives of monthly mean climatic variables. A key
feature of these bioclimatic indices is the two-way combination of climatic factors
such as the mean temperature of the driest quarter, where the driest quarter
may be any consecutive 12 week period within the year. By ignoring the time of
year, seasonal variation in conditions that equate across wide geographic ranges
are automatically taken into account.
While many of the bioclimatic variables appear meaningful, some can result in
unusual spatial patterns with arbitrary sharp boundaries. To avoid this spatial
patterning we generated a suite of simple climatic statistics as the minimum and
maximum of the monthly values—maximum temperature (°C), minimum
temperature (°C), diurnal temperature range (°C), rainfall (mm), evaporation
(mm), rainfall-modified solar radiation (MJ/m2/day), wind run (km/day) and wind
speed (m/s at 9am and 3pm)—for testing as predictor variables in models of
biological response along with other climatic factors. Some of these climatic
statistics replicate bioclimatic indices (e.g. Bioclim variable 5 is maximum
temperature of the warmest period).
The wind datasets (wind run and wind speed at 9am and 3pm) were compiled
from 9-sec resolution monthly variables generated by Williams et al. (2006) to
map suitable areas for macadamia horticulture. These data were created using
version 2 of the 9sec digital elevation model in GDA94 geographics with ANUCLIM
version 5.1. The monthly data were projected to WGS84 geographics and
resampled to 0.01 resolution using the cubic convolution algorithm.
Day length
A commonly used indirect factor in biodiversity modelling, correlated with many
underlying causal factors, is the geographic gradient in north-south or latitudinal
dimensions. The latitudinal gradient in terrestrial species richness is commonly
known as Rapoport’s Rule (Gaston 2000; Stevens 1989; Taylor and Gaines 1999;
Willig et al. 2003). Such positional factors are not determinants of biodiversity
pattern, but correlate with a number of potential causal environmental factors
(Austin 1980; 2002; Gaston 2000). A slightly improved derivative of the northsouth factor related to the energy budget and influencing plant phenology is day
length (e.g. Rathcke and Lacey 1985). Day length is a rescaling of the latitudinal
gradient that is more relevant to biological responses. We therefore applied the
day length model defined by Forsythe et al. (1995) in which the timing of
sunrise/sunset is defined when the centre of the sun is even with the horizon.
These parameters represent the period of effective light for photosynthesis,
applicable to ecological studies (Brock 1981; Running and Coughlan 1988). We
developed monthly estimates of day length based on the 15th day of each month
within a year. Two indices were selected to describe day length – the shortest day
(minimum in June) and the longest day (maximum in December).
Table 1. Definition day length indices.
Label
DL_P0
Description
Day length
Units
Definition
Hours
The monthly length of day in hours
where sunrise/sunset occurs when the
centre of the sun is even with the
horizon, using the calculations in
Forsythe et al. (1995).
Growth Indices (courtesy Janet Stein, March 2008)
The growth Index (GI) is produced with the GROCLIM program from the ANUCLIM
V5.1 package using weekly estimates of long-term mean monthly rainfall,
temperature, radiation and evaporation (Table 1). The index essentially rates the
potential for plant growth between 0 (completely limiting) and 1 (non-limiting).
Four GI layers were produced, one for each of the annual mean GI for C3
microtherm, mesotherm and macrotherm plants and for C4 megatherm plants
(using different default optimal temperature ranges as defined in the ANUCLIM
manual online at:
http://fennerschool.anu.edu.au/publications/software/anuclim/doc/groclim.html)
(Hutchinson et al. 2000). GI is the product of the three component indices
(temperature, light and moisture), the latter (moisture index) is one of the 35
BIOCLIM parameters. The moisture index was computed for a default clay loam
soil type with 150mm available water capacity. The GI grids provide a useful
summary of climatic factors relevant to plant productivity.
Table 2. Definition of growth indices.
Label
Description
Units
C4GI
Growth
index C4
megatherm
plants
index
MEGAGI
Growth
index C3
macrotherm
plants
index
MESOGI
Growth
index C3
mesotherm
plants
index
MICROGI
Growth
index
Definition
Annual mean growth index for C4 megatherm
plants, computed for a clay loam soil type with
150mm available water holding capacity, as the
product of three component indices (temperature,
light and moisture) using an optimum temperature
of 32°C and range 10-45°C.
Annual mean growth index for C3 macrotherm
plants, computed for a clay loam soil type with
150mm available water holding capacity, as the
product of three component indices (temperature,
light and moisture) using an optimum temperature
of 28°C and range 10-38°C.
Annual mean growth index for C3 mesotherm
plants, computed for a clay loam soil type with
150mm available water holding capacity, as the
product of three component indices (temperature,
light and moisture) using an optimum temperature
of 19°C and range 3-36°C.
Annual mean growth index for C3 microtherm
Label
Description
index C3
microtherm
plants
Units
Definition
plants, computed for a clay loam soil type with
150mm available water holding capacity, as the
product of three component indices (temperature,
light and moisture) using an optimum temperature
of 10°C and range 0-20°C.
Atmospheric Water Availability
Measures of atmospheric humidity (relative humidity and vapour pressure deficit)
were computed using published meteorological calculations applied to monthly
ESOCLIM variables: minimum and maximum temperature (overnight minimum
and daytime maximum), wet and dry bulb (9am and 3pm) and dew point (9am
and 3pm) (see http://www.bom.gov.au/climate/cdo/about/definitions9and3.shtml
for definitions of 9am and 3pm climatic statistics). Relative humidity (RH) and
vapour pressure deficit (VPD) require estimates of actual and saturation vapour
pressure at the same temperature. Depending on available meteorological data,
there are different ways to estimate relative humidity and vapour pressure deficit
(Allen et al. 1998). As saturation vapour pressure is related to air temperature, it
can be calculated from the air temperature. Although it is not possible to directly
measure actual vapour pressure it can be derived from relative humidity or
dewpoint temperature with known parameters for atmospheric pressure, latent
heat of vaporisation and the psychrometric constant. Dewpoint temperatures can
be calculated from measurements of wet and dry bulb temperatures
(psychrometer) when air temperatures are greater than 0ºC. We used these
monthly measures of dew point temperature, wet and dry bulb temperatures to
calculate vapour pressure deficit following the equations outlined in Allen et al.
(1998). For relative humidity we adopted the equation used by the Australian
Bureau of Meteorology (after Abbott and Tabony 1985). We used the
psychrometric data method above to estimate actual vapour pressure, with
altitude derived from version 3 of the 9sec DEM (resampled to 1km resolution) to
estimate gridded values of atmospheric pressure. Based on the 9am and 3pm
monthly calculations of VPD and RH, we generated the overall mean, minimum
and maximum values. Essentially RH and VPD have the same data origin in actual
and saturation vapour pressure; one is a ratio of actual to saturation values and
the other, a difference.
Table 3. Definition of atmospheric water availability indices.
Label
Description
Units
Definition
VPD215
Vapour
pressure
deficit 3pm
KPa
The monthly difference between the amount of
moisture in the air (actual vapour pressure) and how
much moisture the air can hold when it is saturated
(saturation vapour pressure) at that temperature
(3pm).
VPD29
Vapour
pressure
deficit 9am
KPa
The monthly difference between the amount of
moisture in the air (actual vapour pressure) and how
much moisture the air can hold when it is saturated
(saturation vapour pressure) at that temperature
(3pm).
RHU215
Relative
Humidity
3pm
%
Approximate monthly relative humidity defined as the
amount of moisture in the air expressed as a
percentage (ratio) of the amount of moisture present
if the air was saturated at that temperature (3pm).
RHU29
Relative
Humidity
9am
%
Approximate monthly relative humidity defined as the
amount of moisture in the air expressed as a
Label
Description
Units
Definition
percentage (ratio) of the amount of moisture present
if the air was saturated at that temperature (9am).
Other atmospheric moisture indices in current use in the literature were also
calculated. These include the aridity index, dryness index and precipitation deficit.
The precipitation deficit has been used to broadly compare the hydrological
regimes of sites and evaluate climate change implications (Harmsen et al. 2009).
A positive value indicates water in excess of crop water requirements and a
negative value indicates a deficit in terms of crop water requirements. An
alternative expression is the aridity index which is the ratio of precipitation to
evaporation (UNEP 1992 cited in Middleton and Thomas 1997), or the inverse
ratio as the dryness index (Zhang et al. 2004). The choice of index to include in
an analysis will depend on the alternative hypotheses of biological stress (e.g.
due to moisture limitations) or productivity factors driving the patterns of
distribution among the biota in question.
Table 4. Definition of aridity and precipitation deficit indices.
Label
ARID
ADEF
Description
Aridity index
Precipitation
deficit
Units
Definition
Dimensionless
The monthly ratio of precipitation to
potential evaporation (pan, free-water
surface). A numerical indicator of the degree
of dryness of the climate at a given location.
Adapted from the index proposed by UNEP
(1992; cited in Middleton and Thomas
(1997)).
Mm
The monthly difference between precipitation
and potential evaporation (pan, free-water
surface), without accounting for soil
buffering capacity on water availability (after
Harmsen et al. (2009), adapted from De
Pauw (2002)). Also known as water deficit or
hydrological deficit. Values are negative
when evaporation demand is greater than
rainfall indicating a water deficit.
Soil Water Availability
Soil moisture and atmospheric humidity are important drivers of biotic responses
and therefore commonly used as physiologically-relevant predictors in models of
biodiversity distribution to improve their generality. To more effectively capture
the interaction between soil and climate, we applied a simple tipping bucket water
balance model, similar to that used in GROCLIM to estimate soil water availability
using the soil water holding capacity attribute derived from the 1:1M to 1:3M
Atlas of Australian Soils (see metadata: 1km resolution terrain and substrate
layers for continental analysis of biodiversity pattern). A continuous soil-water
retention function (for details see Walker and Langridge 1996) allowed spatial
variation in soil water holding capacity and water extraction to be incorporated.
For simplicity, we used a constant soil water retention function scaled between
field capacity and wilting point. The water balance model takes monthly inputs of
rainfall and evaporation, initialised with soil water holding capacities at half their
maximum levels and equilibrated over two annual cycles after which the monthly
outputs—soil moisture content (volumetric water content in mm), surplus water
(mm) and water deficit (mm)—are retained. The soil moisture content is
converted into physiological units of water potential (varying between field
capacity at 0 and wilting point at -1.5 MPa or 15 bars).
Indices of actual evapotranspiration to potential evapotranspiration (PWAT) or to
pan evaporation (EAEO) were also generated for consistency with the alternative
published calculations (Hackett 1988; Specht and Jones 1971). The effectiveness
of one measure over the other depends on how well seasonal variation in canopy
resistance to water loss is estimated. In this case a constant coefficient of
potential evapotranspiration was used (PETCF=0.9), which assumes canopy leaf
areas are seasonally constant.
Table 5. Definition of water balance indices.
Label
WPOT
Description
Soil water
potential
WDEF
Soil water
deficit
SPLS
Soil water
surplus
EAEO
PWAT
Units
Definition
MPa
The monthly soil volumetric water content in
units of pressure potential between field
capacity (0 bars) and wilting point (-15 bars
/ -1.5 MPa), derived from a model of water
balance.
Mm
The monthly residual evaporative demand
that is in excess of soil moisture at wilting
point (-15 Bars) including rainfall, derived
from a model of water balance. Very
negative values represent a particularly
marked soil water deficit
Mm
The monthly precipitation that is in excess of
maximum soil water holding capacity
including evaporative demand, derived from
a model of water balance.
Crop factor
dimensionless
Water
Stress
Index
%
Trends in Seasonal Climatic Variation
The monthly ratio of actual
evapotranspiration to potential (pan, freewater surface) evaporation. Adapted from
the index by Specht and Jones (1971).
Actual evapotranspiration is an output of a
water balance model, and potential
evaporation is an input (ie not adjusted by
the coefficient of potential
evapotranspiration, see PWAT). This ratio
represents a water stress index and has
been termed the ‘crop factor’ (Doorenbos
and Pruitt 1975).
The monthly water stress is the ratio of
actual to potential evapotranspiration
expressed as a percentage. High water
stress occurs when values are low or zero,
low water stress occurs with higher values.
Adapted from the index by (Hackett 1988).
Actual evapotranspiration is an output of a
water balance model, and potential
evapotranspiration is the pan evaporation
(free-water surface) adjusted by the
coefficient of potential evapotranspiration
(PETCF). A constant PETCF value of 0.9 was
used for all months which assumes constant
leaf area index.
Many species respond to the regular progression in seasons utilising
environmental cues to trigger particular phenological responses that result in a
productivity advantage (growth, reproduction) or to avoid extreme conditions
(drought, frost). For example, many tree species are able to adjust, through
acclimation, their optimum temperature for photosynthesis with changes in
environmental temperatures with a lag of days to weeks (Hikosaka et al. 2006).
Rapid rates of change in environmental temperature, for example inland regions
versus coastal regions, may limit this acclimation response and trigger frosttolerance responses such as dormancy instead. Different species therefore occupy
different environments related to their adaptive capacity through various degrees
of specialised phenology responses to environmental cues. While environmental
differences may appear marginal, phenological phenomena are often very
sensitive to small variations in climate, especially temperature which exhibits less
inter-annual variability than rainfall. Phenology has therefore become of particular
interest as an indicator of climate change (e.g., Edwards and Richardson 2004;
Walther et al. 2005). To capture some of the climatic cues potentially correlated
with phenology responses, we generated variables for seasonal rates of change
(trends) in temperature and rainfall as the difference between months in a
forward progression of the seasons (i.e., January values minus December values,
February values minus January values, etc). Intra-annual change in rainfall
patterns were standardised by the number of days in an average month.
Table 6. Definition of seasonal climatic indices.
Label
Description
RTI
Change in
seasonally
varying
minimum
temperatures
RTX
Change in
seasonally
varying
maximum
temperatures
RPREC
Change in
seasonally
varying
rainfall
Units
Definition
°C/day
The monthly average daily difference in
minimum temperatures between successive
months (i.e., January values minus
December values, February values minus
January values, etc), representing
increments of change in the seasonal
progression of overnight temperatures.
°C/day
The monthly average daily difference in
maximum temperatures between successive
months (i.e., January values minus
December values, February values minus
January values, etc), representing
increments of change in the seasonal
progression of daytime temperatures.
mm/day
The monthly average daily difference in
rainfall between successive months (i.e.,
January values minus December values,
February values minus January values, etc),
representing increments of change in the
seasonal progression of rainfall, standardised
by the average number of days in a month.
Rainfall Seasonality
Rainfall across Australia is strongly seasonal and a significant determinant of
biodiversity patterns influencing the timing of faunal reproduction and the balance
between annual and perennial life cycle strategies among plants. A winterdominated rainfall regime occurs in the south and a summer-dominated regime in
the north. Uniform rainfall occurs across much of New South Wales, parts of
eastern Victoria and southern Tasmania. The monsoonal rains of the extreme
north fall between November and March and are often unreliable. More than 80%
of the continent has at least three months each year that are without effective
precipitation, resulting in drought conditions when this occurs during periods of
high temperature. In previous studies of plant distributions in north-eastern NSW,
an index of rainfall seasonality was used with considerable success (Austin 1998).
This index was derived as the ratio of summer to winter rainfall where summer is
defined as the total rainfall over the three months December to February and
winter as the total rainfall June to August, beginning with the solstice months. For
continental Australia, we derived a composite rainfall seasonality index to
emphasise the north-south gradient in conditions from summer to winterdominated rainfall, and applied the same framework in developing an index of
intra-seasonal variability beginning with the equinox months (spring-autumn
dominated rain events) (Equation 1). For comparison, we also generated an index
of annual rainfall seasonality based on the 6 warmest months (October to March)
and 6 coolest months (April to September) and the simpler form of these indices
as previously used in the NSW studies (Austin 1998). Because summer rainfall
volumes are often many magnitudes greater than winter rainfall volumes, two
forms of the index were developed, one of which used the logarithm of rainfall.
Preliminary analyses indicate that the two seasonal indices (SRAIN1 and SRAIN2)
were the most effective predictors (Williams et al. 2010a).
Equation 1: Derivation of rainfall seasonality indices from 12 months of total rainfall.
Alternative form of equation does not use the logarithm of rainfall.
Warm season rainfall (RONDJFM) is the total of rainfall in the six months from
October to March
Cool season rainfall (RAMJJAS) is the total of rainfall in the six months from April to
September
Summer rainfall (RDJF) is the total of rainfall in December, January and February
Winter rainfall (RJJA) is the total of rainfall in June, July and August
Spring rainfall (RSON) is the total of rainfall in September, October and November
Autumn rainfall (RMAM) is the total of rainfall in March, April and May
Seasonality of rainfall (summer-winter) (SRSW) (SLRAIN1):
 log ( R + 1) 
SRSW = +  10 DJF
 − 1 ; else,
log
(
R
+
1
)
10
JJA


 log10 ( RJJA + 1) 
= −
 +1
 log10 ( RDJF + 1) 
if, RDJF ≥ RJJA, then:
SRSW
Seasonality of rainfall (spring-autumn) (SRSA) (SLRAIN2):
if, RSON ≥ RMAM, then:
 log ( R
SRSA = −  10 MAM
 log10 ( RSON
 log10 ( RSON + 1) 
SRSA = + 
 − 1 ; else,
 log10 ( RMAM + 1) 
+ 1) 
 +1
+ 1) 
Annual seasonality of rainfall (warm-cool) (SRWC) (SLRAIN0):
if, RONDJFM ≥ RAMJJAS, then:
 log ( R
+ 1) 
SRWC = +  10 ONDJFM
 − 1 ; else,
 log10 ( R AMJJAS + 1) 
 log ( R
+ 1) 
SRWC = −  10 AMJJAS
 +1
 log10 RONDJFM + 1) 
Alternative form does not use logarithm of rainfall (SRAIN1, SRAIN2, SRAIN0)
Table 7. Definition of rainfall seasonality indices.
Label
SLRAIN0
SRAIN0
SRAIN0MP
Description
annual (log)
rainfall
seasonality
index
annual
rainfall
seasonality
index
annual
rainfall
seasonality
ratio
SLRAIN1
summer or
winter (log)
rainfall
season
SRAIN1
summer or
winter
rainfall
season
Units
Definition
Dimensionless
Annual rainfall seasonality is an index
derived from two ratios of precipitation.
The ratio of warm (Oct-Nov-Dec-Jan-FebMar) to cool (Apr-May-Jun-Jul-Aug-Sep)
season log-rainfall totals (minus 1) are
assigned positive values when rainfall in
the warm season is greater than rainfall in
the cool season. The ratio of cool to warm
season log-rainfall totals (plus 1) are
assigned negative values when rainfall in
the cool season is greater than rainfall in
the warm season.
Dimensionless
Annual rainfall seasonality is an index
derived from two ratios of precipitation.
The ratio of warm (Oct-Nov-Dec-Jan-FebMar) to cool (Apr-May-Jun-Jul-Aug-Sep)
season rainfall totals (minus 1) are
assigned positive values when rainfall in
the warm season is greater than rainfall in
the cool season. The ratio of cool to warm
season rainfall totals (plus 1) are assigned
negative values when rainfall in the cool
season is greater than rainfall in the warm
season.
Dimensionless
Annual rainfall seasonality is the ratio of
warm to cool season precipitation, where
warm season precipitation is defined as
the sum of Oct-Nov-Dec-Jan-Feb-Mar
precipitation and cool season precipitation
is defined as the sum of Apr-May-Jun-JulAug-Sep precipitation (based on Austin
1998).
Dimensionless
Summer or winter rainfall seasonality is an
index derived from two ratios. The ratio of
summer (Dec-Jan-Feb) to winter (Jun-JulAug) log-rainfall totals (minus 1) are
assigned positive values when rainfall in
summer is greater than rainfall in winter.
The ratio of winter to summer log-rainfall
totals (plus 1) are assigned negative
values when rainfall in the winter is
greater than rainfall in summer.
Dimensionless
Summer or winter rainfall seasonality is an
index derived from two ratios. The ratio of
summer (Dec-Jan-Feb) to winter (Jun-JulAug) rainfall totals (minus 1) are assigned
positive values when rainfall in summer is
greater than rainfall in winter. The ratio of
Label
Description
SRAIN1MP
Solstice
rainfall
seasonality
ratio
SLRAIN1
SRAIN1
SRAIN2MP
Spring or
autumn (log)
rainfall
season
Spring or
autumn
rainfall
season
Equinox
rainfall
seasonality
ratio
Units
Definition
winter to summer rainfall totals (plus 1)
are assigned negative values when rainfall
in the winter is greater than rainfall in
summer.
Dimensionless
Solstice rainfall seasonality is the ratio
summer to winter precipitation, where
summer precipitation is defined as the
sum of Dec-Jan-Feb precipitation and
winter precipitation is defined as the sum
of Jun-Jul-Aug precipitation (Austin 1998)
Dimensionless
Spring or autumn rainfall seasonality is an
index derived from two ratios. The ratio of
spring (Sep-Oct-Nov) to autumn (Mar-AprMay) log-rainfall totals (minus 1) are
assigned positive values when rainfall in
spring is greater than rainfall in autumn.
The ratio of autumn to spring log-rainfall
totals (plus 1) are assigned negative
values when rainfall in the autumn is
greater than rainfall in spring.
Dimensionless
Spring or autumn rainfall seasonality is an
index derived from two ratios. The ratio of
spring (Sep-Oct-Nov) to autumn (Mar-AprMay) rainfall totals (minus 1) are assigned
positive values when rainfall in spring is
greater than rainfall in autumn. The ratio
of autumn to spring rainfall totals (plus 1)
are assigned negative values when rainfall
in the autumn is greater than rainfall in
spring.
Dimensionless
Equinox rainfall seasonality is the ratio
spring to autumn precipitation, where
spring precipitation is defined as the sum
of Sep-Oct-Nov precipitation and autumn
precipitation is defined as the sum of MarApr-May precipitation (based on Austin
1998)
Inter-Annual Climatic Variability (temperature)
The seasonality, growing season lengths, climatic extremes and stochastic (interannual variability) nature of rainfall and temperature are strong determinants of
breeding patterns and reproductive success among Australian fauna and flora,
especially in arid and Mediterranean regions (e.g., Lamont et al. 2007). Although
biologists have long-recognised the importance of unpredictability in rainfall on
the patterns of distribution of biota and migration events, there have been few
attempts to develop predictors using the historical climatic data (although see
Zimmermann et al. 2009). Historical climatic data record temporal patterns in the
intensity and duration of extreme events linked to gradients in productivity or
stress across continental Australia. Indices of average extreme cold or hot
conditions were derived from the 5km gridded values of daily climate over 50
years (c.1955-2005) (Jeffrey et al. 2001) resampled to 1km using the CUBIC
option with RESAMPLE in ARC/Info GRID. The temperatures of the hottest or
coldest day per month were averaged over 50 years to define the absolute mean
monthly maximum or minimum temperatures respectively. This is a topic
requiring further research and analysis that is currently under development (M.
Hutchinson and J. Kesteven in prep.).
Table 8. Definition inter-annual temperature variability indices.
Label
Description
TMAXABS
mean
absolute
maximum
temperature
TMINABS
mean
absolute
minimum
temperature
Units
Definition
ºC
The monthly mean absolute maximum
temperature derived from the hottest day of
each month over 50-years (1955 to 2005) of
5km gridded daily climate (Jeffrey et al. 2001)
ºC
The monthly mean absolute minimum
temperature derived from the coldest day of
each month over 50-years (1955 to 2005) of
5km gridded daily climate (Jeffrey et al. 2001)
Dataset Naming Conventions
The following attributes were compiled from the monthly climatic variables and
are represented as individual raster datasets. Derivation method, units and
variable definitions are given in the lineage, above. In each case, the mean,
minimum and maximum values of the 12 monthly outputs were compiled for GDM
analysis. Dataset labels were postfixed ‘I’ or ‘MIN’ for minimum, ‘X’ or ‘MAX’ for
maximum, and ‘M’ or ‘MEAN’ for the average where relevant, except for growth
indices (only the mean annual statistic was derived).
Other statistics of the 12 monthly variables were also computed – standard
deviation and coefficient of variation – but are not included here. For modelling
biodiversity patterns, minimum and maximum statistics are recommended.
Annual mean statistics are often highly correlated with monthly maximum values
and as the integral of the mean monthly variation throughout a year often result
in complex spatial patterns. Many related statistics, for example water balance
and atmospheric water deficit, will be highly correlated. Decisions about choice of
variables to use in a model need to be made according to knowledge of the
biology or ecology of the target species, hypotheses of drivers of distribution
patterns, and by taking into account multicollinearity among environmental
variables.
Table 9. Raster dataset names and descriptive labels for climatic attributes.
Attribute
name (raster
dataset
name)
C4GI
MEGAGI
MESOGI
MICROGI
RHU215_I
RHU215_X
RHU215_M
RH2X
RH2I
RH2M
Descriptive labels
Mean annual growth index C4 megatherm plants
Mean annual growth index C3 macrotherm plants
Mean annual growth index C3 mesotherm plants
Mean annual growth index C3 microtherm plants
Minimum month relative humidity at 3pm (%)
Maximum month relative humidity at 3pm (%)
Mean annual relative humidity at 3pm (%)
Maximum month relative humidity (%)
Minimum month relative humidity (%)
Mean annual relative humidity (%)
Attribute
name (raster
dataset
name)
VPD29_I
VPD29_X
VPD29_M
VPD2X
VPD2I
VPD2M
ADEFI
ADEFX
ADEFM
ARID_X
ARID_I
ARID_M
EVAPI
EVAPX
EVAPM
RAINI
RAINX
RAINM
RPRECX
RPRECI
SLRAIN0
SRAIN0
SRAIN0MP
SLRAIN1
SRAIN1
SRAIN1MP
SLRAIN2
SRAIN2
SRAIN2MP
EAEO_X
EAEO_I
EAEO_M
PWAT_X
PWAT_I
PWAT_M
SPLS_X
SPLS_I
SPLS_M
WDEF_X
WDEF_I
WDEF_M
WPOT_X
WPOT_I
WPOT_N
RADNI
RADNX
RADNM
Descriptive labels
Minimum month vapour pressure deficit at 9am (KPa)
Maximum month vapour pressure deficit at 9am (KPa)
Mean annual vapour pressure deficit at 9am (KPa)
Maximum month vapour pressure deficit (KPa)
Minimum month vapour pressure deficit (KPa)
Mean annual vapour pressure deficit (KPa)
Maximum month precipitation deficit (mm)
Minimum month precipitation deficit (mm)
Mean annual precipitation deficit (mm)
Maximum month aridity index
Minimum month aridity index
Mean annual aridity index
Minimum month evaporation (mm)
Maximum month evaporation (mm)
Mean annual evaporation (mm)
Precipitation of the driest month (mm)
Precipitation of the wettest month (mm)
Mean annual rainfall (mm)
Greatest rainfall difference between successive months (mm/day)
Least rainfall difference between successive months (mm/day)
annual (log) rainfall seasonality index
annual rainfall seasonality index
annual rainfall seasonality ratio
summer or winter (log) rainfall season
summer or winter rainfall season
Solstice rainfall seasonality ratio
Spring or autumn (log) rainfall season
Spring or autumn rainfall season
Equinox rainfall seasonality ratio
Maximum month crop factor
Minimum month crop factor
Mean annual crop factor
Maximum month soil water stress index (%)
Minimum month soil water stress index (%)
Mean annual soil water stress index (%)
Maximum month soil water surplus (mm)
Minimum month soil water surplus (mm)
Mean annual soil water surplus (mm)
Maximum month soil water deficit (mm)
Minimum month soil water deficit (mm)
Minimum month soil water deficit (mm)
Maximum month soil water potential (MPa)
Minimum month soil water potential (MPa)
Mean annual soil water potential (MPa)
Minimum month rainfall-modified solar radiation (MJ/m2/day)
Maximum month rainfall-modified solar radiation (MJ/m2/day)
Mean annual rainfall-modified solar radiation (MJ/m2/day)
Attribute
name (raster
dataset
name)
MAXTI
MAXTX
MAXTM
MINTI
MINTX
MINTM
RTI_X
RTI_I
RTX_X
RTX_I
TMAXABSM
TMAXABSX
TMINABSI
TMINABSM
TRNGA
TRNGI
TRNGX
TRNGM
WINDRI
WINDRX
WINDRM
WINDSP9X
WINDSP9I
WINDSP9M
WINDSP15X
WINDSP15I
WINDSP15M
WINDSPI
WINDSPX
WINDSPM
DL_P0_I
DL_P0_X
DL_P0_M
Descriptive labels
Maximum temperature coolest month (°C)
Maximum temperature hottest month (°C)
Mean annual maximum temperature (°C)
Minimum temperature coldest month (°C)
Minimum temperature warmest month (°C)
Mean annual minimum temperature (°C)
Maximum difference in minimum temperatures (°C/day)
Minimum difference in minimum temperatures (°C/day)
Maximum difference in maximum temperatures (°C/day)
minimum difference in maximum temperatures (°C/day)
Mean annual absolute mean maximum temperature (°C)
Maximum month absolute mean maximum temperature (°C)
Minimum month absolute mean minimum temperature (°C)
Mean annual absolute mean minimum temperature (°C)
Annual Range Temperature (°C)
Minimum month diurnal temperature range (°C)
Maximum month diurnal temperature range (°C)
Mean annual diurnal temperature range (°C)
Minimum month of wind run (km/day)
Maximum month of wind run (km/day)
Mean annual wind run (km/day)
Maximum month of wind speed at 9am (m/s, 9am)
Minimum month of wind speed at 9am (m/s, 9am)
Mean annual wind speed at 9am (m/s, 9am)
Maximum month of wind speed at 3pm (m/s, 3pm)
Minimum month of wind speed at 3pm (m/s, 3pm)
Mean annual wind speed at 3pm (m/s, 3pm)
Minimum month of wind speed at 9am or 3pm (m/s)
Maximum month of wind speed at 9am or 3pm (m/s)
Mean annual wind speed at 9am or 3pm (m/s)
Shortest day length (hours)
Longest day length (hours)
Mean annual day length (hours)
Accuracy
Assessment of the accuracy of version 3 of the DEM is still underway but is likely
to be at least as good as version 2. The standard elevation error of version 2 was
found to vary between about 7.5 and 20m but may be as high as 200m in very
steep or complex terrain (Hutchinson et al. 2001).
Climate estimates are derived from long term monthly mean climate surfaces
(Australian Climate Surfaces version 2.1) through the ESOCLIM module of
ANUCLIM. Rainfall and radiation surface estimates have a relative standard error
of about 10%. Maximum and minimum temperature surface estimates have a
standard error of 0.2 to 0.4 °C (Hutchinson et al. 2000).
CONTACT INFORMATION
Contact Organisation (s)
CSIRO Sustainable Ecosystems
Contact Position
Dr Kristen J Williams, Research Scientist
Mail Address
GPO Box 1700
Locality
Canberra
State
ACT
Country
AUSTRALIA
Postcode
2601
Telephone
02 6246 4213
Facsimile
n/a
Electronic Mail Address
[email protected]
Metadata Date
15-07-2010
DATA TYPE
Spatial representation type
RASTER
PROJECTION
Map projection
GEOGRAPHIC
Datum
WGS84
Map units
DECIMAL DEGREES
SCALE
Scale/ resolution
0.01 degrees
Use Limitation
For internal use within DEWHA, Atlas of Living Australia spatial portal, ANU
Fenner School and CSIRO only. Not to be distributed. Acknowledgement required.
Acknowledgements
These data build upon an original set of National environmental variables and
metadata compiled by Dr. Janet Stein of the ANU Fenner School (Stein 2008).
Monthly climatic variables from ANUCLIM were generated by Tom Harwood of
CSIRO Entomology with technical support provided by Tingbou Xu (ANU Fenner
School). For consistency with DEWHA requirements, the coast-expansion applied
to the environmental data described here follows the approach used by Mr Randal
Storey (ERIN unit) for Maxent Modelling. This work was funded by the
Department of Environment, Water Heritage and the Arts through the Australian
Government’s Caring for our Country initiative under a project led by Dr. Simon
Ferrier of CSIRO Entomology, namely “Harnessing Continent-Wide Biodiversity
Datasets for Prioritising National Conservation Investment”. Drs Mike Austin and
Simon Ferrier contributed discussion which led to the generation of some of the
climatic indices described here. Comments from Randal Storey and Rob de Vries
improved this metadata description.
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