Monitoring Agricultural Drought in Australia - Monitoring and Predicting Agricultural Drought: A Global Study - page 379

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vary greatly from year to year and are therefore not critical for drought
assessment purposes. Drought assessment is concerned more with relative
measures of pasture or crop production (i.e., how production in the cur-
rent season compares with historical production levels). Stored soil mois-
ture, rainfall, air temperature, humidity, and evaporation vary consider-
ably from year to year and contribute more to the year-to-year variation
in pasture and crop production.

D rought Monitoring

Outputs from the GRASP model have been stored as monthly surfaces from
1890 onward and are updated each month. Various model outputs are
available for drought assessment such as soil moisture, pasture growth, and
pasture biomass. Pasture growth, ranked as a percentile against historical
levels, is probably the most appropriate single index of drought in grazing
lands because it is (1) a direct measure of rainfall effectiveness; (2) relatively
insensitive to management practices except in the long term; and (3) highly
correlated with carrying capacity of livestock.
Percentile rainfall and pasture growth maps are output on a monthly,
seasonal, annual, and biennial basis. However, longer term maps may
be required to assess protracted droughts. Twelve-month pasture growth
percentiles are used as the operational basis for drought monitoring. A 12-
month period is appropriate for analyzing drought in grazing lands because
it corrects for the strong seasonality of rainfall and pasture growth in many
parts of the country. For drought assessment purposes, the model output
is aggregated to a district (e.g., shire) level. A threshold of pasture growth
less than 10th percentile is adopted for triggering drought and a threshold
of pasture growth more than the 30th percentile for breaking drought. It
could be argued that the 30th percentile threshold for breaking drought is
too low and that it takes an above-average season to break drought. While
this is a common perception, a risk-averse manager is likely to gear normal
stocking rates to a level that would be safe at least 70% of the time (i.e.,
to a level commensurate with 30th percentile pasture growth).
Based on the above criteria, the spatial model has been used to construct
an historical time-sequence of drought in Queensland on a shire-by-shire
basis (figure 29.5). This modeled time-series is in close agreement with the
record of official droughts from the Queensland drought scheme, described
earlier in this chapter. The overall close agreement between the two time
series provides independent validation both of the AussieGRASS model-
ing framework and the criteria for monitoring drought. The close agree-
ment also clearly dismisses any overall suggestion that official droughts in
Queensland were declared too often in terms of frequency and duration
(e.g., Daly, 1994). The major difference between the two time series oc-
curred in the late 1960s and late 1980s, when the model calculates a higher
proportion of land stricken by drought than evidenced by official drought
declarations (figure 29.5). The far-north of the state is a region of major

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