Agricultural Drought Indices - US Department of Agriculture

Table 1. Accumulated crop water development index (ACWDI) classes and their relationship to crop
development conditions. Source: www.infoseca.sp.gov.br.
ACWDI Classes Crop development conditions
0.8 ≤ ACWDI ≤ 1.0 Very good
0.6 ≤ ACWDI ≤ 0.8 Good
0.4 ≤ ACWDI < 0.6 Reasonable
0.3 ≤ ACWDI < 0.4 Unfavorable
0.2 ≤ ACWDI < 0.3 Critical
0.1 ≤ACWDI < 0.2 Very critical
ACWDI < 0.1
Severe
Another drought index that uses the outputs of the water balance models is the Palmer Drought
Severity Index (PDSI). The PDSI is an agrometeorological drought index, and it responds to
weather and consequent water balance conditions that have been abnormally dry or abnormally
wet. When conditions change from dry to normal or wet, for example, the drought measured by
the PDSI ends (Karl and Knight 1985). The PDSI is calculated based on precipitation and
temperature data, as well as the local SWHC. From these inputs, all the basic terms of the water
balance are determined by Thornthwaite and Mather’s model, including actual evapotranspiration
(ETa), soil water storage (SWS), water deficiency (WD), and water surplus (WS). Complete
descriptions of the equations can be found in the original study of Palmer (1965) and in Alley
(1984).
Strengths, Weaknesses, and Limitations of Water Balance Models for Drought Monitoring
The strengths and weaknesses of a water balance model are related, basically, to their complexity,
as discussed above.
The simpler water balance models, represented here by the Thornthwaite and Mather (1955)
model, have as positive aspects the fact that they are simple to apply, requiring only rainfall and
temperature data (for estimating ETP) and general information from the soil, in terms of water
holding capacity (SWHC). Another advantage of these models is that their outputs are easy to
understand and apply, and they do not require much computational power. Normally, water
deficiency has a high correlation with crop yield losses. On the other hand, these models do not
consider variables such as runoff, rain interception, and detailed soil characteristics, which make
their results limited for more specific studies, generating systematic errors.
The most complex models, which consider the majority of the processes involved with the water
balance, will produce more reliable results when well calibrated for the location, having a very high
correlation with what is happening in the field. However, the complexity will require many complex
input data, which are not readily available for the majority of locations. They are complex to apply;
require detailed information from soil, crop development, crop management, and climate; have
more complex outputs; need higher computational power; and, if not properly calibrated, can
present calibration errors. An example of the calibration problems related to complex water
balance models was presented by Faria and Bowen (2003), when using Ritchie’s model in DSSAT.
As mentioned earlier, users need to find a balance between simplicity and complexity when
choosing a water balance model for drought monitoring with indices. In this way, the most
reasonable option is to evaluate which data are available and which model is the better match for
this data, producing suitable results. Calibration and tests are always recommended.
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