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Figure 12.3 The variation in maize production from 1989 to 2000 for the central southern region of Brazil. Data for 1996-97 was not available.

Growing season

Figure 12.3 The variation in maize production from 1989 to 2000 for the central southern region of Brazil. Data for 1996-97 was not available.

Figure 12.4 Monthly variation in the difference between precipitation (P) and potential evapotranspiration (PET) for different locations in Argentina, Brazil, Chile, and Paraguay.

and Paraguay in figure 12.4. This anomaly can be used as a drought indicator. The anomaly is positive for some hot and humid regions, negative during certain times of the year for other regions, and negative throughout the year for semiarid and arid regions where drought is a common phenomenon.

Aridity Index

The aridity index (AI) is the ratio of annual water deficiency (DEF) to PET which is determined by the methodology proposed by Thornthwaite and Mather (1955):

Based on the above index, various locations in Argentina, Brazil, Chile, and Paraguay have been categorized into different climate types (table 12.2).

Table 12.2 Climatic classification and aridity index based on methodology by Thornthwaite and Mather (1955) for distinctive locations in Argentina, Brazil, Chile, and Paraguay

Country

Location

Aridity index

Climate

Argentina

Rivadavia

0.48

Semiarid

Mendoza

0.75

Arid

Chepes

0.66

Arid

Brazil

Quixeramobim (NE)

0.48

Semiarid

Petrolina (NE)

0.75

Arid

Jaguaribe (NE)

0.58

Semiarid

Fortaleza (NE)

0.33

Subhumid, humid

Cuiaba (W)

0.13

Subhumid dry

Ribeirao Preto (SE)

0.09

Humid

Barretos (SE)

0.13

Subhumid, humid

Campinas (SE)

0.03

Humid

Chile

Los Andes

0.63

Semiarid

Patrerilios

0.89

Arid

La Serena

0.79

Arid

Antofogasta

0.99

Arid

Paraguay

Pedro Peña

0.54

Semiarid

Nueva

0.54

Semiarid

Mariscal

0.45

Semiarid

Distribution of Dry Spells

The occurrence of dry spells lasting more than 10 days in the month of January seriously affects the production of field and grain crops, particularly field and grain crops in the southeastern and central parts of the country (Alfonsi et al., 1979), and the knowledge of the distribution of the dry spells is helpful in identifying, quantifying, and mapping droughts in the region (Arruda and Pinto, 1980).

Drought monitoring and mitigation in Brazil is in its initial stage. Only the state of Sao Paulo conducts weekly drought monitoring—through its Integrated Agrometeorological Information Center (CIIAGRO) and the Instituto Agronomico de Campinas (IAC-APTA) of the Agriculture and Supply Secretariat (Government of the State of Sao Paulo)—using various indices (Brunini et al., 1998) that are described below.

Soil Moisture and Potential Evapotranspiration

The ratio of the actual water availability in soil (W) to the maximum water availability in soil (Wx) and PET can be used to monitor drought conditions, as shown in table 12.3.

Currently a new index, the agricultural drought index (ADI), as shown in table 12.4, is being implemented to monitor crop development stages and drought conditions. The ADI takes into account not only the actual soil water availability, but also the crop phenological stages and rainfall distribution.

Table 12.3 Categorization of drought conditions based on soil water and potential evapotranspiration for the state of Sao Paulo, Brazil

W/Wx

Categorization of drought condition

P s ETp

>0.60

Nil

P s ETp

0.4 s W/Wx < 0.6

Moderate

P < ETp

0 < W/Wx < 0.4

Severe

P = 0

0

Extremely severe

Note: P is accumulated rainfall, ETp is potential evapotranspiration, W is the actual water availability in the soil, and Wx is maximum water availability in the soil.

Note: P is accumulated rainfall, ETp is potential evapotranspiration, W is the actual water availability in the soil, and Wx is maximum water availability in the soil.

Table 12.4 Agricultural drought index for Sao Paulo State based on crop maximum evapotranspiration (CME) and rainfall (P)

Agricultural drought index Crop development conditions

Table 12.4 Agricultural drought index for Sao Paulo State based on crop maximum evapotranspiration (CME) and rainfall (P)

Agricultural drought index Crop development conditions

0.80 < ADI s l.0

Good

0.60 < ADI s 0.80

Favorable

0.40 < ADI s 0.60

Reasonable

0.20 < ADI s 0.40

Unfavorable

ADI s 0.20

Critical

The monthly precipitation anomaly for a location is the difference between monthly precipitation and a historic average (normal) of precipitation for that month. Such an anomaly is the simplest way to monitor drought conditions and rainfall variability from year to year.

Water Deficit Anomaly

Water deficit anomaly is the difference between water deficit for a month and the average water deficit for the month. Water deficit is computed using a water balance methodology (Thornthwaite and Mather, 1955) through a software developed by Brunini and Caputi (2000).

Palmer Drought Severity Index

Currently the CIIAGRO determines the Palmer drought severity index (PDSI; Palmer, 1965) for the Sao Paulo State and makes it available online (http://ciiagro.iac.br). The PDSI is determined as

where z is the monthly value of the precipitation anomaly, and k is climatic characterization of a location.

The normalized value of Ki was determined experimentally by Palmer

(1965) using nine locations in the United States. For the Sao Paulo State (Brunini et al., 2002), Ki was obtained using data collected at 93 locations across Sao Paulo State. Ki is defined as:

where K"is a climatic characterization factor related to moisture departure, and D is the difference between observed precipitation and expected precipitation for a month.

The Palmer index, modified for Brazil, has been used throughout the state of Sao Paulo for monitoring drought conditions both on a monthly and a dekadal basis. Figure 12.5 shows the relationship between PDSI and maize/corn yield.

Standardized Precipitation Index

The standardized precipitation index (SPI) was developed for monitoring precipitation anomalies in the United States (McKee et al., 1993; chapter 9). The SPI is determined based on probabilistic density functions that describe historic precipitation series for different durations (1-72 months). The SPI is simply a z-score of the normal distribution variable, Zi:

Currently the SPI maps are produced for the state of Sao Paulo on a monthly basis, with recurrent periods of 1, 3, 9, 12 and 24 months (SPI-1, SPI-3, SPI-9, SPI-12, and SPI-24) to monitor droughts in the state.

Crop Drought Index

The crop drought index (CDI) is defined as where AET is actual evapotranspiration. The CDI is computed on a 10-day basis considering maximum water holding capacity of the soil as 125 mm. This index, together with the Crop Moisture Index (CMI), the Standardized Precipitation Index (SPI), and the Palmer Drought Severity Index (PDSI) are used on a routine basis to forecast and to monitor drought and dry spells in Sao Paulo state.

The analysis of the results obtained for the state of Sao Paulo show that the above indices (SPI, PDSI, precipitation and deficit anomalies, and the CDI) are helpful in monitoring drought conditions in the state. Yields of various crops were also satisfactorily monitored by PDSI, CDI, and SPI (Mota, 1979; Brunini et al, 2002).

Use of Satellite Data

The use of the satellite data for drought monitoring in Brazil is at its preliminary stage. Nevertheless, studies are being conducted by the Center of

0 2000 4000 6000 8000 10000

Com Yield (Kg/ha)

0 2000 4000 6000 8000 10000

Com Yield (Kg/ha)

Figure 12.5 The relationship between Palmer drought severity index and corn yield.

Study and Research on Agriculture (CEPAGRI—Unicamp; www.cpa.uni camp.br) and mainly by the National Institute of Space Research (Instituto Nacional de Pesquisas Espaciais, www.inpe.br, which are trying to develop technologies for monitoring agricultural droughts.

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