Meteorological Indicators Of Drought

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Drought conditions are basically due to a deficit of water supply in time and/or space. The deficit may be in precipitation, stream flow, or accumulated water in storage reservoirs, ground aquifers, and soil moisture reserves. In describing a drought situation, it is important to understand its duration, spatial extent, severity, initiation, and termination. Depending on the areal extent, a drought can be referred to as a point drought, small-area drought, or a continental drought. The point and small-area drought frequency are very high but are not major sources of concern at the national scale, unless they continue for a prolonged period. When the areal extent of the drought assumes a wide dimension, its assessment and mitigation measures become state and national concerns.

Over time, a number of drought assessment methods have been proposed. Some methods are based on qualitative observations, some on scientific criteria, and others on actual field surveys. However, to date, no comprehensive assessment method is available that has universal appeal. Different countries use different criteria to define and assess the drought situation. It is beyond the scope of this book to enumerate each and every indicator of drought that has been proposed and referred to in the literature. Some of these are very simple and old but still widely used. Others are more comprehensive, having sound scientific bases and holding good promise for application. The National Drought Mitigation Center (Hayes, 1996) has done a detailed comparative evaluation of the most widely used indices and those proposed during the recent past. Another evaluation was performed by Quiring and Papakryiakou (2003).

Percent of Normal

The percent of normal precipitation is one of the simplest measurements of drought for a location. It is calculated by dividing actual precipitation by the normal (considered to be a 30 or more years mean) and multiplying by 100. The percent of normal is calculated for a variety of time scales. Usually the time scales range from a single month, to a group of months representing a particular season, to an annual climatic year.

Analyses using the percent of normal are very effective when used for a single region or a single season. However, it is also easily misunderstood and gives different indications of conditions depending on the location and season.

One of the disadvantages of using the percent of normal precipitation is that the mean, or average, precipitation is often not the same as the median precipitation, which is the value exceeded by 50 percent of the precipitation occurrences in a long-term climatic record, largely because precipitation on monthly or seasonal scales does not have a normal distribution. Use of the percent of normal comparison implies a normal distribution in which the mean and median are considered to be the same. Because of the variety in precipitation records over time and locations, there is no way to determine the frequency of the departures from normal. Therefore, the rarity of an occurring drought is not known and cannot be compared to a different location.

The India Meteorological Department defines drought on the basis of rainfall deficiency during the southwest monsoon season on the basis of the percent of normal rainfall (Murty and Takeuchi, 1996). It employs two measures, the first describing rainfall conditions and the second representing drought severity. Rainfall conditions (based on the average rainfall of the last 70 to 100 years) are described as rainfall thresholds (Table 5.1), with rainfall expressed on a weekly or monthly basis. The intensity of drought is described as drought thresholds (Table 5.2).

A drought-prone area is defined as one in which the probability of drought in a given year is greater than 20 percent. A chronic drought-prone area is defined as one in which the probability of drought in a given year is greater than 40 percent. A drought year is defined as when less than 75 percent of the normal rainfall is received.

The National Institute of Hydrology, India, while analyzing the drought of 1987 (Murty and Takeuchi, 1996) proposed indices describing rainfall deficits, low flows in streams, and a fall in the water table. The drought conditions were classified in terms of runoff as shown in Table 5.3.

TABLE 5.1. Rainfall thresholds

Class

Range

Scanty

-50% or less than the normal

Deficient

-20% to -50% of the normal

Normal

+19% to -19% of the normal

Excess

+20% or more than the normal

TABLE 5.2. Drought thresholds

Class

Range

Moderate drought

Seasonal rainfall -26% to -50% of the normal

Severe drought

Seasonal rainfall < -50% of the normal

TABLE 5.3. Hydrological classification of drought

Drought class

Departure in runoff volume from normal (%)

Severe drought

50 and above

Moderate drought

25 to 50

No drought

Less than 25

In the Philippines, percent of normal index is used to assess the drought situation. A drought warning is issued when less than 40 percent of normal rainfall is received within three consecutive months. In Thailand, a generalized monsoon rainfall index, based on percent of normal rainfall, is also used to assess the impact of rainfall on crop conditions (Murty and Take-uchi, 1996).

To avoid some of the weaknesses within the "percent of normal" approach, Gibbs and Maher (1967) developed the technique of ranking rainfall values in deciles as an indicator of drought. The rainfall occurrences over a long-term precipitation record are divided into sections for each ten percent of the distribution. Each of the sections is called a "decile." The first decile is the rainfall amount not exceeded by the lowest 10 percent of the precipitation occurrences. The second decile is the precipitation amount not exceeded by the lowest 20 percent of occurrences. These deciles continue until the rainfall amount identified by the tenth decile is the largest precipitation amount within the long-term record. By definition, the fifth decile is the median, and it is the precipitation amount not exceeded by 50 percent of the occurrences over the period of record. The deciles are grouped into five classifications, as shown in Table 5.4. The Australian Bureau of Meteorology prepares and displays tables and maps of precipitation deciles for the previous one, three, six, and twelve months across Australia.

The decile method was selected as the meteorological measurement of drought in Australia because it is relatively simple to calculate and requires less data and fewer assumptions than the Palmer Drought Severity Index. In this system, a drought is an exceptional event if it occurs only once in 20 to 25 years (deciles 1 and 2 records) and has lasted longer than 12 months. This uniformity in drought classifications, unlike a system based on the percent of normal precipitation, has been more useful to Australian authorities in

Deciles

TABLE 5.4. Decile ranges and moisture thresholds

Decile range Percent values

Classification

Deciles 1-2 Lowest 20% values

Deciles 3-4 Next 20% values

Deciles 5-6 Middle 20% values

Deciles 7-8 Next highest 20% values

Deciles 9-10 Highest 20% values

Below normal Near normal Above normal

Much below normal

Much above normal determining appropriate drought responses. The disadvantage of the decile system is that a long climatological record is needed to calculate the deciles accurately.

Dependable Rains (DR)

Dependable rains (DR) is defined as the amount of rainfall that occurs in four of every five years (statistically, not consecutively). The index has been applied to the African continent (Le Houerou, Popov, and See, 1993). Dependable rains have potential for use in agricultural planning outside of Africa as well, especially in comparatively dry regions. The concept is, however, not a very good drought-monitoring index.

National Rainfall Index (RI)

The National Rainfall Index compares precipitation patterns and abnormalities on a continental scale. It was utilized to characterize precipitation patterns across Africa (Gommes and Petrassi, 1994). The index is calculated for each country by taking a national annual precipitation average weighted according to the long-term precipitation averages of all the individual stations. The country-size scale is designed to correlate with other countrywide statistics, especially agricultural production.

The RI allows comparisons to be made between years and between countries. RI is well correlated with national crop yields in Africa. Because it is weighted by annual rainfall, those stations in wetter areas of a country have a greater influence on the RI than stations in naturally drier areas. In many countries, especially in Africa, the wetter stations are also located in more agriculturally productive regions. RI has, therefore, a natural bias toward agriculture, and it is a useful tool where country-scale crop production is correlated with rainfall.

RI is independent of absolute amounts of rainfall, which may be localized, and allows general comparisons to be made regarding an entire country. The long-term record makes available a frequency distribution of RI values, which allows historical comparisons to be made, an analysis not possible with the percent of normal. Even if the record is not complete for an individual station, the RI can still be calculated without that station.

The RI may be less useful when looking at overall drought conditions and the hydrological, environmental, and social impacts resulting from drought.

Palmer Drought Severity Index (PDSI)

The Palmer Drought Severity Index measures abnormalities in the moisture supply (Table 5.5). The index developed by Palmer (Palmer, 1965) is based on the supply-and-demand concept of the water balance equation, taking into account several other factors in addition to precipitation deficit at specific locations. The objective of the Palmer Drought Severity Index was to provide a measurement of moisture conditions that were "standardized," so that comparisons using the index could be made between locations and between months.

The PDSI is essentially a meteorological drought index and is based on precipitation and temperature data and the locally available water content (AWC) of the soil (Karl and Knight, 1985). From the inputs, all the basic terms of the water balance equation can be determined, including evapotranspiration, soil recharge, runoff, and moisture loss from the surface layer.

The Palmer Index has been widely used for a variety of applications across the United States. It is most effective in measuring impacts sensitive to soil moisture conditions, such as agriculture. It has also been useful as a drought-monitoring tool and has been used as an indicator on which to base the start or end of drought contingency plans. The index is popular because it provides decision makers with (1) a measurement of the abnormality of recent weather for a region; (2) an opportunity to place current conditions in a historical perspective; and (3) spatial and temporal representations of historical droughts.

TABLE 5.5. Palmer Drought Severity Index classifications for dry and wet periods

Index value

Classification

-4.00 or less -3.00 to-3.99 -2.00 to-2.99 -1.00 to -1.99 -0.50 to-0.99 0.49 to-0.49 0.50 to 0.99 1.00 to 1.99 2.00 to 2.99 3.00 to 3.99 4.00 or more

Extreme drought Severe drought Moderate drought Mild drought Incipient dry spell Near normal Incipient wet spell Slightly wet Moderately wet Very wet Extremely wet

Along with its merits, the Palmer Index also has drawbacks (Alley, 1984; Karl and Knight, 1985):

1. The values quantifying the intensity of a drought and signaling the beginning and end of a drought or wet spell are arbitrarily selected.

2. The Palmer Index is sensitive to the AWC of a soil type. Applying the index for a climate division may be too general.

3. The soil layers within the water balance computations are simplified and may not accurately represent a location.

4. Snowfall, snow cover, and frozen ground are not included in the index.

5. All precipitation is treated as rain, so the timing of PDSI values may be inaccurate in the winter and spring months in regions where snow occurs.

Bhalme and Mooley Drought Index (BMDI)

The BMDI was developed by Bhalme and Mooley in 1980 (Bogardi et al., 1994) and is a simplified version of the Palmer Index. The calculations of BMDI need only precipitation data, but its performance, according to the authors, is comparable to that of PDSI.

The index expresses situations that vary from extreme drought to extreme wet (Table 5.6). BMDI = <-4 for extreme historical drought and proportionally increases to higher values. For normal conditions, BMDI = 0, and for extreme wet, BMDI = >4.

The simplicity of the calculations is the major merit of this index. The index has performed well under Indian and Hungarian climatic conditions. The performance has been equally good in the Great Plains of North America.

TABLE 5.6. Bhalme and Mooley Drought Index based drought categories

Index value

Character of the weather

Greater than 4 4 to 3 3 to 2 2 to 1 1 to -1 -1 to -2

Extremely wet Very wet Moderately wet Slightly wet Near normal Mild drought Moderate drought Severe drought Extreme drought

Surface Water Supply Index (SWSI)

To overcome the limitations of the Palmer Index, Shafer and Dezman (1982) designed the Surface Water Supply Index (SWSI) to be an indicator of surface water conditions. They described the index as "mountain water dependent," in which mountain snowpack is a major component. The intention was to use the index as a complement to the Palmer Index in Colorado.

The SWSI incorporates both hydrological and climatological features into a single index value resembling the Palmer Index for each major river basin in a state. These values would be standardized to allow comparisons between basins. The inputs required are snowpack, stream flow, precipitation, and reservoir storage. Because it is dependent on the season, the SWSI is computed with only the snowpack, precipitation, and reservoir storage in the winter. During the summer months, stream flow replaces snowpack as a component within the SWSI equation. The procedure to determine the SWSI for a particular basin is as follows:

1. Monthly data are collected and summed for all the precipitation stations, reservoirs, and snowpack/stream flow measuring stations over the basin.

2. Each summed component is normalized using a frequency analysis gathered from a long-term data set.

3. Each component has a weight assigned to it depending on its typical contribution to the surface water within that basin, and these weighted components are summed together to determine a SWSI value representing the entire basin.

4. The SWSI is centered on zero and has a range between -4.2 and +4.2.

One of its advantages is that it is simple to calculate and gives a representative measurement of surface water supplies across the region/state. The SWSI has been used to trigger the activation and deactivation of a drought plan in Colorado.

Several characteristics of the SWSI create limitations in its application. The discontinuance of any station means that new stations need to be added to the system and new frequency distributions need to be determined for that component. Additional changes in the water management within a basin, such as flow diversions or new reservoirs, mean that the entire SWSI algorithm for that basin needs to be redeveloped to account for changes in the weight of each component. Thus, it is difficult to maintain a homogeneous time series of the index. Extreme events also cause a problem. If the events are beyond the historical time series, the index will need to be reevaluated to include these events within the frequency distribution of a basin component.

Standardized Precipitation Index (SPI)

The Standardized Precipitation Index (SPI) is based on the fact that a deficit of precipitation has different impacts on the groundwater, reservoir storage, soil moisture, snowpack, and stream flow (McKee, Doesken, and Kleist, 1993). The SPI quantifies the precipitation deficit for multiple time scales (3, 6,12, 24, and 48 months). These time scales reflect the impact of drought on the availability of the different water resources. Soil moisture conditions respond to precipitation anomalies on a relatively short scale, while groundwater, stream flow, and reservoir storage reflect the longer-term precipitation anomalies.

SPI is calculated by taking the difference of the precipitation from the mean for a particular time scale and then dividing by the standard deviation. Because precipitation is not normally distributed for time scales shorter than 12 months, an adjustment is made which allows the SPI to become normally distributed. Thus, the mean SPI for a time scale and location are zeros and the standard deviation is one. This is an advantage, because the SPI is normalized so that wetter and drier climates can be represented in the same way.

A classification system is used to define drought intensities resulting from the SPI (Table 5.7). A drought event occurs any time the SPI is continuously negative and reaches intensity when the SPI is -1.0 or less. The event ends when the SPI becomes positive. Therefore, each drought event has a duration defined by its beginning and end and its intensity for each month that the event continues. An accumulated magnitude of drought can also be measured. It is called the drought magnitude (DM) and is the positive sum

TABLE 5.7. Standardized Precipitation Index

SPI value

Moisture category

2.0 and above

Extremely wet Very wet

Moderately wet Near normal Moderately dry Severely dry Extremely dry

-1.00 to -1.49 -1.50 to -1.99 -2.0 or less of the SPI for all the months within a drought event. This standardization allows the SPI to determine the rarity of a current drought.

The SPI has been used operationally to monitor conditions across Colorado during 1994 and 1995 (McKee, Doesken, and Kleist, 1995). The potential exists for the SPI to provide near-real-time drought monitoring for an entire country. The number of applications using the SPI around the world are increasing, because the index has the advantages of being easily calculated, having modest data requirements, and being independent of the magnitude of mean rainfall, and hence comparable over a range of climatic zones. It does, however, assume the data are normally distributed, which can introduce complications for shorter time periods (Agnew, 2000; Hayes, 2000).

Crop Moisture Index (CMI)

The Crop Moisture Index (CMI) was developed by Palmer in 1968 and uses a meteorological approach to monitor week-to-week crop conditions from procedures he used to calculate the PDSI (Palmer, 1968; McKee, Doesken, and Kleist, 1995). Whereas the PDSI monitors long-term meteorological wet and dry spells, the CMI was designed to evaluate short-term moisture conditions across major crop-producing regions. It is based on the mean temperature and total precipitation for each week and the CMI value from the previous week (Table 5.8). The CMI responds rapidly to changing conditions. It is weighted by location and time, so maps, which commonly display the weekly CMI across a state or a region, can be used to compare moisture conditions at different locations.

The Crop Moisture Index is designed to monitor short-term moisture conditions impacting a developing crop, so it is not a good tool for long-term drought monitoring. The CMI's rapid response to changing short-term conditions may provide misleading information about long-term conditions. The CMI typically begins and ends each growing season near zero. This limitation prevents the CMI from being used to monitor moisture conditions outside the general growing season, especially in drought situations that extend over a year or more.

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