Weather topics > other topics . drought indices
The percent of normal precipitation is one of the simplest measurements of rainfall for a location. Analyses using the percent of normal are very effective when used for a single region or a single season. It is also easily misunderstood and gives different indications of conditions depending on the location and season. It is calculated by dividing actual precipitation by normal precipitation--typically considered to be a 30-year mean--and multiplying by 100%. This can be calculated for a variety of time scales. Usually these time scales range from a single month to a group of months representing a particular season, to an annual or water year. Normal precipitation for a specific location is considered to be 100%. A figure within a special summary released by the Climate Prediction Center shows the percent of normal precipitation for June-August 1993 to highlight the record flooding during that summer in the Midwest. 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% of the precipitation occurrences in a long-term climate record. The reason for this is that precipitation on monthly or seasonal scales does not have a normal distribution. Use of the percent of normal comparison implies a normal distribution where the mean and median are considered to be the same. An example of the confusion this could create can be illustrated by the long-term precipitation record in Melbourne, Australia for the month of January. The median January precipitation is 36.0 mm (1.4 in.), meaning that in half the years less than 36.0 mm is recorded, and in half the years more than 36.0 mm is recorded. However, a monthly January total of 36.0 mm would be only 75% of normal when compared to the mean, which is often considered to be quite dry. Because of the variety in the precipitation records over time and location, there is no way to determine the frequency of the departures from normal. Therefore, the rarity of an occurring drought is not known and can not be compared with a different location. This makes it difficult to link a value of a departure with a specific impact occurring as a result of the departure, inhibiting attempts to mitigate the risks of drought based on the departures from normal and form a plan of response (Willeke et al. 1994). DecilesArranging monthly precipitation data into deciles is another drought-monitoring technique. It was developed by Gibbs and Maher (1967) to avoid some of the weaknesses within the "percent of normal" approach. The technique they developed divided the distribution of occurrences over a long-term precipitation record into sections for each ten percent of the distribution. They called each of these categories a "decile." The first decile is the rainfall amount not exceeded by the lowest 10% of the precipitation occurrences. The second decile is the precipitation amount not exceeded by the lowest 20% 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% of the occurrences over the period of record. The deciles are grouped into five classifications, which are shown in Table 1. The Australian Bureau of Meteorology displays the current precipitation deciles for the previous month and three months across Australia.
The decile method was selected as the meteorological measurement of drought within the Australian Drought Watch System because it is relatively simple to calculate, and requires less data and fewer assumptions than the Palmer Drought Severity Index (Smith et al. 1993). In this system, farmers and ranchers can only request government assistance if the drought is shown to be an event which occurs only once in 20-25 years (deciles 1 and 2 over a 100-year record) and has lasted longer than 12 months (White and O'Meagher 1995). This uniformity in drought classifications, unlike a system based on the percent of normal precipitation, has assisted Australian authorities in determining appropriate drought responses (see discussion of Australia's National Drought Policy). One disadvantage of the decile system is that a long climatological record is needed to calculate the deciles accurately. Palmer Drought Severity Index (PDSI)In 1965, Palmer developed an index to "measure the departure of the moisture supply" (Palmer 1965). Palmer based his index on the supply-and-demand concept of the water balance equation, taking into account more than only the precipitation deficit at specific locations. The objective of the Palmer Drought Severity Index (PDSI), as this index is now called, 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 (Palmer 1965). The PDSI is a "meteorological" drought index and responds to weather 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 without taking into account streamflow, lake and reservoir levels, and other longer-term hydrologic impacts (Karl and Knight 1985). The PDSI is calculated based on precipitation and temperature data, as well as the local Available Water Content (AWC) of the soil. 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. Human impacts on the water balance, such as irrigation, are not considered. Complete descriptions of the equations can be found in the original study by Palmer (1965) and in the more recent analysis by Alley (1984). Palmer developed the PDSI to include the duration of a drought (or wet spell). His motivation was as follows: an abnormally wet month in the middle of a long-term drought should not have a major impact on the index, or a series of months with near normal precipitation following a serious drought does not mean that the drought is over. Therefore, Palmer developed criteria for determining when a drought or a wet spell begins and ends, which adjust the PDSI accordingly. Palmer (1965) described this effort and gave examples, and it is also described in detail by Alley (1984). In near-real time, Palmer's index is no longer a meteorological index but becomes a hydrological index referred to as the Palmer Hydrological Drought Index (PHDI) because it is based on moisture inflow (precipitation), outflow, and storage only, and does not take into account the long-term trend (Karl and Knight 1985). In 1989, a modified method to compute the PDSI was begun operationally (Heddinghaus and Sabol 1991). This modified PDSI differs from the PDSI during transition periods between dry and wet spells. Because of the similarities between these Palmer indices, the terms "Palmer Index" and "Palmer Drought Index" have been used to describe general characteristics of the indices. The Palmer Index varies roughly between -6.0 and +6.0. Palmer arbitrarily selected the classification scale of moisture conditions (Table 2) based on his original study areas in central Iowa and western Kansas (Palmer 1965). Ideally, the Palmer Index is designed so that a -4.0 in South Carolina has the same meaning in terms of the moisture departure from a climatological normal as a -4.0 in Idaho (Alley 1984). The Palmer Index has typically been calculated on a monthly basis, and a long-term archive of the monthly PDSI values for every Climate Division in the United States exists with the National Climatic Data Center from 1895 through the present. In addition, weekly Palmer Index values (actually modified PDSI values) are calculated for the Climate Divisions during every growing season and are available in the Weekly Weather and Crop Bulletin. These weekly Palmer Index maps are also available on the World Wide Web from the Climate Prediction Center.
The Palmer Index is popular and has been widely used for a variety of applications across the United States. It is most effective measuring impacts sensitive to the soil moisture conditions, such as agriculture (Willeke et al. 1994). It has also been useful as a drought monitoring tool and has been used to start or end drought contingency plans (Willeke et al. 1994). Alley (1984) identified three positive characteristics of the Palmer Index that contribute to its popularity: (1) it provides decision makers with a measurement of the abnormality of recent weather for a region; (2) it provides an opportunity to place current conditions in an historical perspective; and (3) it provides spatial and temporal representations of historical droughts. Several states, including New York, Colorado, Idaho, and Utah use the Palmer Index as one part of drought monitoring systems. There are considerable limitations when using the Palmer Index, and these are described in detail by Alley (1984) and Karl and Knight (1985). Drawbacks of the Palmer Index include:
Several other researchers have presented additional limitations of the Palmer Index. McKee et al. (1995) suggested that the PDSI is designed for agriculture, but does not accurately represent the hydrological impacts resulting from droughts of longer time scales. The Palmer Index is also applied within the United States and has little acceptance elsewhere (Kogan 1995). One explanation for this is provided by Smith et al. (1993), who suggested that it does not do well in regions where there are extremes in the variability of rainfall or runoff. Examples in Australia and South Africa were given. Another weakness in the Palmer Index is that the "extreme" and "severe" classifications of drought occur with a greater frequency in some parts of the country than in others (Willeke et al. 1994). "Extreme" droughts in the Great Plains occur with a frequency greater than 10%. This limits the accuracy of comparing the intensity of droughts between two regions, as well as planning response actions based on a certain intensity more difficult. Surface Water Supply Index (SWSI)The Surface Water Supply Index (SWSI) was developed by Shafer and Dezman (1982) to complement the Palmer Index for moisture conditions across the state of Colorado. The Palmer Index is basically a soil moisture algorithm calibrated for relatively homogeneous regions, but it is not designed for large topographic variations across a region and it does not account for snow accumulation and subsequent runoff. Shafer and Dezman designed the SWSI to be an indicator of surface water conditions and described the index as "mountain water dependent," in which mountain snowpack is a major component. The objective of the SWSI was to incorporate both hydrological and climatological features into a single index value resembling the Palmer Index for each major river basin in the state of Colorado (Shafer and Dezman 1982). These values would be standardized to allow comparisons between basins. Four inputs are required within the SWSI: snowpack, streamflow, precipitation, and reservoir storage. Because it is dependent upon the season, the SWSI is computed with only the snowpack, precipitation, and reservoir storage in the winter. During the summer months, streamflow replaces snowpack as a component within the SWSI equation. The procedure to determine the SWSI for a particular basin follows: monthly data are collected and summed for all the precipitation stations, reservoirs, and snowpack/streamflow measuring stations over the basin. Each summed component is normalized using a frequency analysis gathered from a long-term data set. The probability of non-exceedence--the probability that subsequent sums of that component will not be greater than the current sum--is determined for each component based on the frequency analysis. This allows comparisons of the probabilities to be made between the components. 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. Like the Palmer Index, the SWSI is centered around zero and has a range between -4.2 and +4.2. The SWSI has been used, along with the Palmer Index, to trigger the activation and deactivation of the Colorado Drought Plan. One of its advantages is that it is simple to calculate and gives a representative measurement of surface water supplies across the state. It has been modified and applied in other western states as well. These states include Oregon, Montana, Idaho, and Utah. Monthly SWSI maps for Montana are available from the Montana Natural Resource Information System. 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 (Heddinghaus and Sabol 1991). Extreme events also cause a problem if the events are beyond the historical time series, and the index will need to be reevaluated to include these events within the frequency distribution of a basin component. Standardized Precipitation Index (SPI)The understanding that a deficit of precipitation has different impacts on the ground water, reservoir storage, soil moisture, snowpack, and streamflow led McKee et al. (1993) to develop the Standardized Precipitation Index (SPI). The SPI was designed to quantify the precipitation deficit for multiple time scales. 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, streamflow, and reservoir storage reflect the longer-term precipitation anomalies. For these reasons, McKee et al. (1993) originally calculated the SPI for 3, 6, 12, 24, and 48-month time scales. The 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 a location is zero 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. In addition, wet periods can also be monitored using the SPI. McKee et al. (1993) used the classification system shown in Table 3 to define drought intensities resulting from the SPI. McKee et al. (1993) also defined the criteria for a "drought event" for any of the time scales. A drought event occurs any time the SPI is continuously negative and reaches an intensity where the SPI is -1.0 or less. The event ends when the SPI becomes positive. Each drought event, therefore, has a duration defined by its beginning and end, and an intensity for each month that the event continues. The accumulated magnitude of drought can also be measured. McKee et al. (1993) called this the Drought Magnitude (DM), and it is the positive sum of the SPI for all the months within a drought event.
Table 3 also shows the percent of time that the SPI is in each of the drought categories based on an analysis of stations across Colorado (McKee et al. 1993). Because the SPI is standardized, these percentages are expected from a normal distribution of the SPI. The 2.3% of SPI values within the "Extreme Drought" category is a percentage that is typically expected for an "extreme" event (Wilhite 1995). In contrast, the Palmer Index reaches its "extreme" category more than 10% of the time across portions of the central Great Plains. This standardization allows the SPI to determine the rarity of a current drought, as well as the probability of the precipitation necessary to end the current drought (McKee et al. 1993). The SPI has been used operationally to monitor conditions across Colorado during 1994 and 1995 (McKee et al. 1995). Monthly maps of the SPI for Colorado can be found on the Colorado State University home page. It is also being monitored at the Climate Division level for the contiguous United States by the National Drought Mitigation Center and the Western Regional Climate Center (WRCC). Because it is a relatively new index, the SPI has not been widely applied or tested. The potential exists for the SPI to provide near-real time drought monitoring for the entire United States. Currently there is a 10-14 day lag between the end of a particular month and when the precipitation data is available to calculate SPIs for the Climate Divisions. Crop Moisture Index (CMI)The Crop Moisture Index (CMI) is an index that uses a meteorological approach to monitor week-to-week crop conditions. It was developed by Palmer (1968) from procedures within the calculation of the PDSI. 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 within a Climate Division, as well as the CMI value from the previous week. The CMI responds rapidly to changing conditions, and it is weighted by location and time so that maps, which commonly display the weekly CMI across the United States, can be used to compare moisture conditions at different locations. Weekly maps of the CMI are available, generated by Roemer Weather Inc. and Freese-Notis Weather, Inc. Because it is designed to monitor short-term moisture conditions impacting a developing crop, the CMI is not a good long-term drought monitoring tool. The CMI's rapid response to changing short-term conditions may provide misleading information about long-term conditions. For example, a beneficial rainfall during a drought event may allow the CMI value to indicate adequate moisture conditions, while the long-term drought at that location continues to persist. Another characteristic of the CMI that limits its use as a long-term drought monitoring tool is that 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 several years. It also means that the user must be aware of making applications with the CMI information. The CMI, for example, may not be applicable during seed germination at the beginning of a specific crop's growing season. National Rainfall Index (RI)The National Rainfall Index was developed to compare precipitation patterns and abnormalities on a continental scale, and was utilized by Gommes and Petrassi (1994) to characterize recent precipitation patterns across Africa. It 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 country-wide statistics, especially agricultural production. The RI allows comparisons to be made between years and between countries. The strengths of the RI also reveal its limitations. Gommes and Petrassi (1994) demonstrate that the RI is well correlated with national crop yields in Africa. This is helped by the fact that the RI has a natural bias towards agriculture. 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. Thus, if the purpose of the RI is to correlate rainfall with yields on a country-scale, then the RI is useful. This may have less usefulness when looking at overall drought conditions and the hydrological, environmental, and societal impacts resulting from drought. Several additional advantages make it a useful index when applied in Africa (Gommes and Petrassi 1994). First, the RI is independent of absolute amounts of rainfall which may be localized, and allows general comparisons to be made regarding an entire country. Secondly, because of the long-term record, a frequency distribution of RI values is available which allows historical comparisons to be made not possible with the percent of normal. Finally, because a national index results, if the record is not complete for an individual station, the RI can still be calculated without that station. This permits a long-term record of the RI. For example, if there is precipitation data for five stations in Cameroon that date back to 1900, the RI can be calculated back to that point. If the number of stations climbs to 15 by 1960, the RI can still be calculated and is adjusted according to the number of stations. Dependable Rains (DR)Another rainfall monitoring approach which has been applied to the African continent by Le Houérou et al. (1993) is the concept of dependable rains (DR). The researchers definedependable rains as the amount of rainfall that occurs in four of every five years (statistically, not consecutively). They recommend that plans for agricultural production be based on dependable rains. In Africa, the relationship of the DR to the mean is not straightforward and reflects the characteristics of annual precipitation across the continent. Near the Sahara, the DR is approximately 40-50% of the annual mean, while in the 700-800 mm precipitation zone, the DR is about 80% of the mean. These precipitation characteristics illustrate why the percent of normal can be misleading in these locations and may not be useful. The DR could have an impact on agricultural planning outside of Africa as well, especially in more arid regions. For more information, please contact Mike Hayes, Climate Impacts Specialist.
References Alley, W. M., 1984. The Palmer Drought Severity Index: limitations and assumptions. Journal of Climate and Applied Meteorology, 23: 1100-1109. Gibbs, W. J. and J. V. Maher, 1967. Rainfall deciles as drought indicators. Bureau of Meteorology Bulletin No. 48, Commonwealth of Australia, Melbourne. Gommes, R. and F. Petrassi, 1994. Rainfall variability and drought in Sub-Saharan Africa since 1960. Agrometeorology Series Working Paper No. 9, Food and Agriculture Organization, Rome, Italy. Heddinghaus, T. R. and P. Sabol, 1991. A review of the Palmer Drought Severity Index and where do we go from here? In: Proc. 7th Conf. on Applied Climatology, September 10-13, 1991. American Meteorological Society, Boston, pp. 242-246. Karl, T. R. and R. W. Knight, 1985. Atlas of Monthly Palmer Hydrological Drought Indices (1931-1983) for the Contiguous United States. Historical Climatology Series 3-7, National Climatic Data Center, Asheville, NC. Kogan, F. N., 1995. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bulletin of the American Meteorological Society, 76(5): 655-668. Le Houérou, H. N., G. F. Popov, and L. See, 1993. Agro-bioclimatic classification of Africa. Agrometeorology Series Working Paper No. 6, Food and Agriculture Organization, Rome, Italy. McKee, T. B., N. J. Doesken, and J. Kleist, 1995. Drought monitoring with multiple time scales. Preprints, 9th Conference on Applied Climatology, 15-20 January, Dallas, TX, 233-236. McKee, T. B., N. J. Doesken, and J. Kleist, 1993. The relationship of drought frequency and duration to time scales. Preprints, 8th Conference on Applied Climatology, 17-22 January, Anaheim, CA, 179-184. Palmer, W. C., 1968. Keeping track of crop moisture conditions, nationwide: the new Crop Moisture Index, Weatherwise, 21: 156-161. Palmer, W. C., 1965. Meteorological Drought. Research Paper No. 45, U.S. Department of Commerce Weather Bureau, Washington, D.C. Shafer, B. A. and L. E. Dezman, 1982. Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snowpack runoff areas. Proceedings of the Western Snow Conference, 164-175. Smith, D. I., M. F. Hutchinson, and R. J. McArthur, 1993. Australian climatic and agricultural drought: payments and policy. Drought Network News, 5(3): 11-12. White, D. H. And B. O'Meagher, 1995. Coping with exceptional droughts in Australia. Drought Network News, 7(2): 13-17. Wilhite, D. A., 1995. Developing a precipitation-based index to assess climatic conditions across Nebraska. Final report submitted to the Natural Resources Commission, Lincoln, Nebraska. Wilhite, D. A. and M. H. Glantz, 1985. Understanding the drought phenomenon: the role of definitions. Water International, 10(3): 111-120. Willeke, G., J. R. M. Hosking, J. R. Wallis, and N. B. Guttman, 1994. The National Drought Atlas. Institute for Water Resources Report 94-NDS-4, U.S. Army Corps of Engineers. |
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