Agricultural Drought Indices - US Department of Agriculture

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The indices given in Table 1 above can be grouped into the following types. Precipitation-based Indices It is often noted that the simplest indices measure “meteorological drought,” the effects of which can then be measured in terms of agricultural, hydrological, and socioeconomic drought. In this case, drought is normally expressed solely on the basis of the degree of dryness (often in comparison to some “normal” or average amount) and the duration of the dry period. Here, rainfall is the most easily measured meteorological variable, with many records available for more than 100 years in many countries. Additionally, soil moisture that is derived directly from rainfall is commonly the factor that is most limiting to plant growth. It should, however, be noted, that even though the paucity of rain is a main cause of agricultural drought, rainfall data alone may often be insufficient to assess the effect of drought on agricultural productivity. Nevertheless, use of rainfall has considerable value in providing effective summaries of droughts, and, provided the purpose is clearly and precisely defined in terms of activity, location, and timing, rainfall data can greatly assist activities such as drought policy decisions (Heim 2000, 2002; White and Walcott 2009). For rural producers especially, the quantity, intensity, and timing of rain throughout the growing season determine its effectiveness. Therefore, while “simple rainfall-based indices” have considerable value, especially in drought policy decisions, it is also possible for meteorological droughts of apparently equal severity to have differing effects on the productivity of agricultural crops and pastures (e.g., White et al. 1998). In this respect, Heim (2000, 2002) and White and Walcott (2009) provide extensive and worthwhile overviews of commonly applied rainfall-based drought indices. The following is a short summary of such indices. Deciles For countries such as Australia, with especially high interannual rainfall variability, Gibbs and Maher (1967) observed that the occurrence of annual rainfall in the first decile range for the period 1885–1965 corresponded very well with information on drought occurrence (Foley 1957). To aid this appraisal, it is noteworthy that Foley (1957) based “agricultural assessments” on newspaper and other reports of the effects of rainfall in the first decile range on crop yield and livestock numbers. As a consequence, the Australian Bureau of Meteorology now operationally identifies “rainfall deficiencies” rather than defining “droughts,” with a serious rainfall deficiency occurring when the rainfall total over a critical period lies between the 5 th and 10 th percentile, and a severe rainfall deficiency occurs when the total is below the 5 th percentile (White and Walcott 2009). (This approach may also tend to overcome the inherent problems associated with the need for normalized rainfall distributions in the “percent of normal” drought index.) The decile method (Table 2) was selected to describe droughts within the Australian Drought Watch System because of the capability to simply calculate the results, and the method requires less data and fewer assumptions than the Palmer Drought Severity Index (Smith et al. 1993, Hayes 2000). As part of Australian drought policy, growers and producers are advised to only seek exceptional drought assistance if the drought is shown to be an event that 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, Hayes 2000). Table 2. Decile classifications. Decile numbers Rainfall category Description Deciles 1-2 lowest 20% much below normal Deciles 3-4 next lowest 20% below normal Deciles 5-6 middle 20% near normal Deciles 7-8 next highest 20% above normal Deciles 9-10 highest 20% much above normal 175
Accumulated Rainfall Deficits In an extension of the approach of using rainfall deciles, the duration and severity of drought may also be estimated by summing the rainfall anomalies for each month and graphing this accumulation over time. Although cumulative rainfall deficits may be difficult to directly interpret, they can be useful for highlighting a period of rainfall deficit that has been preceded by a period of above-average rainfall. These systems have also been useful in comparing the duration of current meteorological droughts with preceding droughts in the same location. However, the magnitude and importance of the rainfall deficits are very site-specific. They are therefore of limited value in assessing agricultural droughts. In another example, rainfall for more recent months can be weighted according to the seasonal variation in mean rainfall, but may be of little value for operationally determining exceptional droughts at different locations. For example, enhanced rainfall may be more valuable during periods typified by low vegetative cover than at the peak of the growing season (White 1988). Hutchinson Drought Severity Index (HDSI) Smith et al. (1993) calculated 6-monthly and 12-monthly percentiles on a month-by-month basis as the Hutchinson Drought Severity Index (HDSI), but this index may be too sensitive to minor fluctuations in rainfall to facilitate ranking of droughts. These percentiles may underestimate the durations of declared droughts, possibly because of probable historical anomalies in the declaration process. Standardized Precipitation Index (SPI) The Standardized Precipitation Index (SPI) has been developed utilizing the quantification and hence probability of precipitation for multiple time scales, generally between 1 and 48 months but with potential applicability up to 72 months. (McKee et al. 1993, 1995; Edwards and McKee 1997). A strong foundation in this approach stems from the understanding that a deficit of precipitation has markedly different impacts on groundwater, reservoir storage, soil moisture, snowpack, and streamflow (McKee et al. 1993, Hayes 2000). McKee et al. (1993) further defined a drought event as occurring any time the SPI is “continuously negative” and reaches an intensity of -1.0 or less (Table 3). The drought event ends when the SPI becomes positive. The enhanced value of this approach is that each drought event therefore has a recognized duration defined by its beginning and end, and an intensity for each month that the event continues. Additionally, the accumulated magnitude of drought can also be drought magnitude, and it is the positive sum of the SPI for all the months within a drought event (Hayes 2000). Table 3. Values of Standardized Precipitation Index (SPI) related to moisture conditions. SPI Value Moisture condition +2.0 extremely wet +1.5 to 1.99 very wet +1.0 to 1.49 moderately wet -.99 to .99 near normal -1.0 to -1.49 moderately dry -1.5 to -1.99 severely dry -2.0 and less extremely dry Hayes (2000) notes that based on an analysis of stations across Colorado, McKee determined that the SPI is in mild drought 24% of the time, in moderate drought 9.2% of the time, in severe drought 4.4% of the time, and in extreme drought 2.3% of the time (McKee et al. 1993). Importantly, the SPI, which is in widespread use, shows evidence of being able to identify emerging droughts sooner than the Palmer Drought Severity Index (PDSI) in regions of the United States. In particular, Hayes et al. (1999) and Hayes (2000) report that the SPI is now widely 176
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Agricultural Drought Indices Procee
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© World Meteorological Organizatio
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Agricultural Drought Indices Procee
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Preface With the world population p
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Segura River Basin: Spanish Pilot R
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Table 3. Segura River Basin resourc
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Figure 5. Segura River Basin water
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Drought Indicator Expression After
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Table 5. Analysis of drought impact
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Even with this drought action plan
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that complicates water management b
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Understanding the nature of the haz
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Figure 2. U.S. Drought Monitor for
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Summary Drought is a creeping pheno
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Agricultural Drought—WMO Perspect
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c) CAgM-VI (Washington, USA, 1974)
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an unusually large moisture demand.
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the-clock nature of WIGOS, the anal
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and temporal variations in drought
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Support to Regional Institutions WM
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Williams, M.A.J. and R.C. Balling,
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The day after Black Sunday, an Asso
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Meteorological research at these in
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United States is sent to the Secret
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Palmer Drought Severity Index (PDSI
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PDSI as an operational index since
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WAOB, GIS has become an important t
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USDM as the official criteria for F
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Monitoring Drought Risks in India w
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areas recording large incidences of
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The analysis revealed that out of 1
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Figure 3. Aridity anomaly chart of
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Figure 5. Progression of surface we
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Agricultural Drought Indices in Cur
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Meteorological and Agricultural Dro
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Palmer Drought Severity Index Adapt
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Accumulated WD and WS April 2010 Ac
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Soil Water Storage (SWS) September
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hydrological characteristics, and c
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Agricultural Drought Indices in Cur
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application of drought indices has
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The Value of Crop and Pasture Simul
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To assist, many agriculturally-spec
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change in Australia. The PDSI provi
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Mpelasoka, F.S., M.A. Collier, R. S
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supply and demand are fully taken i
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In Germany, the Deutsche Wetterdien
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creation of a system of European su
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The second index is extracted from
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Figure 7. Standardized Soil Water I
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References Durand, Y., E. Brun, L.
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Europe around the Iberian Peninsula
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The incoming rainwater and the atmo
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Figure 6. Example of SPI map for a
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The 1991-95 Drought Episode A long-
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adverse climatic impacts on agricul
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Agricultural Drought Indices in the
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efers to deficiencies in surface an
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Figure 5. Time series of standardiz
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Limitations of Agricultural Drought
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Incorporating a Composite Approach
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Figure 3. Daily gridded SPI by regi
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The North American Drought Monitor:
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process (a) emulates natural cycles
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Summary Given the complexity that d
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Page 137 and 138: Figure 7. Water balance for Brazil,
Page 139 and 140: The soil water balance module in th
Page 141 and 142: Water Balance as a Tool for Agricul
Page 143 and 144: Conclusions The topics discussed pr
Page 145 and 146: Use of Crop Models for Drought Anal
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Page 149 and 150: a model for cassava and potato (Tsu
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Page 153 and 154: management. In research, however, p
Page 155 and 156: Smajstrla, A.G. 1990. Technical Man
Page 157 and 158: Monitoring Regional Drought Conditi
Page 159 and 160: Figure 2 represents the regression
Page 161 and 162: Conclusions and Discussion Several
Page 163 and 164: Experiences During the Drought Peri
Page 165 and 166: During the years 2007 and 2008, for
Page 167 and 168: Figure 6. Management simulation mod
Page 169 and 170: Figure 9. Irrigation diversions in
Page 171 and 172: References Andreu, J., Ferrer Polo,
Page 173 and 174: Table 2. MERIS spectral bands MDS N
Page 175 and 176: Figure 6. Spanish NSDIE during 2008
Page 177 and 178: References Gu, Y., J. F. Brown, J.
Page 179 and 180: Agricultural Drought Indices: Summa
Page 181: Table 1. Agricultural drought indic
Page 185 and 186: Heim (2002) notes that as recently
Page 187 and 188: each calendar day in the base perio
Page 189 and 190: The purpose of the climatic charact
Page 191 and 192: to compare moisture conditions at d
Page 193 and 194: Table 6. Overview of different drou
Page 195 and 196: have been adopted because of the av
Page 197 and 198: TCI = 100 (T max - T) / (T max - T
Page 199 and 200: Figure 1. The February 8, 2011, US
Page 201 and 202: 8) There is a universal interest in
Page 203 and 204: Mavromatis, T. 2007. Drought index
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Agricultural Drought Indices - US Department of Agriculture

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