Agricultural Drought Indices - US Department of Agriculture

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applied in the United States, especially by water resource managers, to provide an early warning of drought and assist in the assessment of drought severity. The SPI is considered simpler to use than the PDSI, although White and Walcott (2009) note that yearly averages of both may be placed into a frequency distribution for comparative purposes (Goodrich and Ellis 2006). Additionally, the SPI may be especially useful for application in developing countries because of its limited data requirements and relative simplicity of calculation. As with many other rainfall-only indices, the SPI is more suited to monitoring meteorological and hydrological droughts than agricultural droughts. Nevertheless, its flexibility in selecting time periods that correspond with growing seasons, or any particular season of interest, does give it additional utility to inform on aspects of agricultural drought. Effective Drought Index (EDI) Rainfall data may be used as a benchmark indicator for providing drought relief assistance. Byun and Wilhite (1999) developed the Effective Drought Index (EDI) as a function of the precipitation needed for a return to normal conditions (PRN), or the recovery from the accumulated rainfall deficit since the beginning of a drought. Morid et al. (2006) found the EDI to be especially responsive to emerging drought conditions in the studies he conducted in Iran, compared with the Decile Index of Gibbs and Maher (1967) and the SPI. The value in practical application of “simple” meteorological drought indices has been recognized in the development of software that automatically generates the EDI and SPI (Smakhtin and Hughes 2007). Percent of Normal This approach (McKee et al. 1993) has value in its simplicity and transparency, especially because all sectors tend to “know what it means,” and it is noteworthy that this approach has strong support in countries such as Indonesia. A downside of this approach is that it does not necessarily detect the extremes in drought conditions, and this can be a problem in very arid areas. This approach also requires a good knowledge of local conditions to make it useful. Hayes (2000) suggests analyses using percent of normal are most effective when used for a single region or a single season. Conversely, percent of normal may be misunderstood and provide different indications of conditions, depending on the location and season. It is calculated by simply dividing actual precipitation by normal (30-year mean) precipitation and multiplying the result by 100%. This approach can be calculated for a variety of time scales, which generally range from a single month to a group of months representing a particular season, up to a year. Hayes (2000) points out that one of the disadvantages of using the percent of normal precipitation is that the mean, or average, precipitation may differ considerably from the median precipitation (which is the value exceeded by 50% of the precipitation occurrences in a long-term climate record) in many world regions, especially those with high year-to-year rainfall variability. Thus, use of the percent of normal comparison requires a normal distribution in rainfall, where the mean and median are considered to be the same. Days without Rainfall Early in the 20 th century, the U.S. Weather Bureau applied “days without rainfall” in an attempt to better identify and quantify drought. In this instance, drought was defined as occurring during any period of 21 or more days with rainfall 30% or more below normal for the period (Henry 1906, Steila 1987). An extension of this approach was through the associated “accumulated precipitation deficit” (see above in this section) or the “accumulated departure from normal.” Heim (2002) provides other examples of these early criteria: 1) 15 consecutive days with no rain, 2) 21 days or more with precipitation less than one-third of normal, 3) annual precipitation that is less than 75% of normal, 4) monthly precipitation that is less than 60% of normal, and 5) any amount of rainfall less than 85% of normal. 177
Heim (2002) notes that as recently as 1957, annual rainfall amount was used as a drought index in a study of drought in Texas. Similar criteria have been employed in other countries: 1) Britain: 15 consecutive days with less than 0.25 mm (0.01 in.); 2) India: rainfall half of normal or less for a week, or actual seasonal rainfall deficient by more than twice the mean deviation; 3) Russia: 10 days with total rainfall not exceeding 5 mm (0.20 in.); 4) Bali: a period of 6 days without rain; 5) Libya: annual rainfall less than 180 mm (7 in.). Heim (2002) notes that most of these indices were developed and thus valid only for their specific application in their specific region and makes the point that indices developed for one region may not be applicable in other regions because the meteorological conditions that result in drought are highly variable around the world. Putting Precipitation-based Indices in Context Although the practical and functional aspects of the (sole) use of meteorological drought indices are now well recognized, it is also known that for agricultural application purposes, the quantity and timing of rain events throughout a growing season largely determines the value of such indices in agricultural production assessments. Meteorological drought indices can be normalized by using appropriate seasonal indices. However, indices based solely on rainfall data do not, by definition, take into account other factors such as ambient temperature, relative or absolute humidity, mean and extreme wind speed, net radiation, evapotranspiration, deep percolation, runoff, soil type, or the agricultural enterprise (White and Walcott 2009). Because the length of a growing season can vary between years depending on when rainfall occurs, tailoring indices to growing seasons to better assess whether drought conditions are affecting crops and pastures may remain a difficult task and negate the value of sole use of meteorological indices in drought assessment. White et al. (1998) and White (2000) note that other important meteorological factors besides rainfall can greatly influence plant growth in a country such as Australia in ways not necessarily foreseen, and so a second step is often recommended in drought assessments besides the sole use of a meteorological drought index. Modification of drought indices, including just slight modification to indices such as the SPI, can provide improved practical application in certain regions (Ntale and Gan 2003). In particular, in a comparison of the PDSI, BMDI, and SPI in different regions of East Africa, the SPI was rated superior to the other indices tested and greatly superior to the PDSI through utilization of this modification approach. Of the seven drought indices assessed in Iran by Morid et al. (2006), the meteorological drought index (the Deciles Index [DI]) (Gibbs and Maher 1967) was rated as “oversensitive,” leading to unrealistically high temporal and spatial variations in wet conditions, especially in summer. However, importantly, the authors noted that this sensitivity could be reduced by using temporal scales larger than 1 month. Additionally, the importance of using long-term precipitation records for drought analyses has been highlighted by Morid at al. (2006), who note that despite utilizing different underlying statistical distributions, the SPI and the China-Z index (CZI) performed similarly in their ability to detect and monitor drought. Interestingly, when a whole range of drought indices have been ranked according to robustness, tractability, transparency, sophistication, extendability, and dimensionality, the overall superior drought indices often emerge as those being simply based on rainfall data inputs and otherwise most relevant to meteorological drought assessment (Keyantash and Dracup 2002). In a study for Oregon (USA), Keyantash and Dracup (2002) found, when making an overall assessment, that the superior drought indices were rainfall deciles, total water deficit, and computed soil moisture, while the SPI also emerged as a highly valuable estimator of drought severity. Simulation studies (e.g., Donnelly et al. 1998, Stafford Smith and McKeon 1998, White et al.1998) have demonstrated that, indeed, grassland and agricultural droughts often coincide with 178
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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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alance models cannot be applied for
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Page 135 and 136: In Figure 4, examples of the normal
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
Page 147 and 148: The parameters used in crop simulat
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 and 182: Table 1. Agricultural drought indic
Page 183: Accumulated Rainfall Deficits In an
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
Page 205: Agricultural Drought Indices Procee
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Agricultural Drought Indices - US Department of Agriculture

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