Agricultural Drought Indices - US Department of Agriculture

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Rainfall anomaly – September 2010 Figure 2. Rainfall anomaly during September 2010 in Brazil. Source: www.inmet.gov.br. Standard Precipitation Index (SPI): SPI was developed primarily for defining and monitoring drought. It allows the user to determine the rarity of a drought at a given time scale of interest for any rainfall station with historic data. SPI is based on the cumulative probability of a given rainfall event occurring at a station (Blain 2005). The historic rainfall data of a given station is fitted to a gamma distribution. This is done through a process of maximum likelihood estimation of the gamma distribution parameters. The process described above allows the rainfall distribution for a station to be effectively represented by a mathematical cumulative probability function. Based on the historic rainfall data, one can then determine the probability of the rainfall being less than or equal to a certain amount. Thus, the probability of rainfall being less than or equal to the average rainfall for that area would be about 0.5, while the probability of rainfall being less than or equal to an amount much smaller than the average would be even lower, depending on the amount. Therefore if a particular rainfall event gives a low probability on the cumulative probability function, this is indicative of a drought event. SPI has been provided by national (INMET and AGRITEMPO) and regional (INFOSECA) M&HSs. Figure 3 presents SPI for 3 months for Brazil, to September 2010. Standard Precipitation Index (SPI) September 2010 – Accumulated for 3 months Extremely wet Severely wet Moderately wet Normal Moderately dry Severely dry Extremely dry Figure 3. SPI accumulated for 3 months (to September 2010) in Brazil. Source: www.inmet.gov.br. 63
Palmer Drought Severity Index Adapted to São Paulo State (PDSI-Adapted): The Palmer Drought Severity Index (PDSI) is widely used to characterize droughts. The PDSI is based on the water balance equation over an area of concern (Palmer 1965). Calculating PDSI requires data on precipitation, temperature (for potential evapotranspiration estimation), soil moisture, and the previous PDSI value. Although precipitation and temperature time series data are easily available for most locations, this is not always the case with soil moisture because of the lack of monitoring networks. PDSI is a drought index that involves aspects related to duration, magnitude, and severity of a drought and also includes information on the onset and termination of a drought event. PDSI was adapted to the climatic conditions of the state of São Paulo by Blain (2005) and has been used by the INFOSECA system as one of their drought monitoring indices, as presented in Figure 4, using the following classification (Table 2). Table 2. PDSI classification used by the INFOSECA system in the state of São Paulo, Brazil. Source: www.infoseca.sp.gov.br/. PDSI interval Classification ≥ 3.00 Extremely wet 2.00 to 2.99 Very wet 1.00 to 1.99 Moderately wet 0.51 to 0.99 Beginning of wet -0.50 to 0.50 Normal -0.99 to -0.51 Beginning of dry -1.99 to -1.00 Moderately dry -2.99 to -2.00 Very dry ≤ -3.00 Extremely dry Adapted SPI – São Paulo August 2010 Legend Figure 4. PDSI adapted for the state of São Paulo, Brazil, for August 2010. Source: www.infoseca.sp.gov.br. Number of days without rain (NDWR): This index simply accumulates the number of days without rain for a given region. When a rain event occurs, NDWR becomes zero and a new accounting starts. Two variations of this index are the number of days without rain above 5 mm (NDWR>5) and 10 mm (NDWR>10), which are considered more appropriate for agricultural drought monitoring. NDWR and NDWR>10 are used by INFOSECA, in the state of São Paulo, and NDWR>5 is used by AGRITEMPO on the national level. 64
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Agricultural Drought Indices Procee
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© World Meteorological Organizatio
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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
Page 19 and 20: Even with this drought action plan
Page 21 and 22: that complicates water management b
Page 23 and 24: Understanding the nature of the haz
Page 25 and 26: Figure 2. U.S. Drought Monitor for
Page 27 and 28: Summary Drought is a creeping pheno
Page 29 and 30: Agricultural Drought—WMO Perspect
Page 31 and 32: c) CAgM-VI (Washington, USA, 1974)
Page 33 and 34: an unusually large moisture demand.
Page 35 and 36: the-clock nature of WIGOS, the anal
Page 37 and 38: and temporal variations in drought
Page 39 and 40: Support to Regional Institutions WM
Page 41 and 42: Williams, M.A.J. and R.C. Balling,
Page 43 and 44: The day after Black Sunday, an Asso
Page 45 and 46: Meteorological research at these in
Page 47 and 48: United States is sent to the Secret
Page 49 and 50: Palmer Drought Severity Index (PDSI
Page 51 and 52: PDSI as an operational index since
Page 53 and 54: WAOB, GIS has become an important t
Page 55 and 56: USDM as the official criteria for F
Page 57 and 58: Monitoring Drought Risks in India w
Page 59 and 60: areas recording large incidences of
Page 61 and 62: The analysis revealed that out of 1
Page 63 and 64: Figure 3. Aridity anomaly chart of
Page 65 and 66: Figure 5. Progression of surface we
Page 67 and 68: Agricultural Drought Indices in Cur
Page 69: Meteorological and Agricultural Dro
Page 73 and 74: Accumulated WD and WS April 2010 Ac
Page 75 and 76: Soil Water Storage (SWS) September
Page 77 and 78: hydrological characteristics, and c
Page 79 and 80: Agricultural Drought Indices in Cur
Page 81 and 82: application of drought indices has
Page 83 and 84: The Value of Crop and Pasture Simul
Page 85 and 86: To assist, many agriculturally-spec
Page 87 and 88: change in Australia. The PDSI provi
Page 89 and 90: Mpelasoka, F.S., M.A. Collier, R. S
Page 91 and 92: supply and demand are fully taken i
Page 93 and 94: In Germany, the Deutsche Wetterdien
Page 95 and 96: creation of a system of European su
Page 97 and 98: The second index is extracted from
Page 99 and 100: Figure 7. Standardized Soil Water I
Page 101 and 102: References Durand, Y., E. Brun, L.
Page 103 and 104: Europe around the Iberian Peninsula
Page 105 and 106: The incoming rainwater and the atmo
Page 107 and 108: Figure 6. Example of SPI map for a
Page 109 and 110: The 1991-95 Drought Episode A long-
Page 111 and 112: adverse climatic impacts on agricul
Page 113 and 114: Agricultural Drought Indices in the
Page 115 and 116: efers to deficiencies in surface an
Page 117 and 118: Figure 5. Time series of standardiz
Page 119 and 120: 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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60 50 40 Error 30 20 Systematic Err
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In Figure 4, examples of the normal
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Figure 7. Water balance for Brazil,
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The soil water balance module in th
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Water Balance as a Tool for Agricul
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Conclusions The topics discussed pr
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Use of Crop Models for Drought Anal
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The parameters used in crop simulat
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a model for cassava and potato (Tsu
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ackground. The system’s most impo
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management. In research, however, p
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Smajstrla, A.G. 1990. Technical Man
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Monitoring Regional Drought Conditi
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Figure 2 represents the regression
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Conclusions and Discussion Several
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Experiences During the Drought Peri
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During the years 2007 and 2008, for
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Figure 6. Management simulation mod
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Figure 9. Irrigation diversions in
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References Andreu, J., Ferrer Polo,
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Table 2. MERIS spectral bands MDS N
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Figure 6. Spanish NSDIE during 2008
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References Gu, Y., J. F. Brown, J.
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Agricultural Drought Indices: Summa
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Table 1. Agricultural drought indic
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Accumulated Rainfall Deficits In an
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Heim (2002) notes that as recently
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each calendar day in the base perio
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The purpose of the climatic charact
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to compare moisture conditions at d
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Table 6. Overview of different drou
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have been adopted because of the av
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TCI = 100 (T max - T) / (T max - T
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Figure 1. The February 8, 2011, US
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8) There is a universal interest in
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Mavromatis, T. 2007. Drought index
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