Agricultural Drought Indices - US Department of Agriculture

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A climate division is a larger, coarser area made up of smaller political units called counties. When the USDM was gearing up in the late 1990s, most data were available weekly and usually in climate division format, except for streamflow and some snow data, which were point-based in nature. The idea was to combine and weight several different indicators/indices that reflect short- and long-term drought across the United States by region or season. The same ranking percentile approach incorporated into the USDM is followed in setting the classes of the OBDI, on both the dry and wet side. The same percentiles classes are used for the wet side, as seen in the figures below. In fact, given the strong emphasis on snow and water resources in the western United States, a customized different weighting scheme is employed across the West, as seen by the magenta outline in Figure 7 below. The Short-Term Blend approximates drought-related impacts that respond to precipitation (and, secondarily, other factors) on time scales ranging from a few days to a few months, such as wildfire danger, non-irrigated agriculture, topsoil moisture, range and pasture conditions, and unregulated streamflow. The Long-Term Blend approximates drought-related impacts that respond to precipitation on time scales ranging from several months to a few years, such as reservoir stores, irrigated agriculture, groundwater levels, and well water depth. The following steps describe the make-up of and steps taken in creating the OBDI each week at NOAA’s Climate Prediction Center. • These products are generated using the Climate Prediction Center’s real-time daily and weekly climate division data, and the National Climatic Data Center’s monthly climate division data archive, back to 1932. • The indices used in the blends and their weights are as follows: o Short-term: 35% Palmer Z-Index; 25% 3-Month Precipitation; 20% 1-Month Precipitation; 13% Climate Prediction Center Soil Moisture Model; and 7% Palmer o (Modified) Drought Index. Long-term: 25% Palmer Hydrologic Drought Index; 20% 12-Month Precipitation; 20% 24-Month Precipitation; 15% 6-Month Precipitation; 10% 60-Month Precipitation; 10% Climate Prediction Center Soil Moisture Model. • All parameters are first rendered as percentiles with respect to 1932-2000 data using a percent rank method. Most parameters are ranked relative to the National Climatic Data Center’s historic climate division data for the current month, except for the Z-Index, which is rendered relative to all months on record (this introduces evaporative seasonality into the short-term blend). • For each blend, the averages of the percentile inputs are calculated, with each input weighted as described above. This yields a “weighted raw average” of the individual component percentiles for each blend. Then, each raw average is compared to its historic (1932-2000) distribution (these have been retrospectively generated from the climate division data archive). The real-time data are compared to ALL retrospective months, not just the current month, since the individual percentile inputs were each generated (for all but the Z-Index) relative to the history of the current month only. This allows for a more confident estimation of the percentile by using more data to define the historical array (12 times as many as if we assessed the blends’ raw weighted averages relative to the current month only). • The precipitation percentile inputs are generated in a somewhat unusual way, combining month-to-date numbers from the Climate Prediction Center with the National Climatic Data Center’s monthly totals for prior months. As daily precipitation totals for the current month are ingested into the x-month totals, an identical proportion of the monthly precipitation that fell during the first month in the x-month period is eliminated (e.g., to determine a 6-month precipitation total, from which a percentile will be calculated and incorporated into the blend, for the period ending September 21, 2002, we add the daily preliminary precipitation amounts for September 1-21 to the 6-month total for March-August 2002, then subtract 21/30 of the March total from the result, since 21/30 of September have been added). This 119
process (a) emulates natural cycles by adding precipitation as it falls but eliminating earlyperiod precipitation evenly over the course of a month, and (b) ensures that the data utilized in real time are as consistent with the historical array as possible. The near-realtime climate division precipitation data are biased in some areas relative to the final NCDC monthly archive, with wet near-real-time biases in the central and northern Rockies particularly extreme. The data are adjusted where appropriate at the end of each month, but the biases remain in the data for all precipitation time scales since the end of the previous calendar month. In addition, the biased near-real-time data are used in the Palmer Drought Index, the Palmer Hydrologic Drought Index, the Z-Index, and CPC’s modeled soil moisture data, and can remain in those calculations for several weeks. The next steps always involve looking at ways to continually improve these OBDI through regional/seasonal assessment and trial and error, along with efforts to move the OBDI to a gridded format in order to increase the spatial resolution down to the county scale. Figure 6. The Short-Term OBDI by climate division. Source: Climate Prediction Center. Figure 7. The Long-Term OBDI by climate division. Source: Climate Prediction Center. 120
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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
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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
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
Page 121 and 122: Incorporating a Composite Approach
Page 123 and 124: Figure 3. Daily gridded SPI by regi
Page 125: The North American Drought Monitor:
Page 129 and 130: Summary Given the complexity that d
Page 131 and 132: alance models cannot be applied for
Page 133 and 134: 60 50 40 Error 30 20 Systematic Err
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
Page 151 and 152: ackground. The system’s most impo
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
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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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