Agricultural Drought Indices - US Department of Agriculture

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Additionally, stress models can be used to ascertain crop yields as a function of soil moisture. This approach has been regularly used as a drought index in assessing exceptional agricultural droughts in winter-grain cropping regions. Application of the knowledge of soil moisture levels as well as application of crop simulation models to assist as an agricultural drought index has been developed in Queensland for winter wheat (Oz-Wheat; see Potgieter et al. 2006) and grain sorghum (Potgieter et al. 2005). An example of the latter is provided in Figure 2, where percentage soil recharge using the Agricultural Production Systems Simulation Model (APSIM) through a winter fallow is provided. Figure 2. Example of percentage soil recharge using the APSIM model through a winter fallow for sorghum-growing regions in eastern Australia. In this example, the excessively dark shaded regions are where soil moisture recharge is over 90% (a recent example for November 2010). The opposite would be expected with major drought occurrence (from A. Potgieter 2005; A. Potgieter, personal communication, November 2010). For drought assessments associated with livestock production, the computer program GRAZPLAN has been developed to aid the management of livestock grazing temperate pastures in southern Australia; the GrassGro decision support system (Moore et al. 1997) predicts pasture growth and quality in a form suitable for input to GrazFeed (Freer et al. 1997), which is the GRAZPLAN animal production model. Donnelly et al. (1998) used GrassGro to provide an index of agricultural drought, especially to assess and rank agricultural droughts at sites near Wellington in the Dubbo Rural Lands Protection Board district of central New South Wales. They were able to estimate pasture production and supplementary feed requirements for the survival of grazing animals in a way that might have practical use in monitoring specific types of agricultural drought. 75
The Value of Crop and Pasture Simulation Models in Drought Assessment in Australia—Use as an Agricultural Drought “Index” The value of crop simulation models to provide value in drought assessment has been a major theme in the implementation of drought policy in Australia. The primary argument for this approach and the simulation of agricultural system performance is that meteorological conditions alone do not sufficiently capture the important issues of rainfall intensity and distribution in relation to the state of the agricultural system. In the models, rainfall at one time of the year can be carried over through fallows to be used at other times of the year. Failure of planting rains at a critical time may downgrade otherwise average seasonal rainfall conditions in terms of production potential. “A crop-soil management systems simulation model can then integrate the meteorological and agricultural dimensions of the production system and assist in creation of more objective criteria for estimating the severity of drought for cereal production regions in Australia” (Keating and Meinke 1998). Governed largely by the demands of providing inputs into national drought policy (and the complexity of implementation of that drought policy in Australia), the use of crop modelling systems has provided a unique opportunity to capture a multitude of crop production inputs that include rainfall timing and effectiveness. In particular, APSIM is a “software environment which consists of models of elements of a system (referred to as modules) and a communications system (engine and module interfaces) that allow modules to share information” (McCown et al. 1996), which can be applied to a variety of dryland cereal production systems in Australia. Importantly, APSIM does this in a manner that can provide a modelled drought index for that particular environment. This approach appears to work especially well in agricultural drought assessment as it has been noted that APSIM can simulate systems that are characterized by both summer and winter cereals interspersed with fallow periods of varying lengths in a region that exhibits extremely high rainfall variability (Keating and Meinke 1998). APSIM can be applied to characterize major agricultural drought periods over the historical climate record, which can reach 100 years (Figure 3). Additionally, Keating and Meinke (1998) point out that APSIM is a “model of a point in space, which is used in this context to assess a phenomenon that is expressed on a regional basis,” an approach that is inherently valuable in agricultural drought assessment. In this approach, the application of APSIM to multiple “points” in a region is used to build up a regional agricultural drought assessment. The approach has been demonstrated to have excellent application for wheat and sorghum cropping systems, appears robust, and constitutes a substantial improvement over using meteorological conditions alone for agricultural drought assessments. One wonders why this concept of application of sophisticated modelling systems such as APSIM in agricultural drought assessment has not been pursued further in order to also create a more generalized but nevertheless robust “agricultural drought index” that could also have widespread global application. Figure 3. Characterizing wheat yield for the Wentworth Shire (county) in southern Australia over a 100-year period in order to assess the level of agricultural drought severity. The period questioned (denoted with “?”) is the period under examination in regard to the effectiveness of reduced rainfall in reducing actual crop yield and thus, provided an assessment of agricultural drought as opposed to meteorological (from Keating and Meinke 1998). 76
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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
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 and 70: Meteorological and Agricultural Dro
Page 71 and 72: Palmer Drought Severity Index Adapt
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: application of drought indices has
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 and 126: The North American Drought Monitor:
Page 127 and 128: process (a) emulates natural cycles
Page 129 and 130: Summary Given the complexity that d
Page 131 and 132: 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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