Agricultural Drought Indices - US Department of Agriculture

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Hoogenboom, G., J.W. Jones, P.W. Wilkens, W.D. Batchelor, W.T. Bowen, L.A. Hunt, N. Pickering, U. Singh, D.C. Godwin, B. Baer, K.J. Boote, J.T. Ritchie, and J.W. White. 1994. Crop models. In DSSAT v3, 2(2) (G. Y. Tsuji, G. Uehara, and S.Salas, eds.). University of Hawaii, Honolulu, Hawaii. Ibarra, R. and T. Hewitt. 1999. Utilizing crop insurance to reduce production risk. Institute of Food and Agricultural Sciences (IFAS Publication FE-198). Florida Cooperative Extension Service, Gainesville, Florida. IBSNAT. 1989. Decision Support System for Agrotechnology Transfer (DSSAT) v2.1. Department of Agronomy and Soil Science, University of Hawaii, Honolulu, Hawaii. Jaynes, D.B. and T.S. Colvin. 1997. Spatiotemporal variability of corn and soybean yield. Agronomy Journal 89(1):30-37. Jones, J.W. 1993. Decision support systems for agricultural development. Pages 459-472 in Proceedings, Systems Approach for Agricultural Development (F.W.T. Penning de Vries, P.S. Teng, and K. Metselaar, eds.). Kluwer Academic Press, Dordrecht, The Netherlands. Jones, C.A. and J.R. Kiniry. 1986. CERES-Maize: A Simulation Model of Maize Growth and Development. Texas A&M University Press, College Station, Texas, pp.103-125. Maidment, D.R. 1993. GIS and hydrologic modeling. Pages 147-167 in Environmental Modeling with GIS (M.F. Goodchild, B.O. Parks, and L.T. Steyaert, eds.). Oxford University Press, New York. Maracchi, G., V. Perarnaoud, and A.D. Kleschenko. 2000. Applications of geographic information systems and remote sensing in agrometeorology. Agricultural and Forest Meteorology 103:119-136. McCown, R.L., G.L. Hammer, J.N.G. Hargreaves, D.P. Holzworth, and D.M. Freebairn. 1996. APSIM: A novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems 50:(3)255-271. Mulla, D.J. and J.S. Schepers. 1997. Key processes and properties for site-specific soil and crop management. Pages 1-8 in The State of Site Specific Management for Agriculture (F.J. Pierce and E.J. Sadler, eds.). ASA/CSSA/SSSA, Madison, Wisconsin. Racsko, P., L. Szeidl, and M.A. Semenov. 1991. A serial approach to local stochastic weather models. Ecological Modeling 57:27-41. Raes, D., P. Steduto, T.C. Hsiao, E. Fereres, and L. Heng. 2008. AquaCrop Calculation Procedure, Prototype Version 2.3a. FAO, Rome, Italy, 64p. Rao, M.N., D.A. Waits, and M.L. Neilsen. 2000. A GIS-based modeling approach for implementation of sustainable farm management practices. Environmental Modelling and Software 15:745-753. Richardson, C.W. 1981. Stochastic simulation of daily precipitation, temperature and solar radiation. Water Resources Research 17:182-190. Richardson, C.W. and D.A. Wright. 1984. WGEN: A model for generating daily weather variables. U.S. Department of Agriculture, Agricultural Research Service, ARS-8, 83pp. Ritchie, J.T. 1985. A user-oriented model of the soil water balance in wheat. Pages 293-305 in Wheat Growth and Modelling (W. Day and R.K. Atkins, eds.). Plenum Publishing Corporation, NATO-ASI Series. Ritchie, J.T. 1986. CERES-Wheat: A General Documentation. USDA–ARS. Grassland, Soil and Water Resource Laboratory, Temple, Texas. Ritchie, J.T. and S. Otter. 1985. Description and performance of CERES-Wheat: A user oriented wheat yield model. ARS Wheat Yield Project, ARS–38. National Technical Information Service, Springfield, Virginia, pp.159–175. Satti, S.R. 2002. GWRAPPS: A GIS-based decision support system for agricultural water resources management. Master of Science Thesis. University of Florida, Gainesville, Florida. Semenov, M.A., R.J. Brooks, E.M. Barrow, and C.W. Richardson. 1998. Comparison of WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Research 10:95-107. Sinclair, T.R. and R.C. Muchow. 2001. System analysis of plant traits to increase grain yield on limited water supplies. Agronomy Journal 93:263-270. 147
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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
Page 47 and 48:
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.
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
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: management. In research, however, p
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 and 184: Accumulated Rainfall Deficits In an
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
Page 205:
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