Agricultural Drought Indices - US Department of Agriculture

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Water Balance—Tools for Integration in Agricultural Drought Indices Paulo Cesar Sentelhas AgMet Group, Department of Biosystems Eng., ESALQ, University of São Paulo Piracicaba, SP, Brazil Abstract An overview of water balance models for integration with agricultural drought indices is presented in this chapter. The basic concepts of water balance for agricultural purposes are discussed, focusing on the complexity of the models used to simulate the soil water content. The models are divided in two groups of complexity according to weather, soil, and plant data availability. Simple models are those based on the computation of rainfall and evapotranspiration, whereas complex models deal with soil water dynamics as a function of the interaction among soil-plant-atmosphere systems. The basic errors associated with these different models are discussed and a clear distinction is made between systematic and calibration errors. The water balance models currently most used for agricultural drought index calculation are presented, with an emphasis on the following models: Thornthwaite and Mather, MUSAG, Ritchie, and Gevaerd and Freitas. Some examples are used to demonstrate both the potential and the limitations of each one of these models and how their results, as actual evapotranspiration and soil moisture, are used to calculate agricultural drought indices. Finally, strengths, weaknesses, and limitations of the water balance models for drought monitoring are presented and discussed. Based on this review, it was concluded that water balance is an indispensable tool for determining agricultural drought indices, but choosing a given model will depend on input data availability, which is related to the complexity of the models and errors associated with them. Introduction Agriculture is an economic activity that depends on several factors to be successful. According to Diepen and Wall (1995), agricultural crop yield can be affected by factors such as abiotic (soil water, soil fertility, soil type, and weather); crop management (soil tillage, soil depth, fertilization, planting density, sowing date, weeding, pests, and disease control); land development (field size, terracing, drainage, and irrigation); socio-economic (infrastructure, market, prices, and costs); and catastrophic (flooding, frosts, hailstorms, and droughts). Among these factors, weather and catastrophes associated with it are the most significant, since they affect crop growth, development, yield, and quality. Several weather parameters such as temperature, relative humidity, solar radiation, wind speed, and rainfall can affect crop yield, but in general, temperature and rainfall are considered the most significant (Boken et al. 2005). For a crop well adapted to a region, the interannual variability of temperature can affect crop cycle duration, and when associated with solar radiation, relative humidity, and wind speed, it can also affect crop water use or crop evapotranspiration. Rainfall is the source of water for the soil, and consequently it affects water availability for crops, which depends on the physical properties of the soil and also on the balance between water inputs and outputs. This balance is called the water balance, and it is an accounting of all water that enters and leaves a given volume of soil over a specified period of time, influencing the soil water content. When the objective is to monitor and evaluate the effect of droughts on agriculture, the use of the water balance data is essential, since it allows the determination of how much water is effectively used by crops as a consequence of soil moisture and atmospheric demand. However, an agricultural drought is very complex to quantify, considering several aspects from the soil-plantatmosphere systems, such as soil water holding capacity, crop type, crop sensitivity to water stress, crop water requirement for each one of its phenological phases, and management practices. Moreover, taking all these aspects into account, the estimation of the water balance by models is very complex and difficult to use in an operational system. On the other hand, very simple water 123
alance models cannot be applied for all conditions because of their empirical limitations, requiring local calibration. The objective of this chapter is to present the basic concepts of the water balance process for agricultural purposes, and discuss the strengths, weaknesses, and limitations of some water balance models used for monitoring agricultural droughts based on indices. Water Balance Basic Concepts Water balance is conceptually the balance between the inputs and outputs of water of reservoirs, which can be a body of water, a catchment, or a volume of soil. For agricultural purposes, the water balance is normally determined for a volume of soil, following the principles of the law of conservation of mass: any change in the water content of the soil during a specific period of time is equal to the difference between the amount of water added to and withdrawn from the soil volume (Zhang et al. 2002). Figure 1 shows the main components of the water balance for an agricultural field, as presented by Zhang et al. (2002). The main water inputs are represented by rainfall (R) and capillary rise (CR), whereas soil evaporation (E), transpiration (T), and deep drainage (DD) are considered the main processes of water output. Surface run off (Ro) and subsurface (lateral) flow (SSF) occur both ways (in and out) and represent the horizontal flow of water when soil is saturated. The computation of all components of the water balance results in the change of soil water storage (∆SWS), which can be positive when the inputs are greater than the outputs or negative when the opposite is observed. The complete water balance equation is usually expressed as ±∆SWS = R + CR – E – T – DD ± Ro ± SSF (1) The water balance equation above has a clear conceptual basis and seems simple in principal, but in practice it is difficult to measure or estimate each of the components. Although rainfall (R) and evapotranspiration (E + T) can be relatively easy to measure or estimate, CR, DD, Ro, and SSF are more complex to determine, requiring site specific measurements of soil water movement. Transpiration Rainfall Soil evaporation Root Zone Deep drainage Infiltration ∆SWS Capilar rise Runoff Lateral flow Figure 1. Schematic representation of the water balance for a cultivated volume of soil. ∆SWS = soil water storage variation. Adapted from Zhang et al. (2002). 124
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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
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Soil Water Storage (SWS) September
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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 and 126: The North American Drought Monitor:
Page 127 and 128: process (a) emulates natural cycles
Page 129: Summary Given the complexity that d
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
Page 177 and 178: References Gu, Y., J. F. Brown, J.
Page 179 and 180: 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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