Agricultural Drought Indices - US Department of Agriculture

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In this chapter, methodologies are presented for the operational estimation of indices at the regional scale related to water deficit in soils and water stress of vegetation, integrating remote sensing and meteorological data. Methodology The estimation of water stress indices from remote sensing has been studied by several authors (Moran et al. 1994, Wang 2001, Fensholt and Sandholt 2003). The classic method for the monitoring and evaluation of water stress of vegetation is the combined use of surface land temperature (LST) and multispectral reflectance of land, with the index of normalized difference vegetation index (NDVI) being derived from them. The range of values defined by LST vs. NDVI gives information about water stress of vegetation and soil moisture conditions (Figure 2). Figure 2. LST-NDVI space for Segura River basin. Time period: September 14-29, 2001. On the base triangular space, several spatio-temporal indices have been proposed. Among them, the Temperature Vegetation Dryness Index (TVDI) developed by Sandholt et al. (2002) to evaluate the state of soil moisture is defined as TVDI where: LST − LST NDVIi NDVIi.min = (1) LSTNDVIi.max − LSTNDVIi.min LST NDVIi max = a + bNDVIi LST NDVIi = a′ + b′ NDVIi . (2) . min (3) 151
Figure 2 represents the regression lines LSTNDVI max and LSTNDVI min , which define the “dry edge” and “wet edge” of the triangle, respectively. The dry edge represents the rate of minimum evapotranspiration (dry areas), while the wet edge gives the maximum value (areas without water restrictions). Then, the Vegetation Temperature Condition Index proposed by Wang et al. (2001), and widely applied by Wan et al. (2004), for monitoring droughts, is defined as VTCI LST NDVIi.max NDVIi = (4) LST NDVIi.max + LST − LST NDVIi.min Finally, the Water Deficit Index (WDI), proposed by Moran et al. (1994), is considered. The WDI is derived from the interpretation of the trapezoid based on the relationship between the difference of surface temperature and air temperature and from the vegetation index. In the present work, the WDI approach proposed by Verstraeten (2001; in Ranjan 2006) is applied, since it only requires Ta (air temperature) as input. This method can be applied at a regional scale, in near to real time. It is useful for surfaces completely covered by vegetation, and can also be applied to mixed surfaces (covered by vegetation and with bare land). WDI quantifies the relative rate of latent heat flux (or evapotranspiration), presenting a value of 0 for surfaces completely wet (evapotranspiration only limited by atmospheric demand) and 1 for dry surfaces where there is no latent heat flux. Following Verstraeten (2001; in Ranjan 2006), WDI ∆LST NDVIi.min NDVIi = (5) ∆LST NDVIi.min − ∆LST − ∆LST NDVIi.max where the equations of the edges of the trapezoid (or space) are defined as the regression lines that identify the minimum and maximum actual evapotranspiration rates: Min ET real (dry edge): Max ET real (wet edge): ∆LST NDVIi max = a + bNDVIi ∆LST NDVIi = a′ + b′ NDVIi . (6) . min (7) Processing of Remote Sensing and Meteorological Data The methodology for the proposed monitoring of drought conditions is based on surface reflectance data and thermal properties derived from the TERRA MODIS (Moderate Resolution Imaging Spectroradiometer) sensor, and from meteorological information (air temperature). The processing was carried out by applying the computational system SORPRESA (García et al. 2007), developed below GRASS GIS. Compositions of 8 days of satellite images for surface temperature, and 16 days for NDVI, both with 1-km spatial resolution (products MOD11A2 and MOD13A2, respectively), were used. In the generation of LST vs. NDVI and (LST-Ta) vs. NDVI spaces, on the basis of 16-day compositions, maximum values of LST were considered. Several authors demonstrated that maximum values of LST represent the surface conditions in a realistic form (Cihlar et al. 1991). The temperature data corresponded to the 42 stations of the Agriculture Information Service (IMIDA, Murcia Region). Analysis of Results The analysis of LST-NDVI spaces, generated for a sufficiently long and representative time period, permits the detection of changes in soil moisture conditions for the whole analysis zone (Figure 3). In general, the dry edge always describes a negative correlation with NDVI, while the wet edge can present positive and negative correlations, which could be related to dry conditions. 152
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Agricultural Drought Indices Procee
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© World Meteorological Organizatio
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Agricultural Drought Indices Procee
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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
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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.
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Europe around the Iberian Peninsula
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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 and 154: management. In research, however, p
Page 155 and 156: Smajstrla, A.G. 1990. Technical Man
Page 157: Monitoring Regional Drought Conditi
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: Agricultural Drought Indices Procee
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