Analyzing spatial patterns of meteorological drought using Standardized Precipitation Index

Meteorological Applications (Impact Factor: 1.34). 12/2007; 14(4):329 - 336. DOI: 10.1002/met.33


Drought is a slow-onset, creeping natural hazard and a recurrent phenomenon in the arid and semi-arid regions of Gujarat (India). In Asia, the standardized precipitation index (SPI) has gained wider acceptance in the detection and the estimation of the intensity, magnitude and spatial extent of droughts. The main advantage of the SPI, in comparison with other indices, is that the SPI enables both determination of drought conditions at different time scales and monitoring of different drought types. This index captures the accumulated deficit (SPI < 0) or surplus (SPI > 0) of precipitation over a specified period, and provides a normalized measure (i.e. spatially invariant Z score) of relative precipitation anomalies at multiple time scales. In the present study, monthly time series of rainfall data (1981–2003) from 160 stations were used to derive SPI, particularly at 3-month time scales. This 3-month SPI was interpolated to depict spatial patterns of meteorological drought and its severity during typical drought and wet years. Correlation analysis was also done to evaluate usefulness of SPI to quantify effects of drought on food grain productivity. Further, time series of SPI were exploited to assess the drought risk in Gujarat. Copyright © 2007 Royal Meteorological Society

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Available from: Patel N. R., Oct 03, 2014
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    • "The importance of climate for crop production is well recognized, and an increasing number of studies have also examined this relationship in South Asia (Parthasarathy et al., 1988; Kumar and Panu, 1997; Zubair, 2002; Selvaraju , 2003; Krishna Kumar et al., 2004; Patel et al., 2007; Welch et al., 2010; Subash and Ram Mohan, 2011; Sarker et al., 2012; Osborne and Wheeler, 2013). Many of the studies have however been limited to the national scale (Parthasarathy et al., 1988; Selvaraju, 2003; Krishna Kumar et al., 2004; Sarker et al., 2012; Osborne and Wheeler, 2013) or to specific provinces or states (Kumar and Panu, 1997; Patel et al., 2007) and focused only on monsoon season precipitation. In this article, we aim to contribute to the discussion on the use of drought indices in assessing climate impacts on rice yield variation by conducting a spatially explicit analysis for the GBM region. "
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    ABSTRACT: Climate variability has major impacts on crop yields and food production in South Asia. The spatial differences of the impact are not, however, well understood. In this study, we thus aim to analyse the spatio-temporal relationship between precipitation and rice yields in the Ganges–Brahmaputra–Meghna region. The effects of rainfall variation on yields were analysed with regression models using the Standardized Precipitation Index (SPI) as an explanatory variable. Our results indicate that in large part of the study region, a strong relationship between precipitation and rice yields exists and the SPI at various lags chosen as the predictor variable performed well in describing the inter-annual yield variability. However, the study demonstrated large spatial variations in the strength of this relationship or optionally in the suitability of the chosen methodology for investigating it. In the mid-plains of the Ganges, which represent very important agricultural areas, precipitation variability has a strong impact on rice yields, while in downstream Ganges as well as in Brahmaputra, where precipitation is more abundant, the relationship was less pronounced. Where the performance of the regression models was weaker, it is likely that yield variation depended on other factors such as management practices or on other climate factors such as temperature. The results further showed that the SPI at 1, 3, 6 and 12 month lags calculated for the monsoon time (June–October) are most commonly the best at explaining the rice yield variability. The SPI can thus be considered a very useful predictor of rice yield variability in some parts of the study region, demonstrating that they could be used for agricultural applications and policy decisions to improve the region's food security.
    Full-text · Article · Sep 2015 · International Journal of Climatology
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    • "On the other hand, the Standardized Precipitation Index (SPI) is a very useful tool as well as an index to monitor meteorological drought which is exclusively based on precipitation data. According to McKee et al. (1993, 1995), SPI gives an easy and flexible way to monitor drought at a different scale (Table 2) ranging from near normal (À0.99) to extreme drought condition (<À2.0) and it has been recommended by various studies for its suitability to estimate meteorological drought at different time lag (Guttman, 1998; Patel et al., 2007; Kumar et al., 2009, 2012; Quiring and Ganesh, 2010; Poonia and Rao, 2012; Dutta et al., 2013; Zhang and Jia, 2013; Belayneh et al., 2014). "
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    ABSTRACT: Owing to its severe effect on productivity of rain-fed crops and indirect effect on employment as well as per capita income, agricultural drought has become a prime concern worldwide. The occurrence of drought is mainly a climatic phenomenon which cannot be eliminated. However, its effects can be reduced if actual spatio-temporal information related to crop status is available to the decision makers. The present study attempts to assess the efficiency of remote sensing and GIS techniques for monitoring the spatio-temporal extent of agricultural drought. In the present study, NOAA-AVHRR NDVI data were used for monitoring agricultural drought through NDVI based Vegetation Condition Index. VCI was calculated for whole Rajasthan using the long term NDVI images which reveals the occurrence of drought related crop stress during the year 2002. The VCI values of normal (2003) and drought (2002) year were compared with meteorological based Standardized Precipitation Index (SPI), Rainfall Anomaly Index and Yield Anomaly Index and a good agreement was found among them. The correlation coefficient between VCI and yield of major rain-fed crops (r > 0.75) also supports the efficiency of this remote sensing derived index for assessing agricultural drought.
    Full-text · Article · Apr 2015 · Egyptian Journal of Remote Sensing and Space Science
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    • "The SPI has the advantages of being easy to calculate, having modest data requirements and being independent of the magnitude of mean rainfall and hence comparable over a range of climatic zones (Agnew, 2000). Patel et al. (2007) indicated that the SPI at a 3-month timescale is effective in capturing seasonal drought patterns over space and time in Gujarat. In their study, Pai et al. (2011) found that the SPI is more suitable than the percent of normal precipitation for district-wise drought monitoring over India during the southwest monsoon season. "
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    ABSTRACT: Drought is a climate based natural hazard occurs in almost all climatic zones irrespective of high or low rainfall areas. Generally, drought is considered as a dry weather condition that lasts over several weeks to months,with no or little rainfall. It happens due to imbalance in water availability. There are several types of drought that can be defined from various perspectives such as agricultural, hydrological, meteorological and socio -economical. Meteorological drought generally defined as a condition, where the annual precipitation is less than the normal for a prolonged period (month, season or year) over an area. Among the several proposed meteorological drought indices, the Standardized Precipitation Index (SPI) is a popular drought index, solely based on precipitation and it measures how much precipitation for a given period of time has deviated from historically observed precipitation of an area. Technically, SPI represents the number of standard deviation of the observed value deviated from the long-term mean, for a normally distributed random variable i.e. Z -variate. SPI can estimate the drought features with different time scales (1, 3, 6, 9, 12, 24 and 48 months), it has been broadly applied to analyze different aspects of droughts. Normally, the ‘‘drought’’ part of the SPI range is arbitrary split into moderately dry ( -1.0 > SPI > -1.49), severely dry (-1.5 > SPI > -1.99) and extremely dry conditions (SPI < -2.0). The present study attempts to assess the teorological drought response to extreme climate condition. Long-term rainfall data (2002-2013) have been taken for Standardized Precipitation Index (SPI) analysis. A detailed spatio -temporal analysis of drought dynamics was carried out using the SPI, which revealed the occurrence of a severe drought in Bundelkhand region during several years. Keywords: Rainfall, Standardized Precipitation Index (SPI), Meteorological Drought, Remote Sensing, GIS.
    Full-text · Article · Jan 2015
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