Analyzing spatial patterns of meteorological drought using standardized precipitation index
ABSTRACT 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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ABSTRACT: Spatial and time behaviours of rainfall shortage and excess are analysed for Catalonia (NE Spain) using a database obtained from 99 rain gauges with monthly totals collected from 1961 to 1990. The distribution of monthly amounts for each rain gauge is modelled by means of the gamma or Poisson-gamma distributions. Then, using an equiprobable transformation, monthly amounts described with these distributions are substituted by values given by the Standardized Precipitation Index (SPI), which follows a standardized normal distribution and provides a unique pluviometric scale. After that, principal component analysis (PCA) is applied to the set of monthly SPIs. A double regionalization of the 99 rain gauges, distinguishing between episodes of rainfall shortage and excess, is achieved by taking into account the rotated factor loadings (RFL) correlating rain gauges and principal components (PC). A time classification of rainfall shortage and excess episodes is also established, considering in this case the factor scores (FS) obtained after a PCA of variables based on monthly SPIs. The spatial regionalization achieved becomes a rough picture of the different topographic domains (Pyrenees, Pre-Pyrenees, Central Basin, Littoral and Pre-Littoral chains and Mediterranean coast), the climatic diversity of Catalonia being enhanced by these results. The time clustering suggests a quite complex behaviour of the rainfall shortage and excess episodes. Moreover, the spatial distribution of these time clusters is very disperse, in such a way that monthly shortage and excess sometimes affect the whole of Catalonia and sometimes just a small area. Besides results obtained from PCA and clustering algorithms, it is worth noticing that the severity of the episodes increases remarkably only for rainfall shortage. In addition, an analysis of the number of rain gauges affected by monthly shortage and excess shows an interesting fact: whereas the number of rain gauges associated with a shortage has an increasing tendency, a significant decreasing tendency for excess is detected in the period 1961–1990. Copyright © 2001 Royal Meteorological SocietyInternational Journal of Climatology 11/2001; 21(13):1669 - 1691. · 2.89 Impact Factor
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ABSTRACT: The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989–2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI–SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.Remote Sensing of Environment. 01/2003;
- Water International - WATER INT. 01/1985; 10(3):111-120.