Large-Scale Assessment of Drought Variability Based on NCEP/NCAR and ERA-40 Re-Analyses

Isabella Bordi, Klaus Fraedrich, Marcello Petitta and Alfonso Sutera

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2006, vol. 20, issue 6, 899-915

Abstract: The impacts of different spatial resolutions and different data assimilation schemes of the available re-analysis data sets (NCEP/NCAR and ERA-40) on the assessment of drought variability are analysed. Particular attention has been devoted to the analysis of the possible existence of a linear trend in the climatic signal. The long-term aspects of drought over the globe during the last forty years have been evaluated by computing the Standardized Precipitation Index (SPI) on 24-month time scale. The SPI, in fact, seems to be a useful tool for monitoring dry and wet periods on multiple time scales and comparing climatic conditions of areas governed by different hydrological regimes. To unveil possible discrepancies between the analyses carried out with the two data sets, we studied the leading space-time variability of drought by applying the principal component analysis (PCA) to the SPI time series. Results suggest that on the global scale, the two re-analyses agree in their first principal component score, but not in the associated loading: both re-analyses capture a linear trend, though the areas where this feature should be most likely observed are not uniquely identified by the two data sets. Moreover, while the ERA-40 unveils the presence of a weak net “global” trend towards wet conditions, the NCEP/NCAR re-analysis suggests that the areas in the world characterised by positive/negative trends balance to zero. At large regional scale, a good agreement of the results with those obtained from the observations are found for the United Stated, while for the European sector the two re-analyses show remarkable differences both in the first loading and in representing the timing of the wet and dry periods. Also for these areas a linear trend, superposed on other short-term fluctuations, is detectable in the first principal component of the SPI field. Copyright Springer Science+Business Media, Inc. 2006

Keywords: drought assessment; re-analysis data; large-scale variability; standardized precipitation index; principal component analysis (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1007/s11269-005-9013-z

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