An analysis of drought in western Iran from 1966 to 2000 is presented using monthly precipitation data observed at 140 gauges uniformly distributed over the area. Drought conditions have been assessed by means of the Standardized Precipitation Index (SPI). To study the long-term drought variability the principal component analysis was applied to the SPI field computed on 12-month time scale. The analysis shows that applying an orthogonal rotation to the first two principal component patterns, two distinct sub-regions having different climatic variability may be identified. Results have been compared to those obtained for the largescale using re-analysis data suggesting a satisfactory agreement. Furthermore, the extension of the large-scale analysis to a longer period shows that the spatial patterns and the associated time variability of drought are subjected to noticeable changes. Finally, the relationship between hydrological droughts in the two sub-regions and El Niño Southern Oscillation events has been investigated finding that there is not clear evidence for a link between the two phenomena.

An analysis, based on rain gauge observations, of the time-space variability of dry and wet periods during the last fifty years in eastern China is presented. The Standardized Precipitation Index (SPI) is used to assess the climatic conditions of the area, and principal component analysis (PCA) is applied to capture the pattern of co-variability of the index at different stations. Results suggest that the northern part of eastern China is experiencing dry conditions more frequently from the 1970s onwards indicated by a negative trend in the SPI time series. Long-term fluctuations characterize the SPI signal and contribute to the power spectrum variance at periods ranging from interdecadal to interannual time scales, that is respectively, 24 years and from 16 to 4-3.7 years. These periodic components provide a useful resource for long-term predictability of dry and wet periods in eastern China

Abstract. Linear and nonlinear trends of drought and wetness are analysed in terms of the gridded Standardized Precipitation Index (SPI) determined from monthly precipitation in Europe (NCEP/NCAR). In characterizing the meteorological and hydrological aspects, the index is computed on a seasonal and on a bi-annual time scale. Two datasets are compared: one from 1949 to 1997 and the other one includes the update of the last decade (to February 2009). The following results are noted: (i) time series of drought and wetness area coverage (number of grid points above/below the severity threshold) show a remarkable linear trend until about the end of the last century, which is reversed in the last (update) decade. This recent trend reversal is an indication of a nonlinear trend, which is more pronounced on the hydrological time scale. (ii) A nonlinear trend analysis is performed based on the time series of the principal component (PC) associated to the first spatial SPI-eigenvector after embedding it in a time delay coordinate system using a sliding window of 70 months (singular spectrum analysis). Nonlinearity appears as a clear feature on the hydrological time scale. (iii) The first spatial EOF-patterns of the shorter and the longer (updated) SPI time series fields show similar structure. An inspection of the SPI time behaviour at selected grid points illustrates the spatial variability of the detected trends.

ABSTRACT:The relationships between large-scale atmospheric circulation types and seasonal regimes of daily precipitation over Iran are assessed using daily precipitation from a high-resolution gridded dataset provided by the Asian Precipitation-Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) Project. Regional spatial modes of daily precipitation variability were identified by S-mode Principal Component Analysis (PCA) with Varimax rotation, applied to the subset of days when at least 10% of all grid-points over Iran received precipitation ≥5 mm. The study refers to the period 1961-2004 and is carried out for each season (excluding summer) separately. To characterize the dynamical features associated with each regional precipitation regime (PR), composites of daily atmospheric fields are computed by only averaging days with rotated PCA scores ≥1.5 (strong positive phase). In autumn and winter, Iran is divided into five PRs, while four PRs are identified in spring. Results suggest that the spatial distribution of precipitation over Iran is largely governed by the geographical position of both the mid-tropospheric trough over the Middle East and the Arabian anticyclone. In fact, in almost all PRs, the trough, as a pre-conditioning factor, leads to regional-scale ascending motions, whereas the Arabian anticyclone induces low-tropospheric moisture transports from southern water bodies into the cyclonic systems near Iran, triggering rain-generating conditions.

Linear and nonlinear trends of drought and wetness are analysed in terms of the gridded Standardized Precipitation Index (SPI) determined from monthly precipitation in Europe (NCEP/NCAR). In characterizing the meteorological and hydrological aspects, the index is computed on a seasonal and on a biannual time scale. Two datasets are compared: one from 1949 to 1997 and the other one includes the update of the last decade (to February 2009). The following results are noted: (i) time series of drought and wetness area coverage (number of grid points above/below the severity threshold) show a remarkable linear trend until about the end of the last century, which is reversed in the last (update) decade. This recent trend reversal is an indication of a non-linear trend, which is more pronounced on the hydrological time scale. (ii) A nonlinear trend analysis is performed based on the time series of the principal component (PC) associated to the first spatial SPI-eigenvector after embedding it in a time delay coordinate system using a sliding window of 70 months (singular spectrum analysis). Nonlinearity appears as a clear feature on the hydrological time scale. (iii) The first spatial EOF-patterns of the shorter and the longer (updated) SPI time series fields show similar structure. An inspection of the SPI time behaviour at selected grid points illustrates the spatial variability of the detected trends.