Spatial and temporal variability of precipitation and drought in Portugal

Martins, Diogo; Raziei, Tayeb; Paulo, Ana A.; Pereira, L. S.

doi:10.5194/nhess-12-1493-2012

Cited by 87 publications

(59 citation statements)

References 30 publications

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“…The first component, with the highest loading in the north, explains 46 % of the total variance and the second one, related to the south, explains 37.3 %. Both components explain 83.3 % of the total variance (Martins et al, 2012). The K-means clustering of the loadings of these two principal components shows three significantly different regions within Portugal relative to drought variability, thus separating the north from the centre and the south sub-regions (Fig.…”

Section: Spi Spatial and Temporal Variability -Drought Clusteringmentioning

confidence: 99%

“…The Standardized Precipitation Index, SPI (McKee et al, 1993(McKee et al, , 1995, which is often used for identification of drought events and to evaluate their severity through well-defined drought classes, is adopted in this study following results obtained in previous studies characterizing droughts in Portugal Santos et al, 2010;Martins et al, 2012).…”

Section: Base Informationmentioning

confidence: 99%

“…These data sets, previously used in other studies (e.g. Paulo et al, 2012;Martins et al, 2012;Raziei et al, 2014) were completed with techniques described by Rosa et al (2010). Series retained did not have more than 250 gaps and all series covered the referred period of 66 years.…”

Section: Base Informationmentioning

confidence: 99%

“…For the Portuguese conditions, where a dry summer period of nearly 6 months occurs, droughts impacting the hydrologic regime are better assessed using the 12-month timescale Santos et al, 2010). Hence, previous studies on drought variability and drought class transitions were performed with the SPI 12-month (Moreira et al, 2006(Moreira et al, , 2012Martins et al, 2012). Therefore, time series of SPI with a 12-month timescale (SPI-12) were computed from the 74 monthly precipitation time series.…”

Section: Base Informationmentioning

confidence: 99%

“…This method allows reduction of the original dimensionality of the data through forming new uncorrelated variables that are linear combinations of the original ones and explain a large portion of the total variance (Sharma, 1996). Here, the PCA was applied in the S-mode (Richman, 1986), using the 74 SPI-12 time series in order to capture the drought variability which shows the co-variability between stations considering its time variability (Martins et al, 2012). Furthermore, to identify regions with different drought variability, the varimax rotation was used (Raziei et al, 2009).…”

Section: Base Informationmentioning

confidence: 99%

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Assessing drought cycles in SPI time series using a Fourier analysis

Moreira

Martins

Pereira

2015

Nat. Hazards Earth Syst. Sci.

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Abstract. In this study, drought in Portugal was assessed using 74 time series of Standardized Precipitation Index (SPI) with a 12-month timescale and 66 years length. A clustering analysis on the SPI Principal Components loadings was performed in order to find regions where SPI drought characteristics are similar. A Fourier analysis was then applied to the SPI time series considering one SPI value per year relative to every month. The analysis focused on the December SPI time series grouped in each of the three identified clusters to investigate the existence of cycles that could be related to the return periods of droughts. The most frequent significant cycles in each of the three clusters were identified and analysed for December and the other months. Results for December show that drought periodicities vary among the three clusters, pointing to a 6-year cycle across the country and a 9.4-year cycle in central and southern Portugal. Both these cycles likely show the influence of the North Atlantic Oscillation (NAO) on the occurrence and severity of droughts in Portugal. Relative to other months it was observed that cycles varied according to the common occurrence of precipitation: for the rainy months -November, December and January -cycles are similar to those for December; for the dry months -May to September -where the lack of precipitation masks the occurrence of drought, the dominant cycles are of short duration and cannot be related to the NAO or other large circulation indices to explain drought variability; for the transition months -February, March, April and October -6-year and 3-year cycles were identified, the latter being more strongly apparent in central and southern Portugal. NAO influence is again identified relative to the 6-year cycles. The short cycles are apparently associated with positive SPI, thus with wetness, not drought. Overall, results confirm the importance of the NAO as a driving force for dry and wet periods.

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Section: Spi Spatial and Temporal Variability -Drought Clusteringmentioning

confidence: 99%

Section: Base Informationmentioning

confidence: 99%

Section: Base Informationmentioning

confidence: 99%

Section: Base Informationmentioning

confidence: 99%

Section: Base Informationmentioning

confidence: 99%

See 3 more Smart Citations

Assessing drought cycles in SPI time series using a Fourier analysis

Moreira

Martins

Pereira

2015

Nat. Hazards Earth Syst. Sci.

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Regionalization of extreme rainfall in India

Bharath

Srinivas

2014

Intl Journal of Climatology

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Regionalization of extreme rainfall is useful for various applications in hydro‐meteorology. There is dearth of regionalization studies on extreme rainfall in India. In this perspective, a set of 25 regions that are homogeneous in 1‐, 2‐, 3‐, 4‐ and 5‐day extreme rainfall is delineated based on seasonality measure of extreme rainfall and location indicators (latitude, longitude and altitude) by using global fuzzy c‐means (GFCM) cluster analysis. The regions are validated for homogeneity in L‐moment framework. One of the applications of the regions is in arriving at quantile estimates of extreme rainfall at sparsely gauged/ungauged locations using options such as regional frequency analysis (RFA). The RFA involves use of rainfall‐related information from gauged sites in a region as the basis to estimate quantiles of extreme rainfall for target locations that resemble the region in terms of rainfall characteristics. A procedure for RFA based on GFCM‐delineated regions is presented and its effectiveness is evaluated by leave‐one‐out cross validation. Error in quantile estimates for ungauged sites is compared with that resulting from the use of region‐of‐influence (ROI) approach that forms site‐specific regions exclusively for quantile estimation. Results indicate that error in quantile estimates based on GFCM regions and ROI are fairly close, and neither of them is consistent in yielding the least error over all the sites. The cluster analysis approach was effective in reducing the number of regions to be delineated for RFA.

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Changes in temporal distribution of precipitation in a Mediterranean area (Tuscany, Italy) 1955–2013

Bartolini

Grifoni

Magno

et al. 2017

Intl Journal of Climatology

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This study investigates changes in precipitation regimes in Tuscany (central Italy) over the 1955–2013 period, by using the precipitation concentration index (PCI) and rainfall amount. The PCI is a dimensionless indicator of the temporal concentration of the precipitation. Different statistical methods were applied over the whole period and two independent sub‐periods (1955–1984 and 1985–2013). A p‐value <0.05 was chosen as level of significance for all the statistical tests. Main results, coming from 35 weather stations, did not show statistically significant trends of the PCI on annual basis and during winter. In spring and summer statistically significant increases were found, respectively, in two stations in northern Apennine (+1.6/50 years) and in four stations in central southern inner areas (+2.3/50 years). In autumn one station in northern Apennine showed a significant decrease (−1.9/50 years). Sub‐periods analysis mainly showed higher values of PCI in the recent sub‐period on annual scale (statistically significant along the coast), in spring (significant in northwest and northern Apennine), in summer (significant in central southern inner areas) and during winter (not significant). Considering the year‐to‐year variability of the annual PCI, statistically significant increasing trends were found in the northern part of Tuscany, while decreasing tendencies were observed in the southern one. Concerning annual rainfall, six stations showed statistically significant decreases (−19%/50 years). On monthly basis, two stations exhibited significant decreasing trends in March and two in December (respectively −48% and −37%/50 years). Two significant decreases were observed in both June and July (respectively −53% and −63%/50 years). One station in northern coast showed instead a significant increase in September (+72%/50 years). This investigation represents a further tile in the larger mosaic of analysis of climate change and its different impact on precipitation regime at regional level and supports studies concerning water management in the areas characterized by strong seasonality.

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Spatial and temporal variability of precipitation and drought in Portugal

Cited by 87 publications

References 30 publications

Assessing drought cycles in SPI time series using a Fourier analysis

Assessing drought cycles in SPI time series using a Fourier analysis

Regionalization of extreme rainfall in India

Changes in temporal distribution of precipitation in a Mediterranean area (Tuscany, Italy) 1955–2013

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