Rainfall-frequency mapping for Greece

Λουκάς, Αθανάσιος; Vasiliades, Lampros; Dalezios, N. R.; Domenikiotis, C.

doi:10.1016/s1464-1909(01)00067-3

Cited by 21 publications

(8 citation statements)

References 6 publications

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“…The clusters are illustrated on the map in Figure 6. This clustering is in good agreement with the classification of Loukas et al (2001) The iabel refer to the station number (listed in Table 1) and the number of the cluster in which that station is included. For exampie.…”

Section: Cluster Analysissupporting

confidence: 82%

Detection of drought events in Greece using daily precipitation

Kalamaras¹,

Michalopoulou

Byun

2010

Hydrology Research

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In this study a method proposed by Byun & Wilhite, which estimates drought severity and duration using daily precipitation values, is applied to data from stations at different locations in Greece. Subsequently, a series of indices is caiculated to facilitate the detection of drought events at these sites. The results provide insight into the trend of drought severity in the region.In addition, the seasonal distribution of days with moderate and severe drought is examined.Finally, the Hierarchical Cluster Anaiysis method is used to identify sites with similar drought features.

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Section: Cluster Analysissupporting

confidence: 82%

Detection of drought events in Greece using daily precipitation

Kalamaras¹,

Michalopoulou

Byun

2010

Hydrology Research

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“…The first one is based on the spatial interpolation of in-situ estimates in order to enable the construction of return level maps. Kohnová et al (2009) and Beguería and Vicente-Serrano (2006) for example interpolated the in-situ extremal distributions, whereas Loukas et al (2001) and Bois (2001, 2002) interpolated directly the in-situ (100-year) return levels. A comparison of different interpolation methods for mapping extreme precipitation can be found in Szolgay et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

Blanchet¹,

Lehning²

2010

Hydrol. Earth Syst. Sci.

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Abstract. For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965-1966 to 2007-2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

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“…This is a relatively large range for a country the size of Greece, and is probably due to the varying climatic conditions (Loukas et al 2001). Natural vegetation sites displayed a relatively larger optimum time-step (10-11 days).…”

Section: Differences At Various Vegetation Categoriesmentioning

confidence: 99%

An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data

Alexandridis

Gitas

Silleos

2008

International Journal of Remote Sensing

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Monitoring vegetation condition is an important issue in the Mediterranean region, in terms of both securing food and preventing fires. The recent abundance of remotely sensed data, such as the daily availability of MODIS imagery, raises the issue of appropriate temporal sampling when monitoring vegetation: undersampling may not accurately describe the phenomenon under consideration, whilst over-sampling would increase the cost of the project without additional benefit. The aim of this work is to estimate the optimum temporal resolution for vegetation monitoring on a nationwide scale using 250 m MODIS/Terra daily images and composites. Specific objectives include: (i) an investigation into the optimum temporal resolution for monitoring vegetation condition during the dry season on a nationwide scale using time-series analysis of Normalized Difference Vegetation Index, NDVI, datasets, (ii) an investigation into whether this temporal resolution differs between the two major vegetation categories of natural and managed vegetation, and (iii) a quality assessment of multi-temporal NDVI composites following the proposed optimum temporal resolution. A timeseries of daily NDVI data is developed for Greece using MODIS/Terra 250 m imagery. After smoothing to remove noise and cloud influence, it is subjected to temporal autocorrelation analysis, and its level of significance is the adopted objective function. In addition, NDVI composites are created at various temporal resolutions and compared using qualitative criteria. Results indicate that the proposed optimum temporal resolution is different for managed and natural vegetation. Finally, quality assessment of the multi-temporal NDVI composites reveals that compositing at the proposed optimum temporal resolution could derive products that are useful for operational monitoring of vegetation.

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Rainfall-frequency mapping for Greece

Cited by 21 publications

References 6 publications

Detection of drought events in Greece using daily precipitation

Detection of drought events in Greece using daily precipitation

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data

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