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Charalampos Skoulikaris   Dr.  University Educator/Researcher 
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Charalampos Skoulikaris published an article in March 2019.
Research Keywords & Expertise
0 water, hydrology, hydroinformatics, GIS
Top co-authors
Georgia Lazoglou

8 shared publications

Department of Meteorology Climatology, School of Geology Aristotle, University of Thessaloniki, GR54124 Thessaloniki,Greece

Christina Anagnostopoulou

3 shared publications

Department of Meteorology Climatology, School of Geology Aristotle, University of Thessaloniki, GR54124 Thessaloniki,Greece

Konstantia Tolika

2 shared publications

Department of Meteorology Climatology, School of Geology Aristotle, University of Thessaloniki, GR54124 Thessaloniki,Greece

3
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9
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Publication Record
Distribution of Articles published per year 

Total number of journals
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3
 
Publications
Article 1 Read 0 Citations Bias Correction of Climate Model’s Precipitation Using the Copula Method and Its Application in River Basin Simulation Georgia Lazoglou, Christina Anagnostopoulou, Charalampos Sko... Published: 22 March 2019
Water, doi: 10.3390/w11030600
DOI See at publisher website ABS Show/hide abstract
During the last few decades, the utilization of the data from climate models in hydrological studies has increased as they can provide data in the regions that lack raw meteorological information. The data from climate models data often present biases compared to the observed data and consequently, several methods have been developed for correcting statistical biases. The present study uses the copula for modeling the dependence between the daily mean and total monthly precipitation using E-OBS data in the Mesta/Nestos river basin in order to use this relationship for the bias correction of the MPI climate model monthly precipitation. Additionally, both the non-corrected and bias corrected data are tested as they are used as the inputs to a spatial distributed hydrological model for simulating the basin runoff. The results showed that the MPI model significantly overestimates the E-OBS data while the differences are reduced sufficiently after the bias correction. The outputs from the hydrological models were proven to coincide with the precipitation analysis results and hence, the simulated discharges in the case of copula corrected data present an increased correlation with the observed flows.
Article 6 Reads 2 Citations River Basin Management Plans as a tool for sustainable transboundary river basins’ management Charalampos Skoulikaris, Antigoni Zafirakou Published: 07 January 2019
Environmental Science and Pollution Research, doi: 10.1007/s11356-019-04122-4
DOI See at publisher website
CONFERENCE-ARTICLE 13 Reads 0 Citations Copula bias correction for extreme precipitation in re-analysis data over a Greek catchment Georgia Lazoglou, Christina Anagnostopoulou, Charalampos Sko... Published: 15 November 2018
Proceedings, doi: 10.3390/ECWS-3-05817
DOI See at publisher website ABS Show/hide abstract

The projection of extreme precipitation events with higher accuracy and reliability, which engender severe socioeconomic impacts more frequently, is considered a priority research topic in the scientific community. Although large scale initiatives for monitoring meteorological and hydrological variables exist, the lack of data is still evident particularly in regions with complex topographic characteristics. The latter results in the use of reanalysis data or data derived from Regional Climate Models, however both datasets are biased to the observations resulting in non-accurate results in hydrological studies. The current research presents a newly developed statistical method for the bias correction of the maximum rainfall amount at watershed scale. In particular, the proposed approach necessitates the coupling of a spatial distribution method, namely Thiessen polygons, with a multivariate probabilistic distribution method, namely copulas, for the bias correction of the maximum precipitation. The case study area is the Nestos river basin where the several extreme episodes that have been recorded have direct impacts to the regional agricultural economy. Thus, using daily data by three monitoring stations and daily reanalysis precipitation values from the grids closest to these stations, the results demonstrated that the bias corrected maximum precipitation totals (greater than 90%) is much closer to the real max precipitation totals, while the respective reanalysis value underestimates the real precipitation totals. The overall improvement of the outputs, shows that the proposed Thiessen-copula method could constitute a significant asset to hydrologic simulations.

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