Automated Author ProfileEleutério, Julian Cardoso
Eleutério, Julian Cardoso
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
ABSTRACT Nonstationary trends in hydrological time series have aroused the interest of experts in recent decades. Consequently, conventional methods used for frequency analysis and quantification of risk associated with the occurrence of extreme climate events require adjustment. This study aims to perform the frequency analysis and the quantification of the risk of precipitation for a ten-day period in Tarauacá - Acre, Brazil. The results have shown that nonstationary Gumbel distribution with time-dependent location and scale best fits the observed data, thus allowing the most reliable measurements for risk of rare events, and determining reference quantiles associated with planning horizons or design of hydraulic structures.
Authors
- Moreira, José Genivaldo Do Vale ;
- Naghettini, Mauro ;
- Eleutério, Julian Cardoso
ABSTRACT Nonstationary trends in hydrological time series have aroused the interest of experts in recent decades. Consequently, conventional methods used for frequency analysis and quantification of risk associated with the occurrence of extreme climate events require adjustment. This study aims to perform the frequency analysis and the quantification of the risk of precipitation for a ten-day period in Tarauacá - Acre, Brazil. The results have shown that nonstationary Gumbel distribution with time-dependent location and scale best fits the observed data, thus allowing the most reliable measurements for risk of rare events, and determining reference quantiles associated with planning horizons or design of hydraulic structures.
Authors
- Moreira, José Genivaldo Do Vale ;
- Naghettini, Mauro ;
- Eleutério, Julian Cardoso