Automated Author ProfileTerziotti, Silvia
Terziotti, Silvia
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: 1.8 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Defining the oxic-suboxic interface is often critical for determining pathways for nitrate transport in groundwater and to streams at the local scale. Defining this interface on a regional scale is complicated by the spatial variability of reaction rates. The probability of oxic groundwater in the Chesapeake Bay watershed was predicted by relating dissolved O2 concentrations in groundwater samples to indicators of residence time and/or electron donor availability using logistic regression. Variables that describe surficial geology, position in the flow system, and soil drainage were important predictors of oxic water. The probability of encountering oxic groundwater at a 30 m depth and the depth to the bottom of the oxic layer were predicted for the Chesapeake Bay watershed. The influence of depth to the bottom of the oxic layer on stream nitrate concentrations and time lags (i.e., time period between land application of nitrogen and its effect on streams) are illustrated using model simulations for hypothetical basins. Regional maps of the probability of oxic groundwater should prove useful as indicators of groundwater susceptibility and stream susceptibility to contaminant sources derived from groundwater.
Authors
- Terziotti, Silvia ;
- Tesoriero, Anthony J. ;
- Abrams, Daniel B.
This USGS Data Release represents the data used to develop multiple linear regression models for estimating the loads of total nitrogen in small streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (less than 585 square kilometers) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. These data support the following publication: Kronholm, S.C., Capel, P.D., and Terziotti, Silvia, 2016, Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams: Environmental Modeling and Assessment, 10 p., http://dx.doi.org/10.1007/s10666-016-9525-3.
Authors
- Terziotti, Silvia ;
- Capel, Paul D. ;
- Kronholm, Scott C.