Automated Author Profile

Terziotti, Silvia

Current S-Index

1.8

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.9

Average Dataset Index per dataset

Total Datasets

2

Total datasets for this author

Average FAIR Score

49.5%

Average FAIR Score per dataset

Total Citations

1

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Depth to 50 percent probability of oxic conditions, Chesapeake Bay Watershed

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.
1 Citation0 Mentions46% FAIR1.5 Dataset Index
10.5066/f78c9tc52017

Data on annual total nitrogen loads and watershed characteristics used to develop a method to estimate the total nitrogen loads in small streams

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.
0 Citations0 Mentions53% FAIR0.3 Dataset Index
10.5066/f7tx3cgb2016