ABSTRACT:

These are the data used in the paper by Kirker, A N, E Cushman, and L Toran. 2024. We used a combination of overland flow monitoring and distributed hydrologic modeling with GSSHA to evaluate the impacts of Stormwater Control Measures (SCMs) in an 23-ha urban catchment. We found that overland flow collected from upslope and downslope of the SCMs had variable concentrations of dissolved nutrients and chloride. Additionally, we determined that the size of a location's upstream contributing area did not always correspond proportionally to its contribution to stream flow. After modeling the installation of SCMs, it was determined that the SCMs did not impact streamflow during high intensity storms, but did decrease flow during lower intensity events. A full model run has been included in the .zip file so that users of Aquavevo's WMS may replicate these results.

ABSTRACT:

These are the datasets used in the paper by Kirker, A N, and L Toran. 2023. We used the distributed hydrologic model GSSHA to simulate overland runoff from impervious and semi-pervious (low vegetation) land covers in a 0.3 km2 urban catchment. To track the arrival of water to a stormwater basin inlet from distinct parts of the catchment, we applied a conservative solute tracer to portions of the gridded model area. These data show the variability in discharge amount and solute arrival for modeled scenarios with different slopes, land uses, and precipitation amounts. A full model run has also been included in the .zip file so that users of Aquavevo's WMS may replicate these results.

ABSTRACT:

These are the datasets used in the paper by Kirker, A N, and L Toran. 2022. δ15N and δ18O isotopic ratios of dissolved nitrate in stormwater runoff were measured at two sites: Ethel Jordan Park (EJ.csv) and Upper Moreland Township School District (UMTSD.csv). At each site, time-based ISCO samples were collected during three storm events. The sample collection time, nitrate concentration, and isotopic composition are given for each sample.Additionally, results of a Bayesian mixing model (MixSIAR) are presented, showing estimated percent contribution to each sample from atmospheric and soil N sources. End-members used for the mixing model are in EndMembers.csv. Rainfall data downloaded for Weather Underground personal weather station sites KPAJENKI4 and KPAWILLO10, and water level data recorded by Onset HOBO pressure loggers are included for each storm event.