ABSTRACT:

NOTE: This data was posted to meet the requirements of the EPA Low Cost Nutrient Sensor Challenge (Phase 2). The data set includes the results of a preliminary test of one type of low cost nutrient sensor and should not be used without consultation with the authors.
Dams and their reservoirs are increasingly being removed from the landscape, often because they are aging and would need costly repairs, have no significant utility and/or to improve anadromous fish passage and connectivity with spawning areas. Understanding the role of reservoirs in nitrate removal will inform ongoing decisions regarding dam removal.Because reservoirs created by dams are potentially effective at removing nitrogen (Gold et al. 2016), such dam removals come with tradeoffs, including reduced nitrate removal. Yet we have a poor understanding of the effectiveness of the reservoirs on smaller rivers that are common in much of New England and elsewhere, as well as how their effectiveness varies during different parts of the growing season and during storm events within season.

Our overarching approach used high frequency nitrate sensors to characterize nitrate concentration patterns and fluxes in different kinds of streams and rivers that drain into and out of reservoirs to understand variability in water quality. From these measurement we can also quantify the effectiveness of reservoirs to retain nitrate across a range of flow conditions. In order to help interpret these nitrate results, we also deployed ancillary high frequency sensors that measure specific conductance and water stage/discharge.

This dataset contains quality controlled level (Level 1) data for all of the variables measured for the EPA Nutrient Sensor Action Challenge. Individual file contain specific variables from data Collected in 2018. We implemented a simple workflow to develop usable datasets (Levels 0 and Level 1, Table 2), . Data was processed using custom Matlab code (Mathworks Inc., Natick, MA), and MS Excel. Unprocessed raw data (Level 0), consisting of multiple data streams at native measurement resolution, were compiled on an ongoing basis.

Low-cost high frequency nitrate sensor outputs data every 6 seconds. While, the other high frequency nitrate sensor (SUNA) output 16 frames of data every 15 minutes. Other sensors ( stage height, conductivity, temperature) provided data at 15-minute intervals. Grab sample (nutrients and chloride), and hand-held sensor measurements ( conductance, temperature, and dissolved oxygen) data was collected weekly or biweekly, in addition to periodic flow measurement data. Level 0 data consists raw sensor files, without an processing performed. For the next level of processing, outliers (Level 1), and bad data points were identified and removed based on existing or historic data, stage height was transformed to discharge by applying site-specific rating curve equation, and temporal aggregation performed and each site’s data was compiled into one CSV file.

Each file header contains site location and an explanation of variable names.

ABSTRACT:

NOTE:This data was posted to meet the requirements of the EPA Low Cost Nutrient Sensor Challenge (Phase 2). The data set includes the results of a preliminary test of one type of low cost nutrient sensor and should not be used without consultation with the authors.

Dams and their reservoirs are increasingly being removed from the landscape, often because they are aging and would need costly repairs, have no significant utility and/or to improve anadromous fish passage and connectivity with spawning areas. Understanding the role of reservoirs in nitrate removal will inform ongoing decisions regarding dam removal.Because reservoirs created by dams are potentially effective at removing nitrogen (Gold et al. 2016), such dam removals come with tradeoffs, including reduced nitrate removal. Yet we have a poor understanding of the effectiveness of the reservoirs on smaller rivers that are common in much of New England and elsewhere, as well as how their effectiveness varies during different parts of the growing season and during storm events within season.

Our overarching approach used high frequency nitrate sensors to characterize nitrate concentration patterns and fluxes in different kinds of streams and rivers that drain into and out of reservoirs to understand variability in water quality. From these measurement we can also quantify the effectiveness of reservoirs to retain nitrate across a range of flow conditions. In order to help interpret these nitrate results, we also deployed ancillary high frequency sensors that measure specific conductance and water stage/discharge.

This dataset contains quality controlled level (Level 1) data for all of the variables measured for the EPA Nutrient Sensor Action Challenge. Individual file contain specific variables from data Collected in 2018. We implemented a simple workflow to develop usable datasets (Levels 0 and Level 1, Table 2), . Data was processed using custom Matlab code (Mathworks Inc., Natick, MA), and MS Excel. Unprocessed raw data (Level 0), consisting of multiple data streams at native measurement resolution, were compiled on an ongoing basis.

Low-cost high frequency nitrate sensor outputs data every 6 seconds. While, the other high frequency nitrate sensor (SUNA) output 16 frames of data every 15 minutes. Other sensors ( stage height, conductivity, temperature) provided data at 15-minute intervals. Grab sample (nutrients and chloride), and hand-held sensor measurements ( conductance, temperature, and dissolved oxygen) data was collected weekly or biweekly, in addition to periodic flow measurement data. Level 0 data consists raw sensor files, without an processing performed. For the next level of processing, outliers (Level 1), and bad data points were identified and removed based on existing or historic data, stage height was transformed to discharge by applying site-specific rating curve equation, and temporal aggregation performed and each site’s data was compiled into one CSV file.

Each file header contains site location and an explanation of variable names.

NOTE: Please note that this dataset is prelimnary and