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| Created: | Mar 01, 2019 at 3:37 p.m. (UTC) | |
| Last updated: | Mar 18, 2019 at 1:22 p.m. (UTC) | |
| Citation: | See how to cite this resource | |
| Content types: | Time Series Content |
| Sharing Status: | Public |
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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
Subject Keywords
Coverage
Spatial
Temporal
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Content
Readme.md
METADATA DOCUMENT
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.
Water Quality at Mill Pond Reservoir on Oyster River , Durham, NH
Developed by: Gopal Mulukutla, with input from Wil Wollheim and Eliza Balch, University of New Hampshire
Contact information: gopal.mulukutla@unh.edu Contact information: Wil.Wollheim@unh.edu
Table1 – Describes the monitoring locations on the Oyster River. Station names referred here are used in aggregated level 1 files archived on Hydroshare.
| Station Name | Station* Description *| Lat | Long | Total Area *km2* | Comment | | --- | --- | --- | --- | --- | --- | | HAM | Hamel Brook | 43.118508 | -70.921881 | 1.8 | Major Inflow to Reservoir | | CLGB | College Brook at Mill Plaza | 43.131306 | -70.924639 | 2.3 | Major Inflow to Reservoir | | ORR | Oyster River at OR Road | 43.126653 | -70.927921 | 44.9 | River Inflow to Reservoir | | OMPD | Oyster River at Mill Pond | 43.130880 | -70.918497 | 50.6 | Outflow from Reservoir |
Table 2: Sensors deployed at each site.
| Description including name of equipment: | Measurement | Location | | --- | --- | --- | | SUNA (Submersible Ultraviolet Nitrate Analyzer) | Nitrate | ORR HAM OMPD | | Realtech GL2000 Series Sensor | Nitrate | ORR | | YSI EXO2 | fDOM*, turbidity*, pH*, conductivity, dissolved oxygen* | OMPD | | Onset Inc. Hobo water level and conductivity logger | Stage height, water temperature and specific conductivity. | ORR CLGB HAM | | Meter Environment, CTD-10 | conductivity, water depth and temperature sensors | OMPD CLGB |
*data for these variables not compiled in the datasets provided here.
Table3: Time frame of deployment of sensors for each site.
| Station Name | Station* Description *| Sensors Deployed | Time Frame of Study | Time Frame of Nitrate Sensor Deployment | | --- | --- | --- | --- | --- | | HAM | Hamel Brook | Stage height, conductivity, temperature, and nitrate | 2018-05-012018-11-19 | 2018-08-20 to2018-10-12 | | CLGB | College Brook at Mill Plaza | Stage height, conductivity, temperature | 2018-05-302018-12-14 | N/A | | ORR | Oyster River at OR Road | Stage height, conductivity, temperature | 2018-05-302018-12-15 | 2018-08-14 to 2018-12-11 | | OMPD | Oyster River at Mill Pond | Stage heightconductivity nitrate | 2018-05-012018-12-20 | 2018-05-01 to2018-12-20 |
Table 4: Summary of variable names in headers, and their description
| Variable Name | Description | | --- | --- | | STAGE.M.HOBO | Stage height in meters | | TEMP.C.HOBO | Temperature in degrees Celsius from continuous sensor | | SPCOND.uScm.HOBO | Specific Conductance in μS/cm from continuous sensor | | NO3.MGL | Sensor based NO3-N milligram per liter | | Cl.MGL.GRAB | Chloride milligram per liter | | NO3.MGL.GRAB | Grab sample based NO3-N milligram per liter | | SO4.MGL.GRAB | Grab sample based SO4 milligram per liter | | NA.MGL.GRAB | Grab sample based Sodium milligram per liter | | K.MGL.GRAB | Grab sample based Potassium in milligram per liter | | MG.MGL.GRAB | Grab sample based Magnesium in milligram per liter | | CA.MGL.GRAB | Grab sample based Calcium in milligram per liter | | TEMP.C.HANDHELD | Temperature in degrees Celsius from handheld sensor | | SPCOND.uScm.HANDHELD | Specific Conductance in μS/cm from handheld probe | | DO.PERCENT.HANDHELD | Dissolved Oxygen Percent Saturation from handheld probe | | DO.MGL.HANDHELD | Dissolved Oxygen concentration in milligram per liter | | STAGE.CM.VISUAL | Stage height in cm from visual in-ground stream gage | | NO3_MGL_REALTECH | Sensor based NO3-N milligram per liter from Realtech sensor | | NO3_FLAG_REALTECH | Bad data flag for sensor based NO3-N milligram per liter from Realtech sensor | | NO3_MGL_MOVAVG_REALTECH | Sensor based NO3-N milligram per liter processed data by removing noise | | NO3_MGL_SUNA | Sensor based NO3-N milligram per liter from SUNA sensor |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| Environmental Protection Agency | EPA Nutrient Sensor Action Challenge | |
| New Hampshire Sea Grant | ||
| USDA, Natural Resources Conservation Service New Hampshire |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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