Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
This resource contains content types with files that need to be updated to match with metadata changes. Show content type files that need updating.
Authors: |
|
|
---|---|---|
Owners: |
|
This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
Type: | Resource | |
Storage: | The size of this resource is 25.1 MB | |
Created: | Aug 26, 2019 at 11:19 a.m. | |
Last updated: | Aug 30, 2019 at 11:20 a.m. | |
DOI: | 10.4211/hs.53bd0030da8d4a0b9286c91069d545c4 | |
Citation: | See how to cite this resource | |
Content types: | Single File Content |
Sharing Status: | Published |
---|---|
Views: | 1684 |
Downloads: | 41 |
+1 Votes: | Be the first one to this. |
Comments: | No comments (yet) |
Abstract
The European Union Water JPI (http://www.waterjpi.eu/) has funded the project PROGNOS (Predicting In-Lake Responses to Change Using Near Real Time Models http://prognoswater.org/) PROGNOS developed an integrated approach that couples high frequency (HF) lake monitoring data to dynamic lake water quality models to forecast short-term changes in lake water quality. Here we provide an archive the the HF monitoring data sets that were used by PROGNOS project Partner NIVA (Norwegian Institute for Water Research) to calibrate and verify the performance of the GOTM (https://gotm.net/) and SELMA models that are coupled by the frame work for aquatic biogeochemical models (https://github.com/fabm-model)
Data were collected from two sources:
a) NIVA's Langtjern monitoring site (http://aquamonitor.no)
b) Publicly available data from the Norwegian Meteorological office (https://thredds.met.no/thredds/catalog.html)
HF frequency data encompasses, at least, from August 2014 to August 2017, except for the carbon concentration at the outlet of the lake where only data until August 2016 was available.
Subject Keywords
Coverage
Spatial
Temporal
Start Date: | |
---|---|
End Date: |
Content
README.md
Langtjern
Lake Langtjern (60°37’N; 9°73’E) is a small and shallow (lake surface 0.227 km2, mean depth 2 m) humic, acid-sensitive, oligotrophic lake located in central Norway. Langtjern has a small catchment area (4.8 km2, ca. 510-750 m.a.s.l) that is dominated by unproductive pine forest, wetlands and bedrock. Yearly precipitation is 750 mm/yr. Langtjern generally experiences long winters and a stable snowpack.
Data
These data were used to model lake carbon processes with the GOTM-FABM family of models. The formats used by GOTM-FABM are described in https://github.com/fabm-model/fabm/wiki/GOTM
Meteorological data
Wind speed, air temperature, relative humidity and precipitation were taken from NIVA's Langtjern long-term ecological monitoring site.
Air temperature and relative humidity are measured using a Campbell Scientific 215. A Young Model 05103-5 measures wind speed and direction. Precipitation is measured with a Geonor T-200B. No local measurements of mean sea level pressure and cloud cover were available but were interpolated with data from the Norwegian Meteorological Institute network of stations.
Filename: langjern-weather.dat
Meterological forcings
Columns:
- Date: YYYY-MM-DD HH:MM:SS
- Wind speed north-south (m/s)
- Wind speed east-west (m/s)
- Mean sea level pressure (millibars)
- Air temperature (oC)
- Relative Humidity (%)
- Cloud Cover (%)
- Precipitation (mm)
Carbon data
Color Dissolved Organic Matter (CDOM) is measured with a TriOS Microflu-CDOM (ex 370nm/em 460nm) (D, small sensor) and UV-absorption with a TriOS ProPS (190-360 nm) (D, large sensor) with a 1 cm light path. Both have mechanical wipers to remove biofilm.
The raw CDOM data has been converted to DOC data with a two-step process: 1) first the CDOM data has been corrected for temperature quenching according to the temperature correction equation recommended by Ryder et al. (2012), then 2) a Gaussian process regression was used to convert the CDOM data into DOC data.
-
The CDOM at a reference temperature (CDOMref) is given by:
CDOMref=CDOM×(1+m/(Tmeas×m+C) ×(Tref−Tmeas))
where CDOM is the raw CDOM signal, Tref is the reference temperature (e.g., 20°C), Tmeas is the measured water temperature, and m and C are the slope and intercept, respectively, of any given regression equation of temperature versus CDOM fluorescence determined in the lab. Since this regression hasn’t been determined for Langtjern, m and C have been optimized to provide the best correlation between CDOMref and weekly DOC data. A simple optimization genetic algorithm written in MATLAB® has been used for this purpose. -
A Gaussian process regression can be used as a non-linear multivariate interpolation tool. In this case, high-frequency DOC data has been expressed as a function of up to 8 predictors including CDOMref, as well as water level and water temperature integrated (or not) over various timescales. In a Gaussian process regression, the coefficient (or vector) for each predictor is variable across time. Different predictors were used for the inflow and outflow.
Inflow to the lake
Filename: langtjern_carbon_inflow.dat
Total carbon inflow to the lake.
Columns:
- Date: YYYY-MM-DD HH:MM:SS
- Carbon concentration (mg/m3)
Outflow from the lake
Filename: langtjern_carbon_inflow.dat
Total carbon outflows from the lake
Columns:
- Date: YYYY-MM-DD HH:MM:SS
- Carbon concentration (mg/m3)
Oxygen data
Oxygen saturation is measured (O2) with an Aanderaa Optode 4175 at depths of 1m and 8m. Oxygen saturation is transformed into a concentration using the water temperature (T) according the formula:
O2 / 100.0 * ( (14.59 - 0.3955 T + 0.0072 T²- 0.0000619 T³) / 31.9988 )
Filename: langtjern_O2.obs
Oxygen concentrations at 1 and 8m.
Columns:
- Date: YYYY-MM-DD HH:MM:SS
- Depth (m)
- Concentration (mmol/m3)
Discharge and water temperature data
Gauge height (gh) is measured with a pressure transducer which is then transformed into discharge according to the following formulas.
- inlet: 2.391(gh-0.345)2.5
- outlet: 3.2136(gh-0.315)2.453
The discharge thus calculated is scaled according to the area it drains compared to the total area draining to the lake.
Water temperature is measured using a thermocouple.
Filename: langtjern-inlet-q.dat
Discharge into the lake and water temperature.
Columns:
- Date: YYYY-MM-DD HH:MM:SS
- Discharge (m3/s)
- Water temperature (oC)
Filename: langtjern-outlet-q.dat
Discharge from the lake and water temperature.
Columns:
- Date: YYYY-MM-DD HH:MM:SS
- Discharge (m3/s)
- Water temperature (oC)
Solar radiation
Global radiation is measured with an Apogee SP-212 sensor. This can be used instead of the cloud cover by the models.
Filename: langtjern-radiation.data
Total incoming solar radiation.
Columns:
- Date: YYYY-MM-DD HH:MM:SS
- Irradiance (W/m2)
Lake water temperature
The lake Langtjern buoy measures water temperature using a thermocouple at different depths.
Filename: langtjern_lake_temperature.dat Water temperature in the lake.
Columns:
- Date: YYYY-MM-DD HH:MM:SS
- Depth (m)
- Water temperature (oC)
Related Resources
The content of this resource is derived from | https://github.com/metno/NGCD/wiki |
The content of this resource is derived from | https://frost.met.no/index.html |
Credits
Funding Agencies
This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
---|---|---|
Forskningsrådet | PREDICTING IN-LAKE RESPONSES TO CHANGE USING NEAR REAL TIME MODELS PROGNOS | 258142 |
European Union ERA-NET WaterWorks2014 Cofunded Call | PROGNOS | WaterWorks2014- PROGNOS |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
Comments
There are currently no comments
New Comment