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Lake Langtjern High-Frequency Monitoring data used for the EU Water JPI project PROGNOS


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Created: Aug 26, 2019 at 11:19 a.m.
Last updated: Aug 30, 2019 at 11:20 a.m.
DOI: 10.4211/hs.53bd0030da8d4a0b9286c91069d545c4
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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.

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Resource Level Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Lake Langjern
Longitude
9.7267°
Latitude
60.3725°

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.

  1. 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.

  2. 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)

References

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

Guerrero, J., F. Clayer (2019). Lake Langtjern High-Frequency Monitoring data used for the EU Water JPI project PROGNOS, HydroShare, https://doi.org/10.4211/hs.53bd0030da8d4a0b9286c91069d545c4

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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