Andreas Musolff

UFZ - Helmholtz-Centre for Environmental Research GmbH | Dr.

Subject Areas: Water quality, Hydrology

 Recent Activity

ABSTRACT:

This data describes water quality parameters and catchment characteristics for 88 catchments draining into German drinking water reservoirs.
This data was used in more detail in Musolff et al. (2017).
The data comprises:
Catchment - number of the catchment
Year - year for which the data was averaged
SUVA254 - Specific ultraviolet absorbance at a wavelenght of 254 nm of the filtered water sample [L m-1 mg-1]
NO3 - nitrate concentration of the filtered water sample [µmol L-1]
Fe - dissolved iron concentration [µmol L-1]
DOC - dissolved organic carbon concentration [mmol L-1]
PO4 - soluble reactive phosphorus concentrations of the filteres water sample [µmol L-1]
Forest - share of the catchment covered by forest following CLC (2016), static metric [%]
TWI90 - topographic wetness index following Beven and Kirkby (1979) using a 10 m digital elevation model, static metric [-]
The hydrochemical data was averaged using Box-Cox-transformation (Box and Cox, 1964) for the samples of each year, arithmetic mean and backtransformation. On average 11 samples per years have been averaged.
The data is stored as a CSV file.

References
Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Journal, 24(1), 43-69.
Box, G. E. P., & Cox, D. R. (1964). An Analysis of Transformations. Journal of the Royal Statistical Society Series B-Statistical Methodology, 26(2), 211-252.
CLC. (2016). CORINE Land Cover 2012 v18.5. . https://land.copernicus.eu/pan-european/corine-land-cover.

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ABSTRACT:

This composite repository contains high-frequency data of discharge, electrical conductivity, nitrate-N, spectral absorbance at 254 nm and water temperature obtained in four neighboring catchments in the Harz mountains, Germany.
The repository contains four files - one for each catchment (WB - Warme Bode, RB - Rappbode, HS - Hassel, SK - Selke). Details on the catchments can be found here: WB - Kong et a.(2019), RV - Werner et al. (2019), HS and SK - Musolff et al. (2015)
Data for the SK catchment is part of the TERENO initiative (https://www.tereno.net/).
Each file states measurements for each timestep using the following columns: "index" (number of observation),"Date.Time" (timestamp in YYYY-MM-DD HH:MM:SS), "WT" (water temperature in degree celsius), "discharge.mm" (discharge in mm/d), "Q.smooth" ( discharge in mm/d smoothed using moving average),"EC.smooth" (electrical conductivity in µS/cm smoothed using moving average), "NO3.smooth" (NO3-N concentrations in mg N/L smoothed using moving average), "SAC.smooth" (spectral absorbance at 254 nm in 1/m, smoothed using moving average); NA - no data

Water quality data and discharge was measured at a high-frequency interval of 15 min in the time period between January 2013 and December 2014. Both, NO3-N and SAC were measured using in-situ UV-VIS probes (TRIOS ProPS, Trios Germany in WB, HS and SK; s::can spectrolyser, scan Austria in RB). EC was measured using in-situ probes (YSI6800, YSI, USA for WB, HS and SK; CTD Diver, Van Essen Canada for RB). Discharge measurements were provided by the state authorities [LHW, 2018] (for WB, HS and SK) or relied on an established stage-discharge relationship (RB, Werner et al. [2019]). Data loggers were maintained every two weeks, including manual cleaning of the UV-VIS probes and grab sampling for subsequent calibration and validation.

Data preparation included five steps: drift corrections, outlier detection, gap filling, calibration and moving averaging:
- Drift was corrected by distributing the offset between mean values one hour before and after cleaning equally among the two weeks maintenance interval as an exponential growth.
- Outliers were detected with a two-step procedure. First, values outside a physically unlikely range were removed. Second, the Grubbs test, to detect and remove outliers, was applied to a moving window of 100 values.
- Data gaps smaller than two hours were filled using cubic spline interpolation.
- The resulting time series were globally calibrated against the lab measured concentration of NO3-N (all stations) and SAC254 (all stations but SK). Here, average probe values one hour before and after sampling were used. EC was calibrated against field values obtained with a handheld WTW probe (WTW Multi 430, Xylem Analytics Germany) for RB while YSI-probe values for WB, HS and SK have been regularly calibrated in field making later corrections obsolete.
- Noise in the signal of both discharge and water quality was reduced by a moving average between 2.5 and 6 hours.

References:
Kong, X. Z., Q. Zhan, B. Boehrer, and K. Rinke (2019), High frequency data provide new insights into evaluating and modeling nitrogen retention in reservoirs, Water Res, 166, 115017.
LHW (2018), Datenportal Gewaesserkundlicher Landesdienst Sachsen-Anhalt (GLD), Landesbetrieb fuer Hochwasserschutz und Wasserwirtschaft Sachsen-Anhalt. accessed 2018-08-15
Musolff, A., C. Schmidt, B. Selle, and J. H. Fleckenstein (2015), Catchment controls on solute export, Advances in Water Resources, 86, 133-146.
Werner, B. J., A. Musolff, O. J. Lechtenfeld, G. H. de Rooij, M. R. Oosterwoud, and J. H. Fleckenstein (2019), High-frequency measurements explain quantity and quality of dissolved organic carbon mobilization in a headwater catchment, Biogeosciences, 16(22), 4497-4516.

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ABSTRACT:

This R code uses joint time series of concentration and discharge to (1) separate discharge events and store them in a data frame (events_h) and (2) analyse C-Q relationships including hysteresis, derive metrics describing these and store them in a data frame (eve.des).
The R code is provided as TXT and as R-file.
The code is written by Qing Zhan, Rémi Dupas, Camille Minaudo and Andreas Musolff.

This code is used and further descriped in this paper:
A. Musolff, Q. Zhan, R. Dupas, C. Minaudo, J. H. Fleckenstein, M. Rode, J. Dehaspe & K. Rinke (2021)
Spatial and Temporal Variability in Concentration-Discharge Relationships at the Event Scale.
Water Resourcers Research Volume 57, Issue 10
https://doi.org/10.1029/2020WR029442

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ABSTRACT:

The water quality and quantity data base Germany (WQQDB) was collected and put together at the UFZ.
It is based on a query to all federal states to provide discharge as well as nutrient and basic water quality metrics from their monitoring programs.
An overview on the providing agencies is given in a CSV-file (data_providers.csv).
This resulted in a collection of water quality and quantity time series from 14 federals states (without Hamburg and Bremen).

Quality data is available for 6086 stations. Time series are on average 11 years long starting from earliest 1954 (very rare), on average starting 1998. For Nitrate concentrations the average frequency is 7 measurements per year. All time series end 2016 latest.
Data is available for in-situ parameters (water temperature, pH, electrical conductivity, oxygen concentration and saturation), for nutrients (nitrate, nitrite, ammonia, mineral and organic nitrogen, total phosphorous, dissolved phosphate, total organic carbon, dissolved organic carbon)
and for sulfate, chloride, magnesium, calcium and suspended solids. Not all data is available for all stations.
For some stations measured discharge at the time of sampling is available. Based on spatial match and on stations naming connections to the water quantity stations as well as GRDC runoff data was established for 501 stations.

Quantity data is available on a daily basis for 894 stations. Time series are on average 40 years long starting earlies 1893, on average starting 1973.

The procedure, how data was imported, transformed and initially quality checked can be found in a TXT-file (procedure.txt).

The WQQDB is composed of two parts:
(1) The raw data archive that is archived in a repository at the UFZ: https://www.ufz.de/record/dmp/archive/7754/de/
(2) The metadata archive that contains information on the stations, time series length, number of measurements, data providers that is stored in HYDROSHARE

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Multidimensional (NetCDF) 0
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Composite Resource Composite Resource

ABSTRACT:

The water quality and quantity data base Germany (WQQDB) was collected and put together at the UFZ.
It is based on a query to all federal states to provide discharge as well as nutrient and basic water quality metrics from their monitoring programs.
An overview on the providing agencies is given in a CSV-file (data_providers.csv).
This resulted in a collection of water quality and quantity time series from 14 federals states (without Hamburg and Bremen).

Quality data is available for 6086 stations. Time series are on average 11 years long starting from earliest 1954 (very rare), on average starting 1998. For Nitrate concentrations the average frequency is 7 measurements per year. All time series end 2016 latest.
Data is available for in-situ parameters (water temperature, pH, electrical conductivity, oxygen concentration and saturation), for nutrients (nitrate, nitrite, ammonia, mineral and organic nitrogen, total phosphorous, dissolved phosphate, total organic carbon, dissolved organic carbon)
and for sulfate, chloride, magnesium, calcium and suspended solids. Not all data is available for all stations.
For some stations measured discharge at the time of sampling is available. Based on spatial match and on stations naming connections to the water quantity stations as well as GRDC runoff data was established for 501 stations.

Quantity data is available on a daily basis for 894 stations. Time series are on average 40 years long starting earlies 1893, on average starting 1973.

The procedure, how data was imported, transformed and initially quality checked can be found in a TXT-file (procedure.txt).

The WQQDB is composed of two parts:
(1) The raw data archive that is archived in a repository at the UFZ: https://www.ufz.de/record/dmp/archive/7754/de/
(2) The metadata archive that contains information on the stations, time series length, number of measurements, data providers that is stored in HYDROSHARE

Show More
Composite Resource Composite Resource

ABSTRACT:

This R code uses joint time series of concentration and discharge to (1) separate discharge events and store them in a data frame (events_h) and (2) analyse C-Q relationships including hysteresis, derive metrics describing these and store them in a data frame (eve.des).
The R code is provided as TXT and as R-file.
The code is written by Qing Zhan, Rémi Dupas, Camille Minaudo and Andreas Musolff.

This code is used and further descriped in this paper:
A. Musolff, Q. Zhan, R. Dupas, C. Minaudo, J. H. Fleckenstein, M. Rode, J. Dehaspe & K. Rinke (2021)
Spatial and Temporal Variability in Concentration-Discharge Relationships at the Event Scale.
Water Resourcers Research Volume 57, Issue 10
https://doi.org/10.1029/2020WR029442

Show More
Composite Resource Composite Resource

ABSTRACT:

This composite repository contains high-frequency data of discharge, electrical conductivity, nitrate-N, spectral absorbance at 254 nm and water temperature obtained in four neighboring catchments in the Harz mountains, Germany.
The repository contains four files - one for each catchment (WB - Warme Bode, RB - Rappbode, HS - Hassel, SK - Selke). Details on the catchments can be found here: WB - Kong et a.(2019), RV - Werner et al. (2019), HS and SK - Musolff et al. (2015)
Data for the SK catchment is part of the TERENO initiative (https://www.tereno.net/).
Each file states measurements for each timestep using the following columns: "index" (number of observation),"Date.Time" (timestamp in YYYY-MM-DD HH:MM:SS), "WT" (water temperature in degree celsius), "discharge.mm" (discharge in mm/d), "Q.smooth" ( discharge in mm/d smoothed using moving average),"EC.smooth" (electrical conductivity in µS/cm smoothed using moving average), "NO3.smooth" (NO3-N concentrations in mg N/L smoothed using moving average), "SAC.smooth" (spectral absorbance at 254 nm in 1/m, smoothed using moving average); NA - no data

Water quality data and discharge was measured at a high-frequency interval of 15 min in the time period between January 2013 and December 2014. Both, NO3-N and SAC were measured using in-situ UV-VIS probes (TRIOS ProPS, Trios Germany in WB, HS and SK; s::can spectrolyser, scan Austria in RB). EC was measured using in-situ probes (YSI6800, YSI, USA for WB, HS and SK; CTD Diver, Van Essen Canada for RB). Discharge measurements were provided by the state authorities [LHW, 2018] (for WB, HS and SK) or relied on an established stage-discharge relationship (RB, Werner et al. [2019]). Data loggers were maintained every two weeks, including manual cleaning of the UV-VIS probes and grab sampling for subsequent calibration and validation.

Data preparation included five steps: drift corrections, outlier detection, gap filling, calibration and moving averaging:
- Drift was corrected by distributing the offset between mean values one hour before and after cleaning equally among the two weeks maintenance interval as an exponential growth.
- Outliers were detected with a two-step procedure. First, values outside a physically unlikely range were removed. Second, the Grubbs test, to detect and remove outliers, was applied to a moving window of 100 values.
- Data gaps smaller than two hours were filled using cubic spline interpolation.
- The resulting time series were globally calibrated against the lab measured concentration of NO3-N (all stations) and SAC254 (all stations but SK). Here, average probe values one hour before and after sampling were used. EC was calibrated against field values obtained with a handheld WTW probe (WTW Multi 430, Xylem Analytics Germany) for RB while YSI-probe values for WB, HS and SK have been regularly calibrated in field making later corrections obsolete.
- Noise in the signal of both discharge and water quality was reduced by a moving average between 2.5 and 6 hours.

References:
Kong, X. Z., Q. Zhan, B. Boehrer, and K. Rinke (2019), High frequency data provide new insights into evaluating and modeling nitrogen retention in reservoirs, Water Res, 166, 115017.
LHW (2018), Datenportal Gewaesserkundlicher Landesdienst Sachsen-Anhalt (GLD), Landesbetrieb fuer Hochwasserschutz und Wasserwirtschaft Sachsen-Anhalt. accessed 2018-08-15
Musolff, A., C. Schmidt, B. Selle, and J. H. Fleckenstein (2015), Catchment controls on solute export, Advances in Water Resources, 86, 133-146.
Werner, B. J., A. Musolff, O. J. Lechtenfeld, G. H. de Rooij, M. R. Oosterwoud, and J. H. Fleckenstein (2019), High-frequency measurements explain quantity and quality of dissolved organic carbon mobilization in a headwater catchment, Biogeosciences, 16(22), 4497-4516.

Show More
Composite Resource Composite Resource

ABSTRACT:

This data describes water quality parameters and catchment characteristics for 88 catchments draining into German drinking water reservoirs.
This data was used in more detail in Musolff et al. (2017).
The data comprises:
Catchment - number of the catchment
Year - year for which the data was averaged
SUVA254 - Specific ultraviolet absorbance at a wavelenght of 254 nm of the filtered water sample [L m-1 mg-1]
NO3 - nitrate concentration of the filtered water sample [µmol L-1]
Fe - dissolved iron concentration [µmol L-1]
DOC - dissolved organic carbon concentration [mmol L-1]
PO4 - soluble reactive phosphorus concentrations of the filteres water sample [µmol L-1]
Forest - share of the catchment covered by forest following CLC (2016), static metric [%]
TWI90 - topographic wetness index following Beven and Kirkby (1979) using a 10 m digital elevation model, static metric [-]
The hydrochemical data was averaged using Box-Cox-transformation (Box and Cox, 1964) for the samples of each year, arithmetic mean and backtransformation. On average 11 samples per years have been averaged.
The data is stored as a CSV file.

References
Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Journal, 24(1), 43-69.
Box, G. E. P., & Cox, D. R. (1964). An Analysis of Transformations. Journal of the Royal Statistical Society Series B-Statistical Methodology, 26(2), 211-252.
CLC. (2016). CORINE Land Cover 2012 v18.5. . https://land.copernicus.eu/pan-european/corine-land-cover.

Show More