Preprints
https://doi.org/10.5194/essd-2022-6
https://doi.org/10.5194/essd-2022-6
 
01 Mar 2022
01 Mar 2022
Status: this preprint is currently under review for the journal ESSD.

Water quality, discharge and catchment attributes for large-sample studies in Germany – QUADICA

Pia Ebeling1, Rohini Kumar2, Stefanie R. Lutz1,3, Tam Nguyen1, Fanny Sarrazin2, Michael Weber2, Olaf Büttner4, Sabine Attinger2, and Andreas Musolff1 Pia Ebeling et al.
  • 1Department of Hydrogeology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, 04318, Germany
  • 2Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research-UFZ, Leipzig, 04318, Germany
  • 3Copernicus Institute of Sustainable Development, Utrecht, 3584 CB, the Netherlands
  • 4Department Aquatic Ecosystems Analysis and Management, Helmholtz Centre for Environmental Research-UFZ, Magdeburg, 39114, Germany

Abstract. Environmental data are the key to define and address water quality and quantity challenges at catchment scale. Here, we present the first large-sample water quality data set for 1386 German catchments covering a large range of hydroclimatic, topographic, geologic, land use and anthropogenic settings. QUADICA (water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany) combines water quality with water quantity data, meteorological and nutrient forcing data, and catchment attributes. The data set comprises time series of riverine macronutrient concentrations (species of nitrogen, phosphorus and organic carbon) and diffuse nitrogen forcing data at catchment scale (nitrogen surplus, atmospheric deposition and fixation). Time series are generally aggregated to an annual basis; however, for 140 stations with long-term water quality and quantity data (more than 20 years), we additionally present monthly median discharge and nutrient concentrations, flow-normalized concentrations and corresponding mean fluxes as outputs from weighted regressions on time, discharge, and season (WRTDS). The catchment attributes include catchment nutrient inputs from point and diffuse sources and characteristics from topography, climate, land cover, lithology and soils. This comprehensive, freely available data collection can facilitate large-sample data-driven water quality assessments at catchment scale as well as mechanistic modeling studies.

Pia Ebeling et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-6', Anonymous Referee #1, 01 Apr 2022
  • RC2: 'Comment on essd-2022-6', Anonymous Referee #2, 14 Apr 2022

Pia Ebeling et al.

Data sets

CCDB - catchment characteristics data base Germany Ebeling, Pia; Kumar, Rohini; Musolff, Andreas https://doi.org/10.4211/hs.82f8094dd61e449a826afdef820a2c19

QUADICA - water quality, discharge and catchment attributes for large-sample studies in Germany Ebeling, Pia; Kumar, Rohini; Weber, Michael; Musolff, Andreas https://doi.org/10.4211/hs.26e8238f0be14fa1a49641cd8a455e29

Pia Ebeling et al.

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Short summary
Environmental data are critical for understanding and managing ecosystems, including for mitigating degraded water quality. To increase data availability, we here present the first large-sample water quality data set of riverine macronutrient concentrations combined with water quantity, meteorological and nutrient forcing data, and catchment attributes. QUADICA covers 1386 German catchments to facilitate large-sample data-driven and modeling water quality assessments.