1. Spatio-temporal controls of C–N–P dynamics across headwater catchments of a temperate agricultural region from public data analysis
- Author
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S. Guillemot, O. Fovet, C. Gascuel-Odoux, G. Gruau, A. Casquin, F. Curie, C. Minaudo, L. Strohmenger, F. Moatar, Sol Agro et hydrosystème Spatialisation (SAS), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), GéoHydrosystèmes COntinentaux (GéHCO EA6293), Université de Tours (UT), Géosciences Rennes (GR), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Physics of Aquatic Systems Laboratory [Lausanne], Ecole Polytechnique Fédérale de Lausanne (EPFL), Riverly (Riverly), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Région Bretagne (grant n°. 16007508), Agence de l’Eau Loire Bretagne (grant n°. 0)., AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Tours, Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), RiverLy - Fonctionnement des hydrosystèmes (RiverLy), Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)
- Subjects
Technology ,010504 meteorology & atmospheric sciences ,[SDV]Life Sciences [q-bio] ,0208 environmental biotechnology ,02 engineering and technology ,Environmental technology. Sanitary engineering ,01 natural sciences ,chemistry.chemical_compound ,Nitrate ,Dissolved organic carbon ,Geography. Anthropology. Recreation ,Temperate climate ,medicine ,GE1-350 ,[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology ,TD1-1066 ,0105 earth and related environmental sciences ,Hydrology ,Base flow ,Seasonality ,medicine.disease ,6. Clean water ,020801 environmental engineering ,Environmental sciences ,chemistry ,[SDU]Sciences of the Universe [physics] ,13. Climate action ,[SDE]Environmental Sciences ,Spatial ecology ,Environmental science ,Spatial variability ,Water quality - Abstract
Characterizing and understanding spatial variability in water quality for a variety of chemical elements is an issue for present and future water resource management. However, most studies of spatial variability in water quality focus on a single element and rarely consider headwater catchments. Moreover, they assess few catchments and focus on annual means without considering seasonal variations. To overcome these limitations, we studied spatial variability and seasonal variation in dissolved C, N, and P concentrations at the scale of an intensively farmed region of France (Brittany). We analysed 185 headwater catchments (from 5–179 km2) for which 10-year time series of monthly concentrations and daily stream flow were available from public databases. We calculated interannual loads, concentration percentiles, and seasonal metrics for each element to assess their spatial patterns and correlations. We then performed rank correlation analyses between water quality, human pressures, and soil and climate features. Results show that nitrate (NO3) concentrations increased with increasing agricultural pressures and base flow contribution; dissolved organic carbon (DOC) concentrations decreased with increasing rainfall, base flow contribution, and topography; and soluble reactive phosphorus (SRP) concentrations showed weaker positive correlations with diffuse and point sources, rainfall and topography. An opposite pattern was found between DOC and NO3: spatially, between their median concentrations, and temporally, according to their seasonal cycles. In addition, the quality of annual maximum NO3 concentration was in phase with maximum flow when the base flow index was low, but this synchrony disappeared when flow flashiness was lower. These DOC–NO3 seasonal cycle types were related to the mixing of flow paths combined with the spatial variability of their respective sources and to local biogeochemical processes. The annual maximum SRP concentration occurred during the low-flow period in nearly all catchments. This likely resulted from the dominance of P point sources. The approach shows that despite the relatively low frequency of public water quality data, such databases can provide consistent pictures of the spatio-temporal variability of water quality and of its drivers as soon as they contain a large number of catchments to compare and a sufficient length of concentration time series.
- Published
- 2021