What do we need to predict groundwater nitrate recovery trajectories?

Denitrification Eutrophication Groundwater Nitrate Predictions Residence time

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
20 Sep 2021
Historique:
received: 02 03 2021
revised: 16 04 2021
accepted: 05 05 2021
pubmed: 26 5 2021
medline: 26 5 2021
entrez: 25 5 2021
Statut: ppublish

Résumé

Nitrate contamination affects many of the Earth's aquifers and surface waters. Large-scale predictions of groundwater nitrate trends normally require the characterization of multiple anthropic and natural factors. To assess different approaches for upscaling estimates of nitrate recovery, we tested the influence of hydrological, historical, and biological factors on predictions of future nitrate concentration in aquifers. We tested the factors with a rich hydrogeological dataset from a fractured bedrock catchment in western France (Brittany). A sensitivity analysis performed on a calibrated model of groundwater flow, denitrification, and nitrogen inputs revealed that trends in nitrate concentration can effectively be approximated with a limited number of key parameters. The total mass of nitrate that entered the aquifer since the beginning of the industrial period needs to be characterized, but the shape of the historical nitrogen input time series can be largely simplified without substantially altering the predictions. Aquifer flow and transport processes can be represented by the mean and standard deviation of the residence time distribution, offering a tractable tool to make reasonable predictions at watershed to regional scales. Apparent sensitivity to denitrification rate was primarily attributable to time lags in oxygen depletion, meaning that denitrification can be simplified to an on/off process, defined only by the time needed for nitrate to reach the hypoxic reactive layer. Obtaining these key parameters at large scales is still challenging with currently available information, but the results are promising regarding our future ability to predict nitrate concentration with integrated monitoring and modeling approaches.

Identifiants

pubmed: 34034194
pii: S0048-9697(21)02732-7
doi: 10.1016/j.scitotenv.2021.147661
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

147661

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Camille Vautier (C)

Univ Rennes, CNRS, Géosciences Rennes, UMR 6118, 35000 Rennes, France.

Tamara Kolbe (T)

Chair of Hydrogeology and Hydrochemistry, Faculty of Geoscience, Geoengineering and Mining, Institute of Geology, Technische Universität Bergakademie Freiberg, 09599 Freiberg, Germany.

Tristan Babey (T)

Department of Earth System Science, Stanford University, Stanford, CA 94305, USA.

Jean Marçais (J)

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAe), RiverLy, Centre de Lyon-Villeurbanne, 69625 Villeurbanne, France.

Benjamin W Abbott (BW)

Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT 84602, USA.

Anniet M Laverman (AM)

Univ Rennes, CNRS, Ecobio, UMR 6553, 35000 Rennes, France.

Zahra Thomas (Z)

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAe), Sol Agro et Hydrosystème Spatialisation, UMR 1069, Agrocampus Ouest, 35042 Rennes, France.

Luc Aquilina (L)

Univ Rennes, CNRS, Géosciences Rennes, UMR 6118, 35000 Rennes, France.

Gilles Pinay (G)

Environnement, Ville et Société, EVS, UMR5600 CNRS, Lyon, France.

Jean-Raynald de Dreuzy (JR)

Univ Rennes, CNRS, Géosciences Rennes, UMR 6118, 35000 Rennes, France; Univ Rennes, CNRS, OSUR (Observatoire des sciences de l'univers de Rennes), UMS 3343, 35000 Rennes, France. Electronic address: jean-raynald.de-dreuzy@univ-rennes1.fr.

Classifications MeSH