A lumped model to simulate nitrate concentration evolution in groundwater at catchment scale-Reference-Cited by-同舟云学术

A lumped model to simulate nitrate concentration evolution in groundwater at catchment scale

Published:2021-05 Issue: Volume:596 Page:125696
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Surdyk Nicolas,Gutierrez Alexis,Baran Nicole,Thiéry Dominique

Funder

Bureau de Recherches Géologiques et Minières

Agence de l'eau Loire-Bretagne

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference28 articles.

1. Agreste, 2018 - On line data - Available at https://agreste.agriculture.gouv.fr/agreste-web/disaron/!searchurl/searchUiid/search/.

2. A lumped conceptual approach for modeling hydrological processes: the case of Scopia catchment area;Charizopoulos;Central Greece. Environ. Earth Sci.,2017

3. Calcul de la fertilisation azotée - Guide méthodologique pour l'établissement des prescriptions locales - Cultures annuelles et prairies;COMIFER;COMIFER,2013

4. Lag time in water quality response to best management practices: a review;Donald;J. Environ. Qual.,2010

5. Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration;Gharari;Hydrol. Earth Syst. Sci.,2015

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Defining the maximum nitrogen surplus in water management plans to recover nitrate polluted aquifers in Spain;Journal of Environmental Management;2024-04

2. Predicting the impact and duration of persistent and mobile organic compounds in groundwater systems using a contaminant mass discharge approach;Journal of Environmental Management;2023-12

3. Dynamic Groundwater Contamination Vulnerability Assessment Techniques: A Systematic Review;Hydrology;2023-09-04

4. A parsimonious model for predicting the NO3−-N concentration in shallow groundwater in intensive agricultural areas using few easily accessible indicators and small datasets based on machine learning;Journal of Hydrology;2023-04

5. Predicting nitrate concentration in river using advanced artificial intelligence techniques;Water, Land, and Forest Susceptibility and Sustainability;2023