Review and assessment of nitrate reduction in groundwater in the Baltic Sea Basin

Højberg, Anker Lajer; Hansen, Anne Lausten; Wachniew, Przemysław; Żurek, Anna; Virtanen, Seija; Arustienė, Jurga; Strömqvist, Johan; Ранкинен, Катри; Refsgaard, Jens Christian

doi:10.1016/j.ejrh.2017.04.001

Cited by 44 publications

(29 citation statements)

References 66 publications

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“…For instance, in the north-west part of the Selke catchment, the relatively lower nitrate outputs (i.e., soil denitrification and terrestrial exports, Figure 8e and f) resulted in nitrate enrichment in the soil moisture and N2NO 2 3 concentration reached up to 45 mg l 21 in recent years (Figure 7a). Although the model was validated at the three gauging stations, nitrate distributions of concentrations and fluxes were in reasonable ranges that suggested by literature (Hofstra & Bouwman, 2005;Wade et al, 2002;Whitehead et al, 1998b) and field measurements. Hofstra and Bouwman (2005) summarized 336 denitrification experiments in agricultural soils and reported mean values ranged 8-51 kg N ha 21 yr 21 , comparing with our modeling mean value 24.5 kg N ha 21 yr 21 .…”

Section: 1029/2017wr022380supporting

confidence: 71%

“…Although the model was validated at the three gauging stations, nitrate distributions of concentrations and fluxes were in reasonable ranges that suggested by literature (Hofstra & Bouwman, 2005;Wade et al, 2002;Whitehead et al, 1998b) and field measurements. Hofstra and Bouwman (2005) summarized 336 denitrification experiments in agricultural soils and reported mean values ranged 8-51 kg N ha 21 yr 21 , comparing with our modeling mean value 24.5 kg N ha 21 yr 21 . Wade et al (2002) reported INCA-N model results of crop/plant uptake, denitrification, mineralization and N-leaching (i.e., terrestrial export in this study) in an England catchment and provided literature ranges respectively.…”

Section: 1029/2017wr022380supporting

confidence: 71%

“…Studies have shown the significance of nitrate retention in subsurface water (Højberg et al, ; Wriedt & Rode, ). However, there are different options on how to conceptualize the subsurface water storage (e.g., the general “groundwater” storage or separated “soil moisture” and “deep groundwater” storages), and how to estimate nitrate concentration therein considering its vertical distribution (Dupas et al, ; Musolff et al, ).…”

Section: Discussionmentioning

confidence: 99%

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A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale

Yang

Jomaa

Zink

et al. 2018

Water Resources Research

104

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Hydrological water quality models have gained wide acceptance from environmental scientists and water managers to address deterioration of surface water quality. Higher spatiotemporal accuracy of such models is increasingly required for better understanding the functional heterogeneity of catchments and improving management decisions at different governance levels. However, balancing spatial representation and model complexity remains challenging. We present a new flexibly designed, fully distributed nitrate transport and removal model (mHM‐Nitrate) at catchment scale. The model was developed mainly based on the mesoscale Hydrological Model (mHM) and the Hydrological Predictions for the Environment (HYPE) model. The mHM‐Nitrate model was tested in the Selke catchment (Central Germany), which is characterized by heterogeneous physiographic and land‐use conditions, using adequate observed hydrological and nitrate data at three nested gauging stations. Long term (1997–2015) daily simulations showed that the model well reproduced the seasonal dynamics of biweekly nitrate observations in forested, agricultural and urban areas. High‐frequency measurements (2010‐2015) were additionally used to validate model performance of simulating short‐term changes in stream‐water concentrations that reflect changes in runoff partitioning and event‐based dilution effects. Uncertainty analysis confirmed the model's robustness. Moreover, model calculations showed that mean terrestrial nitrate input/output (in total 105 kg ha−1 yr−1) and in‐stream removal (8% of mean nitrate load) were in comparable ranges with literature, respectively. The new mHM‐Nitrate model is capable of providing detailed spatial information on nitrate concentrations and fluxes, which can motivate more specific catchment investigations on nitrate transport processes and provide guidance on spatially differentiated agricultural practices and measures.

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Section: 1029/2017wr022380supporting

confidence: 71%

Section: 1029/2017wr022380supporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

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A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale

Yang

Jomaa

Zink

et al. 2018

Water Resources Research

104

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“…The cumulative influx of oxygen and nitrate into the subsurface controls the degree of depletion of reduced compounds and hence the resulting downward mitigation of the redox interface. The rate at which the redox interface mitigates has been accelerated in the past 60–70 years where nitrate, acting as an additional oxidant next to oxygen, has been added to the system (Højberg et al, ). The variability in mitigation rates can be explained by the amount of reduced compounds in the sediment (organic carbon and pyrite), concentration of oxygen and nitrate in the infiltrating water, and the overall vertical flux.…”

Section: Methodsmentioning

confidence: 99%

“…Natural removal of nitrate occurs during the transport from source areas by different natural biogeochemical processes. These processes include denitrification, where nitrate is reduced to N 2 gas through a microbially facilitated process, sedimentation, or the transformation of nitrate into other N‐compounds (Højberg et al, ; Jessen et al, ; Thayalakumaran et al, ). Following the Danish Nature Agency (), nitrate loads in Denmark need to be further reduced by up to 70% in some catchments in order to meet requirements of the EU Water Framework Directive.…”

Section: Introductionmentioning

confidence: 99%

Modeling Depth of the Redox Interface at High Resolution at National Scale Using Random Forest and Residual Gaussian Simulation

Koch

Stisen

Refsgaard

et al. 2019

Water Resources Research

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The management of water resources needs robust methods to efficiently reduce nitrate loads.Knowledge on where natural denitrification takes place in the subsurface is thereby essential. Nitrate is naturally reduced in anoxic environments and high-resolution information of the redox interface, that is, the depth of the uppermost reduced zone is crucial to understand the variability of the denitrification potential. In this study we explore the opportunity to use random forest (RF) regression to model redox depth across Denmark at 100-m resolution based on~13,000 boreholes as training data. We highlight the importance of expert knowledge to guide the RF model in areas where our conceptual understanding is not represented correctly in the training data set by addition of artificial observations. We apply random forest regression kriging in which sequential Gaussian simulation models the RF residuals. The RF model reaches a R 2 score of 0.48 for an independent validation test. Including sequential Gaussian simulation honors observations through local conditioning, and the spread of 800 realizations can be utilized to map uncertainty. Emphasis is put on adequate handling of nonstationarities in variance and spatial correlation of the RF residuals. The RF residuals show no spatial correlation for large parts of the modeling domain, and a local variance scaling method is applied to account for the nonstationary variance. Moreover, we present and exemplify a framework where newly acquired field data can easily be integrated into random forest regression kriging to quickly update local models.Plain Language Summary Nutrients, such as nitrogen in form of nitrate are essential for plant growth. Despite their benefits, nutrient surplus can cause adverse health and ecological effects. In fact, nitrate leaching from agricultural fields is one of the major water resources management challenges in today's agricultural landscapes. During the transport through the subsurface, nitrate can potentially be degraded in anoxic layers below the redox interface. This is referred to as denitrification and its potential varies spatially depending on the local landscape. The location of the redox interface is thus essential knowledge toward a spatially differentiated water resources management. We applied a machine learning method to model the redox depth at 100m spatial resolution across Denmark, based on redox data obtained from~13,000 boreholes and environmental variables that explain redox variability. We highlight the importance of expert knowledge in guiding the machine learning model by adding artificial observations in underrepresented landscapes. As an add-on, a geostatistical model is used to honor local data at grid scale and quantify the spatial uncertainty elsewhere. Lastly, we outline and exemplify a framework that allows an easy integration of newly acquired field data for updating local models of the depth to the redox interface.

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Overview of Water Resources, Quality, and Management in Baltic Sea Countries

Nasr

Negm

2020

Water Resources Quality and Management in Baltic Sea Countries

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Review and assessment of nitrate reduction in groundwater in the Baltic Sea Basin

Cited by 44 publications

References 66 publications

A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale

A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale

Modeling Depth of the Redox Interface at High Resolution at National Scale Using Random Forest and Residual Gaussian Simulation

Overview of Water Resources, Quality, and Management in Baltic Sea Countries

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