A 3D hydrogeochemistry model of nitrate transport and fate in a glacial sediment catchment: A first step toward a numerical model

Kim, Hyojin; Sandersen, Peter B.E.; Jakobsen, Rasmus; Kallesøe, Anders Juhl; Claes, Niels; Blicher-Mathiesen, Gitte; Foged, Nikolaj; Aamand, Jens; Hansen, Birgitte

doi:10.1016/j.scitotenv.2021.146041

Cited by 12 publications

(8 citation statements)

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“…For instance, Kim et al (2021), by synthesizing long-term monitoring data of groundwater and stream chemistry and the geological structure information obtained using subsurface resistivity models and their geological interpretations, identified underlying processes of the spatial and temporal variability of nitrate concentrations in ground-and stream water and also delineated the subsurface into subzones of these processes. 34 Fundamental understanding of nitrate transport and fate and cross-comparisons with other study sites underlain by similar geological settings may contribute to increase the realism of the structural information and the predictive power of catchment N simulations. 38−42 In this study, we constructed a TI encapsulating the 3dimensional (3D) structural information of denitrification zones with their representative denitrification rates, which is a further development of the models of the hydrogeological structure and redox architecture by integrating laboratory measurements of denitrification rates.…”

Section: Introductionmentioning

confidence: 99%

“…The critical element of the MPS simulations is the design of the TIs, which greatly relies on the quality of interpreted knowledge. Nitrate evolution and its temporal and spatial variabilities have indeed been extensively investigated via numerous studies, for example, refs − , and various monitoring programs, for example,, refs − , in many catchments around the world. Such ample information has often not been fully integrated in the modeling framework, mainly because it is conceptual and is available at the point scale; thus, upscaling it could be challenging especially under geologically heterogeneous settings, that is, glacial deposit landscapes.…”

Section: Introductionmentioning

confidence: 99%

“…By employing geophysical information, such knowledge may be better integrated in modeling of the subsurface structure and eventually N transport and fate. For instance, Kim et al (2021), by synthesizing long-term monitoring data of groundwater and stream chemistry and the geological structure information obtained using subsurface resistivity models and their geological interpretations, identified underlying processes of the spatial and temporal variability of nitrate concentrations in ground- and stream water and also delineated the subsurface into subzones of these processes . Fundamental understanding of nitrate transport and fate and cross-comparisons with other study sites underlain by similar geological settings may contribute to increase the realism of the structural information and the predictive power of catchment N simulations. − …”

Section: Introductionmentioning

confidence: 99%

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Upscaling of Denitrification Rates from Point to Catchment Scales for Modeling of Nitrate Transport and Retention

Kim

Jakobsen

Aamand

et al. 2021

Environ. Sci. Technol.

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The spatial and temporal variability of denitrification makes it challenging to integrate conceptual, process-based understandings of nitrate transport and retention into numerical modeling at the catchment scale, although it is critical for the realism and predictive power of the model. In this study, we propose a novel approach where the conceptual understandings of the spatial structure of denitrification zones and the corresponding representative denitrification rates are transformed into a form that can be integrated into a multi-point statistical simulation framework. This is done by constructing a denitrification training image (TI) coupled to a geophysically based TI of the hydrogeological structure. The field observations and laboratory analyses of denitrification rates and the chemistry of water and sediment revealed that the study catchment's subsurface can be characterized by three zones: (1) the oxic zone with no nitrate reduction; (2) the slow-denitrification zone (mean of ln-transformed rate = −1.19 ± 0.52 mg N L −1 yr −1 ); and(3) the high-denitrification zone (mean of ln-transformed rate = 3.86 ± 1.96 mg N L −1 yr −1 ). The underlying controls on the spatial distribution of these zones and the representativeness of denitrification rates were investigated. Then, a TI illustrating the subsurface structure of the denitrification zone was constructed by synthesizing the results of these geochemical interpretations and the hydrogeology TI.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Upscaling of Denitrification Rates from Point to Catchment Scales for Modeling of Nitrate Transport and Retention

Kim

Jakobsen

Aamand

et al. 2021

Environ. Sci. Technol.

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“…To investigate the potential interbasin consistency of riverine nitrogen export, Experiment 2 was conducted, in which the selected models (i.e., LSTM-C-TL and LSTM-F-TL) trained and tested in the NRW were “naively” (i.e., without retraining) used in seven distinct watersheds located on different continents (Figure ). These seven watersheds included the West River Watershed (WRW), another subbasin of the JRW (Figure b); the Lillebæk, Odderbæk, and Uggerby watersheds in Denmark; and the Newport Bay (NPB), North Raccoon River-Sac City (NR-SC), and Upper Kankakee River (UKR) watersheds in the United States. These seven watersheds are significantly different in size, climate, and geographic and socioeconomic conditions (Figure ).…”

Section: Methodsmentioning

confidence: 99%

Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions

Xiong

Zheng

Chen

et al. 2022

Environ. Sci. Technol.

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Terrestrial export of nitrogen is a critical Earth system process, but its global dynamics remain difficult to predict at a high spatiotemporal resolution. Here, we use deep learning (DL) to model daily riverine nitrogen export in response to hydrometeorological and anthropogenic drivers. Long short-term memory (LSTM) models for the daily concentration and flux of dissolved inorganic nitrogen (DIN) were built in a coastal watershed in southeastern China with a typical subtropical monsoon climate. The DL models exhibited excellent accuracy for both DIN concentration and flux, with Nash-Sutcliffe efficiency coefficients (NSEs) up to 0.67 and 0.92, respectively, a performance unlikely to be achieved by generic process-based models with comparable data quality. The flux model ensemble, without retraining, performed well (mean NSE = 0.32–0.84) in seven distinct watersheds in Asia, Europe, and North America, and retraining with multi-watershed data further improved the lowest NSE from 0.32 to 0.68. DL interpretation confirmed that interbasin consistency of riverine nitrogen export exists across different continents, which stems from the similarities in rainfall–runoff relationships. The multi-watershed flux model projects 0.60–12.4% increases in the nitrogen export to oceans from the studied watersheds under a 20% increase in fertilizer consumption, which rises to 6.7–20.1% with a 10% increase in runoff, indicating the synergistic effect of human activities and climate change. The DL-based method represents a successful case of explainable artificial intelligence in environmental science, providing a potential shortcut to a consistent understanding of the global daily-resolution dynamics of riverine nitrogen export under the currently limited data conditions.

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“…Low N retention is also seen in areas with near surface oxic sandy layers with no denitrification potential, and very undulating and incoherent redox structures. In Quaternary deposits, these complex structures are found in several distinct types of deposits and structures such as thrusted layers, geological windows, infills in buried valleys or otherwise heterogenous geological settings 35 , 37 , 38 . In addition, low N retention is observed in groundwater recharge areas where oxic or nitrate reducing anoxic layers have hydraulic connection to the streams.…”

Section: Variation In Groundwater N Retentionmentioning

confidence: 99%

Assessing groundwater denitrification spatially is the key to targeted agricultural nitrogen regulation

Hansen,

Aamand,

Blicher-Mathiesen

et al. 2024

Sci Rep

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Globally, food production for an ever-growing population is a well-known threat to the environment due to losses of excess reactive nitrogen (N) from agriculture. Since the 1980s, many countries of the Global North, such as Denmark, have successfully combatted N pollution in the aquatic environment by regulation and introduction of national agricultural one-size-fits-all mitigation measures. Despite this success, further reduction of the N load is required to meet the EU water directives demands, and implementation of additional targeted N regulation of agriculture has scientifically and politically been found to be a way forward. In this paper, we present a comprehensive concept to make future targeted N regulation successful environmentally and economically. The concept focus is on how and where to establish detailed maps of the groundwater denitrification potential (N retention) in areas, such as Denmark, covered by Quaternary deposits. Quaternary deposits are abundant in many parts of the world, and often feature very complex geological and geochemical architectures. We show that this subsurface complexity results in large local differences in groundwater N retention. Prioritization of the most complex areas for implementation of the new concept can be a cost-efficient way to achieve lower N impact on the aquatic environment.

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A 3D hydrogeochemistry model of nitrate transport and fate in a glacial sediment catchment: A first step toward a numerical model

Cited by 12 publications

References 70 publications

Upscaling of Denitrification Rates from Point to Catchment Scales for Modeling of Nitrate Transport and Retention

Upscaling of Denitrification Rates from Point to Catchment Scales for Modeling of Nitrate Transport and Retention

Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions

Assessing groundwater denitrification spatially is the key to targeted agricultural nitrogen regulation

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