Variations of denitrification in a farming catchment area

Oehler, Francois; Bordenave, Pierre; Durand, Patrick

doi:10.1016/j.agee.2006.10.007

Cited by 42 publications

(44 citation statements)

References 45 publications

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“…Due to high environment heterogeneity in riparian soils, past studies of spatial distribution of the denitrification potential in these systems showed high variations in width and depth, [17][18][19][20][21]. In addition to previously exposed factors in riparian zones, variability can be caused by the great diversity of minerals and organic materials that can be found in different stratigraphic positions, due to sediment transport and settling mechanisms.…”

Section: Eqmentioning

confidence: 99%

“…The main climate parameters were an annual mean temperature of 8.9ºC and an annual accumulated precipitation of 733 mm (Years: 2001 to 2010, grid 20x20 km for temperature and 10x10 km for precipitation), [32]. 21 Evi site is a natural wetland located close to Lake Ejstrup and the city of Ejstrupholm, in Jutland, (55º59'28''N, 9º19'43''E; WGS84). The catchment area is at the border of the maximum extension of the Weichselian glaciers corresponding to the last glacial advance.…”

Section: Description Of the Sitesmentioning

confidence: 99%

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Subsurface nitrate reduction under wetlands takes place in narrow superficial zones

Ribas

Calderer

Martí

et al. 2017

Environmental Technology

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Section: Eqmentioning

confidence: 99%

Section: Description Of the Sitesmentioning

confidence: 99%

Subsurface nitrate reduction under wetlands takes place in narrow superficial zones

Ribas

Calderer

Martí

et al. 2017

Environmental Technology

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“…Our long term goal is developing a model of denitrification at the catchment scale that also addresses the significant emissions from upland areas (Oehler et al, 2007). To achieve this aim, we turned towards simplified modelling approaches also because (1) mechanistic models are developed and validated for homogeneous and simple medium, which is not necessarily appropriate at the catchment scale (Beven, 1993), (2) either the accuracy of measured emissions is poor and/or sampling is too scarce (Groffman et al, 2006), (3) simplified models need inputs that can be obtained either from relatively simple field measurements or directly from simulation models.…”

Section: F Oehler Et Al: Generalized Simplified Denitrification Modelmentioning

confidence: 99%

“…The dasabase was built with datasets easily extractable from the literature (Cosandey et al, 2003;Henault and Germon, 2000;Luo et al, 1999;Oehler et al, 2007;Ryden, 1983;Zaman and Nguyen, 2010). Denitrification rate (Da) rates were measured using the acetylene (C 2 H 2 ) blockage technique (Ryden et al, 1987;Yoshinari et al, 1977).…”

Section: Database and Input Factorsmentioning

confidence: 99%

“…The difference between annual nitrogen flow measured at the catchment outlet and the nitrogen surplus do not provide a reliable estimate of the denitrification at the catchment scale, because of temporary storage processes of nitrogen in the soil, vadose zone or groundwater (Basset-Mens et al, 2006;Molenat and Gascuel-Odoux, 2002;Ruiz et al, 2002). Furthermore, losses by gaseous emission through denitrification are not evenly distributed over the catchment area since they are particularly higher in the riparian zone (Fisher and Acreman, 2004;Haag and Kaupenjohann, 2001;Martin et al, 1999;Oehler et al, 2007;Sebilo et al, 2003). As a result, it is still problematic to up-scale measured emissions to a larger, landscape scale which is the most relevant to assess the impact of agriculture practices and their management.…”

Section: Introductionmentioning

confidence: 99%

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The use of machine learning algorithms to design a generalized simplified denitrification model

2010

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Abstract. We propose to use machine learning (ML) algorithms to design a simplified denitrification model. Boosted regression trees (BRT) and artificial neural networks (ANN) were used to analyse the relationships and the relative influences of different input variables towards total denitrification, and an ANN was designed as a simplified model to simulate total nitrogen emissions from the denitrification process. To calibrate the BRT and ANN models and test this method, we used a database obtained collating datasets from the literature. We used bootstrapping to compute confidence intervals for the calibration and validation process. Both ML algorithms clearly outperformed a commonly used simplified model of nitrogen emissions, NEMIS, which is based on denitrification potential, temperature, soil water content and nitrate concentration. The ML models used soil organic matter % in place of a denitrification potential and pH as a fifth input variable. The BRT analysis reaffirms the importance of temperature, soil water content and nitrate concentration. Generalization, although limited to the data space of the database used to build the ML models, could be improved if pH is used to differentiate between soil types. Further improvements in model performance and generalization could be achieved by adding more data.

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Spatiotemporal investigation of geochemical and hydrological controls on release of soluble reactive phosphorus from the shallow aquifer of a riparian zone

Wang,

Roy,

Power

et al. 2024

J of Env Quality

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Phosphorus (P) that accumulates in agricultural riparian zones can be released under certain hydrological and biogeochemical conditions, thereby limiting the effectiveness of these zones in reducing P loads from field to stream. The study objective was to explore factors that may be contributing to, or limiting, high soluble reactive phosphorus (SRP) concentrations in the shallow aquifer of an alluvial upland riparian zone located in a continental climate. Field investigations including porewater sampling from six vertical nests, soil sampling, and continuous soil moisture, groundwater table, and redox measurements were conducted over 19 months. Porewater SRP concentrations were generally low in the aquifer considering all sampling times (median = 14.7 µg/L; interquartile range [IQR] = 11.1 µg/L, 287 samples). The overall low SRP may be due to low reducible labile soil P (median = 21.1 µgP/g dw, IQR = 10.9 µgP/g dw, 21 samples). However, high SRP concentrations (>52 µg/L, 95% quartile) did occur intermittently in space and time with no clear spatial or temporal patterns. Analyses indicate that most high concentrations were likely not associated with factors previously reported to influence SRP release in riparian aquifers, including redox conditions, pH, and soil drying and wetting. Further, data indicate that internally released or externally supplied SRP may undergo rapid (re‐) sequestration within the aquifer, limiting its vertical or horizontal transport. The study findings highlight the complexity of P behavior in riparian zones and the need for caution when assessing the effectiveness of conservation practices and in interpreting potential impacts of subsurface water quality on stream water quality when monitoring locations are distant from the stream.

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Variations of denitrification in a farming catchment area

Cited by 42 publications

References 45 publications

Subsurface nitrate reduction under wetlands takes place in narrow superficial zones

Subsurface nitrate reduction under wetlands takes place in narrow superficial zones

The use of machine learning algorithms to design a generalized simplified denitrification model

Spatiotemporal investigation of geochemical and hydrological controls on release of soluble reactive phosphorus from the shallow aquifer of a riparian zone

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