Model Based Assessment of Nitrate Pollution of Water Resources on a Federal State Level for the Dimensioning of Agro-environmental Reduction Strategies

Kuhr, Petra; Haider, Josef; Kreins, Peter; Kunkel, Ralf; Tetzlaff, Björn; Vereecken, Harry; Wendland, Frank

doi:10.1007/s11269-012-0221-z

Cited by 29 publications

(19 citation statements)

References 36 publications

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“…The redox conditions tend to be less anaerobic to intermediate in the Geest areas in contrast to the lowlands where strong anaerobic conditions prevail, which was also reported by Eschenbach et al (2018). Conversely, lowlands with sand and gravel deposits in the middle and southern part of Germany often show nitrate-polluted groundwater, due to a lack of cover layer, weakly anaerobic to intermediate redox conditions and intensive agricultural land use (Grimm-Strele et al 2008, Wendland et al 2008, Kuhr et al 2013, Knoll et al 2019. For the pore aquifers of the northern Alpine foothills, the RF NO3 model predicts large areas of nitrate-polluted groundwater, despite generally well-developed cover layers and the associated high protective function of the aquifers in this region.…”

Section: Discussionmentioning

confidence: 78%

Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning

Knoll

Bach

2020

Environ. Res. Lett.

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The protection of water resources and development of mitigation strategies require large-scale information on water pollution such as nitrate. Machine learning techniques like random forest (RF) have proven their worth for estimating groundwater quality based on spatial environmental predictors. We investigate the potential of RF and quantile random forest (QRF) to estimate redox conditions and nitrate concentration in groundwater (1 km × 1 km resolution) using the European Water Framework Directive groundwater monitoring network as well as spatial environmental information available throughout Germany. The RF model for nitrate achieves a good predictive performance with an R 2 of 0.52. Dominant predictors are the redox conditions in the groundwater body, hydrogeological units and the percentage of arable land. An uncertainty assessment using QRF shows rather large uncertainties with a mean prediction interval (MPI) of 53.0 mg l −1 . This study represents the first nation-wide data-driven assessment of the spatial distribution of groundwater nitrate concentrations for Germany.

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Section: Discussionmentioning

confidence: 78%

Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning

Knoll

Bach

2020

Environ. Res. Lett.

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“…Annual global nitrogen (N) production by humans is currently more than double the natural fixation rate and its release into the environment is associated with a range of negative impacts (Kuhr et al, 2013). Excessive nitrogen discharge can cause serious problems from eutrophication to anoxia, with potential implications for human health (van Grinsven et al, 2006;Ward et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Identification and Quantification of Nitrogen in a Reservoir, Jiaodong Peninsula, China

Meng

Sheng

Sun³

et al. 2017

Water Environment Research

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Identification of nitrogen (N) sources is important in water quality control and management. Nitrogen pollution can lead to eutrophication of waterbodies and high concentrations of nitrate in drinking water can pose potential health problems. The 15N isotope and nitrogen fluxes budget approach is useful for determining the source of to surface waters. In this study, mass balance and 15N isotope approaches and nitrogen flux budgets were applied to identify total nitrogen (TN) sources and nitrogen transformation processes in the Menlou Reservoir (MR), Jiaodong Peninsula, China. The different fractions of nitrogen and their 15N isotope signatures were analyzed in the reservoir water, river water, groundwater, soil, and atmospheric precipitation. The results indicate that surface runoff pollution (e.g., fertilizer and animal manure) is the main source of in MR. High concentrations of TN in MR are caused by low nitrogen self-purification (denitrification) rate, low sediment adsorption capacity, and the influx of rich groundwater.

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“…mGROWA emerged from the water balance model GROWA (Kunkel and Wendland, 2002), which has been used in numerous (large-scale) water balance studies in and outside Germany since the nineties (Herrmann et al, 2009;Kuhr et al, 2013;Panagopoulos et al, 2014;Tetzlaff et al, 2009, accepted for publication;Wendland et al, 2009Wendland et al, , 2003. The ideas and concepts underlying the GROWA/mGROWA modelling approach are described in detail in the references specified above and will thus not be repeated here.…”

Section: The Groundwater Recharge Model Mgrowamentioning

confidence: 99%

Simulation of future groundwater recharge using a climate model ensemble and SAR-image based soil parameter distributions — A case study in an intensively-used Mediterranean catchment

Herrmann

Baghdadi²,

Blaschek

et al. 2016

Science of The Total Environment

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Model Based Assessment of Nitrate Pollution of Water Resources on a Federal State Level for the Dimensioning of Agro-environmental Reduction Strategies

Cited by 29 publications

References 36 publications

Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning

Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning

Identification and Quantification of Nitrogen in a Reservoir, Jiaodong Peninsula, China

Simulation of future groundwater recharge using a climate model ensemble and SAR-image based soil parameter distributions — A case study in an intensively-used Mediterranean catchment

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