Linking an economic model for European agriculture with a mechanistic model to estimate nitrogen losses from cropland soil in Europe

Leip, Adrian; Marchi, Giulio; Koeble, Renate; Kempen, Markus; Britz, Wolfgang; Li, C.

doi:10.5194/bgd-4-2215-2007

Cited by 20 publications

(7 citation statements)

References 34 publications

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“…The 20 dataset "Agricultural Landuse2000" from the JRC AFOLU project (Leip et al, 2007) ( 1 0 ) This regression was validated with the French data achieving reasonable results and finally used to calculate the façade areas in Germany and Luxembourg (see Supplementary Information, Appendix A6, Fig. S5).…”

Section: Land Use Datamentioning

confidence: 99%

Modelling biocide and herbicide concentrations in catchments of the Rhine basin

Möser¹,

Wemyss²,

Scheidegger³

et al. 2017

Preprint

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Abstract. Impairment of water quality by organic micropollutants such as pesticides, pharmaceuticals or household 10 chemicals is a problem in many catchments worldwide. These chemicals originate from different urban and agricultural usages and are transferred to surface waters from point or diffuse sources by a number of transport pathways. The quantification of this form of pollution in streams is challenging and especially demanding for diffuse pollution due to the high spatio-temporal concentration dynamics, which requires large sampling and analytical efforts to obtain representative data on the actual water quality. 15 Models can also be used to predict information to which degree streams are affected by these pollutants. However, spatially distributed modeling of water quality is challenging for a number of reasons. Key issues are the lack of such models that incorporate both urban and agricultural sources of organic micropollutants, the large number of parameters to be estimated for many available water quality models, and the difficulty to transfer parameter estimates from calibration sites to areas 20 where predictions are needed.To overcome these difficulties, we used the parsimonious iWaQa model that simulates herbicide transport from agricultural fields and diffuse biocide losses from urban areas (mainly façades and roof materials) and tested its predictive capabilities in the Rhine River basin. The model only requires between one and eight global model parameters per compound that need to 25 be calibrated. Most of the data requirements relate to spatially distributed land use and comprehensive time series of precipitation, air temperature and spatial data on discharge.The model was calibrated with data sets from three different small catchments (0.5 -24.6 km 2 ) for three agricultural herbicides (isoproturon, S-metolachlor, terbuthylazine) and two urban biocides (carbendazim, diuron). Subsequently, it was 30 validated for different years on 12 catchments of much larger size (31 -160'000 km 2 ) without any modification. For most compound-catchment combinations, the model predictions revealed a satisfactory correlation (median r 2 : 0.5) with the observations and the peak concentrations mostly predicted within a factor of two to four (median: 2.1 fold difference for herbicides and 3.2 for biocides respectively). The seasonality of the peak concentration was also well simulated, the predictions of the actual timing of peak concentrations however, was generally poor. 35Limited spatio-temporal data, first on the use of the selected pesticides and second on their concentrations in the river network, restrict the possibilities to scrutinise model performance. Nevertheless, the results strongly suggest that input data and model structure are major sources of predictive uncertainty. The latter is for example seen in background concentrations that are systematically overestimated in certain regions, which is most probably linked to the modelled coupling of 40 background concentrations to land use intensity.H...

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Section: Land Use Datamentioning

confidence: 99%

Modelling biocide and herbicide concentrations in catchments of the Rhine basin

Möser¹,

Wemyss²,

Scheidegger³

et al. 2017

Preprint

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“…Historic data for countries of the former Soviet Union (1970( -1990( ), Yugoslavia (1970, Belgium and Luxemburg (1970), Czechoslovakia (1970 and Ethiopia andEritrea (1970-2012 Livestock numbers are combined with estimates for animal waste per head to estimate the total amount of animal waste produced. Nitrogen excretion rates for cattle, pigs and chicken in developed countries are derived from the CAPRI model 9 for Europe (Leip et al, 2007) and for all other countries and animal types IPCC (2006) (FAO Geonetwork, 2011) with the RIVM land-use map (Goldewijk et al, 2007). Different N2O emission factors are applied to tropical and non-tropical regions.…”

Section: Industrial Processes Statisticsmentioning

confidence: 99%

EDGAR v4.3.2 Global Atlas of the three major Greenhouse Gas Emissions for the period 1970–2012

Janssens-Maenhout

Crippa

Guizzardi³

et al. 2017

Preprint

123

115

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2 use, land-use change and forestry (LULUCF) sector (including forest and savannah burning). Unlike the activities of the LULUCF sector, which are typically estimated top-down from less certain land-use observations, all these activities are estimated bottom-up from standard annual statistics of fuel, products, waste, crops or livestock. We present country-specific emission totals and analyse the trends and variations in emissions of the largest emitting countries together with the EU in more detail, to uncover the effect of changes in human 5 activities with time on each of the gases. The GWP-100 weighted global total GHG emission trend is predominantly determined by the global CO2 trend and in particular, by fuel markets trends, geopolitical changes and financial crises rather than population changes. We also evaluate the uncertainty in emissions for different sectors and three groups of countries (the OECD countries of 1990, the countries with economies in transition in 1990 and the remaining non-Annex I countries). Even though large progress has been made on emission 10 inventory compilation, the uncertainty in global total GHG emissions has not decreased, because of the increasing share of emissions from countries with less developed statistical infrastructure and secondly the decreasing share of emissions from the activities (e.g. coal power plants) for which relatively accurate information is available. Finally, we discuss changes in geospatial distribution with a focus on hot spots and megacities using gridded information. Data is presented online for each source category with annual and monthly 15 global emissions grid-maps of 0.1x0.1 resolution and can be freely accessed from the EDGAR website

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“…This task resulted in an update to the previous data, hereafter referred to as EFSA Spatial Data Version 1.1 (November 2012). This dataset was supplemented with data from the CAPRI land cover database (Leip et al, 2008).…”

Section: Efsa Datasetmentioning

confidence: 99%