Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment

Exbrayat, Jean‐François; Viney, Neil R.; Seibert, Jan; Wrede, S.; Frede, H. G.; Breuer, Lutz

doi:10.5194/hess-14-2383-2010

Cited by 28 publications

(21 citation statements)

References 60 publications

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“…Ensemble modelling is a way to handle this problem (e.g. Viney et al 2005;Exbrayat et al 2010). By including more models in the analysis it is more likely that the dominant processes and initial states (e.g.…”

Section: Uncertainties In the Results From The Model Experimentsmentioning

confidence: 99%

“…Norbert et al 2010;Arheimer et al 2011a, b). Ensemble means from several hydrological models have actually been found to give better performance than each single model used (Viney et al 2005), and recently this approach has also been applied to water quality models (Exbrayat et al 2010). The Balt-HYPE model may contribute to such a model ensemble in the future.…”

Section: Introductionmentioning

confidence: 99%

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Climate Change Impact on Riverine Nutrient Load and Land-Based Remedial Measures of the Baltic Sea Action Plan

Arheimer

Dahné

Donnelly

2012

AMBIO

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To reduce eutrophication of the Baltic Sea, all nine surrounding countries have agreed upon reduction targets in the HELCOM Baltic Sea Action Plan (BSAP). Yet, monitoring sites and model concepts for decision support are few. To provide one more tool for analysis of water and nutrient fluxes in the Baltic Sea basin, the HYPE model has been applied to the region (called Balt-HYPE). It was used here for experimenting with land-based remedial measures and future climate projections to quantify the impacts of these on water and nutrient loads to the sea. The results suggest that there is a possibility to reach the BSAP nutrient reduction targets by 2100, and that climate change may both aggravate and help in some aspects. Uncertainties in the model results are large, mainly due to the spread of the climate model projections, but also due to the hydrological model.

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Section: Uncertainties In the Results From The Model Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Climate Change Impact on Riverine Nutrient Load and Land-Based Remedial Measures of the Baltic Sea Action Plan

Arheimer

Dahné

Donnelly

2012

AMBIO

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“…In recent years multi-model averaging has been widely used to extract information from large ensembles of simulations in studies targeting climate change (Bishop and Abramowitz, 2012;Krishnamurti et al, 1999), rainfallrunoff processes (Georgakakos et al, 2004;Huisman et al, 2009;Shamseldin et al, 1997;Viney et al, 2009) and catchment-scale nutrient exports (Exbrayat et al, 2010(Exbrayat et al, , 2013b. These methods range from simple arithmetic means of model ensembles to more elaborate weighting schemes such as Bayesian that take model performance into account model averaging (Raftery et al, 2005).…”

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confidence: 99%

Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties

et al. 2018

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Abstract. Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in Earth system modelling. Here, we use three global observationally orientated estimates of current net primary productivity (NPP) to perform a reliability ensemble averaging (REA) method using 30 global simulations of the 21st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) "business as usual" emissions scenario. We find that the three REA methods support an increase in global NPP by the end of the 21st century (2095-2099) compared to [2001][2002][2003][2004][2005], which is 2-3 % stronger than the ensemble ISIMIP mean value of 24.2 Pg C y −1 . Using REA also leads to a 45-68 % reduction in the global uncertainty of 21st century NPP projection, which strengthens confidence in the resilience of the CO 2 fertilization effect to climate change. This reduction in uncertainty is especially clear for boreal ecosystems although it may be an artefact due to the lack of representation of nutrient limitations on NPP in most models. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions.

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“…But to our knowledge, the ensemble methodology has received little interest in the nutrient fluxes context to date, in spite of the demonstrated improvement in prediction reliability. Furthermore, the few available studies, including previous publications by our working group, have only been based on re-prediction (hindcasting) efforts rather than scenario analyses (Exbrayat et al, 2010(Exbrayat et al, , 2011Kronvang et al, 2009). Therefore, we present here an example of the potential advantage of using multi-model predictions to assess the impact of a simple management change on the nutrient balance of a well-monitored mesoscale catchment in south-west Western Australia.…”

Section: Introductionmentioning

confidence: 99%

Using multi-model averaging to improve the reliability of catchment scale nitrogen predictions

Exbrayat

Viney

Frede³

2013

Geosci. Model Dev.

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Abstract. Hydro-biogeochemical models are used to foresee the impact of mitigation measures on water quality. Usually, scenario-based studies rely on single model applications. This is done in spite of the widely acknowledged advantage of ensemble approaches to cope with structural model uncertainty issues. As an attempt to demonstrate the reliability of such multi-model efforts in the hydro-biogeochemical context, this methodological contribution proposes an adaptation of the reliability ensemble averaging (REA) philosophy to nitrogen losses predictions. A total of 4 models are used to predict the total nitrogen (TN) losses from the wellmonitored Ellen Brook catchment in Western Australia. Simulations include re-predictions of current conditions and a set of straightforward management changes targeting fertilisation scenarios. Results show that, in spite of good calibration metrics, one of the models provides a very different response to management changes. This behaviour leads the simple average of the ensemble members to also predict reductions in TN export that are not in agreement with the other models. However, considering the convergence of model predictions in the more sophisticated REA approach assigns more weight to previously less well-calibrated models that are more in agreement with each other. This method also avoids having to disqualify any of the ensemble members.

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Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment

Cited by 28 publications

References 60 publications

Climate Change Impact on Riverine Nutrient Load and Land-Based Remedial Measures of the Baltic Sea Action Plan

Climate Change Impact on Riverine Nutrient Load and Land-Based Remedial Measures of the Baltic Sea Action Plan

Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties

Using multi-model averaging to improve the reliability of catchment scale nitrogen predictions

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