Current concepts in nitrogen dynamics for mesoscale catchments

Vaché, Kellie B.; Jülich, Stefan; Frede, Hans‐Georg

doi:10.1623/hysj.53.5.1059

Cited by 43 publications

(41 citation statements)

References 113 publications

(106 reference statements)

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“…A short, general overview of nitrogen processes considered in these models was also provided by Breuer et al (2008).…”

Section: Modelsmentioning

confidence: 99%

“…see Boughton, 2005). However, as emphasised by Breuer et al (2008), there is no single accepted theory of catchment N cycling and models simulating the effects of nitrogen on hydrological and biogeochemical ecosystem functioning are still facing a high degree of uncertainty. Differences between models can be related to the questions they are used to address, involving different descriptions of the nitrogen balance.…”

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confidence: 99%

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

Exbrayat

Viney

Seibert

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. Model predictions of biogeochemical fluxes at the landscape scale are highly uncertain, both with respect to stochastic (parameter) and structural uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a selfdeveloped tool, SWAT and HBV-N-D) designed to simulate hydrological fluxes as well as mobilisation and transport of one or several nitrogen species were applied to the mesoscale River Fyris catchment in mid-eastern Sweden.Hydrological calibration against 5 years of recorded daily discharge at two stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE) ranging between 0.48 and 0.83. Using the calibrated hydrological parameter sets, the parameter uncertainty linked to the nitrogen parameters was explored in order to cover the range of possible predictions of exported loads for 3 nitrogen species: nitrate (NO 3 ), ammonium (NH 4 ) and total nitrogen (Tot-N). For each model and each nitrogen species, predictions were ranked in two different ways according to the performance indicated by two different goodness-of-fit measures: the coefficient of determination R 2 and the root mean square error RMSE. A total of 2160 deterministic Single Model Ensembles (SME) was generated using an increasing number of members (from the 2 best to the 10 best single predictions). Finally the best SME for each model, nitrogen species and discharge station were selected and merged into 330 different Multi-Model Ensembles (MME). The evolution of changes in R 2 and RMSE was used as a performance descriptor of the ensemble procedure.Correspondence to: J.-F. Exbrayat (jean-francois.exbrayat@umwelt.uni-giessen.de) In each studied case, numerous ensemble merging schemes were identified which outperformed any of their members. Improvement rates were generally higher when worse members were introduced. The highest improvements were achieved for the nitrogen SMEs compiled with multiple linear regression models with R 2 selected members, which resulted in the RMSE decreasing by up to 90%.

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“…A short, general overview of nitrogen processes considered in these models was also provided by Breuer et al (2008).…”

Section: Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

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

Exbrayat

Viney

Seibert

et al. 2010

Hydrol. Earth Syst. Sci.

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“…The study of global change impacts on nitrogen dynamics in catchments is the topic of the paper by Breuer et al (2008). The primary mechanisms of change can be grouped broadly into those related to land management, land use, climatic change, as well as nitrogen deposition patterns.…”

Section: This Special Issue On Advances In Ecohydrological Modellingmentioning

confidence: 99%

“…The models analysed are SWAT, SWAT-N, SWIM, INCA, LASCAM, HBV-N, RHESSys and MONERIS. Whilst the review by Breuer et al (2008) was not intended to refer to an exhaustive list of all ecohydrological models that include N cycling, it outlines a classification framework as a means to better understand key differences between common modelling strategies. The authors conclude by proposing the processes that ecohydrological models should include, and suggest additional efforts that should be considered in the course of model development and verification.…”

Section: This Special Issue On Advances In Ecohydrological Modellingmentioning

confidence: 99%

Advances in ecohydrological modelling with SWAT—a review

Krysanova

Arnold

2008

Hydrological Sciences Journal

159

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“…Furthermore, when there is nearly equivalent support in the observed data for multiple models, it is problematic to choose one model over another. Breuer et al (2008) recently reviewed some of the nitrogen hydrobiogeochemical models and discussed the importance of addressing various sources of uncertainty in such models, especially the model structural (i.e. complexity) uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Complexity in microbial metabolic processes in soil nitrogen modeling: a case for model averaging

Ajami

2010

Stoch Environ Res Risk Assess

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Model uncertainty is rarely considered in the field of biogeochemical modeling. The standard biogeochemical modeling approach is to proceed based on one selected model with the ''right'' complexity level based on data availability. However, other plausible models can result in dissimilar answer to the scientific question in hand using the same set of data. Relying on a single model can lead to underestimation of uncertainty associated with the results and therefore lead to unreliable conclusions. Multimodel ensemble strategy is a means to exploit the diversity of skillful predictions from different models with multiple levels of complexity. The aim of this paper is two fold, first to explore the impact of a model's complexity level on the accuracy of the end results and second to introduce a probabilistic multi-model strategy in the context of a process-based biogeochemical model. We developed three different versions of a biogeochemical model, TOUGH-REACT-N, with various complexity levels. Each one of these models was calibrated against the observed data from a tomato field in Western Sacramento County, California, and considered two different weighting sets on the objective function. This way we created a set of six ensemble members. The Bayesian Model Averaging (BMA) approach was then used to combine these ensemble members by the likelihood that an individual model is correct given the observations. Our results demonstrated that none of the models regardless of their complexity level under both weighting schemes were capable of representing all the different processes within our study field. Later we found that it is also valuable to explore BMA to assess the structural inadequacy inherent in each model. The performance of BMA expected prediction is generally superior to the individual models included in the ensemble especially when it comes to predicting gas emissions. The BMA assessed 95% uncertainty bounds bracket 90-100% of the observations. The results clearly indicate the need to consider a multi-model ensemble strategy over a single model selection in biogeochemical modeling study.

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Current concepts in nitrogen dynamics for mesoscale catchments

Cited by 43 publications

References 113 publications

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

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

Advances in ecohydrological modelling with SWAT—a review

Complexity in microbial metabolic processes in soil nitrogen modeling: a case for model averaging

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