Time-dependent global sensitivity analysis of the C-RIVE biogeochemical model in contrasted hydrological and trophic contexts

Wang, Shuaitao; Flipo, Nicolas; Romary, Thomas

doi:10.1016/j.watres.2018.07.033

Cited by 13 publications

(26 citation statements)

References 66 publications

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“…These increases in nitrate and phosphate concentrations are particularly high in stream orders greater than 4 (up to +19% and 42%, respectively, at the outlet for the driest RCP 8.5 projection). Changes in water temperature are even expected to superimpose with potential modifications of phytoplankton biomass and composition as well as oxygen concentrations, as shown by the sensitivity analysis performed by Wang et al [73]. Acting on diffuse sources and, to a lesser extent, on point sources (as discussed above) is therefore even more necessary in a context of climate change.…”

Section: The Context Of Climate Changementioning

confidence: 97%

“…Data typically exhibited a marked seasonal cycle, with low oxygen concentrations (undersaturation) during summer. The seasonal dynamics is first controlled by the temperature dependence of oxygen solubility and second by the increase of microbial activity with temperature [73]. From a minimum concentration around 2 mg L À1 during 2002-2004, the situation has been significantly improved over the last 15 years.…”

Section: High-frequency Analysis Of Oxygen Data Along the Main Seine mentioning

confidence: 99%

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Ecological Functioning of the Seine River: From Long-Term Modelling Approaches to High-Frequency Data Analysis

Garnier

Marescaux

Guillon

et al. 2020

The Handbook of Environmental Chemistry

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At the start of the PIREN-Seine program, organic pollution by the effluent of the Parisian conurbation was responsible for episodic anoxia in the lower Seine River, while nutrients from both point and diffuse sources are used to cause eutrophication, a nuisance for drinking water production from surface water and biodiversity. The implementation of the EU Water Framework Directive led to a drastic decrease of organic carbon, phosphorus and ammonium concentrations in surface waters starting in the early 2000s and to a reduction of the frequency and the amplitude of phytoplankton blooms. However, nitrate contamination from fertiliserintensive agriculture continued to increase or at best levelled off, threatening groundwater resources and causing unbalanced nutrient ratios at the coastal zone where eutrophication still results in harmful algal blooms. High-frequency O 2 data combined with models, which have been developed for 30 years, can help discriminate the contribution of auto-vs. heterotrophic metabolism in the CO 2 supersaturation observed in the Seine River. Despite the impressive improvement in water quality of the Seine River, episodic crises such as summer low-flow conditions still threaten the good ecological status of both river and coastal waters. Modelling scenarios, including further wastewater treatments and structural changes in agriculture and future changes in hydrology under climate changes, provide the basis for a future vision of the ecological functioning of the Seine River network.

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Section: The Context Of Climate Changementioning

confidence: 97%

Section: High-frequency Analysis Of Oxygen Data Along the Main Seine mentioning

confidence: 99%

Ecological Functioning of the Seine River: From Long-Term Modelling Approaches to High-Frequency Data Analysis

Garnier

Marescaux

Guillon

et al. 2020

The Handbook of Environmental Chemistry

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“…Several applications of SA methods can be found for water quality modeling as in Nossent et al (2011) and for DO and N O 3 modeling as in Bailey and Ahmadi (2014). Wang et al (2018) summarized a list of SA applications in hydrological and water quality modeling and applied SA in contrasting hydrological and trophic contexts, where bacterial parameters were identified as the most influential in a 80-h non-bloom low-flow period. However, the sensitivity of water quality models has not been investigated for long-term (= 45 days to be consistent with the batch test for biodegradable fractions of OM (Servais et al, 1995)) low-flow periods against new parameters accounting for boundary condition uncertainties (OM repartition and degradation) as well as against bacterial and physical parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, the sensitivity of C-RIVE (Vilmin et al, 2012;Wang et al, 2018), the biogeochemical module of ProSe-PA (Wang et al, 2019), is investigated against the background of the aforementioned parameters based on 45-and 5-day DO simulations. After incorporation of new parameters to account for repartition and degradation of OM, SA is conducted on a synthetic river system mimicking the Seine.…”

Section: Introductionmentioning

confidence: 99%

How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?

Hasanyar

Romary

Wang

et al. 2022

Preprint

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Abstract. Development of accurate water quality modeling tools is necessary for integrated water quality management of river systems. The existing water quality models can simulate dissolved oxygen (DO) concentration quite well during high flow and phytoplankton blooms in rivers; however, there are discrepancies during the summer low-flow season that are assumed to be due to the uncertainties related to the organic matter contribution of the model boundary conditions. Therefore, we used the C-RIVE biogeochemical model to evaluate the influence of controlling parameters on DO simulations at low flow. Three Sobol sensitivity analyses (SA) were carried out based on a coarse model pre-analysis whose target was to develop SA scenarios providing a reduction in the number of model parameters and computation cost as well as hiding inter-parameter interactions. The parameters studied are related to bacterial (e.g., bacterial growth rate), organic matter (OM; repartition and degradation of OM into constituent fractions), and physical factors (e.g., reoxygenation of the river due to navigation and wind), whose variation ranges are selected based on a detailed literature review. Bacterial growth and mortality rates are found to be by far the two most influential parameters, followed by bacterial growth yield. More refined SA results indicate that the biodegradable fraction of dissolved organic matter (BDOM) and the bacterial growth yield are the most influential parameters under conditions of a high net bacterial growth rate (= growth rate – mortality rate), while bacterial growth yield is independently dominant in low net growth situations. Based on the results of this study, proposals are made for in situ measurement of BDOM under a dense and well-equipped urban area water quality monitoring network that could provide high-frequency data. The results also indicate the need for bacterial community monitoring in order to detect potential bacterial community shifts after transient events such as combined sewer overflows and post-infrastructure improvement in treatment plants. Furthermore, we discuss the integration of BDOM in data assimilation software for better estimation of BDOM contribution from boundary conditions, which would result in improved water quality modeling.

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“…It reduces the input space dimension. Then, in a second step, a Monte Carlo samplingbased sensitivity and ranking analysis is run (see, e.g., Sankar et al, 2018) or Sobol' indices are computed (see, e.g., Morris et al, 2014;Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A global sensitivity analysis approach for marine biogeochemical modeling

Prieur¹,

Viry²,

Blayo³

et al. 2019

Ocean Modelling

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This paper introduces the Sobol' indices approach for global sensitivity analysis (SA), in the context of marine biogeochemistry. Such an approach is particularly well suited for ocean biogeochemical models, which make use of numerous parameters within large sets of differential equations with complex dependencies. This SA allows for a detailed study of the relative influence of a large number of input parameters on output quantities of interest to be chosen. It is able to distinguish between direct effects of these parameters and effects due to interaction between two or more parameters. Although demanding in terms of computation, such a tool is now becoming affordable, thanks to the development of distributed computing environments. An applicative example is presented with the MODECOGeL biogeochemical model, and illustrates the advantages of this approach over standard local SA.

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Time-dependent global sensitivity analysis of the C-RIVE biogeochemical model in contrasted hydrological and trophic contexts

Cited by 13 publications

References 66 publications

Ecological Functioning of the Seine River: From Long-Term Modelling Approaches to High-Frequency Data Analysis

Ecological Functioning of the Seine River: From Long-Term Modelling Approaches to High-Frequency Data Analysis

How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?

A global sensitivity analysis approach for marine biogeochemical modeling

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