Scaling properties of pH fluctuations in coastal waters of the English Channel: pH as a turbulent active scalar

Zongo, Sylvie; Schmitt, François G

doi:10.5194/npg-18-829-2011

Cited by 12 publications

(7 citation statements)

References 40 publications

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“…However, the impossibility of a time‐varying analysis does not permit assessment of the variability of the relative contributions of forcings with time. In addition, the method is not able to document multiannual variability due to the lack of significant variability at time scales greater than 1 yr. Alternatively, the power law behavior (

{E true(f true) \approx f}^{- β}

) of the spectrum trend gives scaling properties of variables (Schmitt et al ; Zongo and Schmitt ; Derot et al ). The scaling exponent β is equal to 0 for noise, 2 for Brownian motion and 5/3 for turbulence (Huang et al ).…”

Section: Resultsmentioning

confidence: 99%

Evaluation of spectral methods for high-frequency multiannual time series in coastal transitional waters: advantages of combined analyses

Jalón‐Rojas

Schmidt

Sottolichio

2016

Limnol. Oceanogr. Methods

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High-frequency monitoring is currently a major component in the management and research of the coastal system responses to ongoing global changes. This monitoring is essential in tidal systems to address the multiscale variability of physico-chemical parameters. The analysis of the resulting multiscale, nonlinear, non-stationary and noisy time series requires adequate techniques; however, to date, there are no standardized methods. Spectral methods might be useful tools to reveal the main variability time scales, and thus their associated forcings. The most widely used methods in coastal systems are Lomb-Scargle Periodogram (LSP), Singular Spectral Analysis (SSA), Continuous Wavelet Transform (CWT), and Empirical Mode Decomposition (EMD), but their relevance for high-frequency, long-term records is still largely unexplored. In this article, these spectral methods are tested and compared using a high-frequency 10-yr turbidity dataset in the Gironde estuary. Advantages and limitations of each method are evaluated on the basis of five criteria: (1) efficiency for incomplete time series, (2) appropriateness for time-varying analysis, (3) ability to recognize processes without complementary environmental variables, (4) capacity to calculate the relative importance of forcings, and (5) capacity to identify long-term trends. SSA is the only analysis method to satisfy all the criteria, even with 70% missing data. Combining methods is also a promising strategy; i.e., SSA 1 LSP for better recognition of processes; CWT 1 SSA and EMD 1 CWT for short-term (seasonal) and long-term (>1 yr) analysis, respectively. The purpose of this methodological framework is to serve as a reference for future postprocessing of data from monitoring programs in coastal waters.

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{E true(f true) \approx f}^{- β}

Section: Resultsmentioning

confidence: 99%

Evaluation of spectral methods for high-frequency multiannual time series in coastal transitional waters: advantages of combined analyses

Jalón‐Rojas

Schmidt

Sottolichio

2016

Limnol. Oceanogr. Methods

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“…However, the measurement of the photosynthetically active radiation (P.A.R) parameter is an exception and for obvious reasons, the sensor is not installed in the tube, but on the top of the sea wall. Each parameter is recorded at a frequency of 20 minutes, except for the three nutrient parameters (nitrates, silicates, and phosphates) which are recorded with a periodicity of 12 hours (Dur et al 2007;Derot et al 2015;Zongo and Schmitt 2011;Huang and Schmitt 2014). Table 1 lists all the parameters that were used in our study.…”

Section: Marel Carnot Datamentioning

confidence: 99%

Benefits of machine learning and sampling frequency on phytoplankton bloom forecasts in coastal areas

Derot

Yajima

Schmitt

2020

Ecological Informatics

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In aquatic ecosystems, anthropogenic activities disrupt nutrient fluxes, thereby promoting harmful algal blooms that could directly impact economies and human health. Within this framework, the forecasting of the proxy of chlorophyll a in coastal areas is the first step to managing these algal blooms. The primary goal was to analyze how phytoplankton bloom forecasts are impacted by different sampling frequencies, by using a machine learning model. The database used in this study was sourced from an automated system located in the English Channel. This device has a sampling frequency of 20 minutes. We considered 12 physicochemical parameters over a six-year period. Our forecast methodology is based on the random forest (RF) model and a sliding window strategy. The lag times for these sliding windows ranged from 12 hours to 3 months with four different sampling times until 1 day.The results indicate that the optimal forecast was obtained for a 20 minutes time step, with an average R² of 0.62. Moreover, the highest values of fluorescence were predicted when the water temperature was approximately 11.8°C. Consequently, we demonstrated that the sampling frequency directly impacts the forecast performance of an RF model. Furthermore, this kind of model can recreate interactions that closely resemble biological processes. Our study suggests that the RF model can utilize the additional information contained in high-frequency datasets. The methodology presented here lays the foundation for the development of a numerical decision-making tool that could help mitigate the impact of these algal blooms.

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“…The anti-correlation between temperature and oxygen showed that higher temperatures may favor larger phytoplankton growth rate, and hence, with a time delay, a lower percentage of oxygen (Huang & Schmitt, 2014). In addition, Zongo & Schmitt, (2011) demonstrated that pH fluctuations in marine waters are strongly influenced by turbulent hydrodynamical transport, and may be considered as a turbulent active scalar (Zongo & Schmitt, 2011).…”

Section: Resultsmentioning

confidence: 99%

MAREL Carnot data and metadata from Coriolis Data Center

Ghosn

Caillault

Charria

et al. 2023

Preprint

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Abstract. The French coast of the Eastern English Channel (ECC) is classified as potential eutrophication zone by the Paris and Oslo Convention (OSPAR), and as moderate to poor according to phytoplankton quality element of the Water Framework Directive (WFD). Indeed, the French part of the EEC is regularly affected by Phaeocystis globosa bloom events, which have detrimental effects on the marine ecosystem, economy as well as public health. Since phytoplankton is an important indicator of water quality, the MAREL Carnot oceanographic multi-sensor station was installed in the Eastern English Channel at the Carnot wall in Boulogne sur Mer in 2004 to monitor water quality and phytoplankton in order to complement results from existing more conventional low resolution monitoring programs, with high frequency data (sampling every 20 minutes). The purpose of this paper is to introduce the MAREL Carnot dataset and show how it can be used for several research objectives. MAREL Carnot collects high frequency, multi-parameter observations from surface water, as well as meteorological measurements, and send data almost immediately to an inshore data center. In this paper, we present several physiochemical and biological parameters measured by this station. In addition, we demonstrated, based on previous research activities, that the MAREL Carnot dataset is useful for evaluating environmental or ecological statuses, marine phytoplankton ecology, physical oceanography, turbulence, as well as public policy. Most importantly, we showed its contribution to Marine Strategy Framework Directive (MSFD) and other regional or universal conventions.

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Scaling properties of pH fluctuations in coastal waters of the English Channel: pH as a turbulent active scalar

Cited by 12 publications

References 40 publications

Evaluation of spectral methods for high-frequency multiannual time series in coastal transitional waters: advantages of combined analyses

Evaluation of spectral methods for high-frequency multiannual time series in coastal transitional waters: advantages of combined analyses

Benefits of machine learning and sampling frequency on phytoplankton bloom forecasts in coastal areas

MAREL Carnot data and metadata from Coriolis Data Center

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