Long-term high frequency phytoplankton dynamics, recorded from a coastal water autonomous measurement system in the eastern English Channel

Derot, Jonathan; Schmitt, François G; Gentilhomme, Valérie; Zongo, Sylvie

doi:10.1016/j.csr.2015.09.015

Cited by 13 publications

(11 citation statements)

References 61 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%

“…Spectral techniques are usually applied independently in coastal research (Kastens ). In studies based on high‐frequency long‐term series, only PSA and EMD were used in the same work (Huang and Schmitt ; Derot et al ); the interest of this combination was illustrated in the previous section. Even if the spectral methods share the same finality of providing the main time scales of variability in time series, the present evaluation shows that they reveal different and, often complementary, aspects of the involved physical processes.…”

Section: Resultsmentioning

confidence: 99%

“…Power Spectrum Analysis (PSA) Oxygen (Rabalais et al 1994;Schmitt et al 2008;Huang and Schmitt 2014;Rafelski et al 2015) Phytoplackton (Platt 1978;Vasseur and Gaedke 2007;Derot et al 2015) Salinity (Sikora and Kjerfve 1985;Reynolds-Fleming and Luettich 2004;Kbaier et al in press) Temperature (Alvarez-Borrego and Alvarez-Borrego 1982;Dur et al 2007;Kbaier et al, in press) Turbidity (Gu ezennec et al 1999;Hoitink 2004;Schmitt et al 2008) Water level (Luettich et al 2002;Reynolds-Fleming and Luettich 2004;Kastens 2014) Waves (Pierson and Marks 1952;S en echal et al 2002;Ruju et al 2014) Continuous Wavelet Transform (CWT) Currents (Jay and Flinchem 1995;Uncles 2002;Matabos et al 2014) Oxygen (Moore et al 2009;Nezlin et al 2009) Phytoplankton (Machu et al 1999;Hodges and Rudnick 2006;Zhang et al 2010a) Water level (Percival and Mofjeld 1997;Buschman et al 2009;Zhang et al 2010b;Guo et al 2015) Waves (Meyers et al 1993;Camayo and Campos 2006) Singular Spectrum Analysis (SSA) Beach shoreline (R o_ zy nski et al 2001) Suspended sediment concentration (Schoellhamer 2002;French et al 2008) Temperature (Keiner and Yan 1997)…”

Section: Spectral Methods Measured Parameter and Referencesmentioning

confidence: 99%

“…Copepods Oxygen (Moore et al 2009;Huang and Schmitt 2014) Phytoplankton (Derot et al 2015) River flow (Huang et al 2009) Water level (Huang et al 2000;Ezer et al 2013) Jal on-Rojas et al…”

Section: River Flow (Knowles 2002) Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

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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%

Section: Resultsmentioning

confidence: 99%

Section: Spectral Methods Measured Parameter and Referencesmentioning

confidence: 99%

“…Copepods Oxygen (Moore et al 2009;Huang and Schmitt 2014) Phytoplankton (Derot et al 2015) River flow (Huang et al 2009) Water level (Huang et al 2000;Ezer et al 2013) Jal on-Rojas et al…”

Section: River Flow (Knowles 2002) Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

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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 importance of niches in defining phytoplankton functional beta diversity during a spring bloom

Louchart,

Lizon,

Debusschere

et al. 2023

Mar Biol

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Long-term high frequency phytoplankton dynamics, recorded from a coastal water autonomous measurement system in the eastern English Channel

Cited by 13 publications

References 61 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

The importance of niches in defining phytoplankton functional beta diversity during a spring bloom

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