# High-frequency monitoring reveals recurrent extreme events in a tropical estuary

Iksandar Leite Costa, Valentine Foulquier, Fabian Blanchard, Yann Rousseau,
Morgana Tagliarolo

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Iksandar Leite Costa, Valentine Foulquier, Fabian Blanchard, Yann Rousseau, Morgana Tagliarolo. High-frequency monitoring reveals recurrent extreme events in a tropical estuary. Continental Shelf Research, 2026, 298, pp.105655. 10.1016/j.csr.2026.105655. hal-05511290

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# High-frequency monitoring reveals recurrent extreme events in a tropical estuary

Iksandar Leite Costa*†, Valentine Foulquier, Fabian Blanchard, Yann Rousseau, Morgana Tagliarolo

Ifremer, UAR LEEISA (CNRS, UG, Ifremer), 275 Route de Montabo, BP477, 97323, Cayenne Cedex, French Guiana, France

## ARTICLE INFO

Keywords:

Global change

Predictions

Tropical ecosystems hypoxia

Salinity

Temperature anomalies

## ABSTRACT

Estuaries of the tropical Guiana Shield exhibit rapid short-term variability superimposed on strong seasonal forcing, yet high-frequency, multi-year observations remain scarce. A four-year record (Jan 2020–Mar 2024; 20-min resolution) of temperature, salinity, and dissolved oxygen from four sites along the Cayenne River estuary was assembled to assess cross-site coherence, diel ranges, seasonal signals, and extreme events—defined as hyposalinity (<5 ppt for ≥2 days), hypoxia (DO < 2 mg L⁻¹ for ≥2 days), and thermal anomalies (daily maxima above month-specific 90th-percentile climatological thresholds for ≥2 days and ≥5 days).

Temperature and salinity showed strong coherence among sites and clear seasonal cycles, with higher values in the dry season and pronounced freshening in the wet season. Oxygen was more heterogeneous, reflecting local biophysical controls. Extreme-event analysis revealed frequent hyposalinity and hypoxia, whereas thermal anomalies were less common and typically shorter. A predictive model driven by atmospheric temperature and river discharge allowed prediction of water temperature and salinity variability and generalized to independent observations, while skill for oxygen was weaker, indicating the need to include additional biogeochemical drivers.

Collectively, these results establish a high-frequency baseline for a data-poor tropical estuary, demonstrate that hydrological and atmospheric variability dominate the physical template, and show both the utility and limits of statistical prediction for anticipating biologically relevant extremes. Sustained high-resolution monitoring, paired with expanded biogeochemical measurements, will improve detection of climate-sensitive events and support adaptive management of estuarine nursery habitats and fisheries resources across tropical systems.

## 1. Introduction

Estuaries are dynamic transition zones where freshwater and marine influences interact, creating complex physicochemical gradients (Levin et al., 2001). Tropical estuaries are among the most productive and valuable ecosystems on the planet as they supply important ecosystem services (Costanza et al., 2014), including environments supporting essential habitats for numerous estuarine and marine vertebrates and invertebrates, and especially fish and crustacean species exploited by fisheries. In fact, many species need these environments for a given phase of their life cycle (nursery, breeding, spawning grounds, feeding areas). These estuarine habitats are thus essential not only for maintaining marine biodiversity but also for some fisheries resources whose population dynamics and biomass production are linked (Blaber, 2013). Hence, understanding the role of the habitat in the population dynamics

may be useful for ecosystem management, conservation, restoration, and sustainable human uses, in the context of local pressures and global change.

Moreover, these environments are highly sensitive to seasonal and climatic variability, which modulates circulation patterns and ecosystem processes (Albuquerque et al., 2022) While temperate estuaries undergo gradual seasonal transitions driven by moderate precipitation regimes (Ducrotoy et al., 2019), tropical estuaries experience more pronounced hydrological shifts dictated by alternating wet and dry seasons (Costa et al., 2018). High sediment loads further contribute to their instability, shaping coastal morphologies such as shifting sand-banks and mudflats (Leuven et al., 2016). Therefore, understanding estuarine circulation on a global scale requires considering regional drivers.

More specifically in South America, estuarine dynamics are shaped

<footnote>* Corresponding author. 275 Route de Montabo, BP477, 97323, Cayenne Cedex, French Guiana, France.
E-mail address: Iksandar.costa@gmail.com (I. Leite Costa).</footnote>
by large river systems and strong oceanic currents (Acha et al., 2004). In the North Brazil Shelf Large Marine Ecosystem (LME), the Amazon River discharges vast amounts of freshwater, forming a plume that extends to French Guiana, Suriname and Guyana, up to the Orinoco estuary in Venezuela, modulating regional salinity and temperature patterns (Muller-Karger et al., 1995; Perillo et al., 2012). These processes influence biogeochemical cycles, such as oxygen availability and organic matter distribution, with significant consequences for coastal and estuarine ecosystems (Tanaka et al., 2021). However, despite existing efforts in some regions, integrated studies remain scarce in tropical estuaries (Vieillard et al., 2020), and systematic data on the physical characteristics, productivity, and ecological dynamics of the LME are still lacking (Isaac and Ferrari, 2017).

The application of remote sensing techniques to monitor estuarine and coastal areas of the LME encounters several obstacles due to the high turbidity (Oliveira et al., 2012) of the waters and the persistent sun glint effect in the area (Miller et al., 2023). In French Guiana, few remote monitoring projects have been developed but they primarily focused on particulate matter and chlorophyll (Loisel et al., 2009) and the scarcity of in-situ data poses a challenge in assessing biogeochemical dynamics. French Guiana's estuarine systems are shaped by strong seasonal hydrological fluctuations, while extensive mangrove forests play a key role in sediment dynamics and nutrient cycling, influencing coastal processes (Proisy et al., 2009). Given the sensitivity of these systems to environmental variability, monitoring key abiotic parameters is essential for evaluating their resilience and long-term dynamics.

Long-term observations are essential for understanding local variability and environmental changes, yet sustained high-frequency monitoring remains limited due to logistical challenges, resource constraints, and the complexity of maintaining continuous data collection in dynamic estuarine environments (Garel and Ferreira, 2011). These observations are especially crucial for detecting extreme events, which, due to their abrupt nature, can have more severe impacts on ecosystems than slow, gradual climatic shifts (Romanou et al., 2024).

Extreme events are characterized by abrupt and intense deviations from typical environmental conditions, driven by natural variability or human-induced factors (Harris et al., 2020). In estuarine systems, these events can lead to sudden changes in salinity, oxygen depletion, or temperature extremes, disrupting the ecological balance and affecting species resilience (Poppeschi et al., 2021; Tomanek, 2014). What constitutes a normal condition in these environments varies depending on seasonal cycles and regional hydrodynamics. However, extreme events push environmental parameters beyond the expected range of natural fluctuations, often surpassing the adaptive capacity of local biota (Harris et al., 2020). Extreme events such as hyposalinity, hypoxia, and thermal anomalies have been documented in tropical estuarine systems where these environmental disruptions can have severe ecological consequences, particularly for organisms with limited tolerance to sudden changes in abiotic conditions (Grant et al., 2017).

Hyposalinity is particularly common during the wet season when high river discharge drastically reduces salinity (Gong and Shen, 2011). Hypoxia can result from restricted circulation (Friedrich et al., 2014), organic matter decomposition (Zhu et al., 2011), or prolonged thermal stress (Earhart et al., 2022). Similarly, extreme temperature fluctuations whether sudden warming events or prolonged heatwaves can push ecosystems beyond their adaptive thresholds. While these phenomena can occur naturally (Wong et al., 2011), changes in their frequency or intensity may signal broader environmental shifts, highlighting the need for continuous monitoring.

Therefore, assessing these variations is essential to determine whether they remain within expected seasonal norms or indicate emerging environmental shifts. This study investigates the temporal and spatial variability of temperature, salinity, and dissolved oxygen in a French Guianan estuary over a four-year period. By analyzing extreme events such as hyposalinity, hypoxia, and thermal anomalies it aims to establish a baseline for estuarine conditions and assess potential

environmental shifts. Salinity and oxygen are expected to follow seasonal discharge patterns, whereas temperature should be primarily driven by atmospheric forcing. Tidal fluctuations and diel (day-night) cycles may additionally modulate short-term variability in mixing, stratification, and surface warming, although their relative influence remains uncertain in this system. The occurrence and intensity of extreme events are further anticipated to reflect broader hydrological and climatic changes, including shifts in freshwater input, circulation patterns, and atmospheric conditions.

## 2. Material and methods

### 2.1. Study area

French Guiana is located on the north-eastern coast of South America, bordered by northern Brazil and southern Suriname (Fig. 1). The maritime zone of French Guiana stretches for approximately 320 km and extends more than 200 nautical miles from the baseline. The continental shelf extends gradually and continuously seaward beyond the Exclusive Economic Zone (EEZ), covering an area of approximately 126,000 km². Along the coast, extensive mudflats dominate the intertidal zone, while offshore, the shelf is characterized by migrating mudbanks (Anthony et al., 2010). The study area focuses on the estuary of the Cayenne River, a critical zone due to its proximity to Cayenne, the main city of French Guiana. This area is therefore particularly vulnerable to anthropogenic influences (Kennish, 2002). Additionally, the estuary is a vital resource for local communities, providing biodiversity that supports fishing and other essential ecosystem services (Gomes, 2022). The Cayenne River, approximately 50 km long, experiences a highly seasonal discharge influenced by a semidiurnal mesotidal regime with an amplitude of up to 2.5 m (Artigas et al., 2003). This dynamic estuarine system drives sediment deposition and erosion, contributing to the movement of mudflats (Frouin et al., 1997).

Four study sites evenly distributed within the estuary of the Cayenne River were selected to represent different habitats (Fig. 1): urban mangrove within or near urbanized areas (CF), estuarine mangrove (S and PC), and estuary (F). All sites are located approximately 5 km apart from each other.

At each location, three HOBO data loggers (Onset Computer Corporation, models U20L-01, U24-001 and U26-001) were deployed to record temperature, salinity and dissolved oxygen over approximately four years (from January 1, 2020, to March 31, 2024). From March 14, 2024, to January 1, 2025, the HOBO probes were replaced with NKE WiMo multiparameter probes (NKE Instrumentation, France). At each site, probes were deployed 2 m below the water surface, fixed to an anchor and resting about 20 cm above the estuarine floor to ensure stability during tidal cycles. Because these tropical estuaries have extremely high levels of suspended particulate matter, light penetration is very limited (Artigas et al., 2003). In French Guiana coastal waters transparency is generally between 0.8 and 3 m depth, but estuarine systems under the influence of the Amazon shows even lower values between 0.19 and 1.60 m (Machado et al., 2022). Consequently, sensors deployed at ~2 m depth are effectively shielded from direct solar irradiance (Gregório and Mendes, 2009; Shi and Wang.).

HOBO probes recorded temperature (−2 to 36 °C, accuracy ± 0.1 °C at 25 °C), salinity (0–10,000 µS/cm, accuracy ± 3%), and dissolved oxygen (0–20 mg/L, accuracy ± 0.2 mg/L up to 8 mg/L). Dissolved oxygen was measured using a luminescence-based optical sensor, which relies on the quenching of a luminescent dye by oxygen molecules. This method is widely regarded as more stable and less prone to drift than electrochemical sensors such as Clark-type electrodes (Chipman et al., 2012; Wang and Wolfbeis, 2014; Wei et al., 2019). No parallel oxygen measurements were conducted in this study; however, HOBO dissolved oxygen probes have been validated in previous estuarine and coastal monitoring applications with strong agreement with Winkler-based laboratory determinations (Bittig and Körtzinger, 2015; Johengen
<figure><img src="image_2.png" /><figcaption>Fig. 1. Study sites map. CF: Crique Fouillée, F: Fourche, PC: Petit Cayenne, S: Soula, M: the Cayenne Suzini meteorological station.</figcaption></figure>

et al., 2016). Probes were factory-calibrated prior to deployment and further calibrated in the laboratory against oxygen-saturated and oxygen-free water standards after each sensor cap replacement. Automatic temperature compensation was applied to salinity and oxygen records internally by the probes. Data were logged automatically every 10 min, and instruments were replaced monthly to minimize biofouling.

NKE WiMo data loggers were deployed at the same locations and depths as the HOBO probes, providing independent high-frequency measurements of the same parameters. According to manufacturer specifications, accuracy levels were ±0.2 °C for temperature (range -2 to +35 °C), ±0.1 mg/L for dissolved oxygen (range 0-23 mg/L), and ±0.5% of the reading or ± 5 µS/cm for conductivity (range 0-10 mS/cm). NKE sondes also logged data at 10 min intervals, ensuring direct comparability with HOBO time series. Unlike the original setup, where three separate data loggers were required to measure temperature, salinity, and oxygen, the NKE data loggers integrate all parameters within a single device, ensuring simultaneous acquisition and reducing deployment complexity.

## 2.2. Data cleaning process

The raw data extracted from the HOBO data loggers, stored on a local server, contained numerous invalid, improper, or incomplete data points due to factors such as biofouling, mud coverage, and battery or memory issues (Annex 1). To ensure consistency, all measurements from the three data loggers were resampled to a 20-min time step by averaging the surrounding values within each interval. At each site, temperature readings from the three data loggers showed high similarity (Table 1),

allowing the creation of a single averaged temperature dataset per site.

Aberrant values considered outside the expected environmental range (temperatures exceeding 37 °C or below 23 °C, oxygen concentrations greater than 10 mg L⁻¹ or below 0 mg L⁻¹, and salinities above 35 ppt or below 0 ppt), were removed from the dataset. These values were typically present when data loggers were exposed to surface conditions during deployment or maintenance. Moreover, battery or memory malfunction also caused a few aberrant repetitions of identical values for extensive periods of time. Those sections were carefully removed and a Z-score methodology (Zhou and Tang, 2016) was also applied to identify and remove outliers.

The absence of probe deployment (due to logistical problems) between July 2021 and January 2022 (Annex 1) caused a large gap in the dataset. Similarly, the apparent discontinuity around May 2021 corresponds to a period without sensor records and was therefore not available for subsequent analyses.

Only a very small fraction of the dataset (<1%) was removed during cleaning, corresponding to clear outliers highlighted as red markers in Annex 1. These points were flagged and excluded to avoid bias in the analyses. Attempts were also made to reconstruct the largest missing segments using ARIMA-based interpolation, but these models did not provide reliable estimates. For transparency and reproducibility, the dataset was therefore retained with its original gaps, ensuring that all available and reliable measurements were included without introducing artefacts from inappropriate imputation.

<table><caption>**Table 1**
Minimums, maximums and means values for, temperature (°C), salinity (ppt) and dissolved oxygen (mg. L⁻¹) in each monitored site.</caption><thead><tr><th rowspan="2">Site</th><th colspan="3">Temperature</th><th colspan="3">Salinity</th><th colspan="3">Oxygen</th></tr><tr><th>Min</th><th>Max</th><th>Mean</th><th>Min</th><th>Max</th><th>Mean</th><th>Min</th><th>Max</th><th>Mean</th></tr></thead><tbody><tr><td>F</td><td>23.51</td><td>35.83</td><td>28.57</td><td>1.1</td><td>28.96</td><td>7.63</td><td>0</td><td>9.99</td><td>3.39</td></tr><tr><td>PC</td><td>23.71</td><td>34.70</td><td>27.83</td><td>0.23</td><td>28.92</td><td>5.485</td><td>0</td><td>9.77</td><td>2.50</td></tr><tr><td>CF</td><td>23.61</td><td>35.8</td><td>28.56</td><td>0.01</td><td>28.89</td><td>7.59</td><td>0</td><td>9.99</td><td>3.38</td></tr><tr><td>S</td><td>24.65</td><td>37.99</td><td>28.63</td><td>1.19</td><td>26.81</td><td>7.97</td><td>0</td><td>8.545</td><td>4.56</td></tr><tr><td>Global</td><td>23.93</td><td>33.24</td><td>28.35</td><td>0.01</td><td>26.94</td><td>6.65</td><td>0.01</td><td>9.39</td><td>3.34</td></tr></tbody></table>
## 2.3. Statistical analysis

All data processing and statistical analyses were performed in R (Wickham et al., 2019). The following R packages were employed: `mgcv` (Wood, 2017), `ggplot2` (Wickham, 2016), `dplyr` (Wickham et al., 2019), and `tidyquant` (Dancho and Vaughan, 2016).

Spearman correlation tests were conducted to assess relationships between sites for each studied parameter (temperature, salinity, and dissolved oxygen). Since sites were highly correlated and exhibited similar patterns (Annex 2), time series from the four sites (Crique, Fourche, Petit Cayenne, Soula) were combined into a single 20-min dataset by averaging values. The single average dataset reduce redundancy and simplify analyses without losing trends.

To assess the potential influence of tidal variability, a high-frequency water level dataset provided for the closest monitoring station (Degrades-Cannes) by the French Naval Hydrographic and Oceanographic Service (SHOM, https://refmar.shom.fr/) was obtained, covering March 2020 to June 2024 with some missing data. The available dataset was integrated with our common 20-min dataset. Observations with missing tidal data were removed, resulting in 43,819 records.

The relationship between tidal height and estuarine variables (temperature, dissolved oxygen, and salinity) was assessed using both linear correlation and Generalized Additive Models (GAMs) to account for potential nonlinear effects.

Diel variability for each parameter (temperature, dissolved oxygen, and salinity) was calculated at the daily scale as the intra-day range (maximum – minimum) for night (18:00–05:59) and day (06:00–17:59) periods. For cross-parameter comparisons, a normalized range was derived as (maximum – minimum) divided by the daily mean. Distributions of both absolute and normalized daily ranges were summarized using kernel density plots.

Extreme events were identified for each parameter based on established thresholds. Hyposalinity events were defined as periods where salinity remained below 5 ppt for at least two consecutive days. This threshold of 5 ppt corresponds to the upper limit of oligohaline waters, which are characterized by a salinity range between 0.5 and 5 ppt. Therefore, a salinity level below 5 ppt marks the transition between oligohaline and mesohaline waters (Taupp and Wetzel, 2014). A minimum duration of two consecutive days was chosen in order to filter out short-term tidal fluctuations and to ensure that only freshwater intrusions with ecological significance were considered. While no standardized duration is explicitly reported in the literature, the two-day criterion represents a pragmatic methodological choice designed to exclude transient tidal variability and focus on ecologically relevant events such as rainfall.

In nearshore and estuarine environments multiple types of hypoxia timescales are possible; including hourly events over the diel-cycle, episodic events ranging from days to weeks, seasonal episodes lasting months or nearly permanent hypoxic conditions in some systems (Testa et al., 2023). In our study, hypoxic events corresponded to oxygen levels falling below 2 mg/L for two consecutive days (Liu et al., 2022). The two-day criterion was applied because in our system diel hypoxic episodes lasting a few hours are regularly present but may not cause lethal or sublethal effects. Extended periods of hypoxia can cause mortality in many marine animals and trigger migrations of mobile organisms, affecting overall ecosystem functioning and food-web structure. (Coffin et al., 2018; Decker et al., 2004).

Different definitions of extreme temperature events exist but they can generally refer to a value that exceeds a threshold (Pol et al., 2017). The determination of the threshold can vary (numerically, experimentally, graphically or arbitrarily) and it can be absolute or relative (Ummenhofer and Meehl, 2017). In this study, extreme temperature events were estimated by a percentile-plus-persistence approach adapted to a 4-year record. High-frequency temperature was aggregated to daily maxima, then a seasonal “typical year” was built by averaging each calendar day across years. From this average year a month-specific

90th-percentile thresholds of daily maximum temperature was estimated. A thermal anomaly was flagged when the daily maximum exceeded the relevant monthly threshold for ≥2 consecutive days. For consistency with other studies focused on global change, a complementary 5-day persistence screen was also implemented across temperature, oxygen, and salinity. Although our definition does not constitute a full climatological baseline in the sense of Hobday et al. (2018), the underlying percentile-based method provides an ecologically meaningful proxy for identifying extreme temperature events within a limited temporal record.

Co-occurrence analyses were then performed to identify days when multiple types of extreme events occurred simultaneously. Specifically, compared the daily time series of thermal anomalies, hypoxic events, and hyposalinity events were compared to detect overlapping occurrences. To facilitate these analyses, we assembled a date-indexed binary event dataset for each stressor (1 = event day, 0 = non-event), flagging a day as 1 if it belonged to any qualifying multi-day run, which enabled straightforward computation of pairwise and triple co-occurrence metrics. This approach allowed us to quantify not only the frequency of each stressor individually but also the proportion of days on which two or more stressors co-occurred.

To assess both the variability of environmental parameters across sites and their interdependencies GAMs were employed. GAMs are a flexible extension of Generalized Linear Models (GLMs) (Laanaya et al., 2017) that allow for non-linear relationships between predictors and response variables by incorporating smooth functions instead of assuming strict linearity (Hastie and Tibshirani, 1986). This makes them particularly suitable for modelling estuarine dynamics (Skov et al., 2016). Here, GAMs were applied to three complementary modelling objectives, each fitted with thin-plate regression splines and estimated using penalized likelihood. Smoothing parameters were selected via data-driven penalized likelihood (*mgcv*), using conservative basis dimensions and standard diagnostics to avoid over-parameterization (Wood et al., 2016).

Site-specific variability was first evaluated by modelling water temperature, dissolved oxygen, and salinity as response variables with site as the predictor. Second, binomial GAMs with a logit link were used to investigate how extreme temperature, hypoxia, and hyposalinity events co-occur. Each extreme was modelled as a binary response with the other extremes as predictors, along with a seasonal smooth term (s (time)). Lastly, additional GAMs were constructed to explain water temperature, salinity, and dissolved oxygen using air temperature, river flow, tidal level and the North Atlantic Oscillation (NAO) index as predictors. These external datasets, obtained from official sources, were used solely as inputs for model construction, and all data points were included without additional outlier filtering.

Air temperature data were collected from the Cayenne Suzini meteorological station (ID: 97302005, coordinates: 4.929667°N, -52.290167°W (Météo-France). For this study, daily mean air temperatures (TM) recorded under shelter were selected. Flow data for an upstream site of the Cayenne River were obtained from the (HydroPortail, 2025) platform. These data were recorded at a 3-h time step, with a maximum data gap of 4320 min (three days). The NAO index values were calculated using 500 mb geopotential height anomalies (NOAA/ESRL PSL). The index is based on the difference between area-averaged 500 mb heights over 55–70°N, 70–10°W and 35–45°N, 70–10°W, with a climatological reference period of 1981–2024. NAO index data were available at a daily time interval, covering the period from 1948 to the near present. NAO index was selected since is a large-scale atmospheric index that strongly influences climate variability across the North Atlantic basin (Jones et al., ). In tropical South America, including the Guiana Shield region, the NAO has been shown to modulate river discharge, precipitation, and coastal circulation (Maksic et al., 2022; Yoon and Zeng, 2010).

The temporal resolutions of the datasets differ (20-min in situ HOBO records, 3-h meteorological observations, 10-min for the tidal level and
daily air temperature and NAO index values). Such discrepancies reflect inherent constraints in environmental monitoring, as cross-disciplinary datasets are rarely collected at identical timestamps. To ensure comparability, all variables were harmonized onto a common daily scale by aggregating high-frequency records to daily means. This harmonization inevitably smooths short-term variability but provides a consistent framework for cross-variable comparisons and modelling, while retaining the ecological relevance of long-term patterns (Hobday et al., 2018; Smale et al., 2019).

A stepwise procedure was adopted to select the best model by iteratively adding or removing predictor variables. The goal was to find a model that balances complexity and predictive accuracy while minimizing overfitting (Laanaya et al., 2017). In total, 85 GAMs were tested, incorporating different predictor lags to account for delayed environmental responses. Time lags of 1 and 2 months were applied for river flow and air temperature, as hydrological and atmospheric influences on estuarine conditions may not be immediate but develop over short to medium timescales. For the NAO index, longer lags of 3 and 6 months were tested, as these reflecting slower climatic oscillations associated with this index, which operates on inter-seasonal and interannual scales (Hurrell, 1995; Stenseth et al., 2003).

For all the tested models the NAO index was consistently non-significant ($p > 0.05$), regardless of the lag applied. This led us to exclude the NAO index from further modelling to improve model parsimony and focus on significant drivers. This refinement process left us with seven significant models. Among these, the two models with the highest explanatory power and best parsimony for temperature and salinity were retained.

For temperature, the selected model was without lag:

$$Temperature_i = \beta_0 + s(Month) + s(Flow_i) + s(Temp_i) + \varepsilon_i$$

For salinity, the selected model had a lag of 1 month for water flow:

$$Salinity_i = \beta_0 + s(Month) + s(Flow_{i-1}) + s(Temp_i) + \varepsilon_i$$

here, $\beta_0$ represents the intercept of the model, $s()$ denotes a smooth function (thin-plate regression spline) used to capture potential nonlinear relationships between the predictors and the response variable, and $\varepsilon_i$ is the residual error term. The subscript i indicates the time step of the observation, while Flow(i-1) represents the river flow lagged by one month.

These two final models were subsequently used to predict temperature and salinity over the full temporal range of the dataset, including filling in gaps and forecasting 2024 data. Predictions for 2024 were then compared to a new dataset collected at the same study sites using NKE data loggers. Model goodness was evaluated by estimating the Root Mean Square Error (RMSE) (Hodson, 2022), which quantifies the average deviation between observed and predicted values while giving greater weight to larger errors, making it particularly sensitive to extreme deviations. The Mean Absolute Error (MAE) was also computed to provide a more straightforward interpretation of average prediction errors, offering insight into the typical discrepancy between predicted and observed values (Chai and Draxler, 2014). Additionally, the coefficient of determination ($R^2$) was calculated to assess the proportion of variance in the observed data explained by the model, indicating its overall explanatory power.

# 3. Results

## 3.1. Spatial and temporal variations of the abiotic factors

Temperature and salinity were highly correlated among sites (Spearman $\rho$ = 0.860–0.936 for temperature; 0.350–0.834 for salinity), whereas dissolved oxygen (DO) correlations were lower ($\rho$ = 0.078–0.632; with fewer viable DO observations) (Annex 1).

Although the site factor was significant ($p < 0.05$), it explained little

variability in the GAMs 5.83% for temperature, 3.19% for salinity, and 8.37% for DO. Mean maximum and minimum values were broadly similar across sites (Table 1).

Since the datasets of the different sites were highly correlated and showed similar means and variation ranges, they were averaged for further analysis to better examine the overall estuarine dynamics. Across the combined dataset, salinity and temperature tracked similar seasonal dynamics with higher values in the dry season, whereas dissolved oxygen was more variable and lacked a stable seasonal signal (Fig. 2). Data availability also differed among variables: oxygen contained 15.86% fewer raw records than temperature and 10.48% fewer than salinity. At the daily scale, oxygen covered 84.9% of days compared with 100% for temperature and 94.1% for salinity deficits of 15.13% and 9.79%, respectively. The oxygen dataset was more limited than the temperature and salinity datasets, since the oxygen probes failed more often than the other sensors. The relationship between estimated water level in the area and the combined dataset was very weak. Correlation coefficients were extremely low ($|r| < 0.06$), and although the GAM smooth terms were statistically significant (likely due to the very large sample size) the effect sizes were negligible. Specifically, water level explained only 0.75% of the deviance in water temperature, 0.65% in dissolved oxygen, and 0.80% in salinity.

Diel variability analysis revealed comparable daily variation during day and night (Fig. 3). Salinity exhibited the largest 24-h fluctuations, with absolute variations reaching up to 22.9 ppt and a mean daily range of 4.1 ppt. The mean range was similar between day and night (4.2 ppt vs. 3.9 ppt, respectively). Dissolved oxygen displayed intermediate diel variability, with a maximum range of 6.3 mg L$^{-1}$ and a mean of 1.4 mg L$^{-1}$, consistent with day and night averages. Temperature showed the smallest diel fluctuations, averaging 1.2 °C with a maximum of 4.1 °C, reflecting the relatively modest day–night ranges observed (1.1–1.2 °C).

When normalized to daily means, oxygen (mean of 0.5 for both day and night) and salinity (mean of 0.9 for both day and night) exhibited much greater relative diel variability than temperature (mean of 0.4 for both day and night) (Fig. 3). These patterns indicate that diel variability is pronounced for salinity and oxygen but remains limited for temperature. Importantly, the similar amplitudes observed during daytime and night-time periods suggest that these fluctuations occur throughout the 24-h cycle rather than being confined to a specific phase.

## 3.2. Extreme events

During the study period, hypoxia accounted for the highest number of events, occurring over 246 days (14% of all recorded days; Table 2). In contrast, hyposalinity events lasted the longest, spanning a total of 443 days (27% of all recorded days). Extreme temperature events were identified using both 2-day and 5-day minimum duration thresholds (Table 2). Based on the more restrictive 5-day threshold, extreme high temperatures occurred on 33 days (3% of all recorded days), whereas the 2-day threshold identified 55 days (5% of all recorded days).

Hyposalinity and hypoxia showed the highest co-occurrence compared to temperature extremes and 12 days showed a triple occurrence off all the extreme events (Fig. 4 and Annex 3).

The GAM analyses of extreme events revealed that the temporal component ($s(time)$) was highly significant in all models, indicating strong time-dependent patterns. Hyposalinity events were significantly more likely on days with extreme high temperature (odds ratio = 4.18). Conversely, extreme temperature events were also associated with extreme low salinity, though with a slightly smaller effect (odds ratio = 3.59). Similarly, extreme low oxygen events were associated with a higher probability of extreme temperature events (odds ratio = 3.59), and extreme high temperatures increased the likelihood of extreme low oxygen events (odds ratio = 4.18). Models including oxygen extremes in combination with salinity extremes did not converge reliably, likely due to the low frequency of simultaneous extreme events.
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Temporal variations of abiotic factors of the Cayenne estuary from January 2022 until March 2024, A; Temperature (°C), B; Salinity (ppt), C; Dissolved Oxygen (mg L⁻¹).</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Kernel density plots showing the distributions of absolute and normalized daily ranges for Day and Night periods for Temperature (°C), Dissolved Oxygen (mg L⁻¹), and Salinity (ppt). Dotted vertical lines indicate the mean of each distribution.</figcaption></figure>
<caption>**Table 2**</caption>

Summary characteristics of the extreme events over the studied period (760 Days). Number of events = N°, mean duration of each event (days) = Mean, standard deviation of the mean duration = S. D

<table><thead><tr><th>Extreme event</th><th>N° EV</th><th>N° Days</th><th>Mean</th><th>S. D.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Temperature (5 days min per event)</td><td>6</td><td>33</td><td>5.5</td><td>0.84</td><td>5</td><td>7</td></tr><tr><td>Temperature (2 days min per event)</td><td>13</td><td>55</td><td>4.23</td><td>1.42</td><td>2</td><td>7</td></tr><tr><td>Salinity (2 days min per event)</td><td>13</td><td>443</td><td>34.1</td><td>49.8</td><td>4</td><td>154</td></tr><tr><td>Dissolved Oxygen (2 days min per event)</td><td>18</td><td>246</td><td>13.7</td><td>10.5</td><td>3</td><td>36</td></tr></tbody></table>

### 3.3. Estuarine system's external factors and their influence on the abiotic factors

To investigate water temperature and salinity variability, a GAM model was applied using air temperature and upstream river flow as input variables (Fig. 5). The oxygen concentration model did not yield satisfactory results, likely due to limited data and extreme temporal variability.

The best GAM models selected after a stepwise procedure showed that both air temperature and river flow have a significant influence on water temperature and salinity (Table 3). Seasonal patterns, represented by the month variable, were able to account for cyclic trends across the time series (Fig. 6). Predictions were performed on datasets containing the aforementioned predictors, and the results were compared to observed values over time. The temporal alignment between observed, predicted data and NKE's (Fig. 6), visualized with time series graphs (see Table 4).

## 4. Discussion

This is the first study to present high-frequency, multi-annual data for estuarine systems in the Guiana shield. The dataset analysis revealed

the interacting processes driving local variability in water temperature, salinity, and dissolved oxygen. Parameters at different sites within the estuary were highly similar, likely reflecting the short distances, shallow depths, and strong mixing in the study area. The amplitude of daily and seasonal fluctuations was pronounced, particularly for salinity. This fine-scale dataset captured recurring extreme conditions, including high temperatures, low salinities, and low oxygen concentrations. Additionally, our modelling approach successfully depicted the relationship between local physical factors and water temperature and salinity.

Across our study region, temperature and salinity were strongly correlated, with minimal contribution from site-specific effects (Annex 4). Despite this, minimum and maximum values followed a clear upstream-downstream pattern, likely driven by tidal dynamics. In contrast, dissolved oxygen exhibited higher spatial variability, reflecting the influence of rapid biogeochemical processes that can vary over short distances. Oxygen concentrations in estuarine waters are strongly controlled by photosynthesis, respiration, and microbial decomposition. These processes respond nonlinearly to light availability, turbidity, and organic-matter inputs and interact with physical mechanisms such as tidal mixing, stratification, and short residence times, modulating ventilation and re-oxygenation rates (Iriarte et al., 2015; Testa et al., 2024). Similar patterns have been observed in systems like Chesapeake Bay, where local hydrodynamics, benthic oxygen demand, and restricted water-column exchange create sharp oxygen gradients (Breitburg et al., 2018).

In the Cayenne estuary, oxygen concentrations are strongly influenced by microbial activity in turbid, mangrove-influenced waters, where high suspended-sediment loads limit light penetration and provide abundant organic matter. These rapid, nonlinear processes likely explain why the GAMs failed to capture oxygen variability, exceeding the temporal and spatial resolution of our predictors. Overall, oxygen dynamics are primarily driven by local biological processes, such as microbial decomposition and primary production, in combination with physical factors including stratification and water residence time (Peña et al., 2010).

Similar dynamics are observed in other tropical and subtropical estuaries, including Amazon-influenced systems in northern Brazil

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Extreme events, Temperature anomaly, hyposalinity and hypoxia (the extreme events are represented with red dots).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 5. External parameters employed in the GAM model (air temperature, NAO index and river flow).</figcaption></figure>

**Table 3**

Summary of bidirectional GAM results for extreme events. Estimate (β): regression coefficient for the predictor on the log-odds scale, S.E.: standard error of the estimate, z: z-value for the significance test of the predictor, p-value: significance of the predictor term, s(time) edf (p-value): effective degrees of freedom (edf) and significance of the smooth term, Adj. R²: adjusted R-squared of the model, D.E.: deviance explained by the model, —: non converging models.

<table><thead><tr><th>Response variable</th><th>Predictor</th><th>Estimate (β)</th><th>S. E.</th><th>z</th><th>p-value</th><th>s(time) edf (p-value)</th><th>Adj. R<sup>2</sup></th><th>D. E.</th></tr></thead><tbody><tr><td>Hyposalinity</td><td>Extreme temperatures</td><td>1.431</td><td>0.465</td><td>3.08</td><td>0.0021</td><td>8.98 (&lt;0.001)</td><td>0.81</td><td>75.9%</td></tr><tr><td>Extreme temperatures</td><td>Hyposalinity</td><td>1.279</td><td>0.432</td><td>2.96</td><td>0.0031</td><td>7.77 (&lt;0.001)</td><td>0.065</td><td>21.4%</td></tr><tr><td>Extreme temperatures</td><td>Hypoxia</td><td>1.279</td><td>0.432</td><td>2.96</td><td>0.0031</td><td>7.77 (&lt;0.001)</td><td>0.065</td><td>21.4%</td></tr><tr><td>Hypoxia</td><td>Extreme temperatures</td><td>1.431</td><td>0.465</td><td>3.08</td><td>0.0026</td><td>8.98 (&lt;0.001)</td><td>0.81</td><td>75.9%</td></tr><tr><td>Hyposalinity</td><td>Hypoxia</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Hypoxia</td><td>Hyposalinity</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>

**Table 4**

GAM model performance metrics (RMSE: Root Mean Square Error, MAE: Mean Absolute Error, R²: Coefficient of Determination).

<table><thead><tr><th>Response</th><th>Predictors</th><th>Sig.</th><th>Dev.</th><th>R<sup>2</sup></th><th>RMSE Val</th><th>MAE Val</th><th>R<sup>2</sup> Val</th></tr></thead><tbody><tr><td>Water temp.</td><td>1. S(Air temp) 2. S(Flow) 3. S(Month)</td><td>p &lt; 0.001</td><td>88.2%</td><td>0.88</td><td>0.6246</td><td>0.4742</td><td>0.8058</td></tr><tr><td>Salinity</td><td>1. S(Air temp) 2. S(Flow lag 1m) 3. S(Month)</td><td>p &lt; 0.001</td><td>72.3%</td><td>0.71</td><td>5.9543</td><td>4.7667</td><td>0.5905</td></tr></tbody></table>

(Coogan et al., 2021), the Pearl River Estuary, and Mobile Bay (Liu et al., 2022), where short-term biological activity and local hydrodynamics generate strong spatial and temporal oxygen heterogeneity. Across these systems, dissolved oxygen emerges as a variable controlled by the interplay of fine-scale biological processes and physical mixing, consistent with the patterns observed in the Cayenne estuary.

The analysis of diel fluctuations revealed distinct patterns, with salinity exhibiting the largest daily changes compared to temperature and oxygen. Surprisingly, these changes were similar between day and night, suggesting that irradiation and primary production are not the main drivers of these rapid variations. Salinity fluctuations primarily reflect tidal forcing, rising during high tide and falling during low tide. When normalized to daily means, oxygen and salinity showed proportionally greater relative variability than temperature. This pattern is typical of many tropical estuaries, including the Mekong Delta, where tidal cycles and riverine inputs generate pronounced salinity oscillations (Eslami et al., 2021). Oxygen variability likely reflects the combined

effects of photosynthesis, respiration, and hydrodynamic mixing over short spatial scales (Duvall et al., 2022; Tian et al., 2022). Temperature changes were more moderate, although rapid increases of 4–5 °C during daytime illustrate the influence of shallow, sun-exposed waters (Haren, 2019). These results highlight the importance of high-frequency monitoring, as single-point measurements may underestimate or misrepresent estuarine dynamics dominated by diel cycles.

High-frequency monitoring revealed recurring extreme environmental events in the Cayenne estuary, including hyposaline, hypoxic, and high-temperature episodes. Hyposaline and hypoxic episodes were highly recurrent and long lasting, while anomalously high-temperature events occurred less often and were typically more limited in time. Hyposalinity events were predominantly observed during the wet season, coinciding with periods of high river discharge following intense rainfall. This pattern has also been reported in the Congo River estuary, where large freshwater plumes significantly alter salinity gradients, impacting species distributions (Martins and Stammer, 2022; White and
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Prediction graphs (red dots represent the predicted data, blue dots the NKE probes data used for validation, and black dots the original dataset values [absolute values]) using the best fitted GAM models.</figcaption></figure>

Toumi). In the Cayenne estuary, these events likely result from heavy precipitation that generates high river outflows, temporarily overriding tidal mixing and reducing estuarine salinity.

The occurrence and co-occurrence of extreme events were highly variable throughout the year, suggesting that seasonal processes, such as strong freshwater inputs during the rainy season, can influence abrupt changes in both temperature and salinity (Feng and Chen, 2023). Extreme low-salinity events were more likely on days with extreme high temperatures, likely due to the complex dynamics in shallow tropical estuaries, where strong freshwater inputs reduce mixing efficiency and increase residence time, promoting near-surface warming even under moderate atmospheric heating (Mariani et al., 2022). This mechanism aligns with our diel analysis, which showed greater relative day-to-day variability in salinity than in temperature. Hyposalinity thus provides a physical template that enables rapid surface warming, whereas heat alone cannot generate a salinity anomaly. Ecologically, these warm- fresh conditions may simultaneously increase metabolic demand and osmoregulatory costs, creating brief periods of heightened vulnerability even in the absence of hypoxia (McBryan et al., 2013).

The occurrence of hypoxic events in our study area were also influ-
enced by extreme high temperatures but hyposalinity episodes did not
seem to correlated to extreme low oxygen concentrations. Elevated
temperatures can accelerate microbial respiration, increasing oxygen
consumption and promoting hypoxia (Rogers et al., 2016). Moreover,
elevated temperatures can also reduce oxygen solubility, affect meta-
bolic rates, and disrupt trophic interactions, leading to shifts in
ecosystem stability (Bahlai et al., 2021). Thermal anomalies, were most
common during the dry season. This pattern is consistent with findings
from the Orinoco Delta, where reduced river discharge and prolonged
atmospheric heat waves lead to significant thermal stress on aquatic
organisms (Warne et al., 2002). These results can have significant
ecological consequences, disrupting benthic communities and altering
fish behaviour, as documented in other estuaries (Coyle et al., 2023;
Johnson et al., 2024; Pollock et al., 2007).

Hyposalinity, hypoxic, and high-temperature events, even if limited
in time, can significantly impact aquatic fauna, particularly during early
developmental stages (Del Rio et al., 2019; Jeffries et al., 2016; Torres
et al., 2021). Several studies showed that fish larvae exposed to
short-term moderate hypoxia displayed depressed juvenile growth

(Altenritter and Walther, n.d.; Zambonino-Infante et al., 2017). More-
over, severe hypoxia can cause massive fish mortality (Diaz, n.d.). Rapid
declines in salinity are also known to produce lethal and sublethal
physiological and behavioural effects in commercially important species
(Domínguez et al., 2021; Torres et al., 2021; Van Thuong et al., 2016).

The GAMs using external factors as predictors performed well in
capturing temperature and salinity dynamics, reflecting gradual vari-
ability resulting from tidal exchange, river inflow, and atmospheric
forcing. In contrast, the models were less effective for oxygen concen-
tration, which disposed of a smaller dataset and a higher spatial vari-
ability. This parameter is probably shaped by rapid, nonlinear processes
such as photosynthesis, respiration, and stratification. This highlights
the suitability of GAMs for hydrological parameters with gradual vari-
ability, but also their limitations in resolving the sharp fluctuations that
characterize estuarine oxygen dynamics. GAMs have already been
effectively utilized in estuarine studies to model complex, nonlinear
relationships between environmental variables and biological re-
sponses. For instance, in the Chesapeake Bay, GAMs were applied to
evaluate long-term water quality trends, successfully capturing the
nonlinear seasonal variations of multiple constituents, including tem-
perature and salinity (Murphy et al., 2019). Similarly, research in the
Xiaoqing River Estuary employed GAMs to elucidate the effects of
environmental factors like salinity and organic matter on benthic species
richness, demonstrating the model's capability to handle intricate
ecological data (Liu et al., 2023).

The GAM performed less well for dissolved oxygen, reflecting the
multiple, partially captured processes that govern oxygen dynamics in
this system. Along the Guianas coast, extremely high suspended-
sediment concentrations and migrating Amazon-derived mud banks
create highly turbid waters, limiting light penetration to the uppermost
layer of the water column, strongly constraining phytoplankton pro-
duction, and generating pronounced small-scale heterogeneity
(Anthony et al., 2019). The absence of high-frequency time series on
estuarine irradiance, turbidity, or chlorophyll is likely the main limita-
tion preventing a more predictive oxygen model (Artigas et al., 2003).

Under these data-limited conditions, more complex modelling ap-
proaches, such as machine learning, would not necessarily perform
better. These methods also often function as "black boxes," providing
limited mechanistic insight (Hochachka et al., 2007). Fully coupled
hydrodynamic-biogeochemical models would require detailed boundary conditions, bathymetry, and forcing fields (tides, winds, light, sediment, and nutrient inputs) (Anthony et al., 2021), which are currently unavailable for the Cayenne estuary and, more broadly, for most tropical estuaries along the Guianas coast. In contrast, GAMs offer a transparent framework to quantify smooth, nonlinear effects of the available drivers and to identify where key processes are missing.

Beyond capturing environmental variability, our modelling approach is particularly useful in a data-limited environment by allowing for the interpolation of periods with missing data since direct measurements of estuarine water parameters can be difficult and expensive. Moreover, the GAMs outputs demonstrated strong predictive capability for estuarine temperature and salinity that are key variables for anticipating stressful conditions for aquatic organisms. Such models can offer valuable tools for monitoring and projecting future shifts in hydrological regimes (Mallick and Dunn, 2024) (e.g., extended drought or extreme climatic changes). These insights can support adaptive management strategies by providing early warnings of extreme events and long-term environmental shifts. Improved monitoring of estuarine variability can enhance our understanding of climate-related stress on ecosystems, facilitate timely management interventions, and support conservation planning.

## 5. Conclusion

This study provides the first high-frequency, multi-annual dataset for the Cayenne River estuary, offering novel insights into the seasonal and diel variability of temperature, salinity, and dissolved oxygen. The results highlight strong seasonal patterns driven primarily by atmospheric forcing and tidal mixing, with oxygen showing more localized fluctuations influenced by biological and physical interactions.

Extreme event analysis revealed that extreme temperature events are a key driver of both hyposalinity and hypoxia episodes, while reciprocal effects exist but are slightly weaker. A pronounced seasonal pattern was depicted for all extreme events. These findings emphasize the role of hydrological and climatic variability in shaping estuarine dynamics.

The application of GAM models successfully captured estuarine temperature and salinity trends but was less effective in describing oxygen variability. However, the model proved valuable for identifying extreme events and filling in data gaps, making it a useful tool for long-term monitoring and environmental forecasting.

These results underscore the importance of continuous high-resolution monitoring to track potential climate-driven shifts in tropical estuaries. Future research should also integrate additional biogeochemical parameters, which are essential to corroborate and mechanistically explain the reported hypoxia events such as nutrient cycling and organic matter dynamics, to refine our understanding of oxygen variability.

Moreover, the estimates of the model presented in this study offer a valuable means to better understand fish and shrimp nursery dynamics, given that biological data have so far been limited to isolated measurements (Rousseau et al., 2017). Additionally, assessing potential anthropogenic influences, including land-use changes and pollution, could help disentangle human-driven impacts from natural estuarine variability. Expanding monitoring efforts across multiple estuaries in French Guiana would improve our ability to anticipate and understand climate-related stressors in these critical coastal ecosystems. This enhanced knowledge could support adaptive management strategies aimed at preserving ecosystem resilience.

### CRediT authorship contribution statement

Iksandar Leite Costa: Writing – review & editing, Writing – original

draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Valentine Foulquier:** Writing – review & editing, Visualization. **Fabian Blanchard:** Writing – review & editing, Supervision, Funding acquisition. **Yann Rousseau:** Writing – review & editing. **Morgana Tagliarolo:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Data curation, Conceptualization.

### Data statement + link

The dataset is freely available: Leite Costa Iksandar, Foulquier Valentine, Blanchard Fabian, Rousseau Yann, Tagliarolo Morgana (2025). Dataset of high-frequency key abiotic factors (temperature, oxygen, salinity) in the tropical Cayenne estuary (French Guiana). SEANOE. https://doi.org/10.17882/105674.

### Funding sources

The authors of this publication would like to express their gratitude to IFREMER for providing the physical and structural resources that facilitated this research. We would also like to thank the University of French Guiana for funding a PhD grant for this project. Furthermore, we extend our thanks to Labex CEBA for providing financial support. This work has benefited from a grant managed by the French National Research Agency (AIBSI: ANR-22-EXES-0005), as part of the France 2030 program. It was also supported by the University of French Guiana and the French Ministry of Higher Education and Research. We would like to thank the University of Guiana for funding the ORCA research project. We also would like to thank the FEAMP for funding the MESH project.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Leite Costa Iksandar reports financial support was provided by University of French Guiana. Morgana Tagliarolo reports financial support was provided by Labex Amazonian Biodiversity Study Centre. Morgana Tagliarolo reports financial support was provided by University of French Guiana. Morgana Tagliarolo reports financial support was provided by FEAMPA. Fabian Blanchard reports financial support was provided by ANR PIA4. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

I would like to express my deepest gratitude to the teams at IFREMER for their invaluable support throughout this research. Their expertise and assistance, particularly during the fieldwork campaigns for retrieving sensor data, have been instrumental in the success of this study.

I also extend my sincere thanks to my co-authors for their significant contributions to this work. Their guidance and collaboration, both in writing and in modelling, have been essential in shaping the final outcome of this research.

Your dedication and commitment have been truly appreciated, and this work would not have been possible without your support.
<figure><img src="image_8.png" /><figcaption>Annex.2. Uncleaned Plots before the cleaning protocols (the black square represents the period with anomalies in the data)</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Annex 3. Venn diagram of the exclusive and shared days of extreme temperature, hyposalinity and hypoxia events estimated by 2-days minimum duration thresholds</figcaption></figure>

<figure><img src="image_10.png" /></figure>
<figure><img src="image_11.png" /><figcaption>Annex.4. Plots showing all parameters data's across all sites for a selected period of the time series</figcaption></figure>

Glossary

CF Cayenne Front (Urban mangrove site)

PC Petit Cayenne (Estuarine mangrove site)

S Soula (Estuarine mangrove site)

F Fourche (Estuary site)

GAM Generalized Additive Model

GLM Generalized Linear Model

NAO North Atlantic Oscillation

NKE Type of multiparameter data loggers replacing HOBO data loggers

PSU Practical Salinity Units

RMSE Root Mean Square Error

MAE Mean Absolute Error

R² Coefficient of Determination
# Data availability

Data will be made available on request.

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