References of "Beckers, Jean-Marie"
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See detailWeb-based application for Data INterpolation Empirical Orthogonal Functions (DINEOF) analysis
Tomazic, Igor ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2014, April)

DINEOF (Data INterpolating Empirical Orthogonal Functions) is a powerful tool based on EOF decomposition developed at the University of Liege/GHER for the reconstruction of missing data in satellite ... [more ▼]

DINEOF (Data INterpolating Empirical Orthogonal Functions) is a powerful tool based on EOF decomposition developed at the University of Liege/GHER for the reconstruction of missing data in satellite datasets, as well as for the reduction of noise and detection of outliers. DINEOF is openly available as a series of Fortran routines to be compiled by the user, and as binaries (that can be run directly without any compilation) both for Windows and Linux platforms. In order to facilitate the use of DINEOF and increase the number of interested users, we developed a web-based interface for DINEOF with the necessary parameters available to run high-quality DINEOF analysis. This includes choosing variable within selected dataset, defining a domain, time range, filtering criteria based on available variables in the dataset (e.g. quality flag, satellite zenith angle …) and defining necessary DINEOF parameters. Results, including reconstructed data and calculated EOF modes will be disseminated in NetCDF format using OpenDAP and WMS server allowing easy visualisation and analysis. First, we will include several satellite datasets of sea surface temperature and chlorophyll concentration obtained from MyOcean data centre and already remapped to the regular grid (L3C). Later, based on user’s request, we plan to extend number of datasets available for reconstruction. [less ▲]

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See detailApproximate and Efficient Methods to Assess Error Fields in Spatial Gridding with Data Interpolating Variational Analysis (DIVA)
Beckers, Jean-Marie ULg; Barth, Alexander ULg; Troupin, Charles ULg et al

in Journal of Atmospheric & Oceanic Technology (2014), 31(2), 515-530

We present new approximate methods to provide error fields for the spatial analysis tool Diva. It is first shown how to replace the costly analysis of a large number of covariance functions by a single ... [more ▼]

We present new approximate methods to provide error fields for the spatial analysis tool Diva. It is first shown how to replace the costly analysis of a large number of covariance functions by a single analysis for quick error computations. Then another method is presented where the error is only calculated in a small number of locations and from there the spatial error field itself interpolated by the analysis tool. The efficiency of the methods is illustrated on simple schematic test cases and a real application in the Mediterranean Sea. These examples show that with these methods one has the possibility for quick masking of regions void of sufficient data and the production of "exact" error fields at reasonable cost. The error-calculation methods can also be generalized for use with other analysis methods such as 3D-Var and are therefore potentially interesting for other implementations. [less ▲]

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See detailUntangling spatial and temporal trends in the variability of the Black Sea Cold Intermediate Layer and mixed Layer Depth using the DIVA detrending procedure
Capet, Arthur ULg; Troupin, Charles ULg; Cartensen, Jacob et al

in Ocean Dynamics (2014), 64(3), 315-324

Current spatial interpolation products may be biased by uneven distribution of measurements in time. This manuscript presents a detrending method that recognizes and eliminates this bias. The method ... [more ▼]

Current spatial interpolation products may be biased by uneven distribution of measurements in time. This manuscript presents a detrending method that recognizes and eliminates this bias. The method estimates temporal trend components in addition to the spatial structure and has been implemented within the Data Interpolating Variational Analysis (DIVA) analysis tool. The assets of this new detrending method are illustrated by producing monthly and annual climatologies of two vertical properties of the Black Sea while recognizing their seasonal and interannual variabilities : the mixed layer depth, and the cold content of its Cold Intermediate Layer (CIL). The temporal trends, given as by-products of the method, are used to analyze the seasonal and interannual variability of these variables over the past decades (1955-2011). In particular, the CIL interannual variability is related to the cumulated winter air temperature anomalies, explaining 88\% of its variation. [less ▲]

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See detailMulti-scale optimal interpolation: application to DINEOF analysis spiced with a local optimal interpolation
Beckers, Jean-Marie ULg; Barth, Alexander ULg; Tomazic, Igor ULg et al

in Ocean Science Discussions (2014), 11

We present a method in which the optimal interpolation of multi-scale processes can be untangled into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of ... [more ▼]

We present a method in which the optimal interpolation of multi-scale processes can be untangled into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of two processes can be obtained by different mathematical formulations involving iterations and analysis focusing on a single process. From the 5 different mathematical equivalent formulations we then select the most efficient ones by analyzing the behavior of the different possibilities in a simple and well controlled test case. The clear guidelines deduced from this experiment are then applied in a real situation in which we combine large-scale analysis of hourly SEVIRI satellite images using DINEOF with a local optimal interpolation using a Gaussian covariance. It is 10 shown that the optimal combination indeed provides the best reconstruction and can therefore be exploited to extract the maximum amount of useful information from the original data [less ▲]

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See detaildivand-1.0: n-dimensional variational data analysis for ocean observations
Barth, Alexander ULg; Beckers, Jean-Marie ULg; Troupin, Charles ULg et al

in Geoscientific Model Development [=GMD] (2014), 7

A tool for multidimensional variational analysis (divand) is presented. It allows the interpolation and analysis of observations on curvilinear orthogonal grids in an arbitrary high dimensional space by ... [more ▼]

A tool for multidimensional variational analysis (divand) is presented. It allows the interpolation and analysis of observations on curvilinear orthogonal grids in an arbitrary high dimensional space by minimizing a cost function. This cost function penalizes the deviation from the observations, the deviation from a first guess and abruptly varying fields based on a given correlation length (potentially varying in space and time). Additional constraints can be added to this cost function such as an advection constraint which forces the analysed field to align with the ocean current. The method decouples naturally disconnected areas based on topography and topology. This is useful in oceanography where disconnected water masses often have different physical properties. Individual elements of the a priori and a posteriori error covariance matrix can also be computed, in particular expected error variances of the analysis. A multidimensional approach (as opposed to stacking 2-dimensional analysis) has the benefit of providing a smooth analysis in all dimensions, although the computational cost is increased. Primal (problem solved in the grid space) and dual formulations (problem solved in the observational space) are implemented using either direct solvers (based on Cholesky factorization) or iterative solvers (conjugate gradient method). In most applications the primal formulation with the direct solver is the fastest, especially if an a posteriori error estimate is needed. However, for correlated observation errors the dual formulation with an iterative solver is more efficient. The method is tested by using pseudo observations from a global model. The distribution of the observations is based on the position of the ARGO floats. The benefit of the 3-dimensional analysis (longitude, latitude and time) compared to 2-dimensional analysis (longitude and latitude) and the role of the advection constraint are highlighted. The tool divand is free software, and is distributed under the terms of the GPL license (http://modb.oce.ulg.ac.be/mediawiki/index.php/divand). [less ▲]

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See detailInterpolation of SLA Using the Data-Interpolating Variational Analysis in the Coastal Area of the NW Mediterranean Sea
Troupin, Charles ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg et al

Poster (2013, October 07)

The spatial interpolation of along-track Sea-Level Anomalies (SLA) data to produce gridded map has numerous applications in oceanography (model validation, data assimilation, eddy tracking, ...). Optimal ... [more ▼]

The spatial interpolation of along-track Sea-Level Anomalies (SLA) data to produce gridded map has numerous applications in oceanography (model validation, data assimilation, eddy tracking, ...). Optimal Interpolation (OI) is often the preferred method for this task, as it leads to the lowest expected error and provides an error field associated to the analyzed field. However, the method suffers from limitations such as the numerical cost (due to the inversion of covariance matrices) as well as the isotropic covariance function, generally employed in altimetry. The Data-Interpolating Variational Analysis (DIVA) is a gridding method based on the minimization of a cost function using a finite-element technique. The cost function penalizes the departures from observations, the smoothness of the gridded field and physical constraints (advection, diffusion, ...). It has been shown that DIVA and OI are equivalent (provided some assumptions on the covariances are made), the main difference is that in DIVA, the covariance function is not explicitly formulated. The technique has been previously applied for the creation of regional hydrographic climatologies, which required the processing of a large number of data points. In this work we present the application and adaptation of Diva to the analysis of SLA in the Mediterranean Sea and the production of weekly maps of SLA in this region. The peculiarities of SLA along-track data are addressed: • number of observations: the finite-element technique coupled to improvements in the matrix inversion (parallel or iterative solvers) lead to a decrease of the computational time, meaning that sub-sampling of the initial data set is not required. • quality of the different missions: the weight attributed to each data point can be easily set according to the satellite that provided the observations, so that different measurement noise variances are considered. • spatial correlation scale: it varies spatially in the domain according to the value of the Rossby radius of deformation. • long-wavelength errors: each data point is associated a class, and a detrending technique allows the determination of the trend for each class, leading to a reduction of the inconsistencies between missions. • anisotropy of physical coastal features: a pseudo-velocity field derived from regional bathymetry enhances the correlations along the main currents. Particular attention will be paid to the influence of this constraint in the coastal area. The analysis and error fields obtained over the Mediterranean Sea are compared with the available gridded products from AVISO. Different ways to compute the error field are compared. The impact of the use of multiple missions to prepare the gridded fields is also examined. In situ measurements from an intensive multi-sensor experiment carried out north of the Balearic Islands in May 2009 serve to assess the quality of the gridded fields in the coastal area. [less ▲]

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See detailHypoxia in the Black Sea northwestern shelf: From eutrophication to climatic stressors.
Capet, Arthur ULg; Beckers, Jean-Marie ULg; Grégoire, Marilaure ULg

in 40th CIESM congress proceedings (2013, October)

Abstract The dynamics of seasonal hypoxia, which affects the Black Sea north-western shelf since the mid 1970's until present days, is investigated by means of a 3D biogeochemical model. Comparison of the ... [more ▼]

Abstract The dynamics of seasonal hypoxia, which affects the Black Sea north-western shelf since the mid 1970's until present days, is investigated by means of a 3D biogeochemical model. Comparison of the model results with in -situ data reveals that the phenomenon may have been underestimated after the mid 1990's due to the distribution of observations. We investigate the mechanism of hypoxia at seasonal scale, and identify the main drivers of its interannual variability. While high nutrients discharge caused severe hypoxia in the 1980's, it was sustained in the 1990's by the pool of organic matter accumulated during the previous years in the sediments layer. With an increasing intensity, climatic stressors intensifies the response of hypoxia to nutrient discharge, and affect the seasonal dynamics of hypoxia by extending its temporal scale. [less ▲]

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See detailWP8 and WP9 developments: Data-Interpolating Variational Analysis (Diva) developments
Troupin, Charles ULg; Barth, Alexander ULg; Ouberdous, Mohamed et al

Conference (2013, September 27)

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See detailApplication of the Data-Interpolating Variational Analysis (DIVA) to sea-level anomaly measurements in the Mediterranean Sea
Troupin, Charles ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg et al

Poster (2013, September 23)

In ocean sciences, numerous techniques are available for the spatial interpolation of in situ data. These techniques mainly differ in the mathematical formulation and the numerical efficiency. Among them ... [more ▼]

In ocean sciences, numerous techniques are available for the spatial interpolation of in situ data. These techniques mainly differ in the mathematical formulation and the numerical efficiency. Among them, DIVA, which is based on the minimization of a cost function using a finite-element technique (figure 1). The cost function penalizes the departure from observations, the smoothness or regularity of the gridded field and can also include physical constraints. The technique is particularly adapted for the creation of climatologies, which required a large to several regional seas or part of the ocean to generate hydrographic climatologies. Sea-level anomalies (SLA) can be deduced from satellite-borne altimeters. The measurements are characterized by a high spatial resolution along the satellite tracks, but often a large distance between neighbour tracks. This implies the use of simultaneous altimetry missions for the construction of gridded maps. An along-track long wave-length error (correlated noise, e.g. due to orbit, residual tidal correction or inverse barometer errors) also affects the measurement and has to be taken into account in the interpolation. In this work we present the application and adaptation of Diva to the analysis of SLA in the Mediterranean Sea and the production of weekly maps of SLA in this region. Determination of the parameters The two main parameters that determines an analysis with DIVA are the correlation length (L) and the signal-to-noise ratio (SNR). Because of the particular spatial distribution of the measurements, the tools implemented in Diva for the analysis parameter determination tend to underestimate L and overestimate SNR, leading to noisy analysis (the observation constraint dominates the regularity constraint). Some adaptations of the tools are necessary to solve this issue. Numerical cost Because of the large number of observations to be processed (in comparison with in situ measurements on a similar period), the interpolation method employed is expected to be numerically efficient. Improvements in the implementation of Diva further improved the numerical performance of the method, especially thanks to the use of a parallel solver for the matrix inversion. The performance of finite-element mesh generator was also enhanced, so that interpolation of a data set of more than 1 million data points on a 100-by-100 grid can be performed in a few minutes on a personal laptop. Analysis and error field The analysis and error fields obtained over the Mediterranean Sea are compared with the available gridded products from AVISO. Different ways to compute the error field are compared. The impact of the use of multiple missions to prepare the gridded fields is also examined. [less ▲]

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See detailDerivation of high resolution TSM data by merging geostationary and polar-orbiting satellite data in the North Sea.
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Vanhellemont, Quinten et al

Conference (2013, September 09)

There is a need for high resolution ocean colour data, both in space and time, for a better assessment of the variability of these data and their influence in the environment, specially at shallow areas ... [more ▼]

There is a need for high resolution ocean colour data, both in space and time, for a better assessment of the variability of these data and their influence in the environment, specially at shallow areas where factors as tides and wind play a role in their dynamics. High spatial resolution is achieved by polar-orbiting satellites, but at a low temporal resolution. The opposite is true for geostationary satellites. In order to exploit the complementary nature of geostationary and polar data, a merging methodology has been developed to obtain a unique estimate of the North Sea Total Suspended Matter (TSM). The largest difficulty in developing a merging methodology is the correct estimation of the error covariance matrix, which can be specially complex for variables like TSM. In this work, the error covariance is not parametrized a priori using an analytical expression, but expressed using a truncated spatial EOF basis calculated by analysing MODIS data using DINEOF (Data INterpolating Empirical Orthogonal Functions). This EOF basis represents more realistically the complex variability of the TSM data sets than the parametric covariance used in most optimal interpolation applications. This EOF basis is subsequently used to merge MODIS and SEVIRI TSM data using an optimal interpolation approach. Results for the North Sea 2009 TSM will be shown, demonstrating the possibilities of this technique. The influence of including variables like winds or tides in the analysis, through multivariate approaches, will be assessed. [less ▲]

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See detailSeasonal hypoxia in the Black Sea north-western shelf. Is there any recovery after eutrophication ?
Capet, Arthur ULg; Beckers, Jean-Marie ULg; Grégoire, Marilaure ULg

in 9th EGU General Assembly (2013, April 11)

The Black Sea North-western shelf (NWS) is a shallow eutrophic area in which seasonal tratification of the water column isolates bottom waters from the atmosphere and prevents entilation to compensate for ... [more ▼]

The Black Sea North-western shelf (NWS) is a shallow eutrophic area in which seasonal tratification of the water column isolates bottom waters from the atmosphere and prevents entilation to compensate for the large consumption of oxygen, due to respiration in the bottom aters and in the sediments. A 3D coupled physical biogeochemical model is used to investigate he dynamics of bottom hypoxia in the Black Sea NWS at different temporal scales from seasonal o interannual (1981-2009) and to differentiate the driving factors (climatic versus eutrophication) f hypoxic conditions in bottom waters. Model skills are evaluated by comparison with 14500 in- itu oxygen measurements available in the NOAA World Ocean Database and the Black Sea ommission data. The choice of skill metrics and data subselections orientate the validation rocedure towards specific aspects of the oxygen dynamics, and prove the model’s ability to esolve the seasonal cycle and interannual variability of oxygen concentration as well as the patial location of the oxygen depleted waters and the specific threshold of hypoxia. During the eriod 1981-2009, each year exhibits seasonal bottom hypoxia at the end of summer. This henomenon essentially covers the northern part of the NWS, receiving large inputs of nutrients rom the Danube, Dniestr and Dniepr rivers, and extends, during the years of severe hypoxia, owards the Romanian Bay of Constanta. In order to explain the interannual variability of bottom ypoxia and to disentangle its drivers, a statistical model (multiple linear regression) is proposed sing the long time series of model results as input variables. This statis- tical model gives a eneral relationships that links the intensity of hypoxia to eutrophication and climate related variables. The use of four predictors allows to reproduce 78% of hypoxia interannual variability: he annual nitrate discharge (N ), the sea surface temperature in the month preceding tratification (T ), the amount of semi-labile organic matter in the sediments (C) and the duration f the stratification (D). Eutrophication (N ,C) and climate (T ,D) predictors explain a similar mount of variability (∼ 35%) when considered separately. A typical timescale of 9.3 years is found to describe the inertia of sediments in the recovering process after eutrophication. From his analysis, we find that under standard conditions (i.e. average atmospheric conditions, ediments in equi- librium with river discharges), the intensity of hypoxia can be linked to the evel of nitrate discharge through a non-linear equation (power law). Bottom hypoxia does not ffect the whole Black Sea NWS but rather exhibits an important spatial variability. This heterogeneous distribution, in addition to the seasonal fluctuations, complicates the monitoring f ottom hypoxia leading to contradictory conclusions when the interpretation is done from different ets of data. We find that it was the case after 1995 when the recovery process was verestimated due to the use of observations concentrated in areas and months not typically ffected by hypoxia. This stresses out the urging need of a dedicated monitoring effort in the WS f the Black Sea focused on the areas and the period of the year concerned by recurrent hypoxic events. [less ▲]

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See detailEstimating Inter-Sensor Sea Surface Temperature Biases using DINEOF analysis
Tomazic, Igor ULg; Alvera Azcarate, Aïda ULg; Troupin, Charles ULg et al

Poster (2013)

Climate studies need long-term data sets of homogeneous quality, in order to discern trends from other physical signals present in the data and to minimise the contamination of these trends by errors in ... [more ▼]

Climate studies need long-term data sets of homogeneous quality, in order to discern trends from other physical signals present in the data and to minimise the contamination of these trends by errors in the source data. Sea surface temperature (SST), defined as one of essential climatology variables, has been increasingly used in both oceanographical and meteorological operational context where there is a constant need for more accurate measurements. Satellite-derived SST provides an indispensable dataset, with both spatially and temporally high resolutions. However, these data have errors of 0.5 K on a global scale and present inter-sensor and inter-regional differences due to their technical characteristics, algorithm limitations and the changing physical properties of the measured environments. These inter-sensor differences should be taken into account in any research involving more than one sensor (SST analysis, long term climate research . . . ). The error correction for each SST sensor is usually calculated as a difference between the SST data derived from referent sensor (e.g. ENVISAT/AATSR) and from the other sensors (SEVIRI, AVHRR, MODIS). However, these empirical difference (bias) fields show gaps due to the satellite characteristics (e.g. narrow swath in case of AATSR) and to the presence of clouds or other atmospheric contaminations. We present a methodology based on DINEOF (Data INterpolation Empirical Orthogonal Functions) to reconstruct and analyse SST biases with the aim of studying temporal and spatial variability of the SST bias fields both at a large scale (European seas) and at a regional scale (Mediterranean Sea) and to perform the necessary corrections to the original SST fields. Two different approaches were taken: by analysing SST biases based on reconstructed SST differences and based on differences of reconstructed SST fields. Corrected SST fields based on both approaches were validated against independent in situ buoy SST data or with ENVISAT/AATSR SST data for areas without in situ data (e.g. eastern Mediterranean). [less ▲]

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See detailDINEOF-based bias correction of SEVIRI sea surface temperature using Metop-A/AVHRR and ENVISAT/AATSR SST
Tomazic, Igor ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2013)

Satellite-derived sea surface temperature (SST) show inter-sensor and inter-regional differences (biases) due to their technical characteristics, multispectral algorithm limitations and the changing ... [more ▼]

Satellite-derived sea surface temperature (SST) show inter-sensor and inter-regional differences (biases) due to their technical characteristics, multispectral algorithm limitations and the changing physical properties of the measured environments. The bias correction is usually calculated as a difference between the SST measurements from two sensors where one is defined as the reference (e.g. ENVISAT/AATSR). These empirical bias fields show gaps due to the satellite characteristics (e.g. narrow swath in case of AATSR) and to the presence of clouds or other atmospheric contamination sources. We present a bias correction approach based on DINEOF (Data Interpolating Empirical Orthogonal Functions) for reconstructing missing data. Two different approaches for deriving SST bias fields were used: analysing SST biases based on reconstructed SST differences or based on differences of the reconstructed SST fields. The method is applied at a large scale (European seas) and at a regional scale (e.g. Mediterranean Sea) to correct SEVIRI and Metop-A/AVHRR SST measurements using ENVISAT/AATSR as a corrector. For SEVIRI we additionally used Metop-A/AVHRR SST as a corrector to analyse the impact of ENVISAT/AATSR failure. Corrected SST fields based on both approaches were validated against independent in situ buoy SST data or with ENVISAT/AATSR SST data for areas without in situ data (e.g. eastern Mediterranean). The method is also compared to the operational bias correction method at Meteo-France/CMS that uses a temporal and spatial averaging. Results show that both approaches lead to near-zero biases when compared to AATSR SST measurements, although the differences of reconstructions exhibit much higher standard deviation (> 0.6 K) compared to the reconstruction of differences (< 0.5 K). Comparison with in situ data expectedly depends on the initial comparison between AATSR and in situ SST for specific regions. [less ▲]

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See detailDrivers, mechanisms and long-term variability of seasonal hypoxia on the Black Sea northwestern shelf – is there any recovery after eutrophication?
Capet, Arthur ULg; Beckers, Jean-Marie ULg; Grégoire, Marilaure ULg

in Biogeosciences (2013), 10

The Black Sea northwestern shelf (NWS) is a shallow eutrophic area in which the seasonal stratification of the water column isolates the bottom waters from the atmosphere. This prevents ventilation from ... [more ▼]

The Black Sea northwestern shelf (NWS) is a shallow eutrophic area in which the seasonal stratification of the water column isolates the bottom waters from the atmosphere. This prevents ventilation from counterbalancing the large consumption of oxygen due to respiration in the bottom waters and in the sediments, and sets the stage for the development of seasonal hypoxia. A three-dimensional (3-D) coupled physical–biogeochemical model is used to investigate the dynamics of bottom hypoxia in the Black Sea NWS, first at seasonal and then at interannual scales (1981–2009), and to differentiate its driving factors (climatic versus eutrophication). Model skills are evaluated by a quantitative comparison of the model results to 14 123 in situ oxygen measurements available in the NOAA World Ocean and the Black Sea Commission databases, using different error metrics. This validation exercise shows that the model is able to represent the seasonal and interannual variability of the oxygen concentration and of the occurrence of hypoxia, as well as the spatial distribution of oxygen-depleted waters. During the period 1981–2009, each year exhibits seasonal bottom hypoxia at the end of summer. This phenomenon essentially covers the northern part of the NWS – which receives large inputs of nutrients from the Danube, Dniester and Dnieper rivers – and extends, during the years of severe hypoxia, towards the Romanian bay of Constanta. An index H which merges the aspects of the spatial and temporal extension of the hypoxic event is proposed to quantify, for each year, the intensity of hypoxia as an environmental stressor. In order to explain the interannual variability of H and to disentangle its drivers, we analyze the long time series of model results by means of a stepwise multiple linear regression. This statistical model gives a general relationship that links the intensity of hypoxia to eutrophication and climate-related variables. A total of 82% of the interannual variability of H is explained by the combination of four predictors: the annual riverine nitrate load (N), the sea surface temperature in the month preceding stratification (Ts), the amount of semi-labile organic matter accumulated in the sediments (C) and the sea surface temperature during late summer (Tf). Partial regression indicates that the climatic impact on hypoxia is almost as important as that of eutrophication. Accumulation of organic matter in the sediments introduces an important inertia in the recovery process after eutrophication, with a typical timescale of 9.3 yr. Seasonal fluctuations and the heterogeneous spatial distribution complicate the monitoring of bottom hypoxia, leading to contradictory conclusions when the interpretation is done from different sets of data. In particular, it appears that the recovery reported in the literature after 1995 was overestimated due to the use of observations concentrated in areas and months not typically affected by hypoxia. This stresses the urgent need for a dedicated monitoring effort in the Black Sea NWS focused on the areas and months concerned by recurrent hypoxic events. [less ▲]

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