Spatial patterns of water quality and plankton from high-resolution continuous in situ sensing along a 537-km nearshore transect of western Lake Superior, 2004

2009 ◽  
pp. 439-472
Author(s):  
Peder M. Yurista ◽  
John R. Kelly
2021 ◽  
Author(s):  
Amandine Declerck ◽  
Matthias Delpey ◽  
Thibaut Voirand ◽  
Ioanna Varkitzi

<p>Keywords: eutrophication; high resolution ocean modeling ; Chla satellite data ; biogeochemistry</p><p>Maliakos Gulf corresponds to mesotrophic waters that can reach eutrophic conditions and are occasionally subject to Harmful Algal Blooms (HAB) (Varkitzi et al. 2018). At the same time, it is an important fish farming and aquaculture production area. A large issue is thus related to the monitoring and forecasting of the risk of occurrence of algae blooms in the Gulf. For this purpose, the present study couples predictions from a high-resolution numerical ocean model with satellite observation to improve the monitoring and anticipation of threats for the local fish farms induced by occasional eutrophication.</p><p>This solution is developed in the frame of the MARINE-EO project (https://marine-eo.eu/). It combines satellite observation with high-resolution ocean modelling to provide detailed information as a support to fish farms management and operations. It is implemented in an operational platform, which provides continuous information in real time as well as short term predictions. The deployed solution uses CMEMS physical products as an input data and offers to refine this solution in order to provide a local information on site using a downscaling strategy. High resolution satellite products and ocean modelling allow to include the impact of local coastal processes on currents and water quality parameters to provide a proper monitoring and forecasting solution at the scale of a specific fish farm.</p><p>To model specific eutrophication processes, a NPZD (Nutrients-Phytoplankton-Zooplankton-Detritus) biogeochemical model is used. Included in the MOHID Water modelling system, the water quality module (Mateus, 2006) considering 18 properties: nutrients and organic matter (nitrogen, phosphorus and silica biogeochemical cycles), oxygen and organisms (phytoplankton and zooplankton) was deployed in the western Aegean Sea. The simulated chlorophyll a concentrations are used to compute a risk level for the eutrophication occurrence. To complete this indicator, another risk level was based on the eutrophication variation following Primpas et al. (2010) formulation. In addition to model forecasts, ocean color observations from the Sentinel-2 MSI and Landsat-8 OLI sensors are used to provide high resolution chlorophyll a concentrations maps in case of bloom events. The processing chain uses the sixth version of the Quasi-Analytical Algorithm initially developed by Lee et al. (2002) and an empirical relation based on a database built using the HydroLight software to compute chlorophyll a concentration.</p><p>Two past eutrophication events monitored in situ (Varkitzi et al. 2018) were studied to assess the accuracy of the developed tool. Although few in situ data were available on environmental input (as rivers flow and nutrient concentrations), it was possible using statistics to reproduce qualitatively these blooms. Finally, an operational demonstration was conducted during 2 months of the 2020 autumn season, to showcase real time monitoring and predictive perspectives.</p>


2018 ◽  
Author(s):  
Hedy M. Aardema ◽  
Machteld Rijkeboer ◽  
Alain Lefebvre ◽  
Arnold Veen ◽  
Jacco C. Kromkamp

Abstract. Marine waters can be highly heterogeneous both on a spatial and temporal scale, yet monitoring is currently mainly limited to low-resolution methods. This study explores the use of two high-resolution methods to study phytoplankton dynamics; Fast Repetition Rate fluorometry (FRRf) to study phytoplankton photosynthesis and scanning flowcytometry (FCM) to study phytoplankton biomass and composition. Measurements were conducted during four cruises on the Dutch North Sea in April, May, June, and August of 2017. Both FRRf and FCM data show spatial heterogeneity with monthly variation. Automated unsupervised Hidden Markov Model (uHMM) spatial clustering resulted in the identification of regions with distinct phytoplankton communities. Manual adjustments were necessary to optimize visualization of some distinct phytoplankton communities. Stepwise multiple linear regression (n = 61) revealed that photophysiology (alpha), phytoplankton biomass (total red fluorescence) and abiotic predictors (Turbidity, DIN, time of the day and temperature) determined integrated water column gross primary productivity. Apart from spatial heterogeneity, the diurnal trend is a significant predictor exposing clear trends with other photophysiological parameters. Consequently, spatial patterns are difficult as temporal and spatial patterns occur simultaneously. Nevertheless, high-resolution monitoring is a very useful supplement in addition to regular low-resolution monitoring.


2020 ◽  
Author(s):  
Majid Bayati ◽  
Mohammad Danesh-Yazdi

<p>The spatiotemporal dynamics of salinity in hypersaline lakes is strongly dependent on the rate of water flow feeding the lake, evaporation rate, and the phenomena of precipitation and dissolution. Although in-situ observations are most reliable in quantifying water quality variables, the spatiotemporal distribution of such data are typically limited or cannot be readily extrapolated for long-term projections. Alternatively, remotely-sensed imagery has facilitated less expensive and stronger ability to estimate water quality over a wide range of spatiotemporal resolutions. This study introduces a machine learning model that leverages in-situ measurements and high-resolution satellite imagery to estimate the salinity concentration in water bodies. To this end, 123 points were sampled in April and July of 2019 across the Lake Urmia surface covering the wide range of salinity fluctuations. Among the artificial neural networks, ANFIS, and linear regression tools examined to determine the relationship between salinity and surface reflectance, artificial neural networks yielded the best accuracy evidenced by R<sup>2</sup> = 0.94 and RMSE = 6.8%. The results show that the seasonal change of salinity is linearly correlated with the volume of water feeding the lake, witnessing that dilution imposes a stronger control on the salinity than bed salt dissolution. The impact of disturbance in the lake circulation due to the causeway is also evident from the sharp changes of salinity around the bridge piers near spring when the mixing of fresh and hypersaline water from the southern and northern parts, respectively, takes place. The results of this study prove the promising potential of machine learning tools fed multi-spectral satellite information to map other water quality metrics than salinity as well.</p>


Author(s):  
I. Theologou ◽  
M. Patelaki ◽  
K. Karantzalos

Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn’t establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r<sup>2</sup>=89.80%), dissolved oxygen (r<sup>2</sup>=88.53%), conductivity (r<sup>2</sup>=88.18%), ammonium (r<sup>2</sup>=87.2%) and pH (r<sup>2</sup>=86.35%), while the total phosphorus (r<sup>2</sup>=70.55%) and nitrates (r<sup>2</sup>=55.50%) resulted in lower correlation rates.


Proceedings ◽  
2019 ◽  
Vol 48 (1) ◽  
pp. 14
Author(s):  
Gordana Kaplan ◽  
Zehra Yigit Avdan ◽  
Serdar Goncu ◽  
Ugur Avdan

In water resources management, remote sensing data and techniques are essential in watershed characterization and monitoring, especially when no data are available. Water quality is usually assessed through in-situ measurements that require high cost and time. Water quality parameters help in decision making regarding the further use of water-based on its quality. Turbidity is an important water quality parameter and an indicator of water pollution. In the past few decades, remote sensing has been widely used in water quality research. In this study, we compare turbidity parameters retrieved from a high-resolution image with in-situ measurements collected from Borabey Lake, Turkey. Here, the use of RapidEye-3 images (5 m-resolution) allows for detailed assessment of spatio-temporal evaluation of turbidity, through the normalized difference turbidity index (NDTI). The turbidity results were then compared with data from 21 in-situ measurements collected in the same period. The actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.84. The research findings support the use of remote sensing data of RadipEye-3 to estimate water quality parameters in small water areas. For future studies, we recommend investigating different water quality parameters using high-resolution remote sensing data.


2004 ◽  
Vol 30 ◽  
pp. 395-406 ◽  
Author(s):  
Timothy B. Johnson ◽  
Michael H. Hoff ◽  
Anett S. Trebitz ◽  
Charles R. Bronte ◽  
Timothy D. Corry ◽  
...  

2017 ◽  
Vol 14 (1) ◽  
pp. 48 ◽  
Author(s):  
Tao Liang ◽  
Yali Tong ◽  
Xiahui Wang ◽  
Lingqing Wang

Environmental contextEutrophication caused by excessive inputs of phosphorus is a prevalent global environmental problem. Reactive phosphorus released from sediments was measured by two new in situ passive sampling techniques capable of high-resolution measurements of phosphorus concentration. The methods provide the scientific evidence for solving the problems associated with deteriorating surface water quality. AbstractInternal phosphorus (P) loading is regarded as a major eutrophication factor and may prevent improvements in lake water quality. Two new in situ passive sampling techniques, high-resolution pore-water equilibrators (HR-Peeper) and zirconium oxide-based diffusive gradients in thin films (Zr-oxide DGT), were combined to measure dissolved reactive phosphorus (DRP) (CPeeper) and labile phosphorus (CDGT) at five sites in South Dongting and West Dongting Lakes. The vertical distribution of CPeeper and CDGT displayed similarity, which demonstrated that the buffering capacity of the labile P in sediments was similar at different depths. The diffusion flux of P from the sediments at the sediment–water interface ranged from 1.9 to 88ng m–2 day–1, with an average value of 38ng m–2 day–1. The P flux at the entrances to the Yuan, Li and Zi Rivers was fairly large at all five sites. The sediments at the five sites released P into overlying water, indicating that the sediments are an important source of P for Dongting Lake.


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