Modelling the impact of water level fluctuations on water quality (suspended particulate matter) in Lake Kinneret, Israel

2000 ◽  
Vol 128 (2-3) ◽  
pp. 101-125 ◽  
Author(s):  
L Håkanson ◽  
A Parparov ◽  
K.D Hambright
2019 ◽  
Vol 11 (23) ◽  
pp. 2785 ◽  
Author(s):  
Zhigang Cao ◽  
Ronghua Ma ◽  
Hongtao Duan ◽  
Kun Xue ◽  
Ming Shen

The temporal resolution of satellite determines how well remote sensing products represent changes in the lake environments and influences the practical applications by end-users. Here, a resampling method was used to reproduce the suspended particulate matter (SPM) dataset in 43 large lakes (>50 km2) on the eastern China plain during 2003–2017 at different temporal resolutions using MODIS Aqua (MODISA) based on Google Earth Engine platform, then to address the impact of temporal resolution on the long-term SPM dataset. Differences between the MODISA-derived and reproduced SPM dataset at longer temporal resolution were higher in the areas with large water dynamics. The spatial and temporal distributions of the differences were driven by unfavorable observation environments during satellite overpasses such as high cloud cover, and rapid changes in water quality, such as water inundation, algae blooms, and macrophytes. Furthermore, the annual mean difference in SPM ranged from 5–10% when the temporal difference was less than 10 d, and the differences in summer and autumn were higher than that of other seasons and surpassed 20% when the temporal resolution was more than 16 d. To assure that difference were less than 10% for long-term satellite-derived SPM datasets, the minimal requirement of temporal resolution should be within 5 d for most of the inland lakes and 3 d for lakes with large changes in water quality. This research can be used to not only evaluate the reliability of historically remote sensing products but also provide a reference for planning field campaigns and applying of high spatial resolution satellite missions to monitor aquatic systems in the future.


2021 ◽  
Vol 14 (6) ◽  
pp. 3695
Author(s):  
Christiane Do Nascimento Monte ◽  
Ana Paula De Castro Rodrigues ◽  
Sara Macedo ◽  
Carolina Ramos Régis ◽  
Edinelson Correa Saldanha ◽  
...  

O Rio Tapajós é uma das maiores bacias hidrográficas da região Norte do país, e o crescimento populacional de algumas cidades amazônicas coloca em risco a qualidade das suas águas. A cidade de Santarém, no Oeste do Pará, é uma das maiores cidades paraenses e não tem uma rede de esgoto eficiente, logo boa parte do esgoto doméstico é lançado in natura em igarapés e no rio Tapajós, o que afeta diretamente a balneabilidade do rio, que é um dos destinos turísticos em ascensão no país, devido às praias de água doce, a qualidade do pescado, que é parte da dieta alimentar da região e pode ser um vetor de doenças, as quais podem aumentar os gastos com a saúde pública. Com o objetivo de avaliar a influência antrópica no rio Tapajós foi realizada uma amostragem em seis pontos do rio na região conhecida como a orla da cidade. Foram analisados parâmetros físico-químicos, biológicos e nutrientes), Apesar de boa parte dos parâmetros estarem em conformidade com a CONAMA 357/05, os parâmetros biológicos foram acima do permitido para a classe II, indicando influência antrópica. Os dados apontaram que a presença de material particulado em suspensão (MPS), coliformes totais e fósforo inorgânico dissolvido (PID), sugerem aumento da degradação da qualidade da água.  O estudo da queda na qualidade de água nos rios amazônicos é importante, pois a relação socioeconômica entre a população e os recursos hídricos é muito importante para a manutenção das funções ambientais, econômicas e sociais na região.   The anthropic influence on the water quality of the Tapajós River, in the city of Santarém-PA A B S T R A C TThe Tapajós River is one of the largest hydrographic basins of the Northern region of the country, and the population growth of some Amazonian cities puts the quality of its waters at risk. The city of Santarém, in western Pará, is one of the largest cities in Pará and does not have an efficient sewage system, so much of the domestic sewage is released in natura into streams and the Tapajós River, which directly affects the balneability of the river, which is one of the tourist destinations on the rise in the country, due to its freshwater beaches, the quality of the fish, which is part of the diet of the region and can be a vector of diseases, which can increase spending on public health. To evaluate the anthropic influence on the Tapajós River, sampling was carried out at six points on the river in the region known as the city edge. Although most of the parameters complied with CONAMA 357/05, the biological parameters were above the permitted for class II, indicating anthropic influence. The data pointed out that the presence of suspended particulate matter (SPM), total coliforms, and dissolved inorganic phosphorus (DIP), suggest increased degradation of water quality.  The study of the decline in water quality in Amazonian rivers is important because the socioeconomic relationship between the population and water resources is very important for the maintenance of environmental, economic, and social functions in the region. Keywords: Tapajós River, sewage, inorganic phosphorus, suspended particulate matter


2012 ◽  
Vol 46 (7) ◽  
pp. 2324-2332 ◽  
Author(s):  
G.S. Bilotta ◽  
N.G. Burnside ◽  
L. Cheek ◽  
M.J. Dunbar ◽  
M.K. Grove ◽  
...  

2006 ◽  
Vol 3 (5) ◽  
pp. 2991-3021 ◽  
Author(s):  
M. Rode ◽  
U. Suhr

Abstract. Monitoring of surface waters is primarily done to detect the status and trends in water quality and to identify whether observed trends arise form natural or anthropogenic causes. Empirical quality of surface water quality data is rarely certain and knowledge of their uncertainties is essential to assess the reliability of water quality models and their predictions. The objective of this paper is to assess the uncertainties in selected surface water quality data, i.e. suspended sediment, nitrogen fraction, phosphorus fraction, heavy metals and biological compounds. The methodology used to structure the uncertainty is based on the empirical quality of data and the sources of uncertainty in data (van Loon et al., 2006). A literature review was carried out including additional experimental data of the Elbe river. All data of compounds associated with suspended particulate matter have considerable higher sampling uncertainties than soluble concentrations. This is due to high variability's within the cross section of a given river. This variability is positively correlated with total suspended particulate matter concentrations. Sampling location has also considerable effect on the representativeness of a water sample. These sampling uncertainties are highly site specific. The estimation of uncertainty in sampling can only be achieved by taking at least a proportion of samples in duplicates. Compared to sampling uncertainties measurement and analytical uncertainties are much lower. Instrument quality can be stated well suited for field and laboratory situations for all considered constituents. Analytical errors can contribute considerable to the overall uncertainty of surface water quality data. Temporal autocorrelation of surface water quality data is present but literature on general behaviour of water quality compounds is rare. For meso scale river catchments reasonable yearly dissolved load calculations can be achieved using biweekly sample frequencies. For suspended sediments none of the methods investigated produced very reliable load estimates when weekly concentrations data were used. Uncertainties associated with loads estimates based on infrequent samples will decrease with increasing size of rivers.


1987 ◽  
Vol 13 (2) ◽  
pp. 147-150 ◽  
Author(s):  
Povl Revsbech ◽  
Jens Korsgaard ◽  
Gunnar R. Lundqvist

2007 ◽  
Vol 11 (2) ◽  
pp. 863-874 ◽  
Author(s):  
M. Rode ◽  
U. Suhr

Abstract. Monitoring of surface waters is primarily done to detect the status and trends in water quality and to identify whether observed trends arise from natural or anthropogenic causes. Empirical quality of river water quality data is rarely certain and knowledge of their uncertainties is essential to assess the reliability of water quality models and their predictions. The objective of this paper is to assess the uncertainties in selected river water quality data, i.e. suspended sediment, nitrogen fraction, phosphorus fraction, heavy metals and biological compounds. The methodology used to structure the uncertainty is based on the empirical quality of data and the sources of uncertainty in data (van Loon et al., 2005). A literature review was carried out including additional experimental data of the Elbe river. All data of compounds associated with suspended particulate matter have considerable higher sampling uncertainties than soluble concentrations. This is due to high variability within the cross section of a given river. This variability is positively correlated with total suspended particulate matter concentrations. Sampling location has also considerable effect on the representativeness of a water sample. These sampling uncertainties are highly site specific. The estimation of uncertainty in sampling can only be achieved by taking at least a proportion of samples in duplicates. Compared to sampling uncertainties, measurement and analytical uncertainties are much lower. Instrument quality can be stated well suited for field and laboratory situations for all considered constituents. Analytical errors can contribute considerably to the overall uncertainty of river water quality data. Temporal autocorrelation of river water quality data is present but literature on general behaviour of water quality compounds is rare. For meso scale river catchments (500–3000 km2) reasonable yearly dissolved load calculations can be achieved using biweekly sample frequencies. For suspended sediments none of the methods investigated produced very reliable load estimates when weekly concentrations data were used. Uncertainties associated with loads estimates based on infrequent samples will decrease with increasing size of rivers.


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