Long-term Water Quality Variations and Chlorophyll a Simulation with an Emphasis on Different Hydrological Periods in Lake Baiyangdian, Northern China

2012 ◽  
pp. 90-102 ◽  
Author(s):  
F. Wang
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Northern China ◽  
Lake Baiyangdian

scholarly journals Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2192
Author(s):  
Xujie Yang ◽  
Yan Jiang ◽  
Xuwei Deng ◽  
Ying Zheng ◽  
Zhiying Yue
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Dynamic Monitoring ◽  
Water Quality Parameter ◽  
Landsat Images ◽  
Chl A ◽  
Donghu Lake ◽  
Cubic Model

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.

Long-term effects of hydrometeorological and water quality conditions on algal dynamics in the Paldang dam watershed, Korea

2014 ◽  
Vol 14 (4) ◽  
pp. 601-608
Author(s):  
D.-W. Kim ◽  
J.-H. Min ◽  
M. Yoo ◽  
M. Kang ◽  
K. Kim
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Chlorophyll A ◽  
Algal Bloom ◽  
Temporal Variations ◽  
Regulated River ◽  
Han River ◽  
Nutrient Levels ◽  
Short Period

The primary goal of this study is to shed light on some important factors that control algal bloom in a large-scale regulated river system. Long-term impacts of environmental conditions on algal dynamics were investigated in the Paldang dam watershed, Korea. Dam inflow, water temperature, chlorophyll-a, TN, PO4-P and TP data collected at five major dams located on the North Han River (NHR) and at four water quality monitoring sites on the South Han River were analyzed for 21 years (1992 to 2012) to examine spatio-temporal variations in each. A pattern of slightly increasing chlorophyll-a and nutrient levels in the NHR since 2001 indicates that algal dynamics were affected by the increased nutrient levels as well as the reduced flow conditions (−10% to −37%). The temporal variations in monthly averaged data collected during summer monsoon seasons (mainly July) over the two decades show that high chlorophyll-a levels observed in both rivers corresponded to the relatively lower flow condition, which means a reduced amount of dam water release due to low or no rainfall over a short period of time, and abnormally high water temperature. This study shows that flow control is most critical for effectively managing algal level in the rivers in the short term, and nutrient management in the watershed is the key to reducing the potential for algal bloom in the long term.

Long-term water quality monitoring in Lake Simcoe based on the empirical method and machine learning

2021 ◽  
Author(s):  
Bo Wang ◽  
Jinhui Huang ◽  
Hongwei Guo
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chlorophyll A ◽  
Total Phosphorus ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Empirical Method ◽  
Quality Monitoring ◽  
Lake Simcoe

<p><strong>Abstract:</strong> The traditional water quality monitoring methods are time-consuming and laborious, which can only reflect the water quality status of single point scale, and have some problems such as irregular sampling time and limited sample size. Remote sensing technology provides a new idea for water quality monitoring, and the temporal resolution of MODIS is one day, which is suitable for long-term, continuous real-time large-scale monitoring of lakes. In this study, Lake Simcoe (located in Ontario, Canada) was selected as the research area. The long-term spatiotemporal changes of chlorophyll-a, transparency, total phosphorus and dissolved oxygen were analyzed by comparing the empirical method, multiple linear regression, random forest and neural network with MODIS data. Finally, the water quality condition of Lake Simcoe is evaluated. The results show that the overall retrieval results of two machine learning models are better than that of the empirical method. The optimal retrieval accuracy R² for four water quality parameters are 0.976, 0.988, 0.943, 0.995, and RMSE are 0.13μg/L, 0.3m, 0.002mg/L and 0.14mg/L, respectively. On the annual scale, the annual mean values of the four water quality parameters during the 10-year period from 2009 to 2018 were 1.37μg/L, 6.9m, 0.0112mg/L and 10.17mg/L, respectively. On the monthly scale, chlorophyll a, total phosphorus and dissolved oxygen first decreased and then increased at the time of year. The higher concentrations of chlorophyll a and total phosphorus in the south and east of Lake Simcoe are related to the input of nutrients from the surrounding residents and farmland.</p><p><strong>Key words: </strong>water quality monitoring; MODIS; empirical method; machine learning</p>

Modelling the Effects of Acid Deposition: Estimation of Long-Term Water Quality Responses in Forested Catchments in Finland

1988 ◽  
Vol 19 (2) ◽  
pp. 99-120 ◽  
Author(s):  
A. Lepistö ◽  
P. G. Whitehead ◽  
C. Neal ◽  
B. J. Cosby
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Base Cations ◽  
Surface Water Quality ◽  
Mineral Weathering ◽  
Co2 Solubility ◽  
Forested Catchments ◽  
Deposition Rates ◽  
Magic Model

A modelling study has been undertaken to investigate long-term changes in surface water quality in two contrasting forested catchments; Yli-Knuutila, with high concentrations of base cations and sulphate, in southern Finland; and organically rich, acid Liuhapuro in eastern Finland. The MAGIC model is based on the assumption that certain chemical processes (anion retention, cation exchange, primary mineral weathering, aluminium dissolution and CO2 solubility) in catchment soils are likely keys to the responses of surface water quality to acidic deposition. The model was applied for the first time to an organically rich catchment with high quantities of humic substances. The historical reconstruction of water quality at Yli-Knuutila indicates that the catchment surface waters have lost about 90 μeq l−1 of alkalinity in 140 years, which is about 60% of their preacidification alkalinity. The model reproduces the declining pH levels of recent decades as indicated by paleoecological analysis. Stream acidity trends are investigated assuming two scenarios for future deposition. Assuming deposition rates are maintained in the future at 1984 levels, the model indicates that stream pH is likely to continue to decline below presently measured levels. A 50% reduction in deposition rates would likely result in an increase in pH and alkalinity of the stream, although not to estimated preacidification levels. Because of the high load of organic acids to the Liuhapuro stream it has been acid before atmospheric pollution; a decline of 0.2 pH-units was estimated with increasing leaching of base cations from the soil despite the partial pH buffering of the system by organic compounds.

Long Term Monitoring of the Macrobenthos of the Upper Clyde Estuary

1984 ◽  
Vol 16 (5-7) ◽  
pp. 359-373 ◽  
Author(s):  
Anne R Henderson
Keyword(s):  
Water Quality ◽  
Seasonal Variation ◽  
Organic Pollution ◽  
Long Term Changes ◽  
Pollution Loading ◽  
Faunal Density ◽  
History Of ◽  
Long Term Monitoring ◽  
Term Monitoring

The sublittoral macrobenthic invertebrate populations of the Upper Clyde Estuary are described. The estuary has a long history of organic pollution. The long term changes in species composition, faunal density and dominance patterns between 1974 and 1980 are presented. The fauna is dominated by brackish, pollution tolerant oligochaetes and polychaetes. Fluctuations in populations can be related to both seasonal variation in environmental conditions and long term improvements in water quality through a reduction in pollution loading to the estuary.

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