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.

scholarly journals Effect of red algal bloom on growth and production of carps

Our Nature ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 48-54
Author(s):  
Ram Bhajan Mandal ◽  
Sunila Rai ◽  
Madhav Kumar Shrestha ◽  
Dilip Kumar Jha ◽  
Narayan Prasad Pandit
Keyword(s):  
Water Quality ◽  
Body Weight ◽  
Chlorophyll A ◽  
Total Nitrogen ◽  
Total Phosphorus ◽  
Algal Bloom ◽  
Total Dissolved Solids ◽  
Dissolved Solids ◽  
Aquaculture Farm ◽  
Red Algal

An experiment was carried to assess the effect of red algal bloom on growth and production of carp, water quality and profit from carp for 120 days at Aquaculture Farm of Agriculture and Forestry University, Chitwan. The experiment included two treatments: carp polyculture in non-red pond and carp polyculture in red pond with algal bloom each with three replicates. Carp fingerlings were stocked at 1 fish/m2 and fed with pellet containing 24% CP at 3% body weight. Net yield of rohu was found significantly higher (p<0.05) in non-red ponds (0.38±0.01 t ha-1) than red ponds (0.24±0.05 t ha-1). Survival of rohu (84.9±1.4%), bighead (95.2±2.0%) and mrigal (88.1±14.4%) were also significantly higher (p<0.05) in non-red ponds than red ponds. Red algal bloom affected DO, nitrate and chlorophyll-a, nitrite, total nitrogen, total phosphorus, total dissolved solids and conductivity. However, overall carp production and profit from carp remained unaffected.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

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.

scholarly journals Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series

2020 ◽  
Vol 12 (21) ◽  
pp. 3622
Author(s):  
Mengmeng Cao ◽  
Kebiao Mao ◽  
Xinyi Shen ◽  
Tongren Xu ◽  
Yibo Yan ◽  
...  
Keyword(s):  
Water Quality ◽  
Human Activities ◽  
Water Surface ◽  
Algal Bloom ◽  
Environmental Changes ◽  
Climatic Factors ◽  
Dongting Lake ◽  
Dynamic Learning ◽  
Traditional Methods

Significant water quality changes have been observed in the Dongting Lake region due to environmental changes and the strong influence of human activities. To protect and manage Dongting Lake, the long-term dynamics of the water surface and algal bloom areas were systematically analyzed and quantified for the first time based on 17 years of Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The traditional methods (index-based threshold algorithms) were optimized by a dynamic learning neural network (DL-NN) to extract and identify the water surface area and algal bloom area while reducing the extraction complexity and improving the extraction accuracy. The extraction accuracy exceeded 94.5% for the water and algal bloom areas, and the analysis showed decreases in the algal bloom and water surface areas from 2001–2017. Additionally, the variations in the water surface and algal bloom areas are greatly affected by human activities and climatic factors. The results of these analyses can help us better monitor human contamination in Dongting Lake and take measures to control the water quality during certain periods, which is crucial for future management. Moreover, the traditional methods optimized by the DL-NN used in this study can be extended to other inland lakes to assess and monitor long-term temporal and spatial variations in algal bloom areas and can also be used to acquire baseline information for future assessments of the water quality of lakes.

scholarly journals Anthropogenic and catchment characteristic signatures in the water quality of Swiss rivers: a quantitative assessment

2019 ◽  
Vol 23 (4) ◽  
pp. 1885-1904 ◽  
Author(s):  
Martina Botter ◽  
Paolo Burlando ◽  
Simone Fatichi
Keyword(s):  
Water Quality ◽  
Anthropogenic Impacts ◽  
Stream Water ◽  
Temporal Variations ◽  
Quality Data ◽  
Anthropogenic Factors ◽  
Water Quality Data ◽  
Catchment Characteristics

Abstract. The hydrological and biogeochemical response of rivers carries information about solute sources, pathways, and transformations in the catchment. We investigate long-term water quality data of 11 Swiss catchments with the objective to discern the influence of major catchment characteristics and anthropic activities on delivery of solutes in stream water. Magnitude, trends, and seasonality of water quality samplings of different solutes are evaluated and compared across catchments. Subsequently, the empirical dependence between concentration and discharge is used to classify the solute behaviors. While the anthropogenic impacts are clearly detectable in the concentration of certain solutes (i.e., Na+, Cl−, NO3, DRP), the influence of single catchment characteristics such as geology (e.g., on Ca2+ and H4SiO4), topography (e.g., on DOC, TOC, and TP), and size (e.g., on DOC and TOC) is only sometimes visible, which is also because of the limited sample size and the spatial heterogeneity within catchments. Solute variability in time is generally smaller than discharge variability and the most significant trends in time are due to temporal variations of anthropogenic rather than natural forcing. The majority of solutes show dilution with increasing discharge, especially geogenic species, while sediment-bonded solutes (e.g., total phosphorous and organic carbon species) show higher concentrations with increasing discharge. Both natural and anthropogenic factors affect the biogeochemical response of streams, and, while the majority of solutes show identifiable behaviors in individual catchments, only a minority of behaviors can be generalized across the 11 catchments that exhibit different natural, climatic, and anthropogenic features.

scholarly journals Spatio-temporal variations of water quality and phytoplankton diversity of the different rivers flowing within Sundarban mangrove wetland ecosystem of Bangladesh

2019 ◽  
Vol 5 (1) ◽  
pp. 61-76
Author(s):  
A Ahmed ◽  
N Akter ◽  
S Hasan ◽  
M Ataullah
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Temporal Variations ◽  
Mangrove Forests ◽  
Water Conductivity ◽  
Phytoplankton Diversity ◽  
A Value ◽  
Positive Loading ◽  
Sundarban Mangrove ◽  
Spatio Temporal

The water of different rivers passing in the Sundarban Mangrove Forests (SMF) of Bangladesh was analyzed to know the spatio-temporal variations in the water quality and phytoplankton diversity of the river. The pH of the waters of all the rivers examined showed a narrow range of variation (6.60-7.8 in 2015 and 6.3 to 7.5 in 2016) indicating the buffering capacity of different rivers. The values of pH showed a slightly decreasing tendency indicating the acidification of river waters which might be due to increase of global CO2 concentration in the atmosphere. The temperature of water, conductivity, salinity, DO (ppm), DO (% sat), K, Na, was found to vary from 27.6 to 30.8° C, 8.00 to 32.30 mS/cm, 5.50 to 23.00 ‰, 3.5 to 6.35 ppm, 46.60 to 82.00 %, 125.00 to 630.00 mg/l, 600 to 4300 ppm, respectively in April 2015; and ranged from 28.8 to 31.0° C, 9.48 to 31.60 mS/cm 5.00 to 24.00 ‰, 0.11 to 5.33 ppm, 1.2 to 95.2 % saturation, 110 to 670 ppm, 4683.5 to 13465.10 ppm, respectively in March 2016. The values of Ca, Mg and Fe were 210 to 500 ppm, 320.0 mg/l to 892.0 mg/l and 0.25 to 0.050 mg/l, respectively in 2015. The amount of DO was very low during 2016 especially in the locations 1, 2, 3 which might be due to cloudy condition during sampling time. Principal component analyses (PCA) of different variables of the year 2015 showed that PC-1 had positive loading of water temperature, air temperature, humidity, pH, conductivity, Salinity, Na, K, Fe, Mg, Zn whereas PC-1 of different variables during 2016 showed positive loading of only water temperature and pH. Maximum number of phytoplankton taxa was recorded from Sela river (Tambulbunia) where 34 taxa (with the unknown ones) were recorded followed by Passur river (near Mongla Ferry Ghat). Coscinodiscus is found to be the dominant genus. Maximum Shannon-Weaver index of diversity was found in Homra Khal (value was 6.825) and minimum was found in Sela river (where the oil tanker sank and value was 0.0). Only one species of phytoplankton was found in this place. Maximum species richness (d) was observed in Passur river at Mongla ferry ghat with a value of 14.81 whereas, maximum evenness (e) was found in location 16 (Homra khal with a value of 5.437). J. Biodivers. Conserv. Bioresour. Manag. 2019, 5(1): 61-76

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

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; 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 R&amp;#178; for four water quality parameters are 0.976, 0.988, 0.943, 0.995, and RMSE are 0.13&amp;#956;g/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 1.37&amp;#956;g/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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Key words: &lt;/strong&gt;water quality monitoring; MODIS; empirical method; machine learning&lt;/p&gt;

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