scholarly journals Decline in Transparency of Lake Hongze from Long-Term MODIS Observations: Possible Causes and Potential Significance

2019 ◽  
Vol 11 (2) ◽  
pp. 177 ◽  
Author(s):  
Na Li ◽  
Kun Shi ◽  
Yunlin Zhang ◽  
Zhijun Gong ◽  
Kai Peng ◽  
...  
Keyword(s):  
Water Quality ◽  
Aquatic Vegetation ◽  
Water Quality Monitoring ◽  
Validation Dataset ◽  
Model Parameters ◽  
Potential Significance ◽  
Important Indicator ◽  
Quality Issue ◽  
Mean Square Errors

Transparency is an important indicator of water quality and the underwater light environment and is widely measured in water quality monitoring. Decreasing transparency occurs throughout the world and has become the primary water quality issue for many freshwater and coastal marine ecosystems due to eutrophication and other human activities. Lake Hongze is the fourth largest freshwater lake in China, providing water for surrounding cities and farms but experiencing significant water quality changes. However, there are very few studies about Lake Hongze’s transparency due to the lack of long-term monitoring data for the lake. To understand long-term trends, possible causes and potential significance of the transparency in Lake Hongze, an empirical model for estimating transparency (using Secchi disk depth: SDD) based on the moderate resolution image spectroradiometer (MODIS) 645-nm data was validated using an in situ dataset. Model mean absolute percentage and root mean square errors for the validation dataset were 27.7% and RMSE = 0.082 m, respectively, which indicates that the model performs well for SDD estimation in Lake Hongze without any adjustment of model parameters. Subsequently, 1785 cloud-free images were selected for use by the validated model to estimate SDDs of Lake Hongze in 2003–2017. The long-term change of SDD of Lake Hongze showed a decreasing trend from 2007 to 2017, with an average of 0.49 m, ranging from 0.57 m in 2007 to 0.42 m in 2016 (a decrease of 26.3%), which indicates that Lake Hongze experienced increased turbidity in the past 11 years. The loss of aquatic vegetation in the northern bays may be mainly affected by decreases of SDD. Increasing total suspended matter (TSM) concentration resulting from sand mining activities may be responsible for the decreasing trend of SDD.

Parameter estimation for eutrophication models in reservoirs

2013 ◽  
Vol 68 (2) ◽  
pp. 319-327 ◽  
Author(s):  
J. M. P. Vieira ◽  
J. L. S. Pinho ◽  
N. Dias ◽  
D. Schwanenberg ◽  
H. F. P. van den Boogaard
Keyword(s):  
Water Quality ◽  
Research Work ◽  
Reservoir Management ◽  
Calibration Procedure ◽  
Production Model ◽  
Model Parameters ◽  
State Variables ◽  
Quality Issue ◽  
Complex Biological Process ◽  
Management Context

Excessive eutrophication is a major water quality issue in lakes and reservoirs worldwide. This complex biological process can lead to serious water quality problems. Although it can be adequately addressed by applying sophisticated mathematical models, the application of these tools in a reservoir management context requires significant amounts of data and large computation times. This work presents a simple primary production model and a calibration procedure that can efficiently be used in operational reservoir management frameworks. It considers four state variables: herbivorous zooplankton, algae (measured as chlorophyll-a pigment), phosphorous and nitrogen. The model was applied to a set of Portuguese reservoirs. We apply the model to 23 Portuguese reservoirs in two different calibration settings. This research work presents the results of the estimation of model parameters.

scholarly journals The use of bed sediments in water quality studies and monitoring programs

2017 ◽  
Vol 375 ◽  
pp. 11-17 ◽  
Author(s):  
Arthur J. Horowitz ◽  
Kent A. Elrick
Keyword(s):  
Water Quality ◽  
Suspended Sediment ◽  
Water Quality Monitoring ◽  
Flood Plain ◽  
Quality Monitoring ◽  
Sediment Chemistry ◽  
Regulatory Requirements ◽  
Bed Sediments ◽  
Monitoring Programs

Abstract. In most water quality monitoring programs, either filtered water (dissolved) or suspended sediment (either whole water or separated suspended sediment) are the traditional sample media of choice. This results both from regulatory requirements and a desire to maintain consistency with long-standing data collection procedures. Despite the fact that both bed sediments and/or flood plain deposits have been used to identify substantial water quality issues, they rarely are used in traditional water quality monitoring programs. The usual rationale is that bed sediment chemistry does not provide the temporal immediacy that can be obtained using more traditional sample media (e.g., suspended sediment, water). However, despite the issue of temporal immediacy, bed sediments can be used to address/identify certain types of water quality problems and could be employed more frequently for that purpose. Examples where bed sediments could be used include: (1) identifying potential long-term monitoring sites/water quality hot spots, (2) establishing a water quality/geochemical history for a particular site/area, and (3) as a surrogate for establishing mean/median chemical values for suspended sediment.

scholarly journals THE EVALUATION OF WATER POLLUTION WITH THE HELP OF REMOTE SENSING TOOLS

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 403-408
Author(s):  
P. Šádek ◽  
J. Struhár
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Water Quality ◽  
Vegetation Index ◽  
Aquatic Vegetation ◽  
Pollution Assessment ◽  
Vegetation Period ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Landsat 8

<p><strong>Abstract.</strong> With the growing population, there is a growing demand for quality drinking water. Especially in developing parts of the world, this is a serious problem. The aim of this work is to test remote sensing methods for water quality monitoring. The presented part of the project is focused on introducing the process of water pollution assessment using vegetation indices, which are derived only using RGB images. Water quality monitoring is based on satellite imagery Landsat 8 and UAV images Phantom 3. As reference data was used in-site measurements in profiles points. In-site measurements were repeated every month in the vegetation period from April to September. Based on regression analysis, the equation for the calculation of the amount of chlorophyll and the statistical evaluation of the quality of these equations is derived for each vegetation index. The best results were achieved using the ratio aquatic vegetation index (RAVI) and ExG (Excess green) indices of 97% and 96.8% respectively.</p>

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;

Modelling Transport of Heavy Metals in Geita Wetland along Mamubi River

2018 ◽  
Vol 37 (1) ◽  
pp. 72-88
Author(s):  
Ezrael J. Massawe ◽  
Richard Kimwaga
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Lake Victoria ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Model Parameters ◽  
Prediction Errors ◽  
Water Quality Simulation ◽  
Suspended Particulate ◽  
Monitoring Stations

The modelling of heavy metals in rivers is highly dependent on hydrodynamics, the transport of suspended particulate matter and the partition between dissolved and particulate phases. This paper presents the development of hydrodynamic model DUFLOW, which is a one dimensional flow and water quality simulation package, that describes the processes governing transformations and transport of heavy metals (Hg, Ni and Cu) along Mabubi River in the Geita wetland. Two monitoring stations were established along Mabubi River at the inlet (MBSP1) and outlet (MBSP2) of the wetland. A set of DUFLOW model inputs representative of the water conditions were collected from the established monitoring stations. The model was calibrated and validated for the prediction of flow and heavy metals (Hg, Ni, and Cu) transport, against a set of measured mean monthly monitoring data. Sensitive model parameters were adjusted within their feasible ranges during calibration to minimize model prediction errors. At the gauging station MBSP2, the calibration results showed the model predicted mean monthly flow within 17% of the measured mean monthly flow while the r2 coefficient and Nash-Sutcliffe (NSE) were 0.83 and 0.79 respectively. At the water quality monitoring station MBSP2, the calibration results showed the model predicted heavy metals (Hg, Ni and Cu) concentrations within 13% and 17% of their respective measured mean monthly concentrations. The mean monthly comparisons r 2 values for heavy metals ranged from 0.75 to 0.88 while the NSE values were between 0.70 and 0.82. The model results and field measurements demonstrated that about 40% of the annual heavy metals loadings which would otherwise reach the Lake Victoria are retained in the wetland. The Mabubi river model can therefore be used for prediction of heavy metals (Hg. Ni and Cu) transformation processes in the Geita wetland.

A Nonparametric Long-Term Trend Analysis Using Water Quality Monitoring Data in Nam-River

2018 ◽  
Vol 27 (11) ◽  
pp. 1029-1048
Author(s):  
Kang-Young Jung ◽  
Myojeong Kim ◽  
Kwang Duck Song ◽  
Kwon Ok Seo ◽  
Seong Jo Hong ◽  
...  
Keyword(s):  
Water Quality ◽  
Trend Analysis ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Term Trend ◽  
Long Term Trend
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