scholarly journals Uncertainty and Sensitivity Analysis of Input Conditions in a Large Shallow Lake Based on the Latin Hypercube Sampling and Morris Methods

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1861
Author(s):  
Min Pang ◽  
Ruichen Xu ◽  
Zhibing Hu ◽  
Jianjian Wang ◽  
Ying Wang
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Wind Speed ◽  
Bottom Layer ◽  
External Input ◽  
Phosphorus Concentration ◽  
Analysis Method ◽  
Chl A ◽  
Tai Lake ◽  
Water Layers

We selected Tai Lake in China as the research area, and based on the Eco-lab model, we parameterized seven main external input conditions: discharge, carbon, nitrogen, phosphorus, wind speed, elevation, and temperature. We combined the LHS uncertainty analysis method and the Morris sensitivity analysis method to study the relationship between water quality and input conditions. The results showed that (1) the external input conditions had an uncertain impact on water quality. Among them, the uncertainties in total nitrogen concentration (TN) and total phosphorus concentration (TP) were mainly reflected in the lake entrance area, and the uncertainties of chlorophyll-a (Chl-a) and dissolved oxygen (DO) were mainly reflected in the lake center area. (2) The external input conditions had different sensitivities to different water layers. The bottom layer was most clearly and stably affected by input conditions. The TN and TP of the three different water layers were closely related to the flux into the lake, with average sensitivities of 83% and 78%, respectively. DO was mainly related to temperature and water elevation, with the bottom layer affected by temperatures as high as 98%. Chl-a was affected by all input factors except nitrogen and was most affected by wind speed, with an average of about 34%. Therefore, the accuracy of external input conditions can be effectively improved according to specific goals, reducing the uncertainty impact of the external input conditions of the model, and the model can provide a scientific reference for the determination of the mid- to long-term governance plan for Tai Lake in the future.

scholarly journals Sensitivity analysis of external conditions based on the MARS-Sobol method: case study of Tai Lake, China

2020 ◽  
Author(s):  
Ruichen Xu ◽  
Yong Pang ◽  
Zhibing Hu
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Sensitivity Analysis ◽  
Computer Calculation ◽  
Scientific Basis ◽  
External Input ◽  
Small Scale ◽  
Chl A ◽  
Tai Lake ◽  
External Conditions

Abstract This study utilized the ECO Lab model calculation samples of Tai Lake, in combination with robust analysis and the GCV test, to promote a faster intelligent application of machine learning and evaluate the MARS machine learning method. The results revealed that this technique can be better trained with small-scale samples, as indicated by the R2 values of the water quality test results, which were all >0.995. In combination with the Sobol sensitivity analysis method, the contribution degree of the parameterized external conditions as well as the relationship with the water quality were examined, which indicated that TP and TN are primarily related to the external input water quality and flow, while Chl-a is related to inflow (36.42%), TP (26.65%), wind speed (25.89%), temperature (8.38%), thus demonstrating that the governance of Chl-a is more difficult. In general, the accuracy and interpretability of MARS machine learning are more in line with the actual situation, and the use of the Sobol method can save computer calculation time. The results of this research can provide a certain scientific basis for future intelligent management of lake environments.

Spatiotemporal Variation of Chlorophyll a and its Relationship with other Water Quality Factors in the Tai Lake

2011 ◽  
Vol 183-185 ◽  
pp. 783-789
Author(s):  
Xing Cai Liu ◽  
Zong Xue Xu ◽  
Guo Qiang Wang
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Quality Factors ◽  
Mean Values ◽  
Chl A ◽  
Four Seasons ◽  
Tai Lake ◽  
Sample Data ◽  
Meiliang Bay

Algae bloom in the Tai Lake is a major issue and affects the water supply to the surrounding cities greatly. Chlorophyll a (Chl-a) is a common indicator that represent the trophic status in lakes. Spatial and temporal variations of Chl-a concentration are analyzed on the basis of sample data at 21 sites during the period of 2001 to 2005. Data at the sites located in the Meiliang Bay, Zhushan and Wulihu show greater fluctuations than that at other sites. A general trend showing that high values in northern part and low values in southern part of the Tai Lake is observed in seasonal mean values of Chl-a concentration for four seasons. Most high Chl-a concentrations occurred in summer (June to August) and autumn (September to November). Quantitative relationships between Chl-a and other water quality factors are investigated at all sites. Relative good relationships are obtained between Chl-a concentration and other water quality factors during 2001 to 2004 by using partial least squared regression. Prediction of Chl-a concentration in 2005 with above models produce worse results, which may be due to the occurrence of some extreme high values of Chl-a concentration in that year. Even though, acceptable predictions are obtained at several sites. Since the water quality in the lake is affected greatly by the inflow of nutrients from rivers, these relationships will be helpful for monitoring Chl-a variation with the combination of hydrological models that is able to simulate the inflow of nutrients.

Algae Migration Characteristics in Fresh Water Red Tide

1993 ◽  
Vol 28 (7) ◽  
pp. 197-201 ◽  
Author(s):  
Dunchun Wang ◽  
Isao Somiya ◽  
Shigeo Fujii
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Fresh Water ◽  
Field Data ◽  
Field Survey ◽  
Red Tide ◽  
Bottom Layer ◽  
Total Biomass ◽  
Quality Indices ◽  
Vertical Distributions

To understand the algae migration characteristics in the fresh water red tide, we performed a field survey in the Shorenji Reservoir located in Nabari City, Japan. From the analysis of the field data, it is found that the patterns of vertical distributions of the indices representing biomass are very different in the morning and the afternoon. Since some water quality indices have reverse fluctuations between the surface and the bottom layer in respect of the time series changes and the total biomass of the vertical water column is relatively constant, it is concluded that vertical and daily biomass variation of red tide alga is caused by its daily migration, that is the movement from the bottom layer to the surface in the morning and the reverse movement in the afternoon.

Long-Term (15 Years) Results of NPS Controls in an Agricultural Watershed upon a Receiving Lake's Water Quality

1993 ◽  
Vol 28 (3-5) ◽  
pp. 441-449 ◽  
Author(s):  
Paul J. Garrison ◽  
Timothy R. Asplund
Keyword(s):  
Water Quality ◽  
Structural Changes ◽  
Soil Phosphorus ◽  
Water Clarity ◽  
Management Control ◽  
Agricultural Watershed ◽  
Control Measures ◽  
Phosphorus Loading ◽  
Phosphorus Concentration ◽  
White Clay

Nonpoint source controls were installed in a 1215 ha agricultural watershed in northeastern Wisconsin in the late 1970. Changes were made in handling of animal wastes and cropping practices to reduce runoff of sediment and nutrients. Modelling results predicted a reduction in phosphorus runoff of 30 percent. The water quality of White Clay Lake has worsened since the installation of NPS controls. The lake's phosphorus concentration has increased from a mean of 29 µg L−1 in the late 1970s to 44 µg L−1 in recent years. Water clarity has declined from 2.7 to 2.1 m and the mean summer chlorophyll levels have increased from 9 to 13 µg L−1 with peak values exceeding 40 µg L−1. Increased phosphorus loading is not the result of elevated precipitation but instead the failure of the control measures to sufficiently reduce P loading. Most of the effort was placed on structural changes while most of the P loading comes from cropland runoff. Further, soil phosphorus concentrations have increased because of artificial fertilizers and manure spreading. The White Clay Lake experience is discouraging since the majority of the polluters in this watershed utilized some NPS control practices, including 76 percent of the farms which installed waste management control facilities.

Developing a Cloud-based Toolbox for Sensitivity Analysis of a Water Quality Model

2021 ◽  
pp. 105068
Author(s):  
Soobin Kim ◽  
Yong Sung Kwon ◽  
JongChel Pyo ◽  
Mayzonee Ligaray ◽  
Joong-Hyuk Min ◽  
...  
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Water Quality Model ◽  
Quality Model

scholarly journals Monitoring and simulating the distribution of phytoplankton in constructed wetlands based on SPOT 6 images

2021 ◽  
Vol 13 (1) ◽  
pp. 454-468
Author(s):  
Yumeng Song ◽  
Jing Zhang
Keyword(s):  
Water Quality ◽  
Water Quality Model ◽  
Quality Model ◽  
Band Ratio ◽  
Entire Study ◽  
Chl A ◽  
Analytical Tools ◽  
Best Fit ◽  
Entire Study Area ◽  
Range Of Values

Abstract We integrated hyperspectral and field-measured chlorophyll-a (Chl-a) data from the Kristalbad constructed wetland in the Netherlands. We developed a best-fit band ratio empirical algorithm to generate a distribution map of Chl-a concentration (C chla) from SPOT 6 imagery. The C chla retrieved from remote sensing was compared with a water quality model established for a wetland pond system. The retrieved satellite results were combined with a water quality model to simulate and predict the changes in phytoplankton levels. The regression model provides good retrievals for Chl-a. The imagery-derived C chla performed well in calibrating the simulation results. For each pond, the modeled C chla showed a range of values similar to the Chl-a data derived from SPOT 6 imagery (10–25 mg m−3). The imagery-derived and prediction model results could be used as the guiding analytical tools to provide information covering an entire study area and to inform policies.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

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