Exploring Dynamics of the Chlorophyll a-Total Phosphorus Relationship at the Lake-Specific Scale: a Bayesian Hierarchical Model

Liang, Zhongyao; Chen, Huili; Wu, Sifeng; Zhang, Xiaoling; Yang, Yu; Liu, Yong

doi:10.1007/s11270-017-3678-9

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…This may be caused by the uncertainty of the model that later caused errors in the calculation of parameter sensitivity. The effect of mymg on TP was obviously higher than that on other indicators except Chl-a, which may be caused by the interaction between cyanobacteria and TP [63,64].…”

Section: Sensitivity Analysis At Different Timesmentioning

confidence: 83%

The Spatiotemporal Characteristics of Water Quality and Main Controlling Factors of Algal Blooms in Tai Lake, China

Pang

et al. 2022

Sustainability

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Taking Tai Lake in China as the research area, a 3D water environment mathematical model was built. Combined with the LHS and Morris uncertainty and sensitivity analysis methods, the uncertainty and sensitivity analysis of total phosphorus (TP), total nitrogen (TN), dissolved oxygen (DO), and chlorophyll a (Chl-a) were carried out. The main conclusions are: (1) The performance assessment of the 3D water environment mathematical model is good (R2 and NSE > 0.8) and is suitable for water quality research in large shallow lakes. (2) The time uncertainty study proves that the variation range of Chl-a is much larger than that of the other three water quality parameters and is more severe in summer and autumn. (3) The spatial uncertainty study proves that Chl-a is mainly present in the northwest lake area (heavily polluted area) and the other three water quality indicators are mainly present in the center. (4) The sensitivity results show that the main controlling factors of DO are ters (0.15) and kmsc (0.12); those of TN and TP are tetn (0.58) and tetp (0.24); and those of Chl-a are its own growth rate (0.14), optimal growth temperature (0.12), death rate (0.12), optimal growth light (0.11), and TP uptake rate (0.11). Thus, TP control is still the key treatment method for algal blooms that can be implemented by the Chinese government.

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Section: Sensitivity Analysis At Different Timesmentioning

confidence: 83%

The Spatiotemporal Characteristics of Water Quality and Main Controlling Factors of Algal Blooms in Tai Lake, China

Pang

et al. 2022

Sustainability

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“…Being the main biogenic elements for phytoplankton biomass, the impact of nitrogen and phosphorus will have important effects on Chl a content in water (Liang et al, 2018; Wu et al, 2010). Generally, phytoplankton will absorb nitrogen and phosphorus and grow rapidly, resulting in water bloom in large lakes (Xu et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…Nitrogen and phosphorus are the main biogenic elements for phytoplankton production, which can lead to the occurrence of eutrophication. Excessive nitrogen and phosphorus loading may stimulate the growth of phytoplankton in lakes, so the Chl a content in lake water is closely related to nitrogen and phosphorus (Liang et al, 2018; Ruan et al, 2008). However, due to limited monitoring, it is difficult to achieve long‐term comprehensive in situ spatiotemporal‐scale monitoring of lake systems.…”

Section: Introductionmentioning

confidence: 99%

Changes in chlorophyll a and its response to nitrogen and phosphorus characteristics over the past three decades in Poyang Lake, China

Jia

et al. 2020

Ecohydrology

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In this study, we explore the relationship between changes in chlorophyll a (Chl a) content, total nitrogen (TN) and total phosphorus (TP) characteristics from 1989 to 2018 in Poyang Lake, China, by applying Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM) image data in combination with in situ monitoring data. Results showed that the average Chl a content in water over this 30‐year period was 5.77 μg/L. Although the water quality status of the lake was generally healthy, proportional Chl a content >10 μg/L (E10) has gradually increased, and the water quality in some areas of the lake has subsequently deteriorated over the past 30 years. Additionally, E10 was significantly positively correlated to the TN/TP. Changes in Chl a content were significantly positively correlated to the ratio of TN flux to TP flux from tributaries into the lake, but negatively correlated to TP flux. Results indicated that Lake can be categorized as phosphorus‐sufficient and nitrogen limited.

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“…Four significant non-optical parameters, TN, TP, NH 3 -N, and NO 3 -N, have been extensively investigated to represent the eutrophication of rivers and lakes, which will cause a critical water pollution issue in many countries like degrading functioning and endangering water security (X. Chen et al, 2018;Liang et al, 2018;Lv & Wu, 2021;Mararakanye et al, 2022). Traditionally, in-situ measurements and the collection of water samples are the major approaches for monitoring water quality.…”

Section: Introductionmentioning

confidence: 99%

Non-optical Water Quality Retrieval from Zhuhai-1 OHS Hyperspectral Images in Taipu River

Yukun

Lin

et al. 2022

Preprint

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Hyperspectral remote sensing is thought to be a useful technology for assessing the condition of inland waters. However, non-optically active water quality parameters are rarely explored in hyperspectral remote sensing applications, despite they are highly valued in the aquatic environment condition. This study intends to evaluate the performance of non-optically active water quality parameters using Zhuhai-1 hyperspectral imagery. Focusing on total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N) and nitrate-nitrogen (NO3-N) in Taipu River, we constructed empirical models to evaluate the precision of water quality inversion from OHS by comparing with Sentinel-2, and determined the sensitive bands of different water quality parameters. The final results showed that the polynomial model based on OHS had the greatest potential in retrieving TN, TP and NH3-N concentration, and the R2 was 0.9678, 0.7924, 0.7682 respectively. The combination of R(510)/R(820) and R(700)/R(806), R(940)/R(820) and R(806)/R(926), R(709)/R(806) and R(746)/R(620) were most sensitive to TN, TP and NH3-N respectively. The OHS and Sentinel-2 both had potential in retrieving NO3-N. The R2 was 0.9791 from OHS and was 0.9513 from Sentinel-2. The sensitive bands of NO3-N were R(596)/R(665) and R(466)/R(580) from OHS, and Red Eage3/Blue and SWIR1/Blue from Sentinel-2. We also analyzed the drivers of the spatial distribution of water quality in Taipu River, the results showed negative impacts of farmland and urban land on water quality, and beneficial impacts of forest land on water quality. This study represented a promising step in hyperspectral remote sensing for retrieving inland non-optically active water quality parameters utilizing Zhuhai-1.

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Exploring Dynamics of the Chlorophyll a-Total Phosphorus Relationship at the Lake-Specific Scale: a Bayesian Hierarchical Model

Cited by 9 publications

References 45 publications

The Spatiotemporal Characteristics of Water Quality and Main Controlling Factors of Algal Blooms in Tai Lake, China

The Spatiotemporal Characteristics of Water Quality and Main Controlling Factors of Algal Blooms in Tai Lake, China

Changes in chlorophyll a and its response to nitrogen and phosphorus characteristics over the past three decades in Poyang Lake, China

Non-optical Water Quality Retrieval from Zhuhai-1 OHS Hyperspectral Images in Taipu River

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