Seasonal dynamics of phytoplankton and its relationship with environmental factors in Lake Hulun

Xiyi, Qian; Li, Jinbiao; Ao, Wen; Bo, Pang; Saru, Bao; Wang, Qi; Liu, Bo; Wang, Zeng-long

doi:10.18307/2022.0603

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…The spatial-temporal distribution of Chl-a in Hulun Lake from 2019 to 2021 was obtained based on the RF model (Figure 6), and the generated Chl-a somewhat displayed a consistent pattern. The results show that the lake's center had a generally lower Chla level than its surroundings, and in line with the conclusions of Qian et al [42] and Cao et al [43] on phytoplankton and algal blooms in Hulun Lake, the regions with relatively high Chl-a concentration were mainly distributed in the northeast, south, and southwest of the lake, which indicates that the distribution of algal blooms in Hulun Lake has a high consistency with Chl-a concentration. In addition to meteorological conditions, nutrients are necessary for the growth of algae.…”

Section: Discussionsupporting

confidence: 89%

Monitoring of Low Chl-a Concentration in Hulun Lake Based on Fusion of Remote Sensing Satellite and Ground Observation Data

Zhang,

Wang

et al. 2024

Remote Sensing

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China’s northern Hulun Lake is a significant body of water internationally. The issue of eutrophication has gained prominence in recent years. The achievement of precise chlorophyll-a (Chl-a) monitoring is crucial for safeguarding Hulun Lake’s ecosystem. The machine learning-based remote sensing inversion method has been shown to be effective in capturing the intricate relationship between independent and dependent variables; however, it lacks a priori knowledge and is limited by the quality of remote sensing data sources. The relationship between independent and dependent variables can be more accurately simulated with the use of suitable auxiliary variables. Therefore, three machine learning models—random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost)—were established in this study using meteorological observation parameters as auxiliary variables combined with Sentinel-2 satellite image remote sensing band combinations as independent variables and measured Chl-a data as dependent variables. The estimation effects before and after the fusion of meteorological ground observation data were compared, and the best model was used to estimate the spatial–temporal variation trend of Chl-a in the regional water body. The results show that (1) the addition of meteorological parameters as auxiliary variables improved the precision of the three machine models; the decision coefficient (R2) rose by 7.25%, 5.71%, and 7.20%, respectively, to 0.76, 0.66, and 0.73. (2) The concentration of Chl-a in the lake region was projected from June to October 2019 to October 2021 using the RF optimal estimating model of meteorological fusion. The northeast, southwest, and south of the lake were where the comparatively high concentration values of Chl-a were located, whereas the lake’s center had a generally low concentration of the substance. Chromatically, Chl-a typically peaked in August after initially increasing and then declining. (3) The three rivers that feed into the river have varying levels of water pollution, with chemical oxygen demand (COD) and total nitrogen (TN) pollution being the most severe. This is what primarily caused the higher levels of Chl-a in the northeast, southwest, and south. This study is crucial for the preservation and restoration of Hulun Lake’s natural ecosystem and offers some technical support for the monitoring of the lake’s concentration of Chl-a.

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Section: Discussionsupporting

confidence: 89%

Monitoring of Low Chl-a Concentration in Hulun Lake Based on Fusion of Remote Sensing Satellite and Ground Observation Data

Zhang,

Wang

et al. 2024

Remote Sensing

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“…The lake is mainly recharged by precipitation, groundwater, the Krulun River, and the Ulson River. Hulun Lake belongs to a semi-arid grassland climate in the middle temperate zone, with dry and windy springs, cool and short summers, rapidly cooling autumns with frost, and cold and long winters [23]. The freezing period of the lake is 170-180 days, and the thickness of the ice surface can reach 1.3 m [17].…”

Section: Study Areamentioning

confidence: 99%

“…Regarding the dominance of cyanobacteria (y), y > 0.02 represents the dominant species [23]. Its calculation formula is as follows:…”

Section: Dominant Species Of Cyanobacteriamentioning

confidence: 99%

“…For example, Cao et al [19] and Liu et al [20] used optical remote sensing images to explore the outbreak area of blooms in Hulun Lake. Wang et al [21], Li et al [22], and Qian et al [23] used lake ecological survey methods to explore the main influencing factors of phytoplankton in Hulun Lake. However, a single-method approach is not sufficient to comprehensively monitor the outbreak area and dominant species of lake blooms, and research on cyanobacterial blooms in Hulun Lake and their influencing factors is lacking.…”

Section: Introductionmentioning

confidence: 99%

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Evolution Characteristics and Driving Factors of Cyanobacterial Blooms in Hulun Lake from 2018 to 2022

Li,

Liu,

Zhang

et al. 2023

Water

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Algal blooms pose serious threats to water environments and the balance of aquatic ecosystems. Moreover, environmental factors may be the primary driver of bloom outbreaks. Studying the characteristics and driving factors of the evolution of cyanobacterial blooms can provide a scientific basis for the restoration of lake water environments and aquatic ecosystems. Based on the Landsat extended time series and practical ecological investigation, this study determined the bloom area and dominant species of cyanobacterial blooms in Hulun Lake, and analyzed their evolution characteristics and primary influencing factors. The results show that the area of bloom outbreak tended to decrease from 2018 to 2020, with the peak area remaining below 150 km2; in contrast, the bloom area showed an increasing trend in 2021–2022, with a maximum of 1970.55 km2 in June 2022. Pearson correlation analysis of bloom area and driving factors showed that the wind speed was the main influencing factor of bloom outbreaks. In 2022, there were five dominant species of cyanobacteria in summer and three in autumn. Redundancy analysis of the dominant species of cyanobacteria and water quality indicators showed that COD, DO, TP, WT, pH, and TN were the main influencing factors of the dominant species of cyanobacteria in summer, and COD, DO, TP, and WT were the main influencing factors in autumn. In general, cyanobacteria in Hulun Lake are sensitive to changes in wind speed and nutrient concentration. An important approach towards effectively reducing the intensity of cyanobacterial blooms in Hulun Lake and coping with the ecological risks associated with the changing environment would be to reduce external loads and regulate the ecosystem structure of the lake. In the future, while studying the law of cyanobacterial bloom outbreaks, attention should be paid to the impact of the cyanobacterial physiological status on bloom outbreaks.

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A dataset of remote sensing-based inherent optical properties of Hulun Lake from 2016 to 2020

WEI,

XU,

HUANG

et al. 2023

CSD

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Hulun Lake, the largest steppe lake in the high latitude semi-arid region of inland China, holds great significance for grassland production and steppe-lake ecosystem. The absorption coefficient of water constitutes is indicative of the optically active components in water, thus conducive to estimating the optically active substances and other related bio-optical parameters of waters. In this study, we used the quasi-analytical algorithm (QAA), a QAA-750E absorption coefficient inversion algorithm suitable for inland waters. Based on Sentinel-3/OLCI remote sensing reflectance data, we produced a dataset of monthly mean phytoplankton absorption coefficient aph(675)and backscattering coefficient bbp(560) of Hulun Lake during 2016-2020. Validation analysis results show that the model-derived b_bp (560) has an R2 value of 0.59, RMSE of 0.55, and MRD of 0.54 (N = 15), indicating a high level of reliability of the dataset. The dataset is expected to offer data support for water quality monitoring and ecological environment protection of Hulun Lake.

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Seasonal dynamics of phytoplankton and its relationship with environmental factors in Lake Hulun

Cited by 6 publications

References 18 publications

Monitoring of Low Chl-a Concentration in Hulun Lake Based on Fusion of Remote Sensing Satellite and Ground Observation Data

Monitoring of Low Chl-a Concentration in Hulun Lake Based on Fusion of Remote Sensing Satellite and Ground Observation Data

Evolution Characteristics and Driving Factors of Cyanobacterial Blooms in Hulun Lake from 2018 to 2022

A dataset of remote sensing-based inherent optical properties of Hulun Lake from 2016 to 2020

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