Integrated modeling framework to evaluate the impacts of multi-source water replenishment on lacustrine phytoplankton communities

被引:22
|
作者
Sun, Bowen [1 ,2 ]
Wang, Guoyu [1 ,2 ]
Chen, Wei [1 ,2 ]
Li, Wenjun [3 ,4 ]
Kong, Fanqing [4 ,5 ]
Li, Na [1 ,2 ]
Liu, Yinzhu [1 ,2 ,6 ]
Gao, Xueping [1 ,2 ]
机构
[1] Tianjin Univ, State Key Lab Hydraul Engn Simulat & Safety, Tianjin 300350, Peoples R China
[2] Tianjin Univ, Sch Civil Engn, Tianjin 300350, Peoples R China
[3] Minist Ecol & Environm Peoples Republ China, Adm Ecol & Environm Haihe River Basin, Tianjin 300061, Peoples R China
[4] Beihai Sea Area, Minist Ecol & Environm Peoples Republ China, Tianjin 300061, Peoples R China
[5] Minist Ecol & Environm Peoples Republ China, Adm Ecol & Environm Haihe River Basin, Ctr Ecoenvironm Monitoring & Sci Res, Tianjin 300061, Peoples R China
[6] 135 Yaguan Rd, Haihe Educ Pk, Tianjin, Peoples R China
关键词
Water diversion project; Ecological restoration; Phytoplankton diversity; Integrated model; Baiyangdian Lake; YANGTZE-RIVER; BAIYANGDIAN LAKE; SPECIES RICHNESS; TROPHIC STATUS; CHLOROPHYLL-A; RESTORATION; QUALITY; PHOSPHORUS; OPTIMIZATION; BIODIVERSITY;
D O I
10.1016/j.jhydrol.2022.128272
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
An extensive artificial water diversion project aimed at alleviating the shortage of ecological water in lakes can change the original hydrological and physicochemical states and further affect the structure and distribution of phytoplankton communities. Therefore, it is important to fully evaluate the ecological restoration effect of the water diversion project on the water-receiving area before implementing water replenishment. For this purpose, we developed an integrated model framework based on the Environmental Fluid Dynamics Code (EFDC) and Random Forest (RF). We adopted a probability distribution method to address the uncertainty during model coupling. This framework was implemented to simulate and predict the evolution of phytoplankton diversity in Baiyangdian Lake (BL) in China. To solve the problem that the phytoplankton biodiversity in BL decreased especially in summer due to human activity, the Yellow River into BL, the south-to-north water diversion project, and joint replenishment of upstream reservoirs have been implemented in recent years. Our framework was used to analyze the biodiversity restoration effects of multi-source water replenishment through different routings. The results show that spatiotemporal coverage should be considered to reduce uncertainty during model coupling. Water replenishment has a positive impact on the biodiversity of BL; however, there are effective areas for phytoplankton diversity restoration, which are related to water quality and quantity, water replenishment routing and internal hydrological connectivity. The Xiaobai River has the most significant water ecological restoration potential among the routings, compared with that of the Baigou and Fu Rivers. Appropriate water replenishment in spring will play a vital role in alleviating the decrease in phytoplankton biodiversity in summer owing to flood control.
引用
收藏
页数:11
相关论文
共 39 条
  • [21] Simulation of water hyacinth growth area based on multi-source geographic information data: An integrated method of WOE and AHP
    Chen, Jinyue
    Chen, Shuisen
    Fu, Rao
    Wang, Chongyang
    Li, Dan
    Jiang, Hao
    Zhao, Jing
    Wang, Li
    Peng, Yongshi
    Mei, Yan
    ECOLOGICAL INDICATORS, 2021, 125
  • [22] Hyperspatial and Multi-Source Water Body Mapping: A Framework to Handle Heterogeneities from Observations and Targets over Large Areas
    d'Andrimont, Raphael
    Marlier, Catherine
    Defourny, Pierre
    REMOTE SENSING, 2017, 9 (03)
  • [23] Exploring the impacts of travel-implied policy factors on COVID-19 spread within communities based on multi-source data interpretations
    Guo, Yuntao
    Yu, Hao
    Zhang, Guohui
    Ma, David T.
    HEALTH & PLACE, 2021, 69
  • [24] A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms
    Karimian, Hamed
    Huang, Jinhuang
    Chen, Youliang
    Wang, Zhaoru
    Huang, Jinsong
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (32) : 79402 - 79422
  • [25] A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms
    Hamed Karimian
    Jinhuang Huang
    Youliang Chen
    Zhaoru Wang
    Jinsong Huang
    Environmental Science and Pollution Research, 2023, 30 : 79402 - 79422
  • [26] Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history
    Gao, Tian
    Zhu, Jiaojun
    Deng, Songqiu
    Zheng, Xiao
    Zhang, Jinxin
    Shang, Guiduo
    Huang, Liyan
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2016, 52 : 155 - 165
  • [27] Towards of multi-source data fusion framework of geo-referenced and non-georeferenced data: prospects for use in surface water bodies
    Villalpando, Fermin
    Tuxpan, Jose
    Ramos-Leal, Jose Alfredo
    Marin, Ana Elizabeth
    GEOCARTO INTERNATIONAL, 2023,
  • [28] A Hybrid Data-Driven Deep Learning Prediction Framework for Lake Water Level Based on Fusion of Meteorological and Hydrological Multi-source Data
    Zhiyuan Yao
    Zhaocai Wang
    Tunhua Wu
    Wen Lu
    Natural Resources Research, 2024, 33 : 163 - 190
  • [29] A Hybrid Data-Driven Deep Learning Prediction Framework for Lake Water Level Based on Fusion of Meteorological and Hydrological Multi-source Data
    Yao, Zhiyuan
    Wang, Zhaocai
    Wu, Tunhua
    Lu, Wen
    NATURAL RESOURCES RESEARCH, 2024, 33 (01) : 163 - 190
  • [30] A robust large-scale surface water mapping framework with high spatiotemporal resolution based on the fusion of multi-source remote sensing data
    Li, Junjie
    Li, Linyi
    Song, Yanjiao
    Chen, Jiaming
    Wang, Zhe
    Bao, Yi
    Zhang, Wen
    Meng, Lingkui
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 118