Dynamic simulation of nutrient distribution in lakes during ice cover growth and ablation

Autor: Haiqing Liao, Rui Cen, Qiuheng Zhu, Yang Fang, Weiying Feng, Wang Xihuan, Matti Leppäranta, Yu Yang
Přispěvatelé: Institute for Atmospheric and Earth System Research (INAR)
Rok vydání: 2021
Předmět:
Environmental Engineering
MIGRATION
Health
Toxicology and Mutagenesis
0208 environmental biotechnology
02 engineering and technology
010501 environmental sciences
Eutrophic lakes
ECOLOGY
Atmospheric sciences
01 natural sciences
Algal bloom
Nutrient
PHYTOPLANKTON
Phase (matter)
Environmental Chemistry
Ice Cover
Ecosystem
Empirical degree-day model
Nutrient migration
0105 earth and related environmental sciences
Pollutant
Public Health
Environmental and Occupational Health
Phosphorus
Nutrients
General Medicine
General Chemistry
Eutrophication
Snow
WINTER LIMNOLOGY
Pollution
6. Clean water
020801 environmental engineering
MODEL
NITROGEN
Lakes
Seasonal ice-cover
WATER-QUALITY
SNOW
13. Climate action
1181 Ecology
evolutionary biology
Environmental science
High-resolution thermodynamic snow and sea-ice model
Stage (hydrology)
SEA-ICE
Zdroj: Chemosphere. 281:130781
ISSN: 0045-6535
DOI: 10.1016/j.chemosphere.2021.130781
Popis: Nutrient transport in seasonally ice-covered lakes is an important factor affecting spring algal blooms in eutrophic waters; because phase changes during the ice growth process redistribute the nutrients. In this study, nutrient transport under static conditions was simulated by using two ice thickness models in combination with an indoor freezing experiment under different segregation coefficient conditions for nutrients. A real-time prediction model for nutrient and pollutant concentrations in ice-covered lakes was established to explore the impact of the ice-on period in eutrophic shallow lakes. The results demonstrated that the empirical degree-day model and the high-resolution thermodynamic snow and sea-ice model (HIGHTSI) could both be used to simulate lake ice thickness. The empirical degree-day model performed better at predicting the maximum ice thickness (measured thickness 0.22-0.55 m; simulated thickness 0.48 m), whereas the HIGHTSI model was more accurate when estimating the mean thickness (5-6% error). When simulating ice growth, the HIGHTSI model considered more meteorological factors impacting ice cover ablation; hence, it performed better during the ablation stage relative to the empirical degree-day model. Two non-dynamic nutrient transport models were developed by combining the segregation coefficient model and the ice thickness prediction model. The HIGHTSI nutrient transport model can be used to predict real-time changes in nutrient concentrations under ice cover, and the degree-day model can be used to predict changes in the lake water ecosystem.
Databáze: OpenAIRE
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