scholarly journals Relationship between the distribution of aquatic plants and the water surface temperature using UAV with thermal infrared sensor.

2020 ◽  
Vol 22 (2) ◽  
pp. 155-163
Author(s):  
Hideyuki NIWA ◽  
Kosuke IMAI ◽  
Syunsuke SUZUKI ◽  
Ryou SHIMIZU ◽  
Shigeharu KOGUSHI
Keyword(s):  
Surface Temperature ◽  
Aquatic Plants ◽  
Water Surface ◽  
Thermal Infrared ◽  
Infrared Sensor ◽  
Water Surface Temperature

Analysis and Modelling of Temperature at Lake’s Water – Atmosphere Interface

2021 ◽  
Author(s):  
Vassilis Z. Antonopoulos ◽  
Soultana K. Gianniou
Keyword(s):  
Heat Transfer ◽  
Energy Balance ◽  
Surface Temperature ◽  
Water Surface ◽  
Heat Transfer Model ◽  
Transfer Model ◽  
Net Radiation ◽  
Water Surface Temperature ◽  
Simulation Results ◽  
Input Variables

Abstract The knowledge of micrometeorological conditions on water surface of impoundments is crucial for the better modeling of the temperature and water quality parameters distribution in the water body and against the climatic changes. Water temperature distribution is an important factor that affects most physical, chemical and biological processes and reactions occurring in lakes. In this work, different processes of water surface temperature of lake’s estimation based on the energy balance method are considered. The daily meteorological data and the simulation results of energy balance components from an integrated heat transfer model for two complete years as well as the lake’s characteristics for Vegoritis lake in northern Greece were used is this analysis.The simulation results of energy balance components from a heat transfer model are considered as the reference and more accurate procedure to estimate water surface temperature. These results are used to compare the other processes. The examined processes include a) models of heat storage changes in relationship to net radiation (Qt(Rn) values, b) net radiation estimation with different approaches, as the process of Slob’s equation with adjusted coefficients to lake data, and c) ANNs models with different architecture and input variables. The results show that the model of heat balance describes the water surface temperature with high accuracy (r2=0.916, RMSE=2.422oC). The ANN(5,6,1) model in which Tsw(i-1) is incorporated in the input variables was considered the better of all other ANN structures (r2=0.995, RMSE=0.490oC). The use of different approaches for simulating net radiation (Rn) and Qt(Rn) in the equation of water surface temperature gives results with lower accuracy.

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