Variability of seawater property after typhoon passage in the Philippine sea of the western North Pacific

2020 ◽  
Author(s):  
Kyung-Hee Oh ◽  
Seok Lee ◽  
Hong Sik Min ◽  
Sok-Kuh Kang
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Mixed Layer ◽  
North Pacific ◽  
Western North Pacific ◽  
Sea Water ◽  
Physical Property ◽  
Sea Surface ◽  
Surface Water Temperature ◽  
Before And After

<p>Sea water temperature and salinity measurements have been collected onboard in September in the Philippine seas of the western North Pacific. This area is close to typhoon occurrence area and is the path through which developed typhoons pass, and also large and small eddies are developed. Therefore variability of sea water property is large.  As a result of analysis, the seawater properties of the upper water showed a big difference before and after the typhoon. After the typhoon, surface water temperature dropped by about 1 degree C and salinity by 1 psu.  Mixed layer became deeper, and changes in seawater properties occurred throughout the upper layers. The depth of the mixed layer was largely different by more than 30-50m, especially the water temperature was changed more than 3 degree C at the depth below thermocline. Real-time sea surface water temperature and salinity measurements show more easily identify the physical property change of sea surface water before and after typhoon.</p>

scholarly journals Urban Land Surface Temperature Monitoring and Surface Thermal Runoff Pollution Evaluation Using UAV Thermal Remote Sensing Technology

2021 ◽  
Vol 13 (20) ◽  
pp. 11203
Author(s):  
Shanshan Xu ◽  
Kun Yang ◽  
Yuanting Xu ◽  
Yanhui Zhu ◽  
Yi Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Surface Temperature ◽  
Water Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Surface ◽  
Surface Water Temperature ◽  
Before And After

With the continuous advancement of urbanization, the impervious surface expands. Urbanization has changed the structure of the natural land surface and led to the intensification of the urban heat island (UHI) effect. This will affect the surface runoff temperature, which, in turn, will affect the surface water temperature of urban lakes. This study will use UAS TIR (un-manned aerial system thermal infrared radiance) remote sensing and in situ observation technology to monitor the urban space surface temperature and thermal runoff in Kunming, Yunnan, in summer; explore the feasibility of UAS TIR remote sensing to continuously observe urban surface temperature during day and night; and analyze thermal runoff pollution. The results of the study show that the difference between UAS TIR LSTs and in situ LSTs (in situ air temperature 10 cm above the ground.) varies with the type of land covers. Urban surface thermal runoff has varying degrees of impact on water bodies. Based on the influence of physical factors such as vegetation and buildings and meteorological factors such as solar radiation, the RMSE between UAS LSTs and in situ LSTs varies from 1 to 5 °C. Land cover types such as pervious bricks, asphalt, and cement usually show higher RMSE values. Before and after rainfall, the in situ data of the lake surface water temperature (LSWT) showed a phenomenon of first falling and then rising. The linear regression analysis results show that the R2 of the daytime model is 0.92, which has high consistency; the average R2 at night is 0.38; the averages R2 before and after rainfall are 0.50 and 0.83, respectively; and the average RMSE is 1.94 °C. Observational data shows that thermal runoff quickly reaches thermal equilibrium with the land surface temperature about 30 min after rainfall. The thermal runoff around the lake has a certain warming effect on LSWT.

scholarly journals An integrated dataset of daily lake surface water temperature over Tibetan Plateau

2021 ◽  
Author(s):  
Linan Guo ◽  
Hongxing Zheng ◽  
Yanhong Wu ◽  
Lanxin Fan ◽  
Mengxuan Wen ◽  
...  
Keyword(s):  
Surface Water ◽  
Tibetan Plateau ◽  
Water Temperature ◽  
Physical Property ◽  
In Situ Observation ◽  
The Tibetan Plateau ◽  
Surface Water Temperature ◽  
Lake Surface ◽  
Night Time ◽  
Spatial Span

Abstract. Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an evident indicator of climate change. By combining the strengths of satellite-based observation and modelling, we have produced an integrated daily lake surface water temperature for 160 lakes across the Tibetan Plateau where in-situ observation is limited. The satellite-based lake-wide mean LSWT in the integrated dataset includes that for the daytime, night-time and for the daily mean for the period 2000–2017. The dataset is comparable with other satellite-based LSWT products (e.g., LSWT from AVHRR and ARC-Lake) and unique for its tempo-spatial span and resolution. Calibrated and validated against the satellite-based LSWT, complete and consistent daily LSWT dataset have been reconstructed and extended to the period 1978–2017 basing on the modified air2water model. According to the reconstructed LSWT dataset, it is found that annual LSWT of lakes in the Tibetan Plateau has increased significantly in the period 1978–2017 with increase rate ranging at 0.01 to 0.4 °C 10 a−1. The warming trends of the lakes are more evident in winter than in summer. The integrated dataset together with the methods introduced herein can contribute to the research community to explore water and heat balance changes and the consequent ecological effects at the Tibetan Plateau in the future researches. Data from this study are openly available via the Zenodo portal, with DOI https://doi.org/10.5281/zenodo.5111400 (Guo et al., 2021).

scholarly journals Long-term variability of water temperature and salinity at the Polish coast

2019 ◽  
Vol 16 (1) ◽  
pp. 115-130
Author(s):  
Małgorzata Świątek
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Sea Water ◽  
Water Salinity ◽  
Surface Water Temperature ◽  
Annual Series ◽  
Baltic Coast ◽  
Annual Water ◽  
The Baltic

Abstract The variability of surface water temperature and water salinity at the south coast of the Baltic in the years 1950–2015 was studied in the article. To that aim, monthly surface water temperature values in Świnoujście, Międzyzdroje, Kołobrzeg (from 1957), Władysławowo, Hel and Gdynia were used, as well as monthly water salinity values in Międzyzdroje, Władysławowo, Hel and Gdynia, all obtained from IMGW-PIB (Institute of Meteorology and Water Management – National Research Institute). Linear regression and Pearson’s simple correlation coefficient of individual monthly, seasonal and annual series of temperature and salinity values over time (in subsequent years) were used to analyse the temporal changes of the examined parameters. In the analysed period a rise in the annual water temperature was recorded in Międzyzdroje, Władysławowo, Hel and Gdynia, while the extent of the changes increased towards the east. There were also positive trends in temperature values in individual months. At the same time, there was a decrease in water salinity, which was also found to be most distinct in the eastern part of the coast. In Władysławowo, Hel and Gdynia, statistically significant drops occurred in nearly all months. During the months featuring statistically insignificant trends, the observed change trends were also negative. Concurrent water temperature increases and water salinity decreases consequently caused a decline in sea water surface density at the Polish Baltic coast.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

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