Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake Kasumigaura
The diel variation of water temperatures is crucial information for lakes. This study investigated such variation in Lake Kasumigaura, the second largest lake in Japan, situated 60 km from the Tokyo metropolitan area, utilizing high-frequency monitoring and deep learning techniques. Data from high-f...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24014158 |
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| author | Senlin Zhu Ryuichiro Shinohara Shin–Ichiro S. Matsuzaki Ayato Kohzu Mirai Watanabe Megumi Nakagawa Fabio Di Nunno Jiang Sun Quan Zhou Francesco Granata |
| author_facet | Senlin Zhu Ryuichiro Shinohara Shin–Ichiro S. Matsuzaki Ayato Kohzu Mirai Watanabe Megumi Nakagawa Fabio Di Nunno Jiang Sun Quan Zhou Francesco Granata |
| author_sort | Senlin Zhu |
| collection | DOAJ |
| description | The diel variation of water temperatures is crucial information for lakes. This study investigated such variation in Lake Kasumigaura, the second largest lake in Japan, situated 60 km from the Tokyo metropolitan area, utilizing high-frequency monitoring and deep learning techniques. Data from high-frequency monitoring of vertical water temperatures at seven depths were employed. A convolutional neural network (CNN) based deep learning model was developed and assessed across three input scenarios. The impact of forecast horizons ranging from one to 48 h ahead on model performance was examined. Results indicate a degradation in model performance with increasing forecast horizons, irrespective of input scenario or water depth. Notably, the CNN model demonstrates superior performance in near-term and medium-term forecasts compared to long-term predictions, underscoring the need for enhanced efforts in long-term forecasting. Overall, the CNN model effectively reproduces vertical water temperature profiles and captures diel variations in lake water temperatures. Incorporating additional input variables does not necessarily improve model performance; however, using surface water temperatures and air temperatures as inputs produces acceptable results for modeling vertical temperature profiles. These findings have implications for lake management in Lake Kasumigaura (e.g., controlling phytoplankton and zooplankton communities) and offer insights into water temperature modeling for other lakes. |
| format | Article |
| id | doaj-art-884954ca26b542ed931fd28c597430e1 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-884954ca26b542ed931fd28c597430e12024-12-16T05:35:42ZengElsevierEcological Indicators1470-160X2024-12-01169112958Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake KasumigauraSenlin Zhu0Ryuichiro Shinohara1Shin–Ichiro S. Matsuzaki2Ayato Kohzu3Mirai Watanabe4Megumi Nakagawa5Fabio Di Nunno6Jiang Sun7Quan Zhou8Francesco Granata9College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, China; Corresponding author.National Institute for Environmental Studies, JapanNational Institute for Environmental Studies, JapanNational Institute for Environmental Studies, JapanNational Institute for Environmental Studies, JapanNational Institute for Environmental Studies, JapanDepartment of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, Frosinone, ItalyCollege of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, ChinaCollege of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, ChinaDepartment of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, Frosinone, ItalyThe diel variation of water temperatures is crucial information for lakes. This study investigated such variation in Lake Kasumigaura, the second largest lake in Japan, situated 60 km from the Tokyo metropolitan area, utilizing high-frequency monitoring and deep learning techniques. Data from high-frequency monitoring of vertical water temperatures at seven depths were employed. A convolutional neural network (CNN) based deep learning model was developed and assessed across three input scenarios. The impact of forecast horizons ranging from one to 48 h ahead on model performance was examined. Results indicate a degradation in model performance with increasing forecast horizons, irrespective of input scenario or water depth. Notably, the CNN model demonstrates superior performance in near-term and medium-term forecasts compared to long-term predictions, underscoring the need for enhanced efforts in long-term forecasting. Overall, the CNN model effectively reproduces vertical water temperature profiles and captures diel variations in lake water temperatures. Incorporating additional input variables does not necessarily improve model performance; however, using surface water temperatures and air temperatures as inputs produces acceptable results for modeling vertical temperature profiles. These findings have implications for lake management in Lake Kasumigaura (e.g., controlling phytoplankton and zooplankton communities) and offer insights into water temperature modeling for other lakes.http://www.sciencedirect.com/science/article/pii/S1470160X24014158Thermal dynamicsDiel variationHigh-frequency monitoringDeep modelingClimate change |
| spellingShingle | Senlin Zhu Ryuichiro Shinohara Shin–Ichiro S. Matsuzaki Ayato Kohzu Mirai Watanabe Megumi Nakagawa Fabio Di Nunno Jiang Sun Quan Zhou Francesco Granata Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake Kasumigaura Ecological Indicators Thermal dynamics Diel variation High-frequency monitoring Deep modeling Climate change |
| title | Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake Kasumigaura |
| title_full | Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake Kasumigaura |
| title_fullStr | Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake Kasumigaura |
| title_full_unstemmed | Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake Kasumigaura |
| title_short | Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake Kasumigaura |
| title_sort | diel temperature patterns unveiled high frequency monitoring and deep learning in lake kasumigaura |
| topic | Thermal dynamics Diel variation High-frequency monitoring Deep modeling Climate change |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24014158 |
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