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Virdis SGP, Kongwarakom S, Juneng L, Padedda BM, Shrestha S. Historical and projected response of Southeast Asian lakes surface water temperature to warming climate. ENVIRONMENTAL RESEARCH 2024; 247:118412. [PMID: 38316380 DOI: 10.1016/j.envres.2024.118412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 02/07/2024]
Abstract
The temperature of surface and epilimnetic waters, closely related to regional air temperatures, responds quickly and directly to climatic changes. As a result, lake surface temperature (LSWT) can be considered an effective indicator of climate change. In this study, we reconstructed and investigated historical and future LSWT across different scenarios for over 80 major lakes in mainland Southeast Asia (SEA), an ecologically diverse region vulnerable to climate impacts. Five different predicting models, incorporating statistical, machine and deep learning approaches, were trained and validated using ERA5 and CHIRPS climatic feature datasets as predictors and 8-day MODIS-derived LSWT from 2000 to 2020 as reference dataset. Best performing model was then applied to predict both historical (1986-2020) and future (2020-2100) LSWT for SEA lakes, utilizing downscaled climatic CORDEX-SEA feature data and multiple Representative Concentration Pathway (RCP). The analysis uncovered historical and future thermal dynamics and long-term trends for both daytime and nighttime LSWT. Among 5 models, XGboost results the most performant (NSE 0.85, RMSE 1.14 °C, MAE 0.69 °C, MBE -0.08 °C) and it has been used for historical reconstruction and future LSWT prediction. The historical analysis revealed a warming trend in SEA lakes, with daytime LSWT increasing at a rate of +0.18 °C/decade and nighttime LSWT at +0.13 °C/decade over the past three decades. These trends appeared of smaller magnitude compared to global estimates of LSWT change rates and less pronounced than concurrent air temperature and LSWT increases in neighbouring regions. Projections under various RCP scenarios indicated continued LSWT warming. Daytime LSWT is projected to increase at varying rates per decade: +0.03 °C under RCP2.6, +0.14 °C under RCP4.5, and +0.29 °C under RCP8.5. Similarly, nighttime LSWT projections under these scenarios are: +0.03 °C, +0.10 °C, and +0.16 °C per decade, respectively. The most optimistic scenario predicted marginal increases of +0.38 °C on average, while the most pessimistic scenario indicated an average LSWT increase of +2.29 °C by end of the century. This study highlights the relevance of LSWT as a climate change indicator in major SEA's freshwater ecosystems. The integration of satellite-derived LSWT, historical and projected climate data into data-driven modelling has enabled new and a more nuanced understanding of LSWT dynamics in relation to climate throughout the entire SEA region.
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Affiliation(s)
- Salvatore Gonario Pasquale Virdis
- Department of Information and Communication Technologies, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4 Klong Luang, Pathum Thani, 12120, Thailand.
| | - Siwat Kongwarakom
- Department of Information and Communication Technologies, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4 Klong Luang, Pathum Thani, 12120, Thailand.
| | - Liew Juneng
- Department of Earth Sciences and Environment, Universiti Kebangsaan Malaysia, Malaysia.
| | - Bachisio Mario Padedda
- Department of Architecture, Design and Urban Planning (DADU), University of Sassari, Via Piandanna 4, 07100, Sassari, Italy.
| | - Sangam Shrestha
- Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4 Klong Luang, Pathum Thani, 12120, Thailand.
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Amadori M, Bresciani M, Giardino C, Dijkstra HA. Slow response of surface water temperature to fast atmospheric variability reveals mixing heterogeneity in a deep lake. Sci Rep 2024; 14:8459. [PMID: 38605068 PMCID: PMC11009398 DOI: 10.1038/s41598-024-58547-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/01/2024] [Indexed: 04/13/2024] Open
Abstract
Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes.
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Affiliation(s)
- Marina Amadori
- Institute for Electromagnetic Sensing of the Environment, National Research Council, 20133, Milan, Italy.
| | - Mariano Bresciani
- Institute for Electromagnetic Sensing of the Environment, National Research Council, 20133, Milan, Italy
| | - Claudia Giardino
- Institute for Electromagnetic Sensing of the Environment, National Research Council, 20133, Milan, Italy
| | - Henk A Dijkstra
- Department of Physics, Institute for Marine and Atmospheric Research Utrecht, Utrecht University, 3584 CC, Utrecht, The Netherlands
- Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38123, Trento, Italy
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Meyer MF, Topp SN, King TV, Ladwig R, Pilla RM, Dugan HA, Eggleston JR, Hampton SE, Leech DM, Oleksy IA, Ross JC, Ross MRV, Woolway RI, Yang X, Brousil MR, Fickas KC, Padowski JC, Pollard AI, Ren J, Zwart JA. National-scale remotely sensed lake trophic state from 1984 through 2020. Sci Data 2024; 11:77. [PMID: 38228637 PMCID: PMC10791641 DOI: 10.1038/s41597-024-02921-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 01/05/2024] [Indexed: 01/18/2024] Open
Abstract
Lake trophic state is a key ecosystem property that integrates a lake's physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.
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Affiliation(s)
- Michael F Meyer
- U.S. Geological Survey, Madison, WI, USA.
- University of Wisconsin - Madison, Madison, WI, USA.
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- Southern Methodist University, Dallas, TX, USA
| | | | - Kate C Fickas
- U.S. Geological Survey, Sioux Falls, SD, USA
- University of California - Santa Barbara, Santa Barbara, CA, USA
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Wang X, Shi K, Zhang Y, Qin B, Zhang Y, Wang W, Woolway RI, Piao S, Jeppesen E. Climate change drives rapid warming and increasing heatwaves of lakes. Sci Bull (Beijing) 2023; 68:1574-1584. [PMID: 37429775 DOI: 10.1016/j.scib.2023.06.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 07/12/2023]
Abstract
Climate change could seriously threaten global lake ecosystems by warming lake surface water and increasing the occurrence of lake heatwaves. Yet, there are great uncertainties in quantifying lake temperature changes globally due to a lack of accurate large-scale model simulations. Here, we integrated satellite observations and a numerical model to improve lake temperature modeling and explore the multifaceted characteristics of trends in surface temperatures and lake heatwave occurrence in Chinese lakes from 1980 to 2100. Our model-data integration approach revealed that the lake surface waters have warmed at a rate of 0.11 °C 10a-1 during the period 1980-2021, being only half of the pure model-based estimate. Moreover, our analysis suggested that an asymmetric seasonal warming rate has led to a reduced temperature seasonality in eastern plain lakes but an amplified one in alpine lakes. The durations of lake heatwaves have also increased at a rate of 7.7 d 10a-1. Under the high-greenhouse-gas-emission scenario, lake surface temperature and lake heatwave duration were projected to increase by 2.2 °C and 197 d at the end of the 21st century, respectively. Such drastic changes would worsen the environmental conditions of lakes subjected to high and increasing anthropogenic pressures, posing great threats to aquatic biodiversity and human health.
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Affiliation(s)
- Xiwen Wang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Kun Shi
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China.
| | - Yunlin Zhang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China.
| | - Boqiang Qin
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Yibo Zhang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - Weijia Wang
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China; College of Nanjing, University of Chinese Academy of Sciences, Nanjing 211135, China
| | - R Iestyn Woolway
- School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey LL57 2DG, UK
| | - Shilong Piao
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Erik Jeppesen
- Department of Ecoscience, Aarhus University, Aarhus C 8000, Denmark; Sino-Danish Centre for Education and Research, Beijing 100039, China; Limnology Laboratory, Centre for Ecosystem Research and Implementation (EKOSAM), Department of Biological Sciences, Middle East Technical University, Ankara 06800, Turkey; Institute of Marine Sciences, Middle East Technical University, Erdeneli-Mersin 33731, Turkey
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