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Algorithmic Characterization of Lake Stratification and Deep Chlorophyll Layers From Depth Profiling Water Quality Data.

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Název: Algorithmic Characterization of Lake Stratification and Deep Chlorophyll Layers From Depth Profiling Water Quality Data.
Autoři: Xu, Wenzhao, Collingsworth, Paris D., Minsker, Barbara
Zdroj: Water Resources Research; May2019, Vol. 55 Issue 5, p3815-3834, 20p
Témata: DEPTH profiling, WATER quality, WATER depth, DATA quality, CHLOROPHYLL, THERMOCLINES (Oceanography), JUDGMENT sampling
Abstrakt: We develop and test algorithms for rapidly and consistently analyzing water quality profile data such as temperature and fluorescence that are used to identify lake thermostratification and deep chlorophyll layers (DCL). Currently, the processing of profile data and identification of key features are manual and subjective, and thus, the results are not comparable from one sampling event to another. In this study, we develop a method to approximate vertical temperature profiles with linear segments using a piecewise linear representation algorithm, from which stratification patterns can be extracted. We also propose an automated peak detection algorithm to identify the location and magnitude of DCL. The algorithms are applied to water quality profile data collected by the United States Environmental Protection Agency Great Lakes National Program Office, which conducts annual depth profiling using conductivity, temperature, depth profilers at fixed locations in the Great Lakes. The algorithms generate similar results to human judgments, with some outliers that show expert errors, algorithm limitations, and ambiguities in defining layers. We also show how the algorithms can analyze the shape of temperature and fluorescence profiles to detect unusual patterns. Lake Superior is used as a case study to reveal spatial and temporal trends of the thermocline, DCL, and the heat storage change from spring to summer. The results reveal that more heat was stored in the eastern basin of the lake. The methods proposed here will help take full advantage of historical depth profiling data and benefit future sampling processes by providing a consistent method. Key Points: Algorithms are developed for automatically identifying lake stratifications and DCL from conductivity‐temperature‐depth profiling dataAlgorithms perform similar to humans and reveal spatial and temporal patterns of lake stratifications and DCL in the Great LakesOpen‐source Python code is provided [ABSTRACT FROM AUTHOR]
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Databáze: Biomedical Index
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Abstrakt:We develop and test algorithms for rapidly and consistently analyzing water quality profile data such as temperature and fluorescence that are used to identify lake thermostratification and deep chlorophyll layers (DCL). Currently, the processing of profile data and identification of key features are manual and subjective, and thus, the results are not comparable from one sampling event to another. In this study, we develop a method to approximate vertical temperature profiles with linear segments using a piecewise linear representation algorithm, from which stratification patterns can be extracted. We also propose an automated peak detection algorithm to identify the location and magnitude of DCL. The algorithms are applied to water quality profile data collected by the United States Environmental Protection Agency Great Lakes National Program Office, which conducts annual depth profiling using conductivity, temperature, depth profilers at fixed locations in the Great Lakes. The algorithms generate similar results to human judgments, with some outliers that show expert errors, algorithm limitations, and ambiguities in defining layers. We also show how the algorithms can analyze the shape of temperature and fluorescence profiles to detect unusual patterns. Lake Superior is used as a case study to reveal spatial and temporal trends of the thermocline, DCL, and the heat storage change from spring to summer. The results reveal that more heat was stored in the eastern basin of the lake. The methods proposed here will help take full advantage of historical depth profiling data and benefit future sampling processes by providing a consistent method. Key Points: Algorithms are developed for automatically identifying lake stratifications and DCL from conductivity‐temperature‐depth profiling dataAlgorithms perform similar to humans and reveal spatial and temporal patterns of lake stratifications and DCL in the Great LakesOpen‐source Python code is provided [ABSTRACT FROM AUTHOR]
ISSN:00431397
DOI:10.1029/2018WR023975