Data for Validation and Sensitivity Analysis of a 1-D Lake Model across Global Lakes

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By Mingyang Guo1, Qianlai Zhuang1, Huaxia Yao2, Malgorzata Golub3, L. Ruby Leung4, Zeli Tan4

1. Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, IN, USA 2. Dorset Environmental Science Centre, Ontario Ministry of Environment, Conservation and Parks, Dorset, ON, Canada 3. Department of Ecology and Genetics/Limnology, Uppsala University, Uppsala, Sweden 4. Pacific Northwest National Laboratory, Richland, WA, USA

This dataset includes the calibration and sensitivity test results of 58 lakes globally using the Arctic Lake Biogeochemistry Model (ALBM) and codes for analysis.

Version 1.0 - published on 18 Jun 2020 doi:10.4231/NPYJ-GE58 - cite this Content may change until committed to the archive on 18 Jul 2020

Licensed under CC0 1.0 Universal



This dataset includes the model calibration and validation results of 58 lakes using ALBM, and the corresponding sensitivity test results. Calib/ contains the metrics of water temperature simulations in the calibration and validation. CART/ contains the summaries of parameter sensitivity tests from Classification and Regression Tree (CART) model training. codes/ contains R codes for data analysis.

Lakes have important influence on weather and climate from local to global scales. However, their prediction using numerical models is notoriously difficult because global lakes are highly heterogeneous across the globe and observations are sparse. Here, we assessed the performance of a 1-D lake model in simulating the thermal structures of 58 lakes with diverse morphometric and geographic characteristics by following the phase 2a local lake protocol of the Inter-sectoral Impact Model Intercomparison Project (ISIMIP2a). The model was calibrated using six years of observation data for each lake and validated using the remaining data. After model calibration, the root-mean-square errors (RMSE) were below 2 °C for 70% and 75% of the lakes for epilimnion temperature and full-profile temperature simulations, with an average of 1.71 °C and 1.43 °C, respectively. The model performance mainly depends on lake shape rather than location, supporting the possibility of grouping model parameters by lake shape for global applications. Furthermore, through machine-learning based parameter sensitivity tests, we identified turbulent heat fluxes, wind-driven mixing and water transparency as the major processes controlling lake thermal and mixing regimes. Snow density is also a sensitive parameter for modeling the ice phenology of high latitude lakes. The relative influence of the key processes and the corresponding parameters mainly depended on lake latitude and depth. Turbulent heat fluxes showed a decreasing importance in affecting lake epilimnion temperature with increasing latitude. Wind-driven mixing was less influential to lake vertical temperature profile for deeper lakes while the impact of light extinction, on the contrary, showed a positive correlation with depth on lake stratification. Our findings may guide improvements in 1-D lake model parameterizations to achieve higher fidelity in simulating global lake thermal dynamics. 

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Due to the file size limit, the raw model calibration output and CART models can't be uploaded. Please contact Mingyang if interested.

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