Prediction of surface water temperature in Xingyun Lake based on interpretable deep learning

Abstract

[Objective] In the context of global warming, the continuous rise in lake surface water temperature(LSWT) has become a key factor in exacerbating lake eutrophication. Therefore, accurately predicting its future trends is of great significance for the early warning of lake eutrophication and water environment management. [Methods] Based on MODIS remote sensing images, daily LSWT data for Xingyun Lake from 2002 to 2024 were acquired. The random forest(RF) algorithm was employed to impute missing values. Subsequently, a parallel LSTM-Transformer deep learning model was developed to predict future LSWT trends under different climate scenarios(SSPs). Furthermore, the SHapley Additive exPlanations(SHAP) method was introduced to identify the key driving factors of LSWT and their directions of influence. [Results] The results indicated that:(1) the warming rate of surface water temperature in Xingyun Lake was 0. 27 ℃/10 a from 2002 to 2024, with a particularly pronounced warming trend in autumn.(2) The LSTM-Transformer fusion model exhibited high prediction accuracy, with a coefficient of determination(R²) of 0. 95 and a root mean square error(RMSE) of 0. 89 ℃. Under future climate scenarios(SSP1-2. 6, SSP2-4. 5, and SSP5-8. 5), the LSWT of Xingyun Lake was projected to continue rising at a rate of 0. 11~0. 42 ℃/10 a.(3) Near-surface air temperature and surface downward longwave radiation were identified as the primary driving factors influencing LSWT variations in Xingyun Lake. [Conclusion] The developed deep learning model exhibits good applicability for predicting surface water temperature in plateau lakes. The framework can provide a basis for adaptive water environment management and early warning of ecological risks in plateau lakes under future climate scenarios.

Keywords

lake surface water temperature / MODIS / Xingyun Lake / deep learning model / LSTM-Transformer / climate scenarios / SHAP / driving factor analysis