How climate change erodes short-term lake-temperature predictability: Informing climate resilient lake forecasting

• Tested 2 weeks ahead surface water temperature forecast skill for 4 climate scenarios • Summer forecast skill was reduced in more extreme climate scenarios for all 5 lakes • Performance reductions were correlated with increased surface temperature volatility • Sampling more often or training with more data can offset climate-driven skill losses • Simulation-informed design of water quality forecasting systems is recommended Climate warming threatens short-term environmental forecast skill, yet its effect on water quality predictability is largely unquantified. Here, we demonstrate a new approach for assessing climate change effects on lake forecasts. Random forest (RF) and gated recurrent unit network models were trained on data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Local Lakes Sector (five central-European lakes, four hydrodynamic models) and then used to forecast daily lake surface temperature 14 days ahead for 2060 - 2100 under four climate scenarios. We then varied (i) sensor-sampling interval (3, 7, 14 days) and (ii) training-set length (1 - 30 years). Under the strongest forcing (SSP585), the summer mean absolute error (MAE) of worst-affected lake, Esthwaite, rose by 0.14°C (from 1.75 to 1.89°C), driven by higher day-to-day temperature volatility (R² = 0.78). For this lake, extending the training set from 5 to 20 years or shortening sampling from 14 to 3 days reduced summer MAE by 0.11 and 0.17°C, effectively offsetting the volatility caused by climate change. In winter, forecast error declined for four lakes because warmer, more stratified conditions simplified surface-layer dynamics. Thus, modest investments in monitoring cadence or historical record length can preserve forecast skill, even under extreme climate change. More broadly, this highlights a largely unexplored potential use for climate scenario projections: informing the design of climate resilient lake monitoring systems.

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Publication Details

Journal
Water Research X
Published
2025-11-23
DOI
https://doi.org/10.1016/j.wroa.2025.100457
Primary Topic
Aquatic Ecosystems and Phytoplankton Dynamics
Type
article
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article

How climate change erodes short-term lake-temperature predictability: Informing climate resilient lake forecasting

Water Research X
Aquatic Ecosystems and Phytoplankton Dynamics
article

How climate change erodes short-term lake-temperature predictability: Informing climate resilient lake forecasting

article en

Abstract

• Tested 2 weeks ahead surface water temperature forecast skill for 4 climate scenarios • Summer forecast skill was reduced in more extreme climate scenarios for all 5 lakes • Performance reductions were correlated with increased surface temperature volatility • Sampling more often or training with more data can offset climate-driven skill losses • Simulation-informed design of water quality forecasting systems is recommended Climate warming threatens short-term environmental forecast skill, yet its effect on water quality predictability is largely unquantified. Here, we demonstrate a new approach for assessing climate change effects on lake forecasts. Random forest (RF) and gated recurrent unit network models were trained on data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Local Lakes Sector (five central-European lakes, four hydrodynamic models) and then used to forecast daily lake surface temperature 14 days ahead for 2060 - 2100 under four climate scenarios. We then varied (i) sensor-sampling interval (3, 7, 14 days) and (ii) training-set length (1 - 30 years). Under the strongest forcing (SSP585), the summer mean absolute error (MAE) of worst-affected lake, Esthwaite, rose by 0.14°C (from 1.75 to 1.89°C), driven by higher day-to-day temperature volatility (R² = 0.78). For this lake, extending the training set from 5 to 20 years or shortening sampling from 14 to 3 days reduced summer MAE by 0.11 and 0.17°C, effectively offsetting the volatility caused by climate change. In winter, forecast error declined for four lakes because warmer, more stratified conditions simplified surface-layer dynamics. Thus, modest investments in monitoring cadence or historical record length can preserve forecast skill, even under extreme climate change. More broadly, this highlights a largely unexplored potential use for climate scenario projections: informing the design of climate resilient lake monitoring systems.

Water Research XVol. 30
University of Stirling (GB), Helmholtz Centre for Environmental Research (DE), Swiss Federal Institute of Aquatic Science and Technology (CH)
Climate action
Openalex Percentile: Top 46%
Aquatic Ecosystems and Phytoplankton Dynamics
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How climate change erodes short-term lake-temperature predictability: Informing climate resilient lake forecasting · Water Research X (2025) | TGRS Research Map | TGRS