Process-Based Forecasts of Lake Water Temperature and Dissolved Oxygen Outperform Null Models, with Variability Over Time and Depth
51 Pages Posted: 19 Jan 2024
Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, variability in forecast performance across time and space has largely been unexamined across multiple ecosystem variables using a process-based modeling approach. To explore how forecast performance varies for water temperature and dissolved oxygen, two freshwater variables important for lake ecosystem functioning, we produced probabilistic forecasts at multiple depths over two open-water seasons in Lake Sunapee, NH, USA. Our forecasting system, FLARE (Forecasting Lake And Reservoir Ecosystems), uses a 1-D coupled hydrodynamic-biogeochemical process model and daily data assimilation to update model initial conditions of water temperature and dissolved oxygen and fit parameters over time. We assessed FLARE performance using both forecast accuracy, via the Continuous Ranked Probability Score, and forecast skill, by comparing FLARE’s forecast accuracy relative to the accuracy of null models, which act as baseline forecasts. Specifically, we calculated FLARE forecast skill relative to both climatology and persistence null models to quantify how much information process-based FLARE forecasts provide over these null models across varying environmental conditions. We found that FLARE water temperature forecasts were always more skillful than FLARE oxygen forecasts. Specifically, temperature forecasts outperformed both null models up to 11 days into the future, as compared to only two days for oxygen. Across different years, we observed variable forecast skill, with performance generally decreasing with depth for both variables. Overall, all temperature forecasts and surface oxygen, but not deep oxygen, forecasts were more skillful than at least one null model >80% of the forecasted period, indicating that our process-based model was able to reproduce the dynamics of these two variables with greater reliability than the null models. However, process-based oxygen forecasts from deeper waters were less skillful than both null models during a majority of the forecasted period, which suggests that deep-water oxygen dynamics are dominated by autocorrelation and seasonal change, which are inherently captured by the null forecasts. Our results highlight that forecast performance varies among lake water quality metrics and that process-based forecasts can provide important information over null models in varying environmental conditions, informing the development of quantitative tools for predicting ecosystem change.
Keywords: baseline model, climatology, ecological forecasting, forecast skill, persistence, water quality
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