Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth
Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, true, out-of-sample probabilistic forecasts remain rare, and variability in forecast performance has largely been unexamined in process-based...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124003674 |
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| author | Whitney M. Woelmer R. Quinn Thomas Freya Olsson Bethel G. Steele Kathleen C. Weathers Cayelan C. Carey |
| author_facet | Whitney M. Woelmer R. Quinn Thomas Freya Olsson Bethel G. Steele Kathleen C. Weathers Cayelan C. Carey |
| author_sort | Whitney M. Woelmer |
| collection | DOAJ |
| description | Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, true, out-of-sample probabilistic forecasts remain rare, and variability in forecast performance has largely been unexamined in process-based forecasts which predict multiple ecosystem variables. 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, which we assessed relative to both climatology and persistence null models to quantify how much information process-based FLARE forecasts provide over 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 in conjunction with null models in varying environmental conditions. Altogether, these process-based forecasts can be used to develop quantitative tools which inform our understanding of future ecosystem change. |
| format | Article |
| id | doaj-art-429faf0800f6486a8d9a28c845739e71 |
| institution | OA Journals |
| issn | 1574-9541 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-429faf0800f6486a8d9a28c845739e712025-08-20T01:54:15ZengElsevierEcological Informatics1574-95412024-11-018310282510.1016/j.ecoinf.2024.102825Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depthWhitney M. Woelmer0R. Quinn Thomas1Freya Olsson2Bethel G. Steele3Kathleen C. Weathers4Cayelan C. Carey5Department of Biological Sciences, 926 West Campus Drive, Virginia Tech, Blacksburg, VA 24061, USA; Corresponding author.Department of Biological Sciences, 926 West Campus Drive, Virginia Tech, Blacksburg, VA 24061, USA; Department of Forest Resources and Environmental Conservation, 310 West Campus Drive, Virginia Tech, Blacksburg, VA 24061, USADepartment of Biological Sciences, 926 West Campus Drive, Virginia Tech, Blacksburg, VA 24061, USACary Institute of Ecosystem Studies, Millbrook, NY 12545, USACary Institute of Ecosystem Studies, Millbrook, NY 12545, USADepartment of Biological Sciences, 926 West Campus Drive, Virginia Tech, Blacksburg, VA 24061, USANear-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, true, out-of-sample probabilistic forecasts remain rare, and variability in forecast performance has largely been unexamined in process-based forecasts which predict multiple ecosystem variables. 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, which we assessed relative to both climatology and persistence null models to quantify how much information process-based FLARE forecasts provide over 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 in conjunction with null models in varying environmental conditions. Altogether, these process-based forecasts can be used to develop quantitative tools which inform our understanding of future ecosystem change.http://www.sciencedirect.com/science/article/pii/S1574954124003674Baseline modelClimatologyEcological forecastingForecast skillPersistenceWater quality |
| spellingShingle | Whitney M. Woelmer R. Quinn Thomas Freya Olsson Bethel G. Steele Kathleen C. Weathers Cayelan C. Carey Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth Ecological Informatics Baseline model Climatology Ecological forecasting Forecast skill Persistence Water quality |
| title | Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth |
| title_full | Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth |
| title_fullStr | Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth |
| title_full_unstemmed | Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth |
| title_short | Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth |
| title_sort | process based forecasts of lake water temperature and dissolved oxygen outperform null models with variability over time and depth |
| topic | Baseline model Climatology Ecological forecasting Forecast skill Persistence Water quality |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124003674 |
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