Опубликована: Янв. 1, 2024
Язык: Английский
Опубликована: Янв. 1, 2024
Язык: Английский
Geoscience Frontiers, Год журнала: 2024, Номер 15(6), С. 101916 - 101916
Опубликована: Авг. 23, 2024
Язык: Английский
Процитировано
8Ecological Applications, Год журнала: 2025, Номер 35(1)
Опубликована: Янв. 1, 2025
Abstract Near‐term, iterative ecological forecasts can be used to help understand and proactively manage ecosystems. To date, more have been developed for aquatic ecosystems than other worldwide, likely motivated by the pressing need conserve these essential threatened increasing availability of high‐frequency data. Forecasters implemented many different modeling approaches forecast freshwater variables, which demonstrated promise at individual sites. However, a comprehensive analysis performance varying models across multiple sites is needed broader controls on performance. Forecasting challenges (i.e., community‐scale efforts generate while also developing shared software, training materials, best practices) present useful platform bridging this gap evaluate how range methods perform axes space, time, systems. Here, we analyzed from aquatics theme National Ecological Observatory Network (NEON) Challenge hosted Initiative. Over 100,000 probabilistic water temperature dissolved oxygen concentration 1–30 days ahead seven NEON‐monitored lakes were submitted in 2023. We assessed varied among with structures, covariates, sources uncertainty relative baseline null models. A similar proportion skillful both variables (34%–40%), although outperformed forecasting (10 out 29) (6 15). These top performing came classes structures. For temperature, found that skill degraded increases horizons, process‐based models, included air as covariate generally exhibited highest performance, most often accounted lower The where observations divergent historical conditions (resulting poor model performance). Overall, NEON provides an exciting opportunity intercomparison learn about strengths diverse suite advance our understanding ecosystem predictability.
Язык: Английский
Процитировано
0Authorea (Authorea), Год журнала: 2024, Номер unknown
Опубликована: Май 1, 2024
Near-term, iterative ecological forecasts can be used to help understand and proactively manage ecosystems.To date, more have been developed for aquatic ecosystems than other worldwide, likely motivated by the pressing need conserve these essential threatened ecosystems.Forecasters implemented many different modelling approaches forecast freshwater variables, which demonstrated promise at individual sites.However, a comprehensive analysis of performance varying models across multiple sites is needed broader controls on performance.Forecasting challenges (i.e., community-scale efforts generate while also developing shared software, training materials, best practices) present useful platform bridging this gap evaluate how range methods perform axes space, time, systems.Here, we analysed from aquatics theme National Ecological Observatory Network (NEON) Forecasting Challenge hosted Initiative.Over 100,000 probabilistic water temperature dissolved oxygen concentration 1-30 days ahead seven NEON-monitored lakes were submitted in 2023.We assessed varied among with structures, covariates, sources uncertainty relative baseline null models.More outperformed forecasting (ten models) (six).These top-performing came classes structures.For temperature, found that process-based included air as covariate generally exhibited highest all sites, most skillful often accounted lower-performing models.The observed where observations divergent historical
Язык: Английский
Процитировано
3Ecological Informatics, Год журнала: 2024, Номер 83, С. 102825 - 102825
Опубликована: Сен. 11, 2024
Язык: Английский
Процитировано
3Ecological Indicators, Год журнала: 2024, Номер 169, С. 112958 - 112958
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
3Water Resources Research, Год журнала: 2024, Номер 60(11)
Опубликована: Ноя. 1, 2024
Abstract Global warming is shifting the thermal dynamics of lakes, with resulting climatic variability heavily affecting their mixing dynamics. We present a dual ensemble workflow coupling climate models lake models. used large set simulations across multiple domains, multi‐scenario, and multi GCM‐ RCM combinations from CORDEX data. forced hydrodynamic by these to explore change impacts on lakes. also quantified contributions different overall uncertainty. employed this investigate effects Lake Sevan (Armenia). predicted for end 21st century, under RCP 8.5, sharp increase in surface temperature substantial bottom , longer stratification periods (+55 days) disappearance ice cover leading shift regime. Increased insufficient cooling during warmer winters points vulnerability change. Our leverages strengths at several levels model chain provide more robust projection same time better uncertainty estimate that accounts Although specific variables, example, summer temperature, single may perform better, full provides has high transferability so our can be blueprint impact studies other systems.
Язык: Английский
Процитировано
2Опубликована: Янв. 1, 2024
Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple variables. However, variability in forecast performance across time and space largely been unexamined ecosystem variables using a process-based modeling approach. To explore how varies water temperature dissolved oxygen, two freshwater important lake functioning, we produced probabilistic forecasts at depths over open-water seasons Lake Sunapee, NH, USA. Our system, FLARE (Forecasting And Reservoir Ecosystems), uses 1-D coupled hydrodynamic-biogeochemical process model daily data assimilation update initial conditions of oxygen fit parameters time. We assessed both accuracy, via the Continuous Ranked Probability Score, skill, by comparing FLARE's accuracy relative null models, which act as baseline forecasts. Specifically, calculated skill climatology persistence models quantify much information provide these varying environmental conditions. found that were always more skillful than outperformed up 11 days future, compared only oxygen. Across different years, observed variable with generally decreasing depth Overall, all surface but not deep least one >80% forecasted period, indicating was able reproduce dynamics greater reliability models. from deeper waters less during majority suggests deep-water are dominated autocorrelation seasonal change, inherently captured results highlight among quality metrics can conditions, informing development quantitative tools predicting change.
Язык: Английский
Процитировано
0KnE Social Sciences, Год журнала: 2024, Номер unknown
Опубликована: Июль 3, 2024
This study utilizes a detailed bibliometric analysis to thoroughly explore the literature surrounding forecasting methods and models in science education. It highlights significant trends, applications, impacts of these methodologies. Leveraging data from Scopus database pinpoints essential themes notable gaps within current body work. The research underscores importance integrating techniques across various scientific disciplines applying address real-world challenges comprehensive is intended contribute richly academic dialogue guide development future educational strategies policies. By identifying discussing key elements, aims enhance understanding implementing settings, ultimately influencing both practice theory Keywords: methods, education,
Язык: Английский
Процитировано
0Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
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