Using near‐term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density

Lofton, Mary E.; Brentrup, Jennifer A.; Beck, Whitney S.; Zwart, Jacob A.; Bhattacharya, Ruchi; Brighenti, Ludmila Silva; Burnet, Sarah H.; McCullough, Ian M.; Steele, Bethel G.; Carey, Cayelan C.; Cottingham, Kathryn L.; Dietze, Michael C.; Ewing, Holly A.; Weathers, Kathleen C.; LaDeau, Shannon L.

doi:10.1002/eap.2590

Cited by 10 publications

(8 citation statements)

References 104 publications

(216 reference statements)

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“…Thus, more frequent DA, which constrains the model's initial conditions, will almost always improve the skill of meteorological forecasts (e.g., Clark et al, 2016; He et al, 2020; Simonin et al, 2017). In contrast, for forecasts of environmental systems in which model initial conditions are less important sources of uncertainty, and model process uncertainty and model driver data uncertainty dominate total uncertainty (e.g., Dietze, 2017a; Heilman et al, 2022; Lofton et al, 2022; Thomas et al, 2020), it is unknown whether more frequent DA can improve forecast skill by generating initial conditions that are more consistent with observations.…”

Section: Introductionmentioning

confidence: 99%

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Wander,

Thomas,

Moore

et al. 2024

Ecosphere

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Ecosystems around the globe are experiencing changes in both the magnitude and fluctuations of environmental conditions due to land use and climate change. In response, ecologists are increasingly using near‐term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near‐term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost‐prohibitive or impossible for forecasting ecological variables that lack high‐frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using Forecasting Lake and Reservoir Ecosystems (FLARE), an open source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1‐ to 35‐day‐ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1‐day‐ahead forecast root mean square error (RMSE) of 0.81°C, mean 7‐day RMSE of 1.15°C, and mean 35‐day RMSE of 1.94°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1‐ to 7‐day‐ahead horizons, but weekly data assimilation resulted in the most skillful forecasts at 8‐ to 35‐day‐ahead horizons. Within a year, forecasts with weekly data assimilation consistently outperformed forecasts with daily data assimilation after the 8‐day forecast horizon during mixed spring/autumn periods and 5‐ to 14‐day‐ahead horizons during the summer‐stratified period, depending on depth. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high‐frequency sensor data.

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Section: Introductionmentioning

confidence: 99%

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Wander,

Thomas,

Moore

et al. 2024

Ecosphere

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“…In addition to improving decision‐making outcomes, uncertainty quantification and partitioning can inform the most effective ways to improve forecast performance (e.g., Lofton, Brentrup, et al, 2022). For example, if uncertainty partitioning identifies that forecast model driver data is the biggest source of forecast uncertainty, then reducing uncertainty in driver data would be a logical next step for improving that forecast system (following Thomas et al, 2020).…”

Section: Discussion and Synthesis: Opportunities To Advance Near‐term...mentioning

confidence: 99%

“…Definitions and examples of terms related to freshwater forecasting. Definitions are adapted from multiple sources (Carey et al, 2022;Dietze, 2017;Lewis et al, 2022;Lofton, Brentrup, et al, 2022;McClure et al, 2021;Thomas et al, 2020), with additional references for select terms provided in the table. There is a 45% chance that dissolved iron concentrations will exceed a drinking water thresh next week Forecast horizon How far into the future a forecast is issued A forecast of stream discharge 1 week into the future (a 1-week horizon) versus 1 day into the future (a 1-day horizon)…”

Section: Ta B L Ementioning

confidence: 99%

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Progress and opportunities in advancing near‐term forecasting of freshwater quality

Lofton

Howard

Thomas

et al. 2023

Global Change Biology

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Near‐term freshwater forecasts, defined as sub‐daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near‐term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water‐related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near‐term forecasts of water quantity and that near‐term water quality forecasts are fewer in number and in the early stages of development (i.e., non‐operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near‐term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end‐user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near‐term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

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“…In addition to improving decision-making outcomes, uncertainty quantification and partitioning (Table 1) can inform the most effective ways to improve forecast accuracy (e.g., Lofton, Brentrup, et al, 2022). For example, if uncertainty partitioning identifies that forecast model driver data is the biggest source of forecast uncertainty, then reducing uncertainty in driver data would be a logical next step for improving that forecast system (following Thomas, Figueiredo, et al, 2020).…”

Section: A Definition Of Forecast That Includes Uncertaintymentioning

confidence: 99%

Progress and opportunities in advancing near-term forecasting of freshwater quality

Lofton

Howard

Thomas

et al. 2022

Preprint

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Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past five years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in early stages of development (i.e., non-operational), despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed, and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events five days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts necessitates substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

show abstract

Using near‐term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density

Cited by 10 publications

References 104 publications

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Progress and opportunities in advancing near‐term forecasting of freshwater quality

Progress and opportunities in advancing near-term forecasting of freshwater quality

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