Improving estimates and forecasts of lake carbon dynamics using data assimilation

Zwart, Jacob A.; Hararuk, Oleksandra; Prairie, Yves T.; Jones, Stuart; Solomon, Christopher T.

doi:10.1002/lom3.10302

Cited by 5 publications

(7 citation statements)

References 89 publications

(109 reference statements)

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“…Overall, our study demonstrates the utility of a workflow for lake and reservoir water temperature forecasting that can be applied to other waterbodies. In addition, FLARE builds the foundation for future water quality data assimilation and forecasting because ecosystem models can easily be coupled to the hydrodynamic model, enabling predictions of dissolved oxygen, algal blooms, and biogeochemical cycling with uncertainty [e.g., Hipsey et al 2013, Page et al 2018, Zwart et al 2019 ]. Importantly, FLARE provides a method for partitioning uncertainty in forecasts that identifies how to prioritize future research to increase confidence in forecasts.…”

Section: Discussionmentioning

confidence: 99%

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

Thomas¹,

Figueiredo

Daneshmand

et al. 2020

Preprint

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We created a near-term iterative lake water temperature forecasting system that uses 13 sensors, data assimilation, and hydrodynamic modeling 14 15 • FLARE quantifies the uncertainty in each daily forecast and provides an open-source, 16 generalizable system for water quality forecasting 17 18• 16-day forecasted temperatures were within 0.91℃ over 100 days in a reservoir case Abstract 22 Freshwater ecosystems are experiencing greater variability due to human activities, necessitating 23 new tools to anticipate future water quality. In response, we developed and operationalized a 24 near-term iterative water temperature forecasting system (FLARE -Forecasting Lake And 25 Reservoir Ecosystems) that is generalizable for lakes and reservoirs. FLARE is composed of: 26 water quality and meteorology sensors that wirelessly stream data, a data assimilation algorithm 27 that uses sensor observations to update predictions from a hydrodynamic model and calibrate 28 model parameters, and an ensemble-based forecasting algorithm to generate forecasts that 29 include uncertainty. Importantly, FLARE quantifies the contribution of different sources of 30 uncertainty (parameters, driver data, initial conditions, and process) to each daily forecast of 31 water temperature at multiple depths. We applied FLARE to a temperate reservoir during a 100-32 day period that encompassed stratified and mixed thermal conditions and found that daily 33 forecasted water temperatures were on average within 0.91℃ at all depths of the reservoir over a 34 16-day forecast horizon. FLARE successfully predicted the onset of fall turnover eight days in 35 advance, and identified meteorology driver data and downscaling as the dominant sources of 36 forecast uncertainty. Overall, FLARE provides an open-source and easily-generalizable system 37 for water quality forecasting for lakes and reservoirs to improve management. 39Lake Model, Water temperature 41 45 freshwater ecosystems, which have been more degraded than any other ecosystem on the planet 46 [Millennium Ecosystem Assessment 2005], are seeking new tools to anticipate future change and 47 ensure clean water for drinking, fisheries, irrigation, industry, and recreation [Brookes et al. 48 2014]. 49 In response to this need, near-term iterative ecological forecasting has emerged as a 50 solution to provide stakeholders, managers, and policy-makers crucial information about future 51 ecosystem conditions [Clark et al. 2001, Dietze et al. 2018, Luo et al. 2011. Here, we define a 52 near-term iterative forecast as a projection of future ecosystem states with fully-specified 53 uncertainties, generated from predictive models that can be constantly updated with new data as 54 they become available [Clark et al. 2001]. Importantly, a near-term iterative forecast is not 55 created from merely one ecosystem simulation, but an ensemble of simulations that enable 56 quantification of the uncertainty in the forecast contributed by different sources [i.e., parameters, 57 driver data, initi...

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Section: Discussionmentioning

confidence: 99%

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

Thomas¹,

Figueiredo

Daneshmand

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Data assimilation, in which satellite and in situ observations are systematically combined with numerical models, provides a range of methodo logies to quantify both the timeevolution of the state of a lake and the lake model parameters (as in reF. 175 ). Also, the new modelling para digm known as processguided deep learning 176 , which aims to integrate process understanding from lake models into advanced machine learning modelling techniques, will provide substantial improvements to our predictive ability of lake responses to climate change.…”

Section: Future Directionsmentioning

confidence: 99%

Global lake responses to climate change

Woolway

Kraemer

Lenters

et al. 2020

Nat Rev Earth Environ

809

391

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| Climate change is one of the most severe threats to global lake ecosystems. Lake surface conditions, such as ice cover, surface temperature, evaporation and water level, respond dramatically to this threat, as observed in recent decades. In this Review, we discuss physical lake variables and their responses to climate change. Decreases in winter ice cover and increases in lake surface temperature modify lake mixing regimes and accelerate lake evaporation. Where not balanced by increased mean precipitation or inflow, higher evaporation rates will favour a decrease in lake level and surface water extent. Together with increases in extreme-precipitation events, these lake responses will impact lake ecosystems, changing water quantity and quality, food provisioning, recreational opportunities and transportation. Future research opportunities, including enhanced observation of lake variables from space (particularly for small water bodies), improved in situ lake monitoring and the development of advanced modelling techniques to predict lake processes, will improve our global understanding of lake responses to a changing climate.

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“…While the increase in use of iterative forecasts over time was not statistically significant, the percentage of papers that use iterative workflows to update model parameters rather than just the initial conditions of the forecast has increased significantly (Table 1). Updating model parameters as new data are incorporated allows the forecasting system to learn over time and potentially make more accurate predictions in the future (Luo et al 2011, Niu et al 2014, Zwart et al 2019. Notes: Statistically significant P-values are in bold.…”

Section: Near-term Ecological Forecasting: State Of the Fieldmentioning

confidence: 99%

Increased adoption of best practices in ecological forecasting enables comparisons of forecastability

Lewis

Woelmer

Wander

et al. 2021

Ecological Applications

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Near‐term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross‐ecosystem analysis of near‐term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near‐term (≤10‐yr forecast horizon) ecological forecasting papers to understand the development and current state of near‐term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near‐term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near‐term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.

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Improving estimates and forecasts of lake carbon dynamics using data assimilation

Cited by 5 publications

References 89 publications

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

Global lake responses to climate change

Increased adoption of best practices in ecological forecasting enables comparisons of forecastability

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