How Does Seasonal Forecast Performance Influence Decision-Making? Insights from a Serious Game

Crochemore, Louise; Cantone, Carolina; Pechlivanidis, Ilias; Photiadou, Christiana

doi:10.1175/bams-d-20-0169.1

Cited by 20 publications

(7 citation statements)

References 50 publications

(14 reference statements)

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“…Consequently, the water storage from the snowmelt season to the next winter in large headwater reservoirs is the most important reservoir management objective. The hydropower sector would greatly benefit from high‐quality forecasts, which are particularly crucial during extreme years (Crochemore et al., 2021).…”

Section: Study Area and Datamentioning

confidence: 99%

Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

Musuuza

Crochemore

Pechlivanidis

2023

Water Resources Research

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Earth observations (EO) and in situ data assimilation (DA) improve hydrological forecasting quality depending on season and assimilated data; effects persist for long periods • DA is relatively more important than meteorological forcing to improve the hydrological forecast quality during all seasons except autumn • Assimilating EO-based snow water equivalent in winter impacts seasonal forecasts for 3 months+, hence it's beneficial to have high-quality snow information

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Section: Study Area and Datamentioning

confidence: 99%

Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

Musuuza

Crochemore

Pechlivanidis

2023

Water Resources Research

Self Cite

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“…Improving the representation of uncertainty in forecasts, as quantified by the reliability of forecast confidence intervals, is important for management (Ramos et al, 2013;Crochemore et al, 2021). Use of ecological forecasts by decision makers is likely to improve if forecast uncertainty is well quantified and confidence intervals are appropriate (Ramos et al, 2013;Buizza 2008;Nadav-Greensberg & Joslyn, 2009).…”

Section: To What Extent Is Relative Forecast Skill Affected By the In...mentioning

confidence: 99%

“…Use of ecological forecasts by decision makers is likely to improve if forecast uncertainty is well quantified and confidence intervals are appropriate (Ramos et al, 2013;Buizza 2008;Nadav-Greensberg & Joslyn, 2009). Underconfidence and overconfidence limit the use of forecasts for management, as underconfident forecasts provide too wide of a range of potential future conditions and overconfident forecasts underestimate the possible range of conditions, with both leading to inappropriate management actions (Crochemore et al, 2021).…”

Section: To What Extent Is Relative Forecast Skill Affected By the In...mentioning

confidence: 99%

What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge

Olsson,

Carey,

Boettiger

et al. 2024

Preprint

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Near-term, iterative ecological forecasts can be used to help understand and proactively manage ecosystems. To date, more forecasts have been developed for aquatic ecosystems than other ecosystems worldwide, likely motivated by the pressing need to conserve these essential and threatened ecosystems. Forecasters have implemented many different modelling approaches to forecast freshwater variables, which have demonstrated promise at individual sites. However, a comprehensive analysis of the performance of varying forecast models across multiple sites is needed to understand broader controls on forecast performance. Forecasting challenges (i.e., community-scale efforts to generate forecasts while also developing shared software, training materials, and best practices) present a useful platform for bridging this gap to evaluate how a range of modelling methods perform across axes of space, time, and ecological systems. Here, we analysed forecasts from the aquatics theme of the National Ecological Observatory Network (NEON) Forecasting Challenge hosted by the Ecological Forecasting Initiative. Over 100,000 probabilistic forecasts of water temperature and dissolved oxygen concentration for 1-30 days ahead across seven NEON-monitored lakes were submitted in 2023. We assessed how forecast performance varied among models with different structures, covariates, and sources of uncertainty relative to baseline null models. More models outperformed the baseline models in forecasting water temperature (ten models) than dissolved oxygen (six). These top-performing models came from a range of classes and structures. For water temperature, we found that process-based models and models that included air temperature as a covariate generally exhibited the highest forecast performance across all sites, and that the most skillful forecasts often accounted for more sources of uncertainty than the lower-performing models. The most skillful forecasts were observed at sites where observations were most divergent from historical Submitted to Ecological Applications 4 conditions (resulting in poor baseline model performance). Overall, the NEON Forecasting Challenge provides an exciting opportunity for a model inter-comparison to learn about the relative strengths of a diverse suite of models and advance our understanding of freshwater ecosystem predictability.

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“…The relative quality (skill) of ECMWF-based streamflow forecasts was evaluated using the Ensemble Streamflow Prediction (ESP) technique as a reference (Wood & Lettenmaier, 2006). ESP provides streamflow forecasts by forcing a hydrological model with a resampled meteorological dataset from past observations and is generally appropriate for assessing short-medium range forecasts (Pappenberger et al, 2015;Bennett et al, 2014) and seasonal streamflow forecasts (Arnal et al, 2018;Crochemore et al, 2021). However, in this study, ESP was applied to the sub-seasonal timescale.…”

Section: Forecast Experimental Setupmentioning

confidence: 99%

Sub-seasonal streamflow forecasts for hydropower dams in the Brazilian Eletrical Interconnected System

Quedi,

Fan,

Siqueira

et al. 2024

RBRH

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Inflow prediction on sub-seasonal timescale have the potential for important contributions to the management of water resources in hydroelectric dam operations. These forecasts challenge the limitations of the medium-term and extend it, bridging a long-standing technical-scientific gap in the forecasting field. In Brazil, the use of sub-seasonal hydrological predictions can boost the hydroelectric production of the National Interconnected System (SIN), since inflow forecast in reservoirs of up to 2 weeks are routinely used using a rain-flow model. This study aimed at the statistical evaluation of hydrological forecasts of up to 6 weeks using a hydrological-hydrodynamic model on a continental scale associated with ensemble precipitation forecasts generated by an atmospheric model, producing future streamflow in the continent basins, and consequently at the SIN’s hydroelectric dams. The statistical evaluation was based on deterministic scores typically used by the SIN operating agent, and additionally we assessed the skill of forecasts based on atmospheric models in relation to simpler forecasts based on the climatology of observed inflows. The performance of the forecasts varies according to the season and geographic location, that is, depending on different hydrological regimes. The best performances were obtained in dams located in the southwest and central-west regions, which have well-defined seasonality, while dams in the south showed greater sensitivity in metrics according to the season. The study presented serves as a technical-scientific contribution for agents and decision makers who seek to improve water resource management by incorporating extended forecasts into the operational chain.

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How Does Seasonal Forecast Performance Influence Decision-Making? Insights from a Serious Game

Cited by 20 publications

References 50 publications

Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge

Sub-seasonal streamflow forecasts for hydropower dams in the Brazilian Eletrical Interconnected System

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