Opportunities for seasonal forecasting to support water management outside the tropics

Jackson-Blake, Leah; Clayer, François; Eyto, Elvira de; French, Andrew N.; Frías, M. D. Rodríguez; Mercado‐Bettín, Daniel; Moore, Tadhg; Puértolas, Laura; Poole, Russell; Rinke, Karsten; Shikhani, Muhammed; Linden, Leon van der; Marcé, Rafael

doi:10.5194/hess-2021-443

Cited by 4 publications

(6 citation statements)

References 37 publications

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“…For example, interviews and focus groups with end users of NOAA's Climate Prediction Center climate outlook visualizations guided updates of NOAA's air temperature and precipitation color maps for improved forecast interpretability (Gerst et al, 2020). Finally, feedback from managers and end users should be sought after forecast dissemination to determine if the forecast product is being successfully implemented for decision‐making support (e.g., Jackson‐Blake et al, 2022).…”

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

confidence: 99%

“…Near‐term forecasts comprise a wide range of time scales (e.g., daily, weekly, seasonal, decadal), each of which is likely associated with different end‐user goals and decisions. For example, a ship captain may be most interested in lake ice conditions over the next several hours to days when deciding whether to embark (Fujisaki‐Manome et al, 2022), while a reservoir manager may look multiple months ahead when planning water releases downstream (Jackson‐Blake et al, 2022; Turner et al, 2020). We observed a relative dearth of near‐term freshwater quality forecasts at multi‐month (seasonal) time scales (but see Mercado‐Bettín et al, 2021; Figure 6), highlighting an opportunity for the development of additional forecasts at this horizon.…”

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

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

confidence: 99%

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

confidence: 99%

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

Lofton

Howard

Thomas

et al. 2023

Global Change Biology

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show abstract

“…As well as providing an easy way of deriving probabilistic forecasts, we found a real benefit of using BNs for forecasting was in stakeholder engagement and model co-development. We found that the easy and transparent visualisation of the model increased stakeholder engagement in the model development process and stakeholders' ability to correctly interpret the probabilistic predictions (Jackson-Blake et al, 2022).…”

Section: Forecasting To Support Water Managementmentioning

confidence: 93%

“…The focus of the WATExR project, a European Union project funded by the European Research Area for Climate Services (ERA4CS), was to help address this gap by developing pilot seasonal forecasting tools for lake water quality and ecology. Tools were co-developed with water managers at five catchmentlake case study sites, with four in Europe and one in South Australia (Jackson-Blake et al, 2022). Tools linked seasonal climate forecasts with models for predicting river discharge, lake water level and water temperature (Mercado-Bettín et al, 2021), water quality, algal bloom risk, and fish migration.…”

Section: Introductionmentioning

confidence: 99%

Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network

Jackson-Blake

Clayer

Haande

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Freshwater management is challenging, and advance warning that poor water quality was likely, a season ahead, could allow for preventative measures to be put in place. To this end, we developed a Bayesian network (BN) for seasonal lake water quality prediction. BNs have become popular in recent years, but the vast majority are discrete. Here, we developed a Gaussian Bayesian network (GBN), a simple class of continuous BN. The aim was to forecast, in spring, mean total phosphorus (TP) and chlorophyll a (chl a) concentration, mean water colour, and maximum cyanobacteria biovolume for the upcoming growing season (May–October) in Vansjø, a shallow nutrient-rich lake in southeastern Norway. To develop the model, we first identified controls on interannual variability in seasonally aggregated water quality. These variables were then included in a GBN, and conditional probability densities were fit using observations (≤39 years). GBN predictions had R2 values of 0.37 (chl a) to 0.75 (colour) and classification errors of 32 % (TP) to 17 % (cyanobacteria). For all but lake colour, including weather variables did not improve the predictive performance (assessed through cross-validation). Overall, we found the GBN approach to be well suited to seasonal water quality forecasting. It was straightforward to produce probabilistic predictions, including the probability of exceeding management-relevant thresholds. The GBN could be sensibly parameterised using only the observed data, despite the small dataset. Developing a comparable discrete BN was much more subjective and time-consuming. Although low interannual variability and high temporal autocorrelation in the study lake meant the GBN performed only slightly better than a seasonal naïve forecast (where the forecasted value is simply the value observed the previous growing season), we believe that the forecasting approach presented here could be particularly useful in areas with higher sensitivity to catchment nutrient delivery and seasonal climate and for forecasting at shorter (daily or monthly) timescales. Despite the parametric constraints of GBNs, their simplicity, together with the relative accessibility of BN software with GBN handling, means they are a good first choice for BN development with continuous variables.

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“…For example, interviews and focus groups with end users of NOAA's Climate Prediction Center climate outlook visualizations guided updates of NOAA's air temperature and precipitation color maps for improved forecast interpretability (Gerst et al, 2020). Finally, feedback from managers and end users should be sought after forecast dissemination to determine if the forecast product is being successfully implemented for decision-making support (e.g., Jackson-Blake et al, 2022).…”

Section: Integration Of End Users Into the Forecast Processmentioning

confidence: 99%

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

Lofton

Howard

Thomas

et al. 2022

Preprint

View full text Add to dashboard Cite

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

Opportunities for seasonal forecasting to support water management outside the tropics

Cited by 4 publications

References 37 publications

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

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

Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network

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

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