Extending the forecast model: Predicting Western Lake Erie harmful algal blooms at multiple spatial scales

Manning, Nathan F.; Wang, Yu-Chen; Long, Christopher; Bertani, Isabella; Sayers, Michael J.; Bosse, Karl R.; Shuchman, Robert A.; Scavia, Donald

doi:10.1016/j.jglr.2019.03.004

Cited by 36 publications

(22 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Areas in category 3 appear to be simply outside the area where Maumee River waters tend to mix due to the predominant water movement patterns. These areas tend to be in the northeastern portions of the western basin, which is consistent with results from other studies (Fang et al, 2019; Manning et al, 2019). By contrast, areas in category 1 experience Maumee River waters annually.…”

Section: Discussionsupporting

confidence: 92%

“…However, our preliminary analysis suggests that the current P targets might have less benefits in the ecologically and economically important areas near the Maumee river mouth and the city of Toledo, Ohio. Other studies have also found that there are strong spatial differences in the extent to which P loads influence different areas of Lake Erie (Manning et al, 2019). Both this analysis and the analysis of Manning et al (2019) are based on statistical associations, but future analyses may benefit from incorporating models on water movement to make predictions about where effects are likely to occur.…”

Section: Discussionmentioning

confidence: 92%

“…While spring/early summer loads of TBP appear to have a relatively strong association with the total biomass of cyanobacteria in western Lake Erie (Bertani et al, 2016), the actual waters of the Maumee River are not mixed randomly throughout the western basin or Lake Erie as a whole. This creates a uneven spatial distribution in the distribution of primary production and thus an uneven distribution of potential effects on ecosystem services (Fang et al, 2019; Manning et al, 2019). A recent analysis by Manning et al (2019) used regression analysis to relate the overall bloom to subregions within the western basin and demonstrated that large bloom events are not affecting all regions of the western basin equally.…”

Section: Introductionmentioning

confidence: 99%

“…This creates a uneven spatial distribution in the distribution of primary production and thus an uneven distribution of potential effects on ecosystem services (Fang et al, 2019; Manning et al, 2019). A recent analysis by Manning et al (2019) used regression analysis to relate the overall bloom to subregions within the western basin and demonstrated that large bloom events are not affecting all regions of the western basin equally. The previously untested implication is that the benefits from TBP load reductions will similarly not be evenly distributed across the western basin of Lake Erie.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Preliminary analysis to estimate the spatial distribution of benefits of P load reduction: Identifying the spatial influence of phosphorus loading from the Maumee River (USA) in western Lake Erie

Larson

Hlavacek

DeJager

et al. 2020

Ecology and Evolution

View full text Add to dashboard Cite

Since the early 2000s, Lake Erie has been experiencing annual cyanobacterial blooms that often cover large portions of the western basin and even reach into the central basin. These blooms have affected several ecosystem services provided by Lake Erie to surrounding communities (notably drinking water quality). Several modeling efforts have identified the springtime total bioavailable phosphorus (TBP) load as a major driver of maximum cyanobacterial biomass in western Lake Erie, and on this basis, international water management bodies have set a phosphorus (P) reduction goal. This P reduction goal is intended to reduce maximum cyanobacterial biomass, but there has been very limited effort to identify the specific locations within the western basin of Lake Erie that will likely experience the most benefits. Here, we used pixel-specific linear regression to identify where annual variation in spring TBP loads is most strongly associated with cyanobacterial abundance, as inferred from satellite imagery. Using this approach, we find that annual TBP loads are most strongly associated with cyanobacterial abundance in the central and southern areas of the western basin. At the location of the Toledo water intake, the association between TBP load and cyanobacterial abundance is moderate, and in Maumee Bay (near Toledo, Ohio),

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preliminary analysis to estimate the spatial distribution of benefits of P load reduction: Identifying the spatial influence of phosphorus loading from the Maumee River (USA) in western Lake Erie

Larson

Hlavacek

DeJager

et al. 2020

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…The examples show a great range of spatial scales, ranging from single case studies of individual lakes (e.g., [38]) to larger sets of lakes [55] and large coastal areas (e.g., [56]). Likewise, the temporal resolution of BN models for cyanobacteria span high-frequency models with real-time data [57] to seasonal forecast [58] and assessment for future decades (this study). This diversity of models suggests that the BN approach is useful for predicting the occurrence and abundance of cyanobacteria, across different scales and systems.…”

Section: Assessment Of the Bayesian Network Modelling Approachmentioning

confidence: 99%

Predicting Lake Quality for the Next Generation: Impacts of Catchment Management and Climatic Factors in a Probabilistic Model Framework

Moe

Couture

Haande

et al. 2019

Water

View full text Add to dashboard Cite

Lake ecosystems across the world are under combined pressures of eutrophication and climate change, which increase the risk of harmful cyanobacteria blooms, reduced ecological status, and degraded ecosystem services. In Europe, the third cycle of river basin management plans (2021–2027) according to the Water Framework Directive must take into account the potential impacts of climate change on water quality, including effects on relevant biological indicators. Here, we applied a Bayesian network as a meta-model for linking future climate and land-use scenarios for the time horizon 2050–2070, via process-based catchment and lake models, to cyanobacteria abundance and ecological status of a eutrophic lake. Building upon previous applications of the model, a new version was developed to include relevant climatic variables such as wind speed. Explorative scenarios showed that the combination of low wind and high temperature gave the most synergistic effects on cyanobacteria under high levels of eutrophication (Chl-a concentration). Considering the management target of good ecological status, however, the climate-related promotion of cyanobacteria blooms contributed most to degrading the ecological status at intermediate levels of eutrophication. Future developments of this model will aim to strengthen the link between climate variables and ecological responses, to make the model also useful for seasonal forecasting.

show abstract

Near‐term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

Woelmer

Thomas

Lofton

et al. 2022

Ecological Applications

View full text Add to dashboard Cite

As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near‐term, iterative forecasts of phytoplankton 1–14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7‐day and 14‐day horizons, a trend that increased up to the 14‐day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.

show abstract

Extending the forecast model: Predicting Western Lake Erie harmful algal blooms at multiple spatial scales

Cited by 36 publications

References 41 publications

Preliminary analysis to estimate the spatial distribution of benefits of P load reduction: Identifying the spatial influence of phosphorus loading from the Maumee River (USA) in western Lake Erie

Preliminary analysis to estimate the spatial distribution of benefits of P load reduction: Identifying the spatial influence of phosphorus loading from the Maumee River (USA) in western Lake Erie

Predicting Lake Quality for the Next Generation: Impacts of Catchment Management and Climatic Factors in a Probabilistic Model Framework

Near‐term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability

Contact Info

Product

Resources

About