2021 journal article
Quantifying uncertainty cascading from climate, watershed, and lake models in harmful algal bloom predictions
SCIENCE OF THE TOTAL ENVIRONMENT, 759.
author keywords: Integrating models; Error analysis; Monte Carlo; Forecasting
MeSH headings : Canada; Harmful Algal Bloom; Lakes; Phosphorus; Uncertainty
TL;DR:
This study integrated an ensemble of four climate models, three watershed models, and four HAB models to show that climate impacts on watershed processes are likely to lead to reductions in future loading, assuming land use and watershed management practices are unchanged.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation
(Web of Science)
13. Climate Action
(Web of Science; OpenAlex)
15. Life on Land
(Web of Science)
Source: Web Of Science
Added: February 15, 2021
In response to increased harmful algal blooms (HABs), hypoxia, and nearshore algae growth in Lake Erie, the United States and Canada agreed to phosphorus load reduction targets. While the load targets were guided by an ensemble of models, none of them considered the effects of climate change. Some watershed models developed to guide load reduction strategies have simulated climate effects, but without extending the resulting loads or their uncertainties to HAB projections. In this study, we integrated an ensemble of four climate models, three watershed models, and four HAB models. Nutrient loads and HAB predictions were generated for historical (1985–1999), current (2002–2017), and mid-21st-century (2051–2065) periods. For the current and historical periods, modeled loads and HABs are comparable to observations but exhibit less interannual variability. Our results show that climate impacts on watershed processes are likely to lead to reductions in future loading, assuming land use and watershed management practices are unchanged. This reduction in load should help reduce the magnitude of future HABs, although increases in lake temperature could mitigate that decrease. Using Monte-Carlo analysis to attribute sources of uncertainty from this cascade of models, we show that the uncertainty associated with each model is significant, and that improvements in all three are needed to build confidence in future projections.
