Ecological forecasting – i.e., making iterative predictions about the future state of ecosystems that account for uncertainty and are updated with new data as they become available – can be a critical tool for the management of aquatic ecosystems. Forecasts of the future state of aquatic ecosystems (e.g., water quantity, water quality, fisheries) are increasingly needed as global change precludes the use of historical conditions for guiding predictions of future aquatic conditions. Recent advances in data availability and ecosystem models have positioned aquatic researchers to increase their use of forecasting for prediction of stream discharge, fisheries, hypoxia, algal blooms, endangered and protected species, drinking water availability, and other metrics of ecosystem functioning. Forecasts benefit a variety of users, including managers, policymakers, and the public, for purposes ranging from preemptive mitigation of water quality and quantity concerns to adaptively protecting aquatic biota and planning recreational activities. Forecasting also allows researchers to refine ecological theory by repeatedly confronting models with data, thereby improving models over time. In this session, we solicit diverse presentations on both methodological and application-based research on forecasting hydrodynamics, biogeochemistry, and ecology in aquatic ecosystems. We invite submissions that examine the role of forecasting to support adaptation and mitigation by engaging management, industry, communities, and other decision-makers in the face of climatic extreme events and other environmental challenges. Our overarching goal is to illuminate the value of ecological forecasting as an approach for guiding climate change adaptation and mitigation in aquatic ecosystems.
Lead Organizer: Mary Lofton, Virginia Tech (melofton@vt.edu)
Co-organizers:
Rafael Marcé, Institut Català de Recerca de l'Aigua (rmarce@icra.cat)
Cayelan Carey, Virginia Tech (cayelan@vt.edu)
Presentations
04:00 PM
DEVELOPING A SUSTAINED DELIVERY, MULTI-STRESSOR FORECASTING SYSTEM IN LAKE ERIE THROUGH PARTNERSHIPS AND LEVERAGING EXISTING INFRASTRUCTURE (8133)
Primary Presenter: Alexandria Hounshell, NOAA (alexandria.hounshell@noaa.gov)
Since 2009 NOAA has been delivering cyanobacteria harmful algal bloom (cyanoHAB) monitoring products and ecological forecasts for western Lake Erie. The initial early warning system was developed using satellite data and hydrodynamic models to provide water resource managers with near-real time information on cyanoHAB location and to forecast likely bloom movement. NOAA later developed a model to predict upcoming seasonal cyanoHAB severity for the western basin. In following years, the seasonal forecast was expanded to include an ensemble of models developed by multiple partners. This year, NOAA is also operationalizing a forecast to predict hypoxic events in Central Lake Erie near-surface waters to support water quality treatment operators. This coupling of forecast infrastructure for cyanoHABs and hypoxia has led to one of the first multi-stressor forecast systems in Lake Erie. Efforts are ongoing to improve forecast accuracy, including developing a mechanistic model to predict seasonal cyanoHAB intensity and ingesting field data into the nowcast/forecast cyanoHAB tracking model. The full suite of forecasting products relies heavily on partnerships to collect field data, develop models and forecasts, provide model inputs, and assist with forecast delivery. As complex water quality issues continue to evolve in Lake Erie due to climate change, changes in nutrient management, and stakeholder needs, NOAA will continue to work closely with federal, state, and local agencies, as well as the research community to further refine this multi-stressor Lake Erie forecasting system.
04:15 PM
A Bayesian spatiotemporal model evaluation of forecasting cyanobacterial harmful algal bloom events. (7988)
Primary Presenter: Kate Meyers, Oak Ridge Institute for Science and Education (Meyers.Kate@EPA.gov)
The U.S. Harmful Algal Bloom and Hypoxia Research Control Act calls for robust approaches to forecasting cyanobacterial harmful algal blooms (cyanoHABs). Accurate forecasting technology could save local communities healthcare costs through the early detection of cyanoHABs and therefore faster advisory warnings. However, most existing forecasting models require time-consuming parametrization and/or are limited to well-sampled individual lake systems. An Integrated Nested Laplace Approximation hierarchical Bayesian spatiotemporal model forecasted weekly lake exceedance of 12 μg/L chlorophyll-a, the World Health Organization’s recreation Alert Level 1 threshold, for 2192 satellite resolved lakes. Model deficiencies were evaluated to improve the functionality of the forecast. We investigate if temporally short events may commonly occur as false negatives, and if reoccurring annual events are prematurely forecasted, resulting in a false positive. We also consider the impacts of lake type and geographic location on these predictions. This evaluation identifies key targets for model improvement with the goal of making the forecast operational in the future.
04:30 PM
PREDICTIVE MODELING FOR HAB MONITORING IN PHOENIX CANALS (8440)
Primary Presenter: Ashley Foster, Arizona State University (ashleynicolefoster16@gmail.com)
The Phoenix canals transport water from the local reservoirs and rivers to treatment plants before being distributed to the community. They also provide a source of recreation to residents in the valley. The canals are stocked with two types of fish, white amur and carp. Blooms of filamentous cyanobacteria have been reported in the canals along with the associated byproducts, such taste and odor compounds. A time series of monthly sampling at multiple canal sites has been conducted since 1998 and parameters such as 2-Methlyisoborneol (MIB) and geosmin, nutrients, organic carbon, and temperature are publicly available through the Regional Drinking Water Monitoring Program. The mean concentrations of MIB and geosmin were 5.5 ng/L and 2.9 ng/L, respectively, in the canal waterways from 1998 to 2022 while filamentous cyanobacteria reached up to 1900 particles/ml during summer of 2002. This study will utilize this public data set to model byproduct hotspots in the Phoenix canals with the use of machine learning and spatial modeling. Principal component analysis selected parameters that are correlated with the occurrence of taste and odor compounds. As a monitoring tool for local resource managers, we created classification trees to determine the location of odorous water samples from environmental measurements and maps predicting compound hotspots. Trees were trained with 80% of data and tested against the remaining 20% to measure model accuracy. We achieved a model accuracy of 87% for MIB and 74% for geosmin.
04:45 PM
CYANOBACTERIA BLOOMS BEHAVIOUR IN LAKE HUME BY CHANGING ITS HYDRODYNAMICS (7878)
Primary Presenter: Duy Nguyen, CSIRO (duy.nguyen@csiro.au)
Managing harmful cyanobacteria in lakes is vital for preserving lake ecosystems and for ensuring good quality water supply from the lake to communities. Lake Hume, a major reservoir of the Murray River system, was identified as a source of cyanobacteria in the downstream river during past mega-blooms. The study aims to assess managed flows from an upstream reservoir on temperature stratification and mixing in the lake to potentially suppress cyanobacteria blooms and thus minimise the risk of cyanobacteria intake from hydropower and irrigation outlets during periods of low dam levels. A one-dimensional vertical hydrodynamic model (Lake1D) was used to simulate 15-year time series data on water temperature and stratification within the lake. The outputs were integrated into a cyanobacteria growth model, driven by water temperature and light. Results indicate that reducing water temperature by 3°C could decrease bloom risk, but it requires an unrealistically high volume of cold-water inflows. High cyanobacteria intake in Lake Hume is a concern when water depths fall below 20m and 10m, for the hydropower and irrigation outlets, respectively. Surface cyanobacteria concentrations obtained from in-situ hyperspectral observations coupled with mixing dynamics enable short-term forecasting of cyanobacteria growth. Varying mixing depth scenarios and initial cell counts underpin the importance of integrating high-resolution water temperature and hyperspectral reflectance data with well-calibrated mixing dynamics for accurate cyanobacteria bloom forecasts.
05:00 PM
Successful forecasting of biodiversity change in natural plankton communities (8220)
Primary Presenter: Luis Gilarranz, Eawag (luis.gilarranz@eawag.ch)
Ecologists have made remarkable discoveries toward a mechanistic understanding of ecosystem dynamics. However, in light of the unprecedented rate of degradation experienced by ecosystems worldwide, just understanding is not enough. We must deliver quantitative predictions. Thanks to time series of both abiotic and biotic drivers, combined with non-parametric modeling, here we provide accurate predictions of biodiversity turnover in the plankton community of a peri-alpine lake. Our results show that not only we can successfully generate anticipatory forecasts of biodiversity change, but we were able to evaluate the relative influence of species interactions, community stability, temperature, light, and nutrients. These results promise a way forward that would allow us to have real-time forecasts of plankton communities using automatized sampling methods.
05:15 PM
Here comes the bloom: forecasting conditions for harmful algal blooms in a large oligotrophic lake (8027)
Primary Presenter: Jonathan Borrelli, Rensselaer Polytechnic Institute (borrejj@gmail.com)
Harmful algal blooms (HABs) may be increasing in frequency and intensity in inland freshwaters. Our knowledge of the mechanisms driving the development of HABs remains limited due to their often-ephemeral nature. The conditions for HABs likely occur well before a HAB is observed at the surface. Yet without foreknowledge, attempting to collect water samples leading up to a HAB is difficult. Lake George, New York, USA is an oligotrophic lake but has experienced several HABs since at least fall, 2020. We deployed and analyzed high-frequency profiling buoy data and high-resolution weather model output to identify potential warning signals that occurred prior to observed HABs. We sought to determine the predictive value of these observations using a near-term iterative forecasting approach. We used an ensemble of one-dimensional lake models driven by NOAA weather forecasts to predict lake temperature and stratification attributes up to 30 days ahead. Prior fall HABs were typically preceded by an increase in Schmidt stability, and we could identify this increase in stability 8-12 days in advance. Using these insights, in 2023 we successfully predicted conditions that resulted in two HABs. Our results demonstrate the capability of parsimonious models to predict HABs days in advance based on proximate weather attributes in oligotrophic lakes. When combined with other models and sensor measurements these forecasts may be generalizable, enabling the ability to accurately forecast rapid changes in phytoplankton community dynamics up to weeks in advance across many types of lakes.
SS42B - Ecological Forecasting as a Tool for Adaptation and Mitigation in Aquatic Ecosystems
Description
Time: 4:00 PM
Date: 5/6/2024
Room: Meeting Room KL