Grantee Research Project Results

Final Report: Development of a multi-scale management tool for predicting and mitigating HABs in Ohio River watersheds

EPA Grant Number: R839269
Title: Development of a multi-scale management tool for predicting and mitigating HABs in Ohio River watersheds
Investigators: Sullivan, Mažeika , Atristain, Miren , Pintor, Lauren , Zhao, Kaiguang
Institution: The Ohio State University
EPA Project Officer: Ludwig-Monty, Sarah
Project Period: January 1, 2018 through December 31, 2020 (Extended to December 31, 2024)
Project Amount: $681,343
RFA: Freshwater Harmful Algal Blooms (2017) RFA Text | Recipients Lists
Research Category: Watersheds , Water Quality , Water

Objective:

The overarching objective of this project was to develop a watershed/catchment classification system to diagnose and manage harmful algal blooms (HABs) in the upper Ohio River basin. We aimed to develop a multi-scale, hierarchical tool that links climate and land use with river physicochemical gradients and ecological condition to predict and prevent HABs. We hypothesized that the timing and magnitude of HABs are related to variability in anthropogenic and natural factors in catchments. Our objectives were to: (1) Determine in-stream characteristics related to the distribution, duration, and intensity of HABs, (2) Determine local climate and land-use features strongly linked with HABs, (3) Assess the ability of our system to scale up to large catchments, and (4) Develop and validate a classification system for use by communities and local and state agencies to predict and prevent HABs.

Summary/Accomplishments (Outputs/Outcomes):

Summary of Findings (Outputs/Outcomes): Overall, we have made substantial progress in developing a robust, multi-year dataset encompassing seven study catchments across Ohio, Indiana and Kentucky. These include Hoover Reservoir, Indian Lake, and Burr Oak Lake in Ohio; Lake Monroe and Patoka Lake in Indiana; and Barren River Lake and Taylorsville Lake in Kentucky. In total, the dataset includes 55 stream sites and 22 reservoir sites.

Our completed monitoring efforts provide a foundation for long-term catchment studies. Over four years of the project, multiparameter sondes were deployed in buoys during various summer and autumn periods to continuously measure key physicochemical and water quality parameters, including water temperature, dissolved oxygen, conductivity, turbidity, pH, chlorophyll, and phycoerythrin. Additionally, continuous temperature data were collected at selected stream sites over three years of the project.

We also characterized hydrologic conditions and nutrient loading across catchments. Updated drainage basin hydrologic parameters were generated using USGS StreamStats v4.16.1 to reflect recent algorithm changes. Monthly flow statistics were used to estimate mean summer flow and, in combination with five years of nutrient concentration data from the Ohio sites, to calculate mean daily summer nutrient loads for total phosphorus (TP), total nitrogen (TN), phosphate (PO₄³⁻), nitrate (NO₃⁻), and ammonium (NH₄⁺). Nutrient loads differed significantly among catchments. We found that total phosphorus and phosphate loads were higher in the mixed-use Hoover catchment compared to the forested Burr Oak catchment. Additionally, total nitrogen, nitrate, and ammonium loads were higher in both the mixed-use Hoover and agricultural Indian Lake catchments compared to Burr Oak.

To better understand nutrient sources, phosphorus concentration data from potential point sources such as golf courses, quarries, row-crop agricultural operations, and dairy farms in Ohio informed orthophosphate isotope sampling in the remaining study catchments. Point-source samples collected across the Indiana and Kentucky catchments were analyzed by an external laboratory for orthophosphate (δ¹⁸O PO₄³⁻) and our data demonstrated variability in orthophosphate signatures, from –17.9‰ at a lumber operation industrial stormwater pond to +79.3‰ at an agricultural stormwater pond on a dairy farm. This variability reflects a diversity of phosphate sources and cycling processes across the study catchments.

Phytoplankton dynamics were also investigated to assess biological responses to environmental variation. Phytoplankton community data from stream and reservoir sites sampled in 2019 and 2021 were analyzed using linear-mixed models based on the Akaike information criterion adjusted for small sample size (AICc) to study the influence of environmental factors on community composition and identify potential drivers of phytoplankton variability. Total chlorophyll a was significantly positively correlated with agricultural land cover (%), which was a strong predictor in all supported models for both total and cyanobacterial chlorophyll a in reservoirs. In streams, the top supported model showed that total chlorophyll a was negatively associated with the molar ratio of dissolved inorganic nitrogen to dissolved inorganic phosphorus (DIN:DIP) and forest cover (%), and was positively associated with ammonium concentrations and water temperature.

Building on research conducted at Ohio stream and reservoir sites sampled since 2016, we sampled fish, aquatic macroinvertebrates, and algae across all study catchments. Fish diversity and species richness varied across stream sites, with the highest mean Shannon-Wiener diversity observed in Barren River (2.84 ± 0.26), and the lowest in Burr Oak (1.32 ± 0.54). Barren River also had the highest mean species richness (26.86 ± 5.21), while Burr Oak had the lowest (8.63 ±5.42).

We have also made significant progress in advancing hydrological modeling by developing a suite of catchment-level metrics and evaluating the use of satellite multispectral imaging as empirical predictors of harmful algal blooms within our classification system. Our results show that data from the Ocean and Land Color Instrument (OLCI) effectively estimate algae-related water quality parameters. Lastly, we deployed 160 SPATT (Solid Phase Adsorption Toxin Tracking) samples for cyanobacterial toxin data. Samples were collected over two years of the project and included results for microcystins, nodularins, anatoxin, cylindrospermopsin, euglenophycin, and saxitoxin.

Ongoing

Following the conclusion of the grant, this project is currently continuing without EPA funding, with the dataset supporting a range of ongoing analyses. These efforts contribute to conference presentations, peer-reviewed publications, and the continued development of the proposed classification system. Watershed and remote sensing modeling efforts are ongoing, focusing on refining data sources, improving modeling techniques, and testing alternative spectral indices to enhance future model performance. Fish and invertebrate data are used in various models to study community dynamics, temporal changes, and the effects of nutrients and environmental factors. Geomorphic data such as substrate size and sediment load are included to study how physical habitat affects communities and ecosystem processes. Buoy and temperature sensor data further support analyses of environmental influences on aquatic communities and primary production.

We are currently estimating equilibrium values from δ¹⁸O in water (δ¹⁸O H₂O) and measured water temperatures and comparing these to observed δ¹⁸O in phosphate (δ¹⁸O PO₄³⁻). Current modeling shows strong potential for this approach to identify critical nutrient source areas. In samples where the expected values exceed observed values, this may indicate phosphate remineralization within biomass. Nutrient load analyses are ongoing beyond the original project period to refine load estimates by incorporating field-measured flow data from all seven catchments to complement modeled values.

Analyses of nutrient and phytoplankton data are also ongoing. Preliminary findings reveal several emerging trends, with further analyses underway in preparation for publication. Agricultural land cover (%) is positively correlated with stream TN and conductivity, suggesting runoff of nutrients and ions from agricultural areas. Stream conductivity is also positively correlated with lake conductivity, indicating potential downstream transport of dissolved ions. Additionally, stream TP is positively associated with TP in reservoir sites. Conductivity shows a strong positive relationship with chlorophyll concentrations of cyanobacteria, green algae, and total phytoplankton, suggesting high conductivity may influence algal growth. In contrast, lake TN did not show significant relationships with phytoplankton groups, suggesting that conductivity may be a more influential driver of phytoplankton biomass in this study. The cyanotoxin data from SPATT analyses will be a key response variable in several ongoing modeling efforts, including the structural equation model, which will inform the development of the proposed classification system.

Journal Articles:

No journal articles submitted with this report: View all 21 publications for this project

Supplemental Keywords:

catchments, cumulative effects, ecological condition, ecosystem, habitat, EPA Regions 4 and 5, Midwest, scaling, sediments, vulnerability

Relevant Websites:

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Progress and Final Reports:

Original Abstract

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.