Key factors affecting nutrient levels and algae growth in stormwater ponds

Nutrient-algae response relationships, primarily measured by chlorophyll-a concentration, help predict algal blooms and lake eutrophication. Key drivers of algal biomass include nitrogen (TN) and phosphorus (TP). While regression models have successfully predicted algal blooms in natural lakes using TN, TP and chlorophyll-a concentrations, the nutrient-response dynamics in stormwater ponds differ significantly due to additional influencing variables.

Stormwater pond management practices affect nutrient concentrations differently than in natural lakes. Pond managers typically control algae and vegetation using chemical treatments, which can suppress chlorophyll-a responses to nutrient levels. This artificial suppression complicates the prediction of algal blooms based solely on TN and TP concentrations.

Vegetation within and surrounding ponds also influences nutrient dynamics. Vegetated littoral shelves, for example, can reduce water column nutrient concentrations by increasing water-vegetation contact and slowing runoff, which promotes particle settling. However, these areas can also contribute nutrients as plant tissue decomposes, particularly when they are managed with herbicides.

Landscape maintenance and land use near stormwater ponds also affect nutrient loading. Impervious surfaces and intensified land use contribute to increased runoff, erosion and nutrient input, degrading water quality. Shoreline erosion, which adds phosphorus and nitrogen to the pond, further elevates nutrient levels. Turfgrass management around ponds can exacerbate these issues, with runoff containing nutrient concentrations from fertilizers and organic debris such as grass clippings, which can contribute to eutrophication.

Given the complexities of nutrient dynamics in stormwater ponds, identifying management variables impacting TN, TP and chlorophyll-a concentrations is essential. Factors such as pond design, vegetation control and landscape practices must be considered for effective management and to ensure that ponds meet water quality standards.

This research highlights the need for pond-specific nutrient models for clear and colored water conditions. These models would provide more accurate predictions of algal blooms and better inform stormwater pond management strategies aimed at reducing nutrient levels and preventing eutrophication. Such an approach would enhance water quality in stormwater ponds and help balance aesthetic goals and ecological health.

Research methods

This study was conducted in a large residential community in the Tampa Bay watershed. Stormwater ponds are an important feature in the community as more than 300 ponds provide water storage, water treatment and aesthetic, recreational and economic value to community members.

A single community pond manager is tasked with making all stormwater pond management decisions, and a single pond management company is contracted to implement those decisions, including chemical and mechanical interventions applied to the stormwater ponds. Landscape management surrounding the stormwater ponds may be conducted by a community-contracted company, individual private contractor or homeowner.

We sampled 36 stormwater ponds in a large residential community in southwest Florida. For four months, we measured water column TP concentration, TN concentration, color and chlorophyll-a concentration at three predetermined locations within each pond monthly. A representative subsample of 12 ponds was then selected from the 36 original ponds and sampled for an additional eight months.

These data established nutrient-response relationships for TP and TN in clear and colored stormwater ponds. These relationships provide an estimate for quantifying the nutrient removal capacity within a stormwater pond. Still, low coefficients of determination for each model suggest that there are other factors influencing nutrient and response dynamics.

Concurrent with the previous study, additional variables were measured at each stormwater pond sample site during the 12 sampling events, including field assessments of landscaping and management decisions. The variables and methods used to quantify the variables are described in Table 1.

Visual evaluation of land use adjacent to stormwater ponds was conducted on-site, and spatial analysis of land use within a 100-meter buffer of the stormwater ponds was performed using ArcGIS Desktop and landscape disturbance gradients identified in the Landscape Development Intensity index.

These variables were evaluated for their significance as predictors of TP, TN and chlorophyll-a concentrations in clear and colored stormwater ponds using a stepwise regression fit model, mixed option, with the probability to enter and leave set at 0.15. Once the significant variables were identified, a fit least squares model procedure was conducted to identify variable coefficients and the model equation.

Results and discussion

The stepwise regression model identified significant predictors for TP, TN and chlorophyll-a concentrations in clear and colored stormwater ponds. This model was refined using least squares fitting, which provided variable coefficients, model equations and coefficients of determination for each model. Predictive models evaluating the impact of different management and landscaping variables for clear and colored stormwater ponds are presented in Table 2, with summary statistics for each significant variable in Table 3.

Two variables, littoral shelf coverage and grass clippings, emerged as significant drivers of TP, TN and chlorophyll-a concentrations in both clear and colored stormwater ponds. Littoral shelf coverage in the ponds ranged from 0 to 60 percent, with a median of 15 percent.

Littoral shelf coverage demonstrated different effects depending on pond type and nutrients. It negatively correlated with TP in colored ponds, positively correlated with TN in clear ponds, and negatively correlated with TN in colored ponds. Additionally, it was positively correlated with chlorophyll-a concentrations in both pond types.

Grass clippings, a byproduct of turf management, were significant predictors of nutrient levels. Grass clippings in 73 percent of the ponds also influenced nutrient dynamics. While they decompose and release nutrients into the water, the study found inverse correlations between grass clippings and TP and TN concentrations but a positive correlation with chlorophyll-a.

Littoral shelves play a crucial role in nutrient and chlorophyll dynamics. These areas, shallow enough to allow sunlight penetration, increase the interaction between the water column, soil and vegetation, promoting nutrient assimilation and particle settling from incoming runoff.

Emergent vegetation in littoral shelves and shoreline plantings also significantly influenced nutrient concentrations. In clear ponds, littoral shelves strongly predict TN, with increased plant coverage correlating to lower TN concentrations. Similarly, shoreline vegetation coverage was inversely correlated with chlorophyll-a concentrations, suggesting that shoreline plantings reduce algal growth.

Submerged aquatic vegetation was also important, inversely correlated with TP and chlorophyll-a concentrations in clear ponds. Previous research supports this, noting that submerged plants obtain phosphorus from sediments rather than the water column, potentially precipitating nutrients out of the water.

Residential fertilizer application was another significant predictor, influencing both TN and TP concentrations. As regulated by local ordinances, the impact of fertilizer restrictions during certain months was also assessed. These restrictions were inversely correlated with TP and TN concentrations in the ponds, suggesting that such regulations may effectively reduce nutrient loads from non-point sources.

Chlorophyll-a concentrations were significant predictors of nutrient levels in stormwater ponds. The concentration of chlorophyll-a was positively correlated with TP in both clear and colored ponds and with TN in clear ponds, reflecting the known relationships between these nutrients and algal growth in freshwater systems.

Filamentous algae coverage, although often seen as an aesthetic problem, was a significant predictor of TN in clear ponds, with a negative correlation to TN concentrations. This result suggests that filamentous algae may play a role in nutrient dynamics, which warrants further study.

Bank erosion around the ponds was another significant factor influencing nutrient concentrations. Contrary to expectations, erosion was inversely correlated with TP and TN concentrations. Soil erosion typically contributes nutrients to water bodies, especially during rain events that cause runoff. However, the results suggest that more subtle processes may be at play, where erosion impacts are not directly reflected in increased nutrient levels in the water column.

Nutrient limitation status also played a crucial role in predicting nutrient and chlorophyll-a concentrations. Ponds were categorized as nitrogen-limited, phosphorus-limited, or co-limited based on the nitrogen-to-phosphorus ratio. The limitation status significantly influenced nutrient dynamics, with nitrogen-limited ponds correlating with higher TP concentrations. Phosphorus-limited and co-limited ponds, on the other hand, showed inverse correlations with TP concentrations, indicating that the limiting nutrient concept is essential for understanding nutrient dynamics in stormwater ponds.

Land use surrounding the ponds also significantly predicted nutrient and chlorophyll-a concentrations, though not entirely in line with conventional understanding. More intensive land use and increased impervious surfaces are expected to increase nutrient runoff. However, the models revealed the opposite effect, except in areas with low-intensity transportation.

Natural areas and wetlands were positively correlated with TN and chlorophyll-a, while low-intensity commercial and high-density residential land use showed inverse correlations. These results suggest that land use around stormwater ponds affects nutrient dynamics in complex and context-dependent ways.

Chemical treatments, often used to manage algae in ponds, significantly predicted chlorophyll-a concentrations. The inverse correlation between chemical treatment and chlorophyll-a indicates that chemical interventions suppress algal growth, masking the true relationship between nutrients and chlorophyll-a. This suggests that stormwater ponds without chemical treatments may experience more significant nutrient responses, complicating efforts to develop standardized nutrient management thresholds.

Similarly, artificial aeration was a significant predictor of TN concentrations, with mixed effects depending on the pond. Aeration can increase the oxidation of organic sediments, affecting nutrient release and removal processes, but further research is needed to understand its full impact on nutrient concentrations.

Understanding the role of these variables in driving nutrient dynamics in stormwater ponds can improve predictions of TN, TP and chlorophyll-a concentrations, leading to better management practices. The findings emphasize the importance of site-specific management strategies tailored to individual stormwater ponds, particularly regarding littoral shelf design, vegetation management, grass clipping control, and land use practices. These strategies can mitigate nutrient loading and algal blooms, helping to maintain water quality and ecological balance in stormwater ponds.

Conclusion

This study evaluated the impact of management and landscaping decisions on stormwater pond nutrient and chlorophyll-a concentrations to identify significant predictor variables of TP, TN and chlorophyll-a water column concentrations within clear and colored stormwater ponds. Models were developed using the significant predictor variables to provide more accurate, site-specific nutrient and water column algae predictions to aid and improve stormwater pond function, use, and aesthetics management.

Reference

Adapted from: Nealis, Charles Patrick. 2015. Chapter 3, Identifying Significant Factors Influencing Stormwater Pond Nutrient And Algae Concentrations, In Managing Expectations: Creating a Community Based Stormwater Pond Nutrient Management Progam. Doctoral Dissertation, Graduate School of the University of Florida.

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