Iowa HALGIS - Harmful Algae Information System

Harmful Algae

Impacts

Public Health

HABs or microcystin are dangerous for people who have physical interactions.

Recreational Activities

Fishing, swimming, and other activities have been affected.

Wildlife

Fishes, birds, and other wild species have been affacted by algal blooms.

Causes

Cyanobacterial Harmful Algal Blooms (CyanoHABs)

Nutrients

Human activities, such as agriculture, urban development, and sewage treatment, can introduce excess nutrients into bodies of water, leading to increased potential for HABs. When these nutrients (i.e., nitrogen and phosphorus) enter the water, they can trigger a process known as eutrophication, where the water becomes enriched with nutrients and promotes the growth of algae. If the algae that grow in response to these excess nutrients are harmful, they can form dense blooms that deplete oxygen levels in the water and produce toxins that are harmful to humans, wildlife, and the ecosystem.
Climate Change

Increased water temperatures: Warmer water temperatures promote the growth and reproduction of algae, which can lead to larger and more frequent HABs. Changes in precipitation patterns: Changes in rainfall patterns can impact nutrient runoff, which can trigger the development of HABs. Changes in water circulation patterns: Climate change can impact water circulation patterns (e.g., flood and drought), which can result in areas of still water that are more susceptible to HABs. Rising sea levels: Rising sea levels can cause saltwater to move further inland, impacting freshwater sources and potentially promoting the growth of harmful saltwater algae in freshwater environments..
Atmospheric Factors

HABs are affected by lake temperature, rainfall distribution, wind speed, and direction. Lakes with higher temperatures are more prone to HABs because algae thrive in warm water. Low rainfall can lower the lake's water level, concentrating it and raising the HAB risk. Strong winds can disrupt thermal stratification in the lake, lowering HABs. However, continuous winds can cause upwelling, which brings cold, nutrient-rich water to the top, boosting algae growth.

About HALGIS

Eutrophication, caused by an influx of nutrients such as fertilizers or pollutants, results in reduced water clarity, unpleasant odors and tastes, a rise of harmful algal blooms (HABs), the loss of aquatic organism populations, increased nutrient concentrations in primary producers, acidification, deoxygenation, and shifts in the aquatic food web. HABs have several negative repercussions on the environment and the economy, while toxins produced by HABs can threaten public health.

In this study, the algal toxin microcystin was modeled using the sparse identification nonlinear dynamics (SINDy) technique. Evaporation was used as a meteorological parameter and dissolved oxygen was used as a water quality indicator. SINDy is an innovative and cutting-edge method for reconstructing the analytical representation of a dynamical system by combining machine learning techniques with sparse regression. In addition, an interactive web platform controlled by the model was developed to promote environmental education, increase public knowledge of HAB-related concerns, and generate better solutions to HAB problems by using what-if scenarios. This online platform makes it possible to monitor the condition of HABs in lakes and see how certain factors affect the growth of harmful algae. On an interactive and user-friendly site, users may effortlessly share photographs of HABs in lakes, enabling others to monitor the lakes' status.

Reference: Baydaroğlu, Ö., Yeşilköy, S., Linderman, M., Demir, I. (2024) Modeling of Harmful Algal Bloom Dynamics and the Model-Based Interactive Framework for Inland Waters

Components and Features

Data Integration

There are data on atmospheric factors and water quality from the sources of the lakes that have HAB problems in Iowa. The water quality data source is AquIA of the Iowa DNR; ECMWF ERA5-reanalysis data on meteorological factors are used.

Analysis

The Sparse Identification of Nonlinear Dynamics (SINDy) method, which is a novel and pioneering approach that combines sparse regression and machine learning methods is employed to reconstruct the analytical representation of a harmful algae formation's dynamical system.

SINDy method reference: Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016). Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the national academy of sciences, 113(15), 3932-3937.