Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River

Abstract

Process-based modeling for predicting harmful cyanobacteria is affected by a variety of factors, including the initial conditions, boundary conditions (tributary inflows and atmosphere), and mechanisms related to cyanobacteria growth and death. While the initial conditions do not significantly affect long-term predictions, the initial cyanobacterial distribution in water is particularly important for short-term predictions. Point-based observation data have typically been used for cyanobacteria prediction of initial conditions. These initial conditions are determined through the linear interpolation of point-based observation data and may differ from the actual cyanobacteria distribution. This study presents an optimal method of applying hyperspectral images to establish the Environmental Fluid Dynamics Code-National Institute of Environment Research (EFDC-NIER) model initial conditions. Utilizing hyperspectral images to determine the EFDC-NIER model initial conditions involves four steps that are performed sequentially and automated in MATLAB. The EFDC-NIER model is established using three grid resolution cases for the Changnyeong-Haman weir section of the Nakdong River Basin, where Microcystis dominates during the summer (July to September). The effects of grid resolution on (1) water quality modeling and (2) initial conditions determined using cumulative distribution functions are evaluated. Additionally, the differences in Microcystis values are compared when applying initial conditions using hyperspectral images and point-based evaluation data. Hyperspectral images allow detailed initial conditions to be applied in the EFDC-NIER model based on the plane-unit cyanobacterial information observed in grids, which can reduce uncertainties in water quality (cyanobacteria) modeling.

Oscillation Flow Dam Operation Method for Algal Bloom Mitigation

“…It is equipped with novel functions, such as the weir function for major domestic rivers, consideration of multiple algal species, and functions for simulating changes in bottom-layer nutrient flux; these functions also consider the vertical migration mechanism of cyanobacteria, cyst generation and germination mechanism, wind stress, and oxidation/reduction conditions (Figure 2). The EFDC-NIER model also allows for analysis of the flow rate and water level in response to artificial hydraulic structures, such as the multifunctional weirs and dams developed as part of the Four Major Rivers Restoration Project [39].…”

“…It is equipped with novel functions, such as the weir function for major domestic rivers, consideration of multiple algal species, and functions for simulating changes in bottomlayer nutrient flux; these functions also consider the vertical migration mechanism of cyanobacteria, cyst generation and germination mechanism, wind stress, and oxidation/reduction conditions (Figure 2). The EFDC-NIER model also allows for analysis of the flow rate and water level in response to artificial hydraulic structures, such as the multifunctional weirs and dams developed as part of the Four Major Rivers Restoration Project [39]. To perform water quality modeling using EFDC-NIER, water quality observation data for total nitrogen (TN) (mg/L), nitrate nitrogen (NO₃−N) (mg/L), ammonia nitrogen (NH₃−N) (mg/L), total phosphorus (TP) (mg/L), water temperature (°C), dissolved total nitrogen (DTN) (mg/L), dissolved total phosphorus (DTP) (mg/L), phosphate (PO₄−P) (mg/L), chlorophyll-a (mg/m 3 ), biochemical oxygen demand (BOD) (mg/L), chemical oxygen demand (COD) (mg/L), dissolved oxygen (DO) (mg/L), and total organic carbon (TOC) (mg/L) were required.…”

Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes

“…The water samples were analyzed for cyanobacteria cell concentration, chlorophyll-a, phycocyanin, and phycoerythrin to calibrate the relationship and model between the in situ sample data and the UAS imagery. These water quality parameters were selected based on the current NH State cyanobacteria monitoring procedures (cell concentration) of the New Hampshire Department of Environmental Protection (NHDES), and standard monitoring practices through pigment analyses for cyanobacteria (chlorophyll-a, phycocyanin, and phycoerythrin) [10,11,13,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. The specific objectives of this study were: 1.…”

Parallelization of a 3-Dimensional Hydrodynamics Model Using a Hybrid Method with MPI and OpenMP

“…Ahn et al [26,27] established a method for short-term prediction of algae by using an improved source code with an operating function of hydraulic river structures and of mechanisms for vertical migration of cyanobacteria and akinete creation and germination. They also proposed an optimal method for predicting algae by applying hyperspectral remote-sensing data to the EFDC-NIER model.…”