Integration of GIS and a Lagrangian Particle-Tracking Model for Harmful Algal Bloom Trajectories Prediction

Rufu Qin,Liangzhao Lin

doi:10.3390/w11010164

Abstract

Harmful algal bloom (HAB) is a major environmental problem in coastal waters around the world. The technologies and approaches for short-term forecasting of the HABs trajectories have obtained increasing attention from researchers. In this paper, we present a straightforward physical-based model based on a non-Fickian Lagrangian particle-tracking scheme for understanding the movement of detected HABs. The model adopts the fractional Brownian motion (fBm) technology, and is coupled with the Delft3D and WRF models and GIS. The fBm based Lagrangian particle-tracking model can flexibly control the scale of the particle clouds diffusion through Hurst value, which can be used to account for uncertainties and adjust for better representing the trajectories of HABs. Simulation results demonstrate that the presented model can successfully predict the trends and the main features of red tide drifting. The developed simulation tool enables users to create the model configuration, manage data inputs, run the model, and generate model maps and animations within a GIS environment. It is believed that the model and the tool outlined herein can be very useful for rapidly evaluating potential areas at risk from the HABs events.

Highlights

As is known, Harmful algal bloom (HAB) is a predominant oceanographic phenomena in coastal waters
The fractional Brownian motion (fBm) based Lagrangian particle-tracking model can flexibly control the scale of the particle clouds diffusion through Hurst value, which can be used to account for uncertainties and adjust for better representing the trajectories of HABs
The red tide was represented as 2000 particles, using the fBm based Lagrangian particle-tracking model (LPTM) to track the trajectories at 30 min intervals, starting from the known position at 10:00:00 a.m. on July 2th 2013, to the locations at 3:00:00 p.m. on

Summary

Introduction

Over the past few decades, known as red tide, blue-green algae or cyanobacteria, HABs have been increasing in frequency worldwide and have posed negative impacts on human health, aquatic ecosystems and the economy [1,2,3] These challenges motivate the development of many new technologies and approaches for bloom detection, monitor, forecasts, and assessment. In situ observations and measurements overcome some of the limitations of satellite system; in situ data are typically scarce and often lack spatial resolution, and significant cost and inefficiency [3,4,6] Most of all, both remote sensing and in situ observation present limitations in providing HAB trajectories’

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Conclusion