Water Quality Assessment Research Division, Water Environment Research Department, National Institute of Environmental Research, Incheon 22689, Korea.
Water Environment Research Department, National Institute of Environmental Research, Incheon 22689, Korea.
Sensors (Basel). 2021 Jan 13;21(2):530. doi: 10.3390/s21020530.
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 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 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.
基于过程的模型预测有害蓝藻受到多种因素的影响，包括初始条件、边界条件（支流流入和大气）以及与蓝藻生长和死亡相关的机制。虽然初始条件不会显著影响长期预测，但水体内初始蓝藻的分布对于短期预测尤为重要。基于点的观测数据通常用于预测初始条件的蓝藻。这些初始条件是通过基于点的观测数据的线性插值确定的，可能与实际蓝藻分布不同。本研究提出了一种将高光谱图像应用于建立环境流体动力学代码-国家环境研究所（EFDC-NIER）模型初始条件的最佳方法。利用高光谱图像确定 EFDC-NIER 模型初始条件涉及四个步骤，这些步骤在 MATLAB 中依次自动执行。针对南洞河流域昌宁-哈曼堰段，建立了 EFDC-NIER 模型，在夏季（7 月至 9 月）期间， 占主导地位。评估了网格分辨率对（1）水质建模和（2）基于累积分布函数确定的初始条件的影响。此外，还比较了使用高光谱图像和基于点的评估数据应用初始条件时 的差异。高光谱图像允许根据网格中观察到的平面单位蓝藻信息在 EFDC-NIER 模型中应用详细的初始条件，从而减少水质（蓝藻）建模的不确定性。
