Prediction of UIva prolifera drift and transportation based on remote sensing data and numerical models: A case study in the offshore area of Jiangsu Province

doi:10.3969/j.issn.1001-909X.2024.01.007

Abstract

Abstract:

UIva prolifera outbreak is one of the most serious marine disasters affecting the global offshore waters, which has attracted the attention of all sectors of society. According to the location of UIva prolifera extracted from satellite data, the Lagrangian drift model was used to track and predict the drift path of UIva prolifera based on the sea-air-wave set model. Compared with satellite observation, this prediction model can better predict the position, distribution and drift transport of UIva prolifera. In addition, this study also discussed the impact of Stokes Drift on the model prediction. The results show that adding Stokes Drift can correct the drift path of UIva prolifera and effectively improve the accuracy of prediction.

Key words: UIva prolifera, model forecast, Stokes Drift, Jiangsu offshore

CLC Number:

LIN Lianjie, DONG Changming, JI Yuxiang, LIM KAM SIAN Kenny Thiam Choy, LI Zhaoxin, JIANG Xingliang, CAO Yuhan, GAO Hui, WANG Shengqiang, CAO Qian. Prediction of UIva prolifera drift and transportation based on remote sensing data and numerical models: A case study in the offshore area of Jiangsu Province[J]. Journal of Marine Sciences, 2024, 42(1): 69-82.

Figures/Tables 22

Fig.1 Schematic diagram of the forecast model process of UIva prolifera in Jiangsu offshore

Fig.2 Diagram of WRF model range setting (The black box area is the calculation range of WRF model, and the red box area is the UIva prolifera forecast range.)

Fig.3 Topographic map of ROMS model

Fig.4 Flow chart of GOCI data preprocessing

Fig.5 Flow chart of algae location information data processing

Fig.6 Comparison between ASCAT observation (a) and WRF model simulation (b) in monthly average sea surface wind speed data at 10 meters in the study area in September 2019

Fig.7 Spatial distribution of relative errors in monthly average sea surface wind speed data at 10 meters observed by ASCAT and simulated by WRF model in the study area in September 2019

Fig.8 Comparison the of monthly average significant wave heights simulated by ERA5 (a) and SWAN model (b) in the study area in September 2019

Fig.9 Spatial distribution of relative errors in monthly average significant wave height simulated by ERA5 and SWAN modes in the study area in September 2019

Fig.10 Comparison of sea surface temperature between satellite observation provided by RSS (a) and simulated by ROMS model (b) in the study area in June 2019

Fig.11 Satellite remote sensing observation images on May 27th-29th, 2017

Fig.12 Comparison of the coverage area of UIva prolifera predicted by the model and satellite observations on May 27th and 28th, 2017 (The dashed box represents the appearance block of UIva prolifera.)

Tab.1 48-hour model prediction and satellite observation of the coverage area and center point position of UIva prolifera (average values of May 27th and 28th, 2017)

区域	浒苔覆盖面积			浒苔中心点位置
区域	卫星观测/km²	模式预报/km²	误差/%	卫星观测	模式预报	误差/km
连云港近岸	38.89	31.56	23.20	34.88 °N,119.25 °E	34.89 °N,119.24 °E	0.01
黄海北部离岸	975.17	1 170.60	16.70	35.01 °N,121.41 °E	34.87 °N,121.23 °E	0.28
长江口	133.82	166.23	19.50	31.48 °N,121.93 °E	31.64 °N,121.86 °E	0.28
江苏近海	1 147.88	1 368.39	16.11

Tab.2 48-hour model prediction and satellite observation of the drift direction of UIva prolifera (May 27th and 28th, 2017)

区域	5月27日浒苔漂移方向/(°)			5月28日浒苔漂移方向/(°)
区域	卫星观测	模式预报	误差	卫星观测	模式预报	误差
连云港近岸	275.49	214.42	61.07	88.85	52.59	36.26
黄海北部离岸	39.88	48.84	-8.96	33.60	137.89	-104.29
长江口	294.16	334.53	-40.37	95.58	86.68	8.90

Fig.13 Satellite remote sensing observation images on June 16th-18th, 2017

Fig.14 Comparison of the coverage area of UIva prolifera predicted by the model and satellite observations on June 16th and 17th, 2017

Tab.3 48-hour model prediction and satellite observation of the coverage area and center point position of UIva prolifera (average values of June 16th and 17th, 2017)

区域	浒苔覆盖面积			浒苔中心点位置
区域	卫星观测/km²	模式预报/km²	误差/%	卫星观测	模式预报	误差/km
黄海北部	1 270.30	1 212.00	4.81	35.61°N,120.85°E	34.88°N,121.17°E	1.20
黄海南部	440.61	155.71	182.97	32.89°N,122.84°E	31.62°N,121.85°E	2.04
江苏近海	1 710.91	1 367.71	25.09

Tab.4 48-hour model prediction and satellite observation of the drift direction of UIva prolifera (June 16th and 17th, 2017)

区域	6月16日浒苔漂移方向/(°)			6月17日浒苔漂移方向/(°)
区域	卫星观测	模式预报	误差	卫星观测	模式预报	误差
黄海北部	58.61	66.13	-7.52	47.53	126.43	-78.90
黄海南部	229.13	224.27	4.86	48.73	268.99	-220.26

Fig.15 Comparison between the distribution of UIva prolifera predicted by the 24-hour forecast considering and without considering the Stokes Drift model and the satellite observation results (May 27th, 2017)

Tab.5 Comparison of 24-hour UIva prolifera coverage area predicted by considering and without considering the Stokes Drift model with satellite observation results (May 27th, 2017)

区域	浒苔覆盖面积/km²			模式预报浒苔覆盖面积与卫星观测结果的误差/%
区域	卫星观测	考虑Stokes漂流模式	不考虑Stokes漂流模式	考虑Stokes漂流模式	不考虑Stokes漂流模式
连云港近岸	34.34	32.26	26.65	0.06	28.84
黄海北部离岸	1 029.70	1 200.77	1 474.32	14.25	30.16
长江口	143.42	175.35	200.60	18.21	28.50
江苏近海	1 207.46	1 408.38	1 701.57	14.27	29.04

Tab.6 Comparison of the 24-hour center position of UIva prolifera predicted by considering and without considering the Stokes Drift model with satellite observation results (May 27th, 2017)

区域	浒苔中心点位置			模式预报浒苔中心点位置与卫星观测结果的误差/km
区域	卫星观测	考虑Stokes漂流模式	不考虑Stokes漂流模式	考虑Stokes漂流模式	不考虑Stokes漂流模式
连云港近岸	34.87°N,119.25°E	34.87°N,119.24°E	34.88°N,119.23°E	0.01	0.02
黄海北部离岸	34.96°N,121.34°E	34.86°N,121.23°E	34.86°N,121.24°E	0.19	0.19
长江口	31.45°N,121.93°E	31.64°N,121.76°E	31.63°N,121.86°E	0.38	0.32

Tab.7 Comparison of 24-hour drift directions of UIva prolifera predicted by considering and without considering the Stokes Drift model with satellite observation results (May 27th, 2017)

区域	浒苔漂移方向/(°)			模式预报浒苔漂移方向与卫星观测结果的误差/(°)
区域	卫星观测	考虑Stokes漂流模式	不考虑Stokes漂流模式	考虑Stokes漂流模式	不考虑Stokes漂流模式
连云港近岸	275.49	279.85	214.42	-4.36	61.07
黄海北部离岸	39.88	40.44	48.84	-0.57	-8.96
长江口	294.16	156.28	334.53	137.88	-40.37

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