TY - JOUR

T1 - Spatial and temporal variability of chlorophyll-a and the modelling of high-productivity zones based on environmental parameters: a case study for the European Arctic Corridor

AU - Кузьмина, Софья Константиновна

AU - Лобанова, Полина Вячеславовна

AU - Чепикова, Светлана

PY - 2024/10

Y1 - 2024/10

N2 - Over the past 20 years, increasing temperature and receding ice-cover have led to 12 changes in the Arctic ecosystem. Our study aims to create models that predict the position of 13 high chlorophyll-a concentration (Chl-a) zones in the European Arctic Corridor (the Barents, 14 Norwegian and Greenland Seas) to monitor these changes. Firstly, we use remotely sensed 15 data to assess spatial and temporal changes in correlation between Chl-a and environmental 16 parameters that could influence Chl-a in the region –Photosynthetically Active Radiation 17 (PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity 18 (SSS) – over the 2010-2019 time period. We found significant correlation (|r| = 0.6-0.8) 19 between Chl-a and PAR and SST, and medium correlation (|r| = 0.4-0.6) between Chl-a and 20 SSS and MLD, correlation was highest during spring periods. Then, using a Random Forest 21 Machine Learning algorithm in the Classifier modification, we created models for each sea to 22 predict the position of high-productivity zones (Chl-a > 1 mg m-3) using environmental 23 parameters. Our results suggested that Chl-a variability in the European Arctic Corridor is 24 mostly determined by PAR (28-32% of Chl-a class variability), SST (25-29%), and SSS (26-25 31%); MLD played a lesser role (12-17%). According to validation, all the models showed 26 high performance scores (f1-score = 66-95%) and slightly underestimated the total area of 27 high productivity.

AB - Over the past 20 years, increasing temperature and receding ice-cover have led to 12 changes in the Arctic ecosystem. Our study aims to create models that predict the position of 13 high chlorophyll-a concentration (Chl-a) zones in the European Arctic Corridor (the Barents, 14 Norwegian and Greenland Seas) to monitor these changes. Firstly, we use remotely sensed 15 data to assess spatial and temporal changes in correlation between Chl-a and environmental 16 parameters that could influence Chl-a in the region –Photosynthetically Active Radiation 17 (PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity 18 (SSS) – over the 2010-2019 time period. We found significant correlation (|r| = 0.6-0.8) 19 between Chl-a and PAR and SST, and medium correlation (|r| = 0.4-0.6) between Chl-a and 20 SSS and MLD, correlation was highest during spring periods. Then, using a Random Forest 21 Machine Learning algorithm in the Classifier modification, we created models for each sea to 22 predict the position of high-productivity zones (Chl-a > 1 mg m-3) using environmental 23 parameters. Our results suggested that Chl-a variability in the European Arctic Corridor is 24 mostly determined by PAR (28-32% of Chl-a class variability), SST (25-29%), and SSS (26-25 31%); MLD played a lesser role (12-17%). According to validation, all the models showed 26 high performance scores (f1-score = 66-95%) and slightly underestimated the total area of 27 high productivity.

M3 - Article

JO - Russian Journal of Earth Sciences

JF - Russian Journal of Earth Sciences

SN - 1681-1178

ER -