Spatial and Temporal Variability of Chlorophyll-a and the Modeling of High-Productivity Zones Based on Environmental Parameters: a Case Study for the European Arctic Corridor

Sofia Kuzmina; Polina Lobanova; Svetlana S. Chepikova

doi:doi:10.2205/2025ES000943

VAK Russia 1.6 UDC 573.22 UDC 55 UDC 550.34 UDC 550.383 CSCSTI 37.01 CSCSTI 37.15 CSCSTI 37.25 CSCSTI 37.31 CSCSTI 38.01 CSCSTI 36.00 CSCSTI 37.00 CSCSTI 38.00 CSCSTI 39.00 CSCSTI 52.00 Russian Classification of Professions by Education 05.00.00 Russian Library and Bibliographic Classification 26 Russian Trade and Bibliographic Classification 63 BISAC SCI

Spatial and Temporal Variability of Chlorophyll-a and the Modeling of High-Productivity Zones Based on Environmental Parameters: a Case Study for the European Arctic Corridor

Published в Russian Journal of Earth Sciences · Volume 25, Issue 1, 2025 · Pages 1–18 · Rubrics: ORIGINAL ARTICLES

DOI 10.2205/2025ES000943 EDN sdycmn

Received: 05.03.2024 Accepted: 15.10.2024 Published: 10.03.2025 Language of publication: ENG

Authors

Sofia Kuzmina ¹ iD · Polina Lobanova ² iD · Svetlana S. Chepikova ³ iD

1 St. Petersburg State University (Department of oceanology)
from 01.01.2022 until now , Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
Russian Federation

2 St. Petersburg State University
Russian Federation

3 State Hydrological Institute
Russian Federation

ABSTRACT

Over the past 20 years, increasing temperature and receding ice-cover have led to changes in the Arctic ecosystem. Our study aims to create models that predict the position of high chlorophyll-a concentration (Chl-a) zones in the European Arctic Corridor (the Barents, Norwegian and Greenland Seas) to monitor these changes. Firstly, we use remotely sensed data to assess spatial and temporal changes in correlation between Chl-a and environmental parameters that could influence Chl-a in the region – Photosynthetically Active Radiation (PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS) – over the 2010–2019 time period. We found significant correlation (∣r∣ = 0.6–0.8) between Chl-a and PAR and SST, and medium correlation (∣r∣ = 0.4–0.6) between Chl-a and SSS and MLD, correlation was highest during spring periods. Then, using a Random Forest Machine Learning algorithm in the Classifier modification, we created models for each sea to predict the position of high-productivity zones (Chl-a > 1 mg m−3) using environmental parameters. Our results suggested that Chl-a variability in the European Arctic Corridor is mostly determined by PAR (28–32% of Chl-a class variability), SST (25–29%), and SSS (26–31%); MLD played a lesser role (12–17%). According to validation, all the models showed high performance scores (F1-score = 66–95%) and slightly underestimated the total area of high productivity.

KEYWORDS

chlorophyll-a ocean productivity Arctic Ocean modeling ocean colour remote sensing Barents Sea Norwegian Sea Greenland Sea

Information Text

Authors:

1. St. Petersburg State University (Department of oceanology)
from 01.01.2022 until now
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung

Russian Federation

2. St. Petersburg State University

Russian Federation

3. State Hydrological Institute

Russian Federation

Type:

Article

Pages:

from 1 to 18

Status:

Published

Subject area:

VAK Russia 1.6
UDC 573.22
UDC 55
UDC 550.34
UDC 550.383
CSCSTI 37.01
CSCSTI 37.15
CSCSTI 37.25
CSCSTI 37.31
CSCSTI 38.01
CSCSTI 36.00
CSCSTI 37.00
CSCSTI 38.00
CSCSTI 39.00
CSCSTI 52.00
Russian Classification of Professions by Education 05.00.00
Russian Library and Bibliographic Classification 26
Russian Trade and Bibliographic Classification 63
BISAC SCI SCIENCE

Language:

English

Keywords:

chlorophyll-a, ocean productivity, Arctic Ocean, modeling, ocean colour, remote sensing, Barents Sea, Norwegian Sea, Greenland Sea

Funding

The authors acknowledge Saint Petersburg State University for a research project no. 116442164.

Text

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Spatial and Temporal Variability of Chlorophyll-a and the Modeling of High-Productivity Zones Based on Environmental Parameters: a Case Study for the European Arctic Corridor

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