Inclusion of Earth Observations and ferrybox data improves status assessments in the Gulf of Finland

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1 Inclusion of Earth Observations and ferrybox data improves status assessments in the Gulf of Finland Kauppila, P., Attila, J., Keto, V., Fleming-Lehtinen, V., Kaitala, S., Kaartokallio, H., Kallio, K., *Eremina, T., *Kochetkova, E., Anttila, S. & Junttila, S. Finnish Environment Institute (SYKE) Russian State Hydromorphological University (RSHU) Scientific symposium 2015 In Malmö, May 6-7, 2015 Foto: Seppo Knuttila

2 Background Reliable information is required for status assessments in coastal marine waters (e.g. HELCOM, WFD, MSFD) Concern of the sufficiency of traditional monitoring programmes to meet the targets Joint use of Earth observations (EO) and data with the traditional water quality monitoring: Usability and comparability of the results? We focus here on chlorophyll a in the Gulf of Finland (GOF) SYKE: Marine Research Centre (Alg@line, mooring buoys, coastal and marine monitoring stations) Data and Information Centre: operational earth observation (EO) processing chain (a part of GMES MarCoast service) RSHU: EO and in situ observations in the eastern GOF 2

3 Monitoring of Chl in SYKE Monitoring stations (MS): coastal and open sea (ICES): Chl samples (0-10 m) automated measurement system onboard merchant ships Flow-through fluorometer measurements and water samples (5 m) Mooring buoys ( ): (Sampling from 2 m depth) EO data (MERIS , MODIS 2012->, Chl on upper surface layer, SYKE processing Mooring buoys Monitoring stations Alg@line ferrybox Muikku Silja Line chain, bio-optical model) Aranda 3

4 EO data of the Baltic Sea match up to the ICES monitoring stations data: Example of the summers ICES EO ICES = The International Council for the Exploration of the Sea 4

5 Comparison of Chl data originating from different data sources in Helsinki region Studied methods differ from each other in relation to observation date / moment, sampling depth and spatial extent of data. Monitoring stations: City of Helsinki However, there is a good overall correspondence between Chl results originating from different data sources. 5

6 Histogram of MERIS data for the Helsinki water body (2011) Chl-a mean median mode N EO MS WFD period 90% percentile EO median EO mean Monitoring st Excellent/Good Good/Moderate Moderate/Poor Poor/Bad Bad lower Histogram: EO observations from all weeks of WFD classification period N Chl-a, [µg/l] 60 a-klorofylli, [µg/l]

7 Annual distributions of EO observations within a water body in the outer archipelago of the eastern GOF 7

8 RSHU EO and in situ data During in-situ and remote sensing observations were made along with the measurements of the spectral upwelling radiance and the spectral downwelling irradiance. The bio-optical algorithm was designed for the L2 MODIS- Aqua data. S. Vazyulya, A. Khrapko, O. Kopelevich,V. Burenkov, T. Eremina, A. Isaev, Regional algorithms for the esbmabon of chlorophyll and suspended mader concentrabon in the Gulf of Finland from MODIS- Aqua satellite data, OCEANOLOGIA, 56 (4), cc. Chl a (ug/l) EO Eastern GOF In situ stations Mean Max

9 In situ ( ), Alg@line ( ) and EO ( ) in SEA-13, HELCOM assessment area in 2008 and 2011 Chl value In situ Mean 2.93 In situ Median 2.91 EO Arithmetic mean 4.44 EO Geometric mean 3.81 EO Median 3.86 EO Mode

10 Distribution of Chl in the GOF in the mid summers of 2008 and Chl values usually elevated near the coast affected by river waters - Interannual variation great

11 Inclusion of EO and data improves status assessments required by HELCOM and EU directives (WFD, MSFD) EO methods, and mooring buoys complement coastal and marine monitoring results especially in offshore waters and during springtime when monitoring with traditional methods is difficult and expensive. EO and Algaline data improve reliability of status assessments by increasing spatial and temporal coverage of monitoring results Comparability: Distributions of data from different sources correspond well with each other when using the same number of samples. The differences in observation time and depth, the number and spatial extent of observations account for most of the discrepancy between the data obtained by different methods. EO methods enable development of indicators for the use of the EU directives (e.g. CSA-index) 11