Federico Filipponi, Emiliana Valentini, Andrea Taramelli Institute for Environmental Protection and Research - ISPRA (Roma, Italy)

federico.filipponi@isprambiente.it

Sea Surface Temperature changes analysis an Essential Climate Variable

for Ecosystem Services provisioning

MultiTemp 2017 Conference – 27-29 June 2017 Bruges (Belgium)

Table of contents • Essential Variables (ECVs) and Ecosystem Services (ESS)

• Methodologies for the analysis of ECVs time series

• Comparison of results

• Scenarios in Ecosystem Services provisioning

• Discussion: Strengths and weaknesses of the different methodologies for spatio-temporal analysis of ECVs multitemporal series; gappy datasets

• Conclusions

Background • Extended time series of Earth Observation products are increasingly

providing consistent information to support applications in a wide variety of domains and disciplines

• There is a need for data analytics to extract information from large time series of EO products

• Datasets are increasingly used for the generation of downstreaming services, like the evaluation of Ecosystem Services (ESS)

• Among provided services need of scenarios under climate change

Essential Variables (ECVs) and Ecosystem Services (ESS)

Sea Surface Temperature (SST) has been widely recognized as an Essential Variable, namely a measurement required for the study, the monitoring and the management of marine environment and climate change.

It is listed among: • Essential Climate Variables (ECVs) • Essential Ocean Variables (EOVs) • measurements for deriving disturbance regime Essential Biodiversity Variable (EBV)

Study area Study area: Mediterranean Sea

Essential Climate Variable: Sea Surface Temperature (SST) from CMEMS Temporal extent: daily gap-free observations 1982-2016

Contains modified data from Copernicus CMEMS [2017, ISPRA]

Objectives • Evaluate indicators of ecosystem changes in marine

environment and the temporal evolution of Ecosystem Services from SST ECV time series by:

• Analyzing temporal signal and detect decadal trends • Identifying spatial and temporal patterns of variability using

different analytical methodologies • Revealing different scenarios of changes for the Ecosystem

Services food provision like the fish grow rates across Mediterranean regions

Workflow

Ecosystem Services food provision

Scenarios under climate change

Time series of SST

Decadal trends

Spatial variability

Temporal variability

Sesonal amplitude

Fish growth model

Ecosystem Services valuation

Empirical Orthogonal Function

Deseasonalized SST time series

Self Organized Maps

Seasonal Trend Decomposition

Multitemporal analysis

Results

Methodologies for the analysis of ECV time series

• Seasonal Trend Decomposition using Loess (Cleveland et al., 1990) • divide up a time series into three components, namely the trend, seasonality and remainder

• Empirical Orthogonal Function (EOF) spatio-temporal analysis (Björnsson and Venegas, 1997) • Rank spatial patterns of variability, their time variation and the importance of

each pattern on the basis of variance.

• Self Organized Maps (SOM) spatio-temporal analysis (Kohonen, 2001) • Data dimensionality reduction based on artificial neural network (ANN) that is trained

using unsupervised learning to produce a low-dimensional, discretised representation

of the input space of the training samples, called a map.

pixel temporal signal

seasonal amplitude

trend signal

trend slope

Seasonal Trend Decomposition

SST trends

Full SST range 5

th percentile 95

th percentile

SST seasonal amplitude Spatial representation of seasonal and trend signals

Higher intra-annual variability

Lower intra-annual variability Higher increasing SST trend in the eastern part of the basin

Seasonal Trend Decomposition

Empirical Orthogonal Function (EOF) analysis Western and

Eastern part of the basin have different

characteristics

Extremely cold winter

• Spatial patterns are strongly related to atmospheric circulation

• Cannot see a clear trend from EOF Expansion Coefficients

• Need result interpretation by expert user knowledge

Self Organized Maps (SOM) analysis

Self Organized Maps (SOM) analysis

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100

150

200

250

300

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SOM 1 SOM 2 SOM 3 SOM 4

• SOM 3 is largely representative of conditions in the 80s and early 90s.

• SOM 1 and SOM 2 show an increasing trend in the frequency of occurrence during the entire considered time period

BMU frequency of occurrence (%)

SOM 1 21.51 SOM 2 34.02 SOM 3 30.88 SOM 4 13.59

• Valentini E., Filipponi F., Nguyen Xuan A., Passarelli F.M., Taramelli A., 2016. Earth Observation for maritime spatial planning: measuring, observing and modeling marine environment to assess potential aquaculture sites. Sustainability 8(6), 519. DOI:10.3390/su8060519.

• Valentini E., Filipponi F., Nguyen Xuan A., Taramelli A., 2016. Marine food provision ecosystem services assessment using EO products. Proceedings of "ESA Living Planet Symposium 2016", Prague (Czech Republic) 9-13 May 2016, ESA SP-740 (CD-ROM). ISBN : 978-92-9221-305-3.

Sea bass: estimated annual growth

Sea bream: estimated annual growth

SST time series

Fish growth model +

SST time series as indicator for Ecosystem Services: food provision

Ecosystem Services: scenarios under climate change

1984-1987

2011-2014

2038-2041

1984-1987

2011-2014

2038-2041

Percentage of days exceeding fish vitality range (high temperatures)

Estimated fish annual growth (g) Sea bream

STRENGHTS: • Capable of extracting the seasonal, trend and residual component • Valuable for the extraction of the seasonal signal in time series • Allow the extraction of temporal trends • Results are not scaled, in the original range of the original values

WEAKNESSES: • Need gap-free multitemporal time series • Work on single pixel temporal profile, not accounting for spatial

variability • User need to define the time windows for the extraction of seasonal

and trend signal

Discussion: Seasonal Trend Decomposition

STRENGHTS: • Capable of extracting the most common patterns • Valuable for reducing dimensionality for big datasets • Allow the interpretation of both spatial and temporal patterns WEAKNESSES: • Need gap-free multitemporal time series • Compute-intensive (lot of memory) • Lack in finding the least occurring patterns or non-linear patterns • Results are scaled, difficult to link to the original values • Generally does not have the ability to split the contribution of different forcings into the different

modes (modes are mixed contributions) • Need result interpretation by expert user because they not always represent real and clear patterns • Expert knowledge is needed to identify relations with environmental forcings

Discussion: Empirical Orthogonal Function (EOF) analysis

STRENGHTS: • Capable of extracting the most common patterns • Valuable for reducing dimensionality for big datasets • Capable of working with not-linear patterns • Results are not scaled, in the original range of the original values • Ability to link single observations to the principal modes (maps) in time

dimension WEAKNESSES: • Need gap-free multitemporal time series • Compute-intensive • User need to initialize the training • Expert knowledge is needed to identify relations with environmental

forcings • Resulting temporal information is discrete

Discussion: Self Organized Maps (SOM) analysis

Discussion: gappy datasets

To deal with gappy RS multitemporal series, expecially for optical multispectral products, the following solutions could be adopted:

• Move to methodologies for the analysis of gappy datasets • Data interpolation of the time series (i.e. DINEOF) • Basic statistics on RS data on the temporal dimension (i.e. monthly

averages) • Adapting the existing methodologies to work with gappy datasets

Conclusions • Mediterranean sea basin showed a SST increase of 1.4 degrees on average

in the period 1982-2016 • Eastern part of the basin show a higher increasing SST trend • Estimated fish annual growth increase at higher SST • Area with temperature exceeding fish vitality range increases • Higher SST can trigger fish deseses and lowering dissolved oxygen in

seawater, causing anoxia events and consequently fish blights • SST increase favors the spreading of alien species (e.g. Lion fish) that are

competing with the endemic species, reducing food availability and thus raising interspecific competition

Conclusions • Extended time series of Earth Observation products provides

consistent information to support the valuation of Ecosystem indicators and Ecosystem Services (ESS)

• data analytics are capable to extract information from large EO product datasets, interpretation of results often requires expert user knowledge

• Use of extended time series of EO product allow the generation of downstreaming services and the scenarios of changes under climate change

Under development Nearly coming: ‘rsta’ package for R programming language: a collection of analytics to perform spatio-temporal

analysis from raster time series.

Act as a front-end to already available functions in various R packages, specifically designed to handle geographic datasets provided as raster time series

Key features: • Analysis: Empirical Orthogonal Function, Empirical Orthogonal Teleconnections, Self

Organized Maps, Seasonal Trend Decomposition using Loess, Breaks For Additive Season and Trend, X-13-ARIMA seasonal adjustment

• Gap-filling: DINEOF, spatio-temporal gapfill, linear and spline interpolation • Code: use of raster masks, the parallel processing support, free and open-source

• Soon available on GitHub: github.com/ffilipponi/rtsa

• Will be presented at ESA EO Open Science 2017 Conference (September 2017)

Acknowledgements The scientific and technical knowledge contained derives from the outcomes of the EC founded projects: • MERMAID - "Innovative Multi-purposE off-shoRe platforMs: plAnnIng, Design and operation",

FP7 OCEAN, Grant Agreement No. 288710 • ECOPOTENTIAL - "Improving future ecosystem benefits through earth observations”, SC

H2020, Grant Agreement No. 641762 • The dissemination and the uptake of the products are funded under the Copernicus Training

and Information Sessions (Copernicus User Uptake Framework Contracts) • This research study has been conducted using the Copernicus Marine Environment

Monitoring Service products.

Federico Filipponi, Emiliana Valentini, Andrea Taramelli Institute for Environmental Protection and Research - ISPRA (Roma, Italy)

federico.filipponi@isprambiente.it

Sea Surface Temperature changes analysis an Essential Climate Variable

for Ecosystem Services provisioning

Thanks for your attention

MultiTemp 2017 Conference – 27-29 June 2017 Bruges (Belgium)