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Detecting spatial and temporal patterns- coherence in
lake water quality
UK-India water centre;
Stirling, June 2017 Marian Scott, Claire Miller and
Ruth O’Donnell
Marian.scott@glasgow.ac.uk
EO has the potential to change
how we monitor and manage
our natural resources.
Using new data streams, we
can assess how lakes in all
parts of the world are
responding to environmental
and climate change
Natural Objectives
In GloboLakes, we want to
Identify patterns of temporal coherence for
individual remotely sensed lake characteristics
& the spatial extent of coherence.
Identify phenological patterns of change in
remotely sensed lake characteristics, evaluate evidence
about state and any long term trends
Big data, big challenges
Huge volumes of data are being generated every day, we need
to have statistical tools to allow us to explore, model and
visualise the results
Our Glonolakes Data
For 1000 lakes, we will have temperature, chlorophyll, TSM,
CDOM….
The data are available at pixel level, ranging from several
thousands to millions, depending on the size of the lake and
the satellite resolution
For each lake pixel we have a time series of measurements.
Big data, big challenges
Why is temporal coherence of interest?
“Limnologists have regarded temporal coherence (synchrony)
as a powerful tool for identifying the relative importance of
local-scale regulators and regional climatic drivers on lake
ecosystems” Yu et al, 2011
“It is well known that lakes can show a regionally coherent
response to large-scale weather patterns. Lakes may also be
synchronously affected by the effects of atmospheric
deposition, meteorological effects on the catchment and
hydrological connectivity. The degree of coherence is
governed by the strength of local, lake-specific factors
compared to the strength of regional drivers.”
UKLeon project
• The synchrony between major fluctuations in a set of time
series is often described as temporal coherence. So we
are trying to identify common patterns.
What is temporal Coherence?
• As well as looking at temporal coherence across lakes, it may also be of
interest to investigate the similarities of different areas within lakes
• Large lakes may have several basins/areas within them that have
different characteristics in terms of the trends, seasonal patterns and
levels of determinands present
• How do we identify which lakes or which areas are coherent?
• Several statistical approaches are available;
▫ Correlation analysis- not recommended
▫ Functional data analysis
▫ Cluster Analysis
▫ Principal Component Analysis
Between and within Lake Coherence
• We have developed some new
statistical tools using
functional data analysis
• For each lake pixel we have a
time series of measurements.
We view the time series as
realisations of an underlying,
unknown smooth function
• We estimate the smooth
functions using a technique
called B-splines and carry out
statistical analysis on the fitted
functions rather than a single
summary value; known as
Functional Data Analysis (FDA)
Between and within Lake Coherence
ARClake Data Example
• Lake Titicaca
• Twice monthly
data
• 281 pixels
(max)
• Lower panels
show average
pattern over
time and
seasonal
pattern
Pixels to Time Series
• Each time series corresponds to a pixel
• Each time series can be smoothed and
an estimate of the trend and seasonal
components can be obtained
• Trend and seasonal components are
centred
• Seasonal pattern is dominant
• We identify coherent (ie similar patterns) groups of lakes or
clusters
• Identifying common patterns and changes is ecologically
important, subsequently we can assess what is driving these
changes
• Functional clustering means we obtain clusters of curves
identified as coherent in terms of temporal dynamics e.g.
seasonal patterns and long term trends.
• We have developed adaptive curve fitting to take into account
unique features of the data such as discontinuities due to ice
cover
Functional Clustering
Lake Titicaca:
• We have used reconstructed data so we have
complete time series over the time period
• An automatic (data driven) procedure was used
to determine the statistically optimal number of
groups
• 2 groups of pixels were identified
• Colouring the seasonal curves by the cluster
memberships highlights the differences between
the two groups
• The bottom plot shows the cluster mean seasonal
pattern for each group (solid line) with the
shaded area representing the variability of the
curves within each group
Lake Titicaca:
Map shows spatial
distribution of the clusters
No spatial information was
included in the clustering
(but spatial correlation can
be incorporated)
Clear separation of the lake
into 2 distinct regions
based on seasonal patterns
• We show a simple illustration based on surface water
temperature
• We use the smooth lake mean temperature curve as the data
point
• We take account of ice, missing data and retrieval uncertainty
• We identify coherent groups of lakes or clusters
• We generate a map
• We can then think about what might be the global and regional
effects driving the patterns- attribution
Functional Clustering on a global scale
Arclake- Global temperature coherence example
• Using a reduced set of 732
lakes and 17 years of bi-
monthly lake surface water
temperature.
• We identified 11 distinct
groups.
• The figure on the right shows
the temporal patterns in
each of the lake clusters
• The map below shows
functional clustering results
Arclake- example of global lake temperature
coherence
Summary
• Lake studies are usually based on small numbers of
lakes. EO lets us look at things on a global scale.
• We need appropriate statistical models and methods
to take full advantage of the new data streams.
• The data streams present interesting statistical
challenges including dealing with missing data, ice
cover and the retrieval uncertainties. We have been
developing new statistical tools based on functional
data analysis to address these new data streams.
acknowledgements
• O'Donnell, Scott and Miller were funded for this work
through the NERC GloboLakes project (NE/J022810/1).
•
• The authors gratefully acknowledge the ARC Lake project
for access to the LSWT data (www.laketemp.net) and the
rest of the GloboLakes team.

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3.3 IUKWC Workshop Freshwater EO - Marian Scott - Jun17

  • 1. Detecting spatial and temporal patterns- coherence in lake water quality UK-India water centre; Stirling, June 2017 Marian Scott, Claire Miller and Ruth O’Donnell Marian.scott@glasgow.ac.uk
  • 2. EO has the potential to change how we monitor and manage our natural resources. Using new data streams, we can assess how lakes in all parts of the world are responding to environmental and climate change
  • 3. Natural Objectives In GloboLakes, we want to Identify patterns of temporal coherence for individual remotely sensed lake characteristics & the spatial extent of coherence. Identify phenological patterns of change in remotely sensed lake characteristics, evaluate evidence about state and any long term trends
  • 4. Big data, big challenges Huge volumes of data are being generated every day, we need to have statistical tools to allow us to explore, model and visualise the results Our Glonolakes Data For 1000 lakes, we will have temperature, chlorophyll, TSM, CDOM…. The data are available at pixel level, ranging from several thousands to millions, depending on the size of the lake and the satellite resolution For each lake pixel we have a time series of measurements.
  • 5. Big data, big challenges
  • 6. Why is temporal coherence of interest? “Limnologists have regarded temporal coherence (synchrony) as a powerful tool for identifying the relative importance of local-scale regulators and regional climatic drivers on lake ecosystems” Yu et al, 2011 “It is well known that lakes can show a regionally coherent response to large-scale weather patterns. Lakes may also be synchronously affected by the effects of atmospheric deposition, meteorological effects on the catchment and hydrological connectivity. The degree of coherence is governed by the strength of local, lake-specific factors compared to the strength of regional drivers.” UKLeon project
  • 7. • The synchrony between major fluctuations in a set of time series is often described as temporal coherence. So we are trying to identify common patterns. What is temporal Coherence?
  • 8. • As well as looking at temporal coherence across lakes, it may also be of interest to investigate the similarities of different areas within lakes • Large lakes may have several basins/areas within them that have different characteristics in terms of the trends, seasonal patterns and levels of determinands present • How do we identify which lakes or which areas are coherent? • Several statistical approaches are available; ▫ Correlation analysis- not recommended ▫ Functional data analysis ▫ Cluster Analysis ▫ Principal Component Analysis Between and within Lake Coherence
  • 9. • We have developed some new statistical tools using functional data analysis • For each lake pixel we have a time series of measurements. We view the time series as realisations of an underlying, unknown smooth function • We estimate the smooth functions using a technique called B-splines and carry out statistical analysis on the fitted functions rather than a single summary value; known as Functional Data Analysis (FDA) Between and within Lake Coherence
  • 10. ARClake Data Example • Lake Titicaca • Twice monthly data • 281 pixels (max) • Lower panels show average pattern over time and seasonal pattern
  • 11. Pixels to Time Series • Each time series corresponds to a pixel • Each time series can be smoothed and an estimate of the trend and seasonal components can be obtained • Trend and seasonal components are centred • Seasonal pattern is dominant
  • 12. • We identify coherent (ie similar patterns) groups of lakes or clusters • Identifying common patterns and changes is ecologically important, subsequently we can assess what is driving these changes • Functional clustering means we obtain clusters of curves identified as coherent in terms of temporal dynamics e.g. seasonal patterns and long term trends. • We have developed adaptive curve fitting to take into account unique features of the data such as discontinuities due to ice cover Functional Clustering
  • 13. Lake Titicaca: • We have used reconstructed data so we have complete time series over the time period • An automatic (data driven) procedure was used to determine the statistically optimal number of groups • 2 groups of pixels were identified • Colouring the seasonal curves by the cluster memberships highlights the differences between the two groups • The bottom plot shows the cluster mean seasonal pattern for each group (solid line) with the shaded area representing the variability of the curves within each group
  • 14. Lake Titicaca: Map shows spatial distribution of the clusters No spatial information was included in the clustering (but spatial correlation can be incorporated) Clear separation of the lake into 2 distinct regions based on seasonal patterns
  • 15. • We show a simple illustration based on surface water temperature • We use the smooth lake mean temperature curve as the data point • We take account of ice, missing data and retrieval uncertainty • We identify coherent groups of lakes or clusters • We generate a map • We can then think about what might be the global and regional effects driving the patterns- attribution Functional Clustering on a global scale
  • 16. Arclake- Global temperature coherence example • Using a reduced set of 732 lakes and 17 years of bi- monthly lake surface water temperature. • We identified 11 distinct groups. • The figure on the right shows the temporal patterns in each of the lake clusters • The map below shows functional clustering results
  • 17. Arclake- example of global lake temperature coherence
  • 18. Summary • Lake studies are usually based on small numbers of lakes. EO lets us look at things on a global scale. • We need appropriate statistical models and methods to take full advantage of the new data streams. • The data streams present interesting statistical challenges including dealing with missing data, ice cover and the retrieval uncertainties. We have been developing new statistical tools based on functional data analysis to address these new data streams.
  • 19. acknowledgements • O'Donnell, Scott and Miller were funded for this work through the NERC GloboLakes project (NE/J022810/1). • • The authors gratefully acknowledge the ARC Lake project for access to the LSWT data (www.laketemp.net) and the rest of the GloboLakes team.