Global Lakes Sentinel Services
Grant number 313256
WP5 Innovation and Application
through Use cases
D.5.3 Use case 2: deep clear lakes with
increasing eutrophication
CNR, WI, EOMAP, BG
2015-09
Global Lakes Sentinel Services (313256)
Global Lakes Sentinel Services
Grant number 313256
WP5 Innovation and Application through Use
cases
D.5.3 Use case 2: deep clear lakes with increasing
eutrophication
CONSIGLIO NAZIONALE DELLE RICERCHE, WATER INSIGHT BV, EOMAP GmbH &
Co.KG, BROCKMANN GEOMATICS
Due date: 2015-08
Submitted: 2015-09
Change records
Version Date
Description
Contributors
0.1
17.06.2015 Initial version
CNR
0.2
23.07.2015 Second version
CNR, BG, BC, EOMAP, WI
0.3
10.09.2015 Third version
CNR, BG, BC, EOMAP, WI
Final
16.09.2015 Final version
CNR
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Consortium
No
Name
Short Name
1
WATER INSIGHT BV
WI
2
SUOMEN YMPARISTOKESKUS
SYKE
3
EOMAP GmbH & Co.KG
EOMAP
4
STICHTING VU-VUMC
VU/VUmc
5
BROCKMANN CONSULT GMBH
BC
6
CONSIGLIO NAZIONALE DELLE RICERCHE
CNR
7
TARTU OBSERVATORY
TO
8
BROCKMANN GEOMATICS SWEDEN AB
BG
Reference
Please refer to this report as: GLaSS Deliverable 5.3, 2015. Global Lakes Sentinel Services,
D5.3: Use case 2: deep clear lakes with increasing eutrophication, CNR, WI, EOMAP, BG.
Available via: www.glass-project.eu/downloads
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Task objective (from DoW)
To demonstrate the applicability of the system developed in the project for a selection of
deep lakes and to assess the changing (as increasing or decreasing) of the trophic status
impacting on water uses.
Scope of this document
The purpose of this document is to describe the methods used to process and analyse EO
time-series data of deep clear lakes to investigate the trophic state trends (temporal and
spatial) and relative results.
Abstract
To demonstrate the applicability of the system developed in the project and to assess
changing of the trophic status impacting water uses, EO data were used for selected deep
waters. EO data provides in fact a synoptic vision and a good sampling frequency suitable for
the evaluation not only of the trophic state but also of changes in trophic state and
phenology.
Seven large deep lakes distributed between Europe, Africa and America were selected within
this case study. The main geomorphologic parameter they share is their high depth and
therefore usually large water volume. Because of their generally large volume the lakes are
generally characterised by clear waters so they are used for all kinds of services (e.g.,
fishing, irrigation, recreation and drinking water). Large water volumes let these lakes
respond very slowly to change in water quality conditions, such as nutrients and other
pollution.
To evaluate the status and trophic tendency we mainly focused on chlorophyll-a
concentrations (chl-a) being a direct proxy of the trophic status. MERIS-derived chl-a
concentrations, as well as other proxies (e.g. light penetration depth), were available within
the consortium thanks to previous works accomplished by project partners.
A number of region of interests (ROI) located in pelagic waters as well as some few other
stations defined depending on the morphology of the lakes, on the presence of river tributary
and on the presence of the permanent sampling stations were selected in each lake to
extract chl-a concentrations and other proxies. Statistical analysis tools were applied to these
measurements to evaluate water quality trends, phytoplankton phenology and peak
abundance. The results overall show almost stable conditions with slight increasing trophic
status trend for Maggiore, Constance and a sub-basin of Lake Michigan, slight decreasing
trophic status trend for Garda and Tanganyika and absolutely stable conditions for Vättern
and Malawi.
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List of abbreviations
Abbreviation
Description
asl
Above sea level
CDOM
Coloured Dissolved Organic Matter
chl-a
Chlorophyll-a
CHM
Chlorophyll-a maximum
EO
Earth Observation
FR
Full Resolution (related to MERIS data)
RS
Reduced Resolution (related to MERIS data)
IOP
Inherent Optical Properties
IQR
Interquartile Range
IWRM
Integrated Water Resources Management
L1B
Level 1B
MERIS
Medium Resolution Imaging Spectrometer
NIR
Near Infra-Red
R2
Coefficient of determination
ROI
Region Of Interest
S-2
Sentinel-2
S-3
Sentinel-3
TOA
Top Of Atmosphere
TSM
Total Suspended Matter
Z90
C2R and MIP transparency product
List of related documents
Short
Description
Date
D5.1
Report socio- economic context & 2014-04
selected case studies
D3.5
Automatical ROI generation tool
2014-09
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Table of contents
Task objective (from DoW) .................................................................................................... 4
Scope of this document ......................................................................................................... 4
Abstract ................................................................................................................................. 4
List of related documents ...................................................................................................... 5
Table of contents ................................................................................................................... 6
1 Introduction ........................................................................................................................ 8
2 Study areas ........................................................................................................................ 8
2.1 Geomorphologic and ecologic characterisation ............................................................ 9
2.2 Socio-economic characterisation ................................................................................ 11
3 Materials and methods ......................................................................................................14
3.1 EO data and algorithms description ............................................................................14
3.2 GLaSS ROIStats tool ..................................................................................................21
3.3 Statistical approach for trend analysis .........................................................................21
3.4 Water levels ................................................................................................................22
4 Results ..............................................................................................................................22
4.1 Lake Garda .................................................................................................................22
Chlorophyll-a .................................................................................................................23
Phenology .....................................................................................................................26
Transparency ................................................................................................................27
Spatial analysis maps ....................................................................................................29
Water levels...................................................................................................................30
4.2 Lake Maggiore ............................................................................................................31
Chlorophyll-a .................................................................................................................32
Phenology .....................................................................................................................33
Transparency ................................................................................................................34
Water levels...................................................................................................................36
4.3 Lake Constance ..........................................................................................................36
Chlorophyll-a .................................................................................................................37
Phenology .....................................................................................................................42
Transparency ................................................................................................................43
Turbidity ........................................................................................................................47
Spatial analysis maps ....................................................................................................49
4.4 Lake Vättern ...............................................................................................................51
Chlorophyll-a .................................................................................................................51
Phenology .....................................................................................................................53
CDOM and Transparency ..............................................................................................53
Turbidity ........................................................................................................................55
Water levels...................................................................................................................56
4.5 Lake Michigan .............................................................................................................57
Chlorophyll-a .................................................................................................................57
Water levels...................................................................................................................62
4.6 Lake Malawi ................................................................................................................62
Chlorophyll-a .................................................................................................................63
Phenology .....................................................................................................................65
4.7 Lake Tanganyika .........................................................................................................65
Chlorophyll-a .................................................................................................................66
Phenology .....................................................................................................................68
5 Conclusions .......................................................................................................................69
Socio-economic overview ..............................................................................................70
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Acknowledgments ................................................................................................................73
References ...........................................................................................................................74
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1 Introduction
This Use Case aims to evaluate the changing of trophic status in deep clear lakes worldwide.
Overall, deep clear lakes represent an important socio-economic resource for the region in
which they are placed so that any effort in improving or preserving water quality is
recommended. In fact, although these lakes are less vulnerable to eutrophication than small
shallow lakes, a continuous input of nutrients has led to increasing eutrophication in many of
them. Conversely, improved practices in water management (e.g. enhanced water treatment
plants) might lead to trophic status decreasing in some other lakes.
Clear example of the vulnerability to eutrophication of these lakes, in particular of perialpine
lakes, is the history of Lake Constance, which in the second half of the 20th century
underwent a change toward worse trophic conditions with high phytoplankton bloom and low
transparency values, now recovered.
Within this study we evaluate water quality focusing on trophic status trend analysis in a
series of deep clear lakes at global scale.
2 Study areas
This document focuses on the study of clear, deep and bluish lakes. These lakes occur in all
areas of the world and perialpine, boreal, temperate tropic lakes and the following are
investigated in this study.




Garda, Maggiore and Constance (perialpine lakes)
Lake Vättern (boreal lake)
Lake Michigan (USA, fifth largest in the world)
Malawi and Tanganyika (tropical lakes in the African rift valley)
The selected lakes are situated in Europe (Garda, Maggiore, Constance and Vättern), North
America (Michigan) and Africa (Malawi and Tanganyika) and cover a range of eco-regions
from 59°N to 14°S spanned from 88°W to 35°E. Moreover, four of the seven selected lakes
are also trans-boundaries, making EO an optimal tool for monitoring water quality with a
synoptic and self-consistent approach (Fig. 1).
•Lake Vättern (Sweden)
•Constance (Germany,
Switzerland and Austria)
•Maggiore (Italy and Switzerland)
Garda (Italy)
Lake Michigan, USA
•Malawi (Malawi, Mozambico and
Tanzania)
•Tanganyika (Burundi, DR
Congo, Tanzania and Zambia)
Figure 1: overview of study area.
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In the sections below we describe the selected lakes according to both geomorphologic and
ecologic parameters as well as based on socio-economic indicators.
2.1 Geomorphologic and ecologic characterisation
The lakes investigated in this study are characterised by large surface area (mean area is
17600 km2) and great depths (mean value is 200 m) (Tab. 1). These features imply that the
lakes have large volumes of water, which generally indicates low trophic status. Additionally,
low levels of phosphorous and nitrogen, which are the most important nutrients for primary
production and principal causes of eutrophication, is a common property. The nutrients can
be brought into the lakes from anthropogenic sources like direct human discharges, or by
run-off from agriculture. Nutrients might also originate from natural external (e.g.,
atmospheric deposition) and internal (e.g., resuspension of nutrients from bottoms due to
complete water column circulation) sources.
Lake
Garda
Maggiore
Constance
Vättern
Michigan
Malawi
Tanganyika
Max
Mean
Altitude
Catchment
depth
Depth
(m asl)
area (km2)
(m)
(m)
370
50.35
346
136
65
2290
212
37
370
177
193
6599
536
55
254
90
395
11500
1940
74
128
41
88
4500
58000
4918
282
85
176
118000
29600
8400
706
292
500
100500
32900
18900
1470
570
773
231000
Table 1: major characteristics of lakes involved of this task.
Surface
(km2)
Volume
(km3)
Residence
time (year)
26.8
4
4.3
60
100
114
440
Located in the southern perialpine region, Lake Garda is the largest Italian lake. Located at
65 m asl, it is in meso-oligotrophic conditions. It is an oligomictic lake in which complete
circulation of water column occurs only in few years. The lake can be divided in two subbasins considering a largest one, extended from North to South-West area, characterised by
deepest bottoms, and the shallower South-Eastern one. They are morphologically divided by
Sant’Andrea fault which mostly prevents water mixing between the two sub-basins. The most
important tributary is Sarca River, which flows in the Northern part of the lake. In the northern
part also a channel from Lake Ledro (a smaller lake affected by algal blooms, mainly
cyanobacteria) flows into the lake for hydropower exploitation. The emissary is Mincio River.
Still located in the southern perialpine region, Lake Maggiore is the second largest Italian
lake by surface and volume. Its northern part and the catchment area (which is the largest of
the southern perialpine lakes) belong to Switzerland. Similar to all southern perialpine lakes,
Maggiore is very narrow with a north-southern elongated shape. Deepest bottoms are
situated in the central and northern parts, shallower bottoms are in the southern and in
Switzerland, in the northern part. It underwent an eutrophication process since the 1960s. In
early 1980s it was classified as meso-eutrophic till the second half of 1980s when lower
phosphorous loads brought the lake towards an oligotrophic state. In fact, after the creation
of wastewater treatment plants in late 1970s, the phosphorous inputs began decreasing. Due
to environmental resilience, phytoplankton community remained instead stable till the end of
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1980s, when medium chl-a concentration finally decreased (AdPO, 2009). Today the lake is
classified as oligotrophic. Regarding the circulation of water column, it is classified as olooligomictic (circulation occurred rarely, usually in winter between January and February,
AdPO, 2009). The most important tributaries are rivers Ticino and Maggia, Toce from Lake
Orta and Tresa from Lake Lugano. The only effluent is Ticino River.
Lake Constance is the third-largest lake in Central Europe in terms of area, and the secondlargest in terms of volume. Even if the deepest part of Lake Constance reaches 250 m of
depth, the lake is quite shallow. The lake can be divided into a larger and deeper part called
Obersee together with Ueberlinger See and a more shallower part called Untersee. One of
the main inflows contributing is the Alpine Rhine in the south-eastern part, carrying a large
amount of suspended particles, especially in the snow melting period, causing highly
dynamics in the south-eastern basin. Other important contributors are located mainly in the
eastern part, including the rivers Bregenzer Ach, Schussen and Argen. For instance, river
Schussen brings high loads of dissolved organic substances, nitrogen and phosphorous into
Lake Constance. After a period of high nutrient inputs in the mid of the last century, the lake
is now in the process of re-oligotrophication due to large water treatment and restoration
activities. Still, pressures caused by high population density, climate change and the various
utilizations of the lake resources threatens the recent positive ecological developments.
Boreal Lake Vättern is located in southern Sweden and it is the sixth largest lake in Europe.
It is composed of one deep and rather narrow basin (width less than 30 km). Lake Vättern
has a relatively small watershed and the seasonal variations in the tributary watercourses
have very little impact on the water quality of the main large basin. Oligotrophic conditions
prevail in this lake, but during the 1950-60s Lake Vättern was exposed to large inputs of
nutrients, which threatened to lead to eutrophication of the lake. The first water management
plan was adopted in 1970 and major efforts to limit nutrients started. Lake Vättern is now a
deep clear lake with high ecological status with respect to nutrients and phytoplankton.
Lake Michigan together with Lakes Superior, Huron, Erie and Ontario constitute the Great
Lakes of North America. Lake Michigan is the second largest of the group by surface area
(and the fifth largest in the world) and the only one entirely located in the United States,
surrounded by four states: Wisconsin, Michigan, Illinois and Indiana (Fuller et al., 1995;
Cherkauer and Sinha, 2010; Pearsall et al., 2012). It covers a surface area of about 58000
km2, with a shoreline 2640 km long, mean depth of 85 m (maximum of 282 m) and holding a
volume of around 4918 km3 (Fuller et al., 1995; GLIN, 2013). The drainage basin spans over
more than 118000 km2, with the main tributaries being the Fox and the Menominee Rivers in
Wisconsin, the St. Joseph, the Kalamazoo and the Grand Rivers in Michigan (GLIN, 2013).
Water residence time is about 100 years, then Lake Michigan discharges into Lake Huron
through the Straits of Mackinac, so in a hydrological sense both constitute only one large
lake (the largest in the world, in fact) (GLIN, 2013). The establishment of the Illinois
Waterway (and previous canals and waterways since the late 1800’s) allowed the connection
of Lake Michigan with the Gulf of Mexico through the Mississippi River (GLIN, 2013). Lake
Michigan is divided into a northern basin and a southern basin by the Milwaukee Reef, both
having a clockwise water flow and small lunar tidal effects have been documented (GLIN,
2013). The lake also has a north-western shallow extension, Green Bay, which its basin
represents one-third of the entire Lake Michigan drainage area (Brazner, 1997). Lake
Michigan’s shoreline is very diverse, from wetlands to enormous metropolitan areas like the
cities of Chicago and Milwaukee, but perhaps the most important features are the many
beaches and the fact that Lake Michigan holds the largest freshwater dune system of the
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world (Fuller et al., 1995; Pearsall et al., 2012; GLIN ,2013). Lake Michigan was once home
for more than 100 fish species, but many have been extinguished due to overfishing and
introduction of exotic species.
The major issues responsible for the degradation of Lake Michigan and its biodiversity are all
related to the intense human pressure, which began as early as the arrival of the first
European settlers.
Lake Malawi, one of the African great lakes, is the ninth largest and third deepest lake on
earth. It is located in the southern part of East African Rift Valley between 9° and 14° South.
Lake Malawi is about 560 km long with the greatest width about 75 km. It consists of a single
basin with the greatest depth of 785 m. It is a meromictic, oligotrophic lake, the production of
phytoplankton is limited mostly by the loss of nutrients to an almost permanent stratified,
anoxic water layer at a depth below 250 m (Eccles, 1962). It has the highest diversity of fish
species in the world (Kidd et al., 2001). The annual surface water resources yield on land is
about 13 Km3 and predominately drains into Lake Malawi and the Shire River. However,
more than 90 percent of this runoff occurs in rainy season (Malawi Integrated Water
Resources Management and Water Efficiency (IWRM/WE) Plan 2008-2012). The Lake
Malawi Ecosystem Management Project (2002), that studied the water quality of the major
contributing rivers, concluded that several rivers are substantially altered by human activities
within their catchment, and that these rivers especially in the southern part of the lake were
already changing the water quality and algal communities in local and larger areas of the
lake. The nutrient and sediment loading to the lake from its tributaries was estimated to have
increased by 50% within the last century (Hecky et al., 1999). However, little systematic
information exists on the spatio-temporal and long-term dynamics of water quality in this lake.
Lake Tanganyika, another African great lake, is the third largest lake in the world by volume
and one of the richest freshwater ecosystems in the world: about 600 of these species exist
nowhere else in the world outside the Lake Tanganyika watershed (www.worldlakes.org).
Lake Tanganyika is situated within the Albertine Rift, the western branch of the East African
Rift. The lake is 673 km long and up to 72 km wide. It is composed of two deep basins, with a
maximum depth of 1470 m. In Lake Tanganyika vertical mixing is only partial as is common in
permanently warm deep lakes, and no annual circulation below a mixed layer of about 50–
150 m depth has been shown. External nutrient loading is low considering the large volume
of the lake and the epilimnium is poor in nutrients. On the other hand, its stability (increased
over the past century) brings below 100–200 m depth an anoxic and relatively rich in
nutrients condition (Verburg et al., 2011).
2.2 Socio-economic characterisation
Waters from most of the studied lakes are regulated by dams and used for several purposes
among which the most important are electrical, agricultural and recreational uses, as well as
for drinking water. This indicates why a continuous and efficient monitoring system is very
important. On the other hand, waters exploitation and impact by human activities are strongly
conditioning the water quality itself. Hence the two, land cover and land use, as well as,
human impact needs to be considered for a proper analysis of the influencing factors on the
lake basin. In particular, an efficient and continuous monitoring program could provide
information on water quality and used as indicator for the measurement of progress during
implementation of water management systems. This progress measurement is suggested not
only by the WFD but also by Integrated Water Resources Management (IWRM) for trans
boundary lakes, like most part of the lakes involved in this task are.
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Lake Garda water is regulated by Salionze dam, located on Mincio, south of the lake. Its
coast are characterized, in the northern part, by mountain slopes mainly covered by forests
and rural territories, while the southern part is mostly urban and in part agricultural: in the last
century in the coastal area urbanization increased due mainly for tourism vocation of the
area. Since the 1980’s a collector has been gathering all sewer discharges around the coast
bringing black waters, after pre-treating, downstream of the dam. The lake represents an
important source of drinking water, for irrigation and for productive activities. It is also a
source for electrical production: water is pumped upstream and stored in Lake Ledro but the
most important uses are bathing and recreational activities linked to tourism which brings to
its coast more than 10 millions of tourists every year (Eulakes project, Final Report). Fishing
activities have strongly diminished mainly due to the increase of alien species at the expense
of autochthonous ones: several invasive species that appeared in the last decades represent
a threat for the lake ecological condition, affecting littoral and pelagic biota (Eulakes project,
Final Report). Water quality changes affect also water drinking and bathing uses, in particular
toxic algal blooms like cyanobacteria (appeared in Lake Garda in the 1990s) make water
unavailable for those purposes.
Lake Maggiore is a trans boundary lake located in Italian and Swiss territories: its watershed
is shared approximately in equal parts between Italy (Regions Piedmont and Lombardy) and
Switzerland (Canton Ticino). The most part of its coasts are urban or agricultural (about 600
000 inhabitants, Mosello et al., 2011); only in the northern part in Italian territories the coast is
covered by forests. The lake is regulated by the Miorina Dam (in Italy), but its level is also
influenced by several reservoirs located upstream, both in Italy and Switzerland, which can
retain a total of 600 million m3 of water mainly for hydropower use. In addition Lake Maggiore
waters are used for tourism and recreational activities, navigation, fishery, drinking use and
as a reservoir for irrigation. In fact its main effluent River Ticino brings water in the Po River
plain, the main agricultural and urban area in Italy, thus the socio-economical system of this
region depends strongly on lake water uses (Mosello et al., 2011). In 1972 the International
Commission for the protection of Italian Swiss Waters (CIPAIS) was born to study the
increasing water eutrophication, considering, among other things, physical, chemical and
biological characteristics of the lake water, including phytoplankton community development.
Lake Constance is a trans boundary lake located on Austrian, German and Swiss territories.
It is not regulated by dams, but water levels fluctuate almost 2 m during the whole year. More
than 4.5 million people in all the three countries depend on Lake Constance for drinking
water. The population density on its shoreline is high (approximately 3 200 people/Km2) and
tourism is an important activity with 10 million tourists per year, resulting in 15 000 full-time
jobs (Wordlakes). Lake Constance is known for its long tradition of local industries including
machine constructions, aircraft and aerospace engineering as well as for its agriculture use in
form of apple farms and wine yards, all of them influencing the water quality of the lake by
their use- and wastewater. Also, fishery has a long tradition dating back to prehistoric times
at Lake Constance, highly depended on the ecological status of the lake water. Since 1959,
the transboundary International Commission for the Protection of Lake Constance (short:
IGKB) coordinates and governs common activities to achieve and sustain the recent
achieved good status of the lake.
Lake Vättern is of historical, cultural, ecological and economic importance. Hydropower
stations were built on the lakes’ tributaries very early and the lake has been exposed to
sewage and wastewater from industries and urban areas since the beginning of the 20th
century. The toxins pcb (polychlorinated biphenyl) and ddt (dichlorodiphenyltrichloroethane)
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also reached unhealthy levels in fatty fish from Lake Vättern as a result of the paper mills’
discharge of chlorinated organic compounds. In 1969, an environment protection law was
passed that came to have great influence on the lakes’ recovery. Authorities, as well as
industry, were forced into a new environmental consciousness. Today, Lake Vättern offers
many important ecosystem services such as drinking water, navigation, recreation and
fisheries. Lake Vättern provides approximately 250 000 inhabitants around the lake with
drinking water and the water can, far from land, be drunk as it is without any purification. The
lake is a popular holiday resort and during the past century, sport fishing has increased in the
lake while commercial fishermen have decreased. Presently, about 20 professional
fishermen work on Lake Vättern. The lake hosts approximately 30 fish species and the
economically most important fish have been whitefish and arctic char.
Lake Malawi is a trans boundary lake located between Malawi, Mozambique and Tanzania.
About 1.5 million people live on its shores. It is a major food source to the resident population
of its shores since its waters are rich in fish, but it has also an important role attracting
international tourism thanks to its shores and islands. With over 800 fish species, 90% of
which are endemic, Lake Malawi is unique and an invaluable natural resource. In addition it
is a major source for irrigation, transport and hydroelectric power generation. It is a
vulnerable resource because about 53% of the water comes from the catchment in Tanzania.
Any major water development activities within the catchment would have serious
consequences for the economy of Malawi (IWRM/WE Plan 2008-2012). In the last few years,
a number of licenses have been issued for oil and gas exploration in Lake Malawi, which
have raised hopes for large oil deposits under the lake. Oil exploration is a very controversial
topic, however, in the local communities because of the danger of fish kills and little hope that
the locals will benefit from the proceeds of oil extraction.
About one million people live around Lake Tanganyika: it is an important source of fish for
consumption and for trade. It is also a vital transport and communications link between the
countries bordering it. In fact it is shared by Tanzania, Congo, Burundi and Zambia. A fiveyear project, "Pollution Control and Other Measures to Protect Biodiversity in Lake
Tanganyika" in 1995 brought to the definition in 2001 of priorities including: urban pollution
from Bujumbura (one of its most important cities), sedimentation from mid-sized catchments,
overfishing in the littoral zone and habitat destruction.
The northern basin of Lake Michigan is more forested, human population is sparser and the
economy is more dependent on natural resources and tourism, while the southern basin is
heavily populated, industrialized and agriculturally rich. Nowadays, twelve million people live
along Lake Michigan’s shores besides the numerous tourists that seek the lakefront (Chicago
alone receives 60 million visitors per year). The lake holds the world’s fifth largest oil refinery,
25% of the United States’ steel production and the Port of Indiana, the busiest of the Great
Lakes (Brammeier, 2001). Green Bay and the Fox River are considerably polluted due to a
high concentration of pulp and paper mills, some areas being hyper-eutrophic (Brazner,
1997). High concentrations of toxic contaminants (including carcinogens) are a problem that
Lake Michigan faces as a whole, not only of the water column but also sediments and
through the trophic food chain (bioaccumulation and biomagnification). Especially worrying
substances are PBDEs (polybrominated diphenyl ethers) (Kuo et al., 2010; Evans et al.,
2011). Despite such an intense human presence, Lake Michigan still holds very important
national parks along its shore, mainly the Indiana Dunes National Park and the Sleeping
Bear Dunes National Lakeshore. Many of the stated fish species are listed as of species of
concern, threatened or endangered by national and state law (MNFI, 2013). Another very
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relevant aspect to ensure public health and the effective safeguard of Lake Michigan’s
biodiversity is related with coalition building between the different states, or regional planning
that incorporates citizens’ concerns and efforts. Examples encompass the Great Lakes Water
Quality Agreement signed in 1978 and further reinforced in 1987, or the Lake Michigan
Federation founded in 1970, which in 2005 became the Alliance for the Great Lakes (AGL,
2013).
3 Materials and methods
3.1 EO data and algorithms description
MERIS FR available L1B products from 2002 to 2012 were used as main source of providing
time series data (Tab. 2 and Tab. 3). MERIS spatial resolution (300 m) and wavebands
covering visible and NIR parts of the spectrum are in fact suitable to study water quality in
inland waters (Giardino et al., 2014; Odermatt et al., 2012a; Bresciani et al., 2011; Matthews,
2014; Ali et al., 2014; Binding et al., 2013). For Lake Constance also some Landsat-7
products from 2002 to 2011 were used. For Lake Vättern only images from May to
September (October in 2002) were taken into account due to the fact that in the other months
the lake freezes. For the large Malawi and Tanganyika lakes MERIS reduced resolution RS
data (1-km resolution) were used for the trend analysis, because the full resolution archive is
quite incomplete until 2005.
Lake
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Garda
0
24
19
28
35
48
43
21
17
16
0
Maggiore
0
31
21
39
41
29
27
18
17
21
0
Constance
14
60
72
56
63
73
81
70
76
92
21
Vättern
16
19
39
41
48
34
45
38
32
40
0
Michigan
16
49
43
51
39
65
92
81
79
69
18
Malawi
92
157
163
163
163
166
169
169
153
158
20
Tanganyika
79
157
155
169
144
172
167
162
144
150
23
TOTAL
217
497
512
547
533
587
624
559
518
546
82
Table 2: number of EO imagery (all MERIS expect for Lake Constance where also some
Landsat-7 products are available) used for statistical analysis.
TOT
Lake
J
F
M
A
M
J
J
A
S
O
N
D
251
Garda
7
9
14
26
23
34
40
37
30
17
7
7
244
Maggiore
4
10
17
25
26
28
37
31
29
18
9
10
678
Constance
26
49
58
70
62
57
78
84
87
56
28
23
352
Vättern
0
0
0
0
74
81
75
64
55
3
0
0
602
Michigan
23
24
47
46
59
72
81
85
63
50
47
49
Malawi
119 109 125 121 141 137 143 148 132 141 136 121 1573
Tanganyika
119 114 116 109 131 141 143 149 129 128 128 109 1516
Table 3: number of images used for each lake and each month from 2002 and 2012. Grey
values is used to indicate a medium number of samples in i-month lower than once per each
year, coloured scale (from yellow to dark-red) is used to indicates a medium number of
samples comprised between twice per year and 7 per year for i-month.
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Different algorithms (Odermatt et al., 2012b) have been implemented for assessing water
quality from MERIS. In this study we used the following methods as previous works from
project partners have shown they are suitable for the lakes investigated within GLaSS.
 C2R (Case-2-Regional) for Garda and Maggiore
 MIP (Modular Inversion and Processing System) for Lake Constance
 FUB (Free University Berlin) for Vättern and Michigan
 CC (CoastColour) for Lake Michigan
 CC and WISP-3 for Malawi and Tanganyika
C2R (Doerffer and Schiller, 2007) is a neural network included in BEAM toolbox useful for the
retrieval of IOPs, water reflectance and water quality parameters such as TSM, chl-a
concentration and CDOM. In Odermatt et al. (2010) and in Bresciani et al. (2011) C2R was
successfully used for the retrieval of chl-a concentration and Z90 values in perialpine lakes
and it was hence used in this project for Lake Garda and Maggiore. Figure 2 shows a
comparison of C2R and in situ data for Lake Garda.
Figure 2: in situ data harvested from 2003 and 2011 in Lake Garda and C2R chl-a
concentration.
MIP algorithms are based on physical inversion schemes that derive bio-physical parameters
from the measured radiance signal at the sensor (Heege et al., 2003) through the
minimization of the difference between satellite measured and modelled spectra of water
surface. It provides estimates of chl-a maximum concentration (CHM), water turbidity (linearly
linked to TSM concentration at low to moderate concentrations as found in Lake Constance)
and Secchi Disk values. Figure 3 shows a comparison of MERIS and Landsat-7 products
obtained with MIP and in situ data for three monitoring stations of Lake Constance. CHM
concentration extracted from both products are generally congruous, excepted for FU station,
in which product from Landsat sometimes overestimates chl-a concentration. Figure 4,
shows an analogous comparison for transparency. Both MERIS and Landsat-7 derived
products well fit with in situ measurements but Landsat underestimate transparency more
often than MERIS, mainly in FU station.
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Figure 3: in situ measurements of chl-a concentration and CHM products from MERIS and
Landsat images extracted from MIP products from 3x3 pixel ROIs, corresponding to
measurements stations.
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Figure 4: in situ measurements of transparency and transparency products from MERIS and
Landsat images extracted from MIP products from 3x3 pixel ROIs, corresponding to
measurements stations.
FUB (Schroeder et al., 2007) allows the retrieval of three water quality parameters (chl-a,
TSM and CDOM) and water leaving reflectance. However, differently from the other
processors, it estimates water quality parameters directly from TOA radiance without
retrieving reflectance values before. In case of Lake Vättern FUB was cross-calibrated with in
situ values as the original processor overestimated chl-a (and underestimates CDOM and
TSM). They were used also for the retrieval of the strong correlation between Secchi Disk
and CDOM which allows to estimate Secchi Disk values directly from CDOM EO products as:
Secchi =0.0727(0.03CDOM)-0.811.
In the analysis of match up data from four Swedish lakes (Vättern, Vänern, Mälaren and
Hjälmaren), the extracted data set was limited to satellite and field data collected on the
same date. Taking all available stations into account, the field sampling date matched the
image date on 154 occasions, during the ten years of MERIS acquisitions. These match ups
has been plotted in Figure 5 below.
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Figure 5: same day match ups between MERIS FUB chl-a and field data.
There is a clear correlation between the two datasets. The most divergent data points are the
four highest concentrations, which correspond to turbid and nutrient rich Lake Hjälmaren.
The algorithm for chl-a estimation is trained for concentrations between 0-50 mg/m3 and
these point are thus out of range. They were therefore excluded in the in order to capture the
general trend as explained by the rest of the data set. The algorithm approximately
overestimates the concentrations with a factor three. The remaining variability depends
mainly on uncertainties and/or limitations in field sampling methodologies and image preprocessing routines (primarily atmospheric correction) as well as temporal differences
between the satellite overpass and the collection of the field sample. One hour might be
enough to change the concentration significantly. Winds, water flow and clear skies make the
water masses move and algae/cyanobacteria become well mixed in the water column or
floating near the surface. In addition to the four points from Lake Hjälmaren, two more points
(max overestimation and max underestimation where removed from the final selection as
displayed in Figure 6 below. By removing these points R2 increases from 0.64 to 0.70.
Figure 6: same day match ups between satellite and field chl-a data, two outliers and four
“out-of-range” concentrations removed.
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In Figure 6 above, data from all four lakes are included, but with a dominating contribution
from Lake Mälaren and Lake Vänern. It would not be possible to build stable relationships
only using Lake Vättern and Lake Hjälmaren data, due to the few match-up points and small
variability in concentrations. All four lakes together exhibit a large range of concentrations,
which makes it possible to establish a relationship for calibration of the MERIS FUB data to in
situ levels. The calibrated match up data from the two clear water stations in Lake Vättern
are plotted in Figure 7 below. The Mean Normalised Error (MNE %) is 11% based on this
data set, which corresponds to a MNE = 0,1 mg/m3 in absolute concentrations. It should be
noted that quite a few of the in situ concentrations are written as e.g. “< 1 mg/m3” in the
protocol. These low concentrations are thus on the limit of detectability in the laboratory
analysis and decreases the variability as can be seen in the figure. However, MERIS FUB
shows significant trend patterns over the season below 1 mg/m3.
Figure 7: same day match ups between calibrated MERIS FUB chl-a data and field data from
the two monitoring stations in Lake Vättern. In situ concentrations = 1 are commonly listed as
<1 in the protocol.
The CC NN algorithm was developed by Dr. Roland Doerffer in the framework of the
CoastColour project focused on coastal complex waters. The algorithm is included in the
BEAM software. The atmospheric correction produces the so-called L2R products, which
include the water leaving reflectances, normalised water leaving reflectances and different
information about atmospheric properties. The procedure is based on the Case2Regional
Processor and the Glint correction processors (implemented in BEAM, too). The AC
correction procedure is based on radiative transfer simulations using a Monte Carlo model
and the Hydrolight model. The simulated radiances are used to train a Neural Network,
which, in turn, is used for the parameterisation of the relationships between TOA radiances
reflectances (RL_toa) and the water leaving radiance reflectances (RLw). Furthermore, it
computes the atmospheric path radiances (RL_path), the downwelling irradiance at water
level (Ed), the aerosol optical thickness (AOT) at 550 nm and three other wavelengths, and
the angstrom exponents of the AOT.
The WISP-3 algorithm was applied to flagged Level 2 MERIS-CC remote sensing
reflectances. The WISP algorithm (Peters et al., 2013) is a semi-analytical approach that
uses an iterative scheme to calculate chl-a, TSM, CDOM. Its parameterisation is based on
the generally accepted SIOP functions used in Hydrolight and in the Coastcolour Round
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Robin Simulations (Nechad and Ruddick, 2012). It uses an iterative scheme, which makes
use of separate algorithms to calculate chl-a, TSM and CDOM. This allows the use of band
ratios for chl-a determination and single band algorithms for TSM and CDOM calculation.
The forward model is based on LUTs and the 4th degree polynomial formulation for the
reflectance function proposed by Park and Ruddick (2005). One attractive aspect of the
iterative approach is that sub-algorithms can be automatically adapted to the concentration
ranges calculated in the previous iteration. From literature we know that low chl-a
concentrations in waters with low TSM are best detected using blue-green band ratios, while
in other cases the red-NIR band ratio provides better results. Similar choices for spectral
bands are known for TSM algorithms. The WISP algorithm is therefore one of the first
algorithms that adapts itself to various conditions.
Figure 8: Validation of chl-a concentrations calculated by the WISP algorithm based on the
Coast Colour data set.
The parameters estimated according to above methods and used in each lake are given in
Tab. 4. In addition to chl-a concentration (which was assumed as main indicator of tropic
status), some others parameters are used as supplementary indicators.
Lake
Garda
Maggiore
Constance
Vättern
Michigan
Water quality parameter
chl-a, Z90
chl-a, Z90
chl-a, turbidity, Secchi disk
chl-a, TSM, CDOM, Secchi disk
chl-a
Processors
Images
C2R
MERIS
C2R
MERIS
MIP
MERIS and Landsat-7
FUB
MERIS and in situ data
FUB and CC
MERIS
CC and
Malawi
chl-a
MERIS
WISP-3
CC and
Tanganyika chl-a
MERIS
WISP-3
Table 4: list of water quality parameters analyzed for each lake and of processors and data
source used for their retrieval.
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3.2 GLaSS ROIStats tool
The GLaSS RoiStats tool was used to extract the water quality parameters within the time
series. For each lake, representative ROIs or stations were selected and statistics were
extracted.
The ROIs were selected to be the most representative of the whole lake itself but allowing
capturing also different behaviour of different sub-basins. Where permanent in situ
monitoring sampling stations exist, ROI matched these stations.
In some cases, the statistics from the whole lake surface (excluding pixel near coast and
shallow waters to avoid mixed pixels, bottom and adjacency effect) where extracted in
addition to the single ROIs. Because many of the selected lakes belong to different countries
(cf. Fig. 1), for Lake Constance as an example, three macro-ROIs were selected according to
national boundaries of Germany, Switzerland and Austria.
The mean, median, percentiles and standard deviation were extracted on daily basis and
then aggregated on monthly and seasonal basis to further investigate the existence of
possible seasonal patterns. These statistics were then exploited to look for possible trends in
time and space, within each lake and between all lakes as described in section 3.4.
3.3 Statistical approach for trend analysis
To analyse and understand possible changes in water quality conditions, several statistical
analysis were conducted on water quality parameters.
Temporal and spatial patterns were analysed plotting daily mean values against time.
Together with monthly box plots, this approach allowed to consider possible differences
between different period and different zones of the same lake. Monthly box plots were built
for each year on the basis of monthly median values to avoid the influence of nonhomogeneous images availability between different years.
Dates of the worst conditions on the basis of each parameter (e.g., high chl-a and low Z90)
were detected in each ROI by looking for the maximum of daily mean chl-a concentrations.
Trend tests were conducted exploiting daily mean values for each ROI and parameter. Since
a seasonal behaviour has been observed for all lakes, the Seasonal Kendall test (Hirsch and
Slack, 1984) was used as in Boyer et al. (1999) and Räike et al. (2003). This test, a
modification of the Mann-Kendall test, is a non-parametric test, which allows taking into
account seasonal patterns in the evaluation of monotonic trend over time (Hirsch and Slack,
1984). In the Mann-Kendall test, the statistic S, which is the difference between the number
of pairwise slopes that are positive minus the number that are negative, is calculated by
listing values in temporal order. The null hypothesis condition for this test is that there is no
temporal trend in the data values with a confidence limit of 0.05. In seasonal modification, the
same statistic is calculated on each season separately and then the results are combined by
summing statistic Si for each season i over all seasons to form the overall test statistic Sk.
Along with this test, the Sen Slope estimator was calculated as estimation of median annual
slope, calculating the median annual slope of all possible pairs of values in each season.
Trend was considered meaningful if p-value was under 0.05 and if the calculated Sen’s Slope
was at least 1% of the median value.
To validate the results, a second test was conducted. It is a seasonally adjusted trend test,
that is a parametric test, which first removes seasonality from data fitting a GAM (generalized
Additive Model, Hastie and Tibshirani, 1990) with 7 degrees of freedom to the annual pattern
(plotting each value in the correspondence of the actual day and month of a unique year).
Residuals, calculated as: data value minus predicted value from GAM plus overall mean of
data. The residuals are then used for a linear regression looking for possible trends. As
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before, the null hypothesis is that slope is not meaningfully different from zero with a
confidence level of 0.05. For this task, the slope was considered also meaningful if estimated
slope per year was at least 1% of the median value.
As an example, spatial patterns were also mapped in Lake Garda and in Lake Constance.
Two maps were produced: a map show, for each pixel the chl-a mean for the whole time
series; a map where pixels are scored with the total number of images of the time series in
which its value exceeds the 95th-percentile (avoiding pixel over the 99th-percentile to avoid
outliers); this number, that gives a sort of highest-values frequency, it is normalized on the
basis of maximum frequency value to understand where highest value mainly occurred.
To detect any changes in phenology, two strategies were adopted: for lakes with clearly
recognizable seasons of higher chl-a concentrations only these seasons were taken into
account, while for the other lakes the whole year was taken into account. For each year or
season the starting date of phenology was defined as the date in which cumulative
concentration exceeded the 25% of total cumulative concentration calculated on the entire
year or season.
A second analysis on phytoplankton abundance was conducted to retrieve the frequency of
occurrences per year and ROI. The event was defined looking for dates in which chl-a
concentration exceeded the sum of median and standard deviation calculated on the basis of
the whole time series in each ROI. The total number of these events was then divided by the
total of available images for each year and ROI. For Lake Garda median and standard
deviation were calculated separately for both the seasons considered in the first analysis.
Finally results from trend analysis and algal blooms analysis of different lakes were
compared to highlight differences and similarities between lakes at different latitudes and
ecological conditions.
3.4 Water levels
In order to evaluate possible trends in other factors which could be influencing water quality
trends in data series, water levels for each lake were evaluated. In Garda and Maggiore in
situ measurements of water levels provided by local authorities were used, while for the other
lakes data were retrieved through Surface monitoring by satellite altimetry (Cretaux et al.,
2011). Similar to water quality data, monthly box plot were used to evaluate seasonal
behaviour and then Seasonal Kendall Test was applied looking for possible trends in time.
4 Results
4.1 Lake Garda
For Lake Garda three ROIs (cf. Fig.9) were evaluated: south-west ROI in front of Desenzano
as representative of the western urbanized part of the lake; south-east ROI in the shallower
part of the lake nearby Peschiera; and the pelagic ROI in the lake centre. The northern part
of the lake was excluded from the analysis because MERIS values might be affected by
errors due adjacency effects.
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Figure 9: ROIs selected in the Lake Garda for statistic extraction.
Chlorophyll-a
Plotting daily mean values in Lake Garda for the three different ROIs, it can be highlighted a
seasonal behaviour of Lake Garda: highest values were recorded in spring and autumn,
while lower concentrations were observed in summer and winter (Fig. 10).
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Figure 10: daily mean values extracted from C2R products from 2003 to 2011 from ROIs
(from up to bottom) in South-West, South-East and Pelagic in Lake Garda.
Considering the three ROIs, an overall very similar time pattern is shown. Deeply observing
the dataset some differences can be appreciated mainly in the magnitude of some chl-a
concentration peaks (and, as it will be shown in the next, also in the anticipation of some of
them) which seems to be more evident in the Pelagic area rather than the other two.
The box plots (Fig. 11), showing statistics for each month, allows evidencing these
differences. Considering maximum value, highest values were recorded in March and in
October, suggesting the occurrence of algal blooms in these two periods. Considering the
Interquartile Range (IQR), high variability was recorded in March and April in the first two
ROIs while in Pelagic ROI high variability was recorded also in June.
Figure 11: monthly box plot for chl-a concentration extracted from C2R products from 2003 to
2011 from ROIs South-West, South-East and Pelagic.
Overall mean values show the presence of higher values in Pelagic and South-East ROIs,
calculated as 3.05 mg/m3 and 2.73 mg/m3 respectively, while for South-West ROIs is 2.51
mg/m3.
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In absolute, maximum mean values were recorded in October 2005 in Pelagic area (13.82
mg/m3) and in South-West (13.30 mg/m3) and in March 2005 in South-East (13.15 mg/m3).
As annual statistic values show, 2005 was the worst year in terms of chl-a concentration (with
highest mean and maximum values) and with the highest standard deviation. In contrast
2006, 2008 and 2010 were the best years (Tab. 5).
SOUTH-WEST
SOUTH-EAST
PELAGIC
YEAR
MEAN
MAX
ST. DEV.
MEAN
MAX
ST. DEV.
MEAN
MAX
ST. DEV.
2003
2.94
10.13
2.68
2.94
10.35
2.70
3.24
12.05
2.67
2004
2.86
11.18
2.83
2.89
10.75
2.80
3.85
12.50
3.26
2005
3.54
13.30
3.65
3.64
13.15
3.71
3.99
13.82
4.07
2006
2.27
5.23
1.23
2.46
6.49
1.48
2.78
7.30
1.61
2007
2.48
12.71
2.44
2.58
8.57
2.06
3.06
13.78
3.14
2008
1.77
6.51
1.22
1.93
5.60
1.27
2.15
7.58
1.75
2009
2.84
9.58
2.08
3.47
7.96
2.20
3.27
9.19
2.34
2010
1.77
5.27
1.07
2.11
6.29
1.31
2.36
8.09
1.94
2011
2.64
7.02
1.94
3.39
8.36
2.63
3.40
8.87
2.61
Table 5: statistics of chl-a concentration (mg/m3) from the whole time series extracted from each
ROI in Lake Garda: in bold type maximum values, in Italic minimum values.
Since a seasonal pattern has been identified, the Seasonal Kendall test was performed on all
the ROIs to look for possible trend in chl-a concentration. Results (Tab. 6) show for all ROIs a
low negative trend from 2005 to 2011, more evident in Pelagic ROI.
Area
Pelagic
Median
(mg/m3)
2.09
Median annual
Sen Slope
-0.111 (-5.3%)
p-value
0.004
South-West
1.88
-0.081 (-4.3%)
0.017
South-East
1.92
-0.066 (-3.4%)
0.050
Table 6: results for chl-a concentration from Seasonal
Kendall test for ROIs South-West, South-East and Pelagic.
Percentage rate is estimated considering median value.
Figure 12: daily mean values and Sen Slope (black line) trend for ROIs South-West, SouthEast and Pelagic.
Seasonally adjusted trend test gave the same results, showing a meaningful negative trend
in all ROIs (more relevant in Pelagic one).
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Area
Slope per year
p-value
Pelagic
-0.192 (-9.1%)
0.004
South-West
-0.177 (-9.4%)
0.002
South-East
-0.115 (-6.0%)
0.046
Table 7: results for chl-a concentration from Seasonal adjusted
trend test for ROIs South-West, South-East and Pelagic.
Percentage rate is estimated considering median value.
Figure 13: de-seasonalised daily mean values and mean slope per year (black line) for ROIs
South-West, South-East and Pelagic.
Phenology
As in Lake Garda phytoplankton growth mainly occurred in spring and autumn, phenology
analysis was conducted on the basis of these two seasons: from March to May and from
September to November.
In spring, the starting date is common to all ROIs, excepted for 2010 when in ROI Pelagic the
phytoplankton growth was a bit later (Fig. 14). Unfortunately, the data availability did not
allow a quantitative comparison between different years. Nevertheless, the results showed
that starting dates occurred always before the beginning of April (still excluding again Pelagic
ROI in 2010).
Figure 14: images cover (black line) and starting date for each year and each ROIs
considering images available from March to May. Vertical lines facilitate time interpretation.
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Figure 15: images cover (black line) and starting date for each year and each ROIs
considering images available from September to October. Vertical lines facilitate time
interpretation.
In autumn, data availability was more homogeneous and results showed a higher variability
within each year among different ROIs (Figure 15). In particular, in 2007, 2008 and 2010,
starting date occurred before in South-East and South-West ROIs and later in Pelagic ROI.
In general, it occurred in September, except for 2005 and 2011, when the phenology starting
date was in the first part of October.
For both periods, the analysis of the percentage rate of images in which chl-a exceeds the
sum of median and standard deviation showed that this type of events occurred mainly in
spring whit higher number of occurrences in South-West ROI, mainly from 2003 and 2005. It
is important to underline the different behaviour of South-East ROI in 2009, when it is the
only in which some events were recorded, and the high percentage of occurrences in 2011,
despite the negative trend previously shown. Also considering autumn 2005 as the year with
the highest percentage rate of occurrences. It was different in spring, when the occurrences
are more numerous from 2007 than before 2005.
Figure 16: percentage rate of images for each year and each ROI in which chl-a
concentration exceeds the sum median and standard deviation in two different seasons: from
March to May (left) and from September to November (right).
Transparency
Observing daily mean values extracted from C2R Z90 products, a very similar behaviour is
clearly recognizable between South-West and Pelagic ROIs, while in South-East area values
are noticeably different (for example at the beginning of 2005 and at the end of 2008) (Fig.
17).
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Figure 17: Z90 daily mean extracted from C2R products from 2003 to 2011 from ROIs SouthWest, South-East and Pelagic.
In particular, looking at monthly box plot differences are clearly recognizable in the first part
of the year. In addition it can be noticed that while in South-East and South-West ROIs an
increase of Z90 median values in spring is well marked, in Pelagic the trend is smoother. In
addition, considering median values, in January the worst water quality was recorded in
Pelagic ROI while in the other ROIs water was clearer. This pattern is in accordance with that
of chl-a concentration (Figure 10) and thus, correlation between these two variables was
tested.
Figure 18: monthly box plot for Z90 extracted from C2R products from 2003 to 2011 from
ROIs South-West, South-East and Pelagic in Lake Garda.
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Figure 19: correlation between chl-a concentration and Z90 from ROIs South-West, SouthEast and Pelagic in Lake Garda. Interpolation line is of type logarithmic.
The best correlation was recorded in ROI Pelagic end South-West (Figure 19), where the two
parameters are quite well correlated (R2 is 0.68 and 0.66 respectively, considering a
logarithmic regression curve) while in South-East ROI the correlation was lower.
In addition, the Seasonal Kendall trend test showed congruence between transparency and
chl-a concentration trends: in fact as shown in Tab. 8, a negative trend was obtained for all
ROIs, indicating a change of lake waters towards better conditions, with again higher slopes
for Pelagic and South-West ROI.
Area
Median
(mg/m3)
-7.35
Median annual
Sen Slope
-0.256 (-3.5%)
p-value
0.003
Pelagic
-7.77
-0.168 (-2.2%)
0.050
South-West
-7.57
-0.142 (-1.9%)
0.034
South-East
Table 8: results for Z90 from Seasonal Kendall Test for ROIs South-West, SouthEast and Pelagic. Percentage rate is estimated considering median value.
Spatial analysis maps
As mentioned before (cf. paragraph 3.4), two maps depicting the spatial patterns were also
produced for Lake Garda.
Figure 20 (left) shows chl-a concentration mean value from 2003 to 2011 calculated on
available images. The highest mean values were recorded mainly in the Northern part of the
lake and between Pelagic and South-Eastern areas. In the first case, it could be due to the
presence of the river, which flows into the lake in that area, but the uncertainty linked to this
area (cf. paragraph 4.1) caused the exclusion from the analysis of this part of the lake.
To explore not only the mean behaviour but to find out where extreme values were more
often recorded, Figure 20 (right) shows the map of the highest-values frequency: the northern
part is excluded before the analysis, allowing to show the pattern of the distribution in the rest
of the lake to avoid adjacency effect. The map in the Pelagic and South-East area shows the
same distribution obtained in the left map but medium-high frequency was calculated also
near Peschiera coasts and coastal belt near Desenzano in South-West area.
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Figure 20: left: chl-a mean concentration calculated on whole time series from 2003 and
2011. Right: normalized highest-values frequency map calculated on whole time series from
2003 and 2011.
Water levels
Daily water level measurements (from Agenzia Interregionale per il Fiume Po (A.I.PO)) were
used in the monthly box plot in Figure 21: the lower values were registered as expected in
summer while the higher ones were recorded in Spring. The seasonal pattern is not similar to
the one of chl-a. The Seasonal Kendall trend test showed a positive trend being the median
annual Sen Slope equal to 4.8 cm (5.5% of the median value, pvalue=0.000).
J
F
M
A
M
J
J
A
S
O
N
D
Figure 21: monthly box plot for water level in Lake Garda (cm) over zero level.
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Figure 22: daily water level and Median Annual Sen Slope (Black line).
4.2 Lake Maggiore
In Lake Maggiore, the ROIs were selected in order to take into account the variability linked
to the presence of Ticino River in the Northern part. For this reason in addition to a central
pelagic ROI, a further western ROI was considered (to evaluate an area highly influenced by
human activities) and a northern one was exploited in the estuarine area of River Ticino.
Figure 23: ROIs selected in the Lake Maggiore for statistic extraction.
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Chlorophyll-a
Analysing daily mean values in the three selected ROIs, similarities are clear in the first two
ROIs (Pelagic and West), while some differences are detectable in the Northern one.
Furthermore, an overall analysis allows retrieving from all the ROIs a seasonal behaviour
with higher values recorded from autumn to spring every year, except the last two years
(2010 and 2011) in which some algal blooms seem to have occurred also during summer.
chl-a (mg/m3)
25
West
20
15
10
5
0
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
25
chl-a (mg/m3)
Pelagic
20
15
10
5
0
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
chl-a (mg/m3)
25
North
20
15
10
5
0
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Figure 24: daily mean values of chl-a concentration extracted from C2R products from 2003
to 2011 from ROIs (from up to bottom) West, Pelagic and North in Lake Maggiore.
The confirmation comes from the monthly box plot in Figure 25, which shows a very low
variability in summer considering IQR but with a maximum rather high than median value. In
addition it can be noticed that maximum values in spring (April) in West and Pelagic ROIs are
rather high than in North ROI. Again in Tab. 9 it is evident that the worst years in terms of chla concentration were 2010 and 2011, but it allows also highlighting differences between
ROIs, mainly in variability and maximum values.
Figure 25: monthly box plot of chl-a concentration from ROIs West, Pelagic and North in
Lake Maggiore.
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WEST
PELAGIC
NORTH
YEAR
MEAN
MAX
ST. DEV.
MEAN
MAX
ST. DEV.
MEAN
MAX
ST. DEV.
2003
1.87
8.39
2.50
2.31
11.48
2.94
1.52
6.57
1.91
1.29
2004
1.79
5.64
1.72
2.34
10.34
2.46
1.61
3.82
2005
2.10
13.44
2.86
2.28
9.84
2.72
1.21
5.05
1.38
2006
1.60
7.42
1.57
1.68
6.78
1.60
1.53
5.54
1.48
2007
1.72
4.59
1.32
2.03
7.57
1.83
1.72
5.93
1.40
2008
1.91
9.92
2.37
1.91
9.73
2.55
1.93
10.56
2.33
2009
1.92
7.35
1.92
2.27
7.19
2.08
2.10
5.52
1.76
2010
4.79
23.30
5.91
5.35
25.00
6.07
3.44
10.00
3.05
2011
5.25
15.57
3.79
5.71
12.37
3.12
4.47
8.85
2.37
Table 9: statistics of chl-a concentration (mg/m3) from the whole time series extracted from each
ROI in Lake Maggiore; in bold type maximum values, in Italic minimum values.
Results from the Seasonal Kendall test show the same positive trend in chl-a concentration
for all the three ROIs (Tab. 10) and thus a worsening in trophic condition, a bit less marked in
Pelagic ROI.
Median annual Sen
Area
Median (mg/m3)
p-value
Slope
West
1.172
0.127 (11%)
0.000
Pelagic
1.278
0.112 (9%)
0.000
North
1.154
0.124 (11%)
0.000
Table 10: results for chl-a concentration from Seasonal Kendall test all ROIs in Lake
Maggiore. Percentage rate is estimated considering median value.
Phenology
Figure 26 shows the date in which cumulative chl-a exceeded the 25% of total cumulative
concentration for each year. It occurred mainly before the end of March, but sometimes even
at the end of June, mainly for the ROI North whose behaviour is often different from the other
two.
Figure 26: images cover (black line) and starting date for each year and each ROIs
considering images available during the whole year. Vertical lines facilitate time
interpretation.
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The analysis on the ratio of the number of images in which the chl-a concentration exceeds
the threshold (the sum of median and standard deviation) showed that the number of
episodes increased in the last three years (2009-2011, Fig. 27).
Figure 27: percentage rate of images for each year and each ROI in which chl-a
concentration exceeds the sum of median and standard deviation.
Transparency
Z90 values plotted in Figure 28 highlight strong differences between the West and Pelagic
ROIs and North ROI. A reason for these differences could be the presence of the river
discharge near to North ROI. In addition, as will be highlighted by the correlation analysis, in
the first two ROIs transparency seems to behave according to chl-a concentration, which is
completely different in North ROI (Fig. 29). Also monthly box plots (Fig. 30) allow noticing this
phenomenon, in addition to a higher variability within each month.
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2003
0
2004
2005
2006
2007
year
2008
2009
2010
2011
2012
-5
Z90 (m)
-10
-15
-20
-25
West
-30
2003
0
2004
2005
2006
2007
year
2008
2009
2010
2011
2012
-5
Z90 (m)
-10
-15
-20
Pelagic
-25
-30
2003
0
2004
2005
2006
2007
year
2008
2009
2010
2011
2012
-5
Z90 (m)
-10
-15
-20
-25
North
-30
Figure 28: daily mean values of Z90 extracted from C2R products from 2003 to 2011 from
ROIs (from up to bottom) West, Pelagic and North in Lake Maggiore.
Figure 29: correlation between chl-a concentration and Z90 from ROIs West, Pelagic and
North in Lake Maggiore. Interpolation line is of type logarithmic.
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Figure 30: monthly box plot of Z90 from ROIs West, Pelagic and North in Lake Maggiore.
Water levels
Water level (m)
In Lake Maggiore monthly mean of in situ measurements were calculated. The Seasonal
Kendall Test allowed showing a positive trend with Median Annual Sen Slope of 4.3 cm
(p=0.024), quite equal to Lake Garda’s.
Figure 31: water levels from lake Maggiore and Median Annual Sen Slope (black line).
4.3 Lake Constance
Six ROIs were used to evaluate the sub-basins features in Lake Constance: Untersee ROI
situated in the smaller homonymous sub-basin, two Pelagic ROIs (North and South) in the
Zeller See, Coastal Austria and Coastal Switzerland ROIs in Obersee near to the outlet of
River Rhine (in order to evaluate a possible influences) and Shallow ROI, sited in the
southern shallower part of the lake.
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Figure 32: ROIs selected in the Lake Constance for statistic extraction at sub-basins scale
(above) and Countries scale (below).
Chlorophyll-a
In the plots of the daily mean values for the three macro-ROIs a very similar pattern is
shown, with Austria ROI showing values a bit higher than the other two areas. This is in line
with the expectation, considering that most part of the ROI includes the area that could be
influenced by Rhine discharge. A seasonal pattern can be individuated at first glance with the
higher values between summer and autumn.
Figure 33: daily mean values of CHM (mg/m3) extracted by MIP products available from 2002
and 2012 from macro-ROIs Austria, Germany and Switzerland in Lake Constance.
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Looking at smaller ROIs (Fig. 34), only results from a coastal ROI and a Pelagic one were
plotted together to evaluate different significant patterns, near and far from the river inflow,
while Untersee ROI and Shallow Austria ROI results were plotted separately considering the
particular position and conformation of this two sub-basin. All the other ROIs were evaluated
but not reported because results did not add any further consideration.
Figure 34: daily mean values of CHM (mg/m3) extracted by MIP products available from 2002
and 2012 from ROIs (from top to bottom) Coastal Austria and Pelagic North, Untersee and
Shallow.
The pattern is very different; showing that the variability in macro-ROIs is very high. In any
case, while lower values were recorded in Untersee and in Pelagic North ROIs and highest
values in Coastal and Shallow Austria ROIs, a seasonal pattern can be again guessed in the
first two ROIs but it is less evident in the Untersse basin and in shallower part of the lake. As
shown in the monthly box plot in Figure 35, highest median values were observed from
spring to autumn in all ROIs except for these last two, which show a different behaviour: in
Shallow ROI median values were higher than in the other ROIs all over the year showing a
higher variability within each month and maximum median value in November; in Untersee
ROI median values are comparable for each month of the year except for November and
December, when higher values and higher variability were recorded. The winter months can
be considered as less reliable due to the lower light availability and reduced image quality
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Figure 35: monthly box plot for chl-a concentration extracted from MIP products from 2002 to
2012 from smaller ROIs in Lake Constance.
Annual mean and standard deviation values were exploited to find out for each ROI the worst
and the most variable year in terms of chl-a concentration (Tab. 11).
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PELAGIC-SOUTH
PELAGIC-NORTH
SHALLOW
YEAR
MEAN
MAX
ST. DEV.
MEAN
MAX
ST. DEV.
MEAN
MAX
ST. DEV.
2002
5.20
9.05
2.05
4.97
8.18
2.06
10.53
14.67
3.07
2003
3.60
7.89
1.29
3.45
6.24
1.22
5.78
10.14
1.54
2004
3.38
7.39
1.67
3.32
7.60
1.76
5.56
13.30
2.39
2005
3.93
8.22
2.00
3.79
9.01
2.05
6.66
14.17
2.58
2006
3.95
9.34
1.76
4.07
8.99
1.84
7.28
15.89
2.79
2007
3.95
7.41
1.59
3.88
7.67
1.76
6.78
15.62
2.67
2008
3.61
9.04
1.60
3.79
7.28
1.49
6.57
15.21
2.54
2009
3.68
6.29
1.48
3.63
6.11
1.46
6.36
13.70
2.47
2010
3.87
8.38
2.09
3.71
7.84
2.04
7.20
16.28
3.24
2011
4.15
9.09
1.87
4.07
8.74
1.80
6.78
11.68
2.26
2012
2.40
3.75
0.83
2.35
3.67
0.77
6.03
8.93
2.25
COASTAL-AUSTRIA
YEAR
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
MEAN
7.14
4.45
4.27
4.95
5.12
4.88
4.38
4.46
5.06
5.00
3.55
MAX
13.31
9.48
8.72
9.57
11.23
10.49
8.64
8.29
16.36
8.73
7.79
ST. DEV.
2.80
1.76
2.01
2.31
2.15
2.22
1.77
2.05
3.20
1.82
2.05
COASTAL-SWITZERLAND
MEAN
7.51
4.38
4.11
5.56
5.04
4.89
4.33
4.39
4.43
4.78
3.32
MAX
11.41
17.18
8.49
11.26
11.94
8.79
9.24
18.69
10.23
7.96
4.63
ST. DEV.
1.87
2.36
1.85
2.52
1.83
1.93
1.75
2.71
2.30
1.54
0.81
UNTERSEE
MEAN
4.02
4.80
4.54
5.09
4.40
4.30
4.48
4.42
4.47
4.45
4.00
MAX
6.20
9.75
8.41
17.89
8.67
10.39
10.31
11.68
7.57
9.73
6.20
ST. DEV.
1.38
1.67
1.65
2.75
1.63
1.79
2.01
1.96
1.48
1.69
1.54
Table 11: statistics of chl-a concentration (mg/m3) from the whole time series extracted from each
ROI: in bold type maximum values, in Italic minimum values.
Maximum mean values were recorded in 2002 in all ROIs except in Untersee basin, but
these high results are affected by the lack of images until June. The highest maximum values
were recorded in 2006 in Pelagic ROI, in 2010 in Austrian ROI and in 2009 in Coastal
Switzerland ROI. As expected, in Untersee ROI mean values are very similar and
comparable from 2002 and 2012, while a higher variability is noticeable between maximum
values (the highest in 2005). Comparing ROIs, highest mean values and highest variability
were confirmed for Shallow ROI, even in absolute terms the highest value was recorded in
Coastal Switzerland ROI (18.69 mg/m3).
Performing the Seasonal Kendall trend test considering the whole lake, a very low positive
trend was estimated (median annual Sen Slope and p-value were respectively 0.061 and
0.002, Fig. 36). Considering all the other ROIs, a significant very low positive trend
(maximum 2.2%) was found in all ROIs except for Coastal Switzerland and Untersee ROIs
for which the no-slope hypothesis could not be rejected (Tab.12).
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Figure 36: CHM (mg/m3) daily mean values extracted from the whole lake Constance and
Sen Slope trend (black line).
Median
Median annual
p-value
3
(mg/m )
Sen Slope
Pelagic South
3.75
+0.083 (2.2%)
0.000
Pelagic North
3.66
+0.081 (2.3%)
0.000
Shallow
6.35
+0.100 (1.6%)
0.004
Coastal Au.
4.76
+0.078 (1.6%)
0.003
Coastal Sw.
4.46
+0.035 (0.7%)
0.171
Untersee
4.25
-0.011 (0.2%)
0.666
Austria
4.62
+0.067 (1.5%)
0.003
Germany
3.88
+0.060 (1.5%)
0.003
Switzerland
3.84
+0.065 (1.7%)
0.001
Whole lake
3.95
+0.061(1.5%)
0.002
Table 12: results for chl-a concentration from Seasonal Kendall test for all ROIs in
lake Constance. Percentage rate is estimated considering median value. Italic
characters indicate no meaningful trend was estimated.
Area
These results were not entirely confirmed by Seasonal adjusted trend test: in particular for
Austria macro-ROI no significant slope was estimated and in general estimated slope values
were lower than that obtained from the previous test (Tab. 13).
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Area
Slope per year
p-value
Pelagic South
+0.073 (1.9%)
0.002
Pelagic North
+0.059 (1.6%)
0.011
Shallow
+0.086 (1.4%)
0.021
Coastal Au.
+0.05 (1.1%)
0.085
Coastal Sw.
-0.014 (0.3%)
0.649
Untersee
-0.035 (0.8%)
0.250
Austria
+0.039 (0.8%)
0.123
Germany
+0.051 (1.3%)
0.019
Switzerland
+ 0.050 (1.3%)
0.019
Whole lake
+0.048 (1.2%)
0.025
Table 13: results for chl-a concentration from Seasonal adjusted trend test for all ROIs
in lake Constance. Percentage rate is estimated considering median value. Italic
characters indicate that null hypothesis was not rejected.
Phenology
The analysis of phenology in Lake Constance was performed considering the whole year:
Figure 37 shows the starting date as the first date in which cumulative chl-a concentration
exceeded 25% of the total cumulative concentration in each year and ROI. Excluding 2002
and 2012 in which MERIS acquired images only in a smaller part of the year, only in 2006
and in 2011 the variability between ROIs was very low. In 2004, 2005 and 2008 the time lag
between the first ROI and the last one where 25% of cumulative concentration was recorded
exceeded a month duration: in particular in 2008 it was recorded very early (in March) for
Pelagic South, Pelagic North, Shallow and Untersee ROIs. In contrast in 2003, 2006 and
2010 in all ROIs it was recorded very late (except for Shallow ROI in 2003 and 2010).
Figure 37: images cover (black line) and starting date for each year and each ROIs
considering all available images. Vertical lines facilitate time interpretation.
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Considering the percentage rate of images in which chl-a exceeds the sum median and
standard deviation, the worst year was 2005 for Coastal Switzerland ROI and 2010 in
Coastal Austria ROIs. In general the worst years in terms of high number of occurrences
were 2005, 2010 and for the Austrian ROIs also 2011, while the best were 2003 and 2009,
mainly in Pelagic North ROI.
In this analysis years 2002 and 2012 were excluded due to the fact that MERIS images
became available only starting from June 2002, excluding months in which concentration was
lower and in 2012 only images till April 4th were available, making results not comparable.
Figure 38: percentage rate of images for each year and each ROI in which chl-a
concentration exceeds the sum median and standard deviation.
Transparency
Plotting daily mean transparency values from Z90 MIP products, a pattern similar to the chl-a
concentration pattern was found. No significant differences are detectable analysing the
three macro-ROIs (only a few times Switzerland ROI showed higher values and so more
transparent waters). In Figure 39 and Figure 40, the same ROIs reported in the previous
chapter are shown: the same pattern with slight differences in magnitude are detectable
between Coastal and Pelagic ROIs while differences are still evident from the other two,
Untersee and Shallow ROIs: in the former the range is very similar but the seasonality
detectable in the first two ROIs is less evident; in the latter lower values (and so less
transparent waters) were recorded all over the period and seasonality is less evident too.
Figure 39: Z90 daily mean extracted from MIP products from 2002 to 2012 from macro-ROIs
Austria, Germany and Switzerland in Lake Constance.
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Figure 40: Z90 daily mean extracted from MIP products from 2002 to 2012 from ROIs (from
top to bottom) Coastal Austria, Pelagic North, Untersee and Shallow in Lake Constance.
The monthly box plot in Figure 41 allows to easier appreciate these phenomena: in particular
while in Pelagic and Coastal ROIs waters are more transparent in the first four months and
the first three months of the year respectively, the pattern is completely different in Untersee
ROI. In Shallow ROI lower values were recorded than in other ROIs but (differently from
CHM) seasonal behaviour is similar to Pelagic and Coastal ROIs with more transparent water
in the first three months of the year.
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Figure 41: monthly box plot for Z90 extracted from MIP products from 2002 to 2012 from all
ROIs in Lake Constance.
Performing the trend test (Fig. 42), results confirmed the trend estimated for chl-a
concentration; in fact, in all ROIs a low but meaningful negative trend was estimated,
suggesting a worsening in water condition. In contrast to chl-a concentration results, slope
percentage is very homogeneous (values varied between 1.4 and 1.8 %) in all ROIs,
excepted for Coastal Switzerland and Untersee where no significant trend was estimated.
Figure 42: Z90 daily mean values extracted from the whole Lake Constance and Sen Slope
trend (black line).
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Median annual
p-value
Sen Slope
54.487
Pelagic South
-0.981 (1.8%)
0.000
54.972
Pelagic North
-0.987 (1.8%)
0.001
33.750
Shallow waters
-0.572 (1.7%)
0.002
42.615
Coastal Au.
-0.763 (1.8%)
0.001
46.207
Coastal Sw.
-0.397 (0.9%)
0.054
52.816
Untersee
-0.150 (0.3%)
0.626
45.521
Austria
-0.658 (1.4%)
0.001
55.033
Germany
-0.779 (1.4%)
0.003
54.626
Switzerland
-0.835 (1.5%)
0.000
53.507
Whole lake
-0.806 (1.5 %)
0.001
Table 14: results for Z90 from Seasonal Kendall trend test for all ROIs in Lake Constance.
Percentage rate is estimated considering median value. Italic characters indicate no
meaningful trend was estimated.
Area
Median (dm)
In order to investigate a possible correlation between chl-a concentration and water
transparency, mean values of CHM of the whole period were plotted against mean values of
Z90 (Fig. 43), both extracted from the whole lake. R2 of a logarithmic regression curve shows
that correlation is quite high (0.89).
Figure 43: correlation between chl-a concentration and transparency from whole Lake
Constance between 2002 and 2012. Interpolation line is of type logarithmic.
Considering all ROIs R2 is high too, varying between 0.9 in Pelagic South ROI and 0.75 in
Coastal Switzerland ROI (Tab. 15).
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Area
R2 CHM-Z90 (log)
0.90
Pelagic South
0.89
Pelagic North
0.85
Shallow waters
0.85
Coastal Au.
0.75
Coastal Sw.
0.79
Untersee
2
Table 15: R on the whole time series in each ROI from the interpolation
between CHM and Z90 mean values. Interpolation line is of type logarithmic.
Turbidity
Turbidity mean values were extracted from the same ROIs used for chl-a concentration and
transparency. Results show that in Austria macro-ROI values are higher than in the other
two. The time pattern within each year is very similar to that estimated for CHM and Z90, with
higher values during summer. In Coastal Austria this behaviour is different, showing a
maximum value in September (even if highest median value still occurred in July) as shown
in the monthly box plot in Figure 46. In addition, in this ROI a higher variability than in other
ROIs was recorded for April. Finally, Shallow ROI shows a different behaviour also for this
parameter with a less marked variability among different months and higher variability within
each month.
Figure 44: daily mean values of turbidity (ETU) extracted by MIP products available from
2002 and 2012 from macro-ROIs Austria, Germany and Switzerland in Lake Constance.
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Figure 45: daily mean values of turbidity (ETU) extracted by MIP products available from
2002 and 2012 from ROIs (from top to bottom) Coastal Austria and Pelagic North, Untersee
and Shallow in Lake Constance.
Figure 46: monthly box plot for turbidity extracted from MIP products from 2002 to 2012 from
all ROIs in Lake Constance.
Trend tests were conducted also to investigate possible trends in turbidity: the Seasonal
Kendall test did not put in evidence any trend in the time series but the seasonal adjusted
trend test showed a negative trend for Untersee and Shallow ROIs. These results are
completely different from that obtained for the other two water quality parameters.
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Median (ETU)
Area
Slope per year
p-value
0.987
- 0.021 (2.1%)
0.013
Untersee
Shallow
1.915
+0.044 (2.3%)
0.025
Table 16: results for turbidity from Seasonal adjusted trend test for ROIs
Untersee and Shallow in Lake Constance.
For this reason, a possible correlation between turbidity and the other two parameters was
explored (Fig. 47). However, none high correlation was found neither between turbidity and
transparency, nor between chl-a concentration and turbidity.
Figure 47: correlation between turbidity and Z90 (left scatterplot) and between CHM and
turbidity (right scatterplot) from whole Lake Constance. Interpolation line is of type logarithmic
for Z90 and linear for CHM.
The same analysis was conducted for each ROI, showing that correlation between turbidity
and transparency decreases from German and Austrian part of the lake to Swiss part and
becomes very low in Untersee basin.
Area
R2 turbidity-Z90 (log)
0.56
Pelagic South
0.54
Pelagic North
0.56
Shallow waters
0.55
Coastal Au.
0.44
Coastal Sw.
0.34
Untersee
2
Table 17: R on the whole time series in each ROI from the
interpolation between turbidity and Z90 mean values.
Interpolation line is of type logarithmic.
Spatial analysis maps
The same maps produced for Lake Garda have been done for Lake Constance. Figure 48
shows CHM mean values from 2002 to 2012 calculated on all available images. Highest
mean concentration has been calculated in Austrian shallower basin and near the coast,
mainly in the southern part of the Lake.
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Figure 48: CHM mean concentration calculated on whole time series.
The map of normalized highest-values frequency was realised at first considering the whole
extension of the lake. As it can be seen in Figure 49, high values occurred mainly in SouthEastern coastal belt. Thus, in order to investigate the distribution of the high value frequency,
this operation was repeated after excluding with a mask all pixels which had after first
analysis a frequency higher than 0.02.
The highest values were anyway more frequency in the South-Eastern belt nearest coastal
area and also in Untersee basin, even if some events in which concentration was higher than
95th occurred also in Pelagic area.
Figure 49: (left) normalized highest-values frequency map calculated on whole time series
from 2002 and 2012. (Right) normalized highest-values frequency map calculated on whole
time series from 2002 and 2012 excluding pixel with frequency higher than 0.02.
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4.4 Lake Vättern
Figure 50: Lake Vättern with ROIs Edeskvarnaan and Jungfrun selected for statistics
extraction.
Chlorophyll-a
In Swedish Lake Vättern, looking at the data from the two stations a seasonal behaviour can
be guessed at first glance, mainly in Jungfrun ROI. In addition, concentration in
Edeskvarnaan is higher in several events, mainly in June and September.
Figure 51: daily mean values extracted from calibrated FUB products from 2002 to 2011 from
ROIs Edeskvarnaan and Jungfrun in Lake Vättern.
Looking at the monthly box plot (Fig. 52) it can be easily noticed that, considering median
values, chl-a concentration is lower in July in both station and higher in September (October
were excluded because data were available only for year 2002).
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Figure 52: monthly box plot for chl-a concentration extracted from calibrated FUB products
from 2002 to 2011 from ROIs Edeskvarnaan and Jungfrun in Lake Vättern.
Tab. 18 shows mean and maximum daily values for both ROIs in each year. The highest
mean and maximum values were recorded in 2002 in Jungfrun ROI but a higher variability
was recorded the same year in Edesvarnaan. As for the case of Lake Constance, these high
results could be affected by the lack of images in some months in 2002. The lowest values
were recorded in 2006. Nevertheless, values are very similar between different years and
ROIs and this fact finds confirmation in trend test results.
EDESKVARNAAN
JUNGFRUN
YEAR
MEAN
MAX
ST. DEV.
MEAN MAX
ST. DEV.
0.22
1.13
1.57
2002
1.11
1.50
0.15
2003
1.02
1.21
0.10
1.03
1.17
0.09
2004
1.06
1.26
0.08
1.04
1.36
0.11
2005
0.99
1.27
0.10
0.98
1.23
0.08
2006
0.99
1.33
0.08
0.98
1.15
0.07
2007
1.06
1.46
0.12
1.05
1.25
0.09
2008
1.01
1.32
0.10
1.03
1.30
0.09
2009
1.05
1.34
0.10
1.05
1.32
0.10
2010
1.03
1.31
0.12
1.04
1.29
0.12
2011
1.03
1.26
0.10
1.03
1.23
0.08
3
Table 18: statistics of chl-a concentration (mg/m ) from the whole time
series extracted from each ROI: in bold type maximum values, in Italic
minimum values.
In fact, the Seasonal Kendall trend test showed that there was no trend in time-series
(median annual Sen slope=0, p-value=0.849 for Edeskvarnaan and p-value=0.949 for
Jungfrun respectively) and the Seasonal adjusted trend test confirmed this results.
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Figure 53: daily mean values of chl-a concentration from Edeskvarnaan ROI in Lake Vättern
and Sen Slope trend (black line).
Phenology
In this case no phenology analysis was conducted due to the lack of information in the first
part of the year. But the analysis of algal blooms was conducted only to identify the number
of events in which concentration exceeds the threshold previously defined. Data from 2002
were excluded considering that they were not homogeneous with the rest of time series
regarding image availability (in Edesvarnann only data from August and September were
available, while for Jungfrun from June, August and September). The worst year with the
highest number of occurrences was 2007, while the best year was 2006, with a very low
percentage rate. In addition, there was an inversion after 2006: from 2003 to 2005 higher
percentage rate was recorded in Edeskvarnaan, while after 2006 (excepted for 2008 in which
results are quite similar) higher percentage values were obtained for Jungfrun station (Fig.
54).
Figure 54: percentage rate of images for each year and each ROI in which chl-a
concentration exceeds the sum of median and standard deviation.
CDOM and Transparency
Secchi Disk values to evaluate transparency were obtained as described in section 3.1
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directly from the aCDOM FUB products. Observing the box plot in Figure 56 aCDOM values follow
the same pattern as chl-a concentrations with low median values in July and higher values in
spring and late summer. In fact, seasonal behaviour shown in Figure 55 is quite the same as
for chl-a. Comparing different ROIs, behaviour is similar but in Jungfrun ROI variability seems
to be higher.
Figure 55: daily mean values of Secchi Disk as estimated from aCDOM values extracted from
FUB products from 2002 to 2011 from ROIs Edeskvarnaan and Jungfrun in Lake Vättern.
Figure 56: monthly box plot for aCDOM (440 nm) extracted from original FUB products from
2002 to 2011 from ROIs Edeskvarnaan and Jungfrun in Lake Vättern.
Also for Secchi Disk the Seasonal Trend Test was conducted and a low significant negative
trend was detected as sum up in Tab. 19 and confirmed by seasonal adjusted trend test.
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Median annual
p-value
Sen Slope
10.838
Edeskvarnaan
-0.177 (-1.6%)
0.035
12.014
Jungfrun
-0.145 (-1.2%)
0.051
Table 19: results for Secchi disk from Seasonal Kendall trend test for ROIs in Lake Vättern.
Percentage rate is estimated considering median value.
Area
Median (dm)
Figure 57: daily mean values of Secchi Disk from Jungfrun ROI in Lake Vättern and Sen
Slope trend (black line).
Turbidity
Regarding turbidity a seasonal pattern is not easy detectable by plotting daily mean values
(Fig. 58).
Figure 58: Turbidity daily mean values extracted from calibrated FUB products from products
from 2002 to 2011 from ROIs Edeskvarnaan and Jungfrun in Lake Vättern.
The monthly box plot also shows a high variability within each month along time series.
Anywhere lower median values were recorded again in July. Finally both Seasonal Kendall
test and seasonal adjusted trend test failed to reject null hypothesis of no slope in time series
(p-value was 0.123 and 0.302 for Edeskvarnaan and Jungfrun respectively).
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EDESKVARNAAN
Turbidity (FNU)
JUNGFRUN
J
F M
A M
J
J
A
S O N D
J
F M
A M J
J
A
S O N D
Figure 59: monthly box plot for turbidity extracted from calibrated FUB products from 2002 to
2011 from ROIs Edeskvarnaan and Jungfrun in Lake Vättern.
Water levels
Water level (m)
Available data from the surface monitored by satellite altimetry were used for lake Vattern.
Monthly box plot didn’t show any particular seasonal behavior: median values were mainly
between 89.3 and 89.5 m during the year. That is, no meaningful trends were detected by the
Seasonal Kendall Test.
J
F
M
A
M
J
J
A
S
O
N
D
Figure 60: monthly box plot for water levels in Lake Vattern.
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4.5 Lake Michigan
To capture the Lake Michigan extension 10 ROIs were identified as Shown in Figure 61.
Figure 61: ROIs selected in the Lake Michigan for statistic extraction at sub-basins scale
(left) and Countries scale (right).
Chlorophyll-a
Considering chl-a concentration, Lake Michigan ROIs can be divided in two subgroups, the
first one comprising the three ROIs in Green bay (Oconto, Chambers and Green bay) and
the second group with all other ROIs. In the first group, as clearly detectable in Figure 62,
there is a meaningful North-South gradient, with higher concentration values in the southern
and narrower part of the bay.
Figure 62: chl-a daily mean values for ROIs Oconto, Chambers and Green bay in Lake
Michigan.
In the second group, values are very similar and no spatial trend can be detected at first
glance. In Figure 63, for simplicity only three stations are reported, the most significant ones,
being collocated respectively in the northern, in the central and in the southern part of the
lake. Differences can be found only in some rare events, which occur in the two more coastal
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stations (Grand Transverse bay and Chicago) and not in the most Pelagic one (Michigan
Central), where chl-a concentration value is quite constant.
Figure 63: chl-a daily mean values for ROIs Grand Transverse bay, Michigan Central and
Chicago in Lake Michigan.
The box plot, built using all values extracted from 2002 to 2012, and mean values map in
Figure 64 confirmed the previous considerations.
Figure 64: box plot for all ROIs in Lake Michigan considering values extracted from 2002 to
2012.
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Figure 65: mean values of chl-a concentration estimated by FUB for June 2009 in Lake
Michigan.
In fact it can be clearly seen both the higher variability of all ROIs, except for Michigan South
and Central, located in pelagic areas of the lake, and the highest median values (in the box
plot) estimated in Oconto station together with the highest mean values in the whole NorthEastern bay, where Oconto, Chambers and Green Bay are located. Seasonal box plot does
not help in this case to determine a seasonal behaviour: it can be only noticed that anomaly
concentration values occurred mainly in winter in almost all ROIs (with the exception of
Oconto one). Only in ROI Grand Traverse Bay there is clearly a seasonal behaviour but,
indeed, higher values occurred in winter (Fig. 66).
Figure 66: monthly box plot for Oconto and Grand Traverse Bay ROIs. Y-axes have different
scales in order to see seasonal behaviour in the second ROI.
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Green Bay
Chambers
Oconto
2002
12.29 12.29
27.64 48.44 19.01
2003
0.52
0.85
0.29
4.48
18.00
6.99
17.95 48.14 16.42
2004
1.18
2.33
0.68
3.24
6.98
3.25
29.69 46.18 14.96
2005
1.58
3.91
1.25
4.73
22.04
7.42
11.45 30.35 10.21
2006
0.74
0.98
0.24
5.57
21.96
7.55
13.80 37.42 16.35
2007
3.01
19.16
6.13
2.70
9.13
3.35
6.34
16.03
6.76
2008
2.10
6.19
1.96
3.24
14.11
4.88
14.26 46.62 15.74
2009
7.74
35.32 15.42
3.66
15.36
5.53
2.62
8.90
3.57
2010
0.91
1.74
0.39
11.67 47.71 15.17
0.71
1.55
0.38
9.05
36.21 14.80
29.36 47.50 16.16
2011
0.89
2.85
1.00
12.93 36.60 20.51 12.89 35.36 19.46
2012
0.35
0.35
Grand Traverse bay
Michigan Central
Chicago
2002
0.71
1.24
0.75
0.35
0.35
0.59
0.83
0.23
5.14
2003
17.60
8.37
0.30
0.45
0.13
0.66
1.10
0.26
2004
0.33
0.82
0.27
0.33
0.54
0.21
0.31
0.42
0.12
2005
2.65
5.42
2.73
0.24
0.24
0.37
0.88
0.44
2006
0.47
1.07
0.33
0.26
0.53
0.24
0.25
0.29
0.06
0.86
2.23
1.19
2007
3.25
9.27
4.46
2.14
5.71
3.10
2008
1.70
9.22
3.00
0.27
0.57
0.16
1.87
6.29
2.49
32.92 10.29
2009
3.64
0.45
0.93
0.28
3.41
31.03
9.71
2010
2.07
10.62
3.21
0.59
0.95
0.35
3.55
15.84
5.66
8.57
47.78 15.12
2011
0.39
1.02
0.28
0.42
1.14
0.37
2012
1.38
3.76
2.06
0.82
0.82
0.90
1.56
0.94
3
Table 20: statistics of chl-a concentration (mg/m ) from the whole time series
extracted from each ROI: in bold type maximum values, in Italic minimum values.
For this reason the phenology analysis was not conducted for this lake, but only the analysis
of the ratio of images in which chl concentration exceeded the sum of median and standard
deviation. As it can be seen in Figure 67, Oconto, Chambers and Green Bay are the ROIs in
which the ratio is higher, but the ratio didn’t change year by year. Only in the Strait of
Mackinac the ratio increased from 2008 to the end of the period analysed.
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Figure 67: percentage rate of images for each year and each ROI in which chl-a
concentration exceeds the sum of median and standard deviation.
The Seasonal Kendall test conducted over data from all ROIs provided the results shown in
Tab. 21 and in Figure 68 where results from Oconto, Michigan South and Chicago are
reported to show respectively, a positive trend, a stable situation, a negative trend: a
significant negative trend (thus an increase in water quality) was, in fact, found only in
Oconto, while a low positive trend (thus a worsening in water quality) was found in Chicago
station.
Median Value
(mg/m3)
Median annual
Sen Slope
p-value
Oconto
8.550
-1.877 (-22%)
0.002
Chambers
0.441
0.026 (5.8%)
0.223
Green Bay
1.156
-0.097 (-8.3%)
0.147
Strait of Mackinac
0.880
-0.030 (-3.4%)
0.842
South Fox
0.388
-0.004 (-1.0%)
0.939
Grand Traverse Bay
0.391
-0.018 (-4.6%)
0.172
Michigan South
0.374
-0.011 (-3.0%)
0.451
Pier Cove
0.251
0.005 (2.0%)
0.482
Michigan Central
0.251
0.000 (0%)
1.000
Chicago
0.293
0.02 (6.8%)
0.032
Table 21: results for chl-a concentration from Seasonal Kendall test for all
ROIs in Lake Michigan. Percentage rate is estimated considering median
value. Italic characters indicate no meaningful trend was estimated.
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Figure 68: daily mean values of chl-a concentration from Oconto, Michigan South and
Chicago ROIs in Lake Michigan and Sen Slope trend (red line).
Water levels
Water level (m)
Available data from the surface monitored by satellite altimetry were used for lake Michigan.
Water levels were clearly higher in summer and lower in winter, on the contrary of chl-a. The
Seasonal Kendall Test didn’t show any significant trend (median Sen Slope -0.002 (<< 1% of
median value)).
J
F
M
A
M
J
J
A
S
O
N
D
Figure 69: monthly box plot of water levels in lake Michigan.
4.6 Lake Malawi
In Lake Malawi four ROIs were used for chl-a concentration extraction in addition to the
whole lake extension (Fig. 70). Only in the Southern part of the lake median values (Tab. 22)
were a bit higher (ROI South).
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Figure 70: Lake Malawi and ROIs selected for statistic extraction.
Chlorophyll-a
As expected in such an equatorial lake, no seasonal trends were highlighted (Fig. 71). Thus
the Seasonal Kendall test was used to be congruent to the other lakes, but also MannKendall test classical version was tested.
10
chl-a (mg/m3)
8
6
4
2
0
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
Figure 71: daily chl-a concentration extracted from ROI Centre South in Lake Malawi.
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Figure 72: example maps of chl-a concentration in Lake Malawi, images from: (left) 10th
October 2007 and (right) 26th September 2010.
Figure 72 shows snapshots of distributions of chl-a concentration in Lake Malawi. The left
map shows a very typical situation, with overall very low concentrations, but somewhat
higher values in the southern part of the lake, in particular along the south-western shore.
The map on the right hand side shows a surface algae bloom that extends over a large area
of the lake. Such blooms occur frequently; their occurrence is mainly controlled by periodic
exchange of nutrient-richt deep water with water from the epilimnion which can be a result of
upwelling, turbulent diffusion or internal waves (seiches) (Eccles, 1962; Bootsma and Hecky,
1999).
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Median Value
(mg/m3)
Median annual
Sen Slope
p-value
CentreNorth
0.528
-0.002 (-0.38%)
0.304
CentreSouth
0.836
0.001 (0.11%)
0.775
North
0.631
0.004 (0.63%)
0.073
South
1.319
0.013 (0.99%)
0.047
Entire lake
0.824
0.003 (0.36%)
0.323
Table 22: results for chl-a concentration from Seasonal Kendall test for all
ROIs in Lake Malawi. Percentage rate is estimated considering median
value. Italic characters indicate that any meaningful trend was estimated.
As shown in Tab. 22 no meaningful trends were detected in all ROIs and the same result was
achieved through Mann-Kendall test.
Phenology
As for Lake Vättern, considering that no seasonal behaviour could be identified, the analysis
of algal blooms was conducted only to identify the number of events in which concentration
exceeds the threshold previously defined. Considering the ratio of images in which chl-a
concentration exceeded the threshold given by the sum of median and standard deviation on
the total number of available images per year, only in Centre South ROI this ratio exceeded
the 20% in 2003 and 2008. In the rest of the period ratio remained under the 20%.
Figure 73: percentage rate of images for each year and each ROI in which chl-a
concentration exceeds the sum of median and standard deviation.
4.7 Lake Tanganyika
In Lake Tanganyika five ROIs were used (Fig. 74) in addition to the whole lake surface for the
extraction of statistic parameters. The ROIs CentreNorth and CentreSouth represent the two
deep basins of the lake (Kigomo and Kipili basin, respectively), with the other ROIs being
shallower.
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Figure 74: Lake Tanganyika and ROIs selected for statistic extraction.
Chlorophyll-a
As shown in Figure 75 and in monthly box plot in Figure 76, the highest values in the lake
were estimated in the central part of the year. Considering this seasonal behavior Seasonal
Kendall test was adopted
35
chl-a (mg/m3)
30
25
20
15
10
5
0
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
Figure 75: daily chl-a concentration extracted from the whole lake surface in Lake
Tanganyika.
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Group Pelagic Period analysed 9 years and 11 months for calendar years 2002 to 2012
35
chl
chl-a (mg/m3)
30
25
20
15
10
5
0
F
M
A
M
J
J
A
S
O
N
D
ge
nn
fe aio
bb
ra
i
m o
ar
zo
ap
r
m ile
ag
g
gi i o
ug
no
lu
gl
ag io
se os
tte to
m
b
ot re
t
no ob
ve re
m
di br
ce e
m
br
e
J
Figure 76: monthly box plot of daily chl-a concentration extracted from the whole lake
Tanganyika surface.
The test (Tab. 23) resulted positive for trends in all the ROIs with a slight decrease of chl-a
concentration (thus a slight improve in water quality), excepted for ROI North.
Median Value
(mg/m3)
Median annual
Sen Slope
p-value
North
1.049
0.005 (0.47%)
0.356
Centre
1.057
-0.017 (-1.61%)
0.004
CentreNorth
0.932
-0.01 (-1.07%)
0.042
CentreSouth
1.322
-0.017 (-1.28%)
0.012
South
1.779
-0.018 (-1.01%)
0.086
Entire lake
1.230
-0.019 (-1.53%)
0.004
Table 23: results for chl-a concentration from Seasonal Kendall test for all
ROIs in Lake Tanganyika. Percentage rate is estimated considering
median value. Italic characters indicate that any meaningful trend was
estimated.
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Sen slope trend for chl for group Pelagic
35
chl:Pelagic
chl-a (mg/m3)
30
25
20
15
10
5
0
1/1/02
1/1/04
1/1/06
1/1/08
Date
1/1/10
1/1/12
Figure 77: chl-a (mg/m3) daily mean values extracted from the lake Tanganyika (ROI Centre)
and Sen Slope trend (black line).
Phenology
The analysis of phenology in Lake Tanganyika was performed considering the whole year.
The 25% of cumulative chl-a concentration was reached between May and before the end of
June almost every year (only in 2011 it occurred some days before).
Figure 78: images cover (black line) and starting date (t25 cumulative) for each year in the
whole lake considering all available images. Vertical lines facilitate time interpretation.
Considering the ratio of images in which chl-a concentration exceeded the threshold given by
the sum of median and standard deviation, the worst year was 2004 but ratio did never
exceed the 20% of available images.
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Figure 79: percentage rate of images for each year and each ROI in which chl-a
concentration exceeds the sum of median and standard deviation.
5 Conclusions
Tab. 24 sums up the tendency towards increasing/or decreasing of trophic status assessed
with MERIS time-series imagery in the seven investigated lakes. Comparing results for all the
lakes a different behaviour was highlighted but the improvement or worsening in water quality
conditions are restrained. The results overall show almost stable conditions with slight
increasing trophic status trend for Maggiore, Constance and the Green Bay of Lake
Michigan, slight decreasing trophic status trend for Garda and Tanganyika and absolutely
stable conditions for Vättern and Malawi. The results obtained with trend test of other trophic
status proxies (e.g., turbidity in Lake Constance, or CDOM in Vättern) are consistent with
those obtained for chl-a concentrations.
Lake
Garda
Maggiore
Constance
Vättern
Min/Max of median
chl-a
1.88/2.09
1.15/1.28
3.66/4.76
1.011/1.018
Median annual Sen
Slope (min/max)
-0.111/-0.066
0.124/0.127
0.060/0.083
No trend
Evaluation
Slight decrease
Slight increase
Slight increase
Stable
Slight
decrease/increase
Michigan
0.251/8.55
-1.877/0.02
depending on the
bay
Malawi
Stable
0.528/1.319
No trend
Tanganyika
Slight decrease
0.932/1.779
-0.01/-0.02
Table 24: summary of the results obtained for each lake from statistical and time trend
analysis based on chl-a concentrations.
Phenology analysis points to those years with higher values of chl-a: 2005 for Garda and
Constance, 2011 for Maggiore, 2007 for Vättern, 2003 and 2008 for Malawi and 2004 for
Tanganyika. In Lake Michigan there was no identification of a bad year.
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As expected, the water levels analysis do not show any link between water quality and levels
due to the large water volume, typical for deep clear lakes. Therefore, the slight change
observed might be due to a combination of meteo-climatic conditions (e.g., windy and cool
winter that facilitates the water column circulation) and anthropic impacts (e.g., underdimensioned water treatment plants). These two drivers are therefore discussed in the
following socio-economic section.
The results presented in this report showed the great capability of MERIS to perform trend
tests analysis on trophic status with focus on chl-a concentration. Being the chl-a
concentration also a key parameter in water quality monitoring plans, this study also supports
the managing practices implemented worldwide for using the water of the lakes. Since four of
the seven lakes investigated in this study are trans boundary, this work might be used to also
emphasize the advantage of integrating EO technologies into the management of aquatic
ecosystems at catchment scale.
As 10-years long time-series might be too short for understating the climatic/anthropogenic
processes we look forwards to the new missions. In particular, we expect that the results
presented here will benefit from the MERIS successor S-3 that will ensure data continuity
collection as needed by time-series analysis.
In addition, the recently launched Sentinel 2 can provide further information on water quality
parameters in the lakes, enhancing both the temporal and spatial resolution. A first example
of turbidity derived using the first Sentinel 2 data provided by ESA and processed with
EOMAP MIP algorithm is given in Figure 80.
Figure 80: MIP derived turbidity of Lake Garda using Sentinel 2 data recorded on 4th July
2015.
Socio-economic overview
Water resources available for our use are limited by nature. For the greatest of their daily
uses (e.g., agriculture, industry and domestic), people depend on good quality freshwater
resources. These are continuously renewed by the natural processes of the hydrological
cycle but when used by humans, water is diverted temporarily from one part of the cycle and
frequently altered in terms of quality.
Among freshwater resources, lakes play a very important role: there are 117 million lakes
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greater than 0.002 km2 covering 3.7% of Earth non-glaciated land area; 27 million water
bodies larger than 0.01 km2 with a total surface area of 4.76 × 106 km2 excluding the
Caspian Sea (Verpoorter et al., 2014).
Lakes and their surroundings offer many options for nature activities and are ecologically,
economically and socio-culturally relevant to the local population and visitors. Lakes areas
have important functions and many economic uses such as:

environmental and macro and micro-climatic functions, in particular habitat
functions for wildlife and for lake water ecological system;

hydrological function for the region (water resources);

regional economically important functions and uses, that depend on the
functionality of the lake, such as tourism, agriculture and viticulture, fishing, reed
use, infrastructural use;

socio-cultural functions such as landscape aesthetics, local customs and traditions;

social function for the population, such as health care and recreation.
Threats to the lake regions biodiversity come mainly from the disruption of the hydrological
water balance due to climate change and anthropogenic pressures (e.g., land use changes,
hydrological modifications, toxic contaminants and invasive species).
Lakes are particularly vulnerable to changes in climate parameters. Climate change could
cause deterioration concerning the water situation in many regions and countries. The main
climate change consequences related to water resources are increases in temperature, shifts
in precipitation pattern and snow cover, increase in the frequency of flooding and droughts
and the possible serious impact of future water-level rises (Blenckner, 2008). The effects of
climate change and climate variability have been analysed in a number of lakes with
significant changes in lake temperature, period of ice cover, stratification strength, bottom
oxygen concentration, spring phytoplankton bloom, clear water phase, fish abundance and
match–mismatch of predator–prey interactions (see for example Schindler et al., 1996;
Scheffer et al., 2001; Winder and Schindler, 2004; Blenckner, 2005).
Lake water quality is heavily depending on direct and indirect human activities. Land-use and
agricultural practices have a very significant effect on water quality end ecological
functionality of the system. Today, non-point sources are especially highly relevant
(Schindler, 2006).
The impacts of these modifications could lead to a decreasing capacity of the lakes to
provide habitat for wildlife and environmental services to key sectors such as tourism or
agriculture.
The human interference with phosphorus and nitrogen cycles is well beyond safe thresholds.
Eutrophication of surface water and coastal zones is expected to increase almost
everywhere until 2030 (UNDESA, 2012). Thereafter, it may stabilize in developed countries,
but is likely to continue to worsen in developing countries. Globally, the number of lakes with
harmful algal blooms will increase by at least 20% until 2050 (UNDESA, 2012).
Due to the absence of proper drainage systems, sewage mixes with stormwater causing
further pollution. It is estimated that up to 90% of all wastewater in developing countries is
discharged untreated directly into rivers, lakes or the oceans, causing major environmental
and health risks (Corcoran et al., 2010). This has huge social and economic impacts due to
increased health care costs and lower labour productivity.
As reported by UNEP (2012), increase in allocation of government expenditure on Water
Resources development was recorded in the past 20 years in over 50% of all countries. The
62% of the high Human Development Index (HDI) countries indicates an increase of
investments from private sources (e.g., banks and private operators, non-profit) for water
resources development.
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Water for agriculture is a high priority for many low HDI countries. Water for environment is a
priority mainly for the very high HDI countries.
As regards the deep clear lakes, the impacts from climate change (combined with changes
in infrastructure and human population, loss of wetlands and invasive species) are not well
understood, but are hypothesized to make increase primary production, including harmful
algal blooms and nuisance macrophyte densities (Kling et al., 2003). Plant and animal
communities will likely shift to more tolerant species, including invasive ones that will expand
their ranges (Wilcox, 2012).
Phytoplankton dynamics are linked to annual fluctuations of temperature, water column
stratification, light availability and consumption (Cloern, 1996). Climate-driven physical
fluctuations exercise strong impacts on aquatic ecosystems because climate is modifying the
abiotic and biotic environments (Winder and Sommer, 2012).
For deep lakes, the dynamics of phytoplankton is strongly correlated to water column mixing,
which, in fact, also affects nutrients availability for phytoplankton growth. Enhanced water
column stratification suppresses the upward flux of nutrients from deep-water layers through
vertical mixing, resulting in more nutrient-depleted conditions in surface waters (Livingstone,
2003; Schmittner, 2005). As a consequence, altered mixing regimes affect the competitive
advantage of specific algal cell types, that are better competitors for nutrients (Falkowski and
Oliver, 2007) and that are able to maintain their vertical position in the surface water
(Huisman et al., 2004).
In deep systems, spring phytoplankton blooms are coupled to the onset of thermal
stratification, which increases the mean light exposure of phytoplankton cells in the mixed
surface layer. Under these conditions, spring blooms are triggered by correlated increases in
temperature and seasonal light availability (e.g., Edwards and Richardson, 2004). A large
number of studies reported that timing and magnitude of seasonal plankton blooms are
shifting in response to climate change (Straile, 2002; Edwards and Richardson, 2004).
Particularly, shifts in plankton spring phenology related to climate change have been shown
in several ecosystems, whereas later in the season other factors like biotic interactions often
complicate the extraction of a clear climate signal.
In addition to climate change, deep clear lakes are affected by both the impacts of point
sources (sewage) and the impacts of diffuse sources (e.g., agriculture). In order to prevent
and reduce the effects of human impacts on lakes, the strategies and proposals expected
are:

modernization and development of the sewerage system;

pre-emptive reduction of point source pollution;

adaption of the design factor of wastewater treatment plants for heavy rain events;

improved wastewater treatment;

modernization of existing wastewater treatment plants (higher purification
efficiency and purification capacity; improved de-nitrification and phosphorous
elimination);

additional tertiary treatment (granular-activated carbon adsorption, chemical
oxidation, disinfection etc.);

reduction of overflow through wastewater routing systems;

increase of storage capacity of the sewerage system and concomitant reduction of
the risk of storm water overflow;

improving industry risk management (register of industrial discharges);

separation of rainwater and sewage.
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Additionally, education of the population is necessary to protect natural resources. This can
be conducted through the mass media and other established means of dispensing
information. Regarding government policies, people must be sensitized to the importance of
the lake and the serious threat they pose to its survival.
By 2005 for Europe as a whole, 38% of the abstracted water was used for agricultural
purposes, while domestic uses, industry and energy production account for 18%, 11%, and
33% respectively. However, large differences exist across the continent.
The future climate change may suppress the turnover in European deep lakes. This implies
the enhancement of anoxic bottom conditions and an increased risk of eutrophication. The
deterioration of oxygen conditions can also be anticipated due to increased bacterial activity
in deep waters and bottom sediment.
In the densely populated parts of Europe, housing and recreational development have led to
extensive deterioration of the shorelines and littoral zones of most of large lakes. Typical
examples of lakes with very extensively altered shorelines are Lake Constance (Ostendorp et
al., 2003) and the Southern part of Lake Garda (EULAKES, 2013). Another serious problem
for the deep European lakes is the hydrological engineering projects, with broadly different
aims and variable degrees of intensity. Generally, the opposite case holds for hydropower:
electricity production mostly calls for unnatural water level alterations and major hydromorphological modifications in the regulated basin (van den Brink et al., 2005).
With respect to deep clear lakes in the United States, the major issues responsible for their
degradation are all related to the intense human pressure. High concentrations of toxic
contaminants and overfishing had terrible effects on the ecology of the food web and in
cascade on the trophic level of the water.
For the African lakes, overfishing, exotic invasive species and increasing economic activity
are depleting the fish stock in lakes and altering water quality. Deforestation and
intensification of agricultural activities in the catchment areas often lead to increased surface
runoff. The deterioration of water quality (e.g., eutrophication) could have an higher impact
on the economy of the local population, in particular on the commercial fish catch.
Acknowledgments
We thank ‘© BOWIS - Daten aus dem Bodensee-Wasser-informationssystem der
Internationalen Gewässerschutzkommission für den Bodensee (IGKB)’ for in situ data of
Lake Constance and ‘CNR-ISE (National Research Council-Institute of Ecosystem Study)’ for
water levels of Lake Maggiore.
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