Webinar - Deltares

Delft3D Phytoplankton modelling:
Concepts of BLOOM
Hans Los
Deltares Webinar
March 2016
Overview of Webinar
1.
2.
3.
4.
5.
6.
7.
Role of primary producers
Position of BLOOM in Delft3D modeling suite
Concepts of BLOOM (overview)
Competition principle (detail)
Existing knowledgebase
Some validation results
Summary and advise for further reading
Aquatic Ecosystem
Aquatic Ecosystem
•
Physical processes
• Exchange atmosphere
• Sedimentation, resuspension
• Horizontal water movement
• Possibly stratification
• Seasonality
Aquatic Ecosystem
•
•
Physical processes
• Exchange atmosphere
• Sedimentation, resuspension
• Horizontal water movement
• Possibly stratification
• Seasonality
Foodweb (eating and being
eaten)
Aquatic Ecosystem: driven by Primary Production
•
•
•
Physical processes
• Exchange atmosphere
• Sedimentation, resuspension
• Horizontal water movement
• Possibly stratification
• Seasonality
Foodweb (eating and being
eaten)
Main driver: primary production
by phytoplankton using Solar
energy and CO2 giving sugar and
O2
Trophic state depends on nutrients
Trophic state depends on nutrients
DELWAQ - BLOOM: nutrients and primary production
• For historic reasons, many different names circulate in papers and
reports such as
• DBS (fresh water mode)
• GEM (marine mode)
• ECO
• DWAQ
• DELWAQ – BLOOM
• DELWAQ(D3D)-BLOOM consists of one generic code for fresh
and marine applications.
• Some differences in selected processes and coefficient values
particularly in definition of phytoplankton species
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
3. BLOOM has been applied and validated world wide in a very
large number of fresh, transitional and marine waters
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
3. BLOOM has been applied and validated world wide in a very
large number of fresh, transitional and marine waters
4. The result is a huge knowledge database with default species
characteristics available to every user
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
3. BLOOM has been applied and validated world wide in a very
large number of fresh, transitional and marine waters
4. The result is a huge knowledge database with default species
characteristics available to every user
5. BLOOM typically operates on a 24 hour time step, which is
characteristic for phytoplankton but it does include diurnal
variations in light even across vertical layers
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
3. BLOOM has been applied and validated world wide in a very
large number of fresh, transitional and marine waters
4. The result is a huge knowledge database with default species
characteristics available to every user
5. BLOOM typically operates on a 24 hour time step, which is
characteristic for phytoplankton but it does include diurnal
variations in light even across vertical layers
6. The mathematical method employed in the model prevents
numerical oscillations or instabilities under all circumstances
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
3. BLOOM has been applied and validated world wide in a very
large number of fresh, transitional and marine waters
4. The result is a huge knowledge database with default species
characteristics available to every user
5. BLOOM typically operates on a 24 hour time step, which is
characteristic for phytoplankton but it does include diurnal
variations in light even across vertical layers
6. The mathematical method employed in the model prevents
numerical oscillations or instabilities under all circumstances
Process formulations BLOOM (1): competition
•
BLOOM is a multi-species
phytoplankton model
Process formulations BLOOM (1): competition
•
BLOOM is a multi-species
phytoplankton model
Process formulations BLOOM (1): competition
•
BLOOM is a multi-species
phytoplankton model
•
Competition between phytoplankton
types is the guiding principle in
BLOOM
Process formulations BLOOM (1): competition
•
BLOOM is a multi-species
phytoplankton model
•
Competition between phytoplankton
types is the guiding principle in
BLOOM
Process formulations BLOOM (1): competition
•
BLOOM is a multi-species
phytoplankton model
•
Competition between phytoplankton
types is the guiding principle in
BLOOM
•
BLOOM selects the optimum
composition based on the ratio of the
net growth rate and the requirements
for each environmental resource
Process formulations BLOOM (1): competition
•
BLOOM is a multi-species
phytoplankton model
•
Competition between phytoplankton
types is the guiding principle in
BLOOM
•
BLOOM selects the optimum
composition based on the ratio of the
net growth rate and the requirements
for each environmental resource
•
Trade-off between growth and
requirement:
• Relatively high potential growth
rates may compensate a relatively
large requirement
Process formulations BLOOM (1): competition
•
BLOOM is a multi-species
phytoplankton model
•
Competition between phytoplankton
types is the guiding principle in
BLOOM
•
BLOOM selects the optimum
composition based on the ratio of the
net growth rate and the requirements
for each environmental resource
•
Trade-off between growth and
requirement:
• Relatively high potential growth
rates may compensate a relatively
large requirement
opportunistic species win when light is high…
Process formulations BLOOM (1): competition
•
BLOOM is a multi-species
phytoplankton model
•
Competition between phytoplankton
types is the guiding principle in
BLOOM
•
BLOOM selects the optimum
composition based on the ratio of the
net growth rate and the requirements
for each environmental resource
•
Trade-off between growth and
requirement:
• Relatively high potential growth
rates may compensate a relatively
large requirement
opportunistic species win when light is high…
…efficient species win when there is little light
Process formulations BLOOM (2): general
•
BLOOM considers various (3 to 20) algal
species (groups), including some nuisance
algae
Process formulations BLOOM (2): general
•
BLOOM considers various (3 to 20) algal
species (groups), including some nuisance
algae (e.g. Phaeocystis,
Process formulations BLOOM (2): general
•
BLOOM considers various (3 to 20) algal
species (groups), including some nuisance
algae (e.g. Phaeocystis, Microcystis
Process formulations BLOOM (2): general
•
BLOOM considers various (3 to 20) algal
species (groups), including some nuisance
algae (e.g. Phaeocystis, Microcystis or Ulva)
Process formulations BLOOM (2): general
•
BLOOM considers various (3 to 20) algal
species (groups), including some nuisance
algae (e.g. Phaeocystis, Microcystis or Ulva)
•
Every species (group) has its own:
• growth response to light conditions
Process formulations BLOOM (2): general
•
BLOOM considers various (3 to 20) algal
species (groups), including some nuisance
algae (e.g. Phaeocystis, Microcystis or Ulva)
•
Every species (group) has its own:
• growth response to light conditions
• growth response to temperature
Process formulations BLOOM (2): general
•
BLOOM considers various (3 to 20) algal
species (groups), including some nuisance
algae (e.g. Phaeocystis, Microcystis or Ulva)
•
Every species (group) has its own:
• growth response to light conditions
• growth response to temperature
• growth response to available nutrients
• stochiometry (composition in C, N, P,
Chlorophyll)
•
Depending on environmental conditions
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
3. BLOOM has been applied and validated world wide in a very
large number of fresh, transitional and marine waters
4. The result is a huge knowledge database with default species
characteristics available to every user
5. BLOOM typically operates on a 24 hour time step, which is
characteristic for phytoplankton but it does include diurnal
variations in light even across vertical layers
6. The mathematical method employed in the model prevents
numerical oscillations or instabilities under all circumstances
Process formulations BLOOM (3): types
•
Variability of stochiometry is modelled as a
distinction of the species in 3 types:
• a nitrogen limited type
• a phosphorus limited type
• an energy limited type
Process formulations BLOOM (3): types
•
Variability of stochiometry is modelled as a
distinction of the species in 3 types:
• a nitrogen limited type
• a phosphorus limited type
• an energy limited type
•
Every type has a constant stochiometry
•
The model computes the optimal
combination of types
•
Different types of same species may be
present at the same time (any linear
combination of types is possible: variable
stochiometry)
•
Types can instantaneously convert into
each other, (groups of) species can’t
(adaptation occurs rapidly, succession
takes more time: days or weeks)
Process formulations BLOOM (4): energy
• At sufficient (= not limiting) availability of nutrients, light and
temperature become the limiting factors
• Analogous to nutrients the available ‘amount’ of light is
calculated
• A ‘critical light efficiency’ is reached when growth averaged over
depth equals all losses:
Process formulations BLOOM (4): energy
• At sufficient (= not limiting) availability of nutrients, light and
temperature become the limiting factors
• Analogous to nutrients the available ‘amount’ of light is
calculated
• A ‘critical light efficiency’ is reached when growth averaged over
depth equals all losses:
PPmaxi ,T * eff cr ,I  Morti ,T  Respi ,T
• This threshold varies:
• per species
• in time and space
Process formulations BLOOM (4): energy
• Blue line: growth vs depth
• Green line: mortality + respiration
vs depth
Process formulations BLOOM (4): energy
Positive net growth
Surplus Energy
• Blue line: growth vs depth
• Green line: mortality + respiration
vs depth
• Low extinction
Process formulations BLOOM (4): energy
Positive net growth
Surplus Energy
Zero net growth
Energy Limitation
PPmaxi ,T * eff cr ,I  Morti ,T  Respi ,T
• Blue line: growth vs depth
• Green line: mortality + respiration
vs depth
• Critical extinction
Process formulations BLOOM (4): energy
Positive net growth
Surplus Energy
• Blue line: growth vs depth
• Green line: mortality + respiration
vs depth
• Extinction too high
Zero net growth
Energy Limitation
Negative net growth
Energy shortage
Process formulations BLOOM (4): energy
Positive net growth
Surplus Energy
• Blue line: growth vs depth
• Green line: mortality + respiration
vs depth
• Extinction too high
• Note: Under stratified conditions,
depth gets smaller, hence higher
potential biomass
Zero net growth
Energy Limitation
Negative net growth
Energy shortage
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
3. BLOOM has been applied and validated world wide in a very
large number of fresh, transitional and marine waters
4. The result is a huge knowledge database with default species
characteristics available to every user
5. BLOOM typically operates on a 24 hour time step, which is
characteristic for phytoplankton but it does include diurnal
variations in light even across vertical layers
6. The mathematical method employed in the model prevents
numerical oscillations or instabilities under all circumstances
Time averaging in BLOOM
• Growth (= biomass increase) has a characteristic time of about 24
hours
• Note that this is not photosynthesis (= production of energy stored
as sugar), which reacts quickly to changes in light
• Photosynthesis is an instantaneous process
• Growth is a time-averaged process
• The light response curves of photosynthesis and growth are not
the same
• So apart from computational aspects, there is also a fundamental
argument to use a relatively long 1 day time step as the default for
BLOOM
Depth averaging in BLOOM
2D (vertically averaged) models:
mixing depth = actual depth
•
1Dv / 3D models:
mixing depth ≠ segment depth: during 24 hours algae
usually visit several vertical layers
• This conflicts with DELWAQ’s usual procedure to
compute process rates in segments independently from
other segments during ∆t
• To deal with this, tracers are introduced in every
vertical layer at the start of the BLOOM time step
• The probability distribution of these tracers is computed
for each layer based on vertical transports
• Based on the mixing pattern of the tracers, the depth
averaged light regime is computed
depth
•
Fate of tracers tropical reservoir model (1)
Vertical distribution tracer layer 4 in a 7 layer model after 24 hours
Fate of tracers tropical reservoir model (2)
Vertical distribution tracer layer 1-7 in a 7 layer model after 24 hours
FRACTIME01
1
STN.3 (2)
STN.3 (4)
FRACTIME02 05 Jul 05 00:00
FRACTIME06 05 Jul 05 00:00
STN.3 (3)
FRACTIME03 05 Jul 05 00:00
FRACTIME07 05 Jul 05 00:00
STN.3 (5)
FRACTIME04 05 Jul 05 00:00
STN.3 (6)
Graph for location STN.3 (1), STN.3 (2), STN.3 (3), STN.3 (4), STN.3 (5), STN.3 (6), STN.3 (7)
0.9
0.95
0.8
0.85
0.75
0.7
0.6
0.65
0.5
0.55
0.4
0.45
0.3
0.35
0.2
0.25
0.1
0.15
0.05
STN.3 (1)
FRACTIME01 05 Jul 05 00:00
FRACTIME05 05 Jul 05 00:00
STN.3 (7)
Fate of tracers tropical reservoir model (3)
3 year simulation of all tracers in layer 1
Graph for location STN.3 (1)
FRACTIME01
1
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
FRACTIME01 STN.3 (1)
FRACTIME06 STN.3 (1)
FRACTIME02 STN.3 (1)
FRACTIME07 STN.3 (1)
FRACTIME03 STN.3 (1)
FRACTIME04 STN.3 (1)
FRACTIME05 STN.3 (1)
Fate of tracers tropical reservoir model (3)
Little mixing
Graph for location STN.3 (1)
FRACTIME01
1
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
FRACTIME01 STN.3 (1)
FRACTIME06 STN.3 (1)
FRACTIME02 STN.3 (1)
FRACTIME07 STN.3 (1)
FRACTIME03 STN.3 (1)
FRACTIME04 STN.3 (1)
FRACTIME05 STN.3 (1)
Fate of tracers tropical reservoir model (3)
Strong mixing
Graph for location STN.3 (1)
FRACTIME01
1
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
FRACTIME01 STN.3 (1)
FRACTIME06 STN.3 (1)
FRACTIME02 STN.3 (1)
FRACTIME07 STN.3 (1)
FRACTIME03 STN.3 (1)
FRACTIME04 STN.3 (1)
FRACTIME05 STN.3 (1)
Depth profile phytoplankton species deep region
Simulated actual depth profile also depends on other factors!
Depth profile phytoplankton species deep region
Simulated actual depth profile also depends on other factors!
Insufficient
nutrients
Insufficient
light
Current development
• Present tracer approach confined to vertical excursions, which
results in some inaccuracies in
• Systems with tidal flats
• Deep lakes with confined, shallow areas (near beaches)
• We now have a beta model version considering both horizontal
and vertical tracer movements
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
3. BLOOM has been applied and validated world wide in a very
large number of fresh, transitional and marine waters
4. The result is a huge knowledge database with default species
characteristics available to every user
5. BLOOM typically operates on a 24 hour time step, which is
characteristic for phytoplankton but it does include diurnal
variations in light even across vertical layers
6. The mathematical method employed in the model prevents
numerical oscillations or instabilities under all circumstances
Competition: 2 types, 1 nutrient
•
•
X-axis: biomass Alg1
Y-axis: biomass Alg2
Competition: 2 types, 1 nutrient
•
•
•
X-axis: biomass Alg1
Y-axis: biomass Alg2
DIN requirement per unit
of biomass (first
competition criterion:
Alg2 is more efficient):
• Alg1 = 0.07
• Alg2 = 0.05
Competition: 2 types, 1 nutrient
•
•
•
X-axis: biomass Alg1
Y-axis: biomass Alg2
DIN requirement per unit
of biomass (first
competition criterion:
Alg2 is more efficient):
• Alg1 = 0.07
• Alg2 = 0.05
• Green: sufficient DIN
Competition: 2 types, 1 nutrient
•
•
•
X-axis: biomass Alg1
Y-axis: biomass Alg2
DIN requirement per unit
of biomass (first
competition criterion:
Alg2 is more efficient):
• Alg1 = 0.07
• Alg2 = 0.05
• Green: sufficient DIN
• Orange line: DIN = 0
(limiting)
Competition: 2 types, 1 nutrient
•
•
•
X-axis: biomass Alg1
Y-axis: biomass Alg2
DIN requirement per unit
of biomass (first
competition criterion:
Alg2 is more efficient):
• Alg1 = 0.07
• Alg2 = 0.05
• Green: sufficient DIN
• Orange line: DIN = 0
(limiting)
• Orange: insufficient DIN
Competition: 2 types, 1 nutrient
•
•
•
Equal growth rate 0.5
per day (second
competition criterion)
Grey line: growth rates
Alg1 = Alg2 = 0.0
Competition: 2 types, 1 nutrient
•
Only Alg1 present, not
yet optimal
Competition: 2 types, 1 nutrient
•
Competition criterion:
Growth divided by
requirement so
•
0.5/0.05 > 0.5/0.07
• Alg2 (= small nutrient
requirement) wins
Competition: 2 types, 1 nutrient
•
Growth rate (Alg1 rapid
grower):
• Alg1 = 0.8 d-1
• Alg2 = 0.5 d-1
• Angle grey line shifted
• DIN requirement per unit
of biomass (Alg2 is more
efficient):
• Alg1 = 0.07
• Alg2 = 0.05
• Alg1 = Alg2 = 0.0
Competition: 2 types, 1 nutrient
•
Only Alg2 present, not
yet optimal
Competition: 2 types, 1 nutrient
•
•
•
Competition criterion:
0.5/0.05 < 0.8/0.07
Alg1 (= fastest growing)
wins
Potential biomass Alg2 >
Alg1
Competition: 2 types, 2 nutrients
•
•
•
•
•
DIN requirement:
• Alg1 < Alg2
PO4 requirement:
• Alg2 < Alg1
Green: sufficient DIN
and PO4
Orange line: DIN = 0
Blue line: PO4 = 0
Other areas: insufficient
DIN or PO4 or both
Competition: 2 types, 2 nutrients
•
•
Equal growth rate 0.5
per day (second
competition criterion)
Alg1 = Alg2 = 0.0
Competition: 2 types, 2 nutrients
•
Only Alg2 present, PO4
limiting, not yet optimal
Competition: 2 types, 2 nutrients
•
Only Alg1 present, DIN
limiting, not yet optimal
Competition: 2 types, 2 nutrients
•
•
•
Alg1 present and limited
by DIN
Alg2 present and limited
by PO4
Two most efficient types
win
Transient conditions in BLOOM
Graph for location NZR9TS135
•
8
7
6
Chlora
5
4
3
2
1
0
27-Jan 03-Feb 10-Feb 17-Feb 24-Feb 03-Mar 10-Mar 17-Mar 24-Mar 31-Mar 07-Apr 14-Apr 21-Apr 28-Apr 05-May 12-May 19-May
Chlora NZR9TS135
So far resource
limitations
Transient conditions in BLOOM
Graph for location NZR9TS135
•
8
7
•
6
Chlora
5
4
3
2
1
0
27-Jan 03-Feb 10-Feb 17-Feb 24-Feb 03-Mar 10-Mar 17-Mar 24-Mar 31-Mar 07-Apr 14-Apr 21-Apr 28-Apr 05-May 12-May 19-May
Chlora NZR9TS135
So far resource
limitations
It takes about 2 weeks to
get growth started
Transient conditions in BLOOM
Graph for location NZR9TS135
•
8
7
•
6
Chlora
5
•
4
3
2
1
0
27-Jan 03-Feb 10-Feb 17-Feb 24-Feb 03-Mar 10-Mar 17-Mar 24-Mar 31-Mar 07-Apr 14-Apr 21-Apr 28-Apr 05-May 12-May 19-May
Chlora NZR9TS135
So far resource
limitations
It takes about 2 weeks to
get growth started
And another 2 weeks to
reach nutrient limited
spring bloom
Transient conditions in BLOOM
Graph for location NZR9TS135
•
8
7
•
6
Chlora
5
•
4
3
2
•
1
0
27-Jan 03-Feb 10-Feb 17-Feb 24-Feb 03-Mar 10-Mar 17-Mar 24-Mar 31-Mar 07-Apr 14-Apr 21-Apr 28-Apr 05-May 12-May 19-May
Chlora NZR9TS135
So far resource
limitations
It takes about 2 weeks to
get growth started
And another 2 weeks to
reach nutrient limited
spring bloom
How does BLOOM deal
with this?
Growth and mortality as limitations
•At the beginning of the
growing season,
• Alg1 = Alg2 = 0.0
Graph for location NZR9TS135
8
7
6
Chlora
5
4
3
2
1
0
27-Jan
03-Feb
10-Feb
17-Feb
24-Feb
03-Mar
10-Mar
17-Mar
24-Mar
31-Mar
07-Apr
Chlora NZR9TS135
14-Apr
21-Apr
28-Apr
05-May
12-May
19-May
Growth and mortality as limitations
Graph for location NZR9TS135
8
7
6
Chlora
5
4
3
2
1
0
27-Jan
03-Feb
10-Feb
17-Feb
24-Feb
03-Mar
10-Mar
17-Mar
24-Mar
31-Mar
07-Apr
Chlora NZR9TS135
14-Apr
21-Apr
28-Apr
05-May
12-May
19-May
•After 1 week
• Alg1 > 0.0
• Alg2 > 0.0
•Maximum biomass increase
over time is restricted
•We call this ‘growth’ limitation
•BLOOM records growth (and
mortality) limitations per
individual species
Growth and mortality as limitations
•After 3 weeks biomasses of
both algae have further
increased
•Still growth limitation
Graph for location NZR9TS135
8
7
6
Chlora
5
4
3
2
1
0
27-Jan
03-Feb
10-Feb
17-Feb
24-Feb
03-Mar
10-Mar
17-Mar
24-Mar
31-Mar
07-Apr
Chlora NZR9TS135
14-Apr
21-Apr
28-Apr
05-May
12-May
19-May
Growth and mortality as limitations
Graph for location NZR9TS135
8
7
6
Chlora
5
4
3
2
1
0
27-Jan
03-Feb
10-Feb
17-Feb
24-Feb
03-Mar
10-Mar
17-Mar
24-Mar
31-Mar
07-Apr
Chlora NZR9TS135
14-Apr
21-Apr
28-Apr
05-May
12-May
19-May
•After 4 weeks resource
limitation
•Alg1 present and limited by
DIN
•Alg2 present and limited by
PO4
Growth and mortality as limitations
• Mortality limitations delimit the
disappearance rate similar to
growth
Growth and mortality as limitations
• Mortality limitations delimit the
disappearance rate similar to
growth
• Growth and mortality limitations
also occur as a result of spatial
gradients in depth, nutrients etc.
Growth and mortality as limitations
Preferred
Alg1
Alg2
• Mortality limitations delimit the
disappearance rate similar to
growth
• Growth and mortality limitations
also occur as a result of spatial
gradients in depth, nutrients etc.
Growth and mortality as limitations
Preferred
Alg1
Alg2
• Mortality limitations delimit the
disappearance rate similar to
growth
• Growth and mortality limitations
also occur as a result of spatial
gradients in depth, nutrients etc.
• Mixing between spatially diverse
areas operates like time: it
extends the necessary period
for reaching resource limited
conditions
Growth and mortality as limitations
Preferred
Alg1
Alg2
• Mortality limitations delimit the
disappearance rate similar to
growth
• Growth and mortality limitations
also occur as a result of spatial
gradients in depth, nutrients etc.
• Mixing between spatially diverse
areas operates like time: it
extends the necessary period
for reaching resource limited
conditions
• Similarly (heavy) grazing by
shell fish also results in
prolonged growth limitations
Practise
•In practise BLOOM considers all
types and all limitations
simultaneously
•More difficult to visualize
•But same principle
Limitations and types in practise
• At station Noordwijk 10 (North Sea)
• Energy is limiting during the winter
half year
Limitations and types in practise
• At station Noordwijk 10 (North Sea)
• Energy is limiting during the winter
half year
•Accordingly BLOOM selects E
limited types in winter
Limitations and types in practise
• At station Noordwijk 10 (North Sea)
• Energy is limiting during the winter
half year
•Accordingly BLOOM selects E
limited types in winter
•Phosphorus and silicate are main
limitations in summer half year
Limitations and types in practise
• At station Noordwijk 10 (North Sea)
• Energy is limiting during the winter
half year
•Accordingly BLOOM selects E
limited types in winter
•Phosphorus and silicate are main
limitations in summer half year
• In summer half year P types are
dominant
• Usually various species are present:
there is a high species diversity
Limitations and types in practise
• At station Noordwijk 70 (North Sea)
• Energy is limiting during the winter half
year
Limitations and types in practise
• At station Noordwijk 70 (North Sea)
• Energy is limiting during the winter half
year
•Accordingly BLOOM selects E limited
types at beginning spring bloom
Limitations and types in practise
• At station Noordwijk 70 (North Sea)
• Energy is limiting during the winter half
year
•Accordingly BLOOM selects E limited
types at beginning spring bloom
•Nitrogen and silicate are main limitations
in summer half year with some P limitation
late summer
Limitations and types in practise
• At station Noordwijk 70 (North Sea)
• Energy is limiting during the winter half
year
•Accordingly BLOOM selects E limited
types at beginning spring bloom
•Nitrogen and silicate are main limitations
in summer half year with some P limitation
late summer
•In summer half year N types are dominant
with some P types end of summer
• Less diversity compared to NW10
Limitations and types in practise
• At station Noordwijk 70 (North Sea)
• Energy is limiting during the winter half
year
•Accordingly BLOOM selects E limited
types at beginning spring bloom
•Nitrogen and silicate are main limitations
in summer half year with some P limitation
late summer
•In summer half year N types are dominant
with some P types end of summer
• Less diversity compared to NW10
• Notice that strong increase in plankton C
in summer is hardly reflected in chlorophyll
levels due to intrinsic adjustment C /
Chlorophyll ratio by BLOOM
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
3. BLOOM has been applied and validated world wide in a very
large number of fresh, transitional and marine waters
4. The result is a huge knowledge database with default species
characteristics available to every user
5. BLOOM typically operates on a 24 hour time step, which is
characteristic for phytoplankton but it does include diurnal
variations in light even across vertical layers
6. The mathematical method employed in the model prevents
numerical oscillations or instabilities under all circumstances
Phytoplankton characteristics (1)
• Freshwater model version considers a subset of:
•
•
•
•
•
•
•
•
•
•
•
Diatoms
Micro flagellates
Green algae
Aphanozomenon (optionally as N-fixer)
Microcystis
Oscillatoria (Planktotrix)
Anabaena
Nodularia
Cylindrospermopsis (N-fixer)
Pseudoanabaena
Chara
• Data on characteristics of many individual species have been
obtained from lab cultures of UvA in the 1980s + other literature
sources
• Adjustments from numerous model applications
Phytoplankton characteristics (2)
• Marine model version considers a subset of:
•
•
•
•
•
•
Diatoms
Micro flagellates
Dinoflagellates (optionally mixotrophic)
Phaeocystis
Ulva
Benthic diatoms
• Data on characteristics of individual species particularly for
Phaeocystis and diatoms have been obtained from lab cultures
• Some adjustments from model applications North Sea
Phytoplankton characteristics (3)
For each species the database contains type-specific information on:
• Stoichiometric ratios (nutrients; chlorophyll)
• Specific extinction coefficient
• Maximum growth rate
• Light requirement
• Dependency diurnal light pattern
• Mortality rate as a function of temperature and salinity
• Partitioning mortality to autolysis (dissolved nutrients), labile and
refractory detritus
• Respiration rate
• Sedimentation rate
BLOOM Highlights
1. It is a true multi species competition model based on an easy
to understand biological principle
2. Phytoplankton species are represented by different types
which allows BLOOM to vary their characteristics as a function
of the environment
3. BLOOM has been applied and validated world wide in a very
large number of fresh, transitional and marine waters
4. The result is a huge knowledge database with default species
characteristics available to every user
5. BLOOM typically operates on a 24 hour time step, which is
characteristic for phytoplankton but it does include diurnal
variations in light even across vertical layers
6. The mathematical method employed in the model prevents
numerical oscillations or instabilities under all circumstances
Calibration & Validation
• First application fresh water model more than 35 years ago,
marine version more than 20 years ago
• Both model versions have been applied very extensively
(worldwide)
• Initially mainly in temperate regions, but many (sub)tropical
application in later years
• The user selects species / types to be included in application
• Usually default coefficients will do, no local tuning required
Validation DBS Lake Veluwe
Simulated species composition Lake Veluwe
Diatoms
Cyanos
Greens
Noordwijk 20km 2002 Validation
[mg/m3]
[mg/m3]
NZR6NW020 Chlorophyll
30
1.2
25
1
20
0.8
15
0.6
10
0.4
5
0.2
NZR6NW020 NO3
0
0
j
f
m
a
Mean
[mg/m3]
m
j
Median
j
a
Model
s
o
n
j
d
f
m
a
Obs2002
m
Mean
[mg/m3]
NZR6NW020 OPO4
j
Median
j
a
Model
s
o
Obs2002
n
d
NZR6NW020 SiO2
1
0.1
0.09
0.9
0.08
0.8
0.07
0.7
0.06
0.6
0.05
0.5
0.04
0.4
0.03
0.3
0.02
0.2
0.01
0.1
0
0
j
f
m
Mean
a
m
Median
j
j
Model
a
s
Obs2002
o
n
d
j
f
m
a
Mean
m
j
Median
j
a
Model
s
o
Obs2002
n
d
Simulated species composition North Sea
Diatoms
Phaeocystis
Terschelling transect 2002 March chlorophyll
[mg/m3]
Chlorophyll
TerschellinS
14
12
10
8
6
4
2
0
0
20
40
60
80
100
120
Model
140
160
180
Obs2002
200
220
Mean
240
260
280
Median
300
320
340
360
380
Venice Lagoon - Year 1987 - Ulva (gC.m-2)
Simulated
Observed
DO summer 1987 Venice Lagoon
Sea of Marmora Phytoplankton result September
Upper layer
Intermediate layer
Lower layer
Tropical reservoir
Han Hongjuan et al in prep
Tropical reservoir
Han Hongjuan et al in prep
Summary
• DELWAQ-BLOOM is a phytoplankton competition model that …
• has been developed and applied for over 35 years now
• and is generic for both fresh water, marine and transitional
(estuarine) systems
• has many applications worldwide
• requires little time for calibration
• and is documented in a large number of peer reviewed papers
• Water quality is often very much dependent on hydrodynamics,
and ideally the two should be modelled in close cooperation!
Additional references
Further reading:
•
•
•
•
•
•
•
•
•
Los, F.J., Mathematical Simulation of algae blooms by the model BLOOM II, Version 2, T68, WL
| Delft Hydraulics Report, 1991.
Van der Molen, D.T., F.J. Los, L. van Ballegooijen, M.P. van der Vat, 1994. Mathematical
modelling as a tool for management in eutrophication control of shallow lakes. Hydrobiologia,
Vol. 275/276: 479-492.
Ibelings, Bas W., Marijke Vonk, Hans F.J. Los and Diederik T. v.d. Molen and Wolf M. Mooij,
2003. Fuzzy modelling of Cyanobacterial waterblooms, validation with NOAA-AVHRR satellite
images, Ecological Applications, 13(5): 1456-1472
Los, F. J., M. T. Villars, & M. W. M. Van der Tol, 2008. A 3- dimensional primary production
model (BLOOM/GEM) and its applications to the (southern) North Sea (coupled physical–
chemical–ecological model). Journal of Marine Systems, 74: 259-294.
Blauw, Anouk N. , Hans F. J. Los, Marinus Bokhorst, and Paul L. A. Erftemeijer., 2009. GEM: a
generic ecological model for estuaries and coastal waters. Hydrobiologia, 618:175–198
Los, F. J., M. Blaas, 2010. Complexity, accuracy and practical applicability of different
biogeochemical model versions. Journal of Marine Systems 81: 44-74.
Los, Hans, 2009. Eco-hydrodynamic modelling of primary production in coastal waters and
lakes using BLOOM, PhD Thesis Wageningen University, ISBN 978-90-8585-329-9.
Los, F.J., T. A. Troost, J.A. v. Beek, 2014. Finding the optimal reduction to meet all targets Applying Linear Programming with a nutrient tracer model of the North Sea Journal of Marine
Systems 131: 91-101
Troost, T.A., A. de Kluijver, F. J. Los, 2014. Evaluation of eutrophication thresholds in the North
Sea in a historical context - a model analysis. Journal of Marine Systems 134: 45-56
Thank You!