Delft3D Phytoplankton modelling: Concepts of BLOOM€¦ · Delft3D Phytoplankton modelling:...

Delft3D Phytoplankton modelling:

Concepts of BLOOM

Hans Los

Deltares Webinar

March 2016

Overview of Webinar

1. Role of primary producers

2. Position of BLOOM in Delft3D modeling suite

3. Concepts of BLOOM (overview)

4. Competition principle (detail)

5. Existing knowledgebase

6. Some validation results

7. Summary and advise for further reading

Aquatic Ecosystem

Aquatic Ecosystem

• Physical processes

• Exchange atmosphere

• Sedimentation, resuspension

• Horizontal water movement

• Possibly stratification

• Seasonality

Aquatic Ecosystem






• Seasonality

• Foodweb (eating and being

eaten)

Aquatic Ecosystem: driven by Primary Production






• 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

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



2. Phytoplankton species are represented by different types

which allows BLOOM to vary their characteristics as a function

of the environment

BLOOM Highlights





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





of the environment



4. The result is a huge knowledge database with default species

characteristics available to every user

BLOOM Highlights





of the environment





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





of the environment








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



• 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

opportunistic species win when light is high…

Process formulations BLOOM (2): general

• BLOOM considers various (3 to 20) algal species (groups), including some nuisance algae


• BLOOM considers various (3 to 20) algal species (groups), including some nuisance algae (e.g. Phaeocystis,


• BLOOM considers various (3 to 20) algal species (groups), including some nuisance algae (e.g. Phaeocystis, Microcystis


• 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 temperature

• growth response to available nutrients

• stochiometry (composition in C, N, P,

Chlorophyll)

• Depending on environmental conditions

BLOOM Highlights





of the environment










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:


• 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:

• This threshold varies:

• per species

• in time and space

TiTiIcrTi RespMorteffPPmax ,,,, *


• Blue line: growth vs depth

• Green line: mortality + respiration

vs depth




vs depth

• Low extinction

Positive net growth

Surplus Energy




vs depth

• Critical extinction

Positive net growth

Surplus Energy

Zero net growth

Energy Limitation

TiTiIcrTi RespMorteffPPmax ,,,, *




vs depth

• Extinction too high

Positive net growth

Surplus Energy

Zero net growth

Energy Limitation

Negative net growth

Energy shortage




vs depth

• Extinction too high

• Note: Under stratified conditions,

depth gets smaller, hence higher

potential biomass

Positive net growth

Surplus Energy

Zero net growth

Energy Limitation

Negative net growth

Energy shortage

BLOOM Highlights





of the environment










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

dep

th

Fate of tracers tropical reservoir model (1)

Vertical distribution tracer layer 4 in a 7 layer model after 24 hours


Vertical distribution tracer layer 1-7 in a 7 layer model after 24 hours

Graph for location STN.3 (1), STN.3 (2), STN.3 (3), STN.3 (4), STN.3 (5), STN.3 (6), STN.3 (7)

FRACTIME01 05 Jul 05 00:00







STN.3 (1)

STN.3 (2)

STN.3 (3)

STN.3 (4)

STN.3 (5)

STN.3 (6)

STN.3 (7)

FRACTIME01

1

0.95

0.9

0.85

0.8

0.75

0.7

0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05


3 year simulation of all tracers in layer 1

Graph for location STN.3 (1)

FRACTIME01 STN.3 (1) FRACTIME02 STN.3 (1) FRACTIME03 STN.3 (1) FRACTIME04 STN.3 (1) FRACTIME05 STN.3 (1)

FRACTIME06 STN.3 (1) FRACTIME07 STN.3 (1)

DecNovOctSepAugJulJunMayAprMarFebJanDecNovOctSepAugJulJunMayAprMarFebJanDecNovOctSepAugJulJunMayAprMarFebJan

FR

AC

TIM

E01

1

0


Little mixing





FR

AC

TIM

E01

1

0


Strong mixing





FR

AC

TIM

E01

1

0

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





of the environment










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





of biomass (first



• Alg1 = 0.07

• Alg2 = 0.05

• Green: sufficient DIN





of biomass (first



• Alg1 = 0.07

• Alg2 = 0.05


• Orange line: DIN = 0

(limiting)





of biomass (first



• Alg1 = 0.07

• Alg2 = 0.05



(limiting)

• Orange: insufficient DIN


• Equal growth rate 0.5

per day (second

competition criterion)

• Grey line: growth rates

• Alg1 = Alg2 = 0.0


• Only Alg1 present, not

yet optimal


• Competition criterion:

Growth divided by

requirement so

• 0.5/0.05 > 0.5/0.07

• Alg2 (= small nutrient

requirement) wins


• Growth rate (Alg1 rapid

grower):

• Alg1 = 0.8 d-1

• Alg2 = 0.5 d-1

• Angle grey line shifted


of biomass (Alg2 is more

efficient):

• Alg1 = 0.07

• Alg2 = 0.05

• Alg1 = Alg2 = 0.0


• Only Alg2 present, not

yet optimal


• 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


and PO4


Blue line: PO4 = 0

• Other areas: insufficient

DIN or PO4 or both


• Equal growth rate 0.5

per day (second

competition criterion)

• Alg1 = Alg2 = 0.0


• Only Alg2 present, PO4

limiting, not yet optimal


• Only Alg1 present, DIN

limiting, not yet optimal


• 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

Chlora NZR9TS135

19-May12-May05-May28-Apr21-Apr14-Apr07-Apr31-Mar24-Mar17-Mar10-Mar03-Mar24-Feb17-Feb10-Feb03-Feb27-Jan

Chlo

ra

8

7

6

5

4

3

2

1

0

• So far resource

limitations



Chlora NZR9TS135


Chlo

ra

8

7

6

5

4

3

2

1

0

• So far resource

limitations

• It takes about 2 weeks to

get growth started



Chlora NZR9TS135


Chlo

ra

8

7

6

5

4

3

2

1

0

• So far resource

limitations


get growth started

• And another 2 weeks to

reach nutrient limited

spring bloom



Chlora NZR9TS135


Chlo

ra

8

7

6

5

4

3

2

1

0

• So far resource

limitations


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


Chlora NZR9TS135


Chlo

ra

8

7

6

5

4

3

2

1

0


•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


Chlora NZR9TS135


Chlo

ra

8

7

6

5

4

3

2

1

0


•After 3 weeks biomasses of

both algae have further

increased

•Still growth limitation


Chlora NZR9TS135


Chlo

ra

8

7

6

5

4

3

2

1

0


•After 4 weeks resource

limitation

•Alg1 present and limited by

DIN

•Alg2 present and limited by

PO4


Chlora NZR9TS135


Chlo

ra

8

7

6

5

4

3

2

1

0


• Mortality limitations delimit the

disappearance rate similar to

growth




growth

• Growth and mortality limitations

also occur as a result of spatial

gradients in depth, nutrients etc.




growth




Preferred

Alg1 Alg2




growth




• Mixing between spatially diverse

areas operates like time: it

extends the necessary period

for reaching resource limited

conditions

Preferred

Alg1 Alg2




growth




• 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

Preferred

Alg1 Alg2

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




half year

•Accordingly BLOOM selects E

limited types in winter




half year



•Phosphorus and silicate are main

limitations in summer half year




half year



•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



• Energy is limiting during the winter half

year




year

•Accordingly BLOOM selects E limited

types at beginning spring bloom




year



•Nitrogen and silicate are main limitations

in summer half year with some P limitation

late summer




year





late summer

•In summer half year N types are dominant

with some P types end of summer

• Less diversity compared to NW10




year





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





of the environment










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


• 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


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





of the environment










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 Greens

Cyanos

Noordwijk 20km 2002 Validation

NZR6NW020 SiO2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

j f m a m j j a s o n d

[mg/m3]

Mean Median Model Obs2002

NZR6NW020 OPO4

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1


[mg/m3]


NZR6NW020 NO3

0

0.2

0.4

0.6

0.8

1

1.2


[mg/m3]


NZR6NW020 Chlorophyll

0

5

10

15

20

25

30


[mg/m3]


Simulated species composition North Sea

Diatoms Phaeocystis

Terschelling transect 2002 March chlorophyll

Chlorophyll TerschellinS

0

2

4

6

8

10

12

14

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380

[mg/m3]

Model Obs2002 Mean Median

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

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!

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