Insight diagram
In Chile, 60% of its population are exposed to levels of Particulate Matter (PM) above international standards. Air Pollution is causing 4,000 premature deaths per year, including health costs over US$8 billion.

The System Dynamics Causal Loop Diagram developed herein shows an initial study of the dynamics among the variables that influences the accumulation of PM in the air, in particular the case of Temuco, in the South of Chile. In Temuco, 97% of the PM inventories comes from the combustion of low quality firewood, which in turns is being burned due to its low price and cultural habits/tradition.
Clone of Air Pollution Dynamics - Firewood Combustion
Insight diagram
Simple model to illustrate oyster growth based on primary production of Phytoplankton as a state variable, forced by light and nutrients, running for a yearly period.

Phytoplankton growth based on on Steele's and Michaelis-Menten equations), where: 

Primary Production=(([Pmax]*[I]/[Iopt]*exp(1-[I]/[Iopt])*[S])/([Ks]+[S]))

Pmax: Maximum production (d-1)
I: Light energy at depth of interest (uE m-2 s-1)
Iopt: Light energy at which Pmax occurs (uE m-2 s-1)
S: Nutrient concentration (umol N L-1)
Ks: Half saturation constant for nutrient (umol N L-1).

Further developments:
- Nutrients as state variable in cycle with detritus from phytoplankton and oyster biomass.
- Light limited by the concentration of phytoplankton.
- Temperature effect on phytoplankton and Oyster growth.


Clone of Clone3f micro algae , biogas , bioelectrcidades
Insight diagram
A simple phosphorus model for a generic lake.
Phosphorus in a Lake
Insight diagram
•Dry Period Case
– 25 years of historical dry period on record (1947-1973)-including drought of record (1947-1956)
–Represents the dry period case
–Future dry cycle includes dry cycle of AMO and overlay of IPCC climate change predictions 
Clone of EA dry conditions 1947-1973
Insight diagram
Simple model to illustrate oyster growth based on primary production of Phytoplankton as a state variable, forced by light and nutrients, running for a yearly period.

Phytoplankton growth based on on Steele's and Michaelis-Menten equations), where: 

Primary Production=(([Pmax]*[I]/[Iopt]*exp(1-[I]/[Iopt])*[S])/([Ks]+[S]))

Pmax: Maximum production (d-1)
I: Light energy at depth of interest (uE m-2 s-1)
Iopt: Light energy at which Pmax occurs (uE m-2 s-1)
S: Nutrient concentration (umol N L-1)
Ks: Half saturation constant for nutrient (umol N L-1).

Further developments:
- Nutrients as state variable in cycle with detritus from phytoplankton and oyster biomass.
- Light limited by the concentration of phytoplankton.
- Temperature effect on phytoplankton and Oyster growth.


Clone of Clone3f micro algae , biogas , bioelectrcidades
Insight diagram
•Dry Period Case
– 25 years of historical dry period on record (1947-1973)-including drought of record (1947-1956)
–Represents the dry period case
–Future dry cycle includes dry cycle of AMO and overlay of IPCC climate change predictions 
Clone of Clone of EA dry conditions 1947-1973
Insight diagram
A system dynamics model of a predator-prey lifecycle relationship




Clone of Predator-Prey relationship
Insight diagram
This model simulates the growth of carp in an aquaculture pond, both with respect to production and environmental effects.

Both the anabolism and fasting catabolism functions contain elements of allometry, through the m and n exponents that reduce the ration per unit body weight as the animal grows bigger.

The 'S' term provides a growth adjustment with respect to the number of fish, so implicitly adds competition (for food, oxygen, space, etc).

 Carp are mainly cultivated in Asia and Europe, and contribute to the world food supply.

Aquaculture currently produces sixty million tonnes of fish and shellfish every year. In May 2013, aquaculture production overtook wild fisheries for human consumption.

This paradigm shift last occurred in the Neolithic period, ten thousand years ago, when agriculture displaced hunter-gatherers as a source of human food.

Aquaculture is here to stay, and wild fish capture (fishing) will never again exceed cultivation.

Recreational fishing will remain a human activity, just as hunting still is, after ten thousand years - but it won't be a major source of food from the seas.

The best way to preserve wild fish is not to fish them.
Clone of CARP - Carp AquacultuRe in Ponds
Insight diagram
HANDY Model of Societal Collapse from Ecological Economics Paper 
see also D Cunha's model at IM-15085
Clone of Clone of Human and Nature Dynamics of Societal Inequality
Insight diagram
How the 4-H club became a marketing thingy for DuPont
Clone of 4-H impact on African Farming
Insight diagram
HANDY Model of Societal Collapse from Ecological Economics Paper 
see also D Cunha's model at IM-15085
Clone of Human and Nature Dynamics of Societal Inequality
Insight diagram
This model implements the one-dimensional version of the advection-dispersion equation for an estuary. The equation is:

dS/dt = (1/A)d(QS)/dx - (1/A)d(EA)/dx(dS/dx) (Eq. 1)

Where S: salinity (or any other constituent such as chlorophyll or dissolved oxygen), (e.g. kg m-3); t: time (s); A: cross-sectional area (m2); Q: river flow (m3 s-1); x: length of box (m); E: dispersion coefficient (m2 s-1).

For a given length delta x, Adx = V, the box volume. For a set value of Q, the equation becomes:

VdS/dt = QdS - (d(EA)/dx) dS (Eq. 2)

EA/x, i.e. (m2 X m2) / (m s) = E(b), the bulk dispersion coefficient, units in m3 s-1, i.e. a flow, equivalent to Q

At steady state, dS/dt = 0, therefore we can rewrite Eq. 2 for one estuarine box as:

Q(Sr-Se)=E(b)r,e(Sr-Se)-E(b)e,s(Se-Ss) (Eq. 3)

Where Sr: river salinity (=0), Se: mean estuary salinity; Ss: mean ocean salinity

E(b)r,e: dispersion coefficient between river and estuary, and E(b)e,s: dispersion coefficient between the estuary and ocean.

By definition the value of E(b)r,e is zero, otherwise we are not at the head (upstream limit of salt intrusion) of the estuary. Likewise Sr is zero, otherwise we're not in the river. Therefore:

QSe=E(b)e,s(Se-Ss) (Eq. 4)

At steady state

E(b)e,s = QSe/(Se-Ss) (Eq 5)

The longitudinal dispersion simulates the turbulent mixiing of water in the estuary during flood and ebb, which supplies salt water to the estuary on the flood tide, and make the sea a little more brackish on the ebb.

You can use the slider to turn off dispersion (set to zero), and see that if the tidal wave did not mix with the estuary water due to turbulence, the estuary would quickly become a freshwater system.
Clone of Estuarine salinity 1D model
Insight diagram
This diagram provides a stylised description of important feedbacks within a shallow-lake system.

Feedbacks in a shallow lake
Insight diagram
This model implements the equations proposed by Ketchum in 1954. The rationale behind the concept is that only phytoplankton that grows above a certain rate will not be flushed out of an estuary.

For biological processes:

Pt  =  Po exp(kt)

Where Pt is the phytoplankton biomass at time t, Po is the initial biomass, and k is the growth rate.

For physical processes:

Pm  =  Po (1-r)^m

Where Pm is the phytoplankton biomass after m tidal cycles, and r is the exchange ratio (proportion of estuary water which does not return each tidal cycle).

By substitution, and replacing t by m in the first equation, we get:

Pm = Poexp(km).(1-r)^m

For phytoplankton to exist in an estuary, Pm = Po (at least), i.e. 1 / (1-r)^m = exp(km)
ln(1) - m.ln(1-r) = km
-m.ln(1-r) = km
k = -ln(1-r)

Ketchum (1954) Relation between circulation and planktonic populations in estuaries. Ecology 35: 191-200.

In 2005, Ferreira and co-workers showed that this balance has direct implications on biodiversity of estuarine phytoplankton, and discussed how this could be relevant for water management, in particular for the EU Water Framework Directive 60/2000/EC (Ecological Modelling, 187(4) 513-523).
Clone of Phytoplankton blooms in estuaries
Insight diagram

This stock and flow diagram is a working draft of a conceptual model of a dune-lake system in the Northland region of New Zealand.

Clone of Stock and flow diagram of phosphorus in a shallow lake
Insight diagram
Simple mass balance model for lakes, based on the Vollenweider equation:

dMw/dt = Min - sMw - Mout

The model was first used in the 1960s to determine the phosphorus concentration in lakes and reservoirs, for eutrophication assessment.
Clone of Vollenweider model
Insight diagram
In Chile, 60% of its population are exposed to levels of Particulate Matter (PM) above international standards. Air Pollution is causing 4,000 premature deaths per year, including health costs over US$8 billion.

The System Dynamics Causal Loop Diagram developed herein shows an initial study of the dynamics among the variables that influences the accumulation of PM in the air, in particular the case of Temuco, in the South of Chile. In Temuco, 97% of the PM inventories comes from the combustion of low quality firewood, which in turns is being burned due to its low price and cultural habits/tradition.
Clone of Air Pollution Dynamics / Firewood Combustion
Insight diagram
As initially proposed by Pr. William M White of Cornell University:

http://www.geo.cornell.edu/eas/education/course/descr/EAS302/302_06Lab11.pdf
http://www.eas.cornell.edu/
Global Carbon Cycle - For ENVS 270
Insight diagram
Clone of Clone of Group 1 Project
Insight diagram
This model implements the one-dimensional version of the advection-dispersion equation for an estuary. The equation is:

dS/dt = (1/A)d(QS)/dx - (1/A)d(EA)/dx(dS/dx) (Eq. 1)

Where S: salinity (or any other constituent such as chlorophyll or dissolved oxygen), (e.g. kg m-3); t: time (s); A: cross-sectional area (m2); Q: river flow (m3 s-1); x: length of box (m); E: dispersion coefficient (m2 s-1).

For a given length delta x, Adx = V, the box volume. For a set value of Q, the equation becomes:

VdS/dt = QdS - (d(EA)/dx) dS (Eq. 2)

EA/x, i.e. (m2 X m2) / (m s) = E(b), the bulk dispersion coefficient, units in m3 s-1, i.e. a flow, equivalent to Q

At steady state, dS/dt = 0, therefore we can rewrite Eq. 2 for one estuarine box as:

Q(Sr-Se)=E(b)r,e(Sr-Se)-E(b)e,s(Se-Ss) (Eq. 3)

Where Sr: river salinity (=0), Se: mean estuary salinity; Ss: mean ocean salinity

E(b)r,e: dispersion coefficient between river and estuary, and E(b)e,s: dispersion coefficient between the estuary and ocean.

By definition the value of E(b)r,e is zero, otherwise we are not at the head (upstream limit of salt intrusion) of the estuary. Likewise Sr is zero, otherwise we're not in the river. Therefore:

QSe=E(b)e,s(Se-Ss) (Eq. 4)

At steady state

E(b)e,s = QSe/(Se-Ss) (Eq 5)

The longitudinal dispersion simulates the turbulent mixiing of water in the estuary during flood and ebb, which supplies salt water to the estuary on the flood tide, and make the sea a little more brackish on the ebb.

You can use the slider to turn off dispersion (set to zero), and see that if the tidal wave did not mix with the estuary water due to turbulence, the estuary would quickly become a freshwater system.
Clone of Estuarine salinity 1 box model (J. Gomes Ferreira)
Insight diagram
This model describes the N isotope dynamics in a marine system where the shallowness of the chemocline prevents significant nitrification near the base of the photic zone.
N isotope dynamics in a suboxic Paleozoic water column
Insight diagram
Koala Prediction
Insight diagram
This model shows the growth of one type of organism as a function of the carrying capacity (i.e., logistic growth).
Clone of Daisy Growth
Insight diagram
Simple mass balance model for lakes, based on the Vollenweider equation:

dMw/dt = Min - sMw - Mout

The model was first used in the 1960s to determine the phosphorus concentration in lakes and reservoirs, for eutrophication assessment.
Clone of Vollenweider model