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

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

This model implements the one-dimensional version of the advection-dispersion equation for an estuary.  It develops the basic 1D model by simulating the mass of salt rather than the concentration. This makes it straightforward to deal with multiple boxes of different volume.  The equation is:  dS/dt
This model implements the one-dimensional version of the advection-dispersion equation for an estuary.

It develops the basic 1D model by simulating the mass of salt rather than the concentration. This makes it straightforward to deal with multiple boxes of different volume.

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 upper 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.

The lower slider allows you to simulate a variable river flow, and understand how dispersion compensates for changes in freshwater input.
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-se
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.
Dynamic system modelling the effect of pest population reductions in transport vectors hubs (e.g. marina or port), by means of a biocontrol agent (e.g. gastropod snail), on the spread of marine pests into new areas.
Dynamic system modelling the effect of pest population reductions in transport vectors hubs (e.g. marina or port), by means of a biocontrol agent (e.g. gastropod snail), on the spread of marine pests into new areas.
	This a simple and "totally accurate" model of the exponential human population.
This a simple and "totally accurate" model of the exponential human population.
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 o
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.
The time-variable solution to a step-function change in inflow concentration for an ideal, completely mixed lake.
The time-variable solution to a step-function change in inflow concentration for an ideal, completely mixed lake.
Very simple model demonstrating growth of phytoplankton using Steele's equation for potential production and Michaelis-Menten equation for nutrient limitation.  Both light and nutrients (e.g. nitrogen) are modelled as forcing functions, and the model is "over-calibrated" for stability.  The phytopla
Very simple model demonstrating growth of phytoplankton using Steele's equation for potential production and Michaelis-Menten equation for nutrient limitation.

Both light and nutrients (e.g. nitrogen) are modelled as forcing functions, and the model is "over-calibrated" for stability.

The phytoplankton model approximately reproduces the spring-summer diatom bloom and the (smaller) late summer dinoflagellate bloom.
 
Oyster growth is modelled only as a throughput from algae. Further developments would include filtration as a function of oyster biomass, oyster mortality, and other adjustments.
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 o
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.
Just recently, there was a huge problem that ended about C8, a deadly chemical, that was poured into lakes and rivers by DuPont, a company that sold nylon. This chemical caused a huge consensus, many birth defects, and cows with green insides spraying blood from many places.
Just recently, there was a huge problem that ended about C8, a deadly chemical, that was poured into lakes and rivers by DuPont, a company that sold nylon. This chemical caused a huge consensus, many birth defects, and cows with green insides spraying blood from many places.
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.  We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale websi
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

Diagrams on generalized knowledge claims and workflow processes from Magliocca 2018 Global Environmental Change  article
Diagrams on generalized knowledge claims and workflow processes from Magliocca 2018 Global Environmental Change article
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 o
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.
This is an edited copy of the original simple building heat flow. I addressed the insulation aspect of this problem by reducing the conductivity values of both the walls and windows. To address a chiller system, I integrated mechanical cooling into the process. To address solar panels, I calculated
This is an edited copy of the original simple building heat flow. I addressed the insulation aspect of this problem by reducing the conductivity values of both the walls and windows. To address a chiller system, I integrated mechanical cooling into the process. To address solar panels, I calculated the effect they may have on electricity prices by adding Solar Panel variables to the equation.
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 o
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.
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.
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.
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-se
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.
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 o
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.