This stock and flow diagram provides a broad description of the key nutrient pathways (N and P) that exist in a dune-lake system subject to external loadings emanating from intensive agriculture.

This model describes the flow of energy from generation to consumption for neighborhoods in the metro Atlanta area. It also calculates the cost of energy production and the number of years it will take to recover that cost.

My AP Environmental Homework for the Cats Over Borneo Assignment

Concepts are designed for Universatility and local variables without forcing a one size fits all model. 

Measurements in the course are designed to maintain a system perspective in all planning and measurement systems. 

this is the Australian food web of the water buffalo

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.

This model implements a very simple shellfish carrying capacity simulation for tidal creeks with freshwater input.

Physics

The 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 top slider to turn off dispersion (set to zero). If the variable being simulated is (a) salinity, you will see that if the tidal wave did not mix with the estuary water due to turbulence, the estuary would quickly become a freshwater system; (b) POM, then the ocean (which typically has less POM) will not contribute a flushing effect and the concentration of POM in the tidal creek or estuary will be higher.

The second slider allows you to simulate a variable river flow, and understand how dispersion compensates for changes in freshwater input.

Biology

Two biological functions are implemented in CREEK, both extremely simplified.

1. Primary production - a constant primary production rate is considered in gC m-3 d-1

2. Oyster filtration - a constant clearance rate (CR) is considered in L ind- 1 h-1, scaled to a certain stocking density S (ind m-3)

Units are normalized, and food depletion is CR * S * POM, in g POM m-3 d-1

The third slider allows for adjustment of different aquaculture densities.

Wild filter-feeding species are included in the model, using an identical clearance rate to the cultivated oysters. Wild species can be turned on or off in the model using the fourth slider.

The model provides three outputs:
1. POM concentration in mg L-1
2. Equivalent in chlorophyll (ug L-1)
3. Total oyster biomass in kg for the whole system

This model adresses the primary production for phytoplankton growth, based on Steele’s light intensity equation and Michaelis-Menten equation for nutrient limitation.

The Streeter-Phelps oxygen dynamics model was originally developed in 1925, almost a century ago.

Play

You can explore the model by hitting the simulate button, and you can use the three sliders below to (i) switch the spill on or off (1 or 0); (ii) define the day when the spill occurs (0 to 15); and (iii) make the model use a constant water temperature (20oC) or a (pre-defined) variable one.

A variable temperature affects oxygen saturation, and therefore also the oxygen deficit and oxygen concentration.

Every model element shows an = sign when you hover over it, and if you click the sign you can view the underlying equation.

If you want to edit the model, you need to create an account in InsightMaker and then clone the model and adapt it to your needs.

Study

Below is a detailed explanation of the model concept.

The model calculates the oxygen deficit (D), defined as Cs-C, where Cs is the saturation concentration of dissolved oxygen (based on temperature, and salinity if applicable), and C is the dissolved oxygen concentration.

Since D = Cs-C, it follows that:
dD/dt = -dC/dt

The rate of change of oxygen concentration with time (dC/dt) depends on two factors, organic decomposition and aeration.

dC/dt = Ka.D - Kd.L

The first term on the right side of the equation is aeration (which adds oxygen to the water), calculated by means of the temperature-dependent aeration parameter Ka.

Ka is also a function of Kr, which depends on wind speed (U) and water depth (z).

The sink term represents oxygen consumption through mineralization (bacterial decomposition) of organic matter.

The organic load L decays in time (or in space, e.g. along a river) according to a first order equation, i.e. dL/dt = -Kd.L

This equation can be integrated to yield L = Lo.exp(Kd.t), where Kd is the decay constant.

This is a model for the mass flow of phosphorus in a stream called "Ljurabäck" in Norrköping during two months. The stream flows from a lake called "Glan" to a large stream called "Motala Ström". 

From Jay Forrester 1971 Book World Dynamics, the earlier, simpler version of the World 3 Limits to Growth Model. adapted from Mark Heffernan's ithink version at Systemswiki.

An element of Perspectives: The Foundation of Understanding and Insights for Effective Action. Register at http://www.systemswiki.org/

Simple energy balance model of a planet with albedo, ocean, and atmosphere.

Simulates Ag biogeochemical cycling using data from Rauch and Pacyna (2009). This Insight forms part of the engaged lear​ning exercise for a SESYNC case study about the human relationship with silver as a natural resource throughout history.

Simple model to illustrate Michaelis-Menten equation for nutrient uptake by phytoplankton.

The equation is:

P = Ppot S / (Ks + S)

Where:

P: Nutrient-limited production (e.g. d-1, or mg C m-2 d-1)
Ppot: Potential production (same units as P)
S: Nutrient concentation (e.g. umol N L-1)
Ks: Half saturation constant for nutrient (same units as S)

The model contains no state variables, just illustrates the rate of production, by making the value of S equal to the timestep (in days). Move the slider to the left for more pronounced hyperbolic response, to the right for linear response.

Fertilizer inflow can cause lake eutrophication. In this simulation, we are studying what happens in a simple lake ecosystem.

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

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).

This diagram provides an accessible description of the key processes that guide the water quality within a lake.

WIP Stock Flow representation of Panarchy Adaptive Cycles

Simple model to illustrate Steele's equation for primary production of phytoplankton.

The equation is:

Ppot = Pmax I/Iopt exp(1-I/Iopt)

Where:

Ppot: Potential production (e.g. d-1, or mg C m-2 d-1)
Pmax: Maximum production (same units as Ppot)
I: Light energy at depth of interest (e.g. uE m-2 s-1)
Iopt: Light energy at which Pmax occurs (same units as I)

The model contains no state variables, just illustrates the rate of production, by making the value of I equal to the timestep (in days). Move the slider to the left for more pronounced photoinhibition, to the right for photosaturation.

Simple model to illustrate Michaelis-Menten equation for nutrient uptake by phytoplankton.

The equation is:

P = Ppot S / (Ks + S)

Where:

P: Nutrient-limited production (e.g. d-1, or mg C m-2 d-1)
Ppot: Potential production (same units as P)
S: Nutrient concentation (e.g. umol N L-1)
Ks: Half saturation constant for nutrient (same units as S)

The model contains no state variables, just illustrates the rate of production, by making the value of S equal to the timestep (in days). Move the slider to the left for more pronounced hyperbolic response, to the right for linear response.

This model prototypes the working of an Smart Grid with Electric Vehicles

The objective is testing the theoretical advantages of batteries (also batteries in Electric Vehicles) in combination with renewable energies. The model considers two houses, that store energy both in Electric Vehicles (Vehicle to Grid), and in a communal battery.

Except when specified otherwise, the units of all variables are expressed in W/h.

Press "Story" in the lower bar for a guided tour over the model. Better seen at 50% zoom.

by Carlos Varela (cvarela@gmx.at)

This model shows the growth of two organisms competing for a limiting resource (space) .