Insight diagram
Koala Prediction
Insight diagram
Modeling forest succession in a northeast deciduous forest.
Clone of Lab1 Forestry Succession Model
Insight diagram
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

Experiment with adjusting the initial number of moose and wolves on the island.
Clone of Isle Royale: Predator Prey Interactions
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
An attempt to model some of the features in the national water market in Australia.
Water Resources
Insight diagram

The Logistic Map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited as an archetypal example of how complex, chaotic behaviour can arise from very simple non-linear dynamical equations. The map was popularized in a seminal 1976 paper by the biologist Robert May, in part as a discrete-time demographic model analogous to the logistic equation first created by Pierre François Verhulst

Mathematically, the logistic map is written

where:

 is a number between zero and one, and represents the ratio of existing population to the maximum possible population at year n, and hence x0 represents the initial ratio of population to max. population (at year 0)r is a positive number, and represents a combined rate for reproduction and starvation. To generate a bifurcation diagram, set 'r base' to 2 and 'r ramp' to 1
To demonstrate sensitivity to initial conditions, try two runs with 'r base' set to 3 and 'Initial X' of 0.5 and 0.501, then look at first ~20 time steps

Clone of Clone of The Logistic Map
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.

Stock and flow diagram of a lake system
Insight diagram
This model demonstrates the various activities occurring across South East Queensland can both negatively and positively impact our endangered Koala population.
Impacts on Koala populations in South East Queensland
Insight diagram
Primitives for Watershed modeling project. Click Clone Insight at the top right to make a copy that you can edit.

The converter in this file contains precipitation for Phoenix only.
Clone of Primitives for Rainwater Harvesting -Phoenix ENVS 270 F21
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
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
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. 

Students will acquire hands on modeling skills using either video learning offered by System Thinking World host and easily viewed through the right hand side of the page conveniently located are links to the video series.  

A facilitator may offer traditional instruction or ideally students from the graduating students are teaching the next group of students and improving the way the materials is presented.

Modelling Social Physics - System Dynamics Projects
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 describes phosphorus cycling in a dune-lake system in the Northland region of New Zealand. It is based on stock and flow diagrams where each orange oval represents an input, while each blue box represents a stock. Each arrow represents a flow. Flows involve a loss from the stock at which they start and add to the stock at which they end.

Clone of Clone of Story of phosphorus dynamics in a shallow lake
Insight diagram
This model is a classic simulation of the production cycle in the ocean, including the effects of the thermocline in switching off advection of dissolved nutrients and detritus to the surface layer.

It illustrates a number of interesting features including the coupling of three state variables in a closed cycle, the use of time to control the duration of advection, and the modulus function for cycling annual temperature data over multiple years.

The model state variables are expressed in nitrogen units (mg N m-3), and the calibration is based on:

Baliño, B.M. 1996. Eutrophication of the North Sea, 1980-1990: An evaluation of anthropogenic nutrient inputs using a 2D phytoplankton production model. Dr. scient. thesis, University of Bergen.
 
Fransz, H.G. & Verhagen, J.H.G. 1985. Modelling Research on the Production Cycle of Phytoplankton in the Southern Bight of the Northn Sea in Relation to Riverborne Nutrient Loads. Netherlands Journal of Sea Research 19 (3/4): 241-250.

This model was first implemented in PowerSim some years ago by one of my M.Sc. students, who then went on to become a Buddhist monk. Although this is a very Zen model, as far as I'm aware, the two facts are unrelated.
Clone of NPD model (Nutrients, Phytoplankton, Detritus)
Insight diagram
Two households with PV systems and Electric Vehicles, sharing a battery and connected to the grid. What are the advantages?


Clone of Vehicle to Grid Simulation
Insight diagram
Here is my systems flow chart for a moose population!
Moose System Flow Chart
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.

Modèle simple pour illustrer la croissance des huîtres sur la base de la production primaire de phytoplancton comme une variable d'état, forcé par la lumière et les éléments nutritifs, en cours d'exécution pour une période annuelle.

La croissance du phytoplancton sur la base de Steele et équations de Michaelis-Menten), où:

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

Pmax: production maximale (d-1)
I: L'énergie lumineuse en profondeur de l'intérêt (Ue m-2 s-1)
Iopt: L'énergie lumineuse à laquelle se produit Pmax (Ue m-2 s-1)
S: concentration des éléments nutritifs (N umol L-1)
KS: Demi constants de saturation en nutriments (N umol L-1).

D'autres développements:
- Les éléments nutritifs comme variable d'état dans le cycle de détritus de phytoplancton et d'huîtres de la biomasse.
- Lumière limitée par la concentration de phytoplancton.
- Effet de la température sur le phytoplancton et la croissance des huîtres.

Clone of Clone of Oyster Growth based on Phytoplankton Biomass
Insight diagram
GSVS4559 water desalination Tahne
Insight diagram

Harvested fishery with endogenous investment and ship deployment policy. Ch 9 p345-360 John Morecroft (2007) Strategic Modelling and Business Dynamics. See simpler models at IM-2990 and IM-2991

Fishery Dynamics with Ship Deployment Policy
Insight diagram
This is step 5 in making a climate model based on our insights of how trees actively contribute to the cooling capcacity of the Earth.​

In the prevoious step we added the reflection of sun energy by Clouds 

In this step we added aabsorption of near infrared by clouds and the biotic pump

Present the temperature of the Earth is 288 Kelvin. Without Earth would be 255 Kelvin. So the energy balance of the Earth add 33 Kelvin.
We optimize in step 4 the variable GHG-effect and the optimal number is 0,29625 in this model.


With Our-Green-Spine we have discovered new insights how trees / forest / green structures are part of the managing system of controlling the temperature of our Earth via their cooling capacity by using water and influencing the water cycle. We want to translate our insights in a climate model. People who to join us please send an email to marcel.planb@gmail.com.
Thanks, Marcel de Berg
Backup of workversion Clone of Model 6 integrating Biotic Pump
Insight diagram
This diagram provides a stylised description of important feedbacks within a shallow-lake system.
Mahinga Kai
Clone of Clone of Key feedbacks in a shallow lake relating to Koura
Insight diagram
Primitives for Watershed modeling project. Click Clone Insight at the top right to make a copy that you can edit.

The converter in this file contains precipitation for Phoenix only.
Clone of Clone of Primitives for Rainwater Harvesting -Phoenix ENVS 270 F21
Insight diagram
Simple model to illustrate an annual cycle for phytoplankton biomass in temperate waters.
Potential primary production uses Steele's equation and a Michaelis-Menten (or Monod) function for nutrient limitation. Respiratory losses are only a function of biomass.
Clone of Phytoplankton model URI