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.
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.
This diagram provides a stylised description of important feedbacks within a shallow-lake system.     Mahinga Kai
This diagram provides a stylised description of important feedbacks within a shallow-lake system.
Mahinga Kai
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.
 Modelo retirado do link   https://insightmaker.com/insight/3230/Food-Chain     Simples modelo da cadeia alimentar
Modelo retirado do link 
https://insightmaker.com/insight/3230/Food-Chain

Simples modelo da cadeia alimentar
33 12 months ago
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   Presen
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
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 sat
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.
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.
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.
European Masters in System Dynamics 2016 New University of Lisbon, Portugal  Simple model to represent oyster individual growth by simulating feeding and metabolism.
European Masters in System Dynamics 2016
New University of Lisbon, Portugal

Simple model to represent oyster individual growth by simulating feeding and metabolism.
The following insight shows the level of crime in the town of Bourke in comparison to the levels of Police and Community Engagement
The following insight shows the level of crime in the town of Bourke in comparison to the levels of Police and Community Engagement
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.
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.
HANDY Model of Societal Collapse from Ecological Economics  Paper   see also D Cunha's model at  IM-15085
HANDY Model of Societal Collapse from Ecological Economics Paper 
see also D Cunha's model at IM-15085
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.
 Harvested fishery with endogenous investment. Ch 9 p340-345 John Morecroft (2007) Strategic Modelling and Business Dynamics

Harvested fishery with endogenous investment. Ch 9 p340-345 John Morecroft (2007) Strategic Modelling and Business Dynamics

The following insight shows the level of crime in the town of Bourke in comparison to the levels of Police and Community Engagement
The following insight shows the level of crime in the town of Bourke in comparison to the levels of Police and Community Engagement
European Masters in System Dynamics 2016 New University of Lisbon, Portugal  Simple model to represent oyster individual growth by simulating feeding and metabolism.
European Masters in System Dynamics 2016
New University of Lisbon, Portugal

Simple model to represent oyster individual growth by simulating feeding and metabolism.
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
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.
M.Sc. in Environmental Engineering SIMA 2018 New University of Lisbon, Portugal   Model to represent oyster individual growth by simulating feeding and metabolism. Model (i) partitions metabolic costs into feeding and fasting catabolism; (ii) adds allometry to clearance rate; (iii) adds temperature
M.Sc. in Environmental Engineering SIMA 2018
New University of Lisbon, Portugal

 Model to represent oyster individual growth by simulating feeding and metabolism. Model (i) partitions metabolic costs into feeding and fasting catabolism; (ii) adds allometry to clearance rate; (iii) adds temperature dependence to clearance rate; (iv) illustrates how clearance rate per gram is used if we multiply by the oyster biomass
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.
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.