The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors. 
   
 Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing th

The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors.

 

Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing the amount of non-renewable resources (which could occur through the development of better exploration technologies) improve our future? Also, experiment with the start date of a low birth-rate, environmentally focused policy.

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 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.
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.
Simple model of the global economy, the global carbon cycle, and planetary energy balance.    The planetary energy balance model is a two-box model, with shallow and deep ocean heat reservoirs. The carbon cycle model is a 4-box model, with the atmosphere, shallow ocean, deep ocean, and terrestrial c
Simple model of the global economy, the global carbon cycle, and planetary energy balance.

The planetary energy balance model is a two-box model, with shallow and deep ocean heat reservoirs. The carbon cycle model is a 4-box model, with the atmosphere, shallow ocean, deep ocean, and terrestrial carbon. 

The economic model is based on the Kaya identity, which decomposes CO2 emissions into population, GDP/capita, energy intensity of GDP, and carbon intensity of energy. It allows for temperature-related climate damages to both GDP and the growth rate of GDP.

This model was originally created by Bob Kopp (Rutgers University) in support of the SESYNC Climate Learning Project.
Interactions between wolves and livestock depending on abundance
Interactions between wolves and livestock depending on abundance
 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.

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.
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.
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale. It was "cloned" from a model that InsightMaker provides to its users, at  https://insightmaker.com/insight/2068/Isle-Royale-Predator-Prey-Interactions  Thanks Scott Fortmann-Roe.
This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale. It was "cloned" from a model that InsightMaker provides to its users, at
https://insightmaker.com/insight/2068/Isle-Royale-Predator-Prey-Interactions
Thanks Scott Fortmann-Roe.

I've created a Mathematica file that replicates the model, at
http://www.nku.edu/~longa/classes/2018spring/mat375/mathematica/Moose-n-Wolf-InsightMaker.nb

It allows one to experiment with adjusting the initial number of moose and wolves on the island.

I used steepest descent in Mathematica to optimize the parameters, with my objective data being the ratio of wolves to moose. You can try my (admittedly) kludgy code, at
http://www.nku.edu/~longa/classes/2018spring/mat375/mathematica/Moose-n-Wolf-InsightMaker-BestFit.nb

{WolfBirthRateFactorStart,
WolfDeathRateStart,
MooseBirthRateStart,
MooseDeathRateFactorStart,
moStart,
woStart} =
{0.000267409,
0.239821,
0.269755,
0.0113679,
591,
23.};

This model illustrates the key processes that influence the water level within Lake Okeechobee.        References:     Southwest Florida Water Management District. (2020). Lake Okeechobee. Retrieved from https://apps.sfwmd.gov/sitestatus/     United States Geological Survey. (2020). USGS Water-Year
This model illustrates the key processes that influence the water level within Lake Okeechobee.


References:

Southwest Florida Water Management District. (2020). Lake Okeechobee. Retrieved from https://apps.sfwmd.gov/sitestatus/

United States Geological Survey. (2020). USGS Water-Year Summary for Site USGS 02276400. Retrieved from https://nwis.waterdata.usgs.gov/nwis/wys_rpt?dv_ts_ids=210619&wys_water_yr=2019&site_no=02276400&agency_cd=USGS&adr_water_years=2006%2C2007%2C2008%2C2009%2C2010%2C2011%2C2012%2C2013%2C2014%2C2015%2C2016%2C2017%2C2018%2C2019&referred_module=

Winchester, J. (2020, October 10). Water releases from Lake Okeechobee to begin next week. Retrieved from https://www.winknews.com/2020/10/09/water-releases-from-lake-okeechobee-to-begin-next-week/


Created By:

Roger Al-Bahou
Carlos Alvarez
Christina Burgess
Devin Hanley
Daniel Harper
This model is based off Meadows economic capital with reinforcing growth loop constrained by a renewable resource model.
This model is based off Meadows economic capital with reinforcing growth loop constrained by a renewable resource model.
Primitives for Watershed modeling project. Click Clone Insight at the top right to make a copy that you can edit.
Primitives for Watershed modeling project. Click Clone Insight at the top right to make a copy that you can edit.
Simple (Kind of) food web of the Cane Toad Species. Includes different levels of consumers including predators.
Simple (Kind of) food web of the Cane Toad Species. Includes different levels of consumers including predators.
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 diagram provides a stylised description of important feedbacks within a shallow-lake system.
This diagram provides a stylised description of important feedbacks within a shallow-lake system.

This diagram describes the key processes that influence the water quality within a Northland lake.
This diagram describes the key processes that influence the water quality within a Northland lake.
 Overview:   This simulation will show the relationship between tree logging forestry and how this can affect mountain biking tourism in Derby Park Tasmania. The main goal of this simulation is to show these two industries can co-exist in the same environment, or increase in demand or production in
Overview: 
This simulation will show the relationship between tree logging forestry and how this can affect mountain biking tourism in Derby Park Tasmania. The main goal of this simulation is to show these two industries can co-exist in the same environment, or increase in demand or production in one sector will affect the result of another.  

Function of the model:
In comparison there are both pros and cons for both sectors working correspondently. Demand for derby park is caused by individual past experience when visiting the park or friends recommendation which increase in the number of demands. Increase in demands will increase in the number of visitors. When visitors visits the park they require make a purchase a bike and pay the park for using the park facilities. All this will adds up to bikers total spending when visiting Derby. When consumer spend it is booting the economy especially in the tourism sector. Similarly tree logging will also contribute financially towards the Tasmania economy. The regeneration stage is relatively low compare to the logging rate. The growth will not cover the loss which can cause some level of damage in the scenery of the park, affecting tourist to view when mountain biking. Visitors overall experience will have the impact towards the demand for mountain biking in derby park, if visitors experience is satisfied they will come back to visit again or visit with group of friends, even words of mouth recommendation will also increase the level of demand of visiting Derby. 

Some key insights base on the simulation:
Based on the simulation of the two models we can see there are some key changes.
Tree logging increase will cause the disturbance of the natural scenery, thus change the overall experience of the visitors, decrease in the level of demand. Tree logging will also have negative impact towards the overall tourist experience thus affect the park facility and track. The natural scenery and the overall experience can affect their experience and if they would continue to recommend this area to friends to increase the demand. 

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.