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 Tucson only. Tucson watersheds are Arroyo Chico, Canada Agua, and Lower Canada del Oro.
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 Tucson only. Tucson watersheds are Arroyo Chico, Canada Agua, and Lower Canada del Oro.
	This a simple and "totally accurate" model of the exponential human population.
This a simple and "totally accurate" model of the exponential human population.
Simple energy balance model of a planet with albedo, ocean, and atmosphere.  This simulation models the stock and flow of energy between a
star, a planet’s surface (primarily its oceans, which are the largest reservoir
of heat), and space.The assumptions governing this model are:  1. The planet abso
Simple energy balance model of a planet with albedo, ocean, and atmosphere.

This simulation models the stock and flow of energy between a star, a planet’s surface (primarily its oceans, which are the largest reservoir of heat), and space.The assumptions governing this model are:

1. The planet absorbs a fraction of the shortwave radiation arriving from its star, with that fraction given by (1-A), where A is albedo. 

2. The planet radiates longwave infrared radiation into space, with the amount of radiation into space given by σΤe4, where σ is the Stefan-Boltzmann constant and Te is the temperature of the effective radiating level.

3. The atmospheric lapse rate is 6 K/km.

4. If there is an imbalance between shortwave radiation absorbed and longwave radiation emitted, the imbalance affects the temperature of the planet. However, it does not do so instantaneously – the imbalance must heat or cool the mixed layer of the ocean.

5. At the start of the simulation, the planet is extremely close to equilibrium given its default parameters. If any of these parameters are changed, the planet will be out of equilibrium, and will have to adjust.

Our computer model details the change in allele frequency of resistant mosquitoes in Africa when the government began spraying DDT. The few mosquitoes that naturally survived the chemical sprays reproduced, and created a large population of resistant mosquitoes. When DDT was sprayed later to prevent
Our computer model details the change in allele frequency of resistant mosquitoes in Africa when the government began spraying DDT. The few mosquitoes that naturally survived the chemical sprays reproduced, and created a large population of resistant mosquitoes. When DDT was sprayed later to prevent the spread of malaria, the DDT was not as effective because of the large amount of DDT-resistant phenotypes in the population.
this is the Australian food web of the water buffalo
this is the Australian food web of the water buffalo
 Clone of IM-1954 to tidy up layout. 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 affec

Clone of IM-1954 to tidy up layout. 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.

all pictures sourced from google images
all pictures sourced from google images

This model uses simple functions (converters, cosine) to simulate the water balance inside a reservoir.
This model uses simple functions (converters, cosine) to simulate the water balance inside a reservoir.
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 version adds diagenesis, using an extra state variable (ph
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 version adds diagenesis, using an extra state variable (phosphorus in the sediment) and incorporates desorption processes that release phosphorus trapped in the sediment back to the water column.

The temporal dynamics of the model simulate the typical development of pollution in time.

1. Low loading, low P concentration in lake
2. High loading, increasing P concentration in lake
3. Desorption rate is low, P in sediment increases
4. Measures implemented for source control, loading reduces
5. P in lake gradually decreases, but below a certain point, desorption increases, and lake P concentration does not improve
6. Recovery only occurs when the secondary load in the sediment is strongly reduced.
 Students in ENVS 270 Online at the University of Arizona: please click Clone Insight at the top to make an editable copy of this model.    As initially proposed by Pr. William M White of Cornell University:    http://www.geo.cornell.edu/eas/education/course/descr/EAS302/302_06Lab11.pdf    http://ww
Students in ENVS 270 Online at the University of Arizona: please click Clone Insight at the top to make an editable copy of this model.

As initially proposed by Pr. William M White of Cornell University:
This model is an attempt to simulate what is commonly
referred to as the “pesticide treadmill” in agriculture and how it played out
in the cotton industry in Central America after the Second World War until
around the 1990s.  

 The cotton industry expanded dramatically in Central America
after WW2,
This model is an attempt to simulate what is commonly referred to as the “pesticide treadmill” in agriculture and how it played out in the cotton industry in Central America after the Second World War until around the 1990s.

The cotton industry expanded dramatically in Central America after WW2, increasing from 20,000 hectares to 463,000 in the late 1970s. This expansion was accompanied by a huge increase in industrial pesticide application which would eventually become the downfall of the industry.

The primary pest for cotton production, bol weevil, became increasingly resistant to chemical pesticides as they were applied each year. The application of pesticides also caused new pests to appear, such as leafworms, cotton aphids and whitefly, which in turn further fuelled increased application of pesticides.

The treadmill resulted in massive increases in pesticide applications: in the early years they were only applied a few times per season, but this application rose to up to 40 applications per season by the 1970s; accounting for over 50% of the costs of production in some regions.

The skyrocketing costs associated with increasing pesticide use were one of the key factors that led to the dramatic decline of the cotton industry in Central America: decreasing from its peak in the 1970s to less than 100,000 hectares in the 1990s. “In its wake, economic ruin and environmental devastation were left” as once thriving towns became ghost towns, and once fertile soils were wasted, eroded and abandoned (Lappe, 1998).

Sources: Douglas L. Murray (1994), Cultivating Crisis: The Human Cost of Pesticides in Latin America, pp35-41; Francis Moore Lappe et al (1998), World Hunger: 12 Myths, 2nd Edition, pp54-55.

This model implements a very simple proxy for vertical dispersion of heat in a lake based on the equation:  dT/dt = 1/A d(EA)/dz (dT/dz)  where: T: temperature (oC); t: time (days); z: depth (m); A: cross-sectional area (m2); E: vertical dispersion coefficient (m2 d-1)  If we consider that E is cons
This model implements a very simple proxy for vertical dispersion of heat in a lake based on the equation:

dT/dt = 1/A d(EA)/dz (dT/dz)

where: T: temperature (oC); t: time (days); z: depth (m); A: cross-sectional area (m2); E: vertical dispersion coefficient (m2 d-1)

If we consider that E is constant (it is in this model), then the equation becomes dT/dt = (EA/A)(d^2T/dz^2) = E(d^2T/dz^2), the classic diffusion equation

The model is simplified by exchanging temperature as a state variable, rather than executing  the full heat balance. This would require a computation of fluxes of atmospheric longwave and shortwave radiation, water longwave radiation, water conduction and convection, and water evaporation and condensation.

The vertical dispersion coefficients are adjusted artificially so that mixing increases at lower temperatures, thus quickly homogenizing the water column in colder months of the year.
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.
 Forcings and feedbacks based on Tom Fiddaman, James Hansen and other feedback and cycle diagrams

Forcings and feedbacks based on Tom Fiddaman, James Hansen and other feedback and cycle diagrams

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)  W
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 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 th
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).
From Schluter et al 2017  article  A framework for mapping and comparing behavioural theories in models of social-ecological systems COMSeS2017  video .   See also Balke and Gilbert 2014 JASSS  article  How do agents make decisions? (recommended by Kurt Kreuger U of S)
From Schluter et al 2017 article A framework for mapping and comparing behavioural theories in models of social-ecological systems COMSeS2017 video. See also Balke and Gilbert 2014 JASSS article How do agents make decisions? (recommended by Kurt Kreuger U of S)