The Eastern Himalayas is a hot spot in ​India. There is an abundance of species living in the area that are threatened by humanity.

Combining electromobility and renewable energies since 2014.

http://www.amsterdamvehicle2grid.nl/

Clone of: 

Simulate an impact of an asteroid of any Diameter at any given Speed!

General global ocean box model, including isotopes.

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.
For approximate Continuous Behavior set 'R Base' to a small number like 0.125To 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

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 uses simple functions (converters, cosine) to simulate the water balance inside a reservoir.

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

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.

This a simple and "totally accurate" model of the exponential human population.

sumatran orangutan food web

A simulation illustrating simple predator prey dynamics. You have two populations.

A model of water flow within the potable water supply chain

A model of an infectious disease and control

Fig 3.1 from Jorgen Randers book 2052 a Global Forecast for the Next Forty Years

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

School assessment

Clone of Pesticide Use in Central America for Lab work