Two households with PV systems and electric vehicles sharing a battery and connected to the grid. What are the advantages?  This model prototypes the working of an Smart Grid with Electric Vehicles   The objective is testing the theoretical advantages of batteries (also batteries in Electric Vehicle
Two households with PV systems and electric vehicles sharing a battery and connected to the grid. What are the advantages?

This model prototypes the working of an Smart Grid with Electric Vehicles

The objective is testing the theoretical advantages of batteries (also batteries in Electric Vehicles) in combination with renewable energies. The model considers two houses, that store energy both in Electric Vehicles (Vehicle to Grid), and in a communal battery.

Except when specified otherwise, the units of all variables are expressed in W/h.

Press "Story" in the lower bar for a guided tour over the model. Better seen at 50% zoom.

by Carlos Varela (cvarela@gmx.at)
This model provides a dynamic simulation of the Sverdrup (1953) paper on the vernal blooming of phytoplankton.  The model simulates the dynamics of the mixed layer over the year, and illustrates how it's depth variation leads to conditions that trigger the spring bloom. In order for the bloom to occ
This model provides a dynamic simulation of the Sverdrup (1953) paper on the vernal blooming of phytoplankton.

The model simulates the dynamics of the mixed layer over the year, and illustrates how it's depth variation leads to conditions that trigger the spring bloom. In order for the bloom to occur, production of algae in the water column must exceed respiration.

This can only occur if vertical mixing cannot transport algae into deeper, darker water, for long periods, where they are unable to grow.

Sverdrup, H.U., 1953. On conditions for the vernal blooming of phytoplankton. J. Cons. Perm. Int. Exp. Mer, 18: 287-295
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.
	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 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.
•Average
(Status Quo) Case

 –Last
30 years of historical EAA data  

 –Used
the past to predict the future 

 –Represents
the status quo case 

 –Includes
the dry portion  and wet portion of AMO
cycle
•Average (Status Quo) Case
–Last 30 years of historical EAA data
–Used the past to predict the future
–Represents the status quo case
–Includes the dry portion  and wet portion of AMO cycle
This is a model representing bushmeat, nutrition, and ebola as it relates to biodiversity and overfishing.
This is a model representing bushmeat, nutrition, and ebola as it relates to biodiversity and overfishing.
 The purpose of this deer management model is to explore the capacity of wildlife management actions to help us adapt to the effects of climate change.

The purpose of this deer management model is to explore the capacity of wildlife management actions to help us adapt to the effects of climate change.

 This model describes nitrogen 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 reduce the stock at which they start and

This model describes nitrogen 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 reduce the stock at which they start and add to the stock at which they end.

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.
A clone of the first model with the addition of a converter to describe the competition between rabbits for available vegetation based on the relationship between rabbit density and rabbit birth rate
A clone of the first model with the addition of a converter to describe the competition between rabbits for available vegetation based on the relationship between rabbit density and rabbit birth rate
 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 amo

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.

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.
 STEM-SM combines a simple ecosystem model (modified version of VSEM; Hartig et al. 2019) with a soil moisture model (Guswa et al. (2002) leaky bucket model). Outputs from the soil moisture model influence ecosystem dynamics in three ways.   (1) The ratio of actual transpiration to maximum evapotran
STEM-SM combines a simple ecosystem model (modified version of VSEM; Hartig et al. 2019) with a soil moisture model (Guswa et al. (2002) leaky bucket model). Outputs from the soil moisture model influence ecosystem dynamics in three ways. 
(1) The ratio of actual transpiration to maximum evapotranspiration (T/ETmax) modifies gross primary productivity (GPP).
(2) Degree of saturation of the soil (Sd) modifies the rate of soil heterotrophic respiration.
(3) Water limitation of GPP (by T/ETmax) and of soil nutrient availability (approximated by Sd) combine with leaf area limitation (approximated by fraction of incident photosynthetically-active radiation that is absorbed) to modify the allocation of net primary productivity to aboveground and belowground parts of the vegetation.

Ecosystem dynamics in turn influence flows of water in to and out of the soil moisture stock. The size of the aboveground biomass stock determines fractional vegetation cover, which modifies interception, soil evaporation and transpiration by plants.

References:
Guswa, A.J., Celia, M.A., Rodriguez-Iturbe, I. (2002) Models of soil moisture dynamics in ecohydrology: a comparative study. Water Resources Research 38, 5-1 - 5-15.

Hartig, F., Minunno, F., and Paul, S. (2019). BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. R package version 0.1.7. https://CRAN.R-project.org/package=BayesianTools

Eastern oyster growth model calibrated for Great Bay.  Developed and implemented by Joao G. Ferreira and Camille Saurel; growth data, driver data, and culture practice from Phil Trowbridge, Ray Grizzle, and Suzanne Bricker.  This is a one box model for an idealized farm with one million oysters seed
Eastern oyster growth model calibrated for Great Bay.

Developed and implemented by Joao G. Ferreira and Camille Saurel; growth data, driver data, and culture practice from Phil Trowbridge, Ray Grizzle, and Suzanne Bricker.

This is a one box model for an idealized farm with one million oysters seeded (one hectare @ a stocking density of 100 oysters per square meter)

1. Run WinShell individual growth model for one year with Great Bay growth drivers;

2. Determine the scope for growth (in dry tissue weight per day) for oysters centered on the five weight classes)
 
3. Apply a classic population dynamics equation:

dn(s,t)/dt = -d[n(s,t)g(s,t)]/ds - u(s)n(s,t)

s: Weight (g)
t: Time
n: Number of individuals of weight s
g: Scope for growth (g day-1)
u: Mortality rate (day-1)

4. Set mortality at 30% per year, slider allows scenarios from 30% to 80% per year

5. Determine harvestable biomass, i.e. weight class 5, 40-50 g (roughly three inches length)