One of the many strengths of GoldSim is its graphically enhanced user interface, which allows you to quickly build your model from reusable components. During a recent webinar, I built a reservoir operations model that receives water from a snowmelt driven watershed using 6 existing models found in our Model Library. Once I understood what my modeling requirements were, I was able to quickly compile a probabilistic system model that is powerful in demonstrating how to operate a dam that services changing water demands and climate scenarios, which protects against downstream flooding.
Showing posts with label markov. Show all posts
Showing posts with label markov. Show all posts
April 24, 2018
Building Blocks in GoldSim
Posted by Jason Lillywhite
One of the many strengths of GoldSim is its graphically enhanced user interface, which allows you to quickly build your model from reusable components. During a recent webinar, I built a reservoir operations model that receives water from a snowmelt driven watershed using 6 existing models found in our Model Library. Once I understood what my modeling requirements were, I was able to quickly compile a probabilistic system model that is powerful in demonstrating how to operate a dam that services changing water demands and climate scenarios, which protects against downstream flooding.
One of the many strengths of GoldSim is its graphically enhanced user interface, which allows you to quickly build your model from reusable components. During a recent webinar, I built a reservoir operations model that receives water from a snowmelt driven watershed using 6 existing models found in our Model Library. Once I understood what my modeling requirements were, I was able to quickly compile a probabilistic system model that is powerful in demonstrating how to operate a dam that services changing water demands and climate scenarios, which protects against downstream flooding.
Labels:
awbm,
flood control,
hydrology,
markov,
melting,
operations,
process,
reservoir,
runoff,
snow17,
snowmelt,
stochastic,
temperature,
WGEN
May 19, 2016
Application of the Markov Process Rainfall Model
Posted by Jason Lillywhite
If you have visited our Model Library lately, you might have noticed that we have a nice little example model that demonstrates the use of a Markov process to simulate daily rainfall. You need to specify some key statistical inputs that have some basis on historic data. How do you develop these inputs? How do you know if the Markov model is realistic? I thought it would be helpful to show how this simple example might be applied in a real-world project and try to answer those questions.
*Note that I made changes to the results on 5/20/2016 after I used GoldSim's optimization function to better calibrate the rate variability.
If you have visited our Model Library lately, you might have noticed that we have a nice little example model that demonstrates the use of a Markov process to simulate daily rainfall. You need to specify some key statistical inputs that have some basis on historic data. How do you develop these inputs? How do you know if the Markov model is realistic? I thought it would be helpful to show how this simple example might be applied in a real-world project and try to answer those questions.
*Note that I made changes to the results on 5/20/2016 after I used GoldSim's optimization function to better calibrate the rate variability.
Labels:
calibration,
climate,
fit,
hydrology,
markov,
model,
optimization,
precipitation,
rainfall,
water
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