<p>I guess the only thing the modeler should know is either the mathematical equation describing the system or the statistical relationship between a predictor and a predictand. You might have "access" to the exact equations or you might need to fit a mathematical model onto empirical observations. Then you need to code these mathematical or statistical relationship into a software (i.e. numerical model).</p>

<p>Fairly general. The devil will be in the details.</p>

CC BY-SA 3.0
2014-08-12T20:44:35.680Z
Once you have an equation it helps to know how to discretize it and analyze it for numerical stability and computational noise.
casey
CC BY-SA 3.0
2014-08-13T14:33:53.300Z
Indeed, thanks! Feel free to edit my answer. :) (or I might add that info later this week when I have some spare time).
Balinus
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2014-08-19T14:54:51.917Z
I'm afraid there is much more to know than simple relationships. Understanding the equations is just one small part of modeling, and most process based models contain hundreds of thousands of equations. Model calibration, model validation, model paramaterization, spinup, hind-casting, forecasting, the list goes on and on. I was hoping someone could direct me towards a modelers bible, if you will. Thank you all for your input. Pun intended.
derelict
CC BY-SA 3.0
2014-08-20T15:47:27.173Z
the question should be divided into such parts (i.e. calbration, validation, etc...). Otherwise, the answer would need to be 40 page long.
Balinus
CC BY-SA 3.0
2014-08-12T14:37:45.930Z
2014-08-12T14:37:45.930Z
Balinus
<p>What books are available that address the fundamental principles all processed based modelers should be aware of?  </p>

<p>Is there a guide, some holy grail of text books for process based simulation models? </p>

<p>Edit:
Process based, or system models such as those used to simulate ecological/biogeochemical/landscape processes. 
As Naught101 mentioned: "process based modelling" refers to a computational-numerical mode built on a conceptual model that is mostly based on physical laws defined by empirical relationships. As opposed to a purely empirical/statistical model. </p>

CC BY-SA 3.0
2014-08-06T19:50:03.413Z
I'm not sure what you mean by process based modeling.... perhaps edit the question to make it a bit more clear?
Neo
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2014-08-07T01:37:41.103Z
Process based models are used for every system that I can think of (e.g., ocean, atmosphere). The question needs more clarification
arkaia
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2014-08-07T02:27:59.853Z
I'm looking for do's and do nots, fundamental principles of process based modeling in general, not a specific system.
derelict
CC BY-SA 3.0
2014-08-17T02:18:11.970Z
This might be more suitable for http://scicomp.stackexchange.com/. If not, you'd be better off removing the "This is a bit off topic, but " part at the start, and editing it to make it more on-topic, if possible (I think it should be). Also, you're generally better off not asking for book recommendations. Those questions don't usually work very well on these sites, especially when they're very general...
naught101
CC BY-SA 3.0
2014-08-19T17:59:36.263Z
-1 This question still needs improvement. What kind of processes are you interested in? Are they described by empirical relationships? Partial differential equations? There is absolutely no answer to modeling "really any process".
milancurcic
CC BY-SA 3.0
2014-09-19T03:31:20.787Z
@Neo: I assume that "process based modelling" refers to a computational-numerical mode built on a conceptual model that is mostly based on physical laws. As opposed to an empirical model.
naught101
CC BY-SA 3.0
2014-09-23T14:04:10.320Z
The help center doesn't mention anything about asking questions that may have long answers. If you cannot answer the question, or are confused by what I'm asking, then don't try to answer the question.
derelict
CC BY-SA 3.0
2014-09-23T21:01:52.170Z
This question is currently closed and thus no one can answer it.  If you wish to have it re-opened, please edit your question to narrow its scope (this will automatically nominate it for re-opening).
casey
CC BY-SA 3.0
2014-09-23T21:05:07.613Z
How is it too broad?
derelict
<untagged>
Princples of process (systems) based modeling
derelict
# Princples of process (systems) based modeling

(Asked by: username_0 on 2014-08-06)

What books are available that address the fundamental principles all processed based modelers should be aware of?

Is there a guide, some holy grail of text books for process based simulation models?

Edit:
Process based, or system models such as those used to simulate ecological/biogeochemical/landscape processes.
As username_3 mentioned: "process based modelling" refers to a computational-numerical mode built on a conceptual model that is mostly based on physical laws defined by empirical relationships. As opposed to a purely empirical/statistical model.

**Tags:** untagged

> **Comments:**
> **username_1**: I'm not sure what you mean by process based modeling.... perhaps edit the question to make it a bit more clear?  
> **username_2**: Process based models are used for every system that I can think of (e.g., ocean, atmosphere). The question needs more clarification  
> **username_0**: I'm looking for do's and do nots, fundamental principles of process based modeling in general, not a specific system.  
> **username_3**: This might be more suitable for http://scicomp.stackexchange.com/. If not, you'd be better off removing the "This is a bit off topic, but " part at the start, and editing it to make it more on-topic, if possible (I think it should be). Also, you're generally better off not asking for book recommendations. Those questions don't usually work very well on these sites, especially when they're very general...  
> **username_4**: -1 This question still needs improvement. What kind of processes are you interested in? Are they described by empirical relationships? Partial differential equations? There is absolutely no answer to modeling "really any process".  
> **username_3**: @username_1: I assume that "process based modelling" refers to a computational-numerical mode built on a conceptual model that is mostly based on physical laws. As opposed to an empirical model.  
> **username_0**: The help center doesn't mention anything about asking questions that may have long answers. If you cannot answer the question, or are confused by what I'm asking, then don't try to answer the question.  
> **username_5**: This question is currently closed and thus no one can answer it.  If you wish to have it re-opened, please edit your question to narrow its scope (this will automatically nominate it for re-opening).  
> **username_0**: How is it too broad?  

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## 1 answers

### Answer by username_6 on 2014-08-12. Score: 1

I guess the only thing the modeler should know is either the mathematical equation describing the system or the statistical relationship between a predictor and a predictand. You might have "access" to the exact equations or you might need to fit a mathematical model onto empirical observations. Then you need to code these mathematical or statistical relationship into a software (i.e. numerical model).

Fairly general. The devil will be in the details.

> **Comments:**
> **username_5**: Once you have an equation it helps to know how to discretize it and analyze it for numerical stability and computational noise.  
> **username_6**: Indeed, thanks! Feel free to edit my answer. :) (or I might add that info later this week when I have some spare time).  
> **username_0**: I'm afraid there is much more to know than simple relationships. Understanding the equations is just one small part of modeling, and most process based models contain hundreds of thousands of equations. Model calibration, model validation, model paramaterization, spinup, hind-casting, forecasting, the list goes on and on. I was hoping someone could direct me towards a modelers bible, if you will. Thank you all for your input. Pun intended.  
> **username_6**: the question should be divided into such parts (i.e. calbration, validation, etc...). Otherwise, the answer would need to be 40 page long.  

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