Revision as of 20:47, 19 January 2011

Motivation

In order to apply hydrological models successfully it is necessary to define model parameters accurately. A direct measurement of the parameters is mostly not possible, too expensive or there is no clear physical relation. For those reasons the parameters are adjusted in a trial and error process in so far that the simulated factors (e.g. runoff) correspond best to the values measured. This task can be time-consuming and difficult if the corresponding model is complex or has a large number of parameters.

Requirements

A basic requirement is, of course, a functioning model. To evaluate the quality of an individual simulation mostly a singular measurement is used. Frequently used examples are the mean square error or the Nash-Sutcliff efficiency. The Jena Adaptable Modelling System the Standard Efficiency Calculator is available for this reason. The image below shows an example configuration of this component
Bild:Eff calc.jpg
The component calculates the efficiencies listed in parameter effMethod with:

• 1 -> Nash - Sutcliff efficiency with power 1
• 2 -> Nash - Sutcliff efficiency with power 2
• 3 -> Nash - Sutcliff efficiency of the logarithmic values with power 1
• 4 -> Nash - Sutcliff efficiency of the logarithmic values with power 2
• 5 -> Index of Agreement (1)
• 6 -> Index of Agreement (2)
• 7 -> regression coefficient
• 8 -> WR2
• 9 -> DSGRAD
• 10 -> absolute volume error
• 11 -> mean square error
• 12 -> PBIAS
• 13 -> PBIAS2

By using the attributes validation and prediction the simulation and validation data are transferred to the component in order to compare them. The time interval in which both time periods should be compared is determined by the attribute effTimeInterval. The modelTimeInterval shows the modeling time. In most applications those two time spans do not correspond. Many models show bad mappings at the beginning of the modeling process since they often need a certain time until internal conditions become stationary. The time of initialising should normally not be considered. Often the qualities of models in a certain time period are of interest (e.g. during snowmelt), so that several efficiency components can be used which consider different time periods.

Calibration

There are two common methods for calibrating a model with the integrated Standard Efficiency Calculator:

Offline Calibration

First, it is possible to carry out a calibration locally on your own computer. However, this uses computing resources for a longer period of time. Step 1

Open the JAMS User Interface Model Editor (JUICE). Load the model you want to calibrate. The following window should appear:
Bild:Juice kalibration 1.jpg

Step 2

Select the entry Kalibrierungswizard under the item Modell. A dialog should appear which offers the configuration of the calibration (see image).
Bild:optimization_wizard.jpg

Step 3

In the middle of the dialog area on the left hand side the potential parameters of the model are listed. Select those parameters from the list which you want to calibrate. If you want to select more than one parameter, hold down CTRL while selecting the parameters. Now you can see several input fields for each parameter on the right hand side. You can choose the lower and upper bound which define the area in which the paramater can vary. In addition, it is possible to define a start value. This makes sense if a parameter is already known which suits as a starting point for the calibration.

Step 4

In the the lower area in the dialog the target function is specified. Either a single criterion or various criteria can be selected (for more than one hold CTRL). By choosing various criteria a multi-criteria optimization problem is created which differs significantly from a (common) one-criterion optimization problem regarding its solution. Since not every optimization method is suitable for a multi-criteria problems either, not all optimizers are at disposal this time.
For every selected optimization criterion a list box appears on the right hand side of this section. It determines whether the criterion will be minimized, maximized, absolutely minimized (i.e. min |f(x)| e.g. for the absolute volume error) or absolutely maximized.

Step 5

When parameters and target criteria are selected, it is necessary to chose an optimization method for the calibration. In the upper area of the dialog you can see a list box which lists several optimization methods. At the moment the following methods are available:

• Shuffled Complex Evolution
• Branch and Bound
• Nelder Mead
• DIRECT
• Gutmann Method
• Random Sampling
• Latin Hypercube Random Sampler
• Gauß Process Optimizer
• NSGA-2
• MOCOM
• Paralleled Random Sampler
• Paralleled SCE

For the application with single target criterion the Shuffled Complex Evolution (SCE) and DIRECT are recommended. Most probably both find a global optimized paramterization. It has been attested that DIRECT shows robust operation and no paramterization is necessary. SCE only needs one parameter: the Anzahl der Komplexe (number of complexes). This parameter indirectly controls whether the parameter search area is browsed rather broadly or if the process quickly concentrates on a (sometimes local) minimum. Mostly the default value 2 can be used. For multi-criteria optimization NSGA - 2 seems to provide very good results. However, there are more detailed analyses to be carried out.

In addition, there are three check boxes available, independent of the selected method.

• irrelevante Komponenten entfernen: deletes components from the model structure which certainly do not influence the calibration criterion
• GUI Komponenten entfernen: deletes all graphical components from the model structure. This option is strongly recommended since e.g. diagrams have a strong negative influence on the calibration.
• Modellstruktur optimieren: at the moment without function

Step 6

In order to finish the configuration, click on the button XML erzeugen. This closes the dialog and a new (modified) model is set up. You can start the calibration by starting the modeling. Please keep in mind that the calibration assistand modifies your output datastores in such a way that for every model run during calibration model parameters and target criteria are given. If you want to avoid this output, delete the file optimization_wizard_optimizer.xml manually.

Online Calibration

As an alternative to offline calibration the process can be carried out on the computing cluster of the Department of Geoinformatics, Hydrology and Modelling at the Friedrich - Schiller - University Jena. This does not occupy any local computing resources and allows a calibration of up to four models at the same time.

Step 1

Open your Internet browser (e.g. Firefox) and go to website [[1]] You will now see the following window and you will require a login. In order to register please contact

• Christian Fischer
• christian.fischer.2@uni-jena.de

Bild:Optas_login_page.png

Step 2 After the registration you will see the main window of the application.
Bild:Optas_main_page.png

Step 3 In order to carry out a new calibration, click on "Create Optimization Run". You will now see the first step of the calibration process.
Bild:Optas_create_step1_page.png In this step you will be asked to enter the data which is necessary. Load the model for the calibration (jam file) into the first file dialog. Pack your working directory of the model as a zip archive. A suitable programs is for example 7-zip. Please note:

• the working directory cannot be in a subfolder
• please do not load any unneccessary files (e.g. in the output file). The upload is limited to 20mb.
• the file default.jap is not allowed to be in the working directory

Now load the packed working directory into the second file dialog. The modeling will usually be carried out by using the current version of JAMS/J2000. Libraries for J2000g and J2000-S are usually available as well. If you need an specially adapted version of J2000, J2000-S or J2000g, please load them (unpacked) into the corresponding