Preparing the SAMIR parameter csv file

All the SAMIR model parameters are written in a csv file. Here is an example of such a file:

ClassName

scale_factor

Default

class1

class2

class3

class4

class5

class6

class7

ClassNumber

1

0

1

2

3

4

5

6

7

FmaxFC

1000

1

Fslope

1000

1.4

Foffset

1000

-0.075

Plateau

1

0

Kcb_max

1000

1.15

Kslope

1000

1.2

Koffset

1000

-0.24

Zsoil

1

2000

Ze

1

300

Init_RU

1000

0.87

DiffE

1

1

DiffR

1

5

REW

1

0

minZr

1

150

maxZr

1

600

p

1000

0.55

FW

1000

1

Irrig_auto

1

1

Irrig_man

1

0

Kcb_stop_irrig

1

0.5

Here is how the file is organised:

  • model parameters are located in the first column (row header), a row contains the values of a parameter for different classes.

  • the file contains three header columns: the parameter name (ClassName leftmost), the scale factors (scale_factor used to manage data types in the SAMIR calculations) and the default (Default) parameter values column.

  • the other columns have the ClassName as a header and contain the different parameter values. If a cell is empty, it will take the Default column value.

  • the ClassNumber row should not be changed for the scale_factor and Default columns. For the classes, it should correspond to the integer values contained in the land cover raster.

You can have different parameter files, the file to use for a simulation is set in the json configuration file. This csv table is then loaded in an object as a pandas DataFrame attribute with the following function:

class modspa_pixel.parameters.params_samir_class.samir_parameters(paramFile: str)[source]

Load all parameters for multiples classes in one object.

Attributes

  1. .table: pd.DataFrame

    pandas DataFrame with all paramters (rows) for all classes (columns)

    It also contains :

    • a scale_factor column (first column) that allows to convert all parameters to integer values for reduced memory usage

    • a Default column (second column) that contains default values to fill in missing values

    Example of the parameter table:

                    scale_factor  Default   class1    class2    class3
    ClassName                                                                      
    ClassNumber                1     0.00     1.00     2.000     3.000
    FminNDVI                1000     0.20     0.20     0.100     0.100
    FmaxNDVI                1000     0.90     0.90     0.900     0.900
    FminFC                  1000     0.90     0.90     0.900     0.900
    FmaxFC                  1000     1.00     0.90     1.000     1.000
    Fslope                  1000     1.40     1.50     1.500     1.500
    Foffset                 1000    -0.10    -0.10    -0.100    -0.100
    Plateau                    1    70.00    70.00    70.000    70.000
    KminNDVI                1000     0.10     0.10     0.100     0.100
    KmaxNDVI                1000     0.90     0.90     0.900     0.900
    KminKcb                 1000     0.00     0.20     0.000     0.000
    KmaxKcb                 1000     0.98     1.00     1.100     1.100
    Kslope                  1000     1.60     1.60     1.600     1.600
    Koffset                 1000    -0.10    -0.10    -0.100    -0.100
    Zsoil                      1  2000.00  1600.00  1550.000  1550.000
    ...                      ...      ...      ...       ...       ...
    Kcmax                   1000     1.15     1.15      1.15      1.15
    Fc_stop                 1000     0.15     0.15      0.15      0.15
    Start_date_Irr             1     0.00     0.00      0.00      0.00
    p_trigger                  1     0.00     0.00      0.00      0.00
    

Methods

__init__(paramFile: str) None[source]

Create pandas table from the csv parameter file.

Arguments

  1. paramFile: str

    path to csv parameter file

This object is used to build a raster for each parameter (spatialized parameters) in the SAMIR functions.