Step by step SWAT-CUP for Calibration

Step by step SWAT-CUP  for Calibration

The detailed information can be find out from

1-     https://www.mssanz.org.au/MODSIM07/papers/24_s17/SWAT-CUP_s17_Abbaspour_.pdf,

2-     http://www.neprashtechnology.ca/Default.aspx)

3-     https://swat.tamu.edu/

Requirements:

 1-     ArcGIS/QGIS, Arc SWAT/QSWAT, SWAT CUP and good processing speed of computer

2-     TxtInOut scenario of swat project run, observed data-sets like discharge (cms), sediment yield (t/ha) etc.

Step by Step procedures

1-Interface of swat-cup

2-Setup the swat-cup project

Simple Click on New for a new swat-cup project

Click next for new swat-cup project

Browse the swat project folder where the TxtInOut is situated 

Select the TxtInOut and click on Open

Click Next 

Chose the Arc-SWAT version and the processor architecture

Select Sufi2 as calibration method

Click Finish to save the swat-cup project

This will take some time to save a new swat-cup project

3: Data input and their formatting

Par_inf.txt provide the facility to select the parameters and their subsequent ranges.

Here three methods are available for parameters as

r: existing parameter value will be replaced by the given value

v: value will be added to the existing parameter values

a: existing parameter is multiplied by (1 + given value)

Generally it was recommended that users should use all types of method during parameters selection

SUFI2_swEdit.def provide the facility to select the simulation number 

Here simulated year and beginning year and skip year can be modified as per the Arc-swat run was done.

Observed _rch.txt: here observed data can paste as per its format and time schedule

Observed _rch.txt: Number of observed variables explains the total outlet observed data taken during the calibration.

Flow_out_1 explains the discharge flow at outlet 1

And 141 is the total data series form beginning year simulation to end year of simulation excluding the warm-up period of model.

The format of observed data input are as follows:

Data point_ observed variable_outlet no_begining year observed value

Var_File_rch.txt: Explains the total variables and outlet information

SUFI2_extract_rch.def: important portion where total variables, variables column number in swat 

output.rch, total reaches in the basin and sub basin reach number, Swat-cup running time step are

 provided.

Like the Observed_rch.txt, here we have to provide the information’s of total observed variables, 

objective function and their threshold value. Here we have to input the all observed variable at

 selected outlets.

After the observed data input, click the calibrate or f5 and a new popup will apper like in the right for Executing. 

Click execute all

Click Y to run the model

Model start running …

Model running … like simulation 1 it will complete all simulation you have selected in par_inf.txt and SUFI2_swEdit.def

After the model will complete the run save it as Iter cal. 

Output of Itercal for the calibration period are 95ppu, dotty plot and the summary_stat, sensitivity analysis etc.

95ppu: It show the 95ppu of all variables and also the  observed and simulated data relation for all data input point.

Here we have taken the 4 observed variables therefore 95ppu can be seen for all 4 variables with observed and model sim.

How to make 95 ppu plot using Excel

Dotty plot shows the parameter value versus objective function.

 The main aim of this plot is to show distribution of sampling point for each simulation and to provide the idea of parameter sensitivity.

Summary show the statistical performance of model during the calibration period.

Here we can find the

p-factor: the percentage of observed data captured by the 95ppu

r-factor: width of 95ppu

R2:Coff. Of determination

NSE: objective function

PBIAS: percentage of biasness

Sensitivity analysis to check the sensitivity of input parameters during the calibration.

Sensitivity analysis at Grid View using the t-stat and the p-value

After the calibration results the performance of model can be evaluated using the Moriasi et al.(2007) and Moriasi et al(2015) criteria of model performance evaluation. After the successful model calibration further model can be validated in the same way using the calibrated parameters.