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The file holds additional model settings to info.txt and is therefore located in the same folder as info.txt. The file is used to define what kind of optimisation to be done if calibration is set in info.txt. There are several different methods to chose from, each with their settings. Which model parameters to calibrate and within which boundaries are information also given in optpar.txt.

Maximum 100 model parameters may be optimised simultaneously. To optimise more parameters, the code needs to be changed (set maxoptpar to a higher value). All parameters are described in the section on par.txt, but not all of them can be calibrated. The objective function of the optimization is defined in info.txt as the combination of criteria chosen, see Performance criteria options.

There are eight methods of optimisation implemented in HYPE as detailed in the table below (read more about them in the tutorial). Additionally, there are two other tasks for output generation, WA and WS, which produce detailed performance and simulation results for all runs performed during optimisation. Tasks WA and WS are compatible with selected optimisation methods only, a denoted in the table. The task of organized scanning SC is a parameter investigation method.

The optpar.txt file may also be used for parameter ensemble simulation (parensemble in info.txt), to know which model parameters to use. The parameter ensemble may use the parameters earlier found by calibration.

Task Description
MCMonte Carlo (MC) simulation with parameter values randomly distributed over the intervals (basic MC-method)
BPprogressive Monte Carlo simulation with parameter space limited by best found so far (alternative MC-method)
SMprogressive Monte Carlo simulation with parameter space reduced in stages (alternative MC-method)
DEDifferential Evolution Markov Chain method (alternative MC-method)
BNoptimisation with Brent method
Q1optimisation with QuasiNewton DFP gradient-based method
Q2optimisation with QuasiNewton BFGS gradient-based method
SDoptimisation with QuasiNewton steepest descent method
SCorganised scanning of two parameters
WAwrite performance result for all simulations (MC, SM or DE)
WSwrite simulation results (basin-, time-, map-, , regional-, or , class-files) for all ensembles in Monte Carlo simulation (MC, BP, or DE)
ASrun parameter ensemble simulations with parameters found in allsim.txt
BSrun parameter ensemble simulations with parameters found in bestsims.txt

File content

The first row is for general comments. It is ignored by the program when reading the file. Next comes a section with calibration settings. It reaches from second to 21st row, and are used to define tasks and other settings. Last comes a section defining the parameters to be calibrated. These occupy row 22 and onward.

In the calibration setting section a row starts with a code indicating a task or other settings. Argument of the code is listed from position 12 and forward on each row. The following options are available for the calibration setting section:

Code Argument Default value Description
tasktwo letter wordFALSEdefine what kind of optimisation to do (see methods above), and if additional results are to be written for the MC-methods
cal_logY/NYESflag for writing a calibration.log file
scan_numxinteger1number of steps taken for the first parameter (SC method)
scan_numyinteger1number of steps taken for the second parameter (SC method)
num_mcinteger1000number of Monte Carlo simulations (per centre point and stage for progressive MC)
num_ensinteger1number of best Monte Carlo simulations to keep and print results from (and use as centre points for next stage of progressive MC) (maximum 999)
num_bpmcinteger200number of simulations per reduced parameter space which the best simulations shall be selected from (BP MC-method)
num_bpmaxinteger100number of reductions of the parameter space for MC simulation (BP MC-method)
num_stagesinteger1number of stages for progressive Monte Carlo (SM MC-method)
num_zoomreal0.9reduction of parameter space (0-1) for each stage of progressive MC (BP MC-method)
DEMC_ngeninteger100number of generations for DEMC method
DEMC_npopinteger25number of populations for DEMC method
DEMC_gammascalereal1scaling of the mutation strength for DEMC method. A new (next generation) parameter candidate is proposed as a mutation of the parent parameter value based on the difference between two random members of the parent population. DEMC_gammascale is a scaling factor for the resulting parameter jump width. Small values will cause smaller mutations, which potentially stabilises the search through large parameter spaces at the cost of convergence speed. A value of 1 will result in no scaling.
DEMC_crossoverreal1crossover probability for DEMC method. Probability that the proposed candidate is chosen instead of the parent parameter. Large DEMC_crossover values mean larger probability that the proposal is chosen. Set to 1, all proposals are accepted. This makes it harder to find an acceptable overall proposal because all parameters are changed in every generation. Set to 0.5, each parameter candidate has only a 50% chance to be accepted into the next proposal.
DEMC_sigmareal0.1sample error standard deviation for DEMC method. Base for the standard deviation of the random perturbation, which adds random noise to the proposed parameter in addition to the gamma-mutation. This value is multiplied with 3rd-row value for each parameter (see description of parameter rows below).
DEMC_accprobinteger0scaling factor for probabilistic acceptance for DEMC method (0 = off (default); >0 = on). If set to off, parameter proposals will only (and always) be accepted if the objective function decreases (= better performance). If turned on, also proposals with higher value of the objective function can be accepted; better performance will give higher probability of acceptance. High values of the scaling factor will also increase the probability of acceptance.
BR_diagStpY/NYESflag for taking a diagonal step at the end of each iteration (BN method)
num_maxItrinteger500max amount of iterations (interrupt non-MonteCarlo methods)
num_maxTiminteger72max calibration time (hours) (interrupt non-MonteCarlo methods)
num_parItrinteger10number of iterations taken into account for parameter change tolerance (interrupt non-MonteCarlo methods)
num_criItrinteger10number of iterations taken into account for criterium change tolerance (interrupt non-MonteCarlo methods)
num_criTolreal0.001tolerance for criteria relative change over last iterations (interrupt non-MonteCarlo methods)
lnS_maxItrinteger500max amount of line search iterations (per line) (non-Monte Carlo methods)
lnS_tolreal0.001general relative tolerance for line search (non-Monte Carlo methods)
QN_nrmTolreal0.001tolerance for gradient norm to be considered zero (QN methods)
QN_pctDervreal0.02factor to offset current parameter values for numerical derivative (QN methods)
QN_stencilinteger2numerical derivative stencil type (2, 4, 6 and 8 allowed) (QN methods)
QN_lambMaxreal0.9factor of parameter interval, used to limit the step length of the line search within given parameter intervals (QN methods)
QN_lambAccreal1.618factor increasing the step length of the line search (QN methods)

From row 22 and onward, model parameters to be calibrated are listed. The parameter is given as it is or with single quotation marks (e.g. 'cevp') followed by its values. For non-general parameters, values for all soil types/land uses/subbasins/parameterregions/etc have to be provided.

Each parameter is defined on three rows:

  • Row 1 specifies lower boundaries of the parameter range
  • Row 2 specifies upper boundaries of the parameter range (the model actually accepts lower and upper boundaries in any order)
  • Row 3 specifies either a minimum step width for parameter change or, in case of the DE method, a parameter specific factor to scale the random noise added to the proposed next-generation parameter, see description of DEMC settings code DEMC_sigma in table above.

NOTE: If lower and upper boundaries are identical, the parameter is omitted. This allows to calibrate a selection of the values for dependent parameters. The omitted parameters will not be included in the result in bestsims.txt and allsim.txt. The omitted parameters will be included in the result respar.txt.

Example of parameter rows in optpar.txt:

wcfc 	0.100 0.020 0.120 0.050 0.250 0.250 0.150 0.050 0.500 0.500 0.050
wcfc 	0.100 0.120 0.120 0.050 0.250 0.250 0.150 0.050 0.500 0.500 0.050
wcfc 	0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001
ttmp  0.0	0.0
ttmp  2.0	2.0
ttmp  0.01	0.01

Explaination of example: The rows starting with wcfc are representing field capacity for 11 soil types, where the second soil type's wcfc is calibrated. Parameter ttmp is threshold temperature for 2 land uses, which are both calibrated.

start/hype_file_reference/optpar.txt.txt ยท Last modified: 2024/01/25 11:37 (external edit)