qmax = 20
ddf = 2
mint = -1
maxt = 1
# Assign the above parameter values into the model parameters object
params.assignvalues(f, smax, qmax, ddf, mint, maxt)
# Initialise the model by loading its climate inputs
model = ExphydroModel(P, PET, T)
# Specify the start and end day numbers of the simulation period.
# This is done separately for the observed and simulated data
# because they might not be of the same length in some cases.
simperiods_obs = [365, 2557]
simperiods_sim = [365, 2557]
# Run the model and calculate objective function value for the simulation period
Qsim = model.simulate(params)
kge = ObjectiveFunction.klinggupta(
Qobs[simperiods_obs[0]:simperiods_obs[1] + 1],
Qsim[simperiods_sim[0]:simperiods_sim[1] + 1])
print('KGE value = ', kge)
# Plot the observed and simulated hydrographs
plt.plot(Qobs[simperiods_obs[0]:simperiods_obs[1] + 1], 'b-')
plt.plot(Qsim[simperiods_sim[0]:simperiods_sim[1] + 1], 'r-')
plt.show()
######################################################################
# Generate 'niter' initial EXP-HYDRO model parameters
params = [ExphydroParameters() for j in range(niter)]
# Initialise the model by loading its climate inputs
model = ExphydroModel(P, PET, T)
# Specify the start and end day numbers of the calibration period.
# This is done separately for the observed and simulated data
# because they might not be of the same length in some cases.
calperiods_obs = [365, 2557]
calperiods_sim = [365, 2557]
# Calibrate the model to identify optimal parameter set
paramsmax = Calibration.montecarlo_maximise(model, params, Qobs, ObjectiveFunction.klinggupta,
calperiods_obs, calperiods_sim)
print 'Calibration run KGE value = ', paramsmax.objval
# Run the optimised model for validation period
Qsim = model.simulate(paramsmax)
kge = ObjectiveFunction.klinggupta(Qobs[calperiods_obs[1]:], Qsim[calperiods_sim[1]:])
print 'Independent run KGE value = ', kge
# Plot the observed and simulated hydrographs
plt.plot(Qobs[calperiods_obs[0]:], 'b-')
plt.hold(True)
plt.plot(Qsim[calperiods_sim[0]:], 'r-')
plt.show()
######################################################################
qmax = 20
ddf = 2
mint = -1
maxt = 1
# Assign the above parameter values into the model parameters object
params.assignvalues(f, smax, qmax, ddf, mint, maxt)
# Initialise the model by loading its climate inputs
model = ExphydroModel(P, PET, T)
# Specify the start and end day numbers of the simulation period.
# This is done separately for the observed and simulated data
# because they might not be of the same length in some cases.
simperiods_obs = [365, 2557]
simperiods_sim = [365, 2557]
# Run the model and calculate objective function value for the simulation period
Qsim = model.simulate(params)
kge = ObjectiveFunction.klinggupta(Qobs[simperiods_obs[0]:simperiods_obs[1]+1],
Qsim[simperiods_sim[0]:simperiods_sim[1]+1])
print 'KGE value = ', kge
# Plot the observed and simulated hydrographs
plt.plot(Qobs[simperiods_obs[0]:simperiods_obs[1]+1], 'b-')
plt.hold(True)
plt.plot(Qsim[simperiods_sim[0]:simperiods_sim[1]+1], 'r-')
plt.show()
######################################################################