def __init__(self, p, pet, t, nsubcats, subcatwts):
""" This method is used to initialise, i.e., create an instance of the ExphydroDistrModel class.
Syntax: ExphydroDistrModel(p, pet, t, npixels)
Args:
(1) p: Daily precipitation time-series (mm/day). Each column is 1 pixel
(2) pet: Daily potential evapotranspiration time-series (mm/day). Each column is 1 pixel
(3) t: Daily mean air temperature time-series (deg C). Each column is 1 pixel
(4) nsubcats: Number of sub-catchments in the catchment
(5) Relative weight of all sub-catchments (array). It is the proportion of area
covered by each sub-catchment. Sum of all array elements is 1.
"""
# The statement below creates nsubcats instances of the lumped EXP-HYDRO model
self.model = [
ExphydroModel(p[:, j], pet[:, j], t[:, j]) for j in range(nsubcats)
]
self.subcatwts = subcatwts # Relative weight of each sub-catchment
self.timespan = p.shape[0] # Time length of the simulation period
self.qsimtmp = numpy.zeros(
self.timespan
) # Variable to temporarily store pixel's streamflow output
self.qsim = numpy.zeros(self.timespan) # Simulated streamflow (mm/day)
def __init__(self, p, pet, t, npixels):
""" This method is used to initialise, i.e., create an instance of the ExphydroDistrModel class.
Syntax: ExphydroDistrModel(p, pet, t, npixels)
Args:
(1) p: Daily precipitation time-series (mm/day)
(2) pet: Daily potential evapotranspiration time-series (mm/day)
(3) t: Daily mean air temperature time-series (deg C)
(4) npixels: Number of pixels in the catchment
"""
# The statement below creates npixel instances of the lumped EXP-HYDRO model
self.model = [ExphydroModel(p, pet, t) for j in range(npixels)]
self.timespan = p.shape[0] # Time length of the simulation period
self.qsimtmp = numpy.zeros(
self.timespan
) # Variable to temporarily store pixel's streamflow output
self.qsim = numpy.zeros(self.timespan) # Simulated streamflow (mm/day)
# MAIN PROGRAM
# Load meteorological and observed flow data
P = numpy.genfromtxt('SampleData/P_test.txt') # Observed rainfall (mm/day)
T = numpy.genfromtxt('SampleData/T_test.txt') # Observed air temperature (deg C)
PET = numpy.genfromtxt('SampleData/PET_test.txt') # Potential evapotranspiration (mm/day)
Qobs = numpy.genfromtxt('SampleData/Q_test.txt') # Observed streamflow (mm/day)
# Specify the no. of iterations
niter = 100
# 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]:])