hymod.par: Function to fit the parameter set of the Hymod...

In albertomontanari/hymodbluecat: An R-package for the hydrological model HyMOD with parameter optimisation and uncertainty assessment with the BLUECAT method in calibration and validation

Description

It calls a Fortran subroutine to make the actual computation of the simulated river flows. It requires the libraries "devtools", "DescTools" and "DEoptim" if the optimisation routine DEoptim is used (see below).

Usage

hymod.par(param.ini,area,tdelta,e,p,qoss,nstep=length(p),qinitial=0,lower=c(10,0.1,0,1,1),upper=c(400,10,0.9,1000,1000),itermax=100,control=list(factr=1e1,fnscale=0.01,parscale=c(100,1,1,1,1)),opt="DEoptim",lambdaln=0,plot=T)

Arguments

param.ini	Vector of the initial value for the 5 Hymod parameters Cmax, beta, alpha, ks, kq. Suitable values may be Cmax=200 mm, beta=0.5, alpha=0.5, ks=Tc where Tc is the time of concentration of the catchment (expressed in number of simulation time steps) and kq=Tc/2.
area	The catchment contributing area given in square kilometers.
Tdelta	The model time step given in seconds.
e	Vector of potential evapotranspiration. It should have the same length as p.
p	Vector of mean areal rainfall. It should have the same length as e.
qoss	Vector of observed river flow in cubic meters per second. It should have the same length as e.
nstep	Number of simulation time steps. Default is the sample size of e and p. It can be lower but not higher.
qinitial	Initial value of river flow. Default is zero.
lower	Lower bound for the model parameter vector. Default is c(10,0.1,0,1,1).
upper	Upper bound for the model parameter vector. Default is c(400,10,0.9,1000,1000).
itermax	Maximum number of trials allowed during optimisation. It used only by DEoptim (see below). Default is 100.
control	Control parameters used by DEoptim. See the help of DEoptim for additional details.
opt	Specifies the optimisation routine to be used. Allowed options are "DEoptim" and "optim" for the DEoptim routine (requires the library DEoptim) and the optim routine (available in R-base), respectively. See the related helps for additional details. Default is "DEoptim".
lambdaln	Specifies if parameter optimisation should be performed by optimising the Nash efficiency (NSE, Nash and Sutcliffe, 1968) or the Nash efficiency computed on a logarithmic transform of observed and simulated data. lambdaln=0 means no transformation (the same result is obtained for lambdaln tending to infinity). For lambdaln tending to 0 the transformation approaches the logarithmic transform.
plot	Specifies if diagnostic is to be returned. If plot=T (the default) a scapperplot of observed versus optimally simulated river flows is returned along with the related Nash efficiency (Nash and Sutcliffe, 1968).

Details

HyMOD is a five-parameter conceptual rainfall-runoff model that was introduced by Boyle (2000). The model is based on the probability-distributed soil storage capacity principle introduced by Moore (1985). In HyMOD, the rainfall-runoff process is represented through a nonlinear tank connected with three identical linear tanks in parallel representing the surface flow and a slow-flow tank representing groundwater flow. The model requires the optimization of five parameters: Cmax (the maximum storage capacity within the watershed), beta (the degree of spatial variability of the soil moisture capacity within the watershed), alpha (a factor for partitioning the flow between two series of tanks) and the two residence time parameters of quick-flow and slow-flow tanks, kq and ks respectively. The input data consist of precipitation and potential evapotranspiration at the given time scale.

Value

The output from DEoptim (or optim) is returned (see the related helps) along with these additional list items:

outputlist$par	The vector of optimised parameters. It is advisable to check that the resulting parameter values are not close to lower and upper limits. If they are, limits can be adjusted accordingly.
outputlist$qoss	The vector of observed calibration data.
outputlist$qsim	The vector of simulated calibration data.
outputlist$eff	The Nash Sutcliffe efficiency of the best parameter set.

Author(s)

Demetris Koutsoyiannis and Alberto Montanari, email: [e-mail].

References

Boyle, D.P., (2000), Multicriteria calibration of hydrological models, Ph.D. dissertation, Dep. of Hydrol. and Water Resour., Univ of Arizona, Tucson.

Moore, R.J., (1985), The probability-distributed principle and runoff production at point and basin scale, Hydrol. Sci. J., 30(2), 273-297.

Nash, J. E., and Sutcliffe, J. V., (1970), River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, doi: 10.1016/0022-1694(70)90255-6. 

Examples

## Load data
data(arnosubbiano)
e=arnosubbiano[,3]
p=arnosubbiano[,2]
qoss=arnosubbiano[,4]
## Run optimisation
pr1=hymod.par(c(100,1,0.5,200,0.5),area=752,tdelta=86400,e,p,qoss,nstep=length(p),qinitial=15,lower=c(10,0.1,0.1,0.1,0.1),upper=c(800,10,0.9,1000,100),opt="DEoptim")

albertomontanari/hymodbluecat documentation built on Jan. 20, 2022, 10:42 p.m.