NSE.HypeSingleVar {HYPEtools}

Nash-Sutcliffe Efficiency

Description

Nash-Sutcliffe Efficiency calculation for imported HYPE outputs with single variables for several catchments, i.e. time and map files, optionally multiple model run iterations combined.

Usage

## S3 method for class 'HypeSingleVar'
NSE(sim, obs, na.rm = TRUE, progbar = TRUE, ...)

Arguments

sim

HypeSingleVar array with simulated variable (one or several iterations).

obs

HypeSingleVar array with observed variable, (one iteration). If several iterations are present in the array, only the first will be used.

na.rm

Logical. If TRUE, incomplete sim-obs pairs will be removed prior to NSE computation.

progbar

Logical, if TRUE progress bars will be printed for main computational steps.

...

ignored

Value

NSE.HypeSingleVar returns a 2-dimensional array of NSE performances for all SUBIDs and model iterations provided in argument sim, with values in the same order as the second and third dimension in sim, i.e. [subid, iteration].

Examples

# Create dummy data, discharge observations with added white noise as model simulations
te1 <- ReadObs(filename = system.file("demo_model", "Qobs.txt", package = "HYPEtools"))
te1 <- HypeSingleVar(x = array(data = unlist(te1[, -1]) + 
                                      runif(n = nrow(te1), min = -.5, max = .5), 
                               dim = c(nrow(te1), ncol(te1) - 1, 1), 
                               dimnames = list(rownames(te1), colnames(te1)[-1])), 
                     datetime = te1$DATE, subid = obsid(te1), hype.var = "cout")
te2 <- ReadObs(filename = system.file("demo_model", "Qobs.txt", package = "HYPEtools"))
te2 <- HypeSingleVar(x = array(data = unlist(te2[, -1]), 
                               dim = c(nrow(te2), ncol(te2) - 1, 1), 
                               dimnames = list(rownames(te2), colnames(te2)[-1])), 
                     datetime = te2$DATE, subid = obsid(te2), hype.var = "rout")
# Nash-Sutcliffe Efficiency
NSE(sim = te1, obs = te2, progbar = FALSE)