rsimilarity_model: Prediction of hydrological similarity to drive transfer of...

In transfR: Transfer of Hydrograph from Gauged to Ungauged Catchments

Prediction of hydrological similarity to drive transfer of hydrograph to ungauged catchments

Description

Quantify the similarity of net rainfall and model this hydrological similarity from catchment similarity predictors

Usage

rsimilarity_model(
  Rn,
  msim = NULL,
  predictors,
  newpredictors,
  FUN = "invKGE",
  power = 0.5,
  symmetrize = mean,
  model = "glmnet",
  args_glmnet = list(s = "lambda.min", lower.limits = 0),
  standardisation = FALSE,
  linearisation = FALSE,
  first_model = NULL,
  parallel = FALSE,
  cores = NULL,
  verbose = TRUE,
  seed = NULL
)

Arguments

Rn	net rainfall matrix of gauged catchments (rows for time index, and columns for catchment index)
msim	similarity matrix of net rainfall time series (as produced by rsimilarity)
predictors	a list of at least two square matrices that could be used to predict similarity of net rainfall. The matrices should be square to describe the similarity of each pair of gauged catchments. High values should indicate a high probability of similarity in net rainfall
newpredictors	a matrix or data frame of predictors to be used as new input to the model being built and from which net rainfall similarity will be predicted. Typically, its rows describe the similarities between an ungauged catchment and several gauged catchments that may be weighted as donors. Each column should correspond to one element of predictors, and each row should correspond to one pair of catchments analysed.
FUN	either a function or a character string specifying the name of a predefined function to quantify the similarity of two net rainfall time series. See rsimilarity for more details
power	exponent applied on Rn time series when computing Rn similarity (generally between -1 and 1, a lower value emphasising the similarity of low flows). Default value is 0.5
symmetrize	a function to combine FUN(Rn[,i],Rn[,j]) and FUN(Rn[,j],Rn[,i]) into one similarity value (typically if FUN=KGE, KGE(Rn[,i],Rn[,j])!=KGE(Rn[,j],Rn[,i])). Default is mean
model	the method to estimate similarity of Rn from descriptors. This must be one of "lm", "glmnet"
args_glmnet	list of arguments to be passed to glmnet::cv.glmnet()
standardisation	logical indicating whether the predictors should be standardised (in particular to make their coefficient comparable)
linearisation	logical indicating whether a power transformation should be applied to the predictors in order to maximise linearity with the target variable, based on the Pearson correlation coefficient
first_model	a first model to use for predicting similarity (generally based on distance, see two_steps of mixr). The actual model will be built on residuals of the first one
parallel	logical indicating whether the computation of Rn similarities should be parallelised
cores	the number of cores to use for parallel execution if parallel is TRUE. If not specified, the number of cores is set to the value of parallel::detectCores()
verbose	logical indicating if warning messages should be written to the console
seed	integer value to be used by set.seed() for reproducible results. Used only if model="glmnet"

See Also

rsimilarity, mixr

Examples

data(Oudon)
obs <- as_transfr(st = Oudon$obs, hl = Oudon$hl)
obs <- velocity(obs, method = "loire2016")
obs <- uh(obs)
obs <- lagtime(obs)
obs <- rapriori(obs)
obs <- inversion(obs)
mdist1 <- hdist(x = obs, y = obs, method = "rghosh", parallel = TRUE, cores=2)
mdist2 <- mdist1^2
rghosh1 <- seq(1000, 5000, by=100)
rghosh2 <- rghosh1^2
res <- rsimilarity_model(Rn = obs$st$RnInv,
                   predictors = list(rghosh1=1/mdist1, rghosh2=1/mdist2),
                   newpredictors = data.frame(rghosh1=1/rghosh1, rghosh2=1/rghosh2),
                   seed=1234)
plot(rghosh1, res$similarity, ylab = "Predicted Rn similarity")
plot(rghosh2, res$similarity, ylab = "Predicted Rn similarity")
# rsimilarity_model() is automatically called by mixr() if mdist is a list
obs <- mixr(obs = obs, mdist = mdist1,
            similarity = list(rghosh1=1/mdist1, rghosh2=1/mdist2),
            parallel = TRUE, cores=2, cv = TRUE, save_donor = TRUE)
obs$similarity_models
obs <- convolution(obs)
plot(obs, i = 1, attribute = c("Qobs", "Qsim"))

transfR documentation built on July 24, 2026, 5:06 p.m.