The dependency structure between hydrological variables is of critical importance to hydrological modelling and forecasting. When a copula capturing that dependence is fitted to a sample, information on the uncertainty of the fit is needed for subsequent hydrological calculations and reasoning. A new method is proposed to report inferential uncertainty in a copula parameter. The method is based on confidence curves constructed with the use of a pseudo maximum likelihood estimator for the copula parameter. The method was tested on synthetic data and then used as a tool in two hydrological examples. The first examines the probability of major floods in two locations on the Rhine River and its tributaries in the same calendar year. In the second example, rainfall–runoff from a karst region in Tunisia was analysed to determine a confidence interval for the delay between precipitation and runoff.

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The availability of water resources is declining, while the demand for water continues to increase for the agricultural sector and drinking water supply in the context of climate change hazards, such as the increasing frequency of extreme weather events (drought, floods). In fact, several studies show that droughts occur more and more frequently, with a duration varying from one month to a few years. Therefore, monitoring these droughts would help in the management of groundwater in the short and medium term and thus would enhance adaptation to climate change. For this purpose, several indices allowing the prediction and characterization of these droughts have been proposed by researchers. Among these, two indices were selected: SPEI (Standardized Precipitation Evapotranspiration Index) and SGI (Standardized Groundwater Index). SPEI makes it possible to take into account precipitation and evapotranspiration on different time scales which facilitates the analysis of drought impact on water resource demands. The same is true for SGI, which allows analysis of the water table level. The objective of this work is to calculate each of these indices for several boreholes in Tunisia to assess correlations between SPEI and SGI, to project SPEI over several horizons under climate change and to predict the evolution of the SGI thanks to the correlations. Correlation between SPEI and SGI time series was examined using three different copula families, Frank, Gumbel and Clayton. Copulas were fitted to a 2D data set consisting of the SPEI time series and the time shifted SGI time series for different lag times. The Kendall @en