In this study, we have presented two different advances for the field of subsurface geostatistics. First, we present data of variogram functions from a variety of different locations around the world. Second, we present a series of geostatistical analyses aimed at examining some of the statistical properties of such variogram functions and their relationship to a number of widely used variogram model functions.

It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.

Permeability of natural systems exhibits heterogeneous spatial variations linked with the size of the measurement support scale. As the latter becomes coarser, the system appearance is less heterogeneous. As such, sets of permeability data associated with differing support scales provide diverse amounts of information. In this contribution, we leverage information theory to quantify the information content of gas permeability datasets collected with four diverse measurement support scales.

Recently, the first global groundwater models were developed to better understand surface-water–groundwater interactions and human water use impacts. However, the reliability of model outputs is limited by a lack of data as well as model assumptions required due to the necessarily coarse spatial resolution. In this study we present the first global maps of model sensitivity according to their parameterization and build a foundation to improve datasets, model design, and model understanding.

This study evaluates the effect of conceptual hydro(geo)logical model (CHM) structure, climate change and groundwater abstraction on future groundwater-level prediction uncertainty. If the current groundwater abstraction trend continues, groundwater level is predicted to decline quickly. Groundwater abstraction in NW Bangladesh should decrease by 60 % to ensure sustainable use. Abstraction scenarios are the dominant uncertainty source, followed by CHM uncertainty and climate model uncertainty.