We investigate the impact of baseflow contribution on concentration–flow (C–Q) relationships across the Australian continent. We developed a novel Bayesian hierarchical model for six water quality variables across 157 catchments that span five climate zones. For sediments and nutrients, the C–Q slope is generally steeper for catchments with a higher median and a greater variability of baseflow contribution, highlighting the key role of variable flow pathways in particulate and solute export.

Drying soil layers (DSLs) have been extensively reported in artificial forestland in the Loess Plateau, China, which has limited water resources and deep loess. To address this issue relating to plant root–soil water interactions, this study developed a root growth model that simulates both the dynamic rooting depth and fine-root distribution. Evaluation vs. field data proved a positive performance. Long-term simulation reproduced the evolution process of the DSLs and revealed their mechanisms.

Many data sources are available to evaluate components of the water cycle (precipitation, evapotranspiration, runoff, and terrestrial water storage). Despite this variety, it remains unclear how different combinations of datasets satisfy the conservation of mass. We conducted the most comprehensive analysis of water budget closure on a global scale to date. Our results can serve as a basis to select appropriate datasets for regional hydrological studies.

Geophysical instruments are often used in hydrological surveys. A geophysical model that couples electrical conductivity in the subsurface layers with measurements from an electromagnetic induction instrument was combined with a machine learning algorithm. The study reveals that this combination can estimate the identifiability of electrical conductivity in a layered soil and provide insight into the best way to configure the instrument for a specific field site.

We present an approach on how to incorporate crop phenology in a regional hydrological model using decision tables and global datasets of rainfed and irrigated cropland with the associated cropping calendar and management practices. Results indicate improved temporal patterns of leaf area index (LAI) and evapotranspiration (ET) simulations in comparison with remote sensing data. In addition, the improvement of the cropping season also helps to improve soil erosion estimates in cultivated areas.

The aim of this study was to develop a high-accuracy micro-lysimeter system for the quantification of non-rainfall water inputs that overcomes existing drawbacks. The micro-lysimeter system had a high accuracy and allowed us to quantify and distinguish between different types of non-rainfall water inputs, like dew and fog. Non-rainfall water inputs occurred frequently in a Swiss Alpine grassland ecosystem. These water inputs can be an important water source for grasslands during dry periods.

In this study, we presented stable isotopes in atmospheric water vapor and precipitation for five extreme winter precipitation events in Nanjing, southeastern China, from December 2018 to February 2019. Our results imply that multiple moisture sources and the rapid shift among them are important conditions for sustaining extreme precipitation events, especially in the relatively cold and dry winter.

This study aims to predict long-term averages of low flow on a hydrologically diverse dataset in Austria. We compared seven statistical learning methods and included a backward variable selection approach. We found that separating the low-flow processes into winter and summer low flows leads to good performance for all the models. Variable selection results in more parsimonious and more interpretable models. Linear approaches for prediction and variable selection are sufficient for our dataset.

Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.

The development of high-resolution global precipitation forecasts and the lack of reliable precipitation forecasts over Africa motivates this work to evaluate the precipitation forecasts from the Global Forecast System (GFS) over the Niger river basin in Africa. The GFS forecasts, at a 15 d accumulation timescale, have an acceptable performance; however, the forecasts are highly biased. It is recommended to apply bias correction to GFS forecasts before their application.

Using specific conductivity (SC) to estimate groundwater inflow to rivers is complicated by bank return waters, interflow, and flows off floodplains contributing to baseflow in all but the driest years. Using the maximum SC of the river in dry years to estimate the SC of groundwater produces the best baseflow vs. streamflow trends. The variable composition of baseflow hinders calibration of hydrograph-based techniques to estimate groundwater inflows.

We investigated how a precipitation post-processor interacts with other tools for uncertainty quantification in a hydrometeorological forecasting chain. Four systems were implemented to generate 7 d ensemble streamflow forecasts, which vary from partial to total uncertainty estimation. Overall analysis showed that post-processing and initial condition estimation ensure the most skill improvements, in some cases even better than a system that considers all sources of uncertainty.