This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.

In extracting a river network from a digital elevation model, an arbitrary pruning area should be specified. As this value grows, the apparent drainage density is reduced following a power function. This reflects the fractal topographic nature. We prove this relationship related to the known power law in the exceedance probability distribution of drainage area. The power-law exponent is expressed with fractal dimensions. Our findings are supported by analysis of 14 real river networks.

This study introduces a hybrid data assimilation scheme for precise streamflow predictions during intense rainfall and hurricanes. Tested in real events, it outperforms traditional methods by up to 50 %, utilizing ensemble and climatological background covariances. The adaptive algorithm ensures reliability with a small ensemble, offering improved forecasts up to 18 h in advance, marking a significant advancement in flood prediction capabilities.

Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.

Neglecting the scientific rationale behind statistical inference leads to logical fallacies and misinterpretations. This study contrasts a model-based approach, rooted in statistical logic, with a test-based approach, widely used in hydro-climatology but problematic. It reveals the impact of dependence in extreme-precipitation analysis and shows that trends in the frequency of extreme events over the past century in various geographic regions can be consistent with the stationary assumption.

For the first time, we analyse the economic and ecological performance of existing multiple big reservoirs on a daily timescale for a major river basin (upper Cauvery) in India, where pre-intervention data were not available but where there are increasing calls for such assessments. Results show that smaller reservoirs on smaller streams that maximize the economic value of stored water are better for the basin economy and the environment. The approach can help to prioritize dam removals.

The research focuses on a 4-million-inhabitant tropical region supplied by a network of open-water reservoirs where the dry season lasts for 8 months (Jun−Dec). We analysed the impact of four climate change scenarios on the evaporation rate and the associated availability (water yield distributed per year). The worst-case scenario shows that by the end of the century (2071−2099), the evaporation rate in the dry season could increase by 6 %, which would reduce stored water by about 80 %.

Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.

The Gran Chaco is a large, dry forest in South America that has been heavily deforested, particularly in the dry Chaco subregion. This deforestation, mainly driven by the expansion of the agricultural frontier, has changed the land's characteristics, affecting the local and regional climate. The study reveals that deforestation has resulted in reduced precipitation, soil moisture, and runoff, and if intensive agriculture continues, it could make summers in this arid region even drier and hotter.

Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.

We focus on past high-flow events to find flood drivers in the Geul. We also explore flood drivers’ trends across various timescales and develop a new method to detect the main direction of a trend. Our results show that extreme 24 h precipitation alone is typically insufficient to cause floods. The combination of extreme rainfall and wet initial conditions determines the chance of flooding. Precipitation that leads to floods increases in winter, whereas no consistent trends are found in summer.

By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.

The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q₉₅ reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.

Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.

Underestimation of precipitation (UoP) in India is a substantial issue not just within gauge-based precipitation datasets but also within state-of-the-art satellite and reanalysis-based datasets. UoP is prevalent across most river basins of India, including those that have experienced catastrophic flooding in the recent past. This paper highlights not only a major limitation of existing precipitation products for India but also other data-related obstacles faced by the research community.

Daily streamflow series for 661 near-natural French catchments are reconstructed over 1871–2012 using two ensemble datasets: HydRE and HydREM. They include uncertainties coming from climate forcings, streamflow measurement, and hydrological model error (for HydrREM). Comparisons with other hydrological reconstructions and independent/dependent observations show the added value of the two reconstructions in terms of quality, uncertainty estimation, and representation of extremes.

We studied groundwater near outlet glaciers of the main Icelandic ice cap. We acquired new data in the field. Two distinct groundwater compartments and their characteristics are identified. We demonstrate the glacial melt recharge impact on the groundwater dynamic. Knowing groundwater systems in a glacial context is crucial to forecast the evolution under climate change of water resources and of potential flood and landslide hazards.