The increase in precipitation as a function of elevation is poorly understood in areas with complex topography. In this article, the reproduction of these orographic gradients is assessed with several precipitation products. The best product is a simulation from a convection-permitting regional climate model. The corresponding seasonal gradients vary significantly in space, with higher values for the first topographical barriers exposed to the dominant air mass circulations.

Floods often take communities by surprise, as they are often considered virtually impossible yet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.

This work employs innovative spatiotemporal modeling to predict soil moisture, with implications for sustainable forest management. By correlating predicted soil moisture with rut depth, it addresses a critical concern of soil damage and ecological impact – and its prevention through adequate planning of forest operations.

Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.

We have developed the first operational system (10 d latency) for estimating irrigation water use from accessible satellite and reanalysis data. As a proof of concept, the method has been implemented over an irrigated area fed by the Kakhovka Reservoir, in Ukraine, which collapsed on June 6, 2023. Estimates for the period 2015–2023 reveal that, as expected, the irrigation season of 2023 was characterized by the lowest amounts of irrigation.

We introduce a comprehensive methodology that combines multi-objective optimization, global sensitivity analysis (GSA) and 3D groundwater modeling to analyze subsurface flow dynamics across large-scale domains. In this way, we effectively consider the inherent uncertainty associated with subsurface system characterizations and their interactions with surface waterbodies. We demonstrate the effectiveness of our proposed approach by applying it to the largest groundwater system in Italy.

There is a limited understanding of the role that topography and climate play in tree water use. Through a cross-site comparison in Luxembourg and Italy, we investigated beech water use along slopes in different climates. Our findings indicate that in landscapes characterized by stronger hydraulic and climatic gradients there is greater spatial variation in tree physiological responses. This highlights how differing growing conditions across landscapes can lead to contrasting tree performances.

Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.

Braided rivers are complex and dynamic systems that are difficult to understand. Here, we proposes a new model of how braided rivers work in the subsurface based on field observations in three braided rivers in New Zealand. We suggest that braided rivers create their own shallow aquifers by moving bed sediments during flood flows. This new conceptualisation considers braided rivers as whole “river systems” consisting of channels and a gravel aquifer, which is distinct from the regional aquifer.

Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.

While groundwater recharge is important for water resources management, nearshore sea levels can obscure this signal. Regression deconvolution has previously been used to remove other influences from groundwater levels (e.g., barometric pressure, Earth tides) by accounting for time-delayed responses from these influences. We demonstrate that it can also remove sea-level influences from measured groundwater levels.