In lowland catchments, flood severity is determined by both the amount of rain and the groundwater depth prior to the rain event. We investigated the trade-off between these two factors and how this affects peaks in the river discharge, for both the current and future climate. We found that with climate change floods will increase in winter and spring, but decrease in fall. The number and severity of floods will increase. This can help water managers to design climate robust water management.

We applied rainfall and tracers on two 8 m wide runoff plots to better understand runoff generation processes in pre-Alpine catchments with low permeability gleysols. The results highlight the frequent occurrence of infiltration and exfiltration of water, the importance of preferential flow, and the quick runoff response (i.e., high celerity) and transport of solutes (i.e., high velocity). These results help to understand why streams in the study region respond very quickly to rainfall. 

We used a random forest approach to produce estimates of monthly precipitation stable isotope variability from 1962–2023, at high resolution across the entire Australian continent. Comprehensive skill and sensitivity testing shows that our random forest models skilfully predict precipitation isotope values in places and times that observations are not available. We make all outputs freely available, facilitating use in fields from ecology and hydrology to archaeology and forensic science.

Evapotranspiration is evaporation that occurs from plants, soil, and water bodies, but determining these components is difficult. We developed a new method that uses measurements such as streamflow and rainfall to determine the portion of plant evaporation. We found differences among vegetation types and climate conditions, and showed that plant water use peaks in moderately dry regions. The results improve understanding of plant-water interactions and can help improve water and climate models.

How landscape features affect water storage and release in catchments remains poorly understood. Here we used water stable isotopes in 12 streams to assess the fraction of precipitation reaching streamflow in less than 2 weeks. More recent precipitation was found when streamflow was high and the fraction was linked to the geology (i.e. high when impermeable, low when permeable). Such information is key for better anticipating streamflow responses to a changing climate.

Recent progress in NN (neural network) accelerated improvements in the performance of catchment modeling. Yet flood modeling remains a very difficult task. Focusing on two headwater streams, we developed N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting) and N-BEATS (Network-Based Expansion Analysis for Interpretable Time Series Forecasting) models and benchmarked them with LSTM (long short-term memory) to predict flooding. N-HiTS and N-BEATS outperformed LSTM for flood predictions. We demonstrated how the proposed models can be augmented with an uncertainty approach to predict flooding that is interpretable without considerable loss in accuracy.

Compound flooding occurs when multiple drivers, such as heavy rain and storm surge, occur simultaneously. Comprehensive compound flood risk assessments require simulating many storm events using flood models, but such historical data are limited. To address this, we developed a statistical framework to generate large numbers of synthetic yet realistic storm events for use in compound flood modeling.

Climate change impacts on a hydrologic phenomenon termed the mid-summer drought (MSD), affecting water and food security of smallholder farmers in Central America, depends on how the MSD was defined. We used many climate model projections and MSD definitions to quantify the sources of uncertainty in future MSD characteristics in Nicaragua for varying levels of global warming. We found that the definition can change where the MSD occurs but it does not add substantially to the total uncertainty.

We studied an arid, endorheic basin in the Chilean Altiplano to understand how rainfall and evaporation affect groundwater and water availability. Using a rainfall-runoff model and 40 years of satellite data, we found that much of the water evaporates and less reaches the aquifers than expected. Our results challenge the idea that the basin is fully closed and suggest that current water budget estimates may need revision, an urgent task under a changing climate.

Flooding is a major problem and predicting it accurately over large areas is tough. This study tested a new approach to forecast floods across a large region in the United States. By dividing the area into smaller areas to develop the prediction models and then combining, the method successfully simulated surface water extent for both high and low flow periods. The results were more accurate than existing approaches with similar methods which can improve flood forecasting for larger areas.

This study introduces a new method of detecting how water moves quickly through certain paths in soil, bypassing the usual, slower flow. By analysing natural water markers in soil samples taken at different depths, we identified unusual flow patterns. Our method is simple and non-invasive, and can be used to cover large areas. This helps us to better understand how water travels through the ground, which is important for managing water resources and protecting the environment.