Here, we represented catchments as networks of variables connected by cause-and-effect relationships. By comparing the performance of statistical and machine learning methods with and without incorporating causal information to predict runoff properties, we showed that causal information can enhance models' robustness by reducing the accuracy drop between the training and testing phases, improving the model's interpretability, and mitigating overfitting issues, especially with small training samples.

Precipitation (P) and soil moisture (SM) are critical components of the climate system but poorly understood in the Arctic. Using NASA's SMAP satellite, we analyzed SM and P patterns in Finland. SM and P correlate strongly in summer and fall but weakly in spring due to snowmelt. While the area of P can be estimated from SM, estimating its intensity is more challenging. Water bodies complicate SM retrieval. The promising results suggest this method could be applied across the Arctic.

This paper presents a new statistical method to estimate probable maximum precipitation (PMP), addressing flaws in traditional approaches. It accounts for uncertainty using the Pearson Type-I distribution. Applied to two Québec stations, it provides more objective PMP estimates. Challenges remain due to short-tailed models applied to heavy-tailed precipitation. Best practice recommendations are also provided for estimating PMP.

Satellite data reveal that woody plants in Texas’s Post Oak Savannah now return almost all rainfall to the atmosphere. In drier regions, once trees and shrubs blanket more than 80 % of the land, yearly water loss to the atmosphere even surpasses rainfall, shifting the region from a water surplus to a deficit and shrinking groundwater recharge. Without brush control, warming and further canopy growth could leave soils drier, streams weaker, and local water supplies increasingly strained.

In water-scarce regions, precipitation is a highly variable and essential resource for crop production. Developing countries like India have an uneven distribution of rain gauges, so reliance on satellite- and reanalysis-based precipitation products is critical for their prudent management. Hence, this study statistically evaluated different precipitation products against station data for water-scarce regions in Tamil Nadu and found that the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 Land (ERA5-Land) performed the best, followed by the Multi-Source Weighted-Ensemble Precipitation (MSWEP).

Thawing permafrost changes how water is stored and moves across landscapes. We measured water inputs and outputs in a basin with thawing peatland complexes and three sub-basins. In addition to yearly changes in precipitation and evapotranspiration, we found that hydrological responses are shaped by thaw-driven landscape connectivity. These findings highlight the need for long-term monitoring of ecosystem service shifts.

The 2021 drought in Quebec showcased the province’s potential vulnerability. This study uses a storyline approach to explore the impacts of future extreme droughts under +2 °C and +3 °C global warming scenarios. Results show that future droughts similar to that of 2021 could worsen significantly, with water levels dropping for longer periods and impacting ecosystems and human activities.

We introduce the ratio of uncertainty to mutual information (RUMI), a new metric to improve rainfall-runoff simulations. RUMI better captures the link between observed and simulated stream flows by considering uncertainty at a core computation step. Tested on 99 catchments and with the GR4J model, it outperforms traditional metrics by providing more reliable and consistent results. RUMI paves the way for more accurate hydrological predictions.

This study compared global satellite and reanalysis precipitation datasets to assess diurnal variability. We found that all datasets capture key diurnal precipitation patterns, with maximum precipitation in the afternoon over land and early morning over the ocean. However, there are differences in the exact timing and amount of precipitation. This suggests that it is better to use a combination of datasets for potential applications rather than relying on a single dataset.