High-quality observational datasets are essential to perform forecast verification and improve weather forecast services. When it comes to verifying precipitation, a high-resolution, global-coverage and good-quality dataset is not yet available. This research analyses the strengths and shortcomings of four observational products that employ complementary measurement techniques to estimate surface precipitation. Satellites provide good spatial coverage, but other products are still more accurate.

Two simple neural networks are trained to detect rainfall events using signal loss from commercial microwave links. Whereas existing rainfall event detection methods have focused on hourly resolution reference data, this study uses weather radar and rain gauges with 5 min and 1 min temporal resolutions, respectively. Our results show that the developed neural networks can detect rainfall events with a higher temporal precision than existing methods.

Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.

We show the results of the 2022 Groundwater Time Series Modelling Challenge; 15 teams applied data-driven models to simulate hydraulic heads, and three model groups were identified: lumped, machine learning, and deep learning. For all wells, reasonable performance was obtained by at least one team from each group. There was not one team that performed best for all wells. In conclusion, the challenge was a successful initiative to compare different models and learn from each other.

This study examined the water use of pine and cypress trees in a semiarid Mediterranean forest environment. We applied a widely used land surface model (Noah-MP) to simulate the water balance of the ecosystem. We found good modeling results for soil moisture. However, the model underestimated the transpiration of the trees during the dry summer months. These findings indicate that more research is needed to improve the modeling of ecosystem responses to climate and land use change.