West Africa faces serious floods, affecting millions of people every year. The FANFAR project co-designed a flood forecasting and warning system at lively workshops together with 50–60 key West African stakeholders. We prioritized FANFAR system configurations that best meet stakeholders’ needs and expectations. Stakeholders preferred a system producing accurate, clear, and accessible flood risk information, which works reliably under difficult West African conditions.

A multinational assessment of radar's ability to capture heavy rain events is conducted. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. Results show a fair agreement, with radar underestimating by 17 %-44 % on average compared with gauges. Despite being adjusted for bias, five of six radar products still exhibited strong conditional biases with intensities of 1–2% per mm/h. Median peak intensity bias was significantly higher, reaching 44 %–67%.

How far can we reach in predicting river flow globally, using integrated catchment modelling and open global data? For the first time, a catchment model was applied world-wide, covering the entire globe with a relatively high resolution. The results show that stepwise calibration provided better performance than traditional modelling of the globe. The study highlights that open data and models are crucial to advance hydrological sciences by sharing knowledge and enabling transparent evaluation.

A state-of-the-art regional climate model ensemble for Europe is investigated for extreme precipitation intensities. The models poorly reproduce short duration events of less than a few hours. Further, there is poor connection to some known hotspots for extreme cases. The model performance is much improved at 12 h durations. Projected future increases scale with seasonal mean temperature change, within a range from a few percent to over 10 percent per degree Celsius.

Hydropower makes up nearly half of Sweden's electrical energy production. Careful reservoir management is required for optimal production throughout the year and accurate seasonal forecasts are essential for this. In this work we develop a seasonal forecast prototype and evaluate its ability to predict spring flood volumes, a critical variable, in northern Sweden. We show that the prototype is better than the operational system on average 65 % of the time and reduces the volume error by ~ 6 %.

New approaches to spring-flood forecasting that reflect the latest developments with respect to analysis and modelling on seasonal timescales are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in spring-flood hindcasts for three Swedish rivers over a 10-year period. When combining all forecasts in a weighted multi-model approach, a mean improvement over all locations and lead times of nearly 5 % was indicated, as compared with today's approach.

The main aims of this paper are the evaluation of five large-scale hydrological models across Europe and the assessment of the suitability of the models for making projections under climate change. While we found large inter-model differences in biases, the skill to simulate interannual variability in discharge did not differ much between the models. Assuming that the skill of a model to simulate interannual variability provides a measure for the model’s ability to make projections under climate

A distribution-based scaling approach was developed and proven useful as a post-process to correct systematic biases in climate modelling outputs (i.e. precipitation, temperature, relative humidity and wind speed) to facilitate the utilisation of climate projections in forest fire risk studies. The result showed reduction of bias in forcing data and an improved description of fire-risk-related indices. Concerning the future climate, southern Sweden is likely to become a more fire-prone region.