As climate change increases the strength and frequency of wildfires in Germany, we must identify which areas are most at risk. We simulated millions of fires under extreme weather to map hazard zones. We found that extreme drought drives fire risk much more than high winds. Open landscapes and areas with dead trees or dense shrubs face the highest danger. Healthy, dense forests remain safer. Forest managers should clear underbrush and maintain thick tree canopies to block the sun and the wind.

Sea-dumped munitions are a threat to economic activities at sea. This study presents the first comprehensive dataset of munition piles in the German Baltic Sea. It was created by mapping, annotating, and analysing three major dump sites. The results show that the piles differ greatly in their size and composition. Many piles lie close to protected areas and a sediment dump site. The dataset can aid discussions on monitoring, risk assessment and prioritisation of piles for clearance.

The Holocene Peatland Model (HPM) is a widely used peatland model to understand and predict long-term peatland dynamics. Here, we test whether the HPM can predict Sphagnum litterbag decomposition rates from oxic to anoxic conditions. Our results indicate that decomposition rates change more gradually from oxic to anoxic conditions and may be underestimated under anoxic conditions, possibly because the effect of water table fluctuations on decomposition rates is not considered.

Decomposition rates for Sphagnum mosses, the main peat-forming plants in northern peatlands, are often derived from litterbag experiments. Here, we estimate initial leaching losses from available Sphagnum litterbag experiments and analyze how decomposition rates are biased when initial leaching losses are ignored. Our analyses indicate that initial leaching losses range between 3 to 18 mass-% and that this may result in overestimated mass losses when extrapolated to several decades.

Spatial proxies, such as coordinates and distances, are often used as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated the conditions under which their use is appropriate. We found that spatial proxies are not always beneficial and should not be used as a default approach without careful consideration. We also provide insights into the reasons behind their suitability, how to detect them, and potential alternatives.