Exploring drought hazard, vulnerability, and related impacts to agriculture in Brandenburg

Abstract. Adaptation to an increasingly dry regional climate requires spatially explicit information about current and future risks. Existing drought risk studies often rely on expert-weighted composite indicators, while empirical evidence on impact-relevant factors is still scarce. The aim of this study is to investigate to what extent hazard and vulnerability indicators can explain observed agricultural drought impacts via data-driven methods. We focus on the German federal state of Brandenburg, 2013–2022, including several consecutive drought years. As impact indicators we use thermal-spectral anomalies (LST/NDVI) on field level, and empirical yield gaps from reported statistics on county level. Empirical associations to the impact indicators on both spatial levels are compared. Non-linear models explain up to about 60 % variance in the yield gap data, with lumped models for all crops being more stable than models for individual crops, and models for the drought years performing better than for the pre-drought years. Meteorological drought in June and soil quality are selected as strongest impact-relevant factors. Rye is found less vulnerable than wheat, despite growing on poorer soils. LST/NDVI only weakly relates to our empirical yield gaps. We recommend comparing different impact indicators on multiple scales to proceed with the development of empirically grounded risk maps.