Drought impact prediction across time and space: limits and potentials of text reports

Abstract

Drought impact prediction can improve early warning and thus preparedness for droughts. Across Europe drought has and will continue to affect environment, society and economy with increasingly costly damages. Impact models are challenged by a lack of data, wherefore reported impacts archived in established inventories may serve as proxy for missing quantitative data. This study develops drought impact models based on the Alpine Drought Impact report Inventory (EDIIALPS) to evaluate the potential to predict impact occurrences. As predictors, the models use drought indices from the Alpine Drought Observatory and geographic variables to account for spatial variation in this mountainous study region. We implemented regression and random forest (RF) models and tested their potential (1) to predict impact occurrence in other regions, e.g. regions without data, and (2) to forecast impacts, e.g. for drought events near real-time. Both models show skill in predicting impacts for regions similar to training data and for time periods that have been extremely dry. Logistic regression outperforms RF models when predicting to very different conditions. Impacts are predicted best in summer and autumn, both also characterised by most reported impacts and therefore highlighting the relevance to accurately predict impacts during these seasons in order to improve preparedness. The model experiments presented reveal how impact-based drought prediction can be approached and complement index-based early warning of drought.