Risk assessment for crop hail damage in Switzerland under current and potential future scenarios

Hailstorms frequently cause severe damage to agriculture in Switzerland highlighting the need to better understand the characteristics of damaging hail events and their spatial and temporal patterns. Such knowledge is essential for improving the assessment and modeling of current and future hail risk to crops.

So far, approaches to assess crop hail damage have included radar, hail pad, and satellite-based methods, however they are often restricted to specific crops or hampered by the limited availability of exposure data and damage reports. Current challenges further include the uncertainty of radar-derived hail size estimates, which are often used as a proxy for hail intensity, as well as the uncertain relationship between hail size and actual crop damage.

To address these challenges and improve the link between observed hail characteristics and agricultural damage, this study combines various radar-based hailstorm indicators, such as the probability of hail (POH), maximum expected severe hail size (MESHS) and thunderstorm radar tracking data, with extensive and high-resolution exposure and damage data provided by the crop insurer Swiss Hail. This includes the policies insured at municipality level as well as damage reports for various crop types from 2014 to 2024. To evaluate which hazard indicators are most relevant for crop damage prediction, a random forest regression approach with feature selection techniques is applied.

First results indicate that combining multiple indicators can achieve robust model performance, particularly when including both municipality-scale hail characteristics (i.e. hail area and duration indicators, MESHS, velocity and mean reflectivity) as well as larger-scale information about the probability of hail and number of storms over a day. The most informative indicator is a proxy integrating the spatial extent and duration of hail occurrence within a municipality.

Based on these findings we perform a risk assessment of potential damage development under prospective future climatological conditions. To do so, we consider recent scientific findings on expected changes in hailstorm frequency and severity through a storyline-scenario approach. The scenarios are built through sampling characteristic hail days from the storm-object-based stochastic hail event set HailStoRe developed for the Swiss Hail Climatology.