Agricultural Drought in Sweden: Assessment of Hazard and Impacts

Sweden is known for its abundance of water resources, while future climatic projections indicate a rise in precipitation and temperature rates. However, droughts had severe effects on the environment, society, and agriculture in 2016, 2017 and 2018, highlighting the need for improved drought monitoring and management. The agricultural sector, in particular, suffered significantly during the 2018, 2021, and 2023 droughts, inquiring the need for enhancements in climate adaptation and preparedness. This study aims to assess the agricultural drought in Sweden with a focus on hazard assessment and its associated impacts. Firstly, we unfold the lessons learnt from continental observatories and national services by evaluating the reliability of the derived information and identifying the added value for local decision making. For this, we compare the simulated soil moisture derived from the LISFLOOD and S-HYPE hydrological models, and we evaluate the modelled simulations against earth observation-based soil moisture from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Based on the LISFLOOD model, we used anomalies of the soil moisture index and the combined drought index at a 10-day average and at a 5 km spatial resolution. Daily runoff and soil moisture data were also used from the S-HYPE model at about 13 km² spatial resolution. Secondly, the spatiotemporal drought hazard is assessed using drought indicators to identify drought frequency and intensity. Here, drought indicators such as Standardized Precipitation Index (SPI), Soil Moisture Anomaly (SMA), and Combined Drought Index (CDI) are computed using S-HYPE model outputs. Finally, we evaluate the utility of integrating data from drought indices (SPI-1, SPI-3, SMA and CDI) and crop yield of wheat and potato to improve the understanding of the links between impacts and statistical indices. The relationships between drought onset and yield response are evaluated for different aggregation of time periods and lags (i.e., monthly). The study outputs are used to assess alternative ways to improve decision-making regarding adaptation strategies to reduce agricultural vulnerability and the capability of addressing the challenges posed by a changing climate.