On the Generalization of Agricultural Drought Classification from Climate Data
This work addresses drought detection for food security under climate change, but it is incremental as it builds on existing methods with a more direct soil moisture index.
The paper tackled agricultural drought classification from climate data by using a soil moisture index from a hydrological model instead of simple drought indices, achieving promising PR-AUC results despite a challenging time-based split and showing models retain predictive capabilities with coarser-resolution inputs.
Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and depend on various external factors, making drought detection in climate data difficult. In contrast to existing works that rely on simple relative drought indices as ground-truth data, we build upon soil moisture index (SMI) obtained from a hydrological model. This index is directly related to insufficiently available water to vegetation. Given ERA5-Land climate input data of six months with land use information from MODIS satellite observation, we compare different models with and without sequential inductive bias in classifying droughts based on SMI. We use PR-AUC as the evaluation measure to account for the class imbalance and obtain promising results despite a challenging time-based split. We further show in an ablation study that the models retain their predictive capabilities given input data of coarser resolutions, as frequently encountered in climate models.