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.
翻译:与其他自然灾害不同,干旱的发起缓慢,取决于各种外部因素,使得气候数据的干旱探测很困难。与以简单的相对干旱指数作为地面实况数据的现有工作相比,我们以水文模型获得的土壤湿度指数(SMI)为基础,这一指数与植被用水不足直接相关。鉴于ERA5-Land气候输入数据为六个月,由MODIS卫星观测的土地利用信息,我们在根据SMI对干旱进行分类时,将不同的模型与不同模型进行比较,而没有相继的诱导偏差。我们使用PR-AUC作为评估措施,以说明阶级不平衡,并获得有希望的结果,尽管时间差异很大。我们还在一项模拟研究中显示,由于气候模型中经常遇到的粗糙分辨率输入数据,这些模型保留了预测能力。