Use of delayed ERA5-Land soil moisture products for improving landslide early warning

Abstract. Previous studies have demonstrated that incorporating ECMWF ERA5-Land soil moisture products can improve the predictive performance of landslide-triggering thresholds. However, these data are released with a five-day latency, which limits their immediate operational use in Landslide Early Warning Systems (LEWSs). In this study, we investigate whether delayed soil moisture data – ranging from 0 to 15 days prior to rainfall events – can still effectively inform landslide-triggering conditions. Specifically, we develop artificial neural networks (ANNs) trained on various delay times and evaluate how detection performances vary with increasing lag. Focusing on Sicily, Italy, our results show that even delayed soil moisture data consistently outperform models based solely on rainfall (TSS = 0.68 vs. 0.59). Notably, TSS reduces only marginally, from 0.78 with no delay to 0.72 with five-day delay, and 0.67 with fifteen-day delay. This performance remains higher than that obtained using only soil moisture data (without precipitation and no delay, TSS = 0.53), as well as those achieved with a traditional power-law threshold based on rainfall intensity and duration (TSS = 0.50) and also through ANN model using rainfall intensity and duration (TSS = 0.59). These findings are, thus, promising for an operational use of ERA5-Land soil moisture products in LEWSs.