Quantifying ecosystem resilience to extreme events: a comparison of single and multiple cropping systems

The stability of managed land systems is increasingly threatened by the rising frequency and severity of extreme events caused by climate change. To maintain productivity and adapt to these changing conditions, these systems must build resilience to adverse impacts. Detecting and understanding the effects of such events is essential for assessing the effectiveness of different management strategies in promoting ecosystem resilience.

In this study, we identify extreme events using model- and observation-based Gross Primary Production (GPP) data. To identify these extremes, the GPP anomalies are calculated and then a statistical technique is used to identify extreme events. Using statistical methods including regression models, the most relevant events are then attributed to meteorological drivers, such as temperature and precipitation. Based on these results, we define a stability measure rooted in the recovery time after an extreme event. This approach is applied to three scenarios: (1) a control experiment considering only GPP influenced by climate, (2) GPP influenced by both climate and land use/land change in single cropping areas, and (3) GPP in multiple cropping areas.

These results will help us contrast whether management strategies can mitigate the impacts of extreme events, and identify which types of events and areas may benefit most from potential targeted interventions to increase ecosystem resilience.