We present a physically based Bayesian network model for inference and prediction of flood duration that allows for a deeper understanding of the nexus of antecedent flow regime, atmospheric blocking, and moisture transport/release mechanisms. Distinct scaling factors at the land surface and regional atmospheric levels are unraveled using this Bayesian network model. Land surface scaling explains the variability in flood duration as a function of cumulative exceedance index, a new measure that represents the evolution of the flood in the basin. Dynamic atmospheric scaling explains the cumulative exceedance index using the interaction between atmospheric blocking system and the synergistic model of wind divergence and atmospheric water vapor. Our findings underline that the synergy between a large persistent low-pressure blocking system and a higher rate of divergent wind often triggers a long-duration flood, even in the presence of moderate moisture supply in the atmosphere. This condition in turn causes an extremely long-duration flood if the basin-wide cumulative flow prior to the flood event was already high. Thus, this new land-atmospheric interaction framework integrates regional flood duration scaling and dynamic atmospheric scaling to enable the coupling of ‘horizontal’ (for example, streamflow accumulation inside the basin) and ‘vertical’ flow of information (for example, interrelated land and ocean-atmosphere interactions), providing an improved understanding of the critical forcing of regional hydroclimatic systems. This Bayesian model approach is applied to the Missouri River Basin, which has the largest system of reservoirs in the United States. Our predictive model can aid in decision support systems for the protection of national infrastructure against long-duration flood events.

我们提出了一个基于物理的贝叶斯网络模型来进行洪水持续时间的推断和预测，从而可以更深入地了解先行流态，大气阻塞和水分传输/释放机制之间的联系。使用该贝叶斯网络模型，可以揭示出陆地表面和区域大气水平的不同比例因子。地表定标解释了洪水持续时间的变化与累积超标指数的关系，这是代表流域洪水演变的一种新度量。动态大气比例缩放是利用大气阻挡系统与风散度和大气水蒸气的协同模型之间的相互作用来解释累积超标指数的。我们的发现强调，即使在大气中存在适度的水分供应的情况下，大型持续性低压阻塞系统与较高风速发散之间的协同作用也经常会引发长期洪水。如果洪水事件发生之前整个流域范围内的累计流量已经很高，那么这种情况将导致持续时间极长的洪水。因此，这一新的陆-气相互作用框架整合了区域洪水持续时间尺度和动态大气尺度，从而实现了“水平”（例如流域内水流的积累）和“垂直”信息流（例如相互关联的土地和大气）的耦合。海洋-大气相互作用），从而使人们更好地了解区域水文气候系统的关键强迫。这种贝叶斯模型方法适用于密苏里州流域，该流域拥有美国最大的水库系统。我们的预测模型可以为决策支持系统提供帮助，以保护国家基础设施免受长期洪灾的影响。