A Modified Atmospheric River Scale for Flood Hazards

Atmospheric rivers (ARs) are key drivers of regional water supply and flood hazard in subtropical and mid-latitude regions, generating interwoven beneficial and hazardous impacts. The original AR scale, developed for early-warning communication, ranks ARs from 1 (“primarily beneficial”) to 5 (“primarily hazardous”) based on atmospheric vapor transport. However, the AR scale does not account for physical processes on the land surface that can strongly influence flood response. Analyzing over 70,000 AR landfalls across 142 catchments in California and central Chile, here we show that runoff efficiency, primarily controlled by antecedent soil moisture, is the dominant source of peak streamflow variability not explained by the AR scale. Based on this insight, we present a simple modification to the AR scale for flood hazards that incorporates antecedent moisture conditions. This modification almost doubles the scale’s correspondence with peak streamflow and increases the number of flood-generating ARs classified as hazardous by over 30%, raising AR flood detection rates to 81% in California and 64% in central Chile. These findings demonstrate that incorporating critical land surface conditions into hazard classification can enhance early-warning tools for communicating hazard likelihood.

大气河流（Atmospheric Rivers, ARs）是亚热带及中纬度区域区域供水与洪水灾害的关键驱动因子，其带来的效益与灾害影响相互交织。最初为预警沟通而开发的AR分级体系，基于大气水汽输送量将AR分为1级（“以益处为主”）至5级（“以灾害为主”）。然而，该AR分级体系未考虑会显著影响洪水响应的陆面物理过程。本研究通过分析加州与智利中部142个流域内逾7万次AR登陆事件，发现产流效率（主要受前期土壤湿度调控）是AR分级体系未解释的洪峰流量变化的主要来源。基于这一发现，我们针对洪水灾害场景对AR分级体系进行了简化改进，将前期土壤湿度条件纳入考量。该改进使分级体系与洪峰流量的契合度提升近一倍，同时将被归类为灾害性的致洪AR数量提升超30%，使得加州的AR洪水检出率升至81%，智利中部的检出率升至64%。上述研究结果表明，将关键陆面条件纳入灾害分级体系，可优化用于传递灾害风险的预警工具。