PDF(Pubmed)
Abstract：
Hydrological predictions at subseasonal-to-seasonal (S2S) time scales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the generalized analog regression downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for 3-month hydrological forecasts for the austral autumn season (March-May) using ensemble hindcasts for 2002-17. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to 1-month lead, evapotranspiration up to 2 months lead, and soil moisture content up to 3 months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: El Niño-Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at 1-month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.
摘要：
亚季节到季节（S2S）时间尺度的水文预测可以支持农业和水电等气候依赖部门的决策。这里,我们介绍了热带南美洲西部（WTSA）的S2S水文预报系统（S2S-HFS）。该系统使用全球NASA戈达德地球观测系统S2S气象预报系统(GEOS-S2S)结合广义模拟回归降尺度算法和NASA陆地信息系统(LIS)。在本实施研究中，我们使用2002-17年的合奏后记评估了南方秋季（3月至5月）的3个月水文预报的系统性能。结果表明，S2S-HFS通常提供预测每月降水高达1个月铅的技能，蒸散量达2个月铅，和土壤水分含量达3个月铅。后播性能更好的生态区位于沿海低地或亚马逊低地森林中。我们进行专门的分析，以了解S2S-HFS中如何代表影响该地区的两个重要的远程连接：厄尔尼诺-南方涛动（ENSO）和南极涛动（AAO）。我们发现，在ENSO的积极阶段和AAO的消极阶段，1个月前所有变量的预测技能都得到了增强。总的来说,这项研究表明，对于WTSA中的许多生态区，S2S-HFS具有有意义的技能，特别是对于长记忆变量，如土壤湿度。降水预报的技巧,然而,预测初始化后迅速衰减，这一现象与世界大部分地区的S2S气象预报一致。
