In this study, the authors explore three persistence approaches in streamflow forecasting motivated by the need for forecasting model skill evaluation. The authors use streamflow observations with 15 min resolution from the year 2008 to 2017 at 140 United States Geological Survey streamflow gauges monitoring the streams and rivers over the State of Iowa. The spatial scale of the basins ranges from about 7 to 37,000 km². The study explores three approaches: simple persistence, gradient persistence, and anomaly persistence. The study shows that persistence forecasts skill has strong dependence on basin scales and weaker but non‐negligible dependence on geometric properties of the river network for a given basin. Among the three approaches explored, anomaly persistence shows highest skill especially for small basins, under about 500 km². The anomaly persistence can serve as a benchmark for model evaluations considering the effect of basin scales and geometric properties of river network of the basin. This study further reiterates that persistence forecasts are hard‐to‐beat methods for larger basin scales at short to medium forecast range.

中文翻译：

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探索流量预测的持久性

在这项研究中，作者探索了由于预测模型技能评估的需要而在流量预测中采用的三种持久性方法。作者使用从2008年到2017年在15个分辨率为15 min的水流观测资料，在140个美国地质调查局水质监测仪上监测爱荷华州的河流和河流。盆地的空间范围从大约7到37,000 km ²。该研究探索了三种方法：简单持久性，梯度持久性和异常持久性。研究表明，持续性预报技巧对流域尺度有很强的依赖性，对给定流域的河网几何特性的依赖性较弱，但不可忽略。在探索的三种方法中，异常持续性显示出最高的技巧，尤其是对于小于500 km ^2的小盆地。考虑流域尺度和流域河网几何特性的影响，异常持续性可以作为模型评估的基准。这项研究进一步重申，持续性预报是在短至中度预报范围内对大型流域尺度难以击败的方法。