Skillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles and a distributed hydrological model to generate ensemble streamflow forecasts at medium-range lead times (1 - 7 days). We demonstrate a case study for machine learning application in postprocessing ensemble streamflow forecasts in the Upper Susquehanna River basin in the eastern United States. For forecast verification, we use different metrics such as skill score and reliability diagram conditioned upon the lead time, flow threshold, and season. The verification results show that the machine learning postprocessor can improve streamflow forecasts relative to low complexity forecasts (e.g., climatological and temporal persistence) as well as deterministic and raw ensemble forecasts. As compared to the raw ensembles, relative gain in forecast skill from postprocessor is generally higher at medium-range timescales compared to shorter lead times; high flows compared to low-moderate flows, and warm-season compared to the cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts.
翻译:精密流流预测可以为水政策和管理各领域的决策提供依据。我们综合了数字天气预测组和分布式水文模型,以产生中程周转时间(1至7天)的混合流预测;我们展示了美国东部上苏克汉纳河流域后处理混合流预测中的机器学习应用案例研究;在预测核查中,我们使用不同的指标,如技术评分和可靠图表,这些指数以铅周期、流量阈值和季节为条件;核查结果显示,机器学习后处理器可以改进与低复杂预测(如气候学和时间持久性)以及确定性和原始混合预测相比的流预测。与原始堆积相比,后处理器预测技能的相对收益一般在中程时间尺度上较高,与较短的准备时间相比;与低温海流相比,流量较高;与冷季相比,温暖海流。总体而言,我们的结果突显了机器在许多方面学习的好处,既可以提高溪流预测的技能和可靠性。