Few ensemble streamflow forecasting systems are designed to operate for ephemeral rivers. In this study, we revise our error model for generating Forecast Guided Stochastic Scenarios (FoGSS) to produce statistically reliable long-range (12-month) forecasts for ephemeral rivers. FoGSS features an error model with four stages: data transformation, bias-correction, an autoregressive error model and the statistical distribution of residuals. We revise the fourth stage of FoGSS with a parameter estimation method that uses data censoring to account for zero values in both observations and forecasts. This allows FoGSS to produce statistically reliable ensemble forecasts in even highly ephemeral streams (with >50% zero flows). We apply FoGSS to conventional ensemble hydrological prediction (ESP) forecasts for 50 Australian catchments, including 26 ephemeral rivers. We show that FoGSS improves the accuracy of ESP forecasts at short lead times, while at long lead times FoGSS forecasts transition to climatology-like forecasts. FoGSS forecasts are reliable in ensemble spread at individual lead times and for volumes aggregated over lead times, even in highly ephemeral rivers. FoGSS forecasts pave the way for operational long-range forecasts in ephemeral rivers, meeting a key need for improved water management.

很少有集合流预报系统设计用于短暂河流。在这项研究中，我们修订了误差模型，以生成预报指导的随机情景（FoGSS），以生成统计上可靠的临时河流的长期（12个月）预报。FoGSS的错误模型具有四个阶段：数据转换，偏差校正，自回归错误模型和残差的统计分布。我们使用参数估计方法修订了FoGSS的第四阶段，该方法使用数据审查在观测值和预测值中都考虑零值。这使FoGSS甚至可以在高度短暂的流（零流量> 50％）中产生统计上可靠的整体预测。我们将FoGSS应用于50个澳大利亚流域的常规总体水文预报（ESP）预报，包括26条短暂的河流。我们表明，FoGSS可以在较短的交货时间内提高ESP预报的准确性，而在较长的交货时间内，FoGSS预报可以过渡到类似气候的预报。FoGSS预测在各个提前期的集合传播以及整个提前期的总量上都是可靠的，即使在高度短暂的河流中也是如此。FoGSS预报为短暂河流中的长期操作预报铺平了道路，满足了改善水管理的关键需求。