While Stochastic Weather Generators (SWGs) are used intensively in climate and hydrological applications to simulate hydroclimatic time series and estimate risks and performance measures linked to climate variability, there have been few investigations into how many realizations are required for a robust estimation of these measures. Given the computational cost and time necessary to force climate-sensitive systems with multiple realizations, the estimation of the optimal number of synthetic time series to generate with a particular SWG for a predefined accuracy when estimating a particular risk or performance measure is particularly important. In this paper, the required number of realizations of five SWGs coupled with a SWAT model (the Soil and Water Assessment Tool) needed in order to achieve a predefined Relative Root Mean Square Error is investigated. The statistical indices used are the mean, standard deviation, skewness, and kurtosis of four hydroclimatic variables: precipitation, maximum and minimum temperature, and annual streamflow obtained for each observed and model-generated time series. While the results vary somewhat across SWGs, variables and indicators, they overall show that the marginal improvement decreases dramatically after 25 realizations. The results also indicate that the benefit of generating more than 100 realizations of climate and streamflow data is very minimal. The methodology presented herein can be applied in further investigations of other set of risk indicators, SWGs, hydrological models, and watersheds to minimize the required workload.

虽然随机天气生成器（SWGs）在气候和水文应用中被大量使用以模拟水文气候时间序列并估算与气候多变性相关的风险和性能指标，但很少有研究对这些指标的可靠估算需要多少实现。给定强制执行具有多种实现方式的气候敏感系统所需的计算成本和时间，在估算特定风险或性能指标时，以特定的SWG为预定的精度生成最佳合成时间序列数的估算尤为重要。在本文中，研究了为实现预定义的相对均方根误差而需要的五​​个SWG与SWAT模型（土壤和水评估工具）的实现数量。所使用的统计指标是四个水文气候变量的平均值，标准差，偏度和峰度：降水，最高和最低温度以及每个观测和模型生成的时间序列获得的年流量。尽管结果在SWG，变量和指标之间有所不同，但总体上表明，在实现25次之后，边际改进显着下降。结果还表明，生成100多个气候和流量数据实现的收益非常小。本文介绍的方法可用于进一步调查其他风险指标集，SWG，