ABSTRACT

Resampling historical time series remains one of the main approaches used to generate long-term probabilistic streamflow forecasts, while there is a need to develop more flexible approaches taking into account non-stationarities. One possible approach is to use a modelling chain consisting of a stochastic weather generator and a hydrological model. However, the ability of this modelling chain to generate adequate probabilistic streamflows must first be evaluated. The aim of this paper is to compare the performance of a stochastic weather generator against resampling historical meteorological time series in order to produce ensemble streamflow forecasts. The comparison framework is based on 30 years of forecasts for a single Canadian watershed. Forecasts resulting from the two methods are evaluated using the continuous ranked probability score (CRPS) and rank histograms. Results indicate that while there are differences between the methods, they nevertheless perform similarly, thus showing that weather generators can be used as substitutes for resampling the historical past.

摘要

对历史时间序列进行重采样仍然是用于生成长期概率流量预测的主要方法之一，同时有必要在考虑非平稳性的情况下开发更灵活的方法。一种可能的方法是使用由随机天气生成器和水文模型组成的建模链。但是，必须首先评估此建模链生成足够概率流的能力。本文的目的是比较随机天气生成器的性能与重采样历史气象时间序列的性能，以产生整体流量预报。比较框架基于对单个加拿大分水岭的30年预测。使用连续排名概率评分（CRPS）和等级直方图评估这两种方法得出的预测。结果表明，尽管这两种方法之间存在差异，但它们的执行方式却相似，这表明天气生成器可以用作重采样历史过去的替代方法。