Complete hydrological time series are crucial for water and energy resources management and modelling in a changing climate. The reliability of the non-parametric stochastic machine learning algorithm MissForest was assessed for gap-filling of daily streamflow time series in a data-scarce region with strong climatic variability. A total of 1,586 reconstructions of streamflows for 1970-2016 were analyzed. Overall, MissForest performed satisfactorily to well, allowing a precise and reliable simulation of the missing data quickly and automatically. MissForest performance increased with the number of predictor records and record length, achieving satisfactory results with 20 or more records having 15 or more years in length. Reconstructed daily streamflow time series of rivers with natural flow regimes were simulated with good performance, which slightly decreased for discharge magnitude alterations by runoff inputs from urbanized areas and water diversion for irrigation. In cases of severe alterations of the flow regime, such as by hydropeaking, MissForest failed at filling daily streamflow series gaps. Reconstructed hydrographs allow analysis of streamflow change and variability and their interactions with key climatic variables.

完整的水文时间序列对于不断变化的气候中的水资源和能源资源管理和建模至关重要。针对气候变化性很强的数据稀缺区域，对非流量随机机器学习算法MissForest的日常流量时间序列的缺口填充进行了评估，以评估其可靠性。分析了1970-2016年的总计1,586个流量重构。总体而言，MissForest的表现令人满意，可以快速，自动地对丢失的数据进行精确而可靠的模拟。MissForest的性能随着预测记录的数量和记录长度的增加而提高，对于20条或更多的长度为15年或15年以上的记录，取得了令人满意的结果。模拟了具有自然流态的河流的每日重建流量序列，并取得了良好的效果，由于来自城市化地区的径流输入和引水灌溉的影响，排放量的变化略有下降。在流量状况发生严重变化的情况下（例如通过水峰），MissForest无法填补每日流量序列的缺口。重建的水文图可以分析水流的变化和变化以及它们与关键气候变量的相互作用。