Water management in the alpine region has an important impact on streamflow. In particular, hydropower production is known to cause hydropeaking i.e., sudden fluctuations in river stage caused by the release or storage of water in artificial reservoirs. Modeling hydropeaking with hydrological models, such as the Soil Water Assessment Tool (SWAT), requires knowledge of reservoir management rules. These data are often not available since they are sensitive information belonging to hydropower production companies. In this short communication, we propose to couple the results of a calibrated hydrological model with a machine learning method to reproduce hydropeaking without requiring the knowledge of the actual reservoir management operation. We trained a support vector machine (SVM) with SWAT model outputs, the day of the week and the energy price. We tested the model for the Upper Adige river basin in North-East Italy. A wavelet analysis showed that energy price has a significant influence on river discharge, and a wavelet coherence analysis demonstrated the improved performance of the SVM model in comparison to the SWAT model alone. The SVM model was also able to capture the fluctuations in streamflow caused by hydropeaking when both energy price and river discharge displayed a complex temporal dynamic.

高寒地区的水管理对河流流量具有重要影响。特别是，已知水力发电会引起水峰，即由人工水库中水的释放或储存引起的河段突然波动。使用诸如土壤水评估工具（SWAT）之类的水文模型对水峰进行建模需要了解水库管理规则。这些数据通常是不可用的，因为它们是属于水电生产公司的敏感信息。在这种简短的交流中，我们建议将校准的水文模型的结果与机器学习方法结合起来，以重现水峰，而无需了解实际的水库管理操作。我们使用SWAT模型输出，星期几和能源价格训练了支持向量机（SVM）。我们对意大利东北部的阿迪杰河上游流域模型进行了测试。小波分析表明，能源价格对河流流量有显着影响，小波相关分析表明，与单独的SWAT模型相比，SVM模型的性能有所提高。当能源价格和河流流量都显示出复杂的时间动态时，SVM模型还能够捕获由水峰引起的水流波动。