ABSTRACT Lack of discharge data for model calibration is challenging for flood prediction in ungauged basins. Since establishment and maintenance of a permanent discharge station is resource demanding, a possible remedy could be to measure discharge only for a few events. We tested the hypothesis that a few flood-event hydrographs in a tropical basin would be sufficient to calibrate a bucket-type rainfall–runoff model, namely the HBV model, and proposed a new event-based calibration method to adequately predict floods. Parameter sets were chosen based on calibration of different scenarios of data availability, and their ability to predict floods was assessed. Compared to not having any discharge data, flood predictions improved already when one event was used for calibration. The results further suggest that two to four events for calibration may considerably improve flood predictions with regard to accuracy and uncertainty reduction, whereas adding more events beyond this resulted in small performance gains.

中文翻译：

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使用单个和多个事件的洪水模型校准的稳健性

摘要 缺乏用于模型校准的流量数据对于未测量流域的洪水预测具有挑战性。由于永久排放站的建立和维护需要资源，因此可能的补救措施是仅测量少数事件的排放。我们检验了热带盆地中的一些洪水事件过程线足以校准桶型降雨-径流模型（即 HBV 模型）的假设，并提出了一种新的基于事件的校准方法来充分预测洪水。参数集的选择基于对不同数据可用性情景的校准，并评估了它们预测洪水的能力。与没有任何流量数据相比，当使用一个事件进行校准时，洪水预测已经有所改善。