ABSTRACT Discharge observations and reliable rainfall forecasts are essential for flood prediction but their availability and accuracy are often limited. However, even scarce data may still allow adequate flood forecasts to be made. Here, we explored how far using limited discharge calibration data and uncertain forcing data would affect the performance of a bucket-type hydrological model for simulating floods in a tropical basin. Three events above thresholds with a high and a low frequency of occurrence were used in calibration and 81 rainfall scenarios with different degrees of uncertainty were used as input to assess their effects on flood predictions. Relatively similar model performance was found when using calibrated parameters based on a few events above different thresholds. Flood predictions were sensitive to rainfall errors, but those related to volume had a larger impact. The results of this study indicate that a limited number of events can be useful for predicting floods given uncertain rainfall forecasts.

中文翻译：

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使用根据有限流量数据和不确定降雨情景校准的参数进行洪水预测。

摘要流量观测和可靠的降雨预报对于洪水预测至关重要，但它们的可用性和准确性往往有限。然而，即使是稀缺的数据，仍然可以进行充分的洪水预报。在这里，我们探讨了使用有限的流量校准数据和不确定的强迫数据会在多大程度上影响用于模拟热带盆地洪水的桶型水文模型的性能。三个发生频率高和低的阈值以上事件被用于校准，并使用 81 个具有不同不确定性程度的降雨情景作为输入来评估它们对洪水预测的影响。当使用基于不同阈值以上的几个事件的校准参数时，发现了相对相似的模型性能。洪水预测对降雨误差很敏感，但与数量相关的影响更大。这项研究的结果表明，鉴于不确定的降雨预报，有限数量的事件可用于预测洪水。