We dealt with a rather frequent and difficult situation while modelling extreme floods, namely, model output uncertainty in data sparse regions. A historical extreme flood event was chosen to illustrate the challenges involved. Our aim was to understand what the causes might have been and specifically to show how input and model parameter uncertainties affect the output. For this purpose, a conceptual model was calibrated and validated with recent data rich time period. Resulting model parameters were used to model the historical event which subsequently resulted in a rather poor hydrograph. Due to the bad model performance, a spatial simulation technique was used to invert the model for precipitation. Constraints, such as taking the precipitation values at historical observation locations in to account, with correct spatial structures and following the observed regional distributions were used to generate realistic precipitation fields. Results showed that the inverted precipitation improved the performance significantly even when using many different model parameters. We conclude that while modelling in data sparse conditions both model input and parameter uncertainties have to be dealt with simultaneously to obtain meaningful results.

中文翻译：

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数据稀疏环境中的水文建模：历史洪水事件的逆向建模

我们在模拟极端洪水时处理了一个相当频繁和困难的情况，即数据稀疏区域的模型输出不确定性。选择了一个历史性的极端洪水事件来说明所涉及的挑战。我们的目标是了解原因可能是什么，特别是要展示输入和模型参数的不确定性如何影响输出。为此，使用最近数据丰富的时间段校准和验证了一个概念模型。产生的模型参数用于对历史事件进行建模，该事件随后导致了相当差的水文过程线。由于模型性能较差，采用空间模拟技术对降水模型进行反演。约束，例如考虑历史观测位置的降水值，使用正确的空间结构并遵循观察到的区域分布来生成真实的降水场。结果表明，即使使用许多不同的模型参数，反演降水也显着提高了性能。我们得出的结论是，在数据稀疏条件下建模时，必须同时处理模型输入和参数不确定性以获得有意义的结果。