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首页> 外文期刊>Physics and chemistry of the earth >Regional parameter allocation and predictive uncertainty estimation of a rainfall-runoff model in the poorly gauged Three Gorges Area (PR China)
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Regional parameter allocation and predictive uncertainty estimation of a rainfall-runoff model in the poorly gauged Three Gorges Area (PR China)

机译：三峡不发达地区降雨径流模型的区域参数分配和预测不确定性估计（中国）

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摘要

In the framework of the IAHS initiative on Predictions in Ungauged Basins, the predictive uncertainty in hydrological simulations constitutes a key issue. The Three Gorges Area located in central China is a poorly gauged macro-scale catchment with an area of about 57,000 km(2) which is frequently hit by floods. The semi-distributed hydrological model PREVAH was implemented in this catchment as a part of the Changjiang Flood Assistance Project. The precipitation correction, being the most sensitive tuneable parameter in the basin, was chosen to be regionally allocated in order to cope with regionally varying precipitation measurement errors due to differences in the measurement network setup. The model was calibrated on one single discharge time series at the basin's outlet by means of the Adaptive Metropolis algorithm. The estimated posteriori parameter probability distribution revealed that the regional allocation of the precipitation correction induced a strong parameter interdependence. The calculated predictive uncertainty is large but nevertheless suggests that additional uncertainty sources should be included to get a sound probabilistic simulation. Despite the large uncertainty and parameter interdependence, the model performance in the flood season of the year 2007 shows that the Adaptive Metropolis algorithm successfully inferred a well behaving best-bin parameter set.
机译：在IAHS关于无塞盆地预报的倡议框架内，水文模拟中的预测不确定性是一个关键问题。位于中国中部的三峡地区是一个规模不大的集水区，面积约57,000公里（2），经常遭受洪水袭击。该流域采用了半分布式水文模型PREVAH，这是长江防洪工程的一部分。降水校正是盆地中最敏感的可调参数，因此选择进行区域分配，以应对由于测量网络设置不同而导致的区域变化的降水测量误差。该模型通过自适应大都会算法在流域出口处的一个单一排放时间序列上进行了校准。估计的后验参数概率分布表明，降水校正的区域分配引起强烈的参数相互依赖性。计算得出的预测不确定性很大，但是尽管如此，仍建议应包括其他不确定性源，以进行合理的概率模拟。尽管存在很大的不确定性和参数相互依存性，但在2007年汛期的模型性能表明，自适应大都会算法成功地推断出性能最佳的参数组。

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