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首页> 外文期刊>Canadian Water Resources Journal >An evaluation of regionalization and watershed classification schemes for continuous daily streamflow prediction in ungauged watersheds
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An evaluation of regionalization and watershed classification schemes for continuous daily streamflow prediction in ungauged watersheds

机译：评估非流域连续日流量的区域化和流域分类方案评估

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Regionalization - the process of transferring hydrological information from gauged to ungauged watersheds - has the potential to perform significantly better if these watersheds are classified in advance. In this study, we demonstrate the benefits of classification by a systematic combination of watershed classification techniques, regionalization methods, and rainfall-runoff models. Basins are first classified, then regionalization methods are applied, for continuous daily streamflow estimation at ungauged watersheds in Ontario, Canada. Nonlinear data-driven methods are used as regionalization and watershed classification schemes to transfer the parameters of two conceptual hydrologic models - namely McMaster University Hydrologiska Byrans Vattenbalansavdelning (MAC-HBV) and Sacramento Soil Moisture Accounting (SAC-SMA) - from gauged to ungauged watersheds. Our results suggest that a certain combination of watershed classification technique, regionalization method and hydrologic model can significantly improve the estimation of continuous streamflow at ungauged basins by improving the accuracy of estimated daily mean, low and peak flows. However, some combinations do not provide a clear improvement when compared to the scenario of unclassified basins. For example, the MAC-HBV model coupled with a counter propagation neural network as a regionalization technique provides a clear improvement in estimated daily mean, low and peak flow when the watersheds are first classified using a nonlinear principal component analysis method. Interestingly, a higher improvement is achieved for low flow as well, which is usually difficult to estimate in ungauged basins.
机译：如果预先对流域进行分类，区域化（将水文信息从有标量的流域转移到未流域的过程）可能会表现出明显更好的潜力。在这项研究中，我们通过分水岭分类技术，区域化方法和降雨径流模型的系统组合展示了分类的好处。首先对流域进行分类，然后应用区域化方法，以连续估算加拿大安大略省未开挖流域的日流量。非线性数据驱动的方法被用作区域化和分水岭分类方案，以转移两个概念性水文模型的参数，即从测量到无分水岭的麦克马斯特大学Hydrologiska Byrans Vattenbalansavdelning（MAC-HBV）和萨克拉曼多土壤水分核算（SAC-SMA） 。我们的结果表明，流域分类技术，区域化方法和水文模型的某种组合可以通过提高估计的日平均流量，低流量和峰值流量的准确性来显着改善非流域流域连续流量的估计。但是，与未分类盆地相比，某些组合不能提供明显的改进。例如，当使用非线性主成分分析方法对流域进行首次分类时，MAC-HBV模型与作为区域化技术的反向传播神经网络相结合，可显着改善估计的日均流量，低流量和峰值流量。有趣的是，对于低流量也实现了更高的改进，这通常在未充填盆地中很难估计。

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