Classifying watersheds prior to regionalization improves streamflow predictions in ungauged basin. Present study aims to assess the ability of combining watershed classification using dimensionality reduction techniques with regionalization methods for reliable streamflow prediction using soil and water assessment tool (SWAT). Isomap and principal component analysis (PCA) are applied to watershed attributes of 30 watersheds from Godavari river basin in India to classify them. The best classification technique is determined by calculating similarity index (SI). The results showed that Isomap is better at classifying hydrologically similar watersheds than PCA with an average SI value of 0.448. The regionalization methods such as global mean, inverse distance weighted (IDW) and physical similarity were applied to transfer the parameters from watersheds of best watershed classification group to the pseudo-ungauged watersheds, using SWAT model. The present study suggests that classifying watersheds with Isomap and regionalization using physical similarity improves the efficiency of streamflow estimation in ungauged basins.

中文翻译：

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利用流域分类和分区技术估算非赋富盆地的径流

在区域化之前对流域进行分类可以改善未流域的流径预报。本研究旨在评估使用降维技术将流域分类与区域化方法相结合的能力，以使用土壤和水评估工具（SWAT）进行可靠的流量预测。将等值图和主成分分析（PCA）应用于印度Godavari流域的30个流域的流域属性以对其进行分类。最佳分类技术是通过计算相似性指数（SI）来确定的。结果表明，Isomap较PCA具有更好的水文相似流域分类能力，其平均SI值为0.448。区域化方法，例如全局平均值，利用SWAT模型，应用逆距离加权（IDW）和物理相似度，将参数从最佳流域分类组的流域转移到伪未复盖的流域。本研究表明，利用等值线图对流域进行分类和使用物理相似度进行区域化可提高未灌流盆地的径流估算效率。