Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review,WIREs Water

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Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review
WIREs Water ( IF 6.8 ) Pub Date : 2020-10-19 , DOI: 10.1002/wat2.1487
Yuhan Guo _{1,

2} , Yongqiang Zhang ₁ , Lu Zhang ₃ , Zhonggen Wang ₁

Affiliation

Runoff prediction in ungauged and scarcely gauged catchments is a key research field in surface water hydrology. There have been numerous studies before and since the launch of the predictions in ungauged basins (PUB) initiative by the International Association of Hydrological Sciences in 2003. This study critically reviews and assesses the decadal progress in the regionalization of hydrological modeling, which is the major tool for PUB, from 2000 to 2019. This paper found that the journal publications have noticeably increased in terms of PUB in the past 7 years, and research countries have been expanded dramatically since 2013. The regionalization methods are grouped into three categories including similarity‐based, regression‐based, and hydrological signature‐based. There are more detailed researches focusing on the interdisciplinary and profound improvement of each regionalization method. Namely, tremendous efforts have been made and lots of improvements have been carried out in the parameterization domain for the post‐PUB period. However, there is still plenty of room to improve the prediction capability in data‐sparse regions (e.g., further verification and proof of multi‐modeling adaptation and uncertainties description). This paper also discusses possible research directions in the future, including PUB in a changing environment and better utilization of multi‐source remote‐sensing information.

中文翻译：

水文模型的区域化，以预测未引水流域的水流量：全面综述

未开垦和稀少流域的径流预测是地表水文学的一个重要研究领域。国际水文科学协会于2003年启动非流域盆地预报（PUB）之前和之后，已经进行了许多研究。该研究批判性地回顾和评估了水文模型区域化的十年进展，这是主要的从2000年到2019年是PUB的工具。本文发现，在过去7年中，就PUB而言，期刊出版物显着增加，而研究国家/地区自2013年以来已得到极大扩展。区域化方法分为三类，包括相似性-基于，基于回归和基于水文特征的。有更多详细的研究集中在每种区域化方法的跨学科和深刻的改进上。即，在PUB后时期，在参数化领域已经做出了巨大的努力，并进行了许多改进。但是，在数据稀疏区域中仍有大量的空间可以提高预测能力（例如，进一步验证和证明多模型适应性和不确定性描述）。本文还讨论了未来可能的研究方向，包括不断变化的环境中的PUB和更好地利用多源遥感信息。在数据稀疏区域中，仍有很大的空间可以提高预测能力（例如，进一步验证和证明多模型适应性和不确定性描述）。本文还讨论了未来可能的研究方向，包括不断变化的环境中的PUB和更好地利用多源遥感信息。在数据稀疏区域中，仍有很大的空间可以提高预测能力（例如，进一步验证和证明多模型适应性和不确定性描述）。本文还讨论了未来可能的研究方向，包括不断变化的环境中的PUB和更好地利用多源遥感信息。

更新日期：2020-12-20

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