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Water Resources Management

Erschienen in:

13.09.2021

Regional Frequency Analysis Based on Precipitation Regionalization Accounting for Temporal Variability and a Nonstationary Index Flood Model

verfasst von: Qianyu Gao, Guofang Li, Jin Bao, Jian Wang

Erschienen in: Water Resources Management | Ausgabe 13/2021

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Abstract

Climate change and human activities have led to nonstationarity in hydrological series. To systematically consider nonstationarity in regional frequency analysis (RFA), the features accounting for temporal variability of data series were developed and a nonstationary index flood model considering the trend and jump mutations was proposed in this study. The features extracted by empirical mode decomposition (EMD) were regarded as attributes to identify homogeneous regions. The fuzzy c-means clustering (FCM) and the combination of the self-organizing feature map and Ward’s agglomerative hierarchical clustering (SOM+Ward) were compared. Then the complete nonstationary RFA was applied to the annual maximum daily precipitation (AMDP) of Jiangxi province, China. The results indicate that the regionalization with the attributes reflecting temporal variability of the data series is more detailed. Moreover, the performance of SOM+Ward is better than FCM. The comparison results of precipitation quantiles, which were estimated by stationary and the proposed nonstationary index model, indicate that ignoring nonstationarity in RFA affects the choice of the best-fit distribution and the determination of index flood. In addition, the complete framework of nonstationary RFA developed in this study can provide more proper information when stations with trend and jump mutations exist in the region.

Vorheriger Artikel Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping

Nächster Artikel Stochastic Design of Flood Control Systems; Uncertainty Propagation and Results Representation

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Titel: Regional Frequency Analysis Based on Precipitation Regionalization Accounting for Temporal Variability and a Nonstationary Index Flood Model
verfasst von: Qianyu Gao
Guofang Li
Jin Bao
Jian Wang
Publikationsdatum: 13.09.2021
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 13/2021
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-021-02959-4

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