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A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds.

Authors :
Li, Kailong
Huang, Guohe
Wang, Shuo
Baetz, Brian
Xu, Weihuang
Source :
Water Resources Research; Feb2022, Vol. 58 Issue 2, p1-31, 31p
Publication Year :
2022

Abstract

Streamflow simulations at daily time steps are vital to water resources management, especially in arid regions. Previously, data‐driven models have been used as an effective tool for daily streamflow simulation. However, the accuracy of conventional data‐driven approaches is affected by the temporal autocorrelation of daily streamflow, especially in irrigated watersheds where the persistence of saturated flows dominates irrigation seasons. This study presents a Stepwise Clustered Regression Tree Ensemble (SCRTE) to address the streamflow autocorrelation. With the provision of a state‐of‐the‐art data‐driven model Stepwise Cluster Analysis (SCA), the SCRTE enables both single‐ and multi‐output settings (i.e., model predictand can be either a scalar or a vector), which can thus address interactions among streamflow values over multiple consecutive days. The autocorrelation effect of daily streamflow is evaluated based on single‐ and multi‐output SCA ensembles, which can then be aggregated according to their performance for various streamflow quantile ranges. To facilitate the irrigation scheduling decision‐making under rigorous transboundary water regulations, the SCRTE is applied to three interconnected watersheds with mixed land use, located in a floodplain of the Yellow River basin in China. The results show that the SCRTE outperforms seven well‐known benchmark models across seven evaluation metrics. Our findings reveal that the SCRTE can reflect the varying effects of autocorrelation over different streamflow quantile ranges, thereby improving the streamflow simulation. The multi‐output SCA ensembles are more capable of addressing the medium flows, while the single‐output one can better simulate the low and high flows. Key Points: A regression tree ensemble is proposed to address the temporal autocorrelation of daily streamflowA Bayesian model average with a stratified sampling ensemble strategy is provided to improve the simulation accuracy for medium flowsAutocorrelation of daily streamflow is particularly important for establishing reliable irrigation‐discharge relationships [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00431397
Volume :
58
Issue :
2
Database :
Complementary Index
Journal :
Water Resources Research
Publication Type :
Academic Journal
Accession number :
155434465
Full Text :
https://doi.org/10.1029/2021WR031065