TY - JOUR
T1 - A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds
AU - Li, Kailong
AU - Huang, Guohe
AU - Wang, Shuo
AU - Baetz, Brian
AU - Xu, Weihuang
N1 - Funding Information:
This research was supported by Canada Research Chair Program, Natural Science and Engineering Research Council of Canada, and MITACS. We appreciate Ningxia Water Conservancy for offering streamflow, groundwater and irrigation information and related help. We would like to express our sincere gratitude to the editor, associate editor and anonymous reviewers for their constructive comments and suggestions.
Funding Information:
This research was supported by Canada Research Chair Program, Natural Science and Engineering Research Council of Canada, and MITACS. We appreciate Ningxia Water Conservancy for offering streamflow, groundwater and irrigation information and related help. We would like to express our sincere gratitude to the editor, associate editor and anonymous reviewers for their constructive comments and suggestions.
Publisher Copyright:
© 2022. American Geophysical Union. All Rights Reserved.
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - autocorrelation
KW - Bayesian inference
KW - data-driven model
KW - irrigation scheduling
KW - regression tree ensemble
UR - http://www.scopus.com/inward/record.url?scp=85125145921&partnerID=8YFLogxK
U2 - 10.1029/2021WR031065
DO - 10.1029/2021WR031065
M3 - Journal article
AN - SCOPUS:85125145921
SN - 0043-1397
VL - 58
JO - Water Resources Research
JF - Water Resources Research
IS - 2
M1 - e2021WR031065
ER -