A stepwise clustered hydrological model for addressing the temporal autocorrelation of daily streamflows in irrigated watersheds

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.