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Multi-scale effects of runoff time series and its improved prediction methods
Zhongzheng He1,2, Jiahao Lu1,2, Yongqiang Wang3
1School of Infrastucture Engineering, Nanchang University, Nanchang, 330031, China.
Scientific Reports
|August 29, 2025
Summary
This study identifies the multi-scale effect of runoff time series (MSER) impacting prediction accuracy. An improved method (MSEIP) enhances cross-time-scale runoff prediction, especially for higher flow rates.
Area of Science:
- Hydrology
- Data Science
- Environmental Modeling
Background:
- Cross-time-scale runoff prediction accuracy is often limited by data characteristics.
- Improving prediction accuracy across different time scales presents a significant challenge in hydrology.
Purpose of the Study:
- To identify the multi-scale effect of runoff time series (MSER) across global hydrological stations.
- To propose and evaluate an MSER-based improved prediction method (MSEIP) for enhanced cross-time-scale runoff prediction.
Main Methods:
- Analysis of 18,250 global hydrological stations to identify MSER.
- Implementation and comparative analysis of Multiple Linear Regression (MLR) and Gaussian Process Regression (GPR) models.
- Evaluation using metrics such as Optimization Proportion (OP) and Optimization Efficiency (OE).
Main Results:
- MSER was found to be applicable to over 73% of hydrological stations, with increased applicability for higher flow rates.
- The MSEIP method showed improved prediction accuracy, with effectiveness decreasing at longer time scales but increasing with higher flow rates.
- MLR excelled at identifying MSER at weekly scales, while GPR performed better at seasonal and yearly scales.
Conclusions:
- MSER is a key factor explaining variations in runoff prediction accuracy across different time scales.
- The MSEIP method offers a viable approach for improving cross-scale runoff prediction accuracy, providing valuable technical support.
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