Articles | Volume 29, issue 4
https://doi.org/10.5194/hess-29-903-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-903-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Feb 2025
Research article |
| 21 Feb 2025
| 21 Feb 2025
Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments
Tian Lan
School of Water and Environment, Chang’an University, Xi’an 710054, China
Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science & Technology, Nanjing 210000, China
Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, China
Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of the 9 Ministry of Water Resources, Chang’an University, Xi’an 710054, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Tongfang Li
CORRESPONDING AUTHOR
School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
Hongbo Zhang
CORRESPONDING AUTHOR
School of Water and Environment, Chang’an University, Xi’an 710054, China
Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, China
Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of the 9 Ministry of Water Resources, Chang’an University, Xi’an 710054, China
Jiefeng Wu
Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science & Technology, Nanjing 210000, China
Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education, Chang’an University, Xi’an 710054, China
School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210000, Jiangsu, China
Yongqin David Chen
School of Humanities and Social Science, The Chinese University of Hong Kong, Shenzhen 518172, China
Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong SAR, China
Chong-Yu Xu
Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, 0316 Oslo, Norway
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- A Hybrid Multi-Strategy Monthly Runoff Forecasting Model Based on Parallel CNN-GRU Architecture, SSA Optimization, and Error Correction Mechanisms L. Wang et al. https://doi.org/10.1007/s11269-025-04457-3
- Multi-model integration framework for monthly runoff prediction based on variational mode decomposition (VMD) and trend-based modeling S. Wang et al. https://doi.org/10.1007/s00477-026-03170-w
- Mathematical Model for Hydropower Plant (HPP) Electricity Forecasting with High Time Resolution V. Alexiev et al. https://doi.org/10.3390/en19092217
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- Cascading effects of cross-year droughts on flow-sediment dynamics across distinct drought types J. Wu et al. https://doi.org/10.1016/j.jhydrol.2025.134601
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- Multi-temporal analysis of runoff evolution in the Poyang lake basin during 1953–2022 Y. Xu et al. https://doi.org/10.1007/s00477-026-03184-4
- Seasonal precipitation-flow-DOC cascade: Long-term monitoring insights J. Wu et al. https://doi.org/10.1016/j.jhydrol.2026.135534
- Reframing urban flood resilience through blue green infrastructure- an integrated cluster analysis and DPSIR approach in the case of Kerala, India P. Ambily et al. https://doi.org/10.1016/j.landusepol.2026.108036
12 citations as recorded by crossref.
- Impacts of human activities and hydrological processes on monthly riverine nitrate sources and transport in the Agro-Pastoral ecotone X. Kong et al. https://doi.org/10.1016/j.jhydrol.2026.135150
- Driving mechanisms of water yield in the Yellow River Basin: Insights from an explainable machine learning approach J. Hu et al. https://doi.org/10.1016/j.ejrh.2025.102610
- A Hybrid Multi-Strategy Monthly Runoff Forecasting Model Based on Parallel CNN-GRU Architecture, SSA Optimization, and Error Correction Mechanisms L. Wang et al. https://doi.org/10.1007/s11269-025-04457-3
- Multi-model integration framework for monthly runoff prediction based on variational mode decomposition (VMD) and trend-based modeling S. Wang et al. https://doi.org/10.1007/s00477-026-03170-w
- Mathematical Model for Hydropower Plant (HPP) Electricity Forecasting with High Time Resolution V. Alexiev et al. https://doi.org/10.3390/en19092217
- Exploring the effects of antecedent rainfall characteristic on streamflow variability in a karst catchment F. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103440
- Cascading effects of cross-year droughts on flow-sediment dynamics across distinct drought types J. Wu et al. https://doi.org/10.1016/j.jhydrol.2025.134601
- Spatiotemporal prediction for groundwater heavy metal contamination using Soft-DTW-based clustering and graph neural network framework Y. He et al. https://doi.org/10.1016/j.watres.2025.125245
- Contrasting CESM and ECMWF for predictive modeling of spatial heterogeneity of drought indices across Colorado and Louisiana regions of the USA N. Choudhari et al. https://doi.org/10.1016/j.ejrh.2026.103351
- Multi-temporal analysis of runoff evolution in the Poyang lake basin during 1953–2022 Y. Xu et al. https://doi.org/10.1007/s00477-026-03184-4
- Seasonal precipitation-flow-DOC cascade: Long-term monitoring insights J. Wu et al. https://doi.org/10.1016/j.jhydrol.2026.135534
- Reframing urban flood resilience through blue green infrastructure- an integrated cluster analysis and DPSIR approach in the case of Kerala, India P. Ambily et al. https://doi.org/10.1016/j.landusepol.2026.108036
Saved (final revised paper)
Latest update: 08 Jun 2026
Short summary
This study develops an integrated framework based on the novel Driving index for changes in Precipitation–Runoff Relationships (DPRR) to explore the controlling changes in precipitation–runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation–runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
This study develops an integrated framework based on the novel Driving index for changes in...
