Articles | Volume 29, issue 15
https://doi.org/10.5194/hess-29-3703-2025
© 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-29-3703-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Aug 2025
Research article |
| 13 Aug 2025
| 13 Aug 2025
Predicting snow cover and frozen ground impacts on large basin runoff: developing appropriate model complexity
Nan Wu
State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Division of Water Resources Engineering, LTH, Lund University, Lund, 22100, Sweden
State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
China Meteorological Administration Hydro-Meteorology Key Laboratory, Hohai University, Nanjing, Jiangsu, 210024, China
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Amir Naghibi
Division of Water Resources Engineering, LTH, Lund University, Lund, 22100, Sweden
Hossein Hashemi
Division of Water Resources Engineering, LTH, Lund University, Lund, 22100, Sweden
Zhongrui Ning
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Division of Water Resources Engineering, LTH, Lund University, Lund, 22100, Sweden
Qinuo Zhang
State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210024, China
Xuejun Yi
Hydrological Center of Shandong Province, Jinan, Shandong, 250002, China
Haijun Wang
Hydrological Center of Shandong Province, Jinan, Shandong, 250002, China
Wei Liu
Hydrological Center of Shandong Province, Jinan, Shandong, 250002, China
Wei Gao
Hydrological Center of Shandong Province, Jinan, Shandong, 250002, China
Jerker Jarsjö
Department of Physical Geography, Stockholm University, Stockholm, 10691, Sweden
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Cited
14 citations as recorded by crossref.
- Soil Amendments in Cold Regions: Applications, Challenges and Recommendations Z. Miao et al. https://doi.org/10.3390/agriculture16030326
- Modelling climate-driven agricultural land use change in a data-limited region: a big data analytics framework for the Kabul River Basin (KRB) M. Zubair et al. https://doi.org/10.1080/10106049.2026.2651496
- Mathematical Modeling and Numerical Simulation of Heat and Mass Transfer in Snow Cover and Soil A. Sibin et al. https://doi.org/10.1134/S0021894425700828
- From empirical to physical constraints: Revisiting the structure of monthly water balance models with global evaluation Z. Ning et al. https://doi.org/10.1016/j.jhydrol.2026.134916
- Advances in Hydrology of Irrigation Districts in Cold Regions M. Yuting et al. https://doi.org/10.70322/hee.2025.10017
- Study on the stability evaluation of loess landslides based on variable weight theory and finite interval cloud model K. Wang et al. https://doi.org/10.3389/feart.2026.1767410
- Integrating dynamic land surface processes and machine learning into a hydrological modeling framework: application to the Yellow River Basin Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.134869
- Ranking water-scarcity management strategies using the TOPSIS method H. Yan & X. Wang https://doi.org/10.1038/s41598-026-48751-5
- Assessing the global performance of a parsimonious soil temperature model for frozen ground prediction D. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135277
- A hybrid deep learning model with topology-aware attention for multi-station water quality prediction at basin scale Z. Yan et al. https://doi.org/10.1016/j.jwpe.2026.110174
- Detecting the spatiotemporal evolution, mutation characteristics and dominant drivers of vegetation drought in the Yangtze River Basin, China F. Wang et al. https://doi.org/10.1016/j.agwat.2026.110388
- Static strength estimation of frozen soils using DEM-verified and optimized tree-based algorithms Q. Shao et al. https://doi.org/10.1016/j.sandf.2026.101774
- The main source of plant water in alpine meadows dominated by supra-permafrost water under decreasing rain conditions Z. Li et al. https://doi.org/10.1016/j.jenvman.2026.130086
- Soil temperature dynamics and its relationship with environmental factors under different frozen soil conditions of the Yellow River source region J. Wu et al. https://doi.org/10.1016/j.ejrh.2026.103517
14 citations as recorded by crossref.
- Soil Amendments in Cold Regions: Applications, Challenges and Recommendations Z. Miao et al. https://doi.org/10.3390/agriculture16030326
- Modelling climate-driven agricultural land use change in a data-limited region: a big data analytics framework for the Kabul River Basin (KRB) M. Zubair et al. https://doi.org/10.1080/10106049.2026.2651496
- Mathematical Modeling and Numerical Simulation of Heat and Mass Transfer in Snow Cover and Soil A. Sibin et al. https://doi.org/10.1134/S0021894425700828
- From empirical to physical constraints: Revisiting the structure of monthly water balance models with global evaluation Z. Ning et al. https://doi.org/10.1016/j.jhydrol.2026.134916
- Advances in Hydrology of Irrigation Districts in Cold Regions M. Yuting et al. https://doi.org/10.70322/hee.2025.10017
- Study on the stability evaluation of loess landslides based on variable weight theory and finite interval cloud model K. Wang et al. https://doi.org/10.3389/feart.2026.1767410
- Integrating dynamic land surface processes and machine learning into a hydrological modeling framework: application to the Yellow River Basin Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.134869
- Ranking water-scarcity management strategies using the TOPSIS method H. Yan & X. Wang https://doi.org/10.1038/s41598-026-48751-5
- Assessing the global performance of a parsimonious soil temperature model for frozen ground prediction D. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135277
- A hybrid deep learning model with topology-aware attention for multi-station water quality prediction at basin scale Z. Yan et al. https://doi.org/10.1016/j.jwpe.2026.110174
- Detecting the spatiotemporal evolution, mutation characteristics and dominant drivers of vegetation drought in the Yangtze River Basin, China F. Wang et al. https://doi.org/10.1016/j.agwat.2026.110388
- Static strength estimation of frozen soils using DEM-verified and optimized tree-based algorithms Q. Shao et al. https://doi.org/10.1016/j.sandf.2026.101774
- The main source of plant water in alpine meadows dominated by supra-permafrost water under decreasing rain conditions Z. Li et al. https://doi.org/10.1016/j.jenvman.2026.130086
- Soil temperature dynamics and its relationship with environmental factors under different frozen soil conditions of the Yellow River source region J. Wu et al. https://doi.org/10.1016/j.ejrh.2026.103517
Saved (final revised paper)
Latest update: 08 Jun 2026
Short summary
This study enhanced a popular water flow model by adding two components: one for snow melting and another for frozen ground cycles. Tested with satellite data and streamflow, the updated model improved accuracy, especially in winter. Frozen ground delays soil drainage, boosting spring runoff by 39 %–77 % and cutting evaporation by 85 %. These findings reveal that frozen ground drives seasonal water patterns.
This study enhanced a popular water flow model by adding two components: one for snow melting...
