Articles | Volume 29, issue 21
https://doi.org/10.5194/hess-29-5913-2025
© 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-29-5913-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Nov 2025
Research article |
| 03 Nov 2025
| 03 Nov 2025
Altitudinal variation in impacts of snow cover, reservoirs and precipitation seasonality on monthly runoff in Tibetan Plateau catchments
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
Jerker Jarsjö
Department of Physical Geography, Stockholm University, Stockholm, 10691, Sweden
Related authors
Nan Wu, Ke Zhang, Amir Naghibi, Hossein Hashemi, Zhongrui Ning, Qinuo Zhang, Xuejun Yi, Haijun Wang, Wei Liu, Wei Gao, and Jerker Jarsjö
Hydrol. Earth Syst. Sci., 29, 3703–3725, https://doi.org/10.5194/hess-29-3703-2025, https://doi.org/10.5194/hess-29-3703-2025, 2025
Short summary
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.
Jin Feng, Ke Zhang, Lijun Chao, Huijie Zhan, and Yunping Li
Earth Syst. Sci. Data, 17, 5039–5064, https://doi.org/10.5194/essd-17-5039-2025, https://doi.org/10.5194/essd-17-5039-2025, 2025
Short summary
Short summary
Understanding how soil moisture affects evapotranspiration (ET) is essential for improving ET estimates. However, many global ET datasets overlook soil moisture constraints, causing large uncertainties. In this study, we developed an improved model that better captures the influence of soil moisture on vegetation and soil evaporation. Our model significantly improves ET estimation accuracy and provides a new long-term global ET dataset to support water cycle and climate research.
Haoyu Jin, Ke Zhang, Moyang Liu, Xuan Yu, Xu Yang, Lijun Chao, Pengfei Zhang, and Guoyan Liu
EGUsphere, https://doi.org/10.5194/egusphere-2025-4289, https://doi.org/10.5194/egusphere-2025-4289, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Heatwaves and heavy rainfall are dangerous on their own. But when they occur in quick succession, extreme heat followed by intense rain, they can create even greater risks. Our findings show that compound heatwave-extreme precipitation events are becoming a distinct and worsening type of climate hazard. They can no longer be treated as isolated events. To build resilience, early warning systems, disaster planning, and adaptation strategies must now account for these compound risks.
Nan Wu, Ke Zhang, Amir Naghibi, Hossein Hashemi, Zhongrui Ning, Qinuo Zhang, Xuejun Yi, Haijun Wang, Wei Liu, Wei Gao, and Jerker Jarsjö
Hydrol. Earth Syst. Sci., 29, 3703–3725, https://doi.org/10.5194/hess-29-3703-2025, https://doi.org/10.5194/hess-29-3703-2025, 2025
Short summary
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.
Louise Petersson Wårdh, Hasan Hosseini, Remco van de Beek, Jafet C. M. Andersson, Hossein Hashemi, and Jonas Olsson
EGUsphere, https://doi.org/10.5194/egusphere-2025-2820, https://doi.org/10.5194/egusphere-2025-2820, 2025
Short summary
Short summary
Extreme rainfall can cause severe damage, especially in cities. However, national meteorological institutes have difficulties to observe such events. In this study we show that rainfall observations collected by local actors, such as municipalities and even citizens, can contribute to better rainfall observations. Sweden’s official monitoring network could not capture the event under study, whereas the complementary sensors contributed to a better understanding of the magnitude of the event.
Jiefan Niu, Ke Zhang, Xi Li, and Hongjun Bao
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-304, https://doi.org/10.5194/hess-2024-304, 2024
Preprint under review for HESS
Short summary
Short summary
This study developed a new method for classifying catchments, combining machine learning techniques with climate and landscape data. By analyzing catchments across China, we identified six climate regions and 35 unique catchment types, each with distinct streamflow patterns. This classification method improves hydrological predictions, especially in areas lacking direct data.
Mohammed Abdallah, Ke Zhang, Lijun Chao, Abubaker Omer, Khalid Hassaballah, Kidane Welde Reda, Linxin Liu, Tolossa Lemma Tola, and Omar M. Nour
Hydrol. Earth Syst. Sci., 28, 1147–1172, https://doi.org/10.5194/hess-28-1147-2024, https://doi.org/10.5194/hess-28-1147-2024, 2024
Short summary
Short summary
A D-vine copula-based quantile regression (DVQR) model is used to merge satellite precipitation products. The performance of the DVQR model is compared with the simple model average and one-outlier-removed average methods. The nonlinear DVQR model outperforms the quantile-regression-based multivariate linear and Bayesian model averaging methods.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
Short summary
Short summary
In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jin Feng, Ke Zhang, Huijie Zhan, and Lijun Chao
Hydrol. Earth Syst. Sci., 27, 363–383, https://doi.org/10.5194/hess-27-363-2023, https://doi.org/10.5194/hess-27-363-2023, 2023
Short summary
Short summary
Here we improved a satellite-driven evaporation algorithm by introducing the modified versions of the two constraint schemes. The two moisture constraint schemes largely improved the evaporation estimation on two barren-dominated basins of the Tibetan Plateau. Investigation of moisture constraint uncertainty showed that high-quality soil moisture can optimally represent moisture, and more accessible precipitation data generally help improve the estimation of barren evaporation.
Cited articles
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, Food and Agriculture Organization of the United Nations, Rome, 1998.
Bai, J., Li, J., Shi, H., Liu, T., and Zhong, R.: Snowmelt Water Alters the Regime of Runoff in the Arid Region of Northwest China, Water, 10, 902, https://doi.org/10.3390/w10070902, 2018.
Bao, Z., Zhang, J., Lian, Y., Wang, G., Jin, J., Ning, Z., Zhang, J., Liu, Y., and Wang, X.: Changes in Headwater Streamflow from Impacts of Climate Change in the Tibetan Plateau, Engineering, https://doi.org/10.1016/j.eng.2023.05.025, 2023.
Berghuijs, W. R., Sivapalan, M., Woods, R. A., and Savenije, H. H. G.: Patterns of similarity of seasonal water balances: A window into streamflow variability over a range of time scales, Water Resour. Res., 50, 5638–5661, https://doi.org/10.1002/2014WR015692, 2014.
Bounoua, L., DeFries, R. S., Imhoff, M. L., and Steininger, M. K.: Land use and local climate: A case study near Santa Cruz, Bolivia, Meteorol. Atmos. Phys., 86, 73–85, https://doi.org/10.1007/s00703-003-0616-8, 2004.
Brutsaert, W. and Hiyama, T.: The determination of permafrost thawing trends from long-term streamflow measurements with an application in eastern Siberia, J. Geophys. Res.-Atmos., 117, D22110, https://doi.org/10.1029/2012JD018344, 2012.
Chen, D. L., Gao, G., Xu, C. Y., Guo, J., and Ren, G. Y.: Comparison of the Thornthwaite method and pan data with the standard Penman-Monteith estimates of reference evapotranspiration in China, Clim. Res., 28, 123–132, https://doi.org/10.3354/cr028123, 2005.
Choudhury, B. J.: Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model, J. Hydrol., 216, 99–110, https://doi.org/10.1016/S0022-1694(98)00293-5, 1999.
Condon, L. E., Atchley, A. L., and Maxwell, R. M.: Evapotranspiration depletes groundwater under warming over the contiguous United States, Nat. Commun., 11, 873, https://doi.org/10.1038/s41467-020-14688-0, 2020.
Cui, T., Li, Y., Yang, L., Nan, Y., Li, K., Tudaji, M., Hu, H., Long, D., Shahid, M., Mubeen, A., He, Z., Yong, B., Lu, H., Li, C., Ni, G., Hu, C., and Tian, F.: Non-monotonic changes in Asian Water Towers' streamflow at increasing warming levels, Nat. Commun., 14, 1176, https://doi.org/10.1038/s41467-023-36804-6, 2023.
Dethier, E. N., Sartain, S. L., Renshaw, C. E., and Magilligan, F. J.: Spatially coherent regional changes in seasonal extreme streamflow events in the United States and Canada since 1950, Sci. Adv., 6, eaba5939, https://doi.org/10.1126/sciadv.aba5939, 2020.
Du, C., Sun, F., Yu, J., Liu, X., and Chen, Y.: New interpretation of the role of water balance in an extended Budyko hypothesis in arid regions, Hydrol. Earth Syst. Sci., 20, 393–409, https://doi.org/10.5194/hess-20-393-2016, 2016.
Fang, H., Huang, L., Wang, J., He, G., and Reible, D.: Environmental assessment of heavy metal transport and transformation in the Hangzhou Bay, China, J. Hazard. Mater., 302, 447–457, https://doi.org/10.1016/j.jhazmat.2015.09.060, 2016.
Fang, Y., Li, H., Wan, W., Zhu, S., Wang, Z., Hong, Y., and Wang, H.: Assessment of Water Storage Change in China's Lakes and Reservoirs over the Last Three Decades, Remote Sens., 11, 1467, https://doi.org/10.3390/rs11121467, 2019.
Gan, G., Wu, J., Hori, M., Fan, X., and Liu, Y.: Attribution of decadal runoff changes by considering remotely sensed snow/ice melt and actual evapotranspiration in two contrasting watersheds in the Tienshan Mountains, J. Hydrol., 610, 127810, https://doi.org/10.1016/j.jhydrol.2022.127810, 2022.
Gao, H., Ding, Y., Zhao, Q., Hrachowitz, M., and Savenije, H. H. G.: The importance of aspect for modelling the hydrological response in a glacier catchment in Central Asia, Hydrol. Process., 31, 2842–2859, https://doi.org/10.1002/hyp.11224, 2017.
Gutenson, J. L., Tavakoly, A. A., Wahl, M. D., and Follum, M. L.: Comparison of generalized non-data-driven lake and reservoir routing models for global-scale hydrologic forecasting of reservoir outflow at diurnal time steps, Hydrol. Earth Syst. Sci., 24, 2711–2729, https://doi.org/10.5194/hess-24-2711-2020, 2020.
Han, P., Long, D., Han, Z., Du, M., Dai, L., and Hao, X.: Improved understanding of snowmelt runoff from the headwaters of China's Yangtze River using remotely sensed snow products and hydrological modeling, Remote Sens. Environ., 224, 44–59, https://doi.org/10.1016/j.rse.2019.01.041, 2019.
He, Z., Zhang, X., Bao, S., Qiao, Y., Sheng, Y., Liu, X., He, X., Yang, X., Zhao, J., Liu, R., and Lu, C.: Multiple climatic cycles imprinted on regional uplift-controlled fluvial terraces in the lower Yalong River and Anning River, SE Tibetan Plateau, Geomorphology, 250, 95–112, https://doi.org/10.1016/j.geomorph.2015.08.010, 2015.
Hock, R.: Temperature index melt modelling in mountain areas, J. Hydrol., 282, 104–115, https://doi.org/10.1016/S0022-1694(03)00257-9, 2003.
Huang, P., Song, J., Cheng, D., Sun, H., Kong, F., Jing, K., and Wu, Q.: Understanding the intra-annual variability of streamflow by incorporating terrestrial water storage from GRACE into the Budyko framework in the Qinba Mountains, J. Hydrol., 603, 126988, https://doi.org/10.1016/j.jhydrol.2021.126988, 2021.
Huang, Y., Zhao, H., Jiang, Y., and Lu, X.: Runoff and its influencing factors in the upper reaches of the Yalong River, Arid Land Geo., 41, 127–133, https://doi.org/10.13826/j.cnki.cn65-1103/x.2018.01.016, 2018.
Huo, J., Liu, C., Yu, X., Jia, G., and Chen, L.: Effects of watershed char and climate variables on annual runoff in different climatic zones in China, Sci. Total Environ., 754, 142157, https://doi.org/10.1016/j.scitotenv.2020.142157, 2021.
Hwang, J. and Devineni, N.: An Improved Zhang's Dynamic Water Balance Model Using Budyko-Based Snow Representation for Better Streamflow Predictions, Water Resour. Res., 58, e2021WR030203, https://doi.org/10.1029/2021WR030203, 2022.
Kazemi, H., Hashemi, H., Maghsood, F. F., Hosseini, S. H., Sarukkalige, R., Jamali, S., and Berndtsson, R.: Climate vs. Human Impact: Quantitative and Qualitative Assessment of Streamflow Variation, Water, 13, 2404, https://doi.org/10.3390/w13172404, 2021.
Kuhn, M., Helfricht, K., Ortner, M., Landmann, J., and Gurgiser, W.: Liquid water storage in snow and ice in 86 Eastern Alpine basins and its changes from 1970–97 to 1998-2006, Ann. Glaciol., 57, 11–18, https://doi.org/10.1017/aog.2016.24, 2016.
Li, L., Ni, J., Chang, F., Yue, Y., Frolova, N., Magritsky, D., Borthwick, A. G. L., Ciais, P., Wang, Y., Zheng, C., and Walling, D. E.: Global trends in water and sediment fluxes of the world's large rivers, Sci. Bull., 65, 62–69, https://doi.org/10.1016/j.scib.2019.09.012, 2020.
Li, X., Zhang, K., Gu, P., Feng, H., Yin, Y., Chen, W., and Cheng, B.: Changes in precipitation extremes in the Yangtze River Basin during 1960-2019 and the association with global warming, ENSO, and local effects, Sci. Total Environ., 760, 144244, https://doi.org/10.1016/j.scitotenv.2020.144244, 2021.
Li, Z. and Quiring, S. M.: Investigating spatial heterogeneity of the controls of surface water balance in the contiguous United States by considering anthropogenic factors, J. Hydrol., 601, 126621, https://doi.org/10.1016/j.jhydrol.2021.126621, 2021.
Liu, J., Zhang, Q., Singh, V. P., and Shi, P.: Contribution of multiple climatic variables and human activities to streamflow changes across China, J. Hydrol., 545, 145–162, https://doi.org/10.1016/j.jhydrol.2016.12.016, 2017.
Liu, J., Zhang, Q., Singh, V. P., Song, C., Zhang, Y., Sun, P., and Gu, X.: Hydrological effects of climate variability and vegetation dynamics on annual fluvial water balance in global large river basins, Hydrol. Earth Syst. Sci., 22, 4047–4060, https://doi.org/10.5194/hess-22-4047-2018, 2018.
Liu, J., You, Y., Zhang, Q., and Gu, X.: Attribution of streamflow changes across the globe based on the Budyko framework, Sci. Total Environ., 794, 148662, https://doi.org/10.1016/j.scitotenv.2021.148662, 2021.
Liu, Z., Wang, T., Han, J., Yang, W., and Yang, H.: Decreases in Mean Annual Streamflow and Interannual Streamflow Variability Across Snow-Affected Catchments Under a Warming Climate, Geophys. Res. Lett., 49, e2021GL097442, https://doi.org/10.1029/2021GL097442, 2022.
Luo, M. and Lau, N.-C.: Synoptic characteristics, atmospheric controls, and long-term changes of heat waves over the Indochina Peninsula, Clim. Dynam., 51, 2707–2723, https://doi.org/10.1007/s00382-017-4038-6, 2018.
Ministry of Water Resources of China: China Hydrological Yearbook, http://mwr.gov.cn (last access: 24 October 2025), 2002.
Miralles, D. G., De Jeu, R. A. M., Gash, J. H., Holmes, T. R. H., and Dolman, A. J.: Magnitude and variability of land evaporation and its components at the global scale, Hydrol. Earth Syst. Sci., 15, 967–981, https://doi.org/10.5194/hess-15-967-2011, 2011.
Miralles, D. G.: GLEAM v4.1, https://doi.org/10.5281/zenodo.14056079, 2024.
New, M., Hulme, M., and Jones, P.: Representing twentieth-century space-time climate variability. Part II: Development of 1901–96 monthly grids of terrestrial surface climate, J. Climate, 13, 2217–2238, https://doi.org/10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2, 2000.
Ning, T., Li, Z., and Liu, W.: Vegetation dynamics and climate seasonality jointly control the interannual catchment water balance in the Loess Plateau under the Budyko framework, Hydrol. Earth Syst. Sci., 21, 1515–1526, https://doi.org/10.5194/hess-21-1515-2017, 2017.
Ning, Z., Wu, N., Zhang, J., Ruan, Y., Tang, Z., Sun, J., Shi, J., Liu, C., and Wang, G.: Wetter trend in source region of Yangtze River by runoff simulating based on Grid-RCCC-WBM, J. Hydrol., 631, 130702, https://doi.org/10.1016/j.jhydrol.2024.130702, 2024.
Qi, J., Wang, L., Zhou, J., Song, L., Li, X., and Zeng, T.: Coupled Snow and Frozen Ground Physics Improves Cold Region Hydrological Simulations: An Evaluation at the upper Yangtze River Basin (Tibetan Plateau), J. Geophys. Res.-Atmos., 124, 12985–13004, https://doi.org/10.1029/2019JD031622, 2019.
Qi, W., Feng, L., Kuang, X., Zheng, C., Liu, J., Chen, D., Tian, Y., and Yao, Y.: Divergent and Changing Importance of Glaciers and Snow as Natural Water Reservoirs in the Eastern and Southern Tibetan Plateau, J. Geophys. Res.-Atmos., 127, e2021JD035888, https://doi.org/10.1029/2021JD035888, 2022.
Rottler, E., Francke, T., Bürger, G., and Bronstert, A.: Long-term changes in central European river discharge for 1869–2016: impact of changing snow covers, reservoir constructions and an intensified hydrological cycle, Hydrol. Earth Syst. Sci., 24, 1721–1740, https://doi.org/10.5194/hess-24-1721-2020, 2020.
Shen, C.: A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists, Water Resour. Res., 54, 8558–8593, https://doi.org/10.1029/2018WR022643, 2018.
Shi, G. and Gao, B.: Attribution Analysis of Runoff Change in the Upper Reaches of the Kaidu River Basin Based on a Modified Budyko Framework, Atmosphere, 13, 1385, https://doi.org/10.3390/atmos13091385, 2022.
Stigter, E. E., Litt, M., Steiner, J. F., Bonekamp, P. N. J., Shea, J. M., Bierkens, M. F. P., and Immerzeel, W. W.: The Importance of Snow Sublimation on a Himalayan Glacier, Front. Earth Sci., 6, 108, https://doi.org/10.3389/feart.2018.00108, 2018.
Tu, X., Singh, V. P., Chen, X., Chen, L., Zhang, Q., and Zhao, Y.: Intra-annual Distribution of Streamflow and Individual Impacts of Climate Change and Human Activities in the Dongijang River Basin, China, Water Resour. Manag., 29, 2677–2695, https://doi.org/10.1007/s11269-015-0963-5, 2015.
USGS EROS: Digital Elevation – Global 30 Arc-Second Elevation (GTOPO30), https://doi.org/10.5066/F7DF6PQS, 2018.
Wang, D. and Tang, Y.: A one-parameter Budyko model for water balance captures emergent behavior in darwinian hydrologic models, Geophys. Res. Lett., 41, 4569–4577, https://doi.org/10.1002/2014GL060509, 2014.
Wang, Z., Wu, R., Chen, S., Huang, G., Liu, G., and Zhu, L.: Influence of Western Tibetan Plateau Summer Snow Cover on East Asian Summer Rainfall, J. Geophys. Res.-Atmos., 123, 2371–2386, https://doi.org/10.1002/2017JD028016, 2018.
Widen-Nilsson, E., Halldin, S., and Xu, C.: Global water-balance modelling with WASMOD-M: Parameter estimation and regionalisation, J. Hydrol., 340, 105–118, https://doi.org/10.1016/j.jhydrol.2007.04.002, 2007.
Wu, N: Budyko code and data, Zenodo [code and data set], https://doi.org/10.5281/zenodo.17357390, 2025.
Wu, J. and Gao, X.: CN05.1 dataset, https://ccrc.iap.ac.cn/resource/detail?id=228 (last access: 24 October 2025), 2021
Wu, C., Yeh, P. J.-F., Hu, B. X., and Huang, G.: Controlling factors of errors in the predicted annual and monthly evaporation from the Budyko framework, Adv. Water Resour., 121, 432–445, https://doi.org/10.1016/j.advwatres.2018.09.013, 2018.
Wu, C., Yeh, P. J.-F., Wu, H., Hu, B. X., and Huang, G.: Global Analysis of the Role of Terrestrial Water Storage in the Evapotranspiration Estimated from the Budyko Framework at Annual to Monthly Time Scales, J. Hydrometeorol., 20, 2003–2021, https://doi.org/10.1175/JHM-D-19-0065.1, 2019.
Wu, N., Zhang, K., Chao, L., Ning, Z., Wang, S., and Jarsjö, J.: Snow cover expansion with contrasting depth thinning in the recent 40 years: Evidence from the Yalong River Basin, South-eastern Tibetan Plateau, J. Hydrol.-Reg. Stud., 53, 101786, https://doi.org/10.1016/j.ejrh.2024.101786, 2024.
Wu S. and Shen M.: The key technical issue and its research advance in Yalong River hydropower development, J. Hydrau. Eng., 15–19, 2007.
Wu, Z., Li, J., Jiang, Z., and Ma, T.: Modulation of the Tibetan Plateau Snow Cover on the ENSO Teleconnections: From the East Asian Summer Monsoon Perspective, J. Climate, 25, 2481–2489, https://doi.org/10.1175/JCLI-D-11-00135.1, 2012.
Xin, J., Sun, X., Liu, L., Li, H., Liu, X., Li, X., Cheng, L., and Xu, Z.: Quantifying the contribution of climate and underlying surface changes to alpine runoff alterations associated with glacier melting, Hydrol. Process., 35, e14069, https://doi.org/10.1002/hyp.14069, 2021.
Xin, Z., Li, Y., Zhang, L., Ding, W., Ye, L., Wu, J., and Zhang, C.: Quantifying the relative contribution of climate and human impacts on seasonal streamflow, J. Hydrol., 574, 936–945, https://doi.org/10.1016/j.jhydrol.2019.04.095, 2019.
Xu, X., Yang, D., and Sivapalan, M.: Assessing the impact of climate variability on catchment water balance and vegetation cover, Hydrol. Earth Syst. Sci., 16, 43–58, https://doi.org/10.5194/hess-16-43-2012, 2012.
Xu, X., Yang, D., Yang, H., and Lei, H.: Attribution analysis based on the Budyko hypothesis for detecting the dominant cause of runoff decline in Haihe basin, J. Hydrol., 510, 530–540, https://doi.org/10.1016/j.jhydrol.2013.12.052, 2014.
Yang, H., Yang, D., Lei, Z., and Sun, F.: New analytical derivation of the mean annual water-energy balance equation, Water Resour. Res., 44, W03410, https://doi.org/10.1029/2007WR006135, 2008.
Yang, T., Wang, C., Chen, Y., Chen, X., and Yu, Z.: Climate change and water storage variability over an arid endorheic region, J. Hydrol., 529, 330–339, https://doi.org/10.1016/j.jhydrol.2015.07.051, 2015.
Yang, Z. and Bai, P.: Evaporation from snow surface: A multi-model evaluation with the FLUXNET2015 dataset, J. Hydrol., 621, 129587, https://doi.org/10.1016/j.jhydrol.2023.129587, 2023.
Yao, L., Libera, D. A., Kheimi, M., Sankarasubramanian, A., and Wang, D.: The Roles of Climate Forcing and Its Variability on Streamflow at Daily, Monthly, Annual, and Long-Term Scales, Water Resour. Res., 56, https://doi.org/10.1029/2020WR027111, 2020.
Ye, S., Li, H.-Y., Li, S., Leung, L. R., Demissie, Y., Ran, Q., and Bloeschl, G.: Vegetation regulation on streamflow intra-annual variability through adaption to climate variations, Geophys. Res. Lett., 42, 10307–10315, https://doi.org/10.1002/2015GL066396, 2015.
Zeng, R. and Cai, X.: Assessing the temporal variance of evapotranspiration considering climate and catchment storage factors, Adv. Water Resour., 79, 51–60, https://doi.org/10.1016/j.advwatres.2015.02.008, 2015.
Zhang, D., Cong, Z., Ni, G., Yang, D., and Hu, S.: Effects of snow ratio on annual runoff within the Budyko framework, Hydrol. Earth Syst. Sci., 19, 1977–1992, https://doi.org/10.5194/hess-19-1977-2015, 2015a.
Zhang, L., Hickel, K., Dawes, W. R., Chiew, F. H. S., Western, A. W., and Briggs, P. R.: A rational function approach for estimating mean annual evapotranspiration, Water Resour. Res., 40, W02502, https://doi.org/10.1029/2003WR002710, 2004.
Zhang, L., Potter, N. J., and Zhang, Y.: Water balance modeling over variable time scales based on the Budyko framework – Model development and testing (vol 360, pg 117, 2008), J. Hydrol., 390, 121–122, https://doi.org/10.1016/j.jhydrol.2010.05.027, 2010.
Zhang, L., Su, F., Yang, D., Hao, Z., and Tong, K.: Discharge regime and simulation for the upstream of major rivers over Tibetan Plateau, J. Geophys. Res.-Atmos., 118, 8500–8518, https://doi.org/10.1002/jgrd.50665, 2013.
Zhang, X., Dong, Q., Zhang, Q., and Yu, Y.: A unified framework of water balance models for monthly, annual, and mean annual timescales, J. Hydrol., 589, 125186, https://doi.org/10.1016/j.jhydrol.2020.125186, 2020.
Zhang, Y., Liu, S., and Ding, Y.: Spatial variation of degree-day factors on the observed glaciers in western China, Acta Geographica Sinica, 61, 89–98, https://doi.org/10.11821/xb200601009, 2006.
Zhang, Y., Enomoto, H., Ohata, T., Kitabata, H., Kadota, T., and Hirabayashi, Y.: Glacier mass balance and its potential impacts in the Altai Mountains over the period 1990-2011, J. Hydrol., 553, 662–677, https://doi.org/10.1016/j.jhydrol.2017.08.026, 2017.
Zhang, Y., Liu, S., and Wang, X.: A dataset of spatial distribution of degree-day factors for glaciers in High Mountain Asia, https://doi.org/10.11922/sciencedb.747, 2019.
Short summary
This study explores how snow dynamics and hydropower reservoirs shape monthly runoff in the Yalong River basin, China. Using 15 years of data and an extended Budyko framework, we found that snow accumulation and melt dominate runoff in high-altitude areas, while reservoirs increasingly influence lower elevations. These factors reduce runoff seasonality at the basin outlet, emphasizing how climate change and human activity alter water availability in cold, mountainous regions.
This study explores how snow dynamics and hydropower reservoirs shape monthly runoff in the...
