Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5755-2025
© 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-29-5755-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Reducing hydrological uncertainty in large mountainous basins: the role of isotope, snow cover, and glacier dynamics in capturing streamflow seasonality
Diego Avesani
Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38123 Trento, Italy
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Fuqiang Tian
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
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Hydrol. Earth Syst. Sci., 29, 2633–2654, https://doi.org/10.5194/hess-29-2633-2025, https://doi.org/10.5194/hess-29-2633-2025, 2025
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Hydrol. Earth Syst. Sci., 29, 2275–2291, https://doi.org/10.5194/hess-29-2275-2025, https://doi.org/10.5194/hess-29-2275-2025, 2025
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Hydrol. Earth Syst. Sci., 29, 1919–1937, https://doi.org/10.5194/hess-29-1919-2025, https://doi.org/10.5194/hess-29-1919-2025, 2025
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Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
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Bruno Majone, Diego Avesani, Patrick Zulian, Aldo Fiori, and Alberto Bellin
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Liying Guo, Jing Wei, Keer Zhang, Jiale Wang, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 26, 1165–1185, https://doi.org/10.5194/hess-26-1165-2022, https://doi.org/10.5194/hess-26-1165-2022, 2022
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Data support is crucial for the research of conflict and cooperation on transboundary rivers. Conventional, manual constructions of datasets cannot meet the requirements for fast updates in the big data era. This study brings up a revised methodological framework, based on the conventional method, and a toolkit for the news media dataset tracking of conflict and cooperation dynamics on transboundary rivers. A dataset with good tradeoffs between data relevance and coverage is generated.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 25, 6151–6172, https://doi.org/10.5194/hess-25-6151-2021, https://doi.org/10.5194/hess-25-6151-2021, 2021
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Hydrological modeling has large problems of uncertainty in cold regions. Tracer-aided hydrological models are increasingly used to reduce uncertainty and refine the parameterizations of hydrological processes, with limited application in large basins due to the unavailability of spatially distributed precipitation isotopes. This study explored the utility of isotopic general circulation models in driving a tracer-aided hydrological model in a large basin on the Tibetan Plateau.
Kunbiao Li, Fuqiang Tian, Mohd Yawar Ali Khan, Ran Xu, Zhihua He, Long Yang, Hui Lu, and Yingzhao Ma
Earth Syst. Sci. Data, 13, 5455–5467, https://doi.org/10.5194/essd-13-5455-2021, https://doi.org/10.5194/essd-13-5455-2021, 2021
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Due to complex climate and topography, there is still a lack of a high-quality rainfall dataset for hydrological modeling over the Tibetan Plateau. This study aims to establish a high-accuracy daily rainfall product over the southern Tibetan Plateau through merging satellite rainfall estimates based on a high-density rainfall gauge network. Statistical and hydrological evaluation indicated that the new dataset outperforms the raw satellite estimates and several other products of similar types.
Yi Nan, Lide Tian, Zhihua He, Fuqiang Tian, and Lili Shao
Hydrol. Earth Syst. Sci., 25, 3653–3673, https://doi.org/10.5194/hess-25-3653-2021, https://doi.org/10.5194/hess-25-3653-2021, 2021
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This study integrated a water isotope module into the hydrological model THREW. The isotope-aided model was subsequently applied for process understanding in the glacierized watershed of Karuxung river on the Tibetan Plateau. The model was used to quantify the contribution of runoff component and estimate the water travel time in the catchment. Model uncertainties were significantly constrained by using additional isotopic data, improving the process understanding in the catchment.
You Lu, Fuqiang Tian, Liying Guo, Iolanda Borzì, Rupesh Patil, Jing Wei, Dengfeng Liu, Yongping Wei, David J. Yu, and Murugesu Sivapalan
Hydrol. Earth Syst. Sci., 25, 1883–1903, https://doi.org/10.5194/hess-25-1883-2021, https://doi.org/10.5194/hess-25-1883-2021, 2021
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The upstream countries in the transboundary Lancang–Mekong basin build dams for hydropower, while downstream ones gain irrigation and fishery benefits. Dam operation changes the seasonality of runoff downstream, resulting in their concerns. Upstream countries may cooperate and change their regulations of dams to gain indirect political benefits. The socio-hydrological model couples hydrology, reservoir, economy, and cooperation and reproduces the phenomena, providing a useful model framework.
Jing Wei, Yongping Wei, Fuqiang Tian, Natalie Nott, Claire de Wit, Liying Guo, and You Lu
Hydrol. Earth Syst. Sci., 25, 1603–1615, https://doi.org/10.5194/hess-25-1603-2021, https://doi.org/10.5194/hess-25-1603-2021, 2021
Liming Wang, Songjun Han, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 25, 375–386, https://doi.org/10.5194/hess-25-375-2021, https://doi.org/10.5194/hess-25-375-2021, 2021
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It remains unclear at which timescale the complementary principle performs best in estimating evaporation. In this study, evaporation estimation was assessed over 88 eddy covariance monitoring sites at multiple timescales. The results indicate that the generalized complementary functions perform best in estimating evaporation at the monthly scale. This study provides a reference for choosing a suitable time step for evaporation estimations in relevant studies.
Cited articles
Andraos, C.: Breaking Uncertainty Barriers: Approximate Bayesian Computation Advances in Rainfall–Runoff Modeling, Water, 16, https://doi.org/10.3390/w16233499, 2024. a
Araya, D., Mendoza, P. A., Muñoz-Castro, E., and McPhee, J.: Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling, Hydrol. Earth Syst. Sci., 27, 4385–4408, https://doi.org/10.5194/hess-27-4385-2023, 2023. a
Asong, Z. E., Elshamy, M. E., Princz, D., Wheater, H. S., Pomeroy, J. W., Pietroniro, A., and Cannon, A.: High-resolution meteorological forcing data for hydrological modelling and climate change impact analysis in the Mackenzie River Basin, Earth Syst. Sci. Data, 12, 629–645, https://doi.org/10.5194/essd-12-629-2020, 2020. a
Betterle, A. and Bellin, A.: Morphological and Hydrogeological Controls of Groundwater Flows and Water Age Distribution in Mountain Aquifers and Streams, Water Resources Research, 60, e2024WR037407, https://doi.org/10.1029/2024WR037407, 2024. a
Beven, K.: A manifesto for the equifinality thesis, Journal of Hydrology, 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006. a, b, c
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, Journal of Hydrology, 249, 11–29, https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. a
Beven, K. J. and Binley, A. M.: The future of distributed models: Model calibration and uncertainty prediction, Hydrological Processes, 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992. a
Birkel, C., Tetzlaff, D., and Dunn, S.: Towards a simple dynamic process conceptualization in rainfall-runoff models using multi-criteria calibration and tracers in temperate, upland catchments, Hydrological Processes, 24, 260–275, https://doi.org/10.1002/hyp.7478, 2009. a
Birkel, C., Soulsby, C., Tetzlaff, D., and Malcolm, I. A.: Modelling catchment-scale water storage dynamics: Reconciling dynamic storage with tracer-inferred passive storage, Hydrological Processes, 25, 3924–3936, https://doi.org/10.1002/hyp.8201, 2011. a
Birkel, C., Soulsby, C., Tetzlaff, D., and Dunn, S. M.: Developing a tracer-aided rainfall-runoff model to simulate hydrological processes in mesoscale catchments, Water Resources Research, 50, 944–961, https://doi.org/10.1002/2013WR014925, 2014. a
Birkel, C., Soulsby, C., and Tetzlaff, D.: Integrating parsimonious models of hydrological connectivity and soil biogeochemistry to simulate stream DOC dynamics, Water Resources Research, 51, 2541–2557, https://doi.org/10.1002/2013JG002551, 2015. a
Blasone, R.-S., Vrugt, J. A., Madsen, H., Rosbjerg, D., Robinson, B. A., and Zyvoloski, G. A.: Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling, Advances in Water Resources, 31, 630–648, https://doi.org/10.1016/j.advwatres.2007.12.003, 2008. a
Boral, S. and Sen, I. S.: Tracing “Third Pole” ice meltwater contribution to the Himalayan rivers using oxygen and hydrogen isotopes, Geochemical Perspectives Letters, 48–53, https://doi.org/10.7185/geochemlet.2013, 2020. a, b
Borriero, A., Kumar, R., Nguyen, T. V., Fleckenstein, J. H., and Lutz, S. R.: Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation, Hydrol. Earth Syst. Sci., 27, 2989–3004, https://doi.org/10.5194/hess-27-2989-2023, 2023. a
Botto, A., Belluco, E., and Camporese, M.: Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope, Hydrol. Earth Syst. Sci., 22, 4251–4266, https://doi.org/10.5194/hess-22-4251-2018, 2018. a
Brazier, R. E., Beven, K. J., Freer, J., and Rowan, J. S.: Equifinality and uncertainty in physically based soil erosion models: application of the GLUE methodology to WEPP – the Water Erosion Prediction Project – for sites in the UK and USA, Earth Surface Processes and Landforms, 25, 825–845, https://doi.org/10.1002/1096-9837(200008)25:8<825::AID-ESP101>3.0.CO;2-3, 2000. a
Chang, Z., Gao, H., Yong, L., Wang, K., Chen, R., Han, C., Demberel, O., Dorjsuren, B., Hou, S., and Duan, Z.: Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China, Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, 2024. a
Chen, J., Kuang, X., Fan, L., and Zheng, C.: Interbasin groundwater flows in the Yarlung Zangbo River Basin and its surrounding areas: A perspective, Science China-Technological Sciences, 68, https://doi.org/10.1007/s11431-024-2845-0, 2025. a
Chen, X., Long, D., Hong, Y., Zeng, C., and Yan, D.: Improved modeling of snow and glacier melting by a progressive two-stage calibration strategy with GRACE and multisource data: How snow and glacier meltwater contributes to the runoff of the Upper Brahmaputra River basin?, Water Resources Research, 53, 2431–2466, https://doi.org/10.1002/2016wr019656, 2017. a
Chen, X., Long, D., Liang, S., He, L., Zeng, C., Hao, X., and Hong, Y.: Developing a composite daily snow cover extent record over the Tibetan Plateau from 1981 to 2016 using multisource data, Remote Sensing of Environment, 215, 284–299, https://doi.org/10.1016/j.rse.2018.06.021, 2018. a
Chowdhury, S. and Sharma, A.: Mitigating parameter bias in hydrological modelling due to uncertainty in covariates, Journal of Hydrology, 340, 197–204, https://doi.org/10.1016/j.jhydrol.2007.04.010, 2007. a
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, Nature Communications, 14, https://doi.org/10.1038/s41467-023-36804-6, 2023. a
Dalla Torre, D., Di Marco, N., Menapace, A., Avesani, D., Righetti, M., and Majone, B.: Suitability of ERA5-Land reanalysis dataset for hydrological modelling in the Alpine region, Journal of Hydrology: Regional Studies, 52, 101718, https://doi.org/10.1016/j.ejrh.2024.101718, 2024. a
Demirel, M. C., Koch, J., Rakovec, O., Kumar, R., Mai, J., Müller, S., Thober, S., Samaniego, L., and Stisen, S.: Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins, Water Resources Research, 60, e2022WR034193, https://doi.org/10.1029/2022WR034193, 2024. a
Di Marco, N., Righetti, M., Avesani, D., Zaramella, M., Notarnicola, C., and Borga, M.: Comparison of MODIS and Model-Derived Snow-Covered Areas: Impact of Land Use and Solar Illumination Conditions, Geosciences, 10, https://doi.org/10.3390/geosciences10040134, 2020. a
Di Marco, N., Avesani, D., Righetti, M., Zaramella, M., Majone, B., and Borga, M.: Reducing hydrological modelling uncertainty by using MODIS snow cover data and a topography-based distribution function snowmelt model, Journal of Hydrology, 599, 126020, https://doi.org/10.1016/j.jhydrol.2021.126020, 2021. a, b, c, d, e, f
Didan, K.: MOD13A3 MODIS/Terra vegetation Indices Monthly L3 Global 1 km SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD13A3.006, 2015. a, b
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S.: Remote sensing of snow – a review of available methods, International Journal of Remote Sensing, 33, 4094–4134, https://doi.org/10.1080/01431161.2011.640964, 2012. a
Efstratiadis, A. and Koutsoyiannis, D.: One decade of multi-objective calibration approaches in hydrological modelling: a review, Hydrological Sciences Journal, 55, 58–78, https://doi.org/10.1080/02626660903526292, 2010. a, b
Fassnacht, S. R., Sexstone, G. A., Kashipazha, A. H., Ignacio Lopez-Moreno, J., Jasinski, M. F., Kampf, S. K., and Von Thaden, B. C.: Deriving snow-cover depletion curves for different spatial scales from remote sensing and snow telemetry data, Hydrological Processes, 30, 1708–1717, https://doi.org/10.1002/hyp.10730, 2016. a
Fenicia, F., Kavetski, D., Reichert, P., and Albert, C.: Signature-Domain Calibration of Hydrological Models Using Approximate Bayesian Computation: Empirical Analysis of Fundamental Properties, Water Resources Research, 54, 3958–3987, https://doi.org/10.1002/2017WR021616, 2018. a
Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., and Burlando, P.: The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model, Water Resources Research, https://doi.org/10.1029/2010WR009824, 2011. a, b
Franks, S. W., Gineste, P., Beven, K. J., and Merot, P.: On constraining the predictions of a distributed model: The incorporation of fuzzy estimates of saturated areas into the calibration process, Water Resources Research, 34, 787–797, https://doi.org/10.1029/97WR03041, 1998. a
Gan, Y., Liang, X.-Z., Duan, Q., Ye, A., Di, Z., Hong, Y., and Li, J.: A systematic assessment and reduction of parametric uncertainties for a distributed hydrological model, Journal of Hydrology, 564, 697–711, https://doi.org/10.1016/j.jhydrol.2018.07.055, 2018. a
Geospatial Data Cloud Site: ASTER GDEM 30M, Geospatial Data Cloud Site [data set], http://www.gscloud.cn/sources/details/310?pid=302 (last access: 1 January 2019), 2019. a
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts, calibration and sharpness, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69, 243–268, https://doi.org/10.1111/j.1467-9868.2007.00587.x, 2007. a
Grinsted, A.: An estimate of global glacier volume, The Cryosphere, 7, 141–151, https://doi.org/10.5194/tc-7-141-2013, 2013. a
Gupta, H., Wagener, T., and Liu, Y.: Reconciling Theory with Observations: Elements of a Diagnostic Approach to Model Evaluation, Hydrological Processes, 22, 3802–3813, https://doi.org/10.1002/hyp.6989, 2008. a
Hao, X., Huang, G., Zheng, Z., Sun, X., Ji, W., Zhao, H., Wang, J., Li, H., and Wang, X.: Development and validation of a new MODIS snow-cover-extent product over China, Hydrol. Earth Syst. Sci., 26, 1937–1952, https://doi.org/10.5194/hess-26-1937-2022, 2022. a
He, Z., Vorogushyn, S., Unger-Shayesteh, K., Gafurov, A., Kalashnikova, O., Omorova, E., and Merz, B.: The Value of Hydrograph Partitioning Curves for Calibrating Hydrological Models in Glacierized Basins, Water Resources Research, 54, 2336–2361, https://doi.org/10.1002/2017WR021966, 2018. a
He, Z., Unger-Shayesteh, K., Vorogushyn, S., Weise, S. M., Kalashnikova, O., Gafurov, A., Duethmann, D., Barandun, M., and Merz, B.: Constraining hydrological model parameters using water isotopic compositions in a glacierized basin, Central Asia, Journal of Hydrology, 571, 332–348, https://doi.org/10.1016/j.jhydrol.2019.01.048, 2019. a
He, Z., Duethmann, D., and Tian, F.: A meta-analysis based review of quantifying the contributions of runoff components to streamflow in glacierized basins, Journal of Hydrology, 603, 126890, https://doi.org/10.1016/j.jhydrol.2021.126890, 2021. a
Hindshaw, R. S., Tipper, E. T., Reynolds, B. C., Lemarchand, E., Wiederhold, J. G., Magnusson, J., Bernasconi, S. M., Kretzschmar, R., and Bourdon, B.: Hydrological control of stream water chemistry in a glacial catchment (Damma Glacier, Switzerland), Chemical Geology, 285, 215–230, https://doi.org/10.1016/j.chemgeo.2011.04.012, 2011. a
Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kaab, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021. a
Huss, M. and Hock, R.: A new model for global glacier change and sea-level rise, Frontiers in Earth Science, 3, https://doi.org/10.3389/feart.2015.00054, 2015. a
Jasechko, S.: Global Isotope Hydrogeology–Review, Reviews of Geophysics, 57, 835–965, https://doi.org/10.1029/2018RG000627, 2019. a
Jasechko, S. and Taylor, R.: Intensive rainfall recharges tropical groundwaters, Environmental Research Letters, 10, 124015, https://doi.org/10.1088/1748-9326/10/12/124015, 2015. a
Jiang, Y., Yang, K., Yang, H., Lu, H., Chen, Y., Zhou, X., Sun, J., Yang, Y., and Wang, Y.: Characterizing basin-scale precipitation gradients in the Third Pole region using a high-resolution atmospheric simulation-based dataset, Hydrol. Earth Syst. Sci., 26, 4587–4601, https://doi.org/10.5194/hess-26-4587-2022, 2022. a
Jin, X., Xu, C. Y., Zhang, Q., and Singh, V.: Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model, Journal of Hydrology, 383, 147–155, https://doi.org/10.1016/j.jhydrol.2009.12.028, 2010. a, b
Khakbaz, B., Imam, B., Hsu, K., and Sorooshian, S.: From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models, Journal of Hydrology, 418–419, 61–77, https://doi.org/10.1016/j.jhydrol.2009.02.021, 2012. a
Khanal, S., Lutz, A. F., Kraaijenbrink, P. D. A., van den Hurk, B., Yao, T., and Immerzeel, W. W.: Variable 21st Century Climate Change Response for Rivers in High Mountain Asia at Seasonal to Decadal Time Scales, Water Resources Research, 57, https://doi.org/10.1029/2020wr029266, 2021. a
Lamontagne, J. and Barber, C.: Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data, Water Resources Research, 56, https://doi.org/10.1029/2020WR027101, 2020. a
Lin, J., Bryan, B. A., Zhou, X., Lin, P., Do, H. X., Gao, L., Gu, X., Liu, Z., Wan, L., Tong, S., Huang, J., Wang, Q., Zhang, Y., Gao, H., Yin, J., Chen, Z., Duan, W., Xie, Z., Cui, T., Liu, J., Li, M., Li, X., Xu, Z., Guo, F., Shu, L., Li, B., Zhang, J., Zhang, P., Fan, B., Wang, Y., Zhang, Y., Huang, J., Li, X., Cai, Y., and Yang, Z.: Making China's water data accessible, usable and shareable, Nature Water, 1, 328–335, https://doi.org/10.1038/s44221-023-00039-y, 2023. a
Liu, S.: The second glacier inventory dataset of China (version 1.0) (2006–2011), National Tibetan Plateau Data Center [data set], https://doi.org/10.3972/glacier.001.2013.db, 2012. a, b
Lutz, A. F., Immerzeel, W. W., Shrestha, A. B., and Bierkens, M. F. P.: Consistent increase in High Asia's runoff due to increasing glacier melt and precipitation, Nature Climate Change, 4, 587–592, https://doi.org/10.1038/nclimate2237, 2014. a
Lutz, A. F., Immerzeel, W. W., Kraaijenbrink, P. D. A., Shrestha, A. B., and Bierkens, M. F. P.: Climate Change Impacts on the Upper Indus Hydrology: Sources, Shifts and Extremes, Plos One, 11, https://doi.org/10.1371/journal.pone.0165630, 2016. a
Ma, J., Li, R., Zheng, H., Li, W., Rao, K., Yang, Y., and Wu, B.: Multivariate adaptive regression splines-assisted approximate Bayesian computation for calibration of complex hydrological models, Journal of Hydroinformatics, 26, 503–518, https://doi.org/10.2166/hydro.2024.232, 2024. a
Majone, B., Avesani, D., Zulian, P., Fiori, A., and Bellin, A.: Analysis of high streamflow extremes in climate change studies: how do we calibrate hydrological models?, Hydrol. Earth Syst. Sci., 26, 3863–3883, https://doi.org/10.5194/hess-26-3863-2022, 2022. a
McGuire, K. J. and McDonnell, J. J.: A review and evaluation of catchment transit time modeling, Journal of Hydrology, 330, 543–563, 2006. a
McKay, M. D., Beckman, R. J., and Conover, W. J.: A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code, Technometrics, 21, 239–245, 1979. a
Mohammadi, B., Gao, H., Feng, Z., Pilesjö, P., Cheraghalizadeh, M., and Duan, Z.: Simulating Glacier Mass Balance and its Contribution to Runoff in Northern Sweden, Journal of Hydrology, 620, https://doi.org/10.1016/j.jhydrol.2023.129404, 2023. a
Molotch, N. P. and Margulis, S. A.: Estimating the distribution of snow water equivalent using remotely sensed snow cover data and a spatially distributed snowmelt model: A multi-resolution, multi-sensor comparison, Advances in Water Resources, 31, 1503–1514, https://doi.org/10.1016/j.advwatres.2008.07.017, 2008. a
Moriasi, D., Arnold, J., Van Liew, M., Bingner, R., Harmel, R., and Veith, T.: Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations, Transactions of the ASABE, 50, https://doi.org/10.13031/2013.23153, 2007. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
Myneni, R., Knyazikhin, Y., and Park, T.: MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid V006 NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD15A2H.006, 2015. a, b
Nan, Y. and Tian, F.: Isotope data-constrained hydrological model improves soil moisture simulation and runoff source apportionment, Journal of Hydrology, 633, 131006, https://doi.org/10.1016/j.jhydrol.2024.131006, 2024. a, b
Nan, Y., He, Z., Tian, F., Wei, Z., and Tian, L.: Can we use precipitation isotope outputs of isotopic general circulation models to improve hydrological modeling in large mountainous catchments on the Tibetan Plateau?, Hydrol. Earth Syst. Sci., 25, 6151–6172, https://doi.org/10.5194/hess-25-6151-2021, 2021a. a
Nan, Y., He, Z., Tian, F., Wei, Z., and Tian, L.: Assessing the influence of water sampling strategy on the performance of tracer-aided hydrological modeling in a mountainous basin on the Tibetan Plateau, Hydrol. Earth Syst. Sci., 26, 4147–4167, https://doi.org/10.5194/hess-26-4147-2022, 2022. a
Nan, Y., Tian, F., Li, Z., and Gui, J.: Longer simulation time step of the tracer-aided hydrological model estimates lower contribution of slow runoff components, Journal of Hydrology, 129889–129889, https://doi.org/10.1016/j.jhydrol.2023.129889, 2023. a
Nan, Y., Tian, F., and Li, Z.: Model Diagnostic Analysis in a Cold Basin Influenced by Frozen Soils With the Aid of Stable Isotope, Water Resources Research, 60, https://doi.org/10.1029/2024WR037218, 2024. a
Nan, Y. and Avesani, D.: Model output for “Reducing Hydrological Uncertainty in Large Mountainous Basins: The Role of Isotope, Snow Cover, and Glacier Dynamics in Capturing Streamflow Seasonality”, Zenodo [data set], https://doi.org/10.5281/zenodo.15605202, 2025a. a
Nan, Y. and Avesani, D.: Dataset for “Reducing Hydrological Uncertainty in Large Mountainous Basins: The Role of Isotope, Snow Cover, and Glacier Dynamics in Capturing Streamflow Seasonality”, Zenodo [data set], https://doi.org/10.5281/zenodo.16634369, 2025b. a
Nan, Y., Tian, F., McDonnell, J., Ni, G., Tian, L., Li, Z., Yan, D., Xia, X., Wang, T., Han, S., and Li, K.: Glacier meltwater has limited contributions to the total runoff in the major rivers draining the Tibetan Plateau, Npj Climate and Atmospheric Science, 8, https://doi.org/10.1038/s41612-025-01060-6, 2025. a
Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models part I – A discussion of principles, Journal of Hydrology, 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
O'Neel, S., Hood, E., Arendt, A., and Sass, L.: Assessing streamflow sensitivity to variations in glacier mass balance, Climatic Change, 123, 329–341, https://doi.org/10.1007/s10584-013-1042-7, 2014. a
Panchanathan, A., Ahrari, A., Ghag, K. S., Mustafa, S., Haghighi, A. T., Kløve, B., and Oussalah, M.: An overview of approaches for reducing uncertainties in hydrological forecasting: Progress and challenges, Earth-Science Reviews, 258, 104956, https://doi.org/10.1016/j.earscirev.2024.104956, 2024. a, b
Raleigh, M. S., Lundquist, J. D., and Clark, M. P.: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework, Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015. a
Reggiani, P., Hassanizadeh, S., Sivapalan, M., and Gray, W. G.: A unifying framework for watershed thermodynamics: constitutive relationships, Advances in Water Resources, 23, 15–39, https://doi.org/10.1016/S0309-1708(99)00005-6, 1999. a
Rodgers, P., Soulsby, C., Waldron, S., and Tetzlaff, D.: Using stable isotope tracers to assess hydrological flow paths, residence times and landscape influences in a nested mesoscale catchment, Hydrol. Earth Syst. Sci., 9, 139–155, https://doi.org/10.5194/hess-9-139-2005, 2005. a, b
Ruelland, D.: Potential of snow data to improve the consistency and robustness of a semi-distributed hydrological model using the SAFRAN input dataset, Journal of Hydrology, 631, 130820, https://doi.org/10.1016/j.jhydrol.2024.130820, 2024. a
Shuai, P., Chen, X., Mital, U., Coon, E. T., and Dwivedi, D.: The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses, Hydrol. Earth Syst. Sci., 26, 2245–2276, https://doi.org/10.5194/hess-26-2245-2022, 2022. a
Stahl, K., Moore, R. D., Shea, J. M., Hutchinson, D., and Cannon, A. J.: Coupled modelling of glacier and streamflow response to future climate scenarios, Water Resources Research, 44, https://doi.org/10.1029/2007WR005956, 2008. a, b
Sun, H., Yao, T., Su, F., Yang, W., and Chen, D.: Spatiotemporal responses of runoff to climate change in the southern Tibetan Plateau, Hydrol. Earth Syst. Sci., 28, 4361–4381, https://doi.org/10.5194/hess-28-4361-2024, 2024. a
Tetzlaff, D., Birkel, C., Dick, J., and Geris, J.: Storage dynamics in hydropedological units control hillslope connectivity, runoff generation, and the evolution of catchment transit time distributions, Water Resources Research, 50, 969–985, https://doi.org/10.1002/2013WR014147, 2014. a
Teweldebrhan, A. T., Burkhart, J. F., and Schuler, T. V.: Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches, Hydrol. Earth Syst. Sci., 22, 5021–5039, https://doi.org/10.5194/hess-22-5021-2018, 2018. a, b, c
Tian, F., Hu, H., Lei, Z., and Sivapalan, M.: Extension of the Representative Elementary Watershed approach for cold regions via explicit treatment of energy related processes, Hydrol. Earth Syst. Sci., 10, 619–644, https://doi.org/10.5194/hess-10-619-2006, 2006. a, b
Tong, R., Parajka, J., Salentinig, A., Pfeil, I., Komma, J., Széles, B., Kubáň, M., Valent, P., Vreugdenhil, M., Wagner, W., and Blöschl, G.: The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model, Hydrol. Earth Syst. Sci., 25, 1389–1410, https://doi.org/10.5194/hess-25-1389-2021, 2021. a, b
Tong, R., Parajka, J., Széles, B., Greimeister-Pfeil, I., Vreugdenhil, M., Komma, J., Valent, P., and Blöschl, G.: The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites, Hydrol. Earth Syst. Sci., 26, 1779–1799, https://doi.org/10.5194/hess-26-1779-2022, 2022. a
Vrugt, J. A. and Sadegh, M.: Toward diagnostic model calibration and evaluation: Approximate Bayesian computation, Water Resources Research, 49, 4335–4345, https://doi.org/10.1002/wrcr.20354, 2013. a
Wu, Y., Sun, J., Blanchette, M., Rousseau, A. N., Xu, Y. J., Hu, B., and Zhang, G.: Wetland mitigation functions on hydrological droughts: From drought characteristics to propagation of meteorological droughts to hydrological droughts, Journal of Hydrology, 617, 128971, https://doi.org/10.1016/j.jhydrol.2022.128971, 2023. a
Xu, M., Yan, M., Kang, J., and Ren, J.: Comparative studies of glacier mass balance and their climatic implications in Svalbard, Northern Scandinavia, and Southern Norway, Environmental Earth Sciences, 67, 1407–1414, https://doi.org/10.1007/s12665-012-1585-3, 2012. a
Xu, R., Tian, F., Yang, L., Hu, H., Lu, H., and Hou, A.: Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high-density rain gauge network, Journal of Geophysical Research: Atmospheres, 122, 910–924, https://doi.org/10.1002/2016JD025418, 2017. a
Yang, J., Reichert, P., and Abbaspour, K. C.: Bayesian uncertainty analysis in distributed hydrologic modeling: A case study in the Thur River basin (Switzerland), Water Resources Research, 43, https://doi.org/10.1029/2006WR005497, 2007. a
Yang, K. and He, J.: China meteorological forcing dataset (1979–2018), National Tibetan Plateau Data Center, https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file, 2019. a, b
Yang, L., Feng, Q., Ning, T., Lu, T., Zhu, M., Yin, X., and Wang, J.: Attributing the streamflow variation by incorporating glacier mass balance and frozen ground into the Budyko framework in alpine rivers, Journal of Hydrology, 628, 130438, https://doi.org/10.1016/j.jhydrol.2023.130438, 2024. a
Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Multi-objective global optimization for hydrologic models, Journal of Hydrology, 204, 83–97, https://doi.org/10.1016/S0022-1694(97)00107-8, 1998. a
Yoshimura, K., Kanamitsu, M., Noone, D., and Oki, T.: Historical isotope simulation using Reanalysis atmospheric data, Journal of Geophysical Research-Atmospheres, 113, https://doi.org/10.1029/2008jd010074, 2008. a
Zhang, M., Nan, Y., and Tian, F.: Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric–hydrological model, Hydrol. Earth Syst. Sci., 29, 1033–1060, https://doi.org/10.5194/hess-29-1033-2025, 2025. a
Zhang, Q., Knowles, J. F., Barnes, R. T., Cowie, R. M., Rock, N., and Williams, M. W.: Surface and subsurface water contributions to streamflow from a mesoscale watershed in complex mountain terrain, Hydrological Processes, 32, 954–967, https://doi.org/10.1002/hyp.11469, 2018. a
Short summary
Our study explores how different data sources (snow cover, glacier mass balance, and water isotopes) can improve hydrological modeling in large mountain basins. Using a Bayesian framework, we show that isotopes are particularly useful for reducing uncertainty in low-flow conditions, while snow and glacier data help during melt seasons. By addressing equifinality, our approach enhances model reliability, improving water management and streamflow predictions in mountainous regions.
Our study explores how different data sources (snow cover, glacier mass balance, and water...