Articles | Volume 28, issue 20
https://doi.org/10.5194/hess-28-4521-2024
© Author(s) 2024. 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-28-4521-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Oct 2024
Research article |
| 16 Oct 2024
| 16 Oct 2024
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Bu Li
State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Institute for Risk and Disaster Reduction, University College London, London, WC1E 6BT, UK
Fuqiang Tian
State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Mahmut Tudaji
State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Li Qin
Gansu Academy for Water Conservancy, Lanzhou 730030, China
Guangheng Ni
CORRESPONDING AUTHOR
State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Mahmut Tudaji, Yi Nan, and Fuqiang Tian
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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We assessed the value of high-resolution data and parameter transferability across temporal scales based on seven catchments in northern China. We found that higher-resolution data do not always improve model performance, questioning the need for such data. Model parameters are transferable across different data resolutions but not across computational time steps. It is recommended to utilize a smaller computational time step when building hydrological models even without high-resolution data.
Zhen Cui and Fuqiang Tian
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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This study investigates stormflow patterns in a forested watershed in north China, highlighting the fact that delayed stormflow is governed by soil water content (SWC) and groundwater level (GWL). When SWC exceeds its storage capacity, excess water infiltrates, recharging groundwater and gradually elevating GWL. Rising GWL enhances subsurface connectivity and lateral flow, synchronizing watershed responses and, in extreme cases, causing a delayed stormflow peak to merge with the direct stormflow peak.
Keer Zhang and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2025-1126, https://doi.org/10.5194/egusphere-2025-1126, 2025
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Spotlighting on Drought-Flood Abrupt Alternation (DFAA) under climate change, this study investigates the mitigating role of reservoirs on DFAA in Lancang-Mekong River Basin. DFAA increase under SSP126 and SSP245, especially upstream Flood-to-Drought (FTD) and downstream Drought-To-Flood (DTF). Reservoirs markedly reduce wet season's FTD and year-round DTF, effectively shorten the monthly span of DFAA. FTD with poorer reservoir control is more challenging than DTF, though DTF is more probable.
Shuo-Jun Mei, Guanwen Chen, Jian Hang, and Ting Sun
EGUsphere, https://doi.org/10.5194/egusphere-2025-1485, https://doi.org/10.5194/egusphere-2025-1485, 2025
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Cities face growing heat challenges due to dense buildings, but predicting surface temperatures is complex because sunlight, airflow, and heat radiation interact. By simulating how sunlight bounces between structures and how heat transfers through materials, we accurately predicted temperatures on roofs, roads, and walls. The model successfully handled intricate city layouts thanks to GPU speed. By revealing which heat matters most, we aim to guide smarter city designs for a warming climate.
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
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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Common intuition holds that higher input data resolution leads to better results. To assess the benefits of high-resolution data, we conduct simulation experiments using data with various temporal resolutions across multiple catchments and find that higher-resolution data do not always improve model performance, challenging the necessity of pursuing such data. In catchments with small areas or significant flow variability, high-resolution data is more valuable.
Diego Avesani, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2025-664, https://doi.org/10.5194/egusphere-2025-664, 2025
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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.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 1033–1060, https://doi.org/10.5194/hess-29-1033-2025, https://doi.org/10.5194/hess-29-1033-2025, 2025
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Owing to differences in the existing published results, we conducted a detailed analysis of the runoff components and future trends in the Yarlung Tsangpo River basin and found that the contributions of snowmelt and glacier melt runoff to streamflow (both ~5 %) are limited and much lower than previous results. The streamflow in this area will continuously increase in the future, but the overestimated contribution of glacier melt could lead to an underestimation of this increasing trend.
Ruidong Li, Jiapei Liu, Ting Sun, Shao Jian, Fuqiang Tian, and Guangheng Ni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3780, https://doi.org/10.5194/egusphere-2024-3780, 2025
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This work presents a new approach to simulate sewer drainage effects for urban flooding with key missing information like flow directions and nodal depths estimated from incomplete information. Tested in Yinchuan, China, our approach exhibits high accuracy in reproducing flood depths and reliably outperforms existing methods in various rainfall scenarios. Our method offers a reliable tool for cities with limited sewer data to improve flood simulation performance.
Khosro Morovati, Keer Zhang, Lidi Shi, Yadu Pokhrel, Maozhou Wu, Paradis Someth, Sarann Ly, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 5133–5147, https://doi.org/10.5194/hess-28-5133-2024, https://doi.org/10.5194/hess-28-5133-2024, 2024
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This study examines large daily river flow fluctuations in the dammed Mekong River, developing integrated 3D hydrodynamic and response time models alongside a hydrological model with an embedded reservoir module. This approach allows estimation of travel times between hydrological stations and contributions of subbasins and upstream regions. Findings show a power correlation between upstream discharge and travel time, and significant fluctuations occurred even before dam construction.
Zhen Cui, Fuqiang Tian, Zilong Zhao, Zitong Xu, Yongjie Duan, Jie Wen, and Mohd Yawar Ali Khan
Hydrol. Earth Syst. Sci., 28, 3613–3632, https://doi.org/10.5194/hess-28-3613-2024, https://doi.org/10.5194/hess-28-3613-2024, 2024
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We investigated the response characteristics and occurrence conditions of bimodal hydrographs using 10 years of hydrometric and isotope data in a semi-humid forested watershed in north China. Our findings indicate that bimodal hydrographs occur when the combined total of the event rainfall and antecedent soil moisture index exceeds 200 mm. Additionally, we determined that delayed stormflow is primarily contributed to by shallow groundwater.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024, https://doi.org/10.5194/hess-28-669-2024, 2024
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Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
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Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
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As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
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We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 26, 4147–4167, https://doi.org/10.5194/hess-26-4147-2022, https://doi.org/10.5194/hess-26-4147-2022, 2022
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Tracer-aided hydrological models are useful tool to reduce uncertainty of hydrological modeling in cold basins, but there is little guidance on the sampling strategy for isotope analysis, which is important for large mountainous basins. This study evaluated the reliance of the tracer-aided modeling performance on the availability of isotope data in the Yarlung Tsangpo river basin, and provides implications for collecting water isotope data for running tracer-aided hydrological models.
Yongping Wei, Jing Wei, Gen Li, Shuanglei Wu, David Yu, Mohammad Ghoreishi, You Lu, Felipe Augusto Arguello Souza, Murugesu Sivapalan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 26, 2131–2146, https://doi.org/10.5194/hess-26-2131-2022, https://doi.org/10.5194/hess-26-2131-2022, 2022
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There is increasing tension among the riparian countries of transboundary rivers. This article proposes a socio-hydrological framework that incorporates the slow and less visible societal processes into existing hydro-economic models, revealing the slow and hidden feedbacks between societal and hydrological processes. This framework will contribute to process-based understanding of the complex mechanism that drives conflict and cooperation in transboundary river management.
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, https://doi.org/10.5194/gmd-15-3041-2022, 2022
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This paper extends the applicability of the SUEWS to extensive pervious areas outside cities. We derived various parameters such as leaf area index, albedo, roughness parameters and surface conductance for non-urban areas. The relation between LAI and albedo is also explored. The methods and parameters discussed can be used for both online and offline simulations. Using appropriate parameters related to non-urban areas is essential for assessing urban–rural differences.
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.
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, https://doi.org/10.5194/gmd-13-4891-2020, 2020
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Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and larger-scale urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, and socio-economic factors and in response to environmental conditions.
Cited articles
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, 2006. a
Bhasme, P., Vagadiya, J., and Bhatia, U.: Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes, J. Hydrol., 615, 128618, https://doi.org/10.1016/j.jhydrol.2022.128618, 2022. a
CDS: Climate Data Store, https://cds.climate.copernicus.eu/datasets (last access: 14 October 2024), 2024. 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, Nat. Commun., 14, 1176, https://doi.org/10.1038/s41467-023-36804-6, 2023. a, b, c, d, e, f, g, h, i
DeBeer, C. M. and Pomeroy, J. W.: Influence of snowpack and melt energy heterogeneity on snow cover depletion and snowmelt runoff simulation in a cold mountain environment, J. Hydrol., 553, 199–213, 2017. a
Didan, K.: MOD13A3 MODIS/Terra vegetation Indices Monthly L3 Global 1 km SIN Grid V006, NASA LP DAAC [data set], https://doi.org/10.5067/MODIS/MOD13A3.006, 2015. a, b
Duan, S. and Ullrich, P.: A comprehensive investigation of machine learning models for estimating daily snow water equivalent over the Western US, Earth and Space Science Open Archive, https://doi.org/10.1002/essoar.10509011.1, 2021. a
Feigl, M., Roesky, B., Herrnegger, M., Schulz, K., and Hayashi, M.: Learning from mistakes-Assessing the performance and uncertainty in process-based models, Hydrol. Process., 36, e14515, https://doi.org/10.1002/hyp.14515, 2022. a
Feng, D., Liu, J., Lawson, K., and Shen, C.: Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy, Water Resourc. Res., 58, e2022WR032404, https://doi.org/10.1029/2022WR032404, 2022. a, b, c
Gao, H., Wang, J., Yang, Y., Pan, X., Ding, Y., and Duan, Z.: Permafrost hydrology of the Qinghai-Tibet Plateau: A review of processes and modeling, Front. Earth Sci., 8, 576838, https://doi.org/10.3389/feart.2020.576838, 2021. 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: 12 May 2022), 2019. a
Grieve, S. W., Mudd, S. M., and Hurst, M. D.: How long is a hillslope?, Earth Surf. Proc. Land., 41, 1039–1054, 2016. a
He, Z. H., Parajka, J., Tian, F. Q., and Blöschl, G.: Estimating degree-day factors from MODIS for snowmelt runoff modeling, Hydrol. Earth Syst. Sci., 18, 4773–4789, https://doi.org/10.5194/hess-18-4773-2014, 2014. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, 2020. a, b
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput., 9, 1735–1780, 1997. a
Höge, M., Scheidegger, A., Baity-Jesi, M., Albert, C., and Fenicia, F.: Improving hydrologic models for predictions and process understanding using neural ODEs, Hydrol. Earth Syst. Sci., 26, 5085–5102, https://doi.org/10.5194/hess-26-5085-2022, 2022. a
Huss, M., Bookhagen, B., Huggel, C., Jacobsen, D., Bradley, R. S., Clague, J. J., Vuille, M., Buytaert, W., Cayan, D. R., Greenwood, G., Mark, B. G., Milner, A. M., Weingartner, R., and Winder, M.: Toward mountains without permanent snow and ice, Earth's Future, 5, 418–435, 2017. a
Jiang, S., Zheng, Y., and Solomatine, D.: Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning, Geophys. Res. Lett., 47, e2020GL088229, https://doi.org/10.1029/2020GL088229, 2020. a
Kashinath, K., Mustafa, M., Albert, A., Wu, J. L., Jiang, C., Esmaeilzadeh, S., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., Singh, A., Manepalli, A., Chirila, D., Yu, R., Walters, R., White, B., Xiao, H., Tchelepi, H. A., Marcus, P., Anandkumar, A., Hassanzadeh, P., and Prabhat: Physics-informed machine learning: case studies for weather and climate modelling, Philos. T. Roy. Soc. A, 379, 20200093, https://doi.org/10.1098/rsta.2020.0093, 2021. a
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. a
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019. a
Kumanlioglu, A. A. and Fistikoglu, O.: Performance Enhancement of a Conceptual Hydrological Model by Integrating Artificial Intelligence, J. Hydrol. Eng., 24, 04019047, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001850, 2019. a
Kuppel, S., Tetzlaff, D., Maneta, M. P., and Soulsby, C.: What can we learn from multi-data calibration of a process-based ecohydrological model?, Environ. Model. Softw., 101, 301–316, 2018. a
Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., and Dadson, S. J.: Benchmarking data-driven rainfal–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models, Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, 2021. a, b
Legates, D. R. and McCabe Jr., G. J.: Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35, 233–241, 1999. a
Levine, S., Finn, C., Darrell, T., and Abbeel, P.: End-to-end training of deep visuomotor policies, J. Mach. Learn. Res., 17, 1334–1373, 2016. a
Li, B.: The code of hybrid hydrological models, Tsinghua University [code], https://cloud.tsinghua.edu.cn/d/1bb19608a7024abfaa3e/ (last access: 6 June 2024), 2024. a
Li, B., Zhou, X., Ni, G., Cao, X., Tian, F., and Sun, T.: A multi-factor integrated method of calculation unit delineation for hydrological modeling in large mountainous basins, J. Hydrol., 597, 126180, https://doi.org/10.1016/j.jhydrol.2021.126180, 2021. a
Li, B., Li, R., Sun, T., Gong, A., Tian, F., Khan, M. Y. A., and Ni, G.: Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: a case study of three mountainous areas on the Tibetan Plateau, J. Hydrol., 620, 129401, https://doi.org/10.1016/j.jhydrol.2023.129401, 2023a. a, b, c, d, e
Liu, Y., Zhang, T., Kang, A., Li, J., and Lei, X.: Research on Runoff Simulations Using Deep-Learning Methods, Sustainability, 13, 1336, https://doi.org/10.3390/su13031336, 2021. a
Lu, D., Konapala, G., Painter, S. L., Kao, S.-C., and Gangrade, S.: Streamflow simulation in data-scarce basins using Bayesian and physics-informed machine learning models, J. Hydrometeorol., 22, 1421–1438, https://doi.org/10.1175/JHM-D-20-0082.1, 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, NASA, https://doi.org/10.5067/MODIS/MYD15A2H.006, 2015. a
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, 2021. a
Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, 1970. a
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., Prieto, C., and Gupta, H. V.: What Role Does Hydrological Science Play in the Age of Machine Learning?, Water Resour. Res., 57, e2020WR028091, https://doi.org/10.1029/2020WR028091, 2021. a
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015. a
Noël, P., Rousseau, A. N., Paniconi, C., and Nadeau, D. F.: Algorithm for delineating and extracting hillslopes and hillslope width functions from gridded elevation data, J. Hydrol. Eng., 19, 366–374, 2014. a
Nourani, V., Khodkar, K., and Gebremichael, M.: Uncertainty assessment of LSTM based groundwater level predictions, Hydrolog. Sci. J., 67, 773–790, 2022. a
Patil, S. D. and Stieglitz, M.: Comparing spatial and temporal transferability of hydrological model parameters, J. Hydrol., 525, 409–417, 2015. a
Quilty, J. M., Sikorska-Senoner, A. E., and Hah, D.: A stochastic conceptual-data-driven approach for improved hydrological simulations, Environ. Model. Softw., 149, 105326, https://doi.org/10.1016/j.envsoft.2022.105326, 2022. a
Shen, C., Appling, A. P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., Baity-Jesi, M., Fenicia, F., Kifer, D., and Li, L.: Differentiable modelling to unify machine learning and physical models for geosciences, Nat. Rev. Earth Environ., 4, 552–567, https://doi.org/10.1038/s43017-023-00450-9, 2023. a, b, c
Solgi, R., Loaiciga, H. A., and Kram, M.: Long short-term memory neural network (LSTM-NN) for aquifer level time series forecasting using in-situ piezometric observations, J. Hydrol., 601, 126800, https://doi.org/10.1016/j.jhydrol.2021.126800, 2021. a
Su, T., Miao, C., Duan, Q., Gou, J., Guo, X., and Zhao, X.: Hydrological response to climate change and human activities in the Three-River Source Region, Hydrol. Earth Syst. Sci., 27, 1477–1492, https://doi.org/10.5194/hess-27-1477-2023, 2023. 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
TPDC: China meteorological forcing dataset (1979–2018), TPDC [data set], https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file, 2024. a
Tsai, W. P., Feng, D., Pan, M., Beck, H., Lawson, K., Yang, Y., Liu, J., and Shen, C.: From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling, Nat. Commun., 12, 5988, https://doi.org/10.1038/s41467-021-26107-z, 2021. a
USGS: MOD15A2H v006 MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid, USGS [data set], https://doi.org/10.5067/MODIS/MOD15A2H.006, 2024. a
van Pelt, S. C., Kabat, P., ter Maat, H. W., van den Hurk, B. J. J. M., and Weerts, A. H.: Discharge simulations performed with a hydrological model using bias corrected regional climate model input, Hydrol. Earth Syst. Sci., 13, 2387–2397, https://doi.org/10.5194/hess-13-2387-2009, 2009. a
Viviroli, D., Archer, D. R., Buytaert, W., Fowler, H. J., Greenwood, G. B., Hamlet, A. F., Huang, Y., Koboltschnig, G., Litaor, M. I., López-Moreno, J. I., Lorentz, S., Schädler, B., Schreier, H., Schwaiger, K., Vuille, M., and Woods, R.: Climate change and mountain water resources: overview and recommendations for research, management and policy, Hydrol. Earth Syst. Sci., 15, 471–504, https://doi.org/10.5194/hess-15-471-2011, 2011. a
Xie, K., Liu, P., Zhang, J., Han, D., Wang, G., and Shen, C.: Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships, J. Hydrol., 603, 127043, https://doi.org/10.1016/j.jhydrol.2021.127043, 2021. a, b
Xu, R., Hu, H., Tian, F., Li, C., and Khan, M. Y. A.: Projected climate change impacts on future streamflow of the Yarlung Tsangpo-Brahmaputra River, Global Planet. Change, 175, 144–159, 2019. a
Yang, K., He, J., Tang, W., Qin, J., and Cheng, C. C.: On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau, Agr. Forest Meteorol., 150, 38–46, 2010. a
Yilmaz, K. K., Gupta, H. V., and Wagener, T.: A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, https://doi.org/10.1029/2007WR006716, 2008. a
Zhong, L., Lei, H., and Gao, B.: Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments, Water Resour. Res., 59, e2022WR034118 , https://doi.org/10.1029/2022WR034118, 2023. a, b, c
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
This paper developed hybrid semi-distributed hydrological models by employing a process-based...
