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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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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Diego Avesani, Yi Nan, and Fuqiang Tian
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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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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.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-3780, https://doi.org/10.5194/egusphere-2024-3780, 2025
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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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Mahmut Tudaji, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-2966, https://doi.org/10.5194/egusphere-2024-2966, 2024
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Zhen Cui and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-2177, https://doi.org/10.5194/egusphere-2024-2177, 2024
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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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This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
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.
Songjun Han and Fuqiang Tian
Hydrol. Earth Syst. Sci., 24, 2269–2285, https://doi.org/10.5194/hess-24-2269-2020, https://doi.org/10.5194/hess-24-2269-2020, 2020
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The complementary principle is an important methodology for estimating actual evaporation by using routinely observed meteorological variables. This review summaries its 56-year development, focusing on how related studies have shifted from adopting a symmetric linear complementary relationship to employing generalized nonlinear functions. We also compare the polynomial and sigmoid types of generalized complementary functions and discuss their future development.
Yu Ma, Guangheng Ni, Chandrasekar V. Chandra, Fuqiang Tian, and Haonan Chen
Hydrol. Earth Syst. Sci., 23, 4153–4170, https://doi.org/10.5194/hess-23-4153-2019, https://doi.org/10.5194/hess-23-4153-2019, 2019
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Raindrop size distribution (DSD) information is fundamental in understanding the precipitation microphysics and quantitative precipitation estimation. This study extensively investigates the DSD characteristics during rainy seasons in the Beijing urban area using 5-year DSD observations from a Parsivel2 disdrometer. The statistical distributions of DSD parameters are examined and the polarimetric radar rainfall algorithms are derived to support the ongoing development of an X-band radar network.
Ting Sun and Sue Grimmond
Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, https://doi.org/10.5194/gmd-12-2781-2019, 2019
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A Python-enhanced urban land surface model, SuPy (SUEWS in Python), is presented with its development (the SUEWS interface modification, F2PY configuration and Python frontend implementation), cross-platform deployment (PyPI, Python Package Index) and demonstration (online tutorials in Jupyter notebooks for users of different levels). SuPy represents a significant enhancement that supports existing and new model applications, reproducibility and enhanced functionality.
Fushan Wang, Guangheng Ni, William J. Riley, Jinyun Tang, Dejun Zhu, and Ting Sun
Geosci. Model Dev., 12, 2119–2138, https://doi.org/10.5194/gmd-12-2119-2019, https://doi.org/10.5194/gmd-12-2119-2019, 2019
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The current lake model in the Weather Research and Forecasting system was reported to be insufficient in simulating deep lakes and reservoirs. We thus revised the lake model by improving its spatial discretization scheme, surface property parameterization, diffusivity parameterization, and convection scheme. The revised model was evaluated at a deep reservoir in southwestern China and the results were in good agreement with measurements.
Mohd Yawar Ali Khan and Fuqiang Tian
Proc. IAHS, 379, 61–66, https://doi.org/10.5194/piahs-379-61-2018, https://doi.org/10.5194/piahs-379-61-2018, 2018
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This study has been conducted on Ramganga River, a major tributary of Ganges River, India, to observe the spatial variation of DOC, dissolved inorganic carbon (DIC), SOC and suspended inorganic carbon (SIC) in river water. The significant conclusions of this investigation revealed that the river and its tributaries show abundance amount of TSC (SOC and SIC) and TDC (DOC and DIC) both in the upstream and downstream. TDC accounts more in river concentration as compared to TSC.
Guanghui Ming, Hongchang Hu, Fuqiang Tian, Zhenyang Peng, Pengju Yang, and Yiqi Luo
Hydrol. Earth Syst. Sci., 22, 3075–3086, https://doi.org/10.5194/hess-22-3075-2018, https://doi.org/10.5194/hess-22-3075-2018, 2018
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The purpose of this research was to detect the effect of plastic film mulching (PFM), a widely applied cultivation method, on soil respiration. We found that soil respiration was not only affected by PFM, but it was also affected by irrigation and precipitation, and whether the PFM increases soil respiration compared to a non-mulched field largely depends on precipitation in the field. The result has an important meaning for agricultural carbon sequestration in the context of global warming.
Ran Xu, Hongchang Hu, Fuqiang Tian, Chao Li, and Mohd Yawar Ali Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-251, https://doi.org/10.5194/hess-2018-251, 2018
Manuscript not accepted for further review
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We provide a comprehensive and updated assessment of the impacts of climate change on YBR streamflow by integrating a physically based hydrological model, regional climate integrations, different bias correction methods, and Bayesian model averaging method. By the year 2035, the annual mean streamflow is projected to change respectively by 6.8 % (12.9 %), −0.4 % (13.1 %), and −4.1 % (19.9 %) under RCP4.5 (8.5) relative to the historical period at the Bahadurabad, the upper Brahmaputra outlet, and Nuxia.
Ting Sun, Zhi-Hua Wang, Walter C. Oechel, and Sue Grimmond
Geosci. Model Dev., 10, 2875–2890, https://doi.org/10.5194/gmd-10-2875-2017, https://doi.org/10.5194/gmd-10-2875-2017, 2017
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The diurnal hysteresis behaviour found between the net storage heat flux and net all-wave radiation has been captured in the Objective Hysteresis Model (OHM). To facilitate use, and enhance physical interpretations of the OHM coefficients, we develop the Analytical Objective Hysteresis Model (AnOHM) using an analytical solution of the one-dimensional advection–diffusion equation of coupled heat and liquid water transport in conjunction with the surface energy balance relationship.
Songjun Han, Fuqiang Tian, Ye Liu, and Xianhui Duan
Hydrol. Earth Syst. Sci., 21, 3619–3633, https://doi.org/10.5194/hess-21-3619-2017, https://doi.org/10.5194/hess-21-3619-2017, 2017
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The history of the co-evolution of the coupled human–groundwater system in Cangzhou (a region with the most serious depression cone in the North China Plain) is analyzed with a particular focus on how the groundwater crisis unfolded and how people attempted to settle the crisis. The evolution of the system was substantially impacted by two droughts. Further restoration of groundwater environment could be anticipated, but the occurrence of drought still remains an undetermined external forcing.
Wen-Yu Yang, Guang-Heng Ni, You-Cun Qi, Yang Hong, and Ting Sun
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-388, https://doi.org/10.5194/amt-2016-388, 2016
Revised manuscript has not been submitted
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Using a dataset consisting of one-year measurements by an X-band radar and distrometer, we found that error corrections greatly improve X-band-radar-based rainfall estimation. Specifically, the greatest improvement is realized by the beam integration. Derivation of localized Z-R relationships for specific rainfall systems is also of great importance. Moreover, wind drift correction improves quantitative estimates and temporal consistency.
Zhenyang Peng, Hongchang Hu, Fuqiang Tian, Qiang Tie, and Sihan Zhao
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-112, https://doi.org/10.5194/hess-2016-112, 2016
Manuscript not accepted for further review
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Preferential flow (PF) occurred by a frequency of 40.7 % in a semi humid catchment. Possibility of PF occurrence is positively correlated with rainfall features, i.e. rainfall amount, duration, maximum and average intensity, among which the rainfall amount is the dominant driven factor of PF. PF is more likely to occur on gentle slopes with thick surface covers, while high antecedent soil moisture is more likely to be consequence of infiltration capacity, rather than an inducer of PF.
Fuqiang Tian, Yu Sun, Hongchang Hu, and Hongyi Li
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-88, https://doi.org/10.5194/hess-2016-88, 2016
Preprint withdrawn
Z. H. He, F. Q. Tian, H. V. Gupta, H. C. Hu, and H. P. Hu
Hydrol. Earth Syst. Sci., 19, 1807–1826, https://doi.org/10.5194/hess-19-1807-2015, https://doi.org/10.5194/hess-19-1807-2015, 2015
D. Liu, F. Tian, M. Lin, and M. Sivapalan
Hydrol. Earth Syst. Sci., 19, 1035–1054, https://doi.org/10.5194/hess-19-1035-2015, https://doi.org/10.5194/hess-19-1035-2015, 2015
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A simplified conceptual socio-hydrological model based on logistic growth curves is developed for the Tarim River basin in western China and is used to illustrate the explanatory power of a co-evolutionary model. The socio-hydrological system is composed of four sub-systems, i.e., the hydrological, ecological, economic, and social sub-systems. The hydrological equation focusing on water balance is coupled to the evolutionary equations of the other three sub-systems.
Z. H. He, J. Parajka, F. Q. Tian, and G. Blöschl
Hydrol. Earth Syst. Sci., 18, 4773–4789, https://doi.org/10.5194/hess-18-4773-2014, https://doi.org/10.5194/hess-18-4773-2014, 2014
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In this paper, we propose a new method for estimating the snowmelt degree-day factor (DDFS) directly from MODIS snow covered area (SCA) and ground-based snow depth data without calibration. Snow density is estimated as the ratio between observed precipitation and changes in the snow volume for days with snow accumulation. DDFS values are estimated as the ratio between changes in the snow water equivalent and difference between the daily temperature and a threshold value for days with snowmelt.
Z. Zhang, H. Hu, F. Tian, X. Yao, and M. Sivapalan
Hydrol. Earth Syst. Sci., 18, 3951–3967, https://doi.org/10.5194/hess-18-3951-2014, https://doi.org/10.5194/hess-18-3951-2014, 2014
Y. Liu, F. Tian, H. Hu, and M. Sivapalan
Hydrol. Earth Syst. Sci., 18, 1289–1303, https://doi.org/10.5194/hess-18-1289-2014, https://doi.org/10.5194/hess-18-1289-2014, 2014
Z. Zhang, F. Tian, H. Hu, and P. Yang
Hydrol. Earth Syst. Sci., 18, 1053–1072, https://doi.org/10.5194/hess-18-1053-2014, https://doi.org/10.5194/hess-18-1053-2014, 2014
L. Yang, F. Tian, Y. Sun, X. Yuan, and H. Hu
Hydrol. Earth Syst. Sci., 18, 775–786, https://doi.org/10.5194/hess-18-775-2014, https://doi.org/10.5194/hess-18-775-2014, 2014
Z. He, F. Tian, H. C. Hu, H. V. Gupta, and H. P. Hu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-1253-2014, https://doi.org/10.5194/hessd-11-1253-2014, 2014
Revised manuscript not accepted
Y. Sun, Z. Hou, M. Huang, F. Tian, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 17, 4995–5011, https://doi.org/10.5194/hess-17-4995-2013, https://doi.org/10.5194/hess-17-4995-2013, 2013
Y. Tang, Q. Tang, F. Tian, Z. Zhang, and G. Liu
Hydrol. Earth Syst. Sci., 17, 4471–4480, https://doi.org/10.5194/hess-17-4471-2013, https://doi.org/10.5194/hess-17-4471-2013, 2013
H. Liu, F. Tian, H. C. Hu, H. P. Hu, and M. Sivapalan
Hydrol. Earth Syst. Sci., 17, 805–815, https://doi.org/10.5194/hess-17-805-2013, https://doi.org/10.5194/hess-17-805-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Long short-term memory networks for enhancing real-time flood forecasts: a case study for an underperforming hydrologic model
Assessing the value of high-resolution rainfall and streamflow data for hydrological modeling: an analysis based on 63 catchments in southeast China
Catchments do not strictly follow Budyko curves over multiple decades, but deviations are minor and predictable
Scale dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
Extended-range forecasting of stream water temperature with deep-learning models
Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
Projections of streamflow intermittence under climate change in European drying river networks
Economic valuation of subsurface water contributions to watershed ecosystem services using a fully integrated groundwater–surface-water model
Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland
Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric–hydrological model
Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments
A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Simulating the Tone River eastward diversion project in Japan carried out 4 centuries ago
Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers
Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study
Heavy-tailed flood peak distributions: what is the effect of the spatial variability of rainfall and runoff generation?
State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models
Improving the hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations
Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments
The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa
Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model
Spatio-temporal patterns and trends of streamflow in water-scarce Mediterranean basins
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
Estimating response times, flow velocities, and roughness coefficients of Canadian Prairie basins
Learning landscape features from streamflow with autoencoders
Hydrological regime index for non-perennial rivers
On the use of streamflow transformations for hydrological model calibration
Simulation-based inference for parameter estimation of complex watershed simulators
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
CONCN: A high-resolution, integrated surface water-groundwater ParFlow modeling platform of continental China
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Assesing the Value of High-Resolution Data and Parameters Transferability Across Temporal Scales in Hydrological Modeling: A Case Study in Northern China
Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
Karst aquifer discharge response to rainfall interpreted as anomalous transport
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Large-sample hydrology – a few camels or a whole caravan?
Comment on “Are soils overrated in hydrology?” by Gao et al. (2023)
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany
Technical note: What does the Standardized Streamflow Index actually reflect? Insights and implications for hydrological drought analysis
Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China
On the importance of plant phenology in the evaporative process of a semi-arid woodland: could it be why satellite-based evaporation estimates in the miombo differ?
Assessing the adequacy of traditional hydrological models for climate change impact studies: A case for long-short-term memory (LSTM) neural networks
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Evolution of river regimes in the Mekong River basin over 8 decades and the role of dams in recent hydrological extremes
Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea
To what extent do flood-inducing storm events change future flood hazards?
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Sebastian Gegenleithner, Manuel Pirker, Clemens Dorfmann, Roman Kern, and Josef Schneider
Hydrol. Earth Syst. Sci., 29, 1939–1962, https://doi.org/10.5194/hess-29-1939-2025, https://doi.org/10.5194/hess-29-1939-2025, 2025
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Accurate early-warning systems are crucial for reducing the damage caused by flooding events. In this study, we explored the potential of long short-term memory networks for enhancing the forecast accuracy of hydrologic models employed in operational flood forecasting. The presented approach elevated the investigated hydrologic model’s forecast accuracy for further ahead predictions and at flood event runoff.
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.
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025, https://doi.org/10.5194/hess-29-1703-2025, 2025
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The quantification of precipitation into evaporation and runoff is vital for water resources management. The Budyko framework, based on aridity and evaporative indices of a catchment, can be an ideal tool for that. However, recent research highlights deviations of catchments from the expected evaporative index, casting doubt on its reliability. This study quantifies deviations of 2387 catchments, finding them minor and predictable. Integrating these into predictions upholds the framework's efficacy.
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart N. Lane, and Francesco Comiti
Hydrol. Earth Syst. Sci., 29, 1725–1748, https://doi.org/10.5194/hess-29-1725-2025, https://doi.org/10.5194/hess-29-1725-2025, 2025
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In this article, we show that by taking the optimal parameters calibrated with a semi-lumped model for the discharge at a catchment's outlet, we can accurately simulate runoff at various points within the study area, including three nested and three neighboring catchments. In addition, we demonstrate that employing more intricate melt models, which better represent physical processes, enhances the transfer of parameters in the simulation, until we observe overparameterization.
Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner
Hydrol. Earth Syst. Sci., 29, 1685–1702, https://doi.org/10.5194/hess-29-1685-2025, https://doi.org/10.5194/hess-29-1685-2025, 2025
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We generate operational forecasts of daily maximum stream water temperature for 32 consecutive days at 54 stations in Switzerland with our best-performing data-driven model. The average forecast error is 0.38 °C for 1 d ahead and increases to 0.90 °C for 32 d ahead given the uncertainty in the meteorological variables influencing water temperature. Here we compare the skill of several models, how well they can forecast at new and ungauged stations, and the importance of different model inputs.
Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1749–1758, https://doi.org/10.5194/hess-29-1749-2025, https://doi.org/10.5194/hess-29-1749-2025, 2025
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Long short-term memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modelling. However, most studies focus on predictions at a daily scale, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting. In this study, we introduce a new architecture, multi-frequency LSTM (MF-LSTM), designed to use inputs of various temporal frequencies to produce sub-daily (e.g. hourly) predictions at a moderate computational cost.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci., 29, 1615–1636, https://doi.org/10.5194/hess-29-1615-2025, https://doi.org/10.5194/hess-29-1615-2025, 2025
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Our study projects how climate change will affect the drying of river segments and stream networks in Europe, using advanced modelling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent and intense and will start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists for evaluating the ecological health of river ecosystem.
Tariq Aziz, Steven K. Frey, David R. Lapen, Susan Preston, Hazen A. J. Russell, Omar Khader, Andre R. Erler, and Edward A. Sudicky
Hydrol. Earth Syst. Sci., 29, 1549–1568, https://doi.org/10.5194/hess-29-1549-2025, https://doi.org/10.5194/hess-29-1549-2025, 2025
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This study determines the value of subsurface water for ecosystem services' supply in an agricultural watershed in Ontario, Canada. Using a fully integrated water model and an economic valuation approach, the research highlights subsurface water's critical role in maintaining watershed ecosystem services. The study informs on the sustainable use of subsurface water and introduces a new method for managing watershed ecosystem services.
Eduardo Acuña Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025, https://doi.org/10.5194/hess-29-1277-2025, 2025
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Data-driven techniques have shown the potential to outperform process-based models in rainfall–runoff simulations. Hybrid models, combining both approaches, aim to enhance accuracy and maintain interpretability. Expanding the set of test cases to evaluate hybrid models under different conditions, we test their generalization capabilities for extreme hydrological events.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
Hydrol. Earth Syst. Sci., 29, 1061–1082, https://doi.org/10.5194/hess-29-1061-2025, https://doi.org/10.5194/hess-29-1061-2025, 2025
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This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.
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.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 29, 903–924, https://doi.org/10.5194/hess-29-903-2025, https://doi.org/10.5194/hess-29-903-2025, 2025
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This study develops an integrated framework based on the novel Driving index for changes in Precipitation–Runoff Relationships (DPRR) to explore the controlling changes in precipitation–runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation–runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci., 29, 785–798, https://doi.org/10.5194/hess-29-785-2025, https://doi.org/10.5194/hess-29-785-2025, 2025
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Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine learning (ML) models are increasingly being applied for flood forecasting. Such models are typically trained on large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets that maximise the spatio-temporal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Joško Trošelj and Naota Hanasaki
Hydrol. Earth Syst. Sci., 29, 753–766, https://doi.org/10.5194/hess-29-753-2025, https://doi.org/10.5194/hess-29-753-2025, 2025
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This study presents the first distributed hydrological simulation which confirms claims raised by historians that the eastward diversion project of the Tone River in Japan was conducted 4 centuries ago to increase low flows and subsequent travelling possibilities surrounding the capital, Edo (Tokyo), using inland navigation. We showed that great steps forward can be made for improving quality of life with small human engineering waterworks and small interventions in the regime of natural flows.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 29, 683–700, https://doi.org/10.5194/hess-29-683-2025, https://doi.org/10.5194/hess-29-683-2025, 2025
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This work investigates how hydrological models are transferred to a period in which climate conditions are different to the ones of the period in which they were set up. The robustness assessment test built to detect dependencies between model error and climatic drivers was applied to three hydrological models in 352 catchments in Denmark, France and Sweden. Potential issues are seen in a significant number of catchments for the models, even though the catchments differ for each model.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci., 29, 627–653, https://doi.org/10.5194/hess-29-627-2025, https://doi.org/10.5194/hess-29-627-2025, 2025
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Water budget non-closure is a widespread phenomenon among multisource datasets which undermines the robustness of hydrological inferences. This study proposes a Multisource Dataset Correction Framework grounded in Physical Hydrological Process Modelling to enhance water budget closure, termed PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 29, 447–463, https://doi.org/10.5194/hess-29-447-2025, https://doi.org/10.5194/hess-29-447-2025, 2025
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Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small catchments compared to large catchments, and spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show effects. The results can improve estimations of probabilities of extreme floods.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht H. Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci., 29, 335–360, https://doi.org/10.5194/hess-29-335-2025, https://doi.org/10.5194/hess-29-335-2025, 2025
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Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping to better prepare for and respond to floods.
Jordy Salmon-Monviola, Ophélie Fovet, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
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To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
Haley A. Canham, Belize Lane, Colin B. Phillips, and Brendan P. Murphy
Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025, https://doi.org/10.5194/hess-29-27-2025, 2025
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The influence of watershed disturbances has proved challenging to disentangle from natural streamflow variability. This study evaluates the influence of time-varying hydrologic controls on rainfall–runoff in undisturbed and wildfire-disturbed watersheds using a novel time-series event separation method. Across watersheds, water year type and season influenced rainfall–runoff patterns. Accounting for these controls enabled clearer isolation of wildfire effects.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci., 28, 5511–5539, https://doi.org/10.5194/hess-28-5511-2024, https://doi.org/10.5194/hess-28-5511-2024, 2024
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Evapotranspiration (ET) is computed from the vegetation (plant transpiration) and soil (soil evaporation). In western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented using the leaf area index (LAI). In this study, we evaluate the importance of the LAI for ET calculation. We take a close look at this interaction and highlight its relevance. Our work contributes to the understanding of terrestrial water cycle processes .
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
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This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Laia Estrada, Xavier Garcia, Joan Saló-Grau, Rafael Marcé, Antoni Munné, and Vicenç Acuña
Hydrol. Earth Syst. Sci., 28, 5353–5373, https://doi.org/10.5194/hess-28-5353-2024, https://doi.org/10.5194/hess-28-5353-2024, 2024
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Hydrological modelling is a powerful tool to support decision-making. We assessed spatio-temporal patterns and trends of streamflow for 2001–2022 with a hydrological model, integrating stakeholder expert knowledge on management operations. The results provide insight into how climate change and anthropogenic pressures affect water resources availability in regions vulnerable to water scarcity, thus raising the need for sustainable management practices and integrated hydrological modelling.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci., 28, 5331–5352, https://doi.org/10.5194/hess-28-5331-2024, https://doi.org/10.5194/hess-28-5331-2024, 2024
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Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. We investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analyses indicate that adding two vegetation parameters is enough to improve the representation of evaporation and that the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
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We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 28, 5173–5192, https://doi.org/10.5194/hess-28-5173-2024, https://doi.org/10.5194/hess-28-5173-2024, 2024
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Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
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The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Pablo Fernando Dornes and Rocío Noelia Comas
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-338, https://doi.org/10.5194/hess-2024-338, 2024
Revised manuscript accepted for HESS
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The Desaguadero-Salado-Chadiluevú-Curacó (DSCC) River is a semiarid river which is severely dammed in its tributaries which collect the snowmelt runoff. This runoff feeds mostly gravitational irrigation systems of very low efficiency. As a result, the DSCC River does not have natural runoff. The proposed Hydrological Regime Index (HRI) is able to discriminate and quantify regime alterations under permanent and non-permanent flow conditions and with low and high impoundment conditions.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
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We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
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Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
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We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-292, https://doi.org/10.5194/hess-2024-292, 2024
Revised manuscript accepted for HESS
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We developed the first high-resolution, integrated surface water-groundwater hydrologic model of the entire continental China using ParFlow. The model shows good performance of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
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This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-2966, https://doi.org/10.5194/egusphere-2024-2966, 2024
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We assessed the value of high-resolution data and parameters transferability across temporal scales based on 7 catchments in northern China. We found that higher resolution data does 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 smaller computational time step when building hydrological models even without high-resolution data.
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279, https://doi.org/10.5194/hess-2024-279, 2024
Revised manuscript accepted for HESS
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Hydrologic models are needed to provide simulations of water availability, floods and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models, and thus cannot easily be used to select an appropriate model for a specific place.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
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A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system and establish the application of the model for simulating flow and transport in such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
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Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
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We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
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Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
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Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Fabián Lema, Pablo A. Mendoza, Nicolás A. Vásquez, Naoki Mizukami, Mauricio Zambrano-Bigiarini, and Ximena Vargas
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-221, https://doi.org/10.5194/hess-2024-221, 2024
Revised manuscript accepted for HESS
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Hydrological droughts affect ecosystems and socioeconomic activities worldwide. Despite they are commonly described with the Standardized Streamflow Index (SSI), there is limited understanding of what it truly reflects in terms of water cycle processes. Here, we used state-of-the-art hydrological models in Andean basins to examine drivers of SSI fluctuations. The results highlight the importance of careful selection of indices and time scales for accurate drought characterization and monitoring.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
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An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
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The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Jean-Luc Martel, François Brissette, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Gabriel Rondeau-Genesse, and Louis-Philippe Caron
EGUsphere, https://doi.org/10.5194/egusphere-2024-2133, https://doi.org/10.5194/egusphere-2024-2133, 2024
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This study compares Long Short-Term Memory (LSTM) neural networks with traditional hydrological models to predict future streamflow under climate change. Using data from 148 catchments, it finds that LSTM models, which learn from extensive data sequences, perform differently and often better than traditional hydrolgical models. The continental LSTM model, which includes data from diverse climate zones, is particularly effective for understanding climate impacts on water resources.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
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The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
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By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
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Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
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Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
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...
